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data/data-pipeline/data_pipeline/ipython/hud_notebook_tabulation_differences_12_27_2021.ipynb diff --git a/data/data-pipeline/data_pipeline/ipython/cal_environ_hud_comparison_12102021.ipynb b/data/data-pipeline/data_pipeline/ipython/cal_environ_hud_comparison_12102021.ipynb index 5ce509a7..088794e3 100644 --- a/data/data-pipeline/data_pipeline/ipython/cal_environ_hud_comparison_12102021.ipynb +++ b/data/data-pipeline/data_pipeline/ipython/cal_environ_hud_comparison_12102021.ipynb @@ -643,7 +643,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -657,7 +657,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.2" + "version": "3.7.4" } }, "nbformat": 4, diff --git a/data/data-pipeline/data_pipeline/ipython/comparative_analysis_se_12_29_2021_spatial.ipynb b/data/data-pipeline/data_pipeline/ipython/comparative_analysis_se_12_29_2021_spatial.ipynb deleted file mode 100644 index bbc5171b..00000000 --- a/data/data-pipeline/data_pipeline/ipython/comparative_analysis_se_12_29_2021_spatial.ipynb +++ /dev/null @@ -1,4019 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Methodology to address fundamental problem 1 itemized in Issue 1024 - follow-up compare tabulations and relative household burden. This time I extend the 12-11 notebook to look at how the percentile ranks affects the proportion of tracts considered as burdened versus the current methodology." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Indicator reviewed: \n", - "\n", - "Socioeconomic Factors Indicator reviewed\n", - "* [Extreme Housing Burden](#housingburden)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Packages" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/sarahluw/.pyenv/versions/3.6.2/envs/my-virtual-env-3.6.2/lib/python3.6/site-packages/geopandas/_compat.py:110: UserWarning: The Shapely GEOS version (3.9.1-CAPI-1.14.2) is incompatible with the GEOS version PyGEOS was compiled with (3.10.1-CAPI-1.16.0). Conversions between both will be slow.\n", - " shapely_geos_version, geos_capi_version_string\n", - "/Users/sarahluw/.pyenv/versions/3.6.2/envs/my-virtual-env-3.6.2/lib/python3.6/site-packages/fuzzywuzzy/fuzz.py:11: UserWarning: Using slow pure-python SequenceMatcher. Install python-Levenshtein to remove this warning\n", - " warnings.warn('Using slow pure-python SequenceMatcher. Install python-Levenshtein to remove this warning')\n" - ] - } - ], - "source": [ - "import math\n", - "import numpy as np\n", - "import os\n", - "import pandas as pd\n", - "\n", - "import seaborn as sns\n", - "import matplotlib.pyplot as plt\n", - "import pandas as pd\n", - "from cenpy import products\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### ETL process for acquiring relevant tables" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### NOTE: If you ran the ETL Process to acquire Table 8 in the other notebook of this draft PR you do not need to run the ETL cell block again" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# Copy and adapt certain sections of code from data_pipeline.utils \n", - "\n", - "def download_hud_dataset():\n", - " DOWNLOAD_FILENAME = \"HUD_ZIPPED.csv\"\n", - " HOUSING_FTP_URL = \"https://www.huduser.gov/portal/datasets/cp/2014thru2018-140-csv.zip\" \n", - " response = requests.get(HOUSING_FTP_URL, verify=True)\n", - " if response.status_code == 200:\n", - " file_contents = response.content\n", - " else:\n", - " sys.exit(\n", - " f\"HTTP response {response.status_code} from url {file_url}. Info: {response.content}\"\n", - " )\n", - "\n", - " # Write the contents to disk.\n", - " file = open(DOWNLOAD_FILENAME, \"wb\")\n", - " file.write(file_contents)\n", - " file.close()\n", - " \n", - "def extract_zipped_download(zip_file_path, unzipped_path):\n", - " with zipfile.ZipFile(zip_file_path, \"r\") as zip_ref:\n", - " zip_ref.extractall(unzipped_path)\n", - " # cleanup temporary file\n", - " os.remove(zip_file_path)\n", - " \n", - "def up_one_directory(path):\n", - " try:\n", - " # from Python 3.6\n", - " parent_dir = Path(path).parents[1]\n", - " # for Python 3.4/3.5, use str to convert the path to string\n", - " # parent_dir = str(Path(path).parents[1])\n", - " shutil.move(path, parent_dir)\n", - " except IndexError:\n", - " # no upper directory\n", - " pass\n", - "\n", - "CURRENT_DIRECTORY = os.getcwd()\n", - "download_hud_dataset()\n", - "extract_zipped_download(CURRENT_DIRECTORY + \"/HUD_ZIPPED.csv\", CURRENT_DIRECTORY) \n", - "up_one_directory(CURRENT_DIRECTORY + \"/140/Table8.csv\")\n", - "shutil.rmtree(\"./140/\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Extreme Housing Burden \n", - "\n", - "The Extreme Housing Burden indicator represents the proportion of low-income households that have to spend more than half their income on rent. These households experience higher levels of stress, report lower health, and may delay medical treatment because of its high cost.\n", - "\n", - "The Extreme Housing Burden indicator measures the percent of households in a census tract that are:\n", - "\n", - "1. Making less than 80% of the Area Median Family Income as determined by the Department of Housing and Urban Development (HUD), and\n", - "2. Paying greater than 50% of their income to housing costs. \n", - "\n", - "This data is sourced from the 2014-2018 Comprehensive Housing Affordability Strategy dataset from the Department of Housing and Urban Development (HUD) using the census tract geographic summary level, and contains cost burdens for households by percent HUD-adjusted median family income (HAMFI) category. This data can be found [here](https://www.huduser.gov/portal/datasets/cp.html). \n", - "\n", - "Because CHAS data is based on American Communities Survey (ACS) estimates, which come from a sample of the population, they may be unreliable if based on a small sample or population size.\n", - "\n", - "The standard error and relative standard error were used to evaluate the reliability of each estimate using CalEnviroScreen’s methodology. \n", - "\n", - "Census tract estimates that met either of the following criteria were considered reliable and included in the analysis [(CalEnviroScreen, 2017, page 129)](https://oehha.ca.gov/media/downloads/calenviroscreen/report/ces3report.pdf ):\n", - "\n", - "- Relative standard error less than 50 (meaning the standard error was less than half of the estimate), OR \n", - "- Standard error less than the mean standard error of all census tract estimates \n", - "\n", - "Formulas for calculating the standard error of sums, proportions, and ratio come from the [American Communities Survey Office](https://www2.census.gov/programs-surveys/acs/tech_docs/accuracy/MultiyearACSAccuracyofData2013.pdf).\n", - "\n", - "Note that this code creates a score and rank by state, for every state." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The relevant variables in table 8 of the CHAS dataset are the following (CHAS data dictionary available [here](https://www.huduser.gov/portal/datasets/cp/CHAS-data-dictionary-14-18.xlsx)):\n", - "\n", - "| Name | Label |\n", - "|---------|-----------------------------------------------------|\n", - "|T1_est1 | Total Occupied housing units | \n", - "|T8_est10 | Owner occupied less than or equal to 30% of HAMFI cost burden greater than 50% |\n", - "|T8_est23 |Owner occupied greater than 30% but less than or equal to 50% of HAMFI\tcost burden greater than 50%|\n", - "|T8_est36 |Owner occupied\tgreater than 50% but less than or equal to 80% of HAMFI\tcost burden greater than 50%|\n", - "|T8_est76 | Renter occupied less than or equal to 30% of HAMFI cost burden greater than 50% |\n", - "|T8_est89 |Renter occupied\tgreater than 30% but less than or equal to 50% of HAMFI\tcost burden greater than 50%|\n", - "|T8_est102|Renter occupied\tgreater than 50% but less than or equal to 80% of HAMFI\tcost burden greater than 50%|\n", - " " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Below I also propose an alternate means for ranking census tracts\n", - "### These steps are outlined and commented below" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/sarahluw/.pyenv/versions/3.6.2/envs/my-virtual-env-3.6.2/lib/python3.6/site-packages/pandas/core/series.py:726: RuntimeWarning: invalid value encountered in sqrt\n", - " result = getattr(ufunc, method)(*inputs, **kwargs)\n", - "/Users/sarahluw/.pyenv/versions/3.6.2/envs/my-virtual-env-3.6.2/lib/python3.6/site-packages/pandas/core/indexing.py:670: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " iloc._setitem_with_indexer(indexer, value)\n" - ] - } - ], - "source": [ - "# Read in the data from https://www.huduser.gov/portal/datasets/cp.html\n", - "housing = pd.read_csv(\"Table8.csv\", \n", - " encoding = \"ISO-8859-1\", \n", - " dtype = {'Tract_ID': object, 'st': object, 'geoid': object})\n", - "\n", - "# Remove data for states that aren't included in the census (e.g. American Samoa, Guam, etc.):\n", - "housing.drop(housing.loc[housing['st'] == '72'].index, inplace = True)\n", - "\n", - "# Combine owner and renter occupied low-income households that make less than 80% of HAMFI into one variable\n", - "housing['summed'] = (housing['T8_est10'] + \n", - " housing['T8_est23'] + \n", - " housing['T8_est36'] + \n", - " housing['T8_est76'] + \n", - " housing['T8_est89'] + \n", - " housing['T8_est102'])\n", - "\n", - "# Create a variable for the standard error of the summed variables\n", - "housing['summed_se'] = np.sqrt((housing['T8_moe10'] / 1.645)**2 + \n", - " (housing['T8_moe23'] / 1.645)**2 + \n", - " (housing['T8_moe36'] / 1.645)**2 + \n", - " (housing['T8_moe76'] / 1.645)**2 + \n", - " (housing['T8_moe89'] / 1.645)**2 + \n", - " (housing['T8_moe102'] / 1.645)**2)\n", - "\n", - "# Remove the first 7 digits in the FIPS Census Tract ID \n", - "housing['geoid'] = housing['geoid'].str[-11:]\n", - "\n", - "# Find the estimate of the proportion of the population that is heavily rent burdened\n", - "housing['hbrd_score'] = housing['summed'] / housing['T8_est1']\n", - "\n", - "# Change rates where the population is 0 to nan\n", - "housing['hbrd_score'].replace(np.inf, np.nan, inplace = True)\n", - "\n", - "# Create function for calculating the standard error, using the proportions standard error formula\n", - "# if the value under the radical is negative, use the ratio standard error formula\n", - "def se_prop(x, y, se_x, moe_y): \n", - " se_y = moe_y / 1.645\n", - " test = se_x**2 - (((x**2)/(y**2))*((se_y)**2))\n", - " se = np.where(test < 0,\n", - " (1/y) * np.sqrt(se_x**2 + (((x**2)/(y**2))*(se_y**2))), \n", - " (1/y) * np.sqrt(se_x**2 - (((x**2)/(y**2))*(se_y**2))))\n", - " return se\n", - "\n", - "housing['se'] = se_prop(housing['summed'], housing['T8_est1'], housing['summed_se'], housing['T8_moe1'])\n", - "\n", - "# Calculate the relative standard error\n", - "housing['rse'] = housing['se'] / housing['hbrd_score']*100\n", - "\n", - "# Change infinite rse's where the housing burden is 0 to np.nan\n", - "housing['rse'].replace(np.inf, np.nan, inplace = True)\n", - "\n", - "# Calculate the mean standard error for each state\n", - "housing['mean_state_se'] = np.zeros(len(housing))\n", - "\n", - "for state in housing['st'].unique():\n", - " mean_se = np.mean(housing[housing['st'] == state]['se'])\n", - " housing['mean_state_se'].loc[housing['st'] == state] = mean_se\n", - " \n", - "# Find census tract estimates that meet both of the following criteria and are thus considered unreliable estimates: \n", - "# RSE less than 50 AND\n", - "# SE less than the mean state SE or housing burdened low income households\n", - "# Convert these scores to nan\n", - "housing.loc[(housing['rse'] >= 50) & (housing['rse'] >= housing['mean_state_se']), 'hbrd_score'] = np.nan\n", - "\n", - "# Rename columns\n", - "housing = housing.rename(columns = {'geoid' :'FIPS_tract_id',\n", - " 'st' : 'state'\n", - " })\n", - "\n", - "# Calculate percentile rank for census tracts with a score above 0, set percentile to 0 if score is 0, for each state\n", - "housing['hbrd_rank'] = housing[\n", - " housing['hbrd_score'] != 0][['hbrd_score',\n", - " 'state']].groupby('state').rank( \n", - " na_option = 'keep', \n", - " pct = True) * 100\n", - "\n", - "housing.loc[housing['hbrd_score'] == 0, 'hbrd_rank'] = 0\n", - "\n", - "# Create final housing burden df\n", - "housingburden = housing.copy()" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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sourcesumlevelFIPS_tract_idnamestatecntytractT8_est1T8_est2T8_est3...T8_moe131T8_moe132T8_moe133summedsummed_sehbrd_scoresersemean_state_sehbrd_rank
02014thru201814001001020100Census Tract 201, Autauga County, Alabama0112010076557050...1212128031.7218070.1045750.04103239.2373140.03660446.298077
12014thru201814001001020200Census Tract 202, Autauga County, Alabama0112020072046565...12121213845.5318740.1916670.06161432.1466590.03660483.269231
22014thru201814001001020300Census Tract 203, Autauga County, Alabama01120300129584060...12121217053.7229210.1312740.04092731.1769990.03660463.653846
32014thru201814001001020400Census Tract 204, Autauga County, Alabama011204001640126015...12121214546.2885100.0884150.02782231.4673970.03660434.615385
42014thru201814001001020500Census Tract 205, Autauga County, Alabama0112050041752320175...171717595147.2216930.1425150.03476024.3901930.03660468.221154
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\n", - "
" - ], - "text/plain": [ - " source sumlevel FIPS_tract_id \\\n", - "0 2014thru2018 140 01001020100 \n", - "1 2014thru2018 140 01001020200 \n", - "2 2014thru2018 140 01001020300 \n", - "3 2014thru2018 140 01001020400 \n", - "4 2014thru2018 140 01001020500 \n", - "\n", - " name state cnty tract T8_est1 \\\n", - "0 Census Tract 201, Autauga County, Alabama 01 1 20100 765 \n", - "1 Census Tract 202, Autauga County, Alabama 01 1 20200 720 \n", - "2 Census Tract 203, Autauga County, Alabama 01 1 20300 1295 \n", - "3 Census Tract 204, Autauga County, Alabama 01 1 20400 1640 \n", - "4 Census Tract 205, Autauga County, Alabama 01 1 20500 4175 \n", - "\n", - " T8_est2 T8_est3 ... T8_moe131 T8_moe132 T8_moe133 summed summed_se \\\n", - "0 570 50 ... 12 12 12 80 31.721807 \n", - "1 465 65 ... 12 12 12 138 45.531874 \n", - "2 840 60 ... 12 12 12 170 53.722921 \n", - "3 1260 15 ... 12 12 12 145 46.288510 \n", - "4 2320 175 ... 17 17 17 595 147.221693 \n", - "\n", - " hbrd_score se rse mean_state_se hbrd_rank \n", - "0 0.104575 0.041032 39.237314 0.036604 46.298077 \n", - "1 0.191667 0.061614 32.146659 0.036604 83.269231 \n", - "2 0.131274 0.040927 31.176999 0.036604 63.653846 \n", - "3 0.088415 0.027822 31.467397 0.036604 34.615385 \n", - "4 0.142515 0.034760 24.390193 0.036604 68.221154 \n", - "\n", - "[5 rows x 280 columns]" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "housingburden.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(73056, 280)" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "housingburden.shape" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### As desired we see a uniform distribution for the percentile rank for burdened households" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Now we compute for a baseline comparison " - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# Owner occupied numerator fields\n", - "OWNER_OCCUPIED_NUMERATOR_FIELDS = [\n", - " # Column Name\n", - " # Line_Type\n", - " # Tenure\n", - " # Household income\n", - " # Cost burden\n", - " # Facilities\n", - " \"T8_est7\",\n", - " # Subtotal\n", - " # Owner occupied\n", - " # less than or equal to 30% of HAMFI\n", - " # greater than 30% but less than or equal to 50%\n", - " # All\n", - " \"T8_est10\",\n", - " # Subtotal\n", - " # Owner occupied\n", - " # less than or equal to 30% of HAMFI\n", - " # greater than 50%\n", - " # All\n", - " \"T8_est20\",\n", - " \n", - " # Subtotal\n", - " # Owner occupied\n", - " # greater than 30% but less than or equal to 50% of HAMFI\n", - " # greater than 30% but less than or equal to 50%\n", - " # All\n", - " \"T8_est23\",\n", - " # Subtotal\n", - " # Owner occupied\n", - " # greater than 30% but less than or equal to 50% of HAMFI\n", - " # greater than 50%\n", - " # All\n", - " \"T8_est33\",\n", - " # Subtotal\n", - " # Owner occupied\n", - " # greater than 50% but less than or equal to 80% of HAMFI\n", - " # greater than 30% but less than or equal to 50%\n", - " # All\n", - " \"T8_est36\",\n", - " # Subtotal\n", - " # Owner occupied\n", - " # greater than 50% but less than or equal to 80% of HAMFI\n", - " # greater than 50%\n", - " # All\n", - "]\n", - "\n", - "# These rows have the values where HAMFI was not computed, b/c of no or negative income.\n", - "OWNER_OCCUPIED_NOT_COMPUTED_FIELDS = [\n", - " # Column Name\n", - " # Line_Type\n", - " # Tenure\n", - " # Household income\n", - " # Cost burden\n", - " # Facilities\n", - " \"T8_est13\",\n", - " # Subtotal\n", - " # Owner occupied\n", - " # less than or equal to 30% of HAMFI\n", - " # not computed (no/negative income)\n", - " # All\n", - " \"T8_est26\",\n", - " # Subtotal\n", - " # Owner occupied\n", - " # greater than 30% but less than or equal to 50% of HAMFI\n", - " # not computed (no/negative income)\n", - " # All\n", - " \"T8_est39\",\n", - " # Subtotal\n", - " # Owner occupied\n", - " # greater than 50% but less than or equal to 80% of HAMFI\n", - " # not computed (no/negative income)\n", - " # All\n", - " \"T8_est52\",\n", - " # Subtotal\n", - " # Owner occupied\n", - " # greater than 80% but less than or equal to 100% of HAMFI\n", - " # not computed (no/negative income)\n", - " # All\n", - " \"T8_est65\",\n", - " # Subtotal\n", - " # Owner occupied\n", - " # greater than 100% of HAMFI\n", - " # not computed (no/negative income)\n", - " # All\n", - "]\n", - "\n", - "OWNER_OCCUPIED_POPULATION_FIELD = \"T8_est2\"\n", - "# Subtotal\n", - "# Owner occupied\n", - "# All\n", - "# All\n", - "# All\n", - "\n", - "OWNER_OCCUPIED_POPULATION_HAMFI_FIELD = \"T8_est3\"\n", - "# Subtotal\n", - "# Owner occupied \n", - "# All\n", - "# All\n", - "# All\n", - "\n", - "# Renter occupied numerator fields\n", - "RENTER_OCCUPIED_NUMERATOR_FIELDS = [\n", - " # Column Name\n", - " # Line_Type\n", - " # Tenure\n", - " # Household income\n", - " # Cost burden\n", - " # Facilities\n", - " \"T8_est73\",\n", - " # Subtotal\n", - " # Renter occupied\n", - " # less than or equal to 30% of HAMFI\n", - " # greater than 30% but less than or equal to 50%\n", - " # All\n", - " \"T8_est76\",\n", - " # Subtotal\n", - " # Renter occupied\n", - " # less than or equal to 30% of HAMFI\n", - " # greater than 50%\n", - " # All\n", - " \"T8_est86\",\n", - " # Subtotal\n", - " # Renter occupied\n", - " # greater than 30% but less than or equal to 50% of HAMFI\n", - " # greater than 30% but less than or equal to 50%\n", - " # All\n", - " \"T8_est89\",\n", - " # Subtotal\n", - " # Renter occupied\n", - " # greater than 30% but less than or equal to 50% of HAMFI\n", - " # greater than 50%\n", - " # All\n", - " \"T8_est99\",\n", - " # Subtotal\n", - " # Renter occupied\tgreater than 50% but less than or equal to 80% of HAMFI\n", - " # greater than 30% but less than or equal to 50%\n", - " # All\n", - " \"T8_est102\",\n", - " # Subtotal\n", - " # Renter occupied\n", - " # greater than 50% but less than or equal to 80% of HAMFI\n", - " # greater than 50%\n", - " # All\n", - "]\n", - "\n", - "# These rows have the values where HAMFI was not computed, b/c of no or negative income.\n", - "RENTER_OCCUPIED_NOT_COMPUTED_FIELDS = [\n", - " # Column Name\n", - " # Line_Type\n", - " # Tenure\n", - " # Household income\n", - " # Cost burden\n", - " # Facilities\n", - " \"T8_est79\",\n", - " # Subtotal\n", - " # Renter occupied\tless than or equal to 30% of HAMFI\n", - " # not computed (no/negative income)\n", - " # All\n", - " \"T8_est92\",\n", - " # Subtotal\n", - " # Renter occupied\tgreater than 30% but less than or equal to 50% of HAMFI\n", - " # not computed (no/negative income)\n", - " # All\n", - " \"T8_est105\",\n", - " # Subtotal\n", - " # Renter occupied\n", - " # greater than 50% but less than or equal to 80% of HAMFI\n", - " # not computed (no/negative income)\n", - " # All\n", - " \"T8_est118\",\n", - " # Subtotal\n", - " # Renter occupied\tgreater than 80% but less than or equal to 100% of HAMFI\n", - " # not computed (no/negative income)\n", - " # All\n", - " \"T8_est131\",\n", - " # Subtotal\n", - " # Renter occupied\n", - " # greater than 100% of HAMFI\n", - " # not computed (no/negative income)\n", - " # All\n", - "]\n", - "\n", - "# T8_est68\tSubtotalRenter occupied\tAll\tAll\tAll\n", - "RENTER_OCCUPIED_POPULATION_FIELD = \"T8_est68\"" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "housingburden[\"current_summed_methodology\"] = housingburden[\n", - " OWNER_OCCUPIED_NUMERATOR_FIELDS\n", - "].sum(axis=1) + housingburden[RENTER_OCCUPIED_NUMERATOR_FIELDS].sum(axis=1)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "housingburden[\"current_methodology_denominator\"] = (\n", - " housingburden[OWNER_OCCUPIED_POPULATION_FIELD]\n", - " + housingburden[RENTER_OCCUPIED_POPULATION_FIELD]\n", - " - housingburden[OWNER_OCCUPIED_NOT_COMPUTED_FIELDS].sum(axis=1)\n", - " - housingburden[RENTER_OCCUPIED_NOT_COMPUTED_FIELDS].sum(axis=1)\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "housingburden[\"current_methodology_denominator_sans_not_computed\"] = (\n", - " housingburden[OWNER_OCCUPIED_POPULATION_FIELD]\n", - " + housingburden[RENTER_OCCUPIED_POPULATION_FIELD]\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "housingburden[\"current_methodology_percent\"] = np.round(\n", - " (housingburden[\"current_summed_methodology\"] / housingburden[\"current_methodology_denominator\"] ), 2) * 100" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Now we construct the distribution of differences in the number of owned and rented burdened households\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Percentiles Comparison" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "final_df = housingburden[['FIPS_tract_id', 'state','hbrd_rank','hbrd_score', 'summed', \n", - " 'current_summed_methodology', 'T8_est1', \n", - " \"current_methodology_denominator_sans_not_computed\",\n", - " 'current_methodology_denominator', 'current_methodology_percent']]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### First notice here that **T8_est1** and **current_methodology_denominator** should represent same or similar aggregates. In general, we cen see that the current computation performed results in a differerntial that undercounts the total occupied and rental households." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/sarahluw/.pyenv/versions/3.6.2/envs/my-virtual-env-3.6.2/lib/python3.6/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " \n" - ] - } - ], - "source": [ - "final_df[\"differences_aggregate_denominator\"] = (\n", - " final_df[\"current_methodology_denominator\"] - final_df[\"T8_est1\"] \n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/sarahluw/.pyenv/versions/3.6.2/envs/my-virtual-env-3.6.2/lib/python3.6/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " \n" - ] - } - ], - "source": [ - "final_df[\"differences_aggregate_denominator_sans_not_computed\"] = (\n", - " final_df[\"current_methodology_denominator\"] - final_df[\"T8_est1\"] \n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(12, 8))\n", - "plt.title('Distribution of differences between aggregate totals that normalizes tabulation of poverty households')\n", - "# Set x-axis label\n", - "plt.xlabel('Aggregate differences in total owner and renter occupied low-income households < 80%')\n", - "# Set y-axis label\n", - "plt.ylabel('Relative Frequency in Support')\n", - "\n", - "sns.histplot(final_df[\"differences_aggregate_denominator\"])" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(12, 8))\n", - "plt.title('Distribution of differences between aggregate totals that normalizes tabulation of poverty households (with removal of not computed fields) ')\n", - "# Set x-axis label\n", - "plt.xlabel('Aggregate differences in total owner and renter occupied low-income households')\n", - "# Set y-axis label\n", - "plt.ylabel('Relative Frequency in Support')\n", - "\n", - "sns.histplot(final_df[\"differences_aggregate_denominator_sans_not_computed\"])" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/sarahluw/.pyenv/versions/3.6.2/envs/my-virtual-env-3.6.2/lib/python3.6/site-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " after removing the cwd from sys.path.\n" - ] - } - ], - "source": [ - "final_df[\"current_methodology_percentile_rank\"] = final_df[\"current_methodology_percent\"].rank(\n", - " pct=True,\n", - " # Set ascending to the parameter value.\n", - " ascending=True,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/sarahluw/.pyenv/versions/3.6.2/envs/my-virtual-env-3.6.2/lib/python3.6/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " \"\"\"Entry point for launching an IPython kernel.\n", - "/Users/sarahluw/.pyenv/versions/3.6.2/envs/my-virtual-env-3.6.2/lib/python3.6/site-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " after removing the cwd from sys.path.\n" - ] - } - ], - "source": [ - "final_df[\"new_threshold_exceeded\"] = (final_df['hbrd_rank'] >= 90)\n", - "\n", - "final_df[\"current_threshold_exceeded\"] = (final_df[\n", - " 'current_methodology_percentile_rank'] >= 0.90)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Only include non-NA tracts for comparison purposes" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "# first save NA tracts that were considered unreliable\n", - "ineligible_tracts = list(final_df[final_df[\"hbrd_rank\"].isna()][\"FIPS_tract_id\"].values)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "5243" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "len(ineligible_tracts)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### How many tracts are ineligible according to CalEnvironScreen but are considerd in Score L?\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "final_current_methodology = final_df[final_df[\"current_methodology_percentile_rank\"] >= 0.90]" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(7323, 15)" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "final_current_methodology.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(53, 15)" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# 53 tracts\n", - "final_current_methodology[\n", - " final_current_methodology.FIPS_tract_id.isin(ineligible_tracts)].shape" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "non_null_df = final_df.copy()" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "non_null_df.to_csv(\"comparison_df_final.csv\")" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(7323, 15)" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# number of tracks eligible\n", - "non_null_df[non_null_df[\"current_methodology_percentile_rank\"] >= 0.90].shape" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(12, 8))\n", - "plt.title(\"Distribution of Percentiles for Housing Burden (Score L)\")\n", - "# Set x-axis label\n", - "plt.xlabel('Percentile (although currently not represented as a percentage)')\n", - "# Set y-axis label\n", - "plt.ylabel('Relative Frequency in Support')\n", - "\n", - "sns.histplot(non_null_df[\"current_methodology_percentile_rank\"])" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "False 65733\n", - "True 7323\n", - "Name: current_threshold_exceeded, dtype: int64" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "non_null_df[\"current_threshold_exceeded\"].value_counts()" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "False 66255\n", - "True 6801\n", - "Name: new_threshold_exceeded, dtype: int64" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "non_null_df[\"new_threshold_exceeded\"].value_counts()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# (non_null_df[\"new_threshold_exceeded\"] == True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Score L Criterion as burden but not CalEnvironscreen" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(2794, 15)" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# this includes uineligible np.nan values too\n", - "predicate_1 = (non_null_df['current_threshold_exceeded'] == True) & (non_null_df['new_threshold_exceeded'] != True)\n", - "\n", - "non_null_df[predicate_1].shape" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [], - "source": [ - "# Where Score L is considered burdened but not in CalEnviroScreen\n", - "score_l_considered_burdened = non_null_df[predicate_1]" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [], - "source": [ - "import us\n", - "\n", - "mapping = us.states.mapping('fips', 'abbr')" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [], - "source": [ - "# Inserted after the basic stats definition.\n", - "# Load demographic data\n", - "import pathlib\n", - "\n", - "DATA_DIR = pathlib.Path.cwd().parent / \"data\"\n", - "COMPARISON_OUTPUTS_DIR = DATA_DIR / \"comparison_outputs\"\n", - "\n", - "demographics_path = DATA_DIR / \"dataset\" / \"census_acs_2019\" / \"usa.csv\"\n", - "\n", - "demographics_df = pd.read_csv(\n", - " demographics_path,\n", - " dtype={\"GEOID10_TRACT\": \"string\"},\n", - ")\n", - "\n", - "# Set some field names\n", - "BLACK_FIELD_NAME = \"Black or African American alone\"\n", - "AMERICAN_INDIAN_FIELD_NAME = \"American Indian and Alaska Native alone\"\n", - "ASIAN_FIELD_NAME = \"Asian alone\"\n", - "HAWAIIAN_FIELD_NAME = \"Native Hawaiian and Other Pacific alone\"\n", - "TWO_OR_MORE_RACES_FIELD_NAME = \"Two or more races\"\n", - "NON_HISPANIC_WHITE_FIELD_NAME = \"Non-Hispanic White\"\n", - "HISPANIC_FIELD_NAME = \"Hispanic or Latino\"\n", - "PERCENT_PREFIX = \"Percent \"\n", - "\n", - "RE_OUTPUT_FIELDS = [\n", - " BLACK_FIELD_NAME,\n", - " AMERICAN_INDIAN_FIELD_NAME,\n", - " ASIAN_FIELD_NAME,\n", - " HAWAIIAN_FIELD_NAME,\n", - " TWO_OR_MORE_RACES_FIELD_NAME,\n", - " NON_HISPANIC_WHITE_FIELD_NAME,\n", - " HISPANIC_FIELD_NAME,\n", - "]\n", - "\n", - "RE_PERCENT_OUTPUT_FIELDS = [PERCENT_PREFIX + field for field in RE_OUTPUT_FIELDS]\n", - "\n", - "columns_to_keep = (\n", - " [\"GEOID10_TRACT\"]\n", - " + RE_OUTPUT_FIELDS\n", - " + RE_PERCENT_OUTPUT_FIELDS\n", - " + ['Percent of individuals < 200% Federal Poverty Line', \n", - " 'Median value ($) of owner-occupied housing units',\n", - " 'Percent individuals age 25 or over with less than high school degree',\n", - " 'Percent enrollment in college or graduate school',\n", - " 'Linguistic isolation (percent)']\n", - ")\n", - "\n", - "\n", - "def highlight_medians(s):\n", - " # highlight if the current median is greater than the median of medians for that series\n", - " is_greater_than_median = s > s.median()\n", - " return ['color: pink; background-color:#7272FE'\n", - " if cell else '' for cell in is_greater_than_median]\n", - "# Join the demographics in.\n", - "merged_df_score_l = score_l_considered_burdened.merge(\n", - " demographics_df[columns_to_keep],\n", - " left_on=\"FIPS_tract_id\",\n", - " right_on=\"GEOID10_TRACT\",\n", - " how=\"inner\"\n", - ")\n", - "\n", - "# these are not converted into percent 0 - 100 scale\n", - "percent_cols = [x for x in merged_df_score_l.columns if \n", - " 'Percent' in x or '(percent)' in x\n", - " ]\n", - "\n", - "merged_df_score_l[\n", - " percent_cols] = merged_df_score_l[\n", - " percent_cols].apply(lambda x: x * 100)\n", - "\n", - "for idx, row in merged_df_score_l.iterrows():\n", - " current_row = str(merged_df_score_l.loc[idx, 'state'])\n", - " state = mapping.get(current_row, None)\n", - " merged_df_score_l.loc[idx, 'state_name'] = state\n", - "\n", - "grouped_stats_score_l = merged_df_score_l.groupby([\"state_name\"]).agg({\n", - " 'GEOID10_TRACT': 'nunique',\n", - " 'Percent of individuals < 200% Federal Poverty Line': [np.median, np.std],\n", - " 'Median value ($) of owner-occupied housing units': [np.median, np.std],\n", - " 'Percent individuals age 25 or over with less than high school degree': [np.median, np.std],\n", - " 'Percent enrollment in college or graduate school': [np.median, np.std],\n", - " 'Percent Black or African American alone': [np.median, np.std],\n", - " 'Percent American Indian and Alaska Native alone': [np.median, np.std],\n", - " 'Percent Non-Hispanic White': [np.median, np.std], \n", - " 'Linguistic isolation (percent)': [np.median, np.std],\n", - " 'Percent Hispanic or Latino': [np.median, np.std],\n", - " 'hbrd_rank': [np.median, np.std],\n", - " 'current_methodology_percent': [np.median, np.std],\n", - " 'current_summed_methodology': [np.median, np.std, np.sum]\n", - "}).reset_index()\n", - "\n", - "\n", - "grouped_stats_score_l.columns = [' '.join(col).strip() for \n", - " col in grouped_stats_score_l.columns.values]\n", - "\n", - "grouped_stats_score_l = grouped_stats_score_l[[x for x in grouped_stats_score_l \n", - " if \"median\" in x and \n", - " \"Percent\" in x] + [\"GEOID10_TRACT nunique\", \n", - " 'current_summed_methodology sum', \"state_name\"]]\n", - "\n", - "grouped_stats_score_l.set_index(\"state_name\", inplace=True)\n", - "\n", - "grouped_stats_score_l = grouped_stats_score_l.rename(columns={\n", - "'Percent of individuals < 200% Federal Poverty Line median'\n", - " : 'Percent of individuals < 200% Federal Poverty Line (median across all tracts)', \n", - "'Percent individuals age 25 or over with less than high school degree median':\n", - "'Percent individuals age 25 or over with less than high school degree (median across all tracts)', \n", - "'Percent enrollment in college or graduate school median'\n", - " :'Percent enrollment in college or graduate school (median across all tracts)',\n", - "'Percent Black or African American alone median':\n", - " 'Percent Black or African American alone (median across all tracts)',\n", - "'Percent American Indian and Alaska Native alone median':\n", - " 'Percent American Indian and Alaska Native alone (median across all tracts)',\n", - "'Percent Non-Hispanic White median':\n", - " 'Percent Non-Hispanic White (median across all tracts)',\n", - "'Percent Hispanic or Latino median':\n", - " 'Percent Hispanic or Latino (median across all tracts)',\n", - "'GEOID10_TRACT nunique': \"Total Number of Unique Tracts\",\n", - "\"current_summed_methodology sum\": \"Total Owned and Rented Burdened Households (Current Aggregation Methodology)\" \n", - "})" - ] - }, - { - "cell_type": "code", - "execution_count": 106, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(2794, 36)" - ] - }, - "execution_count": 106, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "merged_df_score_l.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 108, - "metadata": {}, - "outputs": [], - "source": [ - "merged_df_score_l.to_csv(\"./experiment_4_texas_example/merged_df_score_l_2794.csv\")" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['Percent of individuals < 200% Federal Poverty Line (median across all tracts)',\n", - " 'Percent individuals age 25 or over with less than high school degree (median across all tracts)',\n", - " 'Percent enrollment in college or graduate school (median across all tracts)',\n", - " 'Percent Black or African American alone (median across all tracts)',\n", - " 'Percent American Indian and Alaska Native alone (median across all tracts)',\n", - " 'Percent Non-Hispanic White (median across all tracts)',\n", - " 'Percent Hispanic or Latino (median across all tracts)',\n", - " 'Total Number of Unique Tracts',\n", - " 'Total Owned and Rented Burdened Households (Current Aggregation Methodology)'],\n", - " dtype='object')" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "grouped_stats_score_l.columns" - ] - }, - { - "cell_type": "code", - "execution_count": 117, - "metadata": {}, - "outputs": [], - "source": [ - "top_10_represented_score_l = list(merged_df_score_l[\"state_name\"].value_counts()[:10].keys())" - ] - }, - { - "cell_type": "code", - "execution_count": 118, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "81.53185397279886" - ] - }, - "execution_count": 118, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "representative_score_l = merged_df_score_l[\n", - " merged_df_score_l.state_name.isin(top_10_represented_score_l)]\n", - "\n", - "representative_score_l.shape[0] / merged_df_score_l.shape[0] * 100" - ] - }, - { - "cell_type": "code", - "execution_count": 120, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "g = sns.FacetGrid(representative_score_l[\n", - " representative_score_l.state_name.isin(top_10_represented_score_l[:3])], \n", - " col=\"state_name\", \n", - " margin_titles=True)\n", - "g.map_dataframe(sns.scatterplot, x=\"Median value ($) of owner-occupied housing units\", \n", - " y=\"current_summed_methodology\")\n", - "g.set_axis_labels(\"Median Value of owner occupied units ($)\", \"Number of units\")\n", - "g.set_titles(col_template=\"{col_name} ytacts\", row_template=\"{row_name}\")\n", - "# g.set(xlim=(0, 60), ylim=(0, 12), xticks=[10, 30, 50], yticks=[2, 6, 10])\n", - "g.tight_layout()" - ] - }, - { - "cell_type": "code", - "execution_count": 131, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 0.98, 'Empirical CDF of 81% of the 2794 tracts that are considered burdened using Score L')" - ] - }, - "execution_count": 131, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "ax2 = sns.displot(data=representative_score_l,\n", - " x='Percent individuals age 25 or over with less than high school degree', hue=\"state_name\", kind=\"ecdf\")\n", - "\n", - "ax2.fig.subplots_adjust(top=0.9)\n", - "ax2.fig.suptitle(\"Empirical CDF of 81% of the 2794 tracts that are considered burdened using Score L\", fontsize=16)" - ] - }, - { - "cell_type": "code", - "execution_count": 137, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 0.98, 'Empirical CDF of 81% of the 2794 tracts that are considered burdened using Score L')" - ] - }, - "execution_count": 137, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# split - WA, AZ exceptions\n", - "\n", - "ax1 = sns.displot(data=representative_score_l,\n", - " x=\"Percent Black or African American alone\", hue=\"state_name\", kind=\"ecdf\")\n", - "\n", - "ax1.fig.subplots_adjust(top=0.9)\n", - "ax1.fig.suptitle(\"Empirical CDF of 81% of the 2794 tracts that are considered burdened using Score L\", fontsize=16)" - ] - }, - { - "cell_type": "code", - "execution_count": 145, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 0.98, 'Empirical CDF of 81% of the 2794 tracts that are considered burdened using Score L')" - ] - }, - "execution_count": 145, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "ax = sns.displot(data=representative_score_l,\n", - " x=\"Percent Hispanic or Latino\", hue=\"state_name\", kind=\"ecdf\")\n", - "\n", - "ax.fig.subplots_adjust(top=0.9)\n", - "ax.fig.suptitle(\"Empirical CDF of 81% of the 2794 tracts that are considered burdened using Score L\", fontsize=16)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If the samples are samples from the population - a big assumption - in question and the sampling follows rules and things we take the mean:\n", - "\n", - "The mean of the median distribution (an order statistic) is the best estimate for the median. It's the best least unbiased estimator. The answer is not the median of that distribution.\n", - "\n", - "The answer is also not if the mean is an estimate of the median. Completely unrelated.\n", - "\n", - "Even if the original population is skewed, the distribution of a sampling statistic will be normalized - recall the the central limit theorem for more details\n", - "\n", - "The standard error of that mean should give you what you want to know to confidently make statements of the true population median across all states" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [], - "source": [ - "list_of_columns = list(grouped_stats_score_l.columns)\n", - "values_1 = list(grouped_stats_score_l[list_of_columns].mean())" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [], - "source": [ - "values_1.extend([True, False])" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[57.05272445515469,\n", - " 19.383812769449793,\n", - " 8.132533443213486,\n", - " 33.69280603472747,\n", - " 1.2213947311592683,\n", - " 31.32188596954498,\n", - " 19.378347515748676,\n", - " 62.08888888888889,\n", - " 40949.2,\n", - " True,\n", - " False]" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "values_1" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# CalenviroScreen Burden" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": {}, - "outputs": [], - "source": [ - "predicate_2 = (non_null_df['current_threshold_exceeded'] == False) & (non_null_df['new_threshold_exceeded'] == True)\n", - "\n", - "cal_ej_screen_burdened = non_null_df[predicate_2]" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "metadata": {}, - "outputs": [], - "source": [ - "# Join the demographics in.\n", - "merged_df_score_ejcreen = cal_ej_screen_burdened.merge(\n", - " demographics_df[columns_to_keep],\n", - " left_on=\"FIPS_tract_id\",\n", - " right_on=\"GEOID10_TRACT\",\n", - " how=\"inner\"\n", - ")\n", - "\n", - "# these are not converted into percent 0 - 100 scale\n", - "percent_cols = [x for x in merged_df_score_ejcreen.columns if \n", - " 'Percent' in x or '(percent)' in x\n", - " ]\n", - "\n", - "merged_df_score_ejcreen[\n", - " percent_cols] = merged_df_score_ejcreen[\n", - " percent_cols].apply(lambda x: x * 100)\n", - "\n", - "for idx, row in merged_df_score_ejcreen.iterrows():\n", - " current_row = str(merged_df_score_ejcreen.loc[idx, 'state'])\n", - " state = mapping.get(current_row, None)\n", - " merged_df_score_ejcreen.loc[idx, 'state_name'] = state\n", - "\n", - "grouped_stats_score_ej_screen = merged_df_score_ejcreen.groupby([\"state_name\"]).agg({\n", - " 'GEOID10_TRACT': 'nunique',\n", - " 'Percent of individuals < 200% Federal Poverty Line': [np.median, np.std],\n", - " 'Median value ($) of owner-occupied housing units': [np.median, np.std],\n", - " 'Percent individuals age 25 or over with less than high school degree': [np.median, np.std],\n", - " 'Percent enrollment in college or graduate school': [np.median, np.std],\n", - " 'Percent Black or African American alone': [np.median, np.std],\n", - " 'Percent American Indian and Alaska Native alone': [np.median, np.std],\n", - " 'Percent Non-Hispanic White': [np.median, np.std], \n", - " 'Linguistic isolation (percent)': [np.median, np.std],\n", - " 'Percent Hispanic or Latino': [np.median, np.std],\n", - " 'hbrd_rank': [np.median, np.std],\n", - " 'current_methodology_percent': [np.median, np.std],\n", - " 'current_summed_methodology': [np.median, np.std, np.sum]\n", - "}).reset_index()\n", - "\n", - "\n", - "grouped_stats_score_ej_screen.columns = [' '.join(col).strip() for \n", - " col in grouped_stats_score_ej_screen.columns.values]\n", - "\n", - "grouped_stats_score_ej_screen = grouped_stats_score_ej_screen[[x for x in grouped_stats_score_ej_screen \n", - " if \"median\" in x and \n", - " \"Percent\" in x] + [\"GEOID10_TRACT nunique\", \n", - " 'current_summed_methodology sum', \"state_name\"]]\n", - "\n", - "grouped_stats_score_ej_screen.set_index(\"state_name\", inplace=True)\n", - "\n", - "grouped_stats_score_ej_screen = grouped_stats_score_ej_screen.rename(columns={\n", - "'Percent of individuals < 200% Federal Poverty Line median'\n", - " : 'Percent of individuals < 200% Federal Poverty Line (median across all tracts)', \n", - "'Percent individuals age 25 or over with less than high school degree median':\n", - "'Percent individuals age 25 or over with less than high school degree (median across all tracts)', \n", - "'Percent enrollment in college or graduate school median'\n", - " :'Percent enrollment in college or graduate school (median across all tracts)',\n", - "'Percent Black or African American alone median':\n", - " 'Percent Black or African American alone (median across all tracts)',\n", - "'Percent American Indian and Alaska Native alone median':\n", - " 'Percent American Indian and Alaska Native alone (median across all tracts)',\n", - "'Percent Non-Hispanic White median':\n", - " 'Percent Non-Hispanic White (median across all tracts)',\n", - "'Percent Hispanic or Latino median':\n", - " 'Percent Hispanic or Latino (median across all tracts)',\n", - "'GEOID10_TRACT nunique': \"Total Number of Unique Tracts\",\n", - "\"current_summed_methodology sum\": \"Total Owned and Rented Burdened Households (Current Aggregation Methodology)\" \n", - "})" - ] - }, - { - "cell_type": "code", - "execution_count": 123, - "metadata": {}, - "outputs": [], - "source": [ - "top_10_represented = list(merged_df_score_ejcreen[\"state_name\"].value_counts()[:10].keys())" - ] - }, - { - "cell_type": "code", - "execution_count": 124, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(2272, 36)" - ] - }, - "execution_count": 124, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "merged_df_score_ejcreen.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 125, - "metadata": {}, - "outputs": [], - "source": [ - "# merged_df_score_ejcreen.to_csv(\"./experiment_4_texas_example/merged_df_score_ejcreen_2272.csv\")" - ] - }, - { - "cell_type": "code", - "execution_count": 126, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "43.221830985915496" - ] - }, - "execution_count": 126, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "representative_calenvironscreen = merged_df_score_ejcreen[\n", - " merged_df_score_ejcreen.state_name.isin(top_10_represented)]\n", - "\n", - "representative_calenvironscreen.shape[0] / merged_df_score_ejcreen.shape[0] * 100" - ] - }, - { - "cell_type": "code", - "execution_count": 128, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "TX 206\n", - "PA 114\n", - "WA 93\n", - "NC 90\n", - "MN 89\n", - "Name: state_name, dtype: int64" - ] - }, - "execution_count": 128, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "merged_df_score_ejcreen[\"state_name\"].value_counts()[:5]" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "metadata": {}, - "outputs": [], - "source": [ - "cdc_places = pd.read_csv(\"../data/dataset/cdc_places/usa.csv\")\n", - "\n", - "cdc_le = pd.read_csv(\"../data/dataset/cdc_life_expectancy/usa.csv\")\n", - "\n", - "# all CDC datasets\n", - "temp_cdc = cdc_places.merge(cdc_le, on = \"GEOID10_TRACT\")" - ] - }, - { - "cell_type": "code", - "execution_count": 85, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['FIPS_tract_id', 'state', 'hbrd_rank', 'hbrd_score', 'summed',\n", - " 'current_summed_methodology', 'T8_est1',\n", - " 'current_methodology_denominator_sans_not_computed',\n", - " 'current_methodology_denominator', 'current_methodology_percent',\n", - " 'differences_aggregate_denominator',\n", - " 'differences_aggregate_denominator_sans_not_computed',\n", - " 'current_methodology_percentile_rank', 'new_threshold_exceeded',\n", - " 'current_threshold_exceeded', 'GEOID10_TRACT',\n", - " 'Black or African American alone',\n", - " 'American Indian and Alaska Native alone', 'Asian alone',\n", - " 'Native Hawaiian and Other Pacific alone', 'Two or more races',\n", - " 'Non-Hispanic White', 'Hispanic or Latino',\n", - " 'Percent Black or African American alone',\n", - " 'Percent American Indian and Alaska Native alone',\n", - " 'Percent Asian alone',\n", - " 'Percent Native Hawaiian and Other Pacific alone',\n", - " 'Percent Two or more races', 'Percent Non-Hispanic White',\n", - " 'Percent Hispanic or Latino',\n", - " 'Percent of individuals < 200% Federal Poverty Line',\n", - " 'Median value ($) of owner-occupied housing units',\n", - " 'Percent individuals age 25 or over with less than high school degree',\n", - " 'Percent enrollment in college or graduate school',\n", - " 'Linguistic isolation (percent)', 'state_name'],\n", - " dtype='object')" - ] - }, - "execution_count": 85, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "representative_calenvironscreen.columns" - ] - }, - { - "cell_type": "code", - "execution_count": 129, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "g = sns.FacetGrid(representative_calenvironscreen[\n", - " representative_calenvironscreen.state_name.isin(top_10_represented[:3])], \n", - " col=\"state_name\", \n", - " margin_titles=True)\n", - "g.map_dataframe(sns.scatterplot, x=\"Median value ($) of owner-occupied housing units\", \n", - " y=\"current_summed_methodology\")\n", - "g.set_axis_labels(\"Median Value of owner occupied units ($)\", \"Number of units\")\n", - "g.set_titles(col_template=\"{col_name} ytacts\", row_template=\"{row_name}\")\n", - "# g.set(xlim=(0, 60), ylim=(0, 12), xticks=[10, 30, 50], yticks=[2, 6, 10])\n", - "g.tight_layout()" - ] - }, - { - "cell_type": "code", - "execution_count": 147, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 0.98, 'Empirical CDF of ~43% of the 2272 tracts that are considered burdened using CRHB')" - ] - }, - "execution_count": 147, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "ax2 = sns.displot(data=representative_calenvironscreen,\n", - " x='Percent of individuals < 200% Federal Poverty Line', hue=\"state_name\", kind=\"ecdf\")\n", - "\n", - "ax2.fig.subplots_adjust(top=0.9)\n", - "ax2.fig.suptitle(\"Empirical CDF of ~43% of the 2272 tracts that are considered burdened using CRHB\", fontsize=16) \n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 138, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 0.98, 'Empirical CDF of ~43% of the 2272 tracts that are considered burdened using CRHB')" - ] - }, - "execution_count": 138, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "ax2 = sns.displot(data=representative_calenvironscreen,\n", - " x='Percent individuals age 25 or over with less than high school degree', hue=\"state_name\", kind=\"ecdf\")\n", - "\n", - "ax2.fig.subplots_adjust(top=0.9)\n", - "ax2.fig.suptitle(\"Empirical CDF of ~43% of the 2272 tracts that are considered burdened using CRHB\", fontsize=16)" - ] - }, - { - "cell_type": "code", - "execution_count": 139, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 0.98, 'Empirical CDF of ~43% of the 2272 tracts that are considered burdened using CRHB')" - ] - }, - "execution_count": 139, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# split - WA, AZ exceptions\n", - "\n", - "ax1 = sns.displot(data=representative_calenvironscreen,\n", - " x=\"Percent Black or African American alone\", hue=\"state_name\", kind=\"ecdf\")\n", - "\n", - "ax1.fig.subplots_adjust(top=0.9)\n", - "ax1.fig.suptitle(\"Empirical CDF of ~43% of the 2272 tracts that are considered burdened using CRHB\", fontsize=16)" - ] - }, - { - "cell_type": "code", - "execution_count": 140, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 0.98, 'Empirical CDF of ~43% of the 2272 tracts that are considered burdened using CRHB')" - ] - }, - "execution_count": 140, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "ax = sns.displot(data=representative_calenvironscreen,\n", - " x=\"Percent Hispanic or Latino\", hue=\"state_name\", kind=\"ecdf\")\n", - "\n", - "ax.fig.subplots_adjust(top=0.9)\n", - "ax.fig.suptitle(\"Empirical CDF of ~43% of the 2272 tracts that are considered burdened using CRHB\", fontsize=16)" - ] - }, - { - "cell_type": "code", - "execution_count": 142, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "TX 206\n", - "PA 114\n", - "WA 93\n", - "NC 90\n", - "MN 89\n", - "OH 89\n", - "MI 84\n", - "VA 75\n", - "FL 73\n", - "AZ 69\n", - "Name: state_name, dtype: int64" - ] - }, - "execution_count": 142, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "representative_calenvironscreen.state_name.value_counts()" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(2272, 36)" - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "merged_df_score_ejcreen.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "2272" - ] - }, - "execution_count": 38, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "grouped_stats_score_ej_screen['Total Number of Unique Tracts'].sum()" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [], - "source": [ - "values_2 = list(grouped_stats_score_ej_screen[list_of_columns].mean())" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [], - "source": [ - "values_2.extend([False, True])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Both Met Criterion" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [], - "source": [ - "predicate_3 = (non_null_df['current_threshold_exceeded'] == True) & (non_null_df['new_threshold_exceeded'] == True)\n", - "\n", - "union_df = non_null_df[predicate_3]" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(4529, 15)" - ] - }, - "execution_count": 42, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "union_df.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 241, - "metadata": {}, - "outputs": [], - "source": [ - "# Join the demographics in.\n", - "merged_df_both_met = union_df.merge(\n", - " demographics_df[columns_to_keep],\n", - " left_on=\"FIPS_tract_id\",\n", - " right_on=\"GEOID10_TRACT\",\n", - " how=\"inner\"\n", - ")\n", - "\n", - "# these are not converted into percent 0 - 100 scale\n", - "percent_cols = [x for x in merged_df_both_met.columns if \n", - " 'Percent' in x or '(percent)' in x\n", - " ]\n", - "\n", - "merged_df_both_met[\n", - " percent_cols] = merged_df_both_met[\n", - " percent_cols].apply(lambda x: x * 100)\n", - "\n", - "for idx, row in merged_df_both_met.iterrows():\n", - " current_row = str(merged_df_both_met.loc[idx, 'state'])\n", - " state = mapping.get(current_row, None)\n", - " merged_df_both_met.loc[idx, 'state_name'] = state\n", - "\n", - "grouped_stats_both_met = merged_df_both_met.groupby([\"state_name\"]).agg({\n", - " 'GEOID10_TRACT': 'nunique',\n", - " 'Percent of individuals < 200% Federal Poverty Line': [np.median, np.std],\n", - " 'Median value ($) of owner-occupied housing units': [np.median, np.std],\n", - " 'Percent individuals age 25 or over with less than high school degree': [np.median, np.std],\n", - " 'Percent enrollment in college or graduate school': [np.median, np.std],\n", - " 'Percent Black or African American alone': [np.median, np.std],\n", - " 'Percent American Indian and Alaska Native alone': [np.median, np.std],\n", - " 'Percent Non-Hispanic White': [np.median, np.std], \n", - " 'Linguistic isolation (percent)': [np.median, np.std],\n", - " 'Percent Hispanic or Latino': [np.median, np.std],\n", - " 'hbrd_rank': [np.median, np.std],\n", - " 'current_methodology_percent': [np.median, np.std],\n", - " 'current_summed_methodology': [np.median, np.std, np.sum]\n", - "}).reset_index()\n", - "\n", - "\n", - "grouped_stats_both_met.columns = [' '.join(col).strip() for \n", - " col in grouped_stats_both_met.columns.values]\n", - "\n", - "grouped_stats_both_met = grouped_stats_both_met[[x for x in grouped_stats_both_met \n", - " if \"median\" in x and \n", - " \"Percent\" in x] + [\"GEOID10_TRACT nunique\", \n", - " 'current_summed_methodology sum', \"state_name\"]]\n", - "\n", - "grouped_stats_both_met.set_index(\"state_name\", inplace=True)\n", - "\n", - "grouped_stats_both_met = grouped_stats_both_met.rename(columns={\n", - "'Percent of individuals < 200% Federal Poverty Line median'\n", - " : 'Percent of individuals < 200% Federal Poverty Line (median across all tracts)', \n", - "'Percent individuals age 25 or over with less than high school degree median':\n", - "'Percent individuals age 25 or over with less than high school degree (median across all tracts)', \n", - "'Percent enrollment in college or graduate school median'\n", - " :'Percent enrollment in college or graduate school (median across all tracts)',\n", - "'Percent Black or African American alone median':\n", - " 'Percent Black or African American alone (median across all tracts)',\n", - "'Percent American Indian and Alaska Native alone median':\n", - " 'Percent American Indian and Alaska Native alone (median across all tracts)',\n", - "'Percent Non-Hispanic White median':\n", - " 'Percent Non-Hispanic White (median across all tracts)',\n", - "'Percent Hispanic or Latino median':\n", - " 'Percent Hispanic or Latino (median across all tracts)',\n", - "'GEOID10_TRACT nunique': \"Total Number of Unique Tracts\",\n", - "\"current_summed_methodology sum\": \"Total Owned and Rented Burdened Households (Current Aggregation Methodology)\" \n", - "})" - ] - }, - { - "cell_type": "code", - "execution_count": 242, - "metadata": {}, - "outputs": [], - "source": [ - "values_3 = list(grouped_stats_both_met[list_of_columns].mean())" - ] - }, - { - "cell_type": "code", - "execution_count": 243, - "metadata": {}, - "outputs": [], - "source": [ - "values_3.extend([True, True])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Neither Met Criterion " - ] - }, - { - "cell_type": "code", - "execution_count": 244, - "metadata": {}, - "outputs": [], - "source": [ - "predicate_4 = (non_null_df[\n", - " 'current_threshold_exceeded'] == False) & (\n", - " non_null_df['new_threshold_exceeded'] == False)\n", - "\n", - "negation_union_df = non_null_df[predicate_4]" - ] - }, - { - "cell_type": "code", - "execution_count": 245, - "metadata": {}, - "outputs": [], - "source": [ - "# Join the demographics in.\n", - "merged_df_negation_union = negation_union_df.merge(\n", - " demographics_df[columns_to_keep],\n", - " left_on=\"FIPS_tract_id\",\n", - " right_on=\"GEOID10_TRACT\",\n", - " how=\"inner\"\n", - ")\n", - "\n", - "# these are not converted into percent 0 - 100 scale\n", - "percent_cols = [x for x in merged_df_negation_union.columns if \n", - " 'Percent' in x or '(percent)' in x\n", - " ]\n", - "\n", - "merged_df_negation_union[\n", - " percent_cols] = merged_df_negation_union[\n", - " percent_cols].apply(lambda x: x * 100)\n", - "\n", - "for idx, row in merged_df_negation_union.iterrows():\n", - " current_row = str(merged_df_negation_union.loc[idx, 'state'])\n", - " state = mapping.get(current_row, None)\n", - " merged_df_negation_union.loc[idx, 'state_name'] = state\n", - "\n", - "grouped_stats_both_not_met = merged_df_negation_union.groupby([\"state_name\"]).agg({\n", - " 'GEOID10_TRACT': 'nunique',\n", - " 'Percent of individuals < 200% Federal Poverty Line': [np.median, np.std],\n", - " 'Median value ($) of owner-occupied housing units': [np.median, np.std],\n", - " 'Percent individuals age 25 or over with less than high school degree': [np.median, np.std],\n", - " 'Percent enrollment in college or graduate school': [np.median, np.std],\n", - " 'Percent Black or African American alone': [np.median, np.std],\n", - " 'Percent American Indian and Alaska Native alone': [np.median, np.std],\n", - " 'Percent Non-Hispanic White': [np.median, np.std], \n", - " 'Linguistic isolation (percent)': [np.median, np.std],\n", - " 'Percent Hispanic or Latino': [np.median, np.std],\n", - " 'hbrd_rank': [np.median, np.std],\n", - " 'current_methodology_percent': [np.median, np.std],\n", - " 'current_summed_methodology': [np.median, np.std, np.sum]\n", - "}).reset_index()\n", - "\n", - "\n", - "grouped_stats_both_not_met.columns = [' '.join(col).strip() for \n", - " col in grouped_stats_both_not_met.columns.values]\n", - "\n", - "grouped_stats_both_not_met = grouped_stats_both_not_met[[x for x in grouped_stats_both_not_met \n", - " if \"median\" in x and \n", - " \"Percent\" in x] + [\"GEOID10_TRACT nunique\", \n", - " 'current_summed_methodology sum', \"state_name\"]]\n", - "\n", - "grouped_stats_both_not_met.set_index(\"state_name\", inplace=True)\n", - "\n", - "grouped_stats_both_not_met = grouped_stats_both_not_met.rename(columns={\n", - "'Percent of individuals < 200% Federal Poverty Line median'\n", - " : 'Percent of individuals < 200% Federal Poverty Line (median across all tracts)', \n", - "'Percent individuals age 25 or over with less than high school degree median':\n", - "'Percent individuals age 25 or over with less than high school degree (median across all tracts)', \n", - "'Percent enrollment in college or graduate school median'\n", - " :'Percent enrollment in college or graduate school (median across all tracts)',\n", - "'Percent Black or African American alone median':\n", - " 'Percent Black or African American alone (median across all tracts)',\n", - "'Percent American Indian and Alaska Native alone median':\n", - " 'Percent American Indian and Alaska Native alone (median across all tracts)',\n", - "'Percent Non-Hispanic White median':\n", - " 'Percent Non-Hispanic White (median across all tracts)',\n", - "'Percent Hispanic or Latino median':\n", - " 'Percent Hispanic or Latino (median across all tracts)',\n", - "'GEOID10_TRACT nunique': \"Total Number of Unique Tracts\",\n", - "\"current_summed_methodology sum\": \"Total Owned and Rented Burdened Households (Current Aggregation Methodology)\" \n", - "})" - ] - }, - { - "cell_type": "code", - "execution_count": 246, - "metadata": {}, - "outputs": [], - "source": [ - "values_4 = list(grouped_stats_both_not_met[list_of_columns].mean())\n", - "\n", - "values_4.extend([False, False])" - ] - }, - { - "cell_type": "code", - "execution_count": 247, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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Score L Relative Housing Burden for all households met burden threshold CalEnviroScreen Housing Burden Met Burden Threshold (Ranked Percentile)
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" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 257, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_styled_max_min" - ] - }, - { - "cell_type": "code", - "execution_count": 270, - "metadata": {}, - "outputs": [], - "source": [ - "df_1 = pd.read_excel(\"score_l_burdened_not_calenviroscreen.xlsx\", engine='openpyxl')\n", - "df_1.set_index(\"state_name\", inplace=True)\n", - "df_2 = pd.read_excel(\"calenviroscreen_summary.xlsx\", engine='openpyxl')\n", - "df_2.set_index(\"state_name\", inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 272, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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Percent of individuals < 200% Federal Poverty Line (median across all tracts)Percent individuals age 25 or over with less than high school degree (median across all tracts)Percent enrollment in college or graduate school (median across all tracts)Percent Black or African American alone (median across all tracts)Percent American Indian and Alaska Native alone (median across all tracts)Percent Non-Hispanic White (median across all tracts)Percent Hispanic or Latino (median across all tracts)Total Number of Unique TractsTotal Owned and Rented Burdened Households (Current Aggregation Methodology)
state_name
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AL6.2119174.2839410.3314306.2122780.000000-9.011147-0.630017-28.0-15422.0
AR5.4320521.6018613.94275420.3190490.102459-23.263854-0.896646-35.0-16843.0
AZ13.59487111.979424-0.2720401.361388-0.915677-8.9533618.757832-39.0-23253.0
CA15.71759816.799587-0.7029510.2476470.415820-21.87051940.216194783.0542144.0
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Percent of individuals < 200% Federal Poverty Line (median across all tracts) Percent individuals age 25 or over with less than high school degree (median across all tracts) Percent enrollment in college or graduate school (median across all tracts) Percent Black or African American alone (median across all tracts) Percent American Indian and Alaska Native alone (median across all tracts) Percent Non-Hispanic White (median across all tracts) Percent Hispanic or Latino (median across all tracts) Total Number of Unique Tracts Total Owned and Rented Burdened Households (Current Aggregation Methodology)
state_name
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AL6.2119174.2839410.3314306.2122780.000000-9.011147-0.630017-28.000000-15422.000000
AR5.4320521.6018613.94275420.3190490.102459-23.263854-0.896646-35.000000-16843.000000
AZ13.59487111.979424-0.2720401.361388-0.915677-8.9533618.757832-39.000000-23253.000000
CA15.71759816.799587-0.7029510.2476470.415820-21.87051940.216194783.000000542144.000000
CO5.2679630.8162070.0893705.3386490.149809-24.42590310.133080-34.000000-17296.000000
CT20.3379166.8652140.574592-7.9523960.000000-9.3842333.73425348.00000033786.000000
DC20.2256199.277701-10.58936465.9467630.000000-49.789447-6.12477713.0000009673.000000
DE1.370275-1.231413-4.499021-27.8068720.5818989.5720516.704117-8.000000-2816.000000
FL12.7986417.913089-0.46445013.4531160.000000-19.5679552.96004488.00000071042.000000
GA0.6239272.733393-0.7999511.511527-0.093487-9.6807102.172740-3.00000012615.000000
HI4.8971419.1014610.265372-0.896060-0.094805-35.9393850.3791457.0000004771.000000
IA10.953716-0.708470-4.742017-1.6234740.73201219.552402-7.997991-53.000000-25766.000000
ID4.7221459.6434937.189788-0.311804-1.015539-15.99256919.816490-23.000000-16597.000000
IL5.1553795.303919-0.6307145.0376000.000000-25.0494806.05043593.00000054402.000000
IN2.1136622.4372930.7196577.3157130.0000001.3477450.492553-36.000000-16444.000000
KS5.264502-7.5629430.62746012.2458900.4666341.465143-0.964424-46.000000-21169.000000
KY8.7944366.6839700.862572-0.5751220.578035-6.1331015.455541-57.000000-27542.000000
LA8.3431173.4948501.3988047.9643770.000000-11.833643-1.60731027.00000019739.000000
MA8.9367727.7326661.0050288.4156980.000000-19.35966715.17047538.00000028222.000000
MD9.1005303.424516-1.34189921.8483530.000000-10.271062-2.199229-26.000000-8900.000000
ME12.5650099.122611-3.922820-0.880265-0.439467-5.913473-0.866007-25.000000-14441.000000
MI6.4537851.0685880.6898326.7088500.000000-8.072578-1.350049-23.000000-6868.000000
MN15.92096517.7407803.14160722.4983201.758343-38.33239622.509297-87.000000-46742.000000
MO4.976759-0.085646-3.57740847.1020900.000000-35.919908-2.378652-30.000000-13248.000000
MS8.4848791.040226-0.57274424.8631840.000000-22.9510000.051741-27.000000-12524.000000
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NC8.15338411.292289-2.8119826.4963310.078513-19.50466811.151508-71.000000-45011.000000
ND0.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.000000
NE7.48434337.676269-9.271518-7.5629470.634817-37.67089358.015686-27.000000-13452.000000
NH11.3336927.394888-1.6955912.0796890.000000-14.2165729.124264-16.000000-11851.000000
NJ5.9273278.713014-2.337923-10.3411420.000000-5.62311725.764242151.000000102626.000000
NM0.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.000000
NV7.4854532.632552-0.783104-0.5003270.0769852.5120228.68748512.00000014693.000000
NY12.3690888.434444-0.870792-10.4370370.0000004.47756715.929982573.000000407383.000000
OH5.1664312.6264700.16094516.3221080.000000-16.4005980.628654-21.000000355.000000
OK17.2678635.687201-2.7335255.227426-1.254840-4.62973913.684527-60.000000-25920.000000
OR10.3127027.677240-0.6675781.177527-0.110343-15.16862316.277201-35.000000-26320.000000
PA9.5685676.789819-1.1000452.7685510.000000-7.1880586.730014-63.000000-33293.000000
RI28.09766518.885313-28.0646809.5690740.344864-42.40429539.7406705.0000004138.000000
SC3.7095082.3040213.76689219.0487500.000000-12.7341471.243649-43.000000-22081.000000
SD0.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.000000
TN8.6929494.908793-1.48778131.1622240.000000-26.6405142.135541-31.000000-13021.000000
TX8.2296324.785835-0.0184457.6089030.000000-6.99150311.472775-103.000000-58670.000000
UT12.4011359.2794802.393714-1.0952252.211103-27.44416833.552393-31.000000-18553.000000
VA12.7479834.210496-2.29480120.5814870.000000-12.1926752.823834-48.000000-24962.000000
VT0.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.000000
WA0.8368433.285992-2.8539851.3650100.242227-14.7086995.595930-84.000000-61277.000000
WI15.5521194.422237-0.79604740.591511-0.526995-20.282972-3.363747-17.000000-8795.000000
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from API\n", - "\n", - " - Available data: https://api.census.gov/data.html\n", - " - Variables (aka, fields) you can query for: https://api.census.gov/data/2017/acs/acs5/profile/variables.html\n", - " - Browse variables for a place here: https://www.census.gov/acs/www/data/data-tables-and-tools/data-profiles/2014/\n", - " - Sample query: https://api.census.gov/data/2017/acs/acs5/profile?get=DP05_0001E&for=tract:400100&in=state:06+county:001\n", - " - County FIPS codes: https://www.nrcs.usda.gov/wps/portal/nrcs/detail/?cid=nrcs143_013697\n", - " \n", - "#### Variables name format\n", - "\n", - "More info: https://www.census.gov/data/developers/data-sets/acs-5year/data-notes.html\n", - "\n", - "variable name format: [TableID]_[RowNumber][VariableType]\n", - "\n", - "Example: Variable DP02_0002PE, \"Family households (families)\", represents the percent estimate for table DP02 row number 2.\n", - "\n", - "DP (Data Profile): Table type containing broad social, economic, housing, and demographic information in a total of four profiles.\n", - "\n", - " - DP02: Social Characteristics — includes Education, Marital Status, Relationships, Fertility, Grandparents... \n", - " - DP03: Economic Characteristics — includes Income, Employment, Occupation, Commuting to Work... \n", - " - DP04: Housing Characteristics — includes Occupancy and Structure, Housing Value and Costs, Utilities... \n", - " - DP05: Demographic Characteristics — includes Sex and Age, Race, Hispanic Origin, Housing Units... \n", - "\n", - "Variable suffixes:\n", - "\n", - " - E = estimate\n", - " - M = margin of error\n", - " - PE = percent estimate (of total)\n", - " - PM = margin of error for corresponding PE\n", - " - A = annotation" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "import geopandas as gpd\n", - "import getcensus as gc\n", - "import os\n", - "import pandas as pd\n", - "from shapely import geometry\n", - "# from keys import census_api_key" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# which census dataset\n", - "dataset = 'acs/acs5'\n", - "census_api_key = '7679cb7920268e39c115f3c753fa2885aa9fb0cf'\n", - "# which vintage year\n", - "year = 2017\n", - "\n", - "# which census variables to retrieve for each tract\n", - "variables = {'DP05_0001E':'total_pop', #total pop\n", - " 'DP05_0018E':'median_age', #median age\n", - " 'DP05_0071PE':'pct_hispanic', #pct pop hispanic or latino\n", - " 'DP05_0077PE':'pct_white', #pct pop non-hispanic white alone\n", - " 'DP05_0078PE':'pct_black', #pct pop non-hispanic black\n", - " 'DP05_0080E':'pct_asian', #pct pop non-hispanic asian\n", - " 'DP05_0002PE':'pct_male', #pct pop male\n", - " 'DP04_0007PE':'pct_single_family_home', #pct single family detached homes\n", - " 'DP04_0089E':'med_home_value', #median value of owner occupied units (dollars)\n", - " 'DP04_0037E':'med_rooms_per_home', #median number of rooms in house\n", - " 'DP04_0026PE':'pct_built_before_1940', #pct structure built 1939 or earlier\n", - " 'DP04_0047PE':'pct_renting', #pct renter-occupied housing units\n", - " 'DP04_0005E':'rental_vacancy_rate', #rental vacancy rate\n", - " 'DP04_0049E':'avg_renter_household_size', #average household size of renter-occupied housing units\n", - " 'DP04_0134E':'med_gross_rent', #median gross rent (dollars)\n", - " 'DP03_0062E':'med_household_income', #median household income\n", - " 'DP03_0025E':'mean_commute_time', #mean travel time to work\n", - " 'DP03_0019PE':'pct_commute_drive_alone', #pct commute drove alone\n", - " 'DP03_0128PE':'pct_below_poverty', #pct people with income below povery level\n", - " 'DP02_0057PE':'pct_college_grad_student', #pct who are students currently enrolled in college or grad school\n", - " 'DP02_0079PE':'pct_same_residence_year_ago', #pct residence 1 year ago was same house\n", - " 'DP02_0067PE':'pct_bachelors_degree', #pct bachelor's degree or higher\n", - " 'DP02_0111PE':'pct_english_only', #pct with english only language spoken at home\n", - " 'DP02_0092PE':'pct_foreign_born'} #pct of population foreign born\n", - "\n", - "# data directories\n", - "tracts_path = 'tl_2018_25_tract'\n", - "output_path = 'census_tracts_data.geojson'" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "total_pop\tEstimate!!SEX AND AGE!!Total population\n", - "median_age\tEstimate!!SEX AND AGE!!Total population!!Median age (years)\n", - "pct_hispanic\tPercent Estimate!!HISPANIC OR LATINO AND RACE!!Total population!!Hispanic or Latino (of any race)\n", - "pct_white\tPercent Estimate!!HISPANIC OR LATINO AND RACE!!Total population!!Not Hispanic or Latino!!White alone\n", - "pct_black\tPercent Estimate!!HISPANIC OR LATINO AND RACE!!Total population!!Not Hispanic or Latino!!Black or African American alone\n", - "pct_asian\tEstimate!!HISPANIC OR LATINO AND RACE!!Total population!!Not Hispanic or Latino!!Asian alone\n", - "pct_male\tPercent Estimate!!SEX AND AGE!!Total population!!Male\n", - "pct_single_family_home\tPercent Estimate!!UNITS IN STRUCTURE!!Total housing units!!1-unit, detached\n", - "med_home_value\tEstimate!!VALUE!!Owner-occupied units!!Median (dollars)\n", - "med_rooms_per_home\tEstimate!!ROOMS!!Total housing units!!Median rooms\n", - "pct_built_before_1940\tPercent Estimate!!YEAR STRUCTURE BUILT!!Total housing units!!Built 1939 or earlier\n", - "pct_renting\tPercent Estimate!!HOUSING TENURE!!Occupied housing units!!Renter-occupied\n", - "rental_vacancy_rate\tEstimate!!HOUSING OCCUPANCY!!Total housing units!!Rental vacancy rate\n", - "avg_renter_household_size\tEstimate!!HOUSING TENURE!!Occupied housing units!!Average household size of renter-occupied unit\n", - "med_gross_rent\tEstimate!!GROSS RENT!!Occupied units paying rent!!Median (dollars)\n", - "med_household_income\tEstimate!!INCOME AND BENEFITS (IN 2017 INFLATION-ADJUSTED DOLLARS)!!Total households!!Median household income (dollars)\n", - "mean_commute_time\tEstimate!!COMMUTING TO WORK!!Workers 16 years and over!!Mean travel time to work (minutes)\n", - "pct_commute_drive_alone\tPercent Estimate!!COMMUTING TO WORK!!Workers 16 years and over!!Car, truck, or van -- drove alone\n", - "pct_below_poverty\tPercent Estimate!!PERCENTAGE OF FAMILIES AND PEOPLE WHOSE INCOME IN THE PAST 12 MONTHS IS BELOW THE POVERTY LEVEL!!All people\n", - "pct_college_grad_student\tPercent Estimate!!SCHOOL ENROLLMENT!!Population 3 years and over enrolled in school!!College or graduate school\n", - "pct_same_residence_year_ago\tPercent Estimate!!RESIDENCE 1 YEAR AGO!!Population 1 year and over!!Same house\n", - "pct_bachelors_degree\tPercent Estimate!!EDUCATIONAL ATTAINMENT!!Population 25 years and over!!Percent bachelor's degree or higher\n", - "pct_english_only\tPercent Estimate!!LANGUAGE SPOKEN AT HOME!!Population 5 years and over!!English only\n", - "pct_foreign_born\tPercent Estimate!!PLACE OF BIRTH!!Total population!!Foreign born\n" - ] - } - ], - "source": [ - "# download and display census descriptions of each variable\n", - "variable_descriptions = gc.get_census_variable_descriptions(dataset=dataset, \n", - " year=year, \n", - " variables=variables)\n", - "for v, d in variable_descriptions.items():\n", - " print('{}\\t{}'.format(variables[v], d['label']))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Get vars from ACS DP" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "1478" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# load the tracts in our study area\n", - "gdf = gpd.read_file(tracts_path).sort_values(by='GEOID')\n", - "len(gdf)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Downloading 24 census vars in 25001 for 57 tracts.\n", - "Downloading 24 census vars in 25003 for 39 tracts.\n", - "Downloading 24 census vars in 25005 for 126 tracts.\n", - "Downloading 24 census vars in 25007 for 5 tracts.\n", - "Downloading 24 census vars in 25009 for 163 tracts.\n", - "Downloading 24 census vars in 25011 for 18 tracts.\n", - "Downloading 24 census vars in 25013 for 103 tracts.\n", - "Downloading 24 census vars in 25015 for 36 tracts.\n", - "Downloading 24 census vars in 25017 for 318 tracts.\n", - "Downloading 24 census vars in 25019 for 6 tracts.\n", - "Downloading 24 census vars in 25021 for 130 tracts.\n", - "Downloading 24 census vars in 25023 for 101 tracts.\n", - "Downloading 24 census vars in 25025 for 204 tracts.\n", - "Downloading 24 census vars in 25027 for 172 tracts.\n", - "Wall time: 25.4 s\n" - ] - } - ], - "source": [ - "%%time\n", - "df = gc.get_census_tracts_data(tract_fips=gdf['GEOID'], api_key=census_api_key, dataset=dataset,\n", - " year=year, variables=variables, clean=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# merge the tracts with the acs variables, rename columns, then make sure everything we merged is the same length\n", - "merged = pd.merge(left=gdf.set_index('GEOID'), right=df, how='inner', left_index=True, right_index=True)\n", - "merged = merged.rename(columns=variables)\n", - "assert len(gdf) == len(df) == len(merged)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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STATEFPCOUNTYFPTRACTCENAMENAMELSADMTFCCFUNCSTATALANDAWATERINTPTLAT...mean_commute_timepct_commute_drive_alonepct_below_povertypct_college_grad_studentpct_same_residence_year_agopct_bachelors_degreepct_english_onlypct_foreign_bornstatecounty
2500101010025001010100101Census Tract 101G5020S2504621812765873+42.0598291...13.939.710.747.491.848.888.59.225001
2500101020625001010206102.06Census Tract 102.06G5020S5124091718830100+41.9226356...22.668.011.327.585.452.695.37.825001
2500101020825001010208102.08Census Tract 102.08G5020S5426886111461462+42.0135566...16.869.511.29.599.645.993.69.625001
2500101030425001010304103.04Census Tract 103.04G5020S183476597830612+41.8251080...23.579.54.830.293.451.293.77.025001
2500101030625001010306103.06Census Tract 103.06G5020S178285561730602+41.8593758...17.872.88.210.288.145.196.95.025001
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5 rows Ă— 38 columns

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" - ], - "text/plain": [ - " STATEFP COUNTYFP TRACTCE NAME NAMELSAD MTFCC \\\n", - "25001010100 25 001 010100 101 Census Tract 101 G5020 \n", - "25001010206 25 001 010206 102.06 Census Tract 102.06 G5020 \n", - "25001010208 25 001 010208 102.08 Census Tract 102.08 G5020 \n", - "25001010304 25 001 010304 103.04 Census Tract 103.04 G5020 \n", - "25001010306 25 001 010306 103.06 Census Tract 103.06 G5020 \n", - "\n", - " FUNCSTAT ALAND AWATER INTPTLAT ... mean_commute_time \\\n", - "25001010100 S 25046218 12765873 +42.0598291 ... 13.9 \n", - "25001010206 S 51240917 18830100 +41.9226356 ... 22.6 \n", - "25001010208 S 54268861 11461462 +42.0135566 ... 16.8 \n", - "25001010304 S 18347659 7830612 +41.8251080 ... 23.5 \n", - "25001010306 S 17828556 1730602 +41.8593758 ... 17.8 \n", - "\n", - " pct_commute_drive_alone pct_below_poverty \\\n", - "25001010100 39.7 10.7 \n", - "25001010206 68.0 11.3 \n", - "25001010208 69.5 11.2 \n", - "25001010304 79.5 4.8 \n", - "25001010306 72.8 8.2 \n", - "\n", - " pct_college_grad_student pct_same_residence_year_ago \\\n", - "25001010100 47.4 91.8 \n", - "25001010206 27.5 85.4 \n", - "25001010208 9.5 99.6 \n", - "25001010304 30.2 93.4 \n", - "25001010306 10.2 88.1 \n", - "\n", - " pct_bachelors_degree pct_english_only pct_foreign_born state \\\n", - "25001010100 48.8 88.5 9.2 25 \n", - "25001010206 52.6 95.3 7.8 25 \n", - "25001010208 45.9 93.6 9.6 25 \n", - "25001010304 51.2 93.7 7.0 25 \n", - "25001010306 45.1 96.9 5.0 25 \n", - "\n", - " county \n", - "25001010100 001 \n", - "25001010206 001 \n", - "25001010208 001 \n", - "25001010304 001 \n", - "25001010306 001 \n", - "\n", - "[5 rows x 38 columns]" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "merged.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Save to disk" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "upcast_dispatch = {geometry.Point: geometry.MultiPoint, \n", - " geometry.LineString: geometry.MultiLineString, \n", - " geometry.Polygon: geometry.MultiPolygon}\n", - "\n", - "def maybe_cast_to_multigeometry(geom):\n", - " caster = upcast_dispatch.get(type(geom), lambda x: x[0])\n", - " return caster([geom])\n", - "\n", - "merged['geometry'] = merged['geometry'].apply(maybe_cast_to_multigeometry)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "census_tracts_data.geojson\n", - "Wall time: 4.88 s\n" - ] - } - ], - "source": [ - "%%time\n", - "merged.reset_index().to_file(output_path, driver='GeoJSON')\n", - "print(output_path)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.2" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/data/data-pipeline/data_pipeline/ipython/experiment_1_spatial weights and esda/census/getcensus.py b/data/data-pipeline/data_pipeline/ipython/experiment_1_spatial weights and esda/census/getcensus.py deleted file mode 100644 index 6fb8755a..00000000 --- a/data/data-pipeline/data_pipeline/ipython/experiment_1_spatial weights and esda/census/getcensus.py +++ /dev/null @@ -1,227 +0,0 @@ -# Description: Download tract-level census variables from the API -import requests -import numpy as np -import pandas as pd - - -def get_census_variable_descriptions(dataset, year, variables): - """ - Download descriptions of census variables from the API - """ - url_template = "https://api.census.gov/data/{year}/{dataset}/profile/variables/{variable}.json" - variable_descriptions = {} - - for variable in variables: - url = url_template.format(year=year, dataset=dataset, variable=variable) - response = requests.get(url) - data = response.json() - variable_descriptions[variable] = { - "concept": data["concept"], - "label": data["label"], - } - - return variable_descriptions - - -def get_census_tracts_data( - tract_fips, api_key, dataset, year, variables, max_tracts=1000, clean=False -): - """ - Download census variables (given some year and dataset) for a series of tracts - - limit the max number tracts to download data for in a single api request - """ - - # convert vars to string to send to api - variables_str = ",".join(variables) - - # census dataframe called cd - cd = pd.DataFrame() - - states_counties_tracts = get_states_counties_tracts(tract_fips=tract_fips) - for state in states_counties_tracts: - for county in states_counties_tracts[state]: - - tracts = states_counties_tracts[state][county] - - # if we pass it too many tracts at once, the census api chokes, so - # break up counties with > max_tracts number of tracts into chunks - for tracts_chunk in chunks(tracts, max_tracts): - - # convert tracts to string to send to api - tracts_str = ",".join(tracts_chunk) - print( - "Downloading {} census vars in {}{} for {} tracts.".format( - len(variables), state, county, len(tracts_chunk) - ) - ) - - # get census vars for these tracts and append them to df - df_tmp = get_tracts_census_vars( - api_key=api_key, - dataset=dataset, - variables=variables_str, - state=state, - county=county, - tracts=tracts_str, - year=year, - clean=clean, - ) - - df_tmp["state"] = state - df_tmp["county"] = county - cd = cd.append(df_tmp) - return cd - - -def get_states_counties_tracts(tract_fips): - """ - turn a list of tract fips codes into a nested dict keyed by state, - then keyed by county, finally with tract as the value - """ - - if not isinstance(tract_fips, pd.Series): - raise TypeError("tract_fips must be a pandas series") - - df = pd.DataFrame() - df["state"] = tract_fips.str.slice(0, 2) - df["county"] = tract_fips.str.slice(2, 5) - df["tract"] = tract_fips.str.slice(5) - grouped = df[["state", "county", "tract"]].groupby(["state", "county"]) - - states_counties_tracts = {} - for (state, county), group in grouped: - if state not in states_counties_tracts: - states_counties_tracts[state] = {} - states_counties_tracts[state][county] = group["tract"].tolist() - - return states_counties_tracts - - -def parse_tract_fips(tract_fips): - """ - turn a full tract fips code into a tuple of state, county, tract - """ - - return tract_fips[:2], tract_fips[2:5], tract_fips[5:] - - -def get_tract_ids(fips_codes): - """ - convert a list of full tract fips codes into just tract fips only - """ - - tracts = [] - for fips_code in fips_codes: - _, _, tract_fips = parse_tract_fips(fips_code) - tracts.append(tract_fips) - return tracts - - -def get_tracts_census_vars( - api_key, dataset, variables, state, county, tracts, year, clean -): - """ - download a set of census variables for a state + county + tracts - """ - - url_template = ( - "https://api.census.gov/data/{year}/{dataset}/profile?" - "get={variables}&for=tract:{tracts}&key={api_key}&in=state:{state}+county:{county}" - ) - - url = url_template.format( - api_key=api_key, - dataset=dataset, - variables=variables, - state=state, - county=county, - tracts=tracts, - year=year, - ) - - try: - response = requests.get(url, timeout=30) - json_data = response.json() - except Exception as e: - print(e, response.status_code, response.text, response.url) - - # load as dataframe and index by geoid (state+county+tract) - df = pd.DataFrame(json_data) - df = df.rename(columns=df.iloc[0]).drop(df.index[0]) - df["GEOID10"] = df.apply( - lambda row: "{}{}{}".format(row["state"], row["county"], row["tract"]), - axis="columns", - ) - df = df.set_index("GEOID10").drop( - ["state", "county", "tract"], axis="columns" - ) - - if clean: - df = clean_census_data(df) - - return df - - -def clean_census_data(df): - """ - Clean up the census data results from the API. By default, the census data often - includes non-numeric characters as annotations or missing values. - - # see https://www.census.gov/data/developers/data-sets/acs-5year/data-notes.html - # for estimate and annotation values - - # A '+' following a median estimate means the median falls in the upper interval - # of an open-ended distribution. - - # A '-' entry in the estimate column indicates that either no sample observations - # or too few sample observations were available to compute an estimate, or a ratio - # of medians cannot be calculated because one or both of the median estimates falls - # in the lowest interval or upper interval of an open-ended distribution. - - # An 'N' entry in the estimate and margin of error columns indicates that data for - # this geographic area cannot be displayed because the number of sample cases is too - # small. - - # An '(X)' means that the estimate is not applicable or not available. - """ - - # clean up any non-numeric strings, column by column - df = df.astype(str) - bad_strings = ["-", "N", "(X)", "*"] - for col in df.columns: - - # replace any cell with '-' or 'N' or '(X)' or '*' in this column with NaN - df[col] = df[col].map( - lambda value: np.nan - if any(s in value for s in bad_strings) - else value - ) - - # if every result in this col was replaced by nans, then col is now of type - # float and we can skip the following cleaning step - if not df[col].dtype == np.float64: - # strip out any '+' or ',' or '*' - df[col] = df[col].str.replace("+", "").str.replace(",", "") - - # convert data to floats, assert uniqueness, and return - def convert_float(value): - try: - return float(value) - except: - print("error", value, "\n", df) - return np.nan - - df = df.applymap(convert_float) - - assert df.index.is_unique - - return df - - -def chunks(l, n): - """ - yield successive n-sized chunks from list l - """ - for i in range(0, len(l), n): - yield l[i : i + n] diff --git a/data/data-pipeline/data_pipeline/ipython/experiment_1_spatial weights and esda/census/tl_2018_25_tract/tl_2018_25_tract.cpg b/data/data-pipeline/data_pipeline/ipython/experiment_1_spatial weights and esda/census/tl_2018_25_tract/tl_2018_25_tract.cpg deleted file mode 100644 index 3ad133c0..00000000 --- a/data/data-pipeline/data_pipeline/ipython/experiment_1_spatial weights and esda/census/tl_2018_25_tract/tl_2018_25_tract.cpg +++ /dev/null @@ -1 +0,0 @@ -UTF-8 \ No newline at end of file diff --git a/data/data-pipeline/data_pipeline/ipython/experiment_1_spatial weights and esda/census/tl_2018_25_tract/tl_2018_25_tract.dbf b/data/data-pipeline/data_pipeline/ipython/experiment_1_spatial weights and esda/census/tl_2018_25_tract/tl_2018_25_tract.dbf deleted file mode 100644 index 5007ad5ad78e580509f7853b54c2683b30df9261..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 158564 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weights and esda/census/tl_2018_25_tract/tl_2018_25_tract.shp.ea.iso.xml b/data/data-pipeline/data_pipeline/ipython/experiment_1_spatial weights and esda/census/tl_2018_25_tract/tl_2018_25_tract.shp.ea.iso.xml deleted file mode 100644 index 80c7ea13..00000000 --- a/data/data-pipeline/data_pipeline/ipython/experiment_1_spatial weights and esda/census/tl_2018_25_tract/tl_2018_25_tract.shp.ea.iso.xml +++ /dev/null @@ -1,377 +0,0 @@ - - - - Feature Catalog for the Current Census Tract State-based - - - The Current Census Tract State-based shapefile contains attributes for Census tracts. These are small, relatively permanent statistical subdivisions of a county or equivalent entity. - - - 2018 - - - 2017-06-01 - - - eng - - - utf8 - - - - - - TRACT.shp - - - Current Census Tract State-based - - - false - - - - - - STATEFP - - - Current state Federal Information Processing Series (FIPS) codes - - - - - - - National Standard Codes (ANSI INCITS 38-2009), Federal Information Processing Series (FIPS) - States/State Equivalents - - - - - - - - - - COUNTYFP - - - Current county Federal Information Processing Series (FIPS) code - - - - - - - National Standard Codes (ANSI INCITS 31-2009), Federal Information Processing Series (FIPS) - Counties/County Equivalents - - - - - - - - - - TRACTCE - - - Current census tract code - - - - - - - 000100 to 998999 - - - Census tract number - - - - - - - - 990000 to 990099 - - - Water tract (2010 and continuing) - - - - - - - - 990100 to 998999 - - - Census tract number - - - - - - - - - - GEOID - - - Census tract identifier, a concatenation of Current state Federal Information Processing Series (FIPS) code, county FIPS code, and census tract code - - - - - - - - The GEOID attribute is a concatenation of the state FIPS code, followed by the county FIPS code, followed by the census tract code. No spaces are allowed between the two codes. The State FIPS code is taken from "National Standard Codes (ANSI INCITS 38-2009), Federal Information Processing Series (FIPS) - States". The county FIPS code is taken from "National Standard Codes (ANSI INCITS 31-2009), Federal Information Processing Series (FIPS) - Counties/County Equivalents". The census tract code is taken from the "TRACTCE" attribute. - - - - - - - - - NAME - - - Current census tract name, this is the census tract code converted to an integer or integer plus two-digit decimal if the last two characters of the code are not both zeros. - - - - - - - - Values for this attribute are composed of a set of census tract names. As such, they do not exist in a known, predefined set. - - - - - - - - - NAMELSAD - - - Current translated legal/statistical area description and the census tract name - - - - - - - - Refer to the name in the 'TRACTCE' field and the translated legal/statistical area description code for census tracts - - - - - - - - - MTFCC - - - MAF/TIGER feature class code - - - - - - - G5020 - - - Census Tract - - - - - - - - - - FUNCSTAT - - - Current functional status - - - - - - - S - - - Statistical entity - - - - - - - - - - ALAND - - - Current land area (square meters) - - - - - - - - - - - - - - Range Domain Minimum: 0 - Range Domain Maximum: 9,999,999,999,999 - - - - - - - - - AWATER - - - Current water area (square meters) - - - - - - - - - - - - - - Range Domain Minimum: 0 - Range Domain Maximum: 9,999,999,999,999 - - - - - - - - - INTPTLAT - - - Current latitude of the internal point - - - - - - Decimal degrees - - - - - - - - Range Domain Minimum: -90.000000 - Range Domain Maximum: 90.000000 - - - - - - - - - INTPTLON - - - Current longitude of the internal point - - - - - - Decimal degrees - - - - - - - - Range Domain Minimum: -180.000000 - Range Domain Maximum: 180.000000 - - - - - - - - \ No newline at end of file diff --git a/data/data-pipeline/data_pipeline/ipython/experiment_1_spatial weights and esda/census/tl_2018_25_tract/tl_2018_25_tract.shp.iso.xml b/data/data-pipeline/data_pipeline/ipython/experiment_1_spatial weights and esda/census/tl_2018_25_tract/tl_2018_25_tract.shp.iso.xml deleted file mode 100644 index 4198231e..00000000 --- a/data/data-pipeline/data_pipeline/ipython/experiment_1_spatial weights and esda/census/tl_2018_25_tract/tl_2018_25_tract.shp.iso.xml +++ /dev/null @@ -1,617 +0,0 @@ - - - - tl_2018_25_tract.shp.iso.xml - - - eng - - - UTF-8 - - - - TIGER/Line Shapefile, 2018, Series Information for the Current Census Tract State-based - - - -dataset - - - - - 2017-06-01 - - - ISO 19115 Geographic Information - Metadata - - - 2009-02-15 - - - http://www2.census.gov/geo/tiger/TIGER2018/TRACT/tl_2018_25_tract.zip - - - - - - - - - complex - - - 1478 - - - - - - - - - - - - - Federal Information Processing Series (FIPS), Geographic Names Information System (GNIS), and feature names. - - - - - - - - - - - - - TIGER/Line Shapefile, 2018, state, Massachusetts, Current Census Tract State-based - - - National Geospatial Data Asset (NGDA) Census Tract - - - - - - 2018 - - - publication - - - - - - 2018 - - - - - - - - - The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. - - -Census tracts are small, relatively permanent statistical subdivisions of a county or equivalent entity, and were defined by local participants as part of the 2010 Census Participant Statistical Areas Program. The Census Bureau delineated the census tracts in situations where no local participant existed or where all the potential participants declined to participate. The primary purpose of census tracts is to provide a stable set of geographic units for the presentation of census data and comparison back to previous decennial censuses. Census tracts generally have a population size between 1,200 and 8,000 people, with an optimum size of 4,000 people. When first delineated, census tracts were designed to be homogeneous with respect to population characteristics, economic status, and living conditions. The spatial size of census tracts varies widely depending on the density of settlement. Physical changes in street patterns caused by highway construction, new development, and so forth, may require boundary revisions. In addition, census tracts occasionally are split due to population growth, or combined as a result of substantial population decline. Census tract boundaries generally follow visible and identifiable features. They may follow legal boundaries such as minor civil division (MCD) or incorporated place boundaries in some States and situations to allow for census tract-to-governmental unit relationships where the governmental boundaries tend to remain unchanged between censuses. State and county boundaries always are census tract boundaries in the standard census geographic hierarchy. In a few rare instances, a census tract may consist of noncontiguous areas. These noncontiguous areas may occur where the census tracts are coextensive with all or parts of legal entities that are themselves noncontiguous. For the 2010 Census, the census tract code range of 9400 through 9499 was enforced for census tracts that include a majority American Indian population according to Census 2000 data and/or their area was primarily covered by federally recognized American Indian reservations and/or off-reservation trust lands; the code range 9800 through 9899 was enforced for those census tracts that contained little or no population and represented a relatively large special land use area such as a National Park, military installation, or a business/industrial park; and the code range 9900 through 9998 was enforced for those census tracts that contained only water area, no land area. - - - - In order for others to use the information in the Census MAF/TIGER database in a geographic information system (GIS) or for other geographic applications, the Census Bureau releases to the public extracts of the database in the form of TIGER/Line Shapefiles. - - - - completed - - - - - - - - notPlanned - - - - - - - - NGDA - - - Governmental Units and Administrative and Statistical Boundaries Theme - - - National Geospatial Data Asset - - - theme - - - theme - - - - - NGDA Portfolio Themes - - - - - 2010-02-01 - - - revision - - - - - - http://www.fgdc.gov/initiatives/resources/2013-2-1-ngda-data-themes-fgdc-sc-revised.pdf - - - - - - - - - State or equivalent entity - - - Polygon - - - Census Tract - - - Tract - - - theme - - - - - None - - - - - - - - - - United States - - - U.S. - - - State or Equivalent Entity - - - Massachusetts - - - MA - - - 25 - - - place - - - - - ANSI INCITS 38:2009 (Formerly FIPS 5-2), ANSI INCITS 31:2009 (Formerly FIPS 6-4),ANSI INCITS 454:2009 (Formerly FIPS 8-6), ANSI INCITS 455:2009(Formerly FIPS 9-1), ANSI INCITS 446:2008 (Geographic Names Information System (GNIS)) - - - - - - - - - - otherRestrictions - - - - - - Access Constraints: None - - - Use Constraints:The TIGER/Line Shapefile products are not copyrighted however TIGER/Line and Census TIGER are registered trademarks of the U.S. Census Bureau. These products are free to use in a product or publication, however acknowledgement must be given to the U.S. Census Bureau as the source. -The boundary information in the TIGER/Line Shapefiles are for statistical data collection and tabulation purposes only; their depiction and designation for statistical purposes does not constitute a determination of jurisdictional authority or rights of ownership or entitlement and they are not legal land descriptions.Coordinates in the TIGER/Line shapefiles have six implied decimal places, but the positional accuracy of these coordinates is not as great as the six decimal places suggest. - - - - - - vector - - - eng - - - - - - boundaries - - - The TIGER/Line shapefiles contain geographic data only and do not include display mapping software or statistical data. For information on how to use the TIGER/Line shapefile data with specific software package users shall contact the company that produced the software. - - - - - - - -73.50821 - - - -69.858861 - - - 41.187053 - - - 42.886778 - - - - - - - - publication date - 2017-06 - 2018-05 - - - - - - - - - - - - - true - - - - - - - - - - - - - 2018 - - - - - - - - - https://meta.geo.census.gov/data/existing/decennial/GEO/GPMB/TIGERline/TIGER2017/tract/tl_2018_tract.shp.ea.iso.xml - - - - - - - - - - - TGRSHP (compressed) - - - - PK-ZIP, version 1.93 A or higher - - - - - - - HTML - - - - - - - - WMS - - - 1.3.0 - - - - - - - REST - - - - - - - - - - - The online copy of the TIGER/Line Shapefiles may be accessed without charge. - - - To obtain more information about ordering TIGER/Line Shapefiles visit http://www.census.gov/geo/www/tiger - - - - - - - - - - - http://www2.census.gov/geo/tiger/TIGER2018/TRACT/tl_2018_25_tract.zip - - - Shapefile Zip File - - - - - - - - - - - https://www.census.gov/geo/maps-data/data/tiger-line.html - - - TIGER/Line® Shapefiles - - - Should be used for most mapping projects--this is our most comprehensive dataset. Designed for use with GIS - (geographic information systems). - - - - - - - - - - - https://tigerweb.geo.census.gov/arcgis/services/TIGERweb/tigerWMS_Current/MapServer/WMSServer - - - http://opengis.net/spec/wms - - - TIGERweb/tigerWMS_Current (MapServer) - - - This web mapping service contains the layer for 2010 Census Tracts. - This URL is to be used in mapping software like ArcMap. To use this in a web browser, see the OGC Web Mapping Specification. - - - - download - - - - - - - - - - - - https://tigerweb.geo.census.gov/arcgis/rest/services/TIGERweb/Tracts_Blocks/MapServer - - - http://www.geoplatform.gov/spec/esri-map-rest - - - TIGERweb/Tracts_Blocks (MapServer) - - - This Rest Service contains the 2010 Census Tracts - - - download - - - - - - - - - - - - - - dataset - - - - - - - Data completeness of the TIGER/Line Shapefiles reflects the contents of the Census MAF/TIGER database at the time the TIGER/Line Shapefiles were created. - - - - - - - - Data completeness of the TIGER/Line Shapefiles reflects the contents of the Census MAF/TIGER database at the time the TIGER/Line Shapefiles were created. - - - - - - - - The Census Bureau performed automated tests to ensure logical consistency and limits of shapefiles. Segments making up the outer and inner Boundaries of a polygon tie end-to-end to completely enclose the area. All polygons are tested for closure. -The Census Bureau uses its internally developed geographic update system to enhance and modify spatial and attribute data in the Census MAF/TIGER database. Standard geographic codes, such as FIPS codes for states, counties, municipalities, county subdivisions, places, American Indian/Alaska Native/Native Hawaiian areas, and congressional districts are used when encoding spatial entities. The Census Bureau performed spatial data tests for logical consistency of the codes during the compilation of the original Census MAF/TIGER database files. Most of the codes for geographic entities except states, counties, urban areas, Core Based Statistical Areas (CBSAs), American Indian Areas (AIAs), and congressional districts were provided to the Census Bureau by the USGS, the agency responsible for maintaining the Geographic Names Information System (GNIS). Feature attribute information has been examined but has not been fully tested for consistency. -For the TIGER/Line Shapefiles, the Point and Vector Object Count for the G-polygon SDTS Point and Vector Object Type reflects the number of records in the shapefile attribute table. For multi-polygon features, only one attribute record exists for each multi-polygon rather than one attribute record per individual G-polygon component of the multi-polygon feature. TIGER/Line Shapefile multi-polygons are an exception to the G-polygon object type classification. Therefore, when multi-polygons exist in a shapefile, the object count will be less than the actual number of G-polygons. - - - - - - - - - - TIGER/Line Shapefiles are extracted from the Census MAF/TIGER database by nation, state, county, and entity. Census MAF/TIGER data for all of the aforementioned geographic entities are then distributed among the shapefiles each containing attributes for line, polygon, or landmark geographic data. - - - 2018-01-01T00:00:00 - - - - - online - - - - - Census MAF/TIGER database - - - MAF/TIGER - - - - - - U.S. Department of Commerce, U.S. Census Bureau, Geography Division - - - originator - - - - - - Source Contribution: All line segments - - - - - - - - - - 201706 - 201805 - - - - - - - - - - - - - - - - - - - notPlanned - - - - This was transformed from the Census Metadata Import Format - - - - - \ No newline at end of file diff --git a/data/data-pipeline/data_pipeline/ipython/experiment_1_spatial weights and esda/census/tl_2018_25_tract/tl_2018_25_tract.shx b/data/data-pipeline/data_pipeline/ipython/experiment_1_spatial weights and esda/census/tl_2018_25_tract/tl_2018_25_tract.shx deleted file mode 100644 index b7cde572c6617c29007b30a1cadb5583d65305b9..0000000000000000000000000000000000000000 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/dev/null @@ -1,2042 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Spatial weights and ESDA (Exploratory Spatial Data Analysis) - MA, ACS 2017\n", - "\n", - "ESDA offers direct parallels to many of the aspatial descriptive statistics that are already part of most quantitative research projects: The geographic mean and spatial standard deviation, for example, tell us where the center of our study area is how compact our population is within that area. \n", - "\n", - "I use ESDA to generalize about our observations and to identify outliers and discontinuities that may disrupt our Score L model or suggest hypotheses about relationships we may wish to explore further as it pertains to health disparities and educational disparities.\n", - "\n", - "This is very pertinent to Score L as we make strict assumptions about distributions - for any thresholding approach it assumes that the data represents no measurement bias and no systematic error. Everything is homogeneous and is treated equally.\n", - "\n", - "ESDA can also be important in confirmatory statistical analysis as spatial autocorrelation can bias outcomes in many spatial and aspatial statistical models. \n", - "\n", - "Testing for spatial autocorrelation and locating clusters of correlated residuals will be important in explaining why certain scores are associated together.\n", - "\n", - "\n", - "For example, in section 6, I use LISA's to identify geographical clusters of values or find geographcial outliers. They are a very useful tool that can quickly return areas in which values are concentrated and provide suggestive evidence about the processes that might be at work - namely median income. I will be extending this to educational attainment and job opportunities in a given radius - indicating economic mobility." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/sarahluw/justice40-tool/data/data-pipeline/justice-40-env/lib/python3.7/site-packages/geopandas/_compat.py:110: UserWarning: The Shapely GEOS version (3.8.0-CAPI-1.13.1 ) is incompatible with the GEOS version PyGEOS was compiled with (3.10.0-CAPI-1.16.0). Conversions between both will be slow.\n", - " shapely_geos_version, geos_capi_version_string\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.9.0 2.5.0\n" - ] - } - ], - "source": [ - "import geopandas as gpd\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import pandas as pd\n", - "import pysal as ps\n", - "import seaborn as sns\n", - "from scipy.stats import stats\n", - "\n", - "np.random.seed(0)\n", - "print(gpd.__version__, ps.__version__)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Load the tracts data set" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(1478, 38)" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# load geojson, display shape\n", - "tracts = gpd.read_file('census/census_tracts_data.geojson')\n", - "tracts = tracts.set_index('index')\n", - "tracts.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['STATEFP', 'COUNTYFP', 'TRACTCE', 'NAME', 'NAMELSAD', 'MTFCC',\n", - " 'FUNCSTAT', 'ALAND', 'AWATER', 'INTPTLAT', 'INTPTLON', 'total_pop',\n", - " 'median_age', 'pct_hispanic', 'pct_white', 'pct_black', 'pct_asian',\n", - " 'pct_male', 'pct_single_family_home', 'med_home_value',\n", - " 'med_rooms_per_home', 'pct_built_before_1940', 'pct_renting',\n", - " 'rental_vacancy_rate', 'avg_renter_household_size', 'med_gross_rent',\n", - " 'med_household_income', 'mean_commute_time', 'pct_commute_drive_alone',\n", - " 'pct_below_poverty', 'pct_college_grad_student',\n", - " 'pct_same_residence_year_ago', 'pct_bachelors_degree',\n", - " 'pct_english_only', 'pct_foreign_born', 'state', 'county', 'geometry'],\n", - " dtype='object')" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# what variables are present?\n", - "tracts.columns" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "

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      2500101010025001010100101Census Tract 101G5020S2504621812765873+42.0598291...39.710.747.491.848.888.59.225001MULTIPOLYGON (((-70.25001 42.06410, -70.24959 ...
      2500101020625001010206102.06Census Tract 102.06G5020S5124091718830100+41.9226356...68.011.327.585.452.695.37.825001MULTIPOLYGON (((-70.08245 41.94663, -70.08212 ...
      2500101020825001010208102.08Census Tract 102.08G5020S5426886111461462+42.0135566...69.511.29.599.645.993.69.625001MULTIPOLYGON (((-70.15464 42.07793, -70.15357 ...
      2500101030425001010304103.04Census Tract 103.04G5020S183476597830612+41.8251080...79.54.830.293.451.293.77.025001MULTIPOLYGON (((-70.01480 41.80724, -70.01086 ...
      2500101030625001010306103.06Census Tract 103.06G5020S178285561730602+41.8593758...72.88.210.288.145.196.95.025001MULTIPOLYGON (((-70.01083 41.87511, -70.01028 ...
      \n", - "

      5 rows Ă— 38 columns

      \n", - "
      " - ], - "text/plain": [ - " STATEFP COUNTYFP TRACTCE NAME NAMELSAD MTFCC \\\n", - "index \n", - "25001010100 25 001 010100 101 Census Tract 101 G5020 \n", - "25001010206 25 001 010206 102.06 Census Tract 102.06 G5020 \n", - "25001010208 25 001 010208 102.08 Census Tract 102.08 G5020 \n", - "25001010304 25 001 010304 103.04 Census Tract 103.04 G5020 \n", - "25001010306 25 001 010306 103.06 Census Tract 103.06 G5020 \n", - "\n", - " FUNCSTAT ALAND AWATER INTPTLAT ... \\\n", - "index ... \n", - "25001010100 S 25046218 12765873 +42.0598291 ... \n", - "25001010206 S 51240917 18830100 +41.9226356 ... \n", - "25001010208 S 54268861 11461462 +42.0135566 ... \n", - "25001010304 S 18347659 7830612 +41.8251080 ... \n", - "25001010306 S 17828556 1730602 +41.8593758 ... \n", - "\n", - " pct_commute_drive_alone pct_below_poverty \\\n", - "index \n", - "25001010100 39.7 10.7 \n", - "25001010206 68.0 11.3 \n", - "25001010208 69.5 11.2 \n", - "25001010304 79.5 4.8 \n", - "25001010306 72.8 8.2 \n", - "\n", - " pct_college_grad_student pct_same_residence_year_ago \\\n", - "index \n", - "25001010100 47.4 91.8 \n", - "25001010206 27.5 85.4 \n", - "25001010208 9.5 99.6 \n", - "25001010304 30.2 93.4 \n", - "25001010306 10.2 88.1 \n", - "\n", - " pct_bachelors_degree pct_english_only pct_foreign_born state \\\n", - "index \n", - "25001010100 48.8 88.5 9.2 25 \n", - "25001010206 52.6 95.3 7.8 25 \n", - "25001010208 45.9 93.6 9.6 25 \n", - "25001010304 51.2 93.7 7.0 25 \n", - "25001010306 45.1 96.9 5.0 25 \n", - "\n", - " county geometry \n", - "index \n", - "25001010100 001 MULTIPOLYGON (((-70.25001 42.06410, -70.24959 ... \n", - "25001010206 001 MULTIPOLYGON (((-70.08245 41.94663, -70.08212 ... \n", - "25001010208 001 MULTIPOLYGON (((-70.15464 42.07793, -70.15357 ... \n", - "25001010304 001 MULTIPOLYGON (((-70.01480 41.80724, -70.01086 ... \n", - "25001010306 001 MULTIPOLYGON (((-70.01083 41.87511, -70.01028 ... \n", - "\n", - "[5 rows x 38 columns]" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# inspect the first 5 rows\n", - "tracts.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# calculate pop density in persons per sq km\n", - "tracts['pop_density'] = tracts['total_pop'] / (tracts['ALAND'] / 1e6)\n", - "tracts = tracts.replace([np.inf, -np.inf], np.nan)\n", - "tracts = tracts.dropna(subset=['pop_density'])" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# project spatial geometries from lat-long to a meter-based projection\n", - "utm_ma = '+proj=utm +zone=18 +ellps=WGS84 +datum=WGS84 +units=m +no_defs'\n", - "tracts = tracts.to_crs(utm_ma)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1. Exploratory analysis\n", - "\n", - "Let's see what we've got with a couple key variables of interest." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "count 1471.000000\n", - "mean 2945.508618\n", - "std 4297.767501\n", - "min 0.000000\n", - "25% 336.370316\n", - "50% 1222.493888\n", - "75% 3703.403748\n", - "max 37499.360889\n", - "Name: pop_density, dtype: float64" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# descriptive stats\n", - "tracts['pop_density'].describe()" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "count 1456.000000\n", - "mean 78705.544643\n", - "std 35208.644008\n", - "min 2499.000000\n", - "25% 54200.000000\n", - "50% 74529.500000\n", - "75% 98180.750000\n", - "max 250001.000000\n", - "Name: med_household_income, dtype: float64" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# descriptive stats\n", - "tracts['med_household_income'].describe()" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "# create a function to choropleth map a df column\n", - "def map_variable(df, col, scheme='quantiles', k=10, cmap='plasma', figsize=(10,10)):\n", - " values = df.dropna(subset=[col])\n", - " ax = values.plot(column=col, scheme=scheme, k=k, cmap=cmap, figsize=figsize)\n", - " ax.axis('off')\n", - " plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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      " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# map a variable\n", - "map_variable(tracts, 'pop_density')" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
      " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# map another variable\n", - "map_variable(tracts, 'med_household_income')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "It looks like these two variables might be negatively correlated?" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "r=-0.2572, p=0.0000\n" - ] - } - ], - "source": [ - "# calculate the correlation coefficient and its p-value\n", - "x = tracts.dropna(subset=['pop_density', 'med_household_income'])['pop_density']\n", - "y = tracts.dropna(subset=['pop_density', 'med_household_income'])['med_household_income']\n", - "r, p = stats.pearsonr(x=x, y=y)\n", - "print('r={:.4f}, p={:.4f}'.format(r, p))" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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      " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# scatter plot them with matplotlib\n", - "fig, ax = plt.subplots()\n", - "ax.scatter(x=x, y=y, s=1)\n", - "ax.set_xlim(left=0)\n", - "ax.set_ylim(bottom=0)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "m=-2.1063, b=84941.2430, r^2=0.0662, p=0.0000\n" - ] - } - ], - "source": [ - "# estimate a simple linear regression model with scipy\n", - "m, b, r, p, se = stats.linregress(x=x, y=y)\n", - "print('m={:.4f}, b={:.4f}, r^2={:.4f}, p={:.4f}'.format(m, b, r ** 2, p))" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "m=-0.1150, b=11.9704, r^2=0.1360, p=0.0000\n" - ] - } - ], - "source": [ - "# estimate a log-log simple linear regression model with scipy\n", - "m, b, r, p, se = stats.linregress(x=np.log(x), y=np.log(y))\n", - "print('m={:.4f}, b={:.4f}, r^2={:.4f}, p={:.4f}'.format(m, b, r ** 2, p))" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/sarahluw/justice40-tool/data/data-pipeline/justice-40-env/lib/python3.7/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n", - " FutureWarning\n" - ] - }, - { - "data": { - "image/png": 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\n", 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      " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# plot the log-log regression line with 95% CI using seaborn\n", - "fig, ax = plt.subplots(figsize=(6, 6))\n", - "ax = sns.regplot(np.log(x), np.log(y), marker='.', scatter_kws={'s':2}, ax=ax)\n", - "ax.set_xlim((3, 11))\n", - "ax.set_ylim((9.5, 12.5))\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2. Spatial weights matrix\n", - "\n", - "Spatial weights define the spatial connections among our units of analysis (tracts in this case).\n", - "\n", - "### 2.1. Contiguity-based weights: rook contiguity\n", - "\n", - "Using rook contiguity, two spatial units must share an edge of their boundaries to be considered neighbors. This isn't terribly common in practice (since queen is usually more useful, but it's worth understanding)." - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "# get the tract labels (GEOIDs) and pick one to work with later\n", - "labels = tracts.index.tolist()\n", - "label = labels[700]" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 2.97 s, sys: 174 ms, total: 3.14 s\n", - "Wall time: 3.28 s\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/sarahluw/justice40-tool/data/data-pipeline/justice-40-env/lib/python3.7/site-packages/libpysal/weights/weights.py:172: UserWarning: The weights matrix is not fully connected: \n", - " There are 3 disconnected components.\n", - " warnings.warn(message)\n" - ] - } - ], - "source": [ - "%%time\n", - "\n", - "from libpysal.weights.contiguity import Queen, Rook\n", - "from libpysal.weights import KNN, min_threshold_distance\n", - "from libpysal.weights import DistanceBand, lag_spatial\n", - "# calculate rook spatial weights\n", - "w_rook = Rook.from_dataframe(tracts, ids=labels, id_order=labels)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2.2. Contiguity-based weights: queen contiguity\n", - "\n", - "Using queen contiguity, two spatial units need only share a vertex (a single point) of their boundaries to be considered neighbors." - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 2.33 s, sys: 116 ms, total: 2.45 s\n", - "Wall time: 2.57 s\n" - ] - } - ], - "source": [ - "%%time\n", - "# calculate queen spatial weights\n", - "w_queen = Queen.from_dataframe(tracts, ids=labels, id_order=labels)" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['25017351000', '25017350400', '25017351100', '25017339600', '25017350200']" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# find the neighbors of some tract\n", - "w_queen.neighbors[label]" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[1.0, 1.0, 1.0, 1.0, 1.0]" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# this is a raw contiguity matrix, so weights are binary 1s and 0s meaning neighbor/not\n", - "w_queen.weights[label]" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "5" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# how many neighbors does this tract have?\n", - "w_queen.cardinalities[label]" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "count 1471.000000\n", - "mean 5.859959\n", - "std 1.847070\n", - "min 1.000000\n", - "25% 5.000000\n", - "50% 6.000000\n", - "75% 7.000000\n", - "max 22.000000\n", - "dtype: float64" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# convert cardinalities to series and describe data -- looks right-skewed\n", - "cardinalites_queen = pd.Series(w_queen.cardinalities)\n", - "cardinalites_queen.describe()" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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      " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# look at the distribution of number of neighbors\n", - "ax = cardinalites_queen.hist(bins=20)" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "1471" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# number of observations\n", - "w_queen.n" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "5.859959211420803" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# average number of neighbors\n", - "w_queen.mean_neighbors" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "1" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# min number of neighbors\n", - "w_queen.min_neighbors" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "22" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# max number of neighbors\n", - "w_queen.max_neighbors" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# islands are observations with no neighbors, disconnected \n", - "# in space (can cause modeling problems)\n", - "w_queen.islands" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### Plot a census tract of interest, along with its neighbors:" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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Baxk6NkZTRg73Awl17ZBSPiClDJFShgDvAb8DCCH8gaeB0cAo4GlT7WGA16WU/YBhwHghREStJhedbU9K+XlDwpydnZtwGTpnCQkZRm6uKy+9ZP0DyHfeUejbd4TWMmyS8vJysrOzdfeSTpNo1F1BCNEViAQavFED1wBngyFnAqullLlSyjxgNTBLSlkqpVwPIKWsBPYAXZsq/iwuLi7Mnj2b4cOH64t+GombmxsDB47g2msVqqu1VtMwv/wCvr7e+orpZhIXF0dQUBCurq5aS9GxERp7J30beBioN9xHCNEDCATWmTZ1AWrHv2SYttU+xxeYA6yttfkKIcR+IcSvQohuDYkrKSlhxYoVnD59mqlTpxIeHo6fn19Dpzk04eGX8emnYCvJaYuLjSumhwwZprUUm2Xjxo26e0mn0TRoHIQQlwCnpJSNuY0sAH6VUjYq+78QwgnjKONdKWWqafOfQE8p5RCMI41vLnLu7UKIXUKIXWeruR0/fpw1a9awadMmBg0axKhRoxojw+Ho27cfNTWePPGEbY2yPvpIoXv3vlrLsFmKi4spLi6mZ8+eWkvRsQEac3cYD8wVQqQDC4GpQojvL3LsAv5yKQFkArWf/Luatp3lUyBZSvn22Q1SyhwpZYXpz8+BOh3NUspPpZShUsrQ81MTl5WVUVlZSUpKSkPX5nA4OzszfPgErr9ewdaylm/ZAkVFhgYXPOpcnO3btzNo0CDdPafTIA0aBynlY1LKrlLKnhhv/uuklNeff5wQoh/gB2yttTkaCBdC+JkmosNN2xBCvAD4AP93Xju1f/lzucgkeH0oikLbtm3Jzc1t6ql2z4wZc/j9d4mtBnC99x4MGTJGaxk2zZo1a5g2bZoeyKFTL832KwghnhNCzK21aQGwUNaqai6lzAWeB3aaXs9JKXNNE9xPAAOAPeeFrN5nCm+NA+4DbmqqtgEDBpCQ0GSbYvd07doVDw9//u//rCtFRlP4+muFdu3a4uLiorUUm6W8vJyNGzcyffp0PYBD56I0qYa0lHIDsMH0/r/n7XvmIud8CXx53rYMoM4VTVLKx4DHmqKrNkIIevTowfLly5vbhF0ihGDcuHD+9S8FWy5nceYMrF4NgwYNYc+eXVrLsVmKiorYuXMn06ZNY82aNUhrXQGpoxl299jQu3dvPftqHUycOIW4OMGiRVoraTnvvKMQGKiXEG0pZ86cISkpiUmTJmktRccKsUvjcOjQIa1lWBXe3t506xbELbfYrjupNhs2QHm5Ex06dNBais1z9OhRTp8+zejRo7WWomNl2JVx6NGjBydPntRahtUxbdpc/vc/SE1t+FhbQEp4/30YMkS/oZmDQ4cOUVNTw5AhQ7SWomNF2JVx6N+/P3v37tVahlUxdGgIBQVuvPaaXf1X8913Ch06tNerxJmJXbt24evrS58+fbSWomMl2M0dw9/fn5KSEq1lWBXGFBmhXHutQlWV1mrMS2YmHDigJ+MzJzExMbRr146wsDA9zYaO/RiHIUOGsH37dq1lWBXh4Zfx+eewc6fWSizDRx8pBAcP11qGXbF58+ZzUUy6m8mxsRvjUFRUxPTp0/VhsYmzKTIef9xu/osv4LffwM/PS3/KNTPFxcWsWLGC8vJyIiMjadeundaSdDSgSescrJndu3ejKArBwcHMmjWLiooKtm7dSnl5udbSWh1jiozxzJ1reykymkJxMURHSwYOHMSePTaSQdCGSEpKIiUlhcmTJ6OqKlu3bqWyslJrWTqthF09VqqqyuHDh4mKimLPnj2MGjWK2bNnO1z94fDwS/n1V2w2RUZT+PRThR49Bmotw25RVZX169ezb98+ZsyYwYABA7SWpNNK2JVxqE1BQQExMTFER0fj6elJREQEEydOtPu0C7169cLFxdemU2Q0hdWrwdnZBW9vb62l2DUFBQUsX74cIQQRERH4+/trLUnHwtiNW+li1NTUEB8fT3x8PG3btmXcuHG4u7uzb98+srKytJZnVpycnBg1Koxrr1UoLNRaTetQU2MsBDRmzBC2bo3VWo7dEx8fT0JCAmFhYVRUVLBt2zaqbaFalE6TsduRQ13k5OSwYcMG1qxZQ/v27YmIiGDMmDF2k3xs+vRIoqPB0dJKff+9QqdOvbWW4TCoqsratWs5dOgQM2fO1INA7BT7uCs2kaqqKuLi4li5ciVpaWmEhYUxa9YsAgICtJbWbIKDg3F3b8cddziGO6k2sbGgKE74+PhoLcWhyM3NZfny5bi7uzNr1iz987cz7N6t1BDZ2dlkZ2fj4uLCkCFDGDVqFNnZ2ezduxdVrbcqqtXg7OzMyJGTmTtXIS9PazWtj6rCokUwadJQNm+O0VqOwxEXF0d8fDxTp06lqKiIHTt2UFPTqGKQOlaMQ44c6qKyspJdu3axYsUKTp48yfTp0xk+3HYWWAmBQxqGs/zwg0LHjr20luGwVFdXs2rVKo4cOUJERASBgYFaS9JpIbpxqIPMzExSUlJsJqa7qqqKxMQ4vvmmBjuZPmkyW7YAOOHr66uxEsfm1KlTLFu2jODgYD2iycZx0FtJw/Tu3ZuDBw9qLaPR7N69ky5dqrn9dttwhZkbKeGnn2DQoBCtpehgLEU6ZoxeztWW0Y1DHbRp08Zm5htqs3Hjcl55Bdq311qJNhhdS7o7wxpQVZXU1FQGDtQXKNoqunGoA19fX9zd3QkODtZaSpM4c+YMOTknef99x5wM3LYNqqsNujvDSkhMTKRnz5567isbRTcOdZCZmcny5csxGAzMmjWLyZMn4+bmprWsRrF+fRSzZsG0aVor0YaffoKBA0O0lqFjIiYmhrFjx2otQ6cZ6MbhIqiqSmJiIlFRUcTFxTF69GgiIiLo2rWr1tLqpbq6mj17NvPVVyqO+MBmdC05Vi4ta6aoqAhVVfWSrjaIbhwaQX5+Phs3bmT16tX4+fkRERHBuHHjcHKyzmUihw8n4upaxuOP2968SUvZuRMqKw20bdtWayk6JmJiYggNDUUIobUUnSagG4cmUF1dzYEDB1i5ciUpKSlMmjSJWbNm0d4KZ4BXr17Mv/8NvR0wq8R338GgQbazRsURSEhIYOjQoVrL0GkCunFoJqdOnWLdunWsW7eObt26MXv2bIYPH37RPE2tnb+ppKSE9PRkvvrK8Sanv/5aoXPn7lrL0KlFamoqnTp1wsvLS2spOo1ENw4tpLKykt27d7NixQpOnTrF1KlTCQ8P/9tiLBcXFy699FJmz57dqvnwt2yJYdAglQULWq1LqyA+HrKyBN26ddNaik4t1q5dy5gxY5gyZQpt2rTRWo5OA1in09xGycjIICMjA3d3d4YOHYq/vz9Hjx6lXbt2rF+/noKCAoKCgpg1axYlJSVs3brVoumOpZRs2bKaDz6YxcqVCgUFFuvK6vjkE8F994Vy/PhxraXomKisrGT16tW4u7szYcIEqqqq2LlzJyUlJVpL06kD3ThYgLKyMrZt24YQgsDAQCoqKsjPzwcgJSWFlJQU2rZty6RJk3B2dmb79u3n9pubjIwMSkvzeP11X/75T8fJ2Prjj4IXXmiLwWDQk8BZGWVlZX8zEtXV1ezcuZPi4mKtpenUQkgptdbQYnr06CGfeOIJrWU0C3d3d0JCQvDz8yMhIYG0tDSz9+Hi4sKVV97AtGkKO3aYvXmrJSZGRcpYEhMTtZaiUw9ubm5MmDABVVXZuXMnRUVFWktyGO64447dUsrQuvbpcw4aU1ZWxtatW4mKisLDw4OIiAhGjRpl1gnsyspK4uN38803NRgcZ/DAN98oBAYO1lqGTgOUl5ezZs0aYmNjGT16NNOmTdPLvloBunGwElRVJT4+npUrV5KRkcG0adOYNm0aHh4eZmk/Lm4vAQFV3Hef46x9WLoUAgJ8cHZ21lqKTiOorKxkzZo1bNq0iVGjRjFmzBh9bYSG6MbBCjlx4gSrV69m+/btjBgxgtmzZ9OtWzcMLXzsX7/+T557Drp0MZNQK+f0aYiJgQED9ORvtsRZI3Hy5Elmzpyp52bSCN04WDHFxcVs2rSJVatWERo6mWuvvZmZM+fg6enZrPby8vI4efI4H3/sOBO077yj0KuXvvjKFklPTycmJoYZM2bo6Tc0QDcONkB1dTXV1fB//6ewZEkH5s5dwGWXXUePHk3PIbRx4xomTYLZsy0g1AqJjgYhnPV0GjZKaWkpy5YtY/DgwYSEhGgtx6HQjYONIIRCUhLcf79Cx44KzzzjQb9+M5g//2ZGjhzZaJdTTU0Nu3Zt5IsvVNzdLSzaClBV+PhjQUiIXnjGllmzZg3V1dVMmzbNanOa2Ru6cbARyssL6dvX+L6kBD76SBAYqHDNNc7k5g7h6qtvZurUcNwbccdPSUkBinn2WceYnP7sM0GnTp1aPGejoy0HDx5kz549zJo1Sy8H2wroxsFGyM/PYsSIv9/MpTS6TaZPNzBmjEJsbDeuuOJ65s5d0KCPdvXqJdx1F7RiNg/NOHYMdu2Cvn37aS1Fp4Xk5eWxYsUKxo4d2yy3qk7j0Y2DjXDyZBYhIRdfsBgfDzffbKBbN8Hbb3syduwcLr/8JgYNGlRnOGBZWRlHjiTwzTc1OEK04AcfKAQFhWgtQ8cMqKrKypUrba5So62hGwcbIScnh6Cghu/iubnwyisKnTsr3HGHCy4uo1mw4BYmTgy7wK2yffsWgoJquOkm218l3xBLl4K3t4fZ1o3oaI+Li4vWEuwa3TjYCCUlJbRpI2jswtGaGvj9dxg50sDkyQYyM3sRGTn/b8dIKdm0KYq33pLYezBPeTn89ptk8OAQraXomIn8/Hz8/Py0lmG36MbBhigsrKJ//6aft3cvzJ1rwMPD84I1EidPnqSg4DRvv23/ax8++0yhW7e+WsvQMRMJCQkEBQVpLcNu0Y2DDVFaWtws4wBQVgYJCZKAgIAL9q1du4LLLhNMmNBCgVbO5s3GEqLt2rXTWoqOGcjLy9OjliyIbhxsiOLibAYNav78wIED1FkAp6qqiri4bXzzjYo9pyGSEj7/XDB48EitpeiYCX3ewXI02jgIIQxCiL1CiGV17HtLCLHP9EoSQuTX2nejECLZ9LrRtM1DCLFcCJEohIgXQrxc63hXIcQiIUSKEGK7EKJnyy7RfsjOzmLYsOavTTh40IC3d931ruPjD+LlVcFDD9n32odvvhF06tRZT+hmJ6iqqidWtBBNGTncDyTUtUNK+YCUMkRKGQK8B/wOIITwB54GRgOjgKeFEGdnkF6XUvYDhgHjhRARpu23AnlSyt7AW8ArTbsk++XMmTP06dP8m1pSEjg5XTwv09q1S3j8cejZs9ldWD0pKZCejh4jbwcoikJ1dTVVVVVaS7FLGmUchBBdgUjg80Ycfg3wk+n9TGC1lDJXSpkHrAZmSSlLpZTrAaSUlcAeoKvpnEuBb0zvfwWmCf0xD4DCwkI6dBDNdv0kJYGb28VTDxQWFpKZmcbnn9v35PTHHyv0719nfRMdG2L06NEcOHBAaxl2S2NHDm8DDwP1+hyEED2AQGCdaVMXoHYR3wzTttrn+AJzgLXnnyOlrAYKADsPtGwcqqpSXFxDcwM00tPBw0OpN43Epk3rGTFCcvnlzevDFvjuO2jb1k9f82Dj+Pr6kp2drbUMu6VB4yCEuAQ4JaXc3Yj2FgC/Sikb9egphHDCOMp4V0qZ2phzap17uxBilxBilyPVni0tLadfM7NA1NTAyZNqvVW2VFVl+/a1fPyxSjMzg1s9BQXw44+S4cNHay1Fp5kEBwdbpKSuzl80ZuQwHpgrhEgHFgJThRDfX+TYBfzlUgLIBGqHx3Q1bTvLp0CylPLtus4xGQ8fIOf8jqSUn0opQ6WUoc2tb2CLlJaeOZeArzkkJ8sG8y4dPXqUqqoCXnrJft1Lb7yh0L17kFnLseq0HsHBwRw+fFhrGXZNg78MKeVjUsquUsqeGG/+66SU159/nBCiH+AHbK21ORoIF0L4mSaiw03bEEK8gPHG/3/nNbUUuNH0/kpTf/af36GR5OZmtyhiad8+hc6dGy4Ft3r1Um6+WWCvKfQTEyEuDvo3d+GIjmb4+/tz5swZ9NuCZWn2Y5MQ4jkhxNxamxYAC2vfyKWUucDzwE7T6zkpZa5pgvsJYACwxxQCe5vptC+AtkKIFOBB4NHmarRHsrOzGTSo+ecnJCi0aXPhQrjzqaioIDExjm+/rcFeH65fe00hOHi41jJ0msjIkSOJi4vTWobd06SqGVLKDcAG0/v/nrfvmYuc8yXw5XnbMoA6I5CklOXA/Lr26RhXhfbs2dJw1sZV+dm9eydXXDGAO+4QfPSR/VmIZcvAYHDF39+f3NxcreXoNAIXFxcqKyupqKjQWordY3+/eDunoqICISTNLamblAQeHo1/Jti4cTmvvEKz+7Nmamrgww8Fw4eP01qKTiMZO3asPmpoJXTjYIMUFlY2O8fSyZNgMIhGpx04c+YMZ85k8cEH9jk5/ckngo4dO+qlJ20ENzc3fZTXSujGwQYpK8tvUQW3o0dVfHx8Gn38+vXRhIfDtGnN79NaycyETZtg4MAWTOTotAqDBw8mOTlZaxkOg24cbJD8/JblWEpMhC5dGo5YOkt1dTW7d8fy1Vcqrq7N7tZqefNNhaCgoVrL0GmAbt26kZrapOVQOi1ANw42SFZWFsOGNf/8uDiFDh06N+mcpKTDuLqW8sQT9peYb/VqqK52pn37upMS6mhPx44dycrK0lqGQ6EbBxskJyeH3r2bf/7hwwIXl6ZX0Fq9egkPPgj2VrpXSvjgAxg6dIzWUnQuwrBhwzh48KDWMhwK3TjYIGVlZbi4QB11expFUhK4uDTdP1RSUkJ6ejJffWV/k9NffKHQoUN7fWLaCvHw8KC4uFjPvtrK6MbBRiksrGz2pHRyMnh6Xjz5Xn1s2RLDwIEq11zTvL6tlawsY6W4AS2Z6dexCGPHjmXv3r1ay3A4dONgo5SWFjTbOBQUQHm5bFZWUiklW7as5oMPVOytQuM77ygEBYVoLUOnFoqioCgKjpRc01rQjYONUlCQRUhI8yeH09Ikfn5Nn3cAyMjIoKQkl9desy/3UlQUKIoL/v7+WkvRMREaGkp8fLzWMhwS3TjYKFlZWQwf3rJ60s2thta1a1fatPGxu4pxNTXwyScQEqKn8rYW2rVrp9ds0AjdONgoOTk5BAc3P8fSvn0GfH2blhPDzc2NSy65itGjZ3Hvvc7MmNG8eQtr5osvFDp16qKn8rYSdu3axfDhenJELdB/ATZKaWkpbm7QXA9IYiI4O3s1+vixYydwxRXX8/PP3gQGKvzwQ/P6tXZSU+HQIWO9AB3tyc7Opm1bvRCkFujGwYYpLKxq9qR0YiK4uTVcjFoIQWTklShKX0aOVHjgAYWioub1aSu89JLCwIG6a8laqKmpwdUel+ZbObpxsGFaErGUng7u7vXXk3Z2dmbevOs5dMiHUaMMJCQ0ry9bY/FiKCpyafacjI55yc3Nrbe0rY5l0I2DDVNUlM3gwc2LWFJVyMxU8b1IPKqHhwfz5l3P4sUuREQYKC1tgVAbQ0p48kmFYcMmai1FBygqKmpW2LVOy9CNgw2Tk5NDnz7NP/9iCfjat2/PpZdeyyuvGPjnPw3U2FfEaqNYtAjKy90YPXq81lIcHmdnZ2oc8UuoMbpxsGFyc3MJDGz++Tt3KnTr9vckTUFBQUyfPpfbb1d4+WXH/XrU1MD48QpOTv2JiLhCT6uhIZ07d+b06dNay3A4HPfXbwcUFRXRuXPzw1k//ljg6+tPmzZtAAgNHU1oaBizZyssXGgulbZLRgaMGKGwaZMfl112HW5ublpLckicnZ31sqAaoBsHG6aiogInJ2hC3Z6/kZVlzEYaFhbJtGkRdOgwmFGjFDZtMq9OW6aiAq67TuHTT52ZO/ca3N0bV39bxzx06tRJT9WtEbpxsHGKi6tpSVDN//6n0KaND25uXQkJUUhKMp82e+KxxxTefNPA3LkLzo20dCzPsGHDOHDggNYyHBLdONg45eUVLUpjUVAACxcafewlJWaTZZc8/7zC888bmDNnwUWjvHTMh6enJ4WFhVRXV2stxSHRjYONU1FR0KJJaYB//UtQUSH54IPm52pyFN54w7gQcPbsK+nQoWnpR3Sahp6qW1t042DjFBScZtCglpfunDJF4YorYN48M4iyc778UnDddQrTps3RF8pZCEVRkFJSog9nNcMujIMQzY/YsXVOnsxi2LCWP/GfOAEPPCD4+mtJ165mEGbnLF0KkZEKY8fOoE+fvlrLsTvGjh1LXFyc1jIcGrswDt7e3owZM8YhQw2N9aTNYxy//BI2b5b8/rtET0raMDExxhHX0KGTGDw4RGs5doWXl5e+tkFj7OIWUFBQQFJSEpMmTSI8PJz+/fs7zKKl0tJSXF3BXIkr585V6NYN/vvflruqHIG9e2HMGEFQUCihoWO0lmMXDBgwgJSUFK1l2DSenp5Mnz6dCRMmNDv1iN3cQXNzc1m1ahUAgYGBzJgxg8rKSlJTUzl69Ciqar83u8LCKvr3dyU2tuVtVVdDZKQgJgZWrYItW1repr2TlAShoQqxsYOYMMGd2Nj1WkuyaXr27MnKlSu1lmGT9OjRg4EDB1JYWMjWrVtxcXFh2LBheHt7U1xczN69extdctVujENt0tLSSEtLA4xPIbNmzaKyspL09HTS0tLsLk9LVVUV/v7mS2m8Zw+8+abg998lffsKCgrM1rTdcvy40UBs3BjE1KnurFu3QmtJNkmHDh3IyspCSj1yrrEoikJoaCgBAQEcO3aM6Ojoc/e4kpISNm/eDICvry+DBw/G19eXsrIy9uzZU2+7wh7+E3r06CGfeOKJBo/r06cPQUFBVFVVkZGRQXp6OuXl5a2g0LJcdtmNzJvnavan/D17VI4fF1x6qeNO+DcVb29Yt06lbdvTrF69VL/JNZFp06axZcsWysrKtJZi9Xh6ejJ27FgA4uPjOXHiRKPP9fb2ZsCAAQwdOnS3lDK0rmPscuRwMZKSkkgyLQHu0aMHEydOxGAwUFZWRmpqKidOnLDJUUWbNs6kppq/3alTFdLSJLfeKvniC91ANIbCQpg0SSEqqh0REVcQFfW7Xbs0zY2Li4tuGBrgrOuooKCALVu2NCvct7CwkG3bttV7jEMZh9ocPXqUo0ePAuDk5ET//v0ZOHAgNTU1nD59msOHD9vEl7RPn34UFcHJk+ZvOz8fbrhB8OOPkthYOHzY/H3YI6WlMH26wuLFvkRGXsWKFb/Y5EOHFugJ9urmrOuobdu2HD9+/G+uI0vhsMahNtXV1Rw4cOBcDpcOHTowfvx4nJycyM3N5ciRI+Tm5lqdi8DLy4vQ0AlMnGi5oLM//4QlSyRLlsCQIYLKSot1ZVdUVsKcOQo//ujJnDkLWL78Z6qqqrSWpSmKojQ4itKN6N/x8vJi9OjRCCGIj49nx44drda3bhzqIDs7m+zsbAB8fHwYMGAAfn5+VFVVUVBQQEpKCrm5uRqrhK5du7Jxo2TfPsv2c/31CunpkjfflNxzj+5eaiw1NbBggcJnn7lz2WXXsHz5z3Yxx9UcXFxciIiIoKysDCkle/fu5cyZMxccp+gLbIC/u462bt2qyUpx3Tg0wNn/nLP4+voycOBAvL29qaysJDs7m9TUVEo1qKPp5uaGs7PAYABnZxgxAjIzjfWhzU1YmGD/fsmyZRAVZf727RUp4bbbFE6dcuGuu65h5crfKCws1FpWqzNlyhTWrVtHUVER7u7uDBgwgBEjRlBdXc2ePXvIy8sDcOiU6IqiMHLkSPz9/VvNdVQfunFoIvn5+edCw8CYb37s2LE4OztTVlZGeno6GRkZrfKfmpqayowZQ8jMdCEgAAwGwYIFljEOaWnw+OOCn36SjBolSE42fx/2zOOPK5w4AS+9NJ/Vq5c61OrfgIAAiouLKSoqAqCsrIzdu3cD0KZNGwYOHIi/vz/l5eUUFhbSpUsXMjMztZTcqnh5eTFmjHEB5cGDB9m+fbvGiozoxqGFZGVlnStGoigK/fr1Y8aMGaiqSn5+PmlpaeTl5TUr7bAQAnd3d9q0aYO3tzc+Pj54eXnh6uqKwWCgpKSE3btj8PX1xccnlBtukCxaZDm3z3vvwahRRjfWokXw1FMCB/oNt5j331c4eRK++upSYmNXcezYMa0ltQqjR48mOjq6zn0lJSXn/Oje3t4MHDiQkJAQevXqxe7duzUZkbcWtV1Hmzdvtrprdah1Dq2Nt7c3ffr0oW3btgghqK6upqamhpqaGsrLyykvL0dRFAwGAy4uLudu+gaDAUVREEJQUVFBYWEhubm55OXlnRt+nyU4uA+hoZO44gqF1atb57qCguDbbyUhIcZ8TM8+K6jDfaxzEcLCYMkSlV27YkhOtu/qSmcjABMTE5t0np+fHwMGDMDb25u8vDz27NlDpR1EQ9R2HR07dozExERNXUd33HGHvs5BCwoLC9m1a1ed+zw8PPD09KSmpobq6mrKysqa/OUfOnQ4ffqMICxMYBqltwpHjsD48YIhQ+Drr1XS042jildfFZxnu3TqYP16Y8K+NWsm4+bmwYED+7SWZBEURaF79+7NSoWRl5d3zn3brl07xowZg6enJ9nZ2cTFxdlcAaCzriMpJfHx8VbjOqoP3ThoRGlpaYuGkaGhY+jYcRCjRgm0ylG2fz8MH64wdix8+qnKvffChx/CK68IcnK00WQr7NkDo0cLNm0Kxd3dgx077C+J1eTJk9m5c2eL2zl9+vS5OZpOnTqdSyaXkZHBwYMHrXqRYWBgIP379z83V2ltrqP60I2DDTJkSAhduw4iNFTh+HGt1cDWrTB4sNFIfPyxyl13waefwssvC06d0lqd9ZKcDCNGKMTEDGDSpDbExLSSX7AV8PLyQkpZZ7hqSzg7xyeEoFu3bkyZMgU3NzfS09M5dOiQWftqLue7jqKioqzagF0MPajYxujbtx99+oQyYYJ1GIbabN0KQ4cqhIUJpkyRpKVJPv3UODehUzeZmTBypEJ5eQ9mzJhrN4WrJk6c+LcQcHMjpeTYsWOsW7eOqKgoysrKCA8PZ+TIkRbrsyG8vb2ZMWMGU6dO5dixY6xcuZL4+HibNAzQBOMghDAIIfYKIZbVse8tIcQ+0ytJCJFfa9+NQohk0+vGWttfFEIcF0IUn9fWTUKI07Xau62Z12Z3BAYGEhIykbAwxapDSXfuNLqbxo8XBAVBTIwkOVly333SbHUn7IncXBg/XuHYsfbMnn0lBoNBa0ktIjAwkBMnTrRaKgxVVUlLS2PVqlXk5uZyySWX4Ovr2yp9g/F6Z8+ezaBBg9i8eTNr1qw5F8FoyzRl5HA/kFDXDinlA1LKECllCPAe8DuAEMIfeBoYDYwCnhZC+JlO+9O0rS4WnW1PSvl5EzTaLV27dmXs2GlERAj279daTePYtw+mTRP4+go++khw992S48cly5dLIiLAxu+BZqW0FGbMUNi+3Yc5cxbg7OystaRmM2DAAM1KfB45coTo6GiGDh3KxIkTLdaPoiiMHj2aiIgIPDw8iIqKYsuWLTY1p9AQjTIOQoiuQCTQmBv1NcBPpvczgdVSylwpZR6wGpgFIKXcJqW0ffPaCnTs2JFJk2Zx6aUKFhypWwxVhTffhL59Ffr0EeTmGmtVnzolefVVSXCw1gqtg6oquPJKhSVL3Jk79xqbLHt7tvazliHyVVVVbNy4kdTUVGbPns3YsWPNVhmytusoPT3d5l1H9dHYT+xt4GHAq76DhBA9gEBgnWlTF6C2ZzzDtK0hrhBCTAKSgAeklFbmXW892rVrx9Spl3D11Qrr1jV8vLWTkQH/+AeA4JJL4IknJHfdJUlOhnffFfzyCzSyUJVdIiXcfrvC6dPGdBvLl/+sSV6d5uDm5kabNm3IyMjQWgoAmZmZZGZm0r59eyZNmoSfnx+xsbHn8qY1BVuOOmouDY4chBCXAKeklI2JpF8A/CqlbMmqjj+BnlLKIRhHGt9cRNftQohdQohdjS17Z2v4+fkxY8Zcbr5ZYYUdFhZbtgzGjhUEBAiWLRP897+S7GzJjz9Kxo3TWp22PPGEwgsvGJg7dwEBAQFay2kUkydPtugkdHM5deoU69atIyUlhTFjxhAREcH48eNxcXGp9zxFUc4d7+7ubpeuo/pojFtpPDBXCJEOLASmCiG+v8ixC/jLpQSQCXSr9XdX07aLIqXMkVKencn6HBhxkeM+lVKGSilDPT09G74KG8Pb25tZsy7n7rsVfv1VazWWpbwcnnoKAgMFI0cKAgJg5UrJkRSVf/0L7PC/t1G88YbCP/9pIDz8MgIDA7WWUy8dOnSgoKDAqkc5cXFxZGVlkZmZSWJiImPHjmX27Nn07t37b8d5e3sTHh7O1KlTSUtLY+XKlRw6dMguXUf10aT0GUKIKcBDUspL6tjXD4gCAqWpUdOE9G5guOmwPcAIKWVurfOKpZSetf7udHYuQggxD3hESjmmPl3Wmj6jubRt25bw8Mt46CHBp586ZrTx2jUqEydIyktVFBcnFi2SvP22gqnkhkMxbhwsXy5JStrDvn2tuBS+CURGRhIVFWUT9RjGjBlDWVkZcXFxGAwGevfuTc+ePVFVFWdnZ/Lz89m7d69NFPtqKfWlz2j2nUcI8ZwQYm6tTQuAhbKWtTEZgeeBnabXc2cNgxDiVSFEBuAhhMgQQjxjOu0+IUS8ECIOuA+4qbkabZGOHTsyc+Y8br/d4LCGAeDGmxQqqhS8XnyMNk8+yHUzT7N1i2TfPpXrrgNXV60Vth5btkBoqKBjx2FMnhyutZwLCAkJ4fDhwzZhGAC2bduGu7s7gwcPpqamhsOHDxMdHU1sbOw515EjGIaG0BPvWRHdu3dnwoRwrrlGYdkFq0kcj7vukrz8Yg1evdpDXh6MHEnNK69QMmwSKAqffw4ffCAsUj/bGvH3hxUrVLp3L2bVqj+soqSmoiiEh4cTZYNFPsaOHUtRUREHDx7UWopmWGTkoGNeevUKYvz4cObM0Q3DWT76SHA4RaFqmSlx286dGKZOxbtnW7zefZE7/1HIwQOSmBiVuXPtf91Ebi5MmKDw66+ezJt3He3bt9daEmFhYTaRRK4utm7diqenJ0OHDtVailWiGwcroH//AYwcOZXp0xU2bNBajfUgJfzjHwpVQ0fB5Zf/taOgAPHUU7i398X9spmMdd/Ld9+onMySPP20Steu2mm2NNXVcN99Crfd5sS0aXMJCRne8EkWwsfHh8rKygvSyNsS27Ztw8XFRdO0G9aKbhw0RFEUpkyZRd++45g4UdCKtcNthsREePMtSfEn30NdoYerV+M0MhTvfp3x//lDHr6vgqTDkkOHVB5/XNKjR+trbg1++QVCQxX8/IYza9blmqyoHj9+PNu2bWv1fs3Nzp07qaysZPz48VpLsSp046ARfn5+XH75P0hO7kq/fo4ZhdNYnn9eIbfUlZpvvr34QdnZKPfcg0c7T9znX0Kfo6t44uFKEhIk69erzJljf26npCQYMkRh7Vp/Lr/8etq1a9dqfffp04fjx49TVVXVan1akri4OPLz85k0aZLWUqwG3ThowLBhoUREXMFjj7lwySWKXiCnASorje6l8jlXwfAG3CiqCitWYIiIwMPfA/f5c5jgE8d336qcPCl55hmVzp1bR3drUF4Ot9yicOedzkyffilDhgxrlX579+7NATt7oomPj+fkyZNMmzZNaylWgW4cWhF/f3/mzfsHBkMIoaEKn35qH+mZW4OYGPjjD0npHyuhsWmtVRWWL8dp+HB8+nfBf9GH/OfeclJSJFFRKjNnNr4pa+enn4ypvzt0GMGMGXMtmtl14sSJ7Nmzx2Lta0lSUhKpqanMnDlTaymaYxfGwdpTHBsMBiZMmMrMmVfw9NNuhIYqHD6stSrb4777FCq92yGff77pJ588aXI7eeH+j/lM7ZLAz4tUTpyQPPaYihUE/rSYw4eNRZfi4tozb971eHnVmwqtWXh4eODi4sLJkyfN3ra1kJaWxsGDB4mIiNBaiqbYhXGwxI/AXHTv3p0rr7yR1NRe9O1rTF1tB0tLNCEvD+66S1By76PQqVPzGlFV+O03nAcPwntwT9qv+Ion/lPJ0XTJ0qUqU6aYVXKrU1IC8+Yp/O9/zlxyyVX4+/ubtf3JkyezZYv9lTQ9n8zMTHbv3s0ll1yCotjFbbLJ2MVVl5WVMXbsWK1l/A1XV1dmzryM4cPDuf56J+bMUbCD+h+a89NPsDdOULkiuuWNHT+OcuuttGnvhdtt1zOrzxGWLlE5flzy4IMSM99XW5U33lC45x6FWbMuN9t6iC5dunD69GmHWT186tQpYmNjiYyMbDBJnz1iF8ahoqICV1dXfHx8tJYCwKBBg7niin/w558B9OqlL2ozNzfeqFAVPOhs7u+WU10NP/6Ic78+eA0LplPMTzz7VBUZGZKff1ZtNkPst98Krr9eYfr0uXTo0KHF7YWEhNjtXMPFyM/PZ926dcycORMPDw+t5bQqdmEcADZv3mzRyk+NwcvLi0svvRZ//9FMnqxw770KVpyk0mZJS4MXX5QUv/0ZuLubt/HUVAzXXYdnBy/c7/0nl4UeJ2qlSmqqyj33SLy9zdudpVmyBK67TmHatDktGkGMGDHCITOTApSUlLBq1SrCwsJatfyo1tiNcaiqquLQoUOMGnWxyqOWpWPHjsyZcxVvv+3BoEEKDvaA1eq89ppCVo4z1T//YpkOKivhiy9w7tUTr1ED6L53Mf97vpqTWZLvv1cJrTMbjXWydKkxFHj69LnNMhBOTk4EBASQlpZmAXW2QUVFBdHR0YwePZqOHTtqLadVsBvjAJCamoq3tzetXd9h8OAQwsIu4dprDbz8soKNJKe0aaqrjU/ElWGzYcIEy3Z2+DCGK67Aq5M37g/fy/xJJ1m/TuXwYcntt9tGvYnFi+GGG4wGolMTJ/PDwsKssohPa1NdXc2qVasYOHAgvXr10lqOxbEr4wAQGxvL5MmTW62/4cNH0r17KKNGKfz5Z6t1qwPs3AnffS8pWbQUWiOipLwcPvgAl+5d8BwfQlDScl5/pZrsbMmXX6qEhFheQkv44w+46iqFsLBIundvXF4Rf39/SktLKSwstLA620BKydq1a+natSsDBw7UWo5FsTvjUF5eTmpqaqtkWuzXrz89e4YwfrxCYqLFu9Opg//8R6HMxRf1zTdbt+MDBzDMmYNXFx88nvw31844Tewmlfh4lVtuAWudu1y5EiIjFcaPn0Hv3sENHj927Fh26Em/LiAmJgZPT09GjKizUKVdYHfGASAhIYGOHTvi5uZmsT7atm3L8OHjmTZNcPSoxbrRaYCiIvjnPwWlt9wLWpTSLC2Ft97CtVtH2kwKpe/x1bz1RjWnTkm++EJlWOtks2gSMTEwdarC8OGT6d9/wEWP69evH2lpaXaTP8ncbN++nerqartN2GeXxgFg06ZNhIWFWaz9SZMiue8+wf79FutCp5EsXgyxm6Fi+Spthezdi2HWLLy7+dHmmYe5buZpYjaqJCSo3H47WNNazV27jLUhBgwYz8CBgy/YL4SgV69eHDp0SAN1tkNcXBw5OTlMnTpVaylmx26NQ2lpKZmZmfTt29fsbbdv3x5VdeGbb+wkMY8dcNttCtXdguCuu7SWAsXF8PrruHbtiOfkUIJTo3j9lWpOnpR8+631RDrFx8OECYJBg0bTs+ffR12TJk1i165dGimzLRITE0lLS7O7fEx2axwA9u/fT69evcy+unHo0NF88IExE4OOdZCZCc+/ICl+/k2woDuxyezdiyEy0jg38dj/cfUUY6RTcrLKXXeB1us2ExNh9myFceOmnVso5+npicFg4NSpU9qKsyHS0tLYv38/kZGRdpNuwz6uoh5iYmLMmoLXYDDQoUMHvv7a7j86m+OttxSKKlxQP/xQaykXUloK775rjHQaN5TA+D95+cUqsrIkCxeqaOm23rIFrr9eYdq0S/Dx8WHixIkOkT/J3GRlZbFt2zYiIyNxcnLSWk6Lsfs7XElJCceOHWPIkCFmaS84OJi4ODh+3CzN6ZiRykpjYr7SK26ELl20lnNxDh7EcNllxnUTD97F5aMzWLlCJT1d8sAD2uR0WrIE/vMfwezZV5Kbm0t5eXnri7ADcnJy2LBhAxERERYNiGkN7N44gLGIR8eOHXE3Q6qFPn1Ceesth/jYbJLFiyE+Aap++U1rKQ1TXg4ff4xzYA+8Rvan67afee6/xpxOf/yhEhbWuvUmfvtNQVEUEvW47BZRVFTEmjVrmD59eqsvyDUnDnOXM0f0ko+PD+7ubixebB5NOpbhjjsUqoaMAlsq+ZiUhGHBAjzbe+J+581EDkhl8R8qGRkqjzwiaY0KoK+/rpKZeYyCggLLd2bnlJWVER0dzaRJk8yeNr21cBjjUFZWxrFjxxg0aFCz2xg2bDSffw562Ld1ExcHS5ZKKr79UWspTaeqCr75Bue+wXiHBNFxzfc89Uglx45Kli1TmT7dMqOJESPg8sth8+Z15m/cQamqqiI6OpqRI0fS2QZr0zqMcQA4ePAgXbp0aZZ7SVEUunTpzkcfOdRHZrP06CFx7tzBNhIfXYz0dJQbb6RNe0/cbr2O8F7J/ParyolM81avEwI+/1xl//4d+oI3M1NTU8OqVavo168fvXv31lpOk3C4O11MTEyzFqwEBwcTHw9HjlhAlI5ZGTYMhgwRqIv/MK45sHWqq+Gnn3Ae0A/vob1oH/U1T/ynivR0ycqVLa+Ffd110LVrJQcP6is6LYGUknXr1tGpU6cWeS5aG4czDmVlZSQlJRHaxJVIwcEjePddh/u4bJKnnqrBRVTg9OKLWksxP0ePotxyC23atcH9lmuZ1v0wPy9SOX1aEhXV9HQdnp7wzjsqW7ZovLrcAdi0aRPu7u6MHDlSaymNwi7udj4+PgwePLjRi92Sk5Px9vbGu5GVWzw8PPDxacPvv7dEpU5r0KMHzJypIPbsMk4+2Cs1NbBwIc4DB+A9qAfO+acZOlSwaZMkPV3y7LPQmJ/Ds8+qFBWd4eTJk5bXrMPOnTupqKjQvDBZY7AL41BYWEhBQQGjRo0iPDyc2bNnM3LkyHrLhm7atIlJjYxmGTJkGL/+KnGQ0rk2zcMP10BVOc7PPKO1lNYjIwOhKERGCjp0EDz7rGDePElOjmTDBnnRBXbBwXDHHRATY4Z63DqNZv/+/Zw6dcqsi3Mtge0v48Po0zt27BjHjh0DjEnDAgICCA4OxtfXF2dnZ8rKykhOTiYzMxMwRhLs37+fCRMmEBsbW2/73bv35Y477MKO2jX+/sb60k4Zx2CdA0XdTJxIgfQ+V33wq6/gq68EgwbBXXdJoqIk+fnwzTeC554zLhYE+OQTlaSkg5TpTz2tzuHDhykvL2fmzJlER1uncbYL43A+UkpOnz7N6dOnz23z8vKiW7du9OnTB1dXV1RV5fjx4zg7OxMQEMCZM2fqbKtTp04UFips29Za6nWay913q6gVlbg8/bTWUlqV4vse49W3nS/YfvCgccX4v/8N8+fDv/8tuf9+Y0bWFSsEw4bV8Ouveq0GrTh69Cjl5eVERkaycuVKq6vPLaSUWmtoMT169JBPPPFEk85xcXGhc+fOdO3aFX9/f5YuXVrncTNmzOWzz9rz0kv6yMGacXeHrCyJR/kpnLt0wWFqtXbuTOmhNDp2c6GoqOHDR4wwGgcwRu7pq6G1x8/PjwkTJhAdHU3l2WFdK3HHHXfsllLWGZ3jsHe8yspK0tPTiY2NZfv27XWGtxqT7LXnu+8c9mOyGW65RUJNFU7PP+84hgGouv1ufvrFqVGGAWDiROPT6fbt23XDYCXk5eWxbt06Zs6ciYcVlRDU73pAdnY2FRUVBAQE/G17UFAQBw9CRoZGwnQahZMTPPWUpI2hDPHll1rLaT2cnam6817ebEKurz17FI4eVQkOHk7//v0tKE6nKZSUlLBq1SrCwsLw9fXVWg6gG4dzbN++nfHjx9OnT59z2wYMGE5JiSAyUvu8+zoX56qrwNW5BuX113GkkDJ5+eXEp7jRlGJtMTEQGKhw663OtGs3jvnzb2bQoEGI1szwp1MnFRUVREdHM3r0aDp16qS1HMedc6gLRVHo1asXgYGBKIqCwWCgoKAIf/8e+Pu7k54uiYqC9esV0tMhOxvOnDEuYNXRjuRklcBOZRi6doX8fK3ltBqF2+K5+dUBLVp/ExkJL71UQ7dukv37t+iuJitACEFYWBjHjh0jJSXFon3VN+dgl9FKzUVVVVJSUkhJSTm3oM44QbQJRVHo0KED06b1Zv787ri4uODmZsDdXVBcLFm7Fn74QWHlSod6eNWciAhoFyDhs88cyjAwZgzlXXuzZEnLmlm+HJYvNzBnDrz11gQGDRrDrl0bSE9PN4tMnaZzNt3GhAkT8PDwYL9Gher1kUMLEULg4eFB37596dp1AD4+7mzYIPnuO4Vly2j0RKFO89i5s4aQgVU49Q6CEye0ltNqFP+xisfXTee998znDlIUWLAAXntNxdm5gm3b1pCVlWW29nWazogRI3BycmL79u0WaV+PVrIgUkpKSkrYs2cPS5d+z88/f4uv7w5ef72IU6ck69er3HEH9OyptVL7Y8wY6NtHwC8/O5RhoE8f1ImT+eIL884TqCr8+CP07Knw9NNujBsXyaWXXoufn59Z+9FpPLt376a4uJjJkye3et/6yMGCODs707t3b7p370/btn4UFgqWL4dlyxTWr9dHFS1l+fIaZkypxHlYCCQlaS2n1Sj97HvePHktTz1l2Ulkd3e4/36Vxx6D0tJiYmKiyHck150VERwcTPfu3Vm7dq1Z261v5KAbh1bE39+f4OA+tG8fhJ+fB4mJkj/+gKgohV27jE9uOo2jVy84cEDivGUtzjNmaC2n9ejYkdKEo3Tv7UJOTut06e1tNBIPPaQbCS3p0aMH/fr1M2u6Dd2tZCXk5uayffs2/vzzB3744UtOnlzF9dcfZ8WKSgoKJMuXq9x2G3TvrrVS6+ehh2qgqgzn55/XWkqrUnH/f/h+kVOrGQaAwkJ4/nmFbt0UPvzQk1mzrmTWrMtwctLjWVqTo0ePsn//fmbPnt0q/TV65CCEMAC7gEwp5SXn7XsLOFug2QNoL6X0Ne27EXjStO8FKeU3pu0vAjcAflJKz1ptuQLfAiOAHOBqKWV6fdpsZeRQHx4eHvTq1YuuXfvRtq0veXmcc0Ft2GAfNWvMha8vZGRInDOScOnXT2s5rYeXF2VHT9F/mBtHj2onw88PPvqohogI2LZtrR7Z1MoEBAQwZswYVqxY0eJ8TOYKZb0fSAAuKIIgpXzg7HshxL3AMNN7f+BpIBSQwG4hxFIpZR7wJ/A+kHxec7cCeVLK3kKIBcArwNVN0GmTlJaWcvDgQQ4ePAhA27ZtGTu2L3Pn9sLPz534eMnPPwuWLhU4eij6HXeo1FRU0ua557SW0qpU334n0eucNTUMAHl5sGCBgUsuga+/nk6/fmdYs2YZ1fqCn1bhzJkzbNq0icjISIvmY2qUW0kI0RWIBD5vxOHXAD+Z3s8EVkspc00GYTUwC0BKuU1KWVec3KXAN6b3vwLThAMu38zJyWHbti38+ef3/PDDl+TmruWOO06yfXsNmZkq772nEhZmTB3hSDg5wUMPgQel8MsvWstpVVRvP/wCrMcTvGwZBAcrbN3aliuvvJGeekheq1FQUMCaNWsIDw/H00J10hv7TXsbeBiodwwjhOgBBAJnk+l3AY7XOiTDtK0+zp0jpawGCoC2jdRpl9TU1JCWlsaaNX+ycOEXbN36B2FhSSxaVEFenmTJEpUbboAOHbRWannmzwcnpQbltdegqkprOa2Ky/NPMbxPEffcYz1BJGdHEdddZ2DEiOn6XEQrUlZWRnR0NBMnTrwgL5w5aNA4CCEuAU5JKXc3or0FwK9SSounxRRC3C6E2CWE2FXsYA75nJwcNm+O4Y8/vuGPP76nffudvPBCPqmpKklJKq++Kpk0yT5HFU8+WYOXezXKJ59oLaX1qa7Ga85UXn4JrC1nnj6K0IaqqipWrVrFsGHD6Natm1nbbszIYTwwVwiRDiwEpgohvr/IsQv4y6UEkAnUVtzVtK0+zp0jhHACfDBOTP8NKeWnUspQKWWopYZVtkBZWRn798excuXP/PDDFyQmLmfOnCP8+msFBQWS6GiV22831la2dcaNg65dBfLbb42PrI7I7t24ffw2i/+QOF9Y30dTao8ihg+fzpw5V1tVCmp7paamhjVr1hAUFPS3xKEtpUnrHIQQU4CHzo9WMu3rB0QBgdLUqGlCejcw3HTYHmCElDK31nnF50Ur3Q0MllL+yzQhfbmU8qr6dNlDtJIlcHNzo1evXucW4eXmwoYNsGOHwv79cOCAbd1jFy+uIXJGFU7DhjrUore6KI5P57Oo7jz4b+ucjmvTBp56SuWee+DEiWPExKylxoHqbGjF+PHjKS4uJi4urlHHm20RXG3jIIR4DtglpVxq2vcM4CalfPS8c24BHjf9+aKU8ivT9leBa4HOwAngcynlM0IIN+A7jBFPucACKWVqfbp049A42rVrR/fu3fH374KHhy8+Pi4UF8OhQ5Jt2xT27hXs32+871pb4EmXLpCcLDHsiMFlyhSt5WhP165Upx2jUyfBRSrcWgU9e8I779QQFgYHD+7kwAFtksg5EqGhoRgMhkblY9JXSOtcFC8vL9q3b0+nTl3w8upEmzZt8PQ0cOyYZM8e2L7dOMrYvx9OndJO53vv1XDz9ZW0WTAPrLQge2sir7qK+Ee/Z/BwK/MtXYSJE+Gzz2oICKhm48blF63ZrmMeBg8ejJ+fHzExMfUepxsHnSbh5OSEn58fnTt3JiCgC23atMXHx5XKSkhIgO3bjRXF9u83FrG39CijfXtIS5M4ZyTj3LevZTuzBRSF4oTjXHFvZ1at0lpM41EUuPNOlZdegpycbNavj6LKwSLOWhNjpuiu9eZj0us56DSJ6upqTp8+zenTp4G/fJceHh60b9+eqVM7M29eZ7y8/FAU+OEH+PRTQSPdnE3mkUdU1IoKnB97zDId2Bjy6qs5UtjOpgwDGHOHffCBws8/wzvvtOfKK2/QXU0W5PDhw5SXlzNr1iyioqKafL71rKjRsXpKS0tJT09n69YtJCTspLJSsmCBYOBA2LRJkpIieeABSbt25uvT3x/uuEPgmn8S/vjDfA3bKopCyXNv8u9HbcOdVBenT8O11xqYPdtAQMAo5s27Xo9qshBn8zHNnDmzyefqxkGnWYwaNZUHHlCIioIpUwS+voL33xfcfrvk2DHJmjWSefPAVFCv2fznPypqZTnOjz8OduACbSly/nyO5LfFzJmbNSE2FgYOVPj4YzfmzbuW4cNHaC3JLjlx4gSHDh1iRhOzF+vGQafJhIQMIyNDYeHCv7apKrz9NvTvrxAYKEhNFXzwgeTMGcknn0hGNON37+cH99wjcCs+Az//bDb9tkzxk6/w2H9td9RwPlVV8N//Kowfr+DtPYx58663WDoIR+b48eOkpKQwbdq0Rp+jGwedJmEwGOjffzh33mm46IP8yZNw++3QubMgIkIQFCRZu1aSlib5z38kHTs2rq9//1tFVlbg/OijerELgNmzyTZ0YuVKrYWYn7g4GDRI4f333bj00gVMnBiGA6ZUsyhpaWmkp6cTHh7eqON146DTJCZODCMmBjZvbtzxmzfD9OkKvr6C114T3HijJDVVsmGDZP58cHWt+zwfH7j/foFbWQ4sWmS+C7Bhip56lSeeaaGfzoqpqoLnnlMYOlTh9OlezJ9/M127dtVall1x5MgRDh061KiaELpx0Gk0rq6udO3ak/vvNzTr/A8/ND4ddu8uiI8XvPWW0e30xReS0aP/fuz//Z+KWlVhnGvQV9bCxIkUdQrm11+1FmJ5jhyByZMN/OtfTowdO4tZsy7D2dpyhdgwGRkZ7Ny5k8jIyHqP042DTqOZOHEqv/wiOXKkZe2cOQN3323MkzR1qqBzZ0l0tHEi+/HHJb17wwMPCNwrC4xxsjoUPfUK/33exaG8a4sWQVCQQmxsW6688gb69NHXuJiL06dPs3PnznqP0Y2DTqNwcXGhY8cuPPts80YNF2PnToiIMLqdnntOsGCBJDkZFGpwev55fdQAMHQoVUNG8N13WgtpffLy4LrrDMybZ6Bv34nMnXs1bm5uWsuyC041kPLALoyD/mWxPBMnTuWPPyRpaZbr4/PPYcgQhaQkFQ+nCsRXX1muMxui5JFnefkNZyxU8MsmWLsWevdW+PlnTy6//HqGDg3RWpLdYxfGwdXVVTcQFsTZ2ZmOHbvy9NPmHTXUxciR0KkT8O23UFpq8f6snh49ICKCjz/WI3dKS+H++w1Mm6bQrl0ol156Da4Xi2jQaTF2YRyKioqaFL+r0zQmTAhj2bKWzzU0hunTJU4GgVCcwALVrWyN8gce45MvnSkq0lqJ9bBjBwwYoPDzzx5cccU/CArqrbUku8QujIOqqiQmJjJmzBitpdgdzs7OdO7cnSeftPyoAeDllwWzIwW/t72NsqPZFG+Og3nzwNA6/VsV/v7IG2/i9df1UcP5VFTAffcZuOwyhcGDp+iFhSyAXRgHMMbvurq6WqSWqiMzceJU/vxTkpLSOv1JaSxINH++oGMnhQe+GsLB536j5EwplV//CGasdGXtVN11H38scyYrS2sl1svatcaIpu++82LevGuZPHkaimI3tzVNsatPMTY2ljFjxuhfDjPh4uJCp07deOopbZ7aCwuNk9SDBwtCx7rwQe4C8rclUJiYibzzLrDneSY3N6ruf4jnX9S/yw1RXAyPPKIwZIhCWlpPrrrqZvrqqd1bjF1982pqati2bRtTp07VpP9OnTpp0q+lmDIlnD/+aL1RQ30kJsKDDwratVe48dHObFrwPmWniyj/bTkMGaK1PLOj3nQLW3a7kpiotRLbITUVIiIMzJ9voHfvicydu0APVGkBdmUcAM6cOUNubq5ZC203hoiICEJDQ3FpaRpSK8HT05N27Try8MPW5euvrobFi2HyZEHfAU68fiiCnLV7KUg5hfrY48Zsfa1M5f0PUfb1woYPbCxOTpQ9/jxPPaOXW2kOq1ZBcLDCTz/pYa8twe6MA8CePXvo1atXqz01TJ48mcTERNavX09YWFir9Glppk69hDffxKr93cePw1NPCdp3UJh/Zzv+HPUCZRlnKN60t/Umsd3cqHnqGUoir6bGTOsy1H/cwP50L7ZtM0tzDklZGTz4oEJYmIK/fyjz5l1PmzZttJZlU9ilcQCIiYlh+vTpFu9n+PDh5Ofnk5aWRnFxMQUFBXTo0MHi/VoSDw8PPD29eOUV2/h6qCqsXg2XzRN06qzw7+9COPDsb5SeKaXiu0UwcKDl+r7xJrbucSUiAiquuhGuvLJlDRoMlD71Mg8/rucSMge7dhlrRnzwgRuXXXYNgwbZnwvSUtjGr78ZlJaWsm/fPosaiJ49e+Lh4UFcrfqYO3fuZOTIkRbrszUYPHgI0dEqJSVaK2k6BQXw6acwZIhg2GgX3s2aT+7G/RQeOY16z33m7cxgoOwxo/tn1y549FEo/vwnaEEmUXnNNSRm+xIba0adDk5VFTz7rMLkyQrdu4/ikkvm4+Sku+wawm6NAxizDx4/fpzx48ebvW0/Pz+Cg4OJPe9XXFNTQ2JiIsOGDTN7n61Fp059+Pln65praA5JSfDww8ZJ7Pl3BlDx6ttwyy1ma19efTUJWT5s2WL8+733BFGrFUq27GueS0tRKHn6NX3UYCF274Z+/RTWrvVm/vwbGTxYH0XUh10bBzAW2S4rK2Po0KFma9PNzY1x48axbt26OvenpKTQoUMHm3w6cXJywtvblWbUI7daVNU4SfnLL5KiVz9E3n5Hy+cjhKD4yZd54um/38j/8Q+F9EI/KtY3/dFfXnUVKfltWb++ZdJ0Lk5JCdxwg4HISAP+/iO54oobad++vdayrBK7Nw5gnKD28vKiV69eLW5LURRmzJjBunXrqKknY+i2bduYMmVKi/trbYKDg9m7V6WwUGsl5uemmxSuudGVuDs/ovhkEep/nwF39+Y1dsklZKkdWLXq75vLyyE8XKGw32iqP/ui8e0JQfHTr/HIE/qooTXYtAkGDDDw5JMuhIXNZfbsy/WaEefhEMYBjAvkevXq1eKnhJkzZ7J582bKysrqPS4/P5+Kigp8fX1b1F9r07v3EH780fZdSnUhJSxfDsOGCWZd5s7aCf+lNLuQyq++hyYGERQ99SqPP1132PKJEzB1qqDs6puNExGN0Xb55Rwrv9DY6FgOVYWPPxb07Kmwfr0fV1zxD9q1a6e1LKvBYYwDwLp16wgNDW12AfOwsDAOHTpEbm5uo47fvn0748aNa1ZfWuDs7IyPjxd//KG1EsuzeTOEhwtCRjjxXfW1lB05QemazdAY9+PkyRQE9Kr3czp4EC69VFD65P/g6qsbbLL41v9j+x79yVUL8vONrqZ77zUwY8aljBhh2wEl5sKhjIOqqqxevZopU6Y0ebHaiBEjOHPmDEePHm30OZWVlWRmZhIUFNRUqZowevRYVq+WZGZqraT1SE6G2/4p6Npd4aUNY8lbt5eiA2nGdRIXoeixF3nmxYarsq1fD/+8HUo+/xFmzqz3WK/P3+Lq+SpffqnadVYQa+bbbwWjRin4+w9l1qzLtJajOQ5lHACqqqpYt24dM2fObHQOpqCgIFxdXTlw4ECT+9u/fz/9+vVr8nmtjRCCrl2Deekl+3QpNURuLrzwgqBjJ8G9r/ck9bXfKMosQN53P9QOLBg0iJrhI/n++8a1++OPgvv/T1Dy6wqoL63L77/TZvQgrppZyIEDkgEDWnY9Os0jIQGGDlXIymrLnDlXIYTjZsR1OOMAxjUQmzZtIiIiosFjAwICCAwMZMvZeMUmIqVkz549Vj853b9/f9LTYft2rZVoS2UlfPMNBPUWXHmzN7uuf4uS7CLU514Ad3dK//M0r7/jTEVF49v84gvBvx8SlCxZDRMnXvzAhATa9GhHj6Rodu6UPPusij5H2voUFcGUKQbi4724+upbuPrqW7nssusdbsLaLoxDc/K45+fns2fPHsLDwy96jLu7O6NGjWJ9C2MLs7KyKCwsZNCgQS1qx5L06zeSF190zFHDxVi1CkaNEkyb7cbaMY9Tll0A8+bxwQdNf5r85BPBI49CyfL1UN88VHU1zpEReNwwnwfvriQlRTbkkdKxAOXlxiR+YWEGRoww8Pvvrsybd71DJfKzC+NgMBialaI3KyuLI0eOMHny5Av2KYrC9OnTWbt2bb0hq41lz549BAQEWGXmVmMNDGd+/11rJdbJ9u3Gyetd+5z46WeF/PzmtfPBBwpP/ldQEhUDo0bVf/Bvv+HZxZcuW37m119U1q5VCQ5uXr86zaO6GvbsgSNH4M47DXz8sYFLL73WYYoK2YVxKCoqonPnzvTu3fRygUeOHCE3N/eClBcRERFs2rSJiqb4Dxpg48aNhISEWN2Xa/ToSXz0kfHHoFM3imIMZPrww5b5oN9+W+GZ5wQlq7cYC2bXR0UFhmsW4DmiHxPaJbJvn+Ttt1W8vVskQaeZPPaYwv/+p3DppdfQsWNHreVYHLswDgDr16+nR48eBAYGNvncAwcO4ObmxhBTXYDp06cTFxdHfnMfES+ClJJ169YxbZr1VKsyGAz4+fnz+efWocdaeeEFyMw0Pkm2lNdfV3jmeUHJ2q0NjyAAkpNxGTIQj+su57arizh6VHLHHRIbXIBv87z6qsKttxqYOvUSBg60XjexObCrO8LatWvp3bs33bp1a9J5fn5++Pj44O/vz8yZM8nMzCQjI8MiGisqKoiNjWWmlTiSBw4cxJ49kmPHtFZi3dx+u+Sxx8wXufL66wpPPysoWbMFJk1q3EmLF9Omiz8+H/yP116uIT1d5eqrwYEDajThl19g8mSF4OAxTJsWYbcRTXZlHABWr17NwIED6dy5c6OOb9u2LePHj2fFihVs3LiRhIQEEhISLKoxLy+PpKQkiyQEbCpBQSG8954+EV0fTz8NeXmwdKl5233jDYWHHlYoXb4eLrmkcSepKuLJJ/Hq7k/HdT/w2Sc1JCZKGhF4p2NG9uyBwYMVMjI6c8UVN9hlrQi7Mw4AUVFRDB8+HO8GnLN+fn6MHTuWFStWoKoqUkqOtdIj9JEjR6iqqmr1inW18fLywtXVmcWLNZNgE9x9t3HUIKX52/74Y8Gt/1QoWbi0aRlji4ow3HADXr07EpS0nJ8XqWzZotKjh/k16tTNqVMQFmbgtdecufTSaxhgZ4tT7NI4AKxYsYJJkyZddCW0j4/PuRGD2tBSVwuxY8cOevTooVn+pfHjp/Lll8bYfp26efRRKC2F336zXB8LF8JllwkK3/qc6i++btrJZ85gmDMHz0GBjHA5wKF4ycsvS33SupWQEl5+WWHGDIXAwHFcdtl1zU7PY23YrXFQVZUNGzbUOfnr5eXFpEmTNDUMZ1m/fj0TJkxo9fTeLi4uBAS048037fYrYBYeeMByo4barFkDQ4cKjoy/gZJ9SdDUG8yxY7iEhuBx983cc3MJx49JHnhAYiclza2ebdugd2+FDz9059JLFxAWFo6hNcrUWhC7vjMUFxezc+dOwsP/+o9q06YNU6ZMYeXKlZobBoDq6mo2bNjQ6hPUo0ePZ+VKyfHjrdqtTfHPf0JNDSxa1Dr9pacbM8YuPxxEcdopGDGi6Y188w1tOvvi/cbT/O8F1Zy1jXQaoLzcWHFu4ECF5ORuzJ9/k83kVasLuzYOAGfOnGHHjh2Eh4fj5eXF1KlTiY6OptqKgvqLi4uJi4sjLCysVfpTFIWuXXvpK6Ib4PHHJS++SIMJ9sxJWRlcfbXC48+5UbJxJzWvvdH0Rmpq4LPPUKtq+PVX82vUqZ+jRyEy0sC11xoYNCiMOXOutsmV1XZvHAByc3PZunUrM2bMYN++fVRaoZM9IyODvLw8s1asuxhDhgwlPh727rV4VzbLtGnQti189ZU2YYrvvScYOVKQHPkAxYczoInrdyr+72G+X+jEmTMWEqjTIMuXQ69eCgsXenHFFdczcuRorSU1CYcwDmDMpbR48WJ69OjBmDFjtJZTJ/v27cPLy6tZK72bQnBwCM8/r48a6uONNyQffGCcjNaKhAQYPETw9s+dKd2fgvrEU4070ccH9fY7efF/DvPztlpKS+HBBxXGjVOAQVx55U1WmUKnLhr97RFCGIQQe4UQy+rY95YQYp/plSSEyK+170YhRLLpdWOt7SOEEAeEEClCiHeFaSWJEOIZIURmrfZmt/Aaz6GqKrGxseTk5BAREWE1q5RrExsbS48ePSy2PD8wMJDCQgMrV1qkebsgOBj69IG339Z+cVN1NTz1lGDSFIWUG56lOCWrwbmI6rvu5c9VzvrCRiti/34YMcLAgw+6MHFiJJGRV+Dq6qq1rHppyt3xfqDO1WFSygeklCFSyhDgPeB3ACGEP/A0MBoYBTwthPAznfYR8E8g2PSaVavJt862J6Vc0QSNjSI5OZlt27YRGRmJe3NrCFuQ9evXExISgo+Pj9nbDgmZwPPPKxaPvrFlPvxQ5bffIDtbayV/sXs3DBggePK9DhSv30nFb39CXTm63NyofOARnn1OHxlaG1Ia08EHBiosW+bLlVf+g9DQRqRP0YhGGQchRFcgEvi8EYdfA/xkej8TWC2lzJVS5gGrgVlCiE6At5Rym5RSAt8ClzVVfEvIy8tj9erVzJgxozW7bRSqqrJmzRomTJhg1iR9/v7+ODu7NrpQja2yY4fK9u2S7t2bfq63N4wdK3jpJe1HDedTUwPvvCMI7iNYJmdTkpmPvOfevx2j3nIrm3e7ceiQRiJ1GiQ/H/71LwPjxyvAYKt1NTV25PA28DBQb9yGEKIHEAisM23qAtQOlswwbetien/+9rPcI4TYL4T4stZIw+yUl5dbVdRSbaqrq1mzZg1Tp05tcknTizF+/HTeeosmFaqxNSZNgv79BYmJkkOHJD/+2LRY/7feMj6lW/PN9eRJuPJKhUvmOZNy7zsUHc2Fq64Cg4HSR57lqWf0jHy2QFwchIYaXU0TJhhdTdbkyWjQOAghLgFOSSl3N6K9BcCvUsqWFED4CAgCQoAsoM5YPiHE7UKIXUKIXcXFxc3qqHv37mRlZTVXp8WpqKhocknTi+Hu7o6Pjw8ffGB98yzm5K23JJ98AjfeqDBunCAwEE6elDz6aOPOv+wyyQsvWN+ooS42bIC+/QS3/NuP468vpPTYaY4W+Dp8NT9borar6Y8/fLniiuuYNi3CKqrONeZOMR6YK4RIBxYCU4UQF3NMLOAvlxJAJlA7RWpX07ZM0/vztyOlzJZS1kgpVeAzjHMVFyCl/FRKGSqlDG3ucvX+/fuz18rjOUtLS4mJiWH27JbNy48fP4Uff1TJyTGTMCskMBD694c33zTe3PfvN7qIbrpJcM89kmPHZL357f7zHygogNWrW0mwGZASfv0VAgMFb3/tR/eeCmvXSlohIlrHjBQUwL33GujfX2H79s5cddWNTJw4RdNV1g0aBynlY1LKrlLKnhhv/uuklNeff5wQoh/gB2yttTkaCBdC+JncQ+FAtJQyCygUQowxRSndACwxtVPb+TYPONi8S6sff39/sxbysSQFBQXs3r2badOmNet8JycnOnbswiuv2Pck5QcfqCxdKjlx4u/bly6FXr0E774LP/wgiY+XjK4j5PzeeyUvvmgbo4bzqamBJ56Azp0FZ85INm+WLFki0TCvo04zOHYMrrvOwMiRCkePBnHllTfSoUMHTbQ028cghHhOCDG31qYFwELTBDMAUspc4Hlgp+n1nGkbwF0YJ7hTgCPA2eDKV00hrvuBMOCB5mqsj2HDhhEbG2uJpi1CVlYWp06datYiudDQUWzaJElJsYAwK8HTEyZNErz4Yt1f6cpKeP11Qffugj//hLVrJTt2yHOlN2fPNk5G//BDK4q2AMXFxhXWPXsKnJwke/dKvv++eZPzOtqRkGCsYf3RRwamTbuE4cOHt7qGJhkHKeUGKeUlpvf/lVIurbXvGSnlBZ5dKeWXUsreptdXtbbvklIOklIGSSnvOWtUpJT/kFIOllIOkVLONY0yzIqnpyeKoljtZPTFOHDgAL6+vk16khBC0KNHf7tf9Pbmm8YV3wcO1H9cQQE8+qggKEiwdy/s2ydZu1by5puS994z5sexB86cgchIhf79BV27QkKC5KOPJBo9hOo0Az8/uPlmyc6dMeTk5DB79mwGDhzYav3b9+zkRRgxYgSbN2/WWkaz2LRpEyNGjGh0rpb+/Qdw9CjY6OU2miuvlDz7bONdQtnZcMcdgkGDjG6Ybt3g/fdt06VUH8eOwZQpgtBQQWio5MgRyXPPqXUukdCxHoSARYtUCgqOk5yczNGjR1mxYgXV1dVERkbSq1cvi2twOOPg5uaGu7s7pVrmRWgBqqqyfv16pk+f3qjjBwwYxX//a9+jhscfh5wcY9rrppKWZnTD+PgIq1r0Zm4SEmDkSIWZMwULFsDx45LbbpNYYZIAHeDRR1WGDasgJubv0RGHDx9mxYoVtGnThsjISNq3b28xDQ731Rg+fDjbtm3TWkaLKCsrY/fu3Q0aiMDAQIqKDGYvb2lt3H235LnnWvbUb2MexmazeTP06aPwwAOCF16A5GRJCwPhdMzMpEnGB57o6N/rLCsgpeTAgQNERUURGBjIrFmz8PLyMrsOhzIOzs7O+Pj4kJ+fr7WUFpOVlUV2djYhISEXPSYkZCLPPGPfqTKuugpcXY3V1HQaz7ffQseOgp9+Evzwg3FyfpT1ZnJwGDp0gN9/V9m6dS0lJSX1HltTU8P27dvZsGEDISEhhIeHm23BLDiYcRg2bBg7d+7UWobZOHDgAD4+PnUm6WvXrh1CuNh89E1DPP+85LXXoKpKayW2yZNPQocOgvh4wdq1kj//lFg4KbBOPfz4o0p2dippaWmNPqe8vJxNmzaxZcsWJk+ezCgzWXmHMQ4Gg4GAgADO2FmC+02bNjF8+PALJqhHj57CO+/Y900zNBS6dIGPP7a/ieTWpLISbr4ZevQQGAySuDjJZ59JLOjO1qmDm26ShIRUsWXLhmadX1xczP79+82WsNNhjMPgwYPZv3+/1jLMTl0T1C4uLvj6+vDZZ/b93/vee5KvvjKGp+q0nNxcmD1bISREEBIiSU2VvPCCbHI5a52m07kzvPsubNzYsrr2rq6uuLq6miXlv33fPUwIIejSpQsZGRkNH2yDlJWVsXPnznMGIjR0JKtWSbuOvuncGYYONS5s0zEvycnGyKZZswTz50uOH5fcfbfECtL92C3ff69y7Fgyp0+fblE7x44dY+XKlXTv3p2IiAiam1oIHMQ4DBgwgMTERK1lWJTs7GxOnjzJ8OHD6dKlL2+/bd/hq++/rxIdLTl6VGsl9ktsLPTtq/CvfwkeewzS0yXz52utyv64+WbJ0KFVbN0aY5b2ampq2LFjBxs2bGDEiBGEh4c3q4a1QxiHHj16cOTIEa1lWJyDBw/i5eWFqqoMHdr8oaktMH264N13HeLrqzmLFkHXroK33xZ89JHk4EHJlClaq7IPunSBd95puTupLsrKyti4cSNbtmxh7NixTJ8+HSenxqdzt/tfV3BwMOnp6VrLaDViYmJQ1QJefllg5VUIW8ShQ/DTT3Un0NOxDK+9Bu3bC9auFSxdKlm9WtK3r9aqbJuvv1Y5ejSpxe6k+iguLmb9+vXs2LGDqVOnMnbs2Ead5xDG4ZA1V24xM716BeHu3pbHHpN2XdRnzBjB558bwy8fesi+R0nWhKrC/fcbRxJlZZI9eySffCIJCNBame0RGgqjRkm2bdvUKv0VFhayatUqjh8/TmRkJH0aSNlr18ahR48eVl3Mx5wIIZg4cTpDhkxhxgyFd96x6/9awBijP2OG4JFHBKtXS/z9tVbkOBQWwty5xsimkSMlaWmSRx+Vdj1aNTdPPaVy5MhBs7uTGiIjI4Ply5c3WFDIru8gtlDMxxy4u7szd+61nDnTkwEDFGw8O0iT2LrVGJ/v5WXMPNrIEbOOmUhOhuHDFa68UnDnnXD0qOTqq7VWZf306QPTpsG+fXs00xAfH1/vfrs1Du3bt6eoqEhrGRana9euzJt3LZ995s6UKQp2tsavUZSWGt1MH35oHEE8+aSqJ5RrZaKjjUb65ZcFH3wg2b9fN9T18cILKqmph6my4lWqdvET8vb2ZsKECfTr14+2bdvi5OTE0KFD2bp1a8Mn2zCjRo1j3LhZXHmlgaeeUmjl0anV8eyzxvTU99wj2LRJYoZ1QDpN5O23jZPWmzcLVq2SrFwp6ddPa1XWRa9exuJSu3ZZ9xDfLoxDYWEh27Zto6SkhKCgIKZNm0ZOTk6r+/Jak1mzLsfDYwBDhihER2utxnrYtcs4WVpRYXQzzZyptSLHQ1XhzjuhWzdBdbVk927Jd9+pdO6stTLr4OabJVlZmVY9agA7MQ4A1dXVHD9+nB07dhAdHc2ePdr58loDIQRr1sDx41orsT6qq2HqVMEzzwh+/VXy1lv66l4tyM831sqoroZx406QlKTy4otqvek4/PxASvjqK5WRI1tNaqshBNx6q2T/futPAGo3xsHRWL9+OTfcABqUlrUZ3nkHhg8X3HIL3H671mock5tvlhQVFbJmzQqWLFnIddflcvSoyu23Swx1LOLPy4MNG1Ruuklh1apqsrNV3nxTJTS09bVbgilTwMWl2iYSgOrGwUYpLy9n375t/PijShMWPToclZXg7AyLF2utxPFwd4dnnpHs2rUBMC7Gio7+nU2b/uT558tITlaJiLjwvBkzFHbuVMnMTGXTpj+IiEhl9epqjh5VeeQRlU6dWvc6zMn996ukpR3UWkaj0I2DDXPo0EF8fEr5z3/sd26lpXz0kcrSpZLMTK2VOB733adSXl5A9nkZILOzs1m8+HtSUzfw449VbN6sMnToX/urq2HOHIUOHXrj6+tLTMw6fv75S+LilvOvf53myBGVDRtU5s/HptZVtG0L4eGCgwdtIzu0bhxsnA0blvHEE+gFWurA0xMmThS88IL+NW9tXF3hkUdg27a1Fz3myJEUfvnla5yd9xIbq/Ljjypduhj3ZWcbDcTo0WHn6hNkZWWxatUSfvzxKwyGrbz7bglnzkg++8w25iduvFFy6lQOlZWVWktpFPqvxsYpLCzk8OGDfPutPno4n9deg7g4OGgbo3i74h//gPLyEnJzc+s9TkrJ3r27+eWXbwgJSePwYZWXXjJOWm/bBg8/DOHh8/62mrempoZDh+JZuvQHli79iTFjkli1yuh2+u9/VXr1svTVNY/77pMcOGDd4au10Y2DHbB793b69Knk1lvtuFh0M7jySslLL+n1HlobIYypIfbubXzOoKqqKjZtWsvixT9xzTU5HDum8q9/ST79VGHJEifCwy+r87zi4mK2bInh55+/ZN++ZdxwQxZxcSr796vccw+0a2emi2oh48eDt3cNJ06c0FpKo9GNgxXg4+NDv379cG2mA1VKyc6dG/j8c4EZ64vbNLfcYiyRunKl1kocjzlzwMOjqlnFtUpKSoiO/oONG5fyzDOlpKSoLF2qkJ3tw7hxk+s99+TJk6xdu5yffvqCrKw1/Oc/Zzh6VCU2VuWmm8BM1TObxb33qhw9mqCdgGagGwcrQAhBcHAwYWFhTJgwAUNdMX710LVrV8aPn8HLL6vYiDvT4jz6qOSNN3D4VeNa8PTTKvHx21vUxqlTp1iy5AeOHFnPl19W4eMDgwb1JTi4/kyiYHxYSk9PJyrqd3788StqamJ54YUCsrIkK1aozJvXuhPZPj4wZ45g//59rdepGdCDIK2A/Px8qquriYqKIiAggIiICOLi4jjeiBVuo0aNp2fP/lx1lUJUVCuItQEGDTIWUfniC92l1NoMGwa9e0sWLTpslvaOHDlCamoqgwcPIShoNGFhUygpKW60e6ampobExEQSExNxcXGhf//+fPjhYDw93Vm8WPLZZwqbNhkX3lmKq6+G3NwCysvLLdeJBdCNg5VQYSq+cObMGZYtW8b48ePp3bs3sbGxdS6zd3V1ZcaMyzh1yoshQxR9pXQt3n5b5eefBfn5WitxPO6/X+XYsSSkGe+2Ukr2748jPv4g06fPYPTo0SQlJXHo0KEm9VNZWUlcXBxxcXG0adOGwYOH8McffamqcuLTTwVffimwRF2w229XSUqyvYwNunFoZVxdXWnXrh2dO3fG09MTJycnDAYDUkoURTmXD2rz5s34+Pgwc+ZMDh06RGpq6rk2OnXqxOTJEXz1lcJDDxnTE+j8xdChgpMnjaGsxcVaq3EcvL1h/nzBb7/tskj7NTU1REcbh8fBwcHMnj2b5ORkkpKSmtxWSUkJ27dvZfv2rQQEBHDNNaN48MHOJCTA++8r/PKLMdtvS+nWDfr3hx9/TG34YCtDNw4WRAiBv78/PXv2xNfXFxcXF6qqqsjKyuLQoUMUN3DnKigoYNmyZYwaNYrAwEBiY2OpqKggLGwmS5Y48X//1zrXYWtMmiT47TdJerrkzjsFv/yitSLH4PrrITc3n7KyMov3lZycTHJyMoMHDyYyMpIDBw5w7NixZrV15swZ1q1bgaIoBAUF8b//hfL++558/bXkvfcUmmF7znHNNSrZ2adsMgmoMOfwTyt69Oghn3jiCU01KIpCQEAAXbp0OWcIFEUhJyeH5ORk8vLyWtS+p6cnkydPJiUlhdzcXGbMuJSJExX27TOPfnvkzjvhhRckCQlwyy2iRT9ynYY5ckQlMTGqWVFKLWXUqFH4+vqyefNmSkpKWtyep6cnI0aMoUuXnhw7Bp9/Lli0SNDUSNSUFJXExJVkWukS/TvuuGO3lLLOzFW6cWgBzs7ODBgwgPbt2+Pk5MTp06dJS0sjJyfHYn2GhITQoUMHTpw4SVDQSAYPVjh1ymLd2TwuLvDTTyozZwpefVXy3HN6gJ4lmDQJFi+u4pdfvtJMg4uLC1OnTuXEiRPs32+eFBVCCLp3706/fsNp164tCQnw+ecKv/0Gp0/Xf+7QoRATU8PChV+YRYsl0I2DmfHx8SEkJIQ2bdqwd+/eVq9T7ebmxtSpU3Fy8uDECV/GjlX0ENYG+Okn6NpVMnGiHsFkCdavV3Fz22UV4ZqDBw+mffv2bNy4kWozTsgpikJgYCB9+gwjIMCXXbvgww8VliyBujxp77+vEhZ2hNjY9WbTYG5042AmjF+MPlRWVrJ161bNQ9MGDBhASMg4liyB66/Xn4gvhqJATo7kyisFay+e6kenmQweDFu21LBo0dfU1NRoLQcAX19fJkyYwObNm1vs0q0Lg8FAcHAfgoOH4e3dht9/N4bFxsYaw2IVBXJzVVasWGTV5YrrMw76hHQDGAwGBg0aRJcuXcjIyCDaisquHTp0iPT0dObOvYb/+z+Vt9/WDURdPPccnDyJbhgsxNNPq6SkHLIawwDGtUMrVqwgPDycI0eOkJycbNb2jesnEkhMTMDDw4PBg4eyeHE/qqoMfPaZ4PBhQXV1tVUbhobQRw4Xwc3NjWHDhuHn50dcXJzVTigBeHl5MWfO1cybp7BmjdZqrI/sbMnddwt+/VVrJfZHt26QmKiyaNE3Vlv2cvz48dTU1LBtm+WT3gUEBDBkyEg6duxMZWUp27fHNmoxq1boI4cm0KVLF/r374/BYGD79u3k28BKqqKiImJiovj11whCQwUpKVorsh7uusuYY+n337VWYp/cf79KZmaG1RoGMK4Z6t27NzNmzGDdunUWHeEYw2KNCb2cnJzo168fAwYMoLq6mp07d1JYWGixvs2NbhwwLkwbPHgw7dq149SpU6xbt87m4pIzMjJITNzN6tXDGTpUwYa+gxbl0Uclzz0n9BxLFqBNG7j9dsGyZZu1ltIgKSkp5OTkMGvWLNatW9cqazGqq6s5ePAgBw8exNPTk0GDBuHv78+ZM2fYs2ePWSfLLYFDG4fOnTvTv39/nJyc2L17N7t2WWZlZ2sRF7eHqVM7snhxZ6ZPVxz+hnj11cYb2DffaK3EPrnxRklhYYHN+NXz8vJYu3YtM2bMYP369Q0uQjUnxcXF59xaHTt2ZOLEibi5uZGcnEyKlQ71Hc44ODs7M2TIEDp06MCZM2dYv369zY0S6mP9+pXMnXsNr7/uwYMPOvYE9f/+J3nxRTClrdIxI4oCjz8u2bfP+kcNtSkvL2flypVERES0uoE4y8mTJzl58iQGg4HevXsTHh4OwK5duxosjtSaOIxxcHd3Z/jw4fj4+LBr1y52796ttSSLIKUkKuo3brnlOvbsEXz/vWPG9c+ebazZ+/HHjnn9luayy8DZudKqAzUuRnV1teYGAowRT4cPH+bw4cO4u7szcOBARo8eTWFhITt37tS8nKjdGwd/f39CQkJwdnZm69atNjUh1FwqKipYs2YJH300j8OHBTt3aq2o9XnzTckbb0hKS3XjYAmee04lPn6r1jKaTW0DsW7dOrOk3GgJZWVl59zabdu2ZcyYMXh6enLs2DEOalTnttF+ByGEQQixVwixrI59bwkh9pleSUKI/Fr7bhRCJJteN9baPkIIcUAIkSKEeFcIIUzb/YUQq03HrxZC+DXnwrp3787MmTMZMmQIsbGxREdHO4RhOEtOTg47d25kxQqVTp20VtO6TJpkrOfw7ruO7VazFJMnQ5cuNWZfO9DaVFdXEx0dzdSpU5tdhdES5OTkEBMTw8qVKykpKWH69OnMnDmTjh07tqqOpowc7gcSAO/zd0gpHzj7XghxLzDM9N4feBoIBSSwWwixVEqZB3wE/BPYDqwAZgErgUeBtVLKl4UQj5r+fqQxAmsvWDt58qRVLVjTguTkZAICOrFyZR9CQx0ntfc770g++ggKCrRWYp88/bTK4cN7tZZhFiorK1m7di3Tp08nOjraqiKIpJSkpaWRlpZ2rlBRSEgI5eXl7Nq1y+LusEY9WgkhugKRwOeNOPwa4CfT+5nAaillrskgrAZmCSE6Ad5Sym3SuArvW+Ay0zmXAmfjS76ptf2iGAwGJk6cyMyZMzlz5gwrVqxgzx7bK65hCbZujaF9+2Keftp+Jt3ro0cP6NsX3npLdydZggEDYORIycGDB7SWYjZKS0vZvHkz06dPx+TAsDrOFiqKiopiz549DBkyhIiICEaOHImTk2VmBxrb6tvAw4BXfQcJIXoAgcA606YuQO3lgRmmbV1M78/fDtBBSnk2k91JoEND4jw9Pdm7d69mE0vWztq1S/m//7uWxYvBTufhz/HBByrLlkFWlnX+yG2dJ59USUtLsqpUGeYgPz+fffv2MXXqVNZaeZ6VwsJCtmzZAhjDYidMmICHhwepqakkJiaarZ8GjYMQ4hLglJRytxBiSgOHLwB+lVKa5ZsjpZRCiDrzewghbgduB+Oks24YLk5paSl7927hl1/GMWCAgo2Vsm00bm4webJg0iTdMFiC7t3h0kvhl192aC3FIpw8eZI2bdowceJENm3apLWcRnE2LPZsxtgZM2agKAr79+9vcbboxriVxgNzhRDpwEJgqhDi+4scu4C/XEoAmUC3Wn93NW3LNL0/fztAtsnthOnfOqsVSCk/lVKGSilDPT09G3EZjk1CwiGcnPJ5+WX7dS+98gokJMBe+3CHWx3PPqty7Fj6uXrn9siRI0coLCwkJCREaylNQlVVjhw5wurVq1m/fj3t27dn1qxZTJ06FW/vC6aJG0WDxkFK+ZiUsquUsifGm/86KeX15x8nhOgH+AG149uigXAhhJ8p6igciDa5jQqFEGNMUUo3AEtM5ywFzkY13Vhru04LWbfuT269VTJxotZKLMO110peeEEfNViCHj3gqqtg+3bbeKJuCXFxcfj6+tK9e3etpTSL2vMTO3fuZODAgcyePZsxY8Y0aX6i2TMZQojngF1SyqWmTQuAhbJWmlcpZa4Q4nngbKT9c1LKs0sA7wK+BtwxRimtNG1/GfhZCHErcBS4qrkadf5ORUUF27dvZNGiKfTpo2BPnrh774XycvjzT62V2CfPPady9GiqXY8aarNhwwZmz55NQUEBBTYc9lZUVMTWrcbn9fbt25+bn0hJSSGpgbq5espuByQ8/FLWrWvHrbfazzqAo0eNo4bPPtNaif3RuTMkJ6v88st3DmMcwFj5bfbs2URHR1t11tmmoigKQUFBBAYGEhgYeNGU3fZzd9BpNOvXr+DKKyUzZ2qtxDxccgn4+MB332mtxD65916VrKwTDmUYwOjH37hxI1OnTtVaillRVZXk5GRWrVpV73G6cXBAqqqq2LJlNd9/r+Lrq7WalvPyy5K33pJ2G4WlJa6ucOedsHv3Fq2laEJRURHJycmMGTNGaymtjm4cHJRjx46Rl5fJ55/bdvRSaCgEBsL77+tfZUtwzTVQWlpiE0WvLEVqaipCCIKDg7WW0qrovygHJiZmFdOm1TB/vtZKms8HH0i+/BJycrRWYp889pjKgQO2m2DPXGzdupWgoCD8/JqV6s0m0Y2DA1NTU0NMzEo++0zSyjm9zEJAAAweDK+9poevWoKJE6FduxrS09O1lmIVrFq1ivHjx2MwGLSW0iroxsHBOXnyJBkZKfzwg+25l159FbZsgWPHtFZinzzyiMqRI/aTQ6mlqKrKtm3bmGivC4XOQzcOOmzdupGhQ6u4/XbbCmuOjJS88UbjRw1WmlPNKunWDcLCYP/+fVpLsSrOnDlDUVERffv21VqKxdGNQzNRFPv56FRVZf36P3n9dUlgoNZqGkfnzsbw1cbmSHv0UVBVePBBiVe96SN1AO6+W+XEiRNWlcLaWti9eze9evXCy86/SPoiOBOKopzLp1Jfum+DwcCwYcPo1KkTGRkZ7DVzIp/AwEC6d++OlBIpJaqqUlNTg6qq596f/bumpobq6mqqqqqorKw893dlZeW5V1P+f0eOHEtNzUBGj1aw9rLajz8OV1whGTGiccMBV1coKZEYDIKSEsnChZIvv1TYuhXs4CdgVlxc4NQpSXT0Lw4dpVQfTk5OzJw5k5UrV9p0Dfo77rjjoovg7L5MaEMIIRg0aBDdu3cnLi6OgQMH0qZNmwvKBp41Cu3bt2fPnj3s2rWLyMhI4uLizPrl6NatGxs3bjz3t5OTE4qi4OTkdO5lMBgwGAw4OTnh7OyMs7Mz7u7uODs74+rqipubG66urjg7OyOEQFXVc8YG+Nu/5786dpQ8/7zKE09Y98hoxgzJmjWNP76iAjp3FqSmSlatWsyoUf2YN683VVUGPvtM8MkngoyMhttxBObPh7KyUt0w1EN1dTW7d+9m3LhxxMbGai3HIji0cejfvz+9evUiISGB5cuXA8YSfWPHjmXdOmNJCldXV4YNG4a/vz+7d+8+V+cVYP/+/QwZMoR9+/aZTdP5xUbODutbq9i4u/t27r77GjIz4cMPrddA9O7d9II+p07Bjz9Kxo8fSGzsBmATbdu25ZprxvDgg53YsgXeekshKgqrHzlZkocfVjl0aLvWMqyekydPEhgYSK9evUhNTdVajtmx3l+/BQkKCiIyMhKA5cuX/+0/tqysDICuXbsyadIkwsLCOHz4MCtWrCA7O/tv7Rw/fpwuXbqYdf5B60pUZWVlrFz5K6+8Iq12/YO7O/j6wrvvqrz1lsqMGcZaDo3BzQ3Ky/8aFebk5LB27XIWLvyaNm128PXXFaSnq9xyCzg7W0a/NTNsGPTsKTly5IjWUmyCrVu3MmDAAK1lWASHMQ4Gg4HBgwcze/ZsPD09Wb58OQkJCXUeu2HDBnr37s2uXbuIiooiLy/vou2eHT2YC2uY6C4sLGTVqiV8+aXKtGlaq7mQq66CwsJi9u37kxkzkvjuu3Ly8iRbtqg89JBk8OC6z3Nzg3nzBAkJhy7YV11dzYED+/njj284eDCaF14oJitL5cEHVdq0sfAFWRH//rfKsWPJTZqrcnTsKSlfbbS/E7UCo0aNYubMmRQWFrJixQri4uLqPV5VVTZs2EBpaWmDbZt79KD1yOEsZ86cISYmij/+UAmtc7pKO/79b5WEhB1kZ2ezeXMMS5Z8y08/fU1p6XruvDOLTZuqycpSefttlTFj/gphveIKKCoquWA+6XyOHz/On3/+SEzMEh54oIATJyQvv6zSpUu9p9k8PXrAvHng5eWutRSbwt5Kpp7FIYyDn58fK1as4OjRoxZp31yjBw8PD9wa6x9pBTIyMti5cyOrVklaK6zbzw+uu84YMVMXAwca3R7n+3irqqo4cuQIa9YsY9GiL9m8+Q/Cw4+wfHkVp0+rfPyxyuOPqxw+vKvuhuvg9OnTrFjxC1FRv3DllVkkJan8+adqt8WSnnlG5ejRNNLS0hg1apTWcmwCDw8Puw33dQjjcODAAYt+2VsyevD392fcuHHMnDmTcePGWUBdy0hJSSYxcQcbN7bOk/Ojj6p8+mkNGRkqd90lL5hLuP12lczM9AbdHjk5OWzatJ5ff/2KNWt+Ydy4RLy980lJSWmypvz8fNauXc6iRd/QseM+liypIjlZ5bbbwMOjyc1dwKRJ8OabaqsZ4Lro3t0YpbRjRyxJSUm4uLjQq1cv7QRZMWFhYXTs2JGQkBAmTZp0rpiOveEw6xwiIiJYuXJlvce0hMDAQDw9PTlwoP50A4qi0K1bNwIDA/Hw8CAvL4+4uLhzLqwZM2awevVqi+lsLqNGjcPbewChoQr1TMG0CA8PyMqSrFixCHd3d0aODMPT04svvpC8+65CRgacOaOycuXPFBYWWkZEI+nRoweDBo3F29uLL7+UvPOOQlpa09sRAhITVTw8TuHr257ERHjjDYXff4dWClAD4IsvVMaNSycm5q/44NmzZ7N582abroRmCS699FLS09PJyckhMzPTpudn6lvn4BAjB4CEhASGDRtmsfbT0tLo1q1bnXMGBoOBPn36MH36dGbOnIm3tzcxMTGsWLGCrVu3/m1uw1rmHM5nx44tVFUdY9061SxPy3WxYIExf35hYSHZ2dksW7aQ5csXEhl5jPh4ld27VcrLKzQ3DABHjx5l+fKFLFu2kIiI46SkSNq3b3o7c+dC27ZVrFixlIULv6S4OIa33irh1CmVN99UCQoyv/bz6dbN+NmfXx86KiqKiRMn4uyIYVv1cOTIEYqKisjIyLBpw9AQDmMc0tPT6dSpk8X76N27N2A0CL1792b69OmEh4ejqipr1qxh5cqVHDhw4KIL56zVOABs3LgKH59TREWpF50TaAn33HPhnEBRUREbNqzip5++oqRkBzt3rjN/xy3AeJM4wqFDklOnmn7+K6+o7N9vLKSjqipJSUksXfoDK1f+TGTkMeLiVLZsUeudh2kp//2vyvHjRy+o9KaqKjExMYSFhVmmYxvl4MGD9O/f36p/q+bAYYwDQHJyskVjkg8dOkS/fv3OGQQhxDmD0BxftzWyZs2fBAbm88cfKubMXBwYCH36cNH4+pqaGg4c2M+JE5nm69RMDBw4huefb/pPac4cCAioJjk5+YJ9hYWFJqP4JVVVm3ntNWNo7RNPqHToYA7VRjp3Nhb0OX/UUFtHamoqo0ePNl+ndkBCQgJDhw7VWoZFcTjjEGjhzHJr1qw5ZxDq+tE3hLU/jUgpiYr6neHDi/n+e9VsmU5vvFGSnZ1tc8P0du3aoShu/PZb0899+eW/Rg0XQ1VVEhMT+PPPH1m37nduu+0MaWkqUVEqkZG02EA/9JBKVlYm5fXUWE1JScHJyUmfoK5FamoqnTt3xtXVVWspFsNhjIO7uztTpkw5twLaUli6fWtAVVWWL/+FadPK+eijlueZuPRS+Pe/Jbt3216OmhEjJvHqq9DUUPfZs6Fjx2qSk5MafU5ubi6rVi1m4cJv8PXdyZdflnPqlMrLL6v06NFE4YCXF/zzn4IdO+oeNdRm8+bN9OvXD29v76Z3ZKds2rSJsWPHai3DYti9cXBxcWH8+PFMnjyZHTt2nMuZZK1Y+8jhLDU1NSxbtogrr6zk5ZebbyACAuCbbyRr1/5Z70p0a8TDwwN/fz8++6zpP6OXXlI5cGBbs/qtqqoiLi6OxYu/Zd2637niikzi41VWrTKmEmnsV+if/1TJy8ujuLi4UcevWrWKSZMmOUwltIYoKirCYDDg6+urtRSLYLfGwWAwMHLkSKZPn058fDxRUVGNWvGs03j+v71zj66quvP453cTSCJiDMQgiIJOYVmcIUyGVjthMJQRQkrBpSyLo9VxWmkdR6fjqoPCarvUumzVrmldtrKsaNvpyEPwQfMgD0ISQCGEkJDwMCAqQngEyIMkQLi5e/44+8ptcm/CTW6455zsz1p35dx99jnn9805+/zufv32hQsXyMlZzQ9+0MnixX1zEE884ePUqaPd4lY5ga9+dRLZ2RDuSM+MDLjhhk7q6j7utw2nT5+muDiPVav+wPDhFaxY0cHnn/t48UXFpk0+XnrJR7DWoNhYa42LiorS7jtD4PV62bJlCxkZGf222y2Ulpa6dsKg65yDiJCamkpmZiaHDh0iNzfXUaGHnVJz8HPu3DlyclazZImPRYvCcxDDhsGjj0JFhfOakwCuu+5mVqwIvwg995yPvXt3RLR/xev1smtXFWvX/oHq6lzmzfuECxdKmT//MLt2+diyxceCBReDCd5zD3R2nqWhoSGs6zQ2NnL06FHS0tIiZruT8Xq9tLe3c60TF2HvBVc5h5tvvpmsrCyamprIyclx5K9Rp3XIArS1tZGXt5aXXw4vkuujj/poaWlylPP2ExcXR2JiAgUF4R2XlgapqYrdu2sHxjCgvr6e0tJi9u/fz8aN61m58i06Oz/k1VfbOXHCqk0884yPmpqeO8NDsWfPHhITExk7dmyELXcmmzdvdqWzdIVz8Hg8zJ07FxEhJydnwGIoDTQxMTGOdA4Azc3NFBWt4803fcya1Xv+hASrWWPrVnv3AYViwoQJVFYqukwN6JVnnvFx4EDtZV09rLOzk7179/DBB3+moGAN8+Z9QUJCM5/2ZUq3ZuPGjUyZMoVhgylkbQ8cPHjQdUNbXeEc/KuYhQrB7RRiY2MdHeGxoaGB0tI81qyxoqH2xGOP+Whvb+H06dOXx7gIc/58B5Mm+WhuVuTm+vjhD2HChJ6PmTgRZsyAnTt3XB4jg9DU1ERJST45Oe/0+1wFBQVkZGTYIsx8tNm3bx9jxoxxlbN0xV1VSnHs2DHHt/vFxMQ4PsLjkSNH2Lp1A+vX+0KuqzB0KDz7rFBVteXyGhdB9u+v45133mTdurdJTNzKU0+dZMeOTk6d8vH++z4WLaJbIL2f/MTHZ5/td/w99tPR0cHOnTtdPZwzHDZs2MC0adOibUbEcIVzAKiurnZ8u58bnANYcaaqq7dQUuIL+mu6owMWL1ZMmzabhARnrx3Q1tZGbW0t+fnvsmLFcgoKVnPttdtZsuQk5eWdNDb6yM7u5Ikn4K67oKKib8NX7Up9fT0xMTFcf/310TYl6nR0dHDkyBFuueWWaJsSEVzjHLxeL42NjY4ecxwbG+sK5wCwb99ePv64gs2bfdxwQ/f9v/mNh1/9ysPcud9x1SzTlpYWqquryc9/l5Url5Obu5KkpG08/ngDe/fu7ha/yA2UlZWRmprqqvvYV2praxk3bhxXDFR0ysuIa5wDQGVlJbf11thtY2JiYly15GBNTRVffFHDli0+grX4Pfech9//PpZ58+517YultbVV1yzeY8cOd8b9B2tp3dtvvz3aZtiCoqIipk+fHm0z+o2rnMP58+dpb2/nyiuvjLYpfWLIkCGuC7+xY8c2Ghvr2LTJx4gR3fcvXux+BzEYaG1tpb6+PqLrqTuVjo4ODh486Phmblc5B4CKigrS09OjbUafiIuLo+NyrvBymfjoozK83s8pLfUxfHj3/cZBuIPa2lpSUlIYM2ZMtE2JOnV1dSQnJ5OUlBRtU/qM65xDe3s7Z8+eZXiwt5DNiY+P7zE6ppMpKyskPr6eDRt8BOuDNg7CHRQVFZGWlubY2nskKSoqcvRILtc5B4Dy8nJHDikbOnSoa50DQHFxLikpJ8nLC75YkHEQ7mD9+vVkZGQM+hXk/Is3TZkyJdqm9AlXOodz587R3NzsuCpdXFyc6/oculJQ8AFf+Uoza9cGXyzIOAjn4/V6KSsrMx3UWGthjB492pGjl1zpHMDqe3Balc7tNQewJizm5a1l6tS2kIsFGQfhfFpaWjh27JjpoMYKNeLElgzXOoeOjg5Onz5NcnJytE25ZNwyCa43rMWCVjNz5jmWLQseYyjQQcTHx19mCw2RoLa2ltGjRzMi2DC1QYS/JcNpHfWudQ4TJkxgxIgRtLS0RNuUS2YwxajxLxZ0990dvPhizw7i299eaByEQyksLCQ9Pd3xM+H7y/bt2x03c9p1b6PY2FhmzJhBUlISubm5rhwa6hYuXLhAdvYqHn7Yy9KlxkG4EZ/PR2FhITNnzhzUTYQ+nw8RsVXfQ28jOl3lHFJSUsjMzKSqqory8vJomxM2TlvoJxKcP3+ev/xlFU8+6ePxx0M7iGXLjINwKufOnaO4uJg77riDocGGqQ0Stm3bFvWRSykpKcyaNYvZs2f3WpO5ZOcgIjEislNEskPsv0dE9ojIbhF5OyD9lyJSqz/fCUj/pohU6vQ/ikisTs8QkWYRqdKfn16KfWlpaUyePJns7GzHrUXsZzA6B4CzZ8+Sm/sOP/+54qGHgq9nsWSJh9deMw7CqbS3t1NSUsLs2bNJTEyMtjlRobm5OSra4+PjmT59OnPmzGHs2LGUlpaSn5/P1q09B4GMDeMa/wnsBa7qukNEJgBPA+lKqUYRSdHp3wLSgClAHFAiInlAK/BHYKZSqk5EngUeBJbrU25SSs29VMMSExNpbW2lsrIyDDkGO3HmzBny8tbyyit3c+ZMDGvWdM+zdKkHiOWRRxaSk7ParAnuMFpbW8nJySErK4vy8nJOnDgRbZMuO21tbSQmJtIc7sLjYeLxeJg8eTJjxoyhtbWVmpqasH80X1LNQUTGAt8C3giR5WHgt0qpRgCllP+uTwLKlFJepVQbsAvIBEYCHUqpOp2vELg7LMsDaG1tpa6urveMNmew1hz8NDU1UVi4jrfe8jFnTvA8S5d6eOmlWObPv9fREXgHKz6fj+zsbG699dZB2QdRU1PDxIkTB+z8ycnJZGZmMmPGDE6ePEleXh5lZWV9ak251GalXwP/DYRa23AiMFFEtojIVhHJ1OnVQKaIXCEiycAM4HrgJBArIlN1vgU63c83RKRaRPJEpNcufievnhbIYBqtFIqGhgZKSnJZvdpHRoa1YE7Xd8gLL3j40Y88zJlzt+MmOhosSkpKHB1Bua80NjYOyDObmppKVlYW48ePp7i4mA0bNnD48OF+LTvca7OSiMwFTiildohIRg/nmQBkAGOBMhH5O6VUgYh8DfgQaAA+AjqVUkpEFgL/IyJxQAHgf8NXAuOUUq0ikgW8r8/d1a5FwCLANeOoB3vNwU99fT2bNxeQnz+bjg4fcXEejh9X7N4NlZXCnj1CdbXw9NMeXnjhLvLz33PscqODlTNnzhATE8PIkSM5depUtM25rMTExODxePq9jrjH4yE9PZ1hw4axb98+qqurI2ShxaX0OaQD8/SLOh64SkT+rJS6PyDPYWCbUuoC8KmI1GG90LcrpZ4HngfQHdV1AEqpj4B/0umzsGofKKW+nJiglMoVkd+JSLJS6mSgUUqp14HXAcaNG9d392gjTM3hIocOHWLNmhW0tbUhIiQmJjJq1CjuvHMU992XQkLCMBIThzB0aAwLFixg+fLlrqlBDhZKSkrIzMwkNzc32qZcVo4fP84111zD8ePH+3R8QkLCl5Gnq6uraWhoiKR5XyLhVDt0zeHHXTuLdTPSvUqpB3Xz0U6sTugm4Gql1CkRmQy8DUxRSnlFJEUpdULXHHKB55VSxSJyLXBc1y6+DqzBqkmENFREGoDPuyQnYzVfuQGjxZ4YLfbEaLl0ximlrgm2I5zRSn+FHmFUoZRaB+QDs0RkD1bz0JPaIcQDm3RzSQtwv1LKHx/iSd1k5QFeU0oV6/QFwCMi4gXOAgt7cgwAwcSJSIVSamqw/E7DaLEnRos9MVoidO3+dFjYGfOA2BOjxZ4YLfYkmlpMI7fBYDAYuuFm5/B6tA2IIEaLPTFa7InREgFc26xkMBgMhr7j5pqDwWAwGPqKUsp2H+C/gN1ALbACa37FfwAHAAUkB+RNAt7DCs1RDvxtwL5M4GN93FMB6TcC23T6KmCoTo/T3w/o/eNtpOUzoAaowhol5k8fgRV+ZL/+m6TTBXhFX2cXkDZAWv5P/49rgTeBIb1dHyuO1n79eTAg/R+0xgP6WOlJo020dOp7UgWss9EzFkrLzViTUc9jDUsPPI9dy0tftHyGs8rLffq6NViThlOjfV/6JX4gPsB1wKdAgv6+GvhX4O+B8fqmB75QXwJ+FvCwbNDbMcAnwE3AUKxQHpMCzrlQby8DHtHb/w4s09sLgVV20BLwsCcHucaL/gcGeAr4pd7OAvL0Q38b1iTFgdCSpa8hugA80tP1sQrnQf03SW/7C2i5ziv62Dk9aYy2Fr2vNcQ1ov2MhdKSAnwNa2LqjwPOY+fyEpYWh5aXf+RiOZjDxfIStfti12alWCBBh/G+AqhXSu1USn0WJO8koBhAKbUPGC8io4CvAweUUgeVUh3ASmC+WJMuvok1uQ6s6LB36u35+jt6/0zpf0yLSGjpiUCbu2r5k7LYClwtIqP7pSS4llx9DYX1ch/by/VnA4VKqdPKCtRYiBV/azRwlVJqqz7Xnwh+XwI1RltLUGzyjAXVopQ6oZTaDlzocg47l5dwtfSELcuLUupDXR4AtnLx2YvafbGdc1BKHQFeBg4BR4FmpVRBD4dUA3cB6BnV47D+sdcBXwTkO6zTRgJN6uJkPH86gcfo/c06f7S1gNUEVSAiO3RcKT+jlFJH9fYxwO9MQukfEC0iMgT4LrC+l+v3lH44hL2hNEZbC0C8iFTogJN36jTbPGNBtITC9uUlDC3gvPISyPewajE92TXg98V2zkFEkrA8343AGGCYiNzfwyG/wPLyVcBjWKE7bBFkJ8Japiml0rCqnI+KyPSuB+tfIypyCi5yCVp+hxWefdNAXN9PJDRGWMs4ZU1S+hfg1yLyN/2xLVzscl8iQYS1OLK8iMgMLOeweCDsCgfbOQfgn4FPlVINygrk9y5We1xQlFItSqmHlFJTgAeAa7DasY/w12HAx+q0U1gv4Ngu6QQeo/cn6vzR1uL/JYKy1sp4D6u6CXDcX/3Vf/1raYTSH3EtIvIzbesTAflDXb+n9LFB0iG0xmhrCbwvB4ESrP4kWzxjIbSEwtblJUwtTiwv6PhzbwDzlVL+/2PU7osdncMh4Dax1oAQYCbWCnRBEZGrRcS/MO33sbxxC7AdmCAiN+r9C7FGkyhgI1YMJ7BGznygt9fp7+j9xTp/VLWIyDARGa7zDANmYY126GpzVy0PiMVtWNVaf3U6YlpE5PtY/Qj3KqUCYxCHur4/DleS/nU1C8jX+1pE5DZ9/gcIfl8CNUZVi9YQByBWwMl0YI8dnrEetITCtuUlXC1OLC8icgOWA/muurgIGkTzvqh+9MgP1Ad4BtiHdUP/F2to1uNY7WpeoB54Q+f9BlYY8I/1Pzcp4DxZet8nwNKA9JuwOoMOAO8AcTo9Xn8/oPffZAct2t5q/dndRctIYAPW0LwiYIROF+C3WnsNMHWAtHj1Nar056e9XR/4N/0/PgA8FJA+VZ/7E+BVLg5lDaox2lqwfgnW6PtSA3zPRs9YKC3X6mevBStq8mGsgQBg3/ISlhacWV7eABoD0gOH30blvpgZ0gaDwWDohh2blQwGg8EQZYxzMBgMBkM3jHMwGAwGQzeMczAYDAZDN4xzMBgMBkM3jHMwGAwGQzeMczAYDAZDN4xzMBgMBkM3/h/BqUKfFiVqKAAAAABJRU5ErkJggg==\n", - "text/plain": [ - "
      " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(figsize=(6, 6))\n", - "tracts.plot(ax=ax, facecolor='#999999', edgecolor='w', linewidth=0.5)\n", - "\n", - "# plot some tract of interest in red\n", - "tract = tracts.loc[[label]]\n", - "tract.plot(ax=ax, facecolor='r', edgecolor='w', linewidth=1)\n", - "\n", - "# plot the neighbors in blue\n", - "neighbors = tracts.loc[w_queen[label]]\n", - "neighbors.plot(ax=ax, facecolor='b', edgecolor='w', linewidth=1)\n", - "\n", - "# zoom to area of interest\n", - "xmin, ymin, xmax, ymax = neighbors.unary_union.bounds\n", - "ax.axis('equal')\n", - "ax.set_xlim(xmin, xmax)\n", - "ax.set_ylim(ymin, ymax)\n", - "\n", - "ax.set_title('Neighbors of tract {}'.format(label))\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
      " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 15.3 s, sys: 297 ms, total: 15.6 s\n", - "Wall time: 15.8 s\n" - ] - } - ], - "source": [ - "%%time\n", - "# draw a contiguity graph of the tracts\n", - "fig, ax = plt.subplots(figsize=(12, 12))\n", - "tracts.plot(ax=ax, facecolor='#333333', edgecolor='w', linewidth=0.2)\n", - "\n", - "for label, neighbors in w_queen:\n", - " tract_point = np.hstack(tracts.loc[label, 'geometry'].centroid.xy)\n", - " neighbor_points = np.vstack(tracts.loc[neighbors, 'geometry'].apply(lambda g: (g.centroid.x, g.centroid.y)).values)\n", - " for neighbor_point in neighbor_points:\n", - " ax.plot(*zip(tract_point, neighbor_point), color='r', linewidth=0.3)\n", - "\n", - "ax.axis('off')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 3.3. Distance-based weights: *k*-nn\n", - "\n", - "Find the *k*-nearest neighbors of each tract" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 198 ms, sys: 6.1 ms, total: 204 ms\n", - "Wall time: 204 ms\n" - ] - } - ], - "source": [ - "%%time\n", - "# k-nearest neighbors finds the closest k tract centroids to each tract centroid\n", - "w_knn = KNN.from_dataframe(tracts, k=6)" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['25017388100',\n", - " '25017325100',\n", - " '25017324102',\n", - " '25027715100',\n", - " '25027713100',\n", - " '25017388200']" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# they all have exactly k neighbors\n", - "w_knn.neighbors[label]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 3.4. Distance-based weights: distance band\n", - "\n", - "Here tracts are considered neighbors of some tract if they are within a given threshold distance of it. Distance band weights can be specified to take on continuous values rather than binary (1s and 0s), with these values being the inverse distance between each pair of \"neighboring\" units. The default (linear) distance-decay exponent is thus -1. If we set it to -2, we get gravity model weights." - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "13724.836623444824" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# first determine the minimum nearest neighbor distance so each unit is assured of at least one neighbor\n", - "x = tracts.centroid.x\n", - "y = tracts.centroid.y\n", - "coords = np.array([x, y]).T\n", - "threshold = min_threshold_distance(coords)\n", - "threshold" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 470 ms, sys: 48.5 ms, total: 518 ms\n", - "Wall time: 521 ms\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/sarahluw/justice40-tool/data/data-pipeline/justice-40-env/lib/python3.7/site-packages/scipy/sparse/data.py:117: RuntimeWarning: divide by zero encountered in reciprocal\n", - " return self._with_data(data ** n)\n", - "/Users/sarahluw/justice40-tool/data/data-pipeline/justice-40-env/lib/python3.7/site-packages/libpysal/weights/weights.py:172: UserWarning: The weights matrix is not fully connected: \n", - " There are 2 disconnected components.\n", - " warnings.warn(message)\n" - ] - } - ], - "source": [ - "%%time\n", - "# default alpha=-1 and gravity weights alpha=-2\n", - "w_dist = DistanceBand.from_dataframe(tracts, threshold=threshold, binary=False, alpha=-1)" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "23" - ] - }, - "execution_count": 36, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# how many distance-band neighbors does our tract have?\n", - "len(w_dist.neighbors[label])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### Map the neighbors, colored by weight from nearest to furthest:" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
      " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(figsize=(6, 6))\n", - "tracts.plot(ax=ax, facecolor='k', edgecolor='gray', linewidth=1)\n", - "\n", - "# get the tract of interest and its neighbors/weights\n", - "tract = tracts.loc[[label]]\n", - "weights = pd.Series(w_dist[label])\n", - "neighbors = tracts.loc[weights.index, ['geometry']]\n", - "\n", - "# plot the tract's neighbors in blues by weight\n", - "neighbors['weights_scaled'] = weights\n", - "neighbors.plot(ax=ax, column='weights_scaled', cmap='Blues_r', edgecolor='gray', linewidth=1, scheme='Fisher_Jenks')\n", - "\n", - "# plot the tract of interest in white\n", - "tract.plot(ax=ax, facecolor='w', edgecolor='r', linewidth=2)\n", - "\n", - "# zoom to area of interest\n", - "xmin, ymin, xmax, ymax = neighbors.unary_union.bounds\n", - "ax.set_xlim(xmin, xmax)\n", - "ax.set_ylim(ymin, ymax)\n", - "ax.axis('off')\n", - "\n", - "ax.set_title('Neighbors of tract {}'.format(label))\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 3.5. Standardizing weights\n", - "\n", - "A spatial weights matrix with raw values (e.g., binary 1s and 0s for neighbor/not) is not always the best for analysis. Some sort of standardization is useful." - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'25017323100': 1.0,\n", - " '25017388100': 1.0,\n", - " '25017388200': 1.0,\n", - " '25027713100': 1.0,\n", - " '25027715100': 1.0,\n", - " '25017325100': 1.0,\n", - " '25017324102': 1.0}" - ] - }, - "execution_count": 38, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# inspect the neighbors and weights of our tract\n", - "w_queen[label]" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'O'" - ] - }, - "execution_count": 39, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# check the current transformation of the weights matrix (O = original)\n", - "w_queen.get_transform()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Typically, we want to apply a row-based transformation, so every row of the matrix sums up to 1." - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'25017323100': 0.14285714285714285,\n", - " '25017388100': 0.14285714285714285,\n", - " '25017388200': 0.14285714285714285,\n", - " '25027713100': 0.14285714285714285,\n", - " '25027715100': 0.14285714285714285,\n", - " '25017325100': 0.14285714285714285,\n", - " '25017324102': 0.14285714285714285}" - ] - }, - "execution_count": 40, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# transform the queen weights\n", - "w_queen.set_transform('R')\n", - "w_queen[label]" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'25017322300': 0.036906174957608374,\n", - " '25017322400': 0.03669354346102883,\n", - " '25017323100': 0.04347181611211989,\n", - " '25017324101': 0.04592164256831342,\n", - " '25017324102': 0.056594838296610474,\n", - " '25017325100': 0.06842299654463031,\n", - " '25017326101': 0.04023024252197761,\n", - " '25017326102': 0.03293236456578337,\n", - " '25017363103': 0.03714321358160922,\n", - " '25017363104': 0.033046004657601,\n", - " '25017363201': 0.043844541849521704,\n", - " '25017363202': 0.04279001257490172,\n", - " '25017364101': 0.03451617785752894,\n", - " '25017388100': 0.0746887833852934,\n", - " '25017388200': 0.048948730448574576,\n", - " '25027709202': 0.03537185994394643,\n", - " '25027709400': 0.03424336705864242,\n", - " '25027709501': 0.03743833903988393,\n", - " '25027712102': 0.03697605691301035,\n", - " '25027713100': 0.05291230504948427,\n", - " '25027715100': 0.055217220523758256,\n", - " '25027716200': 0.03619543705790678,\n", - " '25027716300': 0.03549433103026463}" - ] - }, - "execution_count": 41, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# transform the linear-decay distance-based weights\n", - "w_dist.set_transform('R')\n", - "w_dist[label]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "PySAL supports the following transformations:\n", - "\n", - " - O: original, returning the object to the initial state\n", - " - B: binary, with every neighbor having assigned a weight of 1\n", - " - R: row-based, with all the neighbors of a given observation adding up to 1\n", - " - V: variance stabilizing, with the sum of all the weights being constrained to the number of observations\n", - "\n", - "**It can take a long time to calculate a weights matrix for a large data set.**\n", - "\n", - "Once you've created yours, you might want to save it to disk to re-use in subsequent analyses." - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": {}, - "outputs": [], - "source": [ - "# save your matrix to disk\n", - "import libpysal\n", - "f = libpysal.io.open('tracts_queen.gal', 'w')\n", - "f.write(w_queen)\n", - "f.close()" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/sarahluw/justice40-tool/data/data-pipeline/justice-40-env/lib/python3.7/site-packages/libpysal/weights/weights.py:172: UserWarning: The weights matrix is not fully connected: \n", - " There are 3 disconnected components.\n", - " warnings.warn(message)\n" - ] - }, - { - "data": { - "text/plain": [ - "{'25017323100': 1.0,\n", - " '25017388100': 1.0,\n", - " '25017388200': 1.0,\n", - " '25027713100': 1.0,\n", - " '25027715100': 1.0,\n", - " '25017325100': 1.0,\n", - " '25017324102': 1.0}" - ] - }, - "execution_count": 43, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# read a matrix from disk (notice its transformation)\n", - "w_queen = libpysal.io.open('tracts_queen.gal', 'r').read()\n", - "w_queen[label]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 4. Spatial lag\n", - "\n", - "Spatial lag tells us about values near some spatial unit. Calculate spatial lag of the `med_household_income` variable. If the spatial weights matrix is row-standardized, then the spatial lag is the average value of an observation's neighbors." - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": {}, - "outputs": [], - "source": [ - "# pick a variable to investigate\n", - "col = 'med_household_income'" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": {}, - "outputs": [], - "source": [ - "# first drop null rows\n", - "tracts_not_null = tracts[[col, 'geometry']].dropna()\n", - "y = tracts_not_null[col]" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "metadata": {}, - "outputs": [], - "source": [ - "# recompute spatial weights for these observations then row-standardize\n", - "w_queen = Queen.from_dataframe(tracts_not_null)\n", - "w_queen.set_transform('R')" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "metadata": {}, - "outputs": [], - "source": [ - "# compute spatial lag\n", - "y_lag = lag_spatial(w_queen, y)" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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      med_household_incomemed_household_income_lag
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      " - ], - "text/plain": [ - " med_household_income med_household_income_lag\n", - "index \n", - "25015820300 44855 51203\n", - "25021421200 71629 80727\n", - "25009203302 95729 81047\n", - "25017342101 46341 54003\n", - "25001013200 71641 79544\n", - "25009221400 48389 60375\n", - "25015821700 74635 71043\n", - "25021416102 119688 99906\n", - "25017335301 84231 96330\n", - "25001015200 86786 73224" - ] - }, - "execution_count": 48, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "col_lag = '{}_lag'.format(col)\n", - "data_lag = pd.DataFrame(data={col:y, col_lag:y_lag}).astype(int)\n", - "data_lag.sample(10)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 6. Spatial autocorrelation\n", - "\n", - "Statistical models typically assume that the observations are independent of each other. This assumption is violated when a variable's value at one location is correlated with its value at nearby locations. This is called spatial autocorrelation, and is common in the real world due to proximity-based spillover effects. Substantive spatial autocorrelation can be explained by social or economic theory that describes a spatial relationship. Nuisance spatial autocorrelation stems from data problems.\n", - "\n", - "*Positive spatial autocorrelation*: nearby values tend to be more similar (e.g. income, home values, temperature, rainfall)\n", - "\n", - "*Negative spatial autocorrelation*: nearby values tend to be more dissimilar (e.g. fire stations, grocery stores)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 6.1. Moran's I\n", - "\n", - "Moran's I measures *global* spatial autocorrelation: do values tend to be near other (dis)similar values. Values > 1 indicate positive spatial autocorrelation, and values < 1 indicate negative spatial autocorrelation." - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "metadata": {}, - "outputs": [], - "source": [ - "# calculate the statistic\n", - "from esda.moran import Moran\n", - "mi = Moran(data_lag[col], w_queen)" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.6243797297762772" - ] - }, - "execution_count": 50, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# show the I value\n", - "mi.I" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.001" - ] - }, - "execution_count": 51, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# statistical inference: show the p value\n", - "mi.p_sim" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If we generated a large number of maps with the same values but randomly allocated over space, and calculated Moran's I for each of these maps, only 0.1% of them would display a larger absolute value than the one we computed from the real-world data set. Thus there is a 0.1% chance of getting the observed value of Moran's I if the spatial distribution of our variable is random. We can conclude that the variable's distribution is statistically-significantly postively spatially autocorrelated. We'll talk more about inference in the other notebook" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 6.2. Moran plots\n", - "\n", - "A Moran plot scatter plots the spatially-lagged values (y-axis) vs the original variable's values (x-axis). Moran's I equals the slope of the line in a Moran plot, which makes this all a bit easier to conceptualize." - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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      " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(figsize=(6, 6))\n", - "sns.regplot(x=col, y=col_lag, data=data_lag, scatter_kws={'s':1})\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "metadata": {}, - "outputs": [], - "source": [ - "# standardize the vector (i.e., calculate z-scores)\n", - "y_std = (y - y.mean()) / y.std()" - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "metadata": {}, - "outputs": [], - "source": [ - "# compute the spatial lag of the standardized vector and save it as a series with same index\n", - "y_std_lag = pd.Series(lag_spatial(w_queen, y_std), index=y_std.index, name=col_lag)" - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
      " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# standardized moran's plot, ignoring outliers beyond 3 std devs\n", - "fig, ax = plt.subplots(figsize=(6, 6))\n", - "ax.set_xlim(-3, 3)\n", - "ax.set_ylim(-3, 3)\n", - "plt.axvline(0, c='k', alpha=0.5)\n", - "plt.axhline(0, c='k', alpha=0.5)\n", - "sns.regplot(ax=ax, x=y_std, y=y_std_lag, scatter_kws={'s':1})\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Notice the 95% confidence interval shading and the positive slope. Given the value of Moran's I that we calculated earlier (and its p-value), we can conclude that the slope of the line is statistically-significantly different from zero. This plot makes it visually apparent." - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "m=0.6244, b=0.0336, r^2=0.6210, p=0.0000\n" - ] - } - ], - "source": [ - "# estimate a simple linear regression model (of the line above)\n", - "m, b, r, p, se = stats.linregress(x=y_std, y=y_std_lag)\n", - "print('m={:.4f}, b={:.4f}, r^2={:.4f}, p={:.4f}'.format(m, b, r ** 2, p))" - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.6243797297762772" - ] - }, - "execution_count": 57, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# the slope is the same as moran's I, calculated earlier\n", - "mi.I" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 6.3. LISAs\n", - "\n", - "Local Indicators of Spatial Autocorrelation: are there specific areas with high concentrations of (dis)similar values?\n", - "\n", - "Moran's I tells us about spatial clustering across the data set as a whole. However, it does not tell us where these clusters occur. For that, we need a local measure. Essentially, we will classify the data set's observations into four groups based on the four quadrants of the Moran plot:\n", - "\n", - " 1. **HH**: high value near other high values (*hot spots*)\n", - " 1. **LL**: low value near other low values (*cold spots*)\n", - " 1. **HL**: high value near low values (*spatial outliers*)\n", - " 1. **LH**: low value near high values (*spatial outliers*)" - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
      " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# standardized moran's plot again, from subsection above\n", - "fig, ax = plt.subplots(figsize=(6, 6))\n", - "ax.set_xlim(-3, 3)\n", - "ax.set_ylim(-3, 3)\n", - "ax.axvline(0, c='k', ls=':', alpha=0.5)\n", - "ax.axhline(0, c='k', ls=':', alpha=0.5)\n", - "ax.text(1.25, 1.25, 'HH', fontsize=20)\n", - "ax.text(1.25, -1.75, 'HL', fontsize=20)\n", - "ax.text(-1.75, 1.25, 'LH', fontsize=20)\n", - "ax.text(-1.75, -1.75, 'LL', fontsize=20)\n", - "ax.set_title('Moran Plot')\n", - "sns.regplot(ax=ax, x=y_std, y=y_std_lag, scatter_kws={'s':1})\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "metadata": {}, - "outputs": [], - "source": [ - "# calculate LISA values using the queen spatial weights\n", - "from esda.moran import Moran_Local\n", - "lisa = Moran_Local(data_lag[col], w_queen)" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "metadata": {}, - "outputs": [], - "source": [ - "# set the statistical-significance threshold (alpha)\n", - "alpha = 0.05" - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "False 915\n", - "True 541\n", - "Name: significant, dtype: int64" - ] - }, - "execution_count": 61, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# identify whether each observation is significant or not\n", - "# p-value interpretation same as earlier with moran's I\n", - "data_lag['significant'] = lisa.p_sim < alpha\n", - "data_lag['significant'].value_counts()" - ] - }, - { - "cell_type": "code", - "execution_count": 62, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "LL 644\n", - "HH 524\n", - "LH 165\n", - "HL 123\n", - "Name: quadrant, dtype: int64" - ] - }, - "execution_count": 62, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# identify the quadrant each observation belongs to\n", - "data_lag['quadrant'] = lisa.q\n", - "data_lag['quadrant'] = data_lag['quadrant'].replace({1:'HH', 2:'LH', 3:'LL', 4:'HL'})\n", - "data_lag['quadrant'].sort_values().value_counts()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### Now map the tracts, colored according to their LISA quadrants, to identify clusters:" - ] - }, - { - "cell_type": "code", - "execution_count": 63, - "metadata": {}, - "outputs": [], - "source": [ - "# merge the original tracts and LISA quadrants data together\n", - "tracts_lisa = gpd.GeoDataFrame(pd.merge(tracts, data_lag, how='left', left_index=True, right_index=True))" - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
      " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# create figure and axis then draw the basemap of tracts\n", - "fig, ax = plt.subplots(figsize=(9, 9))\n", - "tracts_lisa.plot(ax=ax, facecolor='#999999', edgecolor='k', linewidth=0.1)\n", - "\n", - "# plot each quandrant's tracts (if significant LISA statistic) in a different color\n", - "quadrant_colors = {'HH':'r', 'LL':'b', 'LH':'skyblue', 'HL':'pink'}\n", - "for q, c in quadrant_colors.items():\n", - " mask = tracts_lisa['significant'] & (tracts_lisa['quadrant']==q)\n", - " rows = tracts_lisa.loc[mask]\n", - " rows.plot(ax=ax, color=c, edgecolor='k', linewidth=0.1)\n", - "\n", - "ax.axis('off')\n", - "fig.savefig('clusters.png', bbox_inches='tight', dpi=600)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Gray tracts have statistically-insignificant LISA values. In red we see clusters of tracts with high values surrounded by other high values. In blue we see clusters of tracts with low values surrounded by other low values. In pink, we see the first type of spatial outliers: tracts with high values but surrounded by low values. Finally, in light blue we see the other type of spatial outlier: tracts with low values surrounded by other tracts with high values." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.2" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/data/data-pipeline/data_pipeline/ipython/experiment_4_texas_example/spatial_analysis_1_12292021_cal_environscreeen_economic.ipynb b/data/data-pipeline/data_pipeline/ipython/experiment_4_texas_example/spatial_analysis_1_12292021_cal_environscreeen_economic.ipynb deleted file mode 100644 index 7a6e34d5..00000000 --- a/data/data-pipeline/data_pipeline/ipython/experiment_4_texas_example/spatial_analysis_1_12292021_cal_environscreeen_economic.ipynb +++ /dev/null @@ -1,2257 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Spatial weights and ESDA (Exploratory Spatial Data Analysis) - TX, ACS 2019\n", - "\n", - "ESDA offers direct parallels to many of the aspatial descriptive statistics that are already part of most quantitative research projects: The geographic mean and spatial standard deviation, for example, tell us where the center of our study area is how compact our population is within that area. \n", - "\n", - "I use ESDA to generalize about our observations and to identify outliers and discontinuities that may disrupt our Score L model or suggest hypotheses about relationships we may wish to explore further as it pertains to health disparities and educational disparities.\n", - "\n", - "This is very pertinent to Score L as we make strict assumptions about distributions - for any thresholding approach it assumes that the data represents no measurement bias and no systematic error. Everything is homogeneous and is treated equally.\n", - "\n", - "ESDA can also be important in confirmatory statistical analysis as spatial autocorrelation can bias outcomes in many spatial and aspatial statistical models. \n", - "\n", - "Testing for spatial autocorrelation and locating clusters of correlated residuals will be important in explaining why certain scores are associated together.\n", - "\n", - "\n", - "For example, in section 6, I use LISA's to identify geographical clusters of values or find geographcial outliers. They are a very useful tool that can quickly return areas in which values are concentrated and provide suggestive evidence about the processes that might be at work - namely median income. I will be extending this to educational attainment and job opportunities in a given radius - indicating economic mobility." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/sarahluw/.pyenv/versions/3.6.2/envs/my-virtual-env-3.6.2/lib/python3.6/site-packages/geopandas/_compat.py:110: UserWarning: The Shapely GEOS version (3.9.1-CAPI-1.14.2) is incompatible with the GEOS version PyGEOS was compiled with (3.10.1-CAPI-1.16.0). Conversions between both will be slow.\n", - " shapely_geos_version, geos_capi_version_string\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.9.0 2.3.0\n" - ] - } - ], - "source": [ - "import geopandas as gpd\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import pandas as pd\n", - "import pysal as ps\n", - "import seaborn as sns\n", - "from scipy.stats import stats\n", - "\n", - "np.random.seed(0)\n", - "print(gpd.__version__, ps.__version__)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Load the tracts data set" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(5265, 38)" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# load geojson, display shape\n", - "tracts = gpd.read_file('census/census_tracts_data.geojson')\n", - "tracts = tracts.set_index('index')\n", - "tracts.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['STATEFP', 'COUNTYFP', 'TRACTCE', 'NAME', 'NAMELSAD', 'MTFCC',\n", - " 'FUNCSTAT', 'ALAND', 'AWATER', 'INTPTLAT', 'INTPTLON', 'total_pop',\n", - " 'median_age', 'pct_hispanic', 'pct_white', 'pct_black', 'pct_asian',\n", - " 'pct_male', 'pct_single_family_home', 'med_home_value',\n", - " 'med_rooms_per_home', 'pct_built_before_1940', 'pct_renting',\n", - " 'rental_vacancy_rate', 'avg_renter_household_size', 'med_gross_rent',\n", - " 'med_household_income', 'mean_commute_time', 'pct_commute_drive_alone',\n", - " 'pct_below_poverty', 'pct_college_grad_student',\n", - " 'pct_same_residence_year_ago', 'pct_bachelors_degree',\n", - " 'pct_english_only', 'pct_foreign_born', 'state', 'county', 'geometry'],\n", - " dtype='object')" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# what variables are present?\n", - "tracts.columns" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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      STATEFPCOUNTYFPTRACTCENAMENAMELSADMTFCCFUNCSTATALANDAWATERINTPTLAT...pct_commute_drive_alonepct_below_povertypct_college_grad_studentpct_same_residence_year_agopct_bachelors_degreepct_english_onlypct_foreign_bornstatecountygeometry
      index
      48001950100480019501009501Census Tract 9501G5020S4833066197864313+31.9714680...84.016.330.64775.091.24495.00.448001MULTIPOLYGON (((-95.69702 31.97597, -95.69699 ...
      48001950401480019504019504.01Census Tract 9504.01G5020S16549991296734+31.7346372...100.00.034.24838.075.24800.01.648001MULTIPOLYGON (((-95.84791 31.75753, -95.84806 ...
      48001950402480019504029504.02Census Tract 9504.02G5020S724720392627857+31.8000515...100.021.533.87506.069.77501.01.048001MULTIPOLYGON (((-95.98357 31.78925, -95.98354 ...
      48001950500480019505009505Census Tract 9505G5020S2313205299223+31.7878850...85.119.428.24423.084.04178.00.748001MULTIPOLYGON (((-95.68769 31.81041, -95.68741 ...
      48001950600480019506009506Census Tract 9506G5020S20653883329641+31.7502049...79.917.439.35109.071.24890.00.048001MULTIPOLYGON (((-95.70774 31.72559, -95.70765 ...
      \n", - "

      5 rows Ă— 38 columns

      \n", - "
      " - ], - "text/plain": [ - " STATEFP COUNTYFP TRACTCE NAME NAMELSAD MTFCC \\\n", - "index \n", - "48001950100 48 001 950100 9501 Census Tract 9501 G5020 \n", - "48001950401 48 001 950401 9504.01 Census Tract 9504.01 G5020 \n", - "48001950402 48 001 950402 9504.02 Census Tract 9504.02 G5020 \n", - "48001950500 48 001 950500 9505 Census Tract 9505 G5020 \n", - "48001950600 48 001 950600 9506 Census Tract 9506 G5020 \n", - "\n", - " FUNCSTAT ALAND AWATER INTPTLAT ... \\\n", - "index ... \n", - "48001950100 S 483306619 7864313 +31.9714680 ... \n", - "48001950401 S 16549991 296734 +31.7346372 ... \n", - "48001950402 S 72472039 2627857 +31.8000515 ... \n", - "48001950500 S 23132052 99223 +31.7878850 ... \n", - "48001950600 S 20653883 329641 +31.7502049 ... \n", - "\n", - " pct_commute_drive_alone pct_below_poverty \\\n", - "index \n", - "48001950100 84.0 16.3 \n", - "48001950401 100.0 0.0 \n", - "48001950402 100.0 21.5 \n", - "48001950500 85.1 19.4 \n", - "48001950600 79.9 17.4 \n", - "\n", - " pct_college_grad_student pct_same_residence_year_ago \\\n", - "index \n", - "48001950100 30.6 4775.0 \n", - "48001950401 34.2 4838.0 \n", - "48001950402 33.8 7506.0 \n", - "48001950500 28.2 4423.0 \n", - "48001950600 39.3 5109.0 \n", - "\n", - " pct_bachelors_degree pct_english_only pct_foreign_born state \\\n", - "index \n", - "48001950100 91.2 4495.0 0.4 48 \n", - "48001950401 75.2 4800.0 1.6 48 \n", - "48001950402 69.7 7501.0 1.0 48 \n", - "48001950500 84.0 4178.0 0.7 48 \n", - "48001950600 71.2 4890.0 0.0 48 \n", - "\n", - " county geometry \n", - "index \n", - "48001950100 001 MULTIPOLYGON (((-95.69702 31.97597, -95.69699 ... \n", - "48001950401 001 MULTIPOLYGON (((-95.84791 31.75753, -95.84806 ... \n", - "48001950402 001 MULTIPOLYGON (((-95.98357 31.78925, -95.98354 ... \n", - "48001950500 001 MULTIPOLYGON (((-95.68769 31.81041, -95.68741 ... \n", - "48001950600 001 MULTIPOLYGON (((-95.70774 31.72559, -95.70765 ... \n", - "\n", - "[5 rows x 38 columns]" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# inspect the first 5 rows\n", - "tracts.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# calculate pop density in persons per sq km\n", - "tracts['pop_density'] = tracts['total_pop'] / (tracts['ALAND'] / 1e6)\n", - "tracts = tracts.replace([np.inf, -np.inf], np.nan)\n", - "tracts = tracts.dropna(subset=['pop_density'])\n", - "\n", - "# project spatial geometries from lat-long to a meter-based projection\n", - "utm_tx = '+proj=utm +zone=18 +ellps=WGS84 +datum=WGS84 +units=m +no_defs'\n", - "tracts = tracts.to_crs(utm_tx)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "calenvironscreen_df = pd.read_csv(\"merged_df_score_ejcreen_2272.csv\", dtype={\n", - " \"FIPS_tract_id\": str\n", - "})\n", - "\n", - "calenvironscreen_df.set_index(\"FIPS_tract_id\", inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "df_merged = calenvironscreen_df.merge(tracts, how='inner', left_index=True, right_index=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(206, 75)" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_merged.shape" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1. Exploratory analysis\n", - "\n", - "Let's see what we've got with a couple key variables of interest." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "count 5253.000000\n", - "mean 1283.221970\n", - "std 1360.394266\n", - "min 0.000000\n", - "25% 198.253540\n", - "50% 1030.673949\n", - "75% 1937.892611\n", - "max 23721.665788\n", - "Name: pop_density, dtype: float64" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# descriptive stats\n", - "tracts['pop_density'].describe()" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "count 5202.000000\n", - "mean 64130.328720\n", - "std 33451.776234\n", - "min 8907.000000\n", - "25% 41998.000000\n", - "50% 55771.000000\n", - "75% 76667.000000\n", - "max 250001.000000\n", - "Name: med_household_income, dtype: float64" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# descriptive stats\n", - "tracts['med_household_income'].describe()" - ] - }, - { - "cell_type": "code", - "execution_count": 86, - "metadata": {}, - "outputs": [], - "source": [ - "# create a function to choropleth map a df column\n", - "def map_variable(df, col, scheme='quantiles', k=10, cmap='plasma', figsize=(10,10)):\n", - " values = df.dropna(subset=[col])\n", - " ax = values.plot(column=col, scheme=scheme, k=k, cmap=cmap, figsize=figsize, legend = True)\n", - " ax.axis('off')\n", - " plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 87, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
      " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# map a variable\n", - "map_variable(tracts, 'pop_density')" - ] - }, - { - "cell_type": "code", - "execution_count": 88, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
      " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# map another variable\n", - "map_variable(tracts, 'med_household_income')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "It looks like these two variables might be negatively correlated?" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "r=-0.0802, p=0.0000\n" - ] - } - ], - "source": [ - "# calculate the correlation coefficient and its p-value\n", - "x = tracts.dropna(subset=['pop_density', 'med_household_income'])['pop_density']\n", - "y = tracts.dropna(subset=['pop_density', 'med_household_income'])['med_household_income']\n", - "r, p = stats.pearsonr(x=x, y=y)\n", - "print('r={:.4f}, p={:.4f}'.format(r, p))" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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      " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# scatter plot them with matplotlib\n", - "fig, ax = plt.subplots()\n", - "ax.scatter(x=x, y=y, s=1)\n", - "ax.set_xlim(left=0)\n", - "ax.set_ylim(bottom=0)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "m=-1.9798, b=66681.6564, r^2=0.0064, p=0.0000\n" - ] - } - ], - "source": [ - "# estimate a simple linear regression model with scipy\n", - "m, b, r, p, se = stats.linregress(x=x, y=y)\n", - "print('m={:.4f}, b={:.4f}, r^2={:.4f}, p={:.4f}'.format(m, b, r ** 2, p))" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "m=0.0013, b=10.9475, r^2=0.0000, p=0.6713\n" - ] - } - ], - "source": [ - "# estimate a log-log simple linear regression model with scipy\n", - "m, b, r, p, se = stats.linregress(x=np.log(x), y=np.log(y))\n", - "print('m={:.4f}, b={:.4f}, r^2={:.4f}, p={:.4f}'.format(m, b, r ** 2, p))" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/sarahluw/.pyenv/versions/3.6.2/envs/my-virtual-env-3.6.2/lib/python3.6/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n", - " FutureWarning\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
      " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# plot the log-log regression line with 95% CI using seaborn\n", - "fig, ax = plt.subplots(figsize=(6, 6))\n", - "ax = sns.regplot(np.log(x), np.log(y), marker='.', scatter_kws={'s':2}, ax=ax)\n", - "ax.set_xlim((3, 11))\n", - "ax.set_ylim((9.5, 12.5))\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2. Spatial weights matrix\n", - "\n", - "Spatial weights define the spatial connections among our units of analysis (tracts in this case).\n", - "\n", - "### 2.1. Contiguity-based weights: rook contiguity\n", - "\n", - "Using rook contiguity, two spatial units must share an edge of their boundaries to be considered neighbors. This isn't terribly common in practice (since queen is usually more useful, but it's worth understanding)." - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "# get the tract labels (GEOIDs) and pick one to work with later\n", - "# tracts_subset = tracts[tracts.index.isin(df_merged.index.values)]\n", - "labels = tracts.index.tolist()\n", - "label = labels[10]" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/sarahluw/.pyenv/versions/3.6.2/envs/my-virtual-env-3.6.2/lib/python3.6/site-packages/libpysal/weights/_contW_lists.py:31: ShapelyDeprecationWarning: Iteration over multi-part geometries is deprecated and will be removed in Shapely 2.0. Use the `geoms` property to access the constituent parts of a multi-part geometry.\n", - " return list(it.chain(*(_get_boundary_points(part.boundary) for part in shape)))\n", - "/Users/sarahluw/.pyenv/versions/3.6.2/envs/my-virtual-env-3.6.2/lib/python3.6/site-packages/libpysal/weights/_contW_lists.py:29: ShapelyDeprecationWarning: Iteration over multi-part geometries is deprecated and will be removed in Shapely 2.0. Use the `geoms` property to access the constituent parts of a multi-part geometry.\n", - " return list(it.chain(*(list(zip(*shape.coords.xy)) for shape in shape)))\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 18.2 s, sys: 756 ms, total: 19 s\n", - "Wall time: 19.1 s\n" - ] - } - ], - "source": [ - "%%time\n", - "\n", - "from libpysal.weights.contiguity import Queen, Rook\n", - "from libpysal.weights import KNN, min_threshold_distance\n", - "from libpysal.weights import DistanceBand, lag_spatial\n", - "# calculate rook spatial weights\n", - "w_rook = Rook.from_dataframe(tracts, ids=labels, id_order=labels)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2.2. Contiguity-based weights: queen contiguity\n", - "\n", - "Using queen contiguity, two spatial units need only share a vertex (a single point) of their boundaries to be considered neighbors." - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 15.8 s, sys: 625 ms, total: 16.5 s\n", - "Wall time: 16.9 s\n" - ] - } - ], - "source": [ - "%%time\n", - "# calculate queen spatial weights\n", - "w_queen = Queen.from_dataframe(tracts, ids=labels, id_order=labels)" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['48213951400',\n", - " '48213950300',\n", - " '48001950902',\n", - " '48213950400',\n", - " '48001950401',\n", - " '48161000100',\n", - " '48001950100',\n", - " '48001950402',\n", - " '48213950902',\n", - " '48213951100',\n", - " '48001950901']" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# find the neighbors of some tract\n", - "w_queen.neighbors[label]" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# this is a raw contiguity matrix, so weights are binary 1s and 0s meaning neighbor/not\n", - "w_queen.weights[label]" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "11" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# how many neighbors does this tract have?\n", - "w_queen.cardinalities[label]" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "count 5253.000000\n", - "mean 6.291643\n", - "std 2.106332\n", - "min 1.000000\n", - "25% 5.000000\n", - "50% 6.000000\n", - "75% 7.000000\n", - "max 23.000000\n", - "dtype: float64" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# convert cardinalities to series and describe data -- looks right-skewed\n", - "cardinalites_queen = pd.Series(w_queen.cardinalities)\n", - "cardinalites_queen.describe()" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
      " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# look at the distribution of number of neighbors\n", - "ax = cardinalites_queen.hist(bins=20)" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "5253" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# number of observations\n", - "w_queen.n" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "6.291642870740529" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# average number of neighbors\n", - "w_queen.mean_neighbors" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "1" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# min number of neighbors\n", - "w_queen.min_neighbors" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "23" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# max number of neighbors\n", - "w_queen.max_neighbors" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# islands are observations with no neighbors, disconnected \n", - "# in space (can cause modeling problems)\n", - "len(w_queen.islands)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### Plot a census tract of interest, along with its neighbors:" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
      " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(figsize=(6, 6))\n", - "tracts.plot(ax=ax, facecolor='#999999', edgecolor='w', linewidth=0.5)\n", - "\n", - "# plot some tract of interest in red\n", - "tract = tracts.loc[[label]]\n", - "tract.plot(ax=ax, facecolor='r', edgecolor='w', linewidth=1)\n", - "\n", - "# plot the neighbors in blue\n", - "neighbors = tracts.loc[w_queen[label]]\n", - "neighbors.plot(ax=ax, facecolor='b', edgecolor='w', linewidth=1)\n", - "\n", - "# zoom to area of interest\n", - "xmin, ymin, xmax, ymax = neighbors.unary_union.bounds\n", - "ax.axis('equal')\n", - "ax.set_xlim(xmin, xmax)\n", - "ax.set_ylim(ymin, ymax)\n", - "\n", - "ax.set_title('Neighbors of tract {}'.format(label))\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 3.3. Distance-based weights: *k*-nn\n", - "\n", - "Find the *k*-nearest neighbors of each tract" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/sarahluw/.pyenv/versions/3.6.2/envs/my-virtual-env-3.6.2/lib/python3.6/site-packages/libpysal/weights/util.py:1073: ShapelyDeprecationWarning: The array interface is deprecated and will no longer work in Shapely 2.0. Convert the '.coords' to a numpy array instead.\n", - " data = np.vstack([np.array(shape.centroid) for shape in first_choice])\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 1.13 s, sys: 35.2 ms, total: 1.16 s\n", - "Wall time: 1.17 s\n" - ] - } - ], - "source": [ - "%%time\n", - "# k-nearest neighbors finds the closest k tract centroids to each tract centroid\n", - "w_knn = KNN.from_dataframe(tracts, k=6)" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['48001950402',\n", - " '48001950401',\n", - " '48001950500',\n", - " '48001950901',\n", - " '48001950600',\n", - " '48001950700']" - ] - }, - "execution_count": 35, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# they all have exactly k neighbors\n", - "w_knn.neighbors[label]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 3.4. Distance-based weights: distance band\n", - "\n", - "Here tracts are considered neighbors of some tract if they are within a given threshold distance of it. Distance band weights can be specified to take on continuous values rather than binary (1s and 0s), with these values being the inverse distance between each pair of \"neighboring\" units. The default (linear) distance-decay exponent is thus -1. If we set it to -2, we get gravity model weights." - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "91602.1289312889" - ] - }, - "execution_count": 36, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# first determine the minimum nearest neighbor distance so each unit is assured of at least one neighbor\n", - "x = tracts.centroid.x\n", - "y = tracts.centroid.y\n", - "coords = np.array([x, y]).T\n", - "threshold = min_threshold_distance(coords)\n", - "threshold" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/sarahluw/.pyenv/versions/3.6.2/envs/my-virtual-env-3.6.2/lib/python3.6/site-packages/libpysal/weights/util.py:1073: ShapelyDeprecationWarning: The array interface is deprecated and will no longer work in Shapely 2.0. Convert the '.coords' to a numpy array instead.\n", - " data = np.vstack([np.array(shape.centroid) for shape in first_choice])\n", - "/Users/sarahluw/.pyenv/versions/3.6.2/envs/my-virtual-env-3.6.2/lib/python3.6/site-packages/scipy/sparse/data.py:117: RuntimeWarning: divide by zero encountered in reciprocal\n", - " return self._with_data(data ** n)\n", - "/Users/sarahluw/.pyenv/versions/3.6.2/envs/my-virtual-env-3.6.2/lib/python3.6/site-packages/libpysal/weights/weights.py:172: UserWarning: The weights matrix is not fully connected: \n", - " There are 2 disconnected components.\n", - " There is 1 island with id: 48443950100.\n", - " warnings.warn(message)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 7.52 s, sys: 750 ms, total: 8.27 s\n", - "Wall time: 8.33 s\n" - ] - } - ], - "source": [ - "%%time\n", - "# default alpha=-1 and gravity weights alpha=-2\n", - "w_dist = DistanceBand.from_dataframe(tracts, threshold=threshold, binary=False, alpha=-1)" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "127" - ] - }, - "execution_count": 39, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# how many distance-band neighbors does our tract have?\n", - "len(w_dist.neighbors[label])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### Map the neighbors, colored by weight from nearest to furthest:" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
      " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(figsize=(6, 6))\n", - "tracts.plot(ax=ax, facecolor='k', edgecolor='gray', linewidth=1)\n", - "\n", - "# get the tract of interest and its neighbors/weights\n", - "tract = tracts.loc[[label]]\n", - "weights = pd.Series(w_dist[label])\n", - "neighbors = tracts.loc[weights.index, ['geometry']]\n", - "\n", - "# plot the tract's neighbors in blues by weight\n", - "neighbors['weights_scaled'] = weights\n", - "neighbors.plot(ax=ax, column='weights_scaled', cmap='Blues_r', edgecolor='gray', linewidth=1, scheme='Fisher_Jenks')\n", - "\n", - "# plot the tract of interest in white\n", - "tract.plot(ax=ax, facecolor='w', edgecolor='r', linewidth=2)\n", - "\n", - "# zoom to area of interest\n", - "xmin, ymin, xmax, ymax = neighbors.unary_union.bounds\n", - "ax.set_xlim(xmin, xmax)\n", - "ax.set_ylim(ymin, ymax)\n", - "ax.axis('off')\n", - "\n", - "ax.set_title('Neighbors of tract {}'.format(label))\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 3.5. Standardizing weights\n", - "\n", - "A spatial weights matrix with raw values (e.g., binary 1s and 0s for neighbor/not) is not always the best for analysis. Some sort of standardization is useful." - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'48213951400': 1.0,\n", - " '48213950300': 1.0,\n", - " '48001950902': 1.0,\n", - " '48213950400': 1.0,\n", - " '48001950401': 1.0,\n", - " '48161000100': 1.0,\n", - " '48001950100': 1.0,\n", - " '48001950402': 1.0,\n", - " '48213950902': 1.0,\n", - " '48213951100': 1.0,\n", - " '48001950901': 1.0}" - ] - }, - "execution_count": 43, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# inspect the neighbors and weights of our tract\n", - "w_queen[label]" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'O'" - ] - }, - "execution_count": 44, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# check the current transformation of the weights matrix (O = original)\n", - "w_queen.get_transform()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Typically, we want to apply a row-based transformation, so every row of the matrix sums up to 1." - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'48213951400': 0.09090909090909091,\n", - " '48213950300': 0.09090909090909091,\n", - " '48001950902': 0.09090909090909091,\n", - " '48213950400': 0.09090909090909091,\n", - " '48001950401': 0.09090909090909091,\n", - " '48161000100': 0.09090909090909091,\n", - " '48001950100': 0.09090909090909091,\n", - " '48001950402': 0.09090909090909091,\n", - " '48213950902': 0.09090909090909091,\n", - " '48213951100': 0.09090909090909091,\n", - " '48001950901': 0.09090909090909091}" - ] - }, - "execution_count": 45, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# transform the queen weights\n", - "w_queen.set_transform('R')\n", - "w_queen[label]" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "('WARNING: ', '48443950100', ' is an island (no neighbors)')\n" - ] - }, - { - "data": { - "text/plain": [ - "{'48001950100': 0.014979349650672217,\n", - " '48001950401': 0.020309161978431498,\n", - " '48001950402': 0.027131414696176744,\n", - " '48001950500': 0.017319400840410566,\n", - " '48001950600': 0.016637060192047873,\n", - " '48001950700': 0.015244318419098868,\n", - " '48001950800': 0.01408277142653615,\n", - " '48001950901': 0.016907375123011943,\n", - " '48001950902': 0.012200189058361914,\n", - " '48001951000': 0.009177966871647854,\n", - " '48073950100': 0.005665470063253171,\n", - " '48073950200': 0.007562144118312603,\n", - " '48073950300': 0.008237583597924463,\n", - " '48073950400': 0.007526067312122535,\n", - " '48073950500': 0.00794901702501413,\n", - " '48073950600': 0.007274746294178255,\n", - " '48073950700': 0.00780389323825469,\n", - " '48073950801': 0.006242441029268617,\n", - 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" '48423002004': 0.0062903757929929525,\n", - " '48423002006': 0.006446009311873876,\n", - " '48423002007': 0.006164926496797508,\n", - " '48423002008': 0.006705674003381338,\n", - " '48423002009': 0.006060951844700154,\n", - " '48423002101': 0.00530503989256266,\n", - " '48423002102': 0.005359990467584851,\n", - " '48423002200': 0.006474205519002173,\n", - " '48423980000': 0.006586479545901954,\n", - " '48467950100': 0.005051437574980212,\n", - " '48467950200': 0.004943369352290823,\n", - " '48467950300': 0.004783563865037059,\n", - " '48467950600': 0.005942567063798955,\n", - " '48467950700': 0.005935711750343549,\n", - " '48467950800': 0.005996467435603836,\n", - " '48467950900': 0.0073887097114497015,\n", - " '48467951000': 0.00716038582911822}" - ] - }, - "execution_count": 46, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# transform the linear-decay distance-based weights\n", - "w_dist.set_transform('R')\n", - "w_dist[label]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "PySAL supports the following transformations:\n", - "\n", - " - O: original, returning the object to the initial state\n", - " - B: binary, with every neighbor having assigned a weight of 1\n", - " - R: row-based, with all the neighbors of a given observation adding up to 1\n", - " - V: variance stabilizing, with the sum of all the weights being constrained to the number of observations\n", - "\n", - "**It can take a long time to calculate a weights matrix for a large data set.**\n", - "\n", - "Once you've created yours, you might want to save it to disk to re-use in subsequent analyses." - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "metadata": {}, - "outputs": [], - "source": [ - "# save your matrix to disk\n", - "import libpysal\n", - "f = libpysal.io.open('tracts_queen.gal', 'w')\n", - "f.write(w_queen)\n", - "f.close()" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'48213951400': 1.0,\n", - " '48213950300': 1.0,\n", - " '48001950902': 1.0,\n", - " '48213950400': 1.0,\n", - " '48001950401': 1.0,\n", - " '48161000100': 1.0,\n", - " '48001950100': 1.0,\n", - " '48001950402': 1.0,\n", - " '48213950902': 1.0,\n", - " '48213951100': 1.0,\n", - " '48001950901': 1.0}" - ] - }, - "execution_count": 48, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# read a matrix from disk (notice its transformation)\n", - "w_queen = libpysal.io.open('tracts_queen.gal', 'r').read()\n", - "w_queen[label]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 4. Spatial lag\n", - "\n", - "Spatial lag tells us about values near some spatial unit. Calculate spatial lag of the `med_household_income` variable. If the spatial weights matrix is row-standardized, then the spatial lag is the average value of an observation's neighbors." - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "metadata": {}, - "outputs": [], - "source": [ - "# pick a variable to investigate\n", - "col = 'med_household_income'" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "metadata": {}, - "outputs": [], - "source": [ - "# first drop null rows\n", - "tracts_not_null = tracts[[col, 'geometry']].dropna()\n", - "y = tracts_not_null[col]" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/sarahluw/.pyenv/versions/3.6.2/envs/my-virtual-env-3.6.2/lib/python3.6/site-packages/libpysal/weights/_contW_lists.py:31: ShapelyDeprecationWarning: Iteration over multi-part geometries is deprecated and will be removed in Shapely 2.0. Use the `geoms` property to access the constituent parts of a multi-part geometry.\n", - " return list(it.chain(*(_get_boundary_points(part.boundary) for part in shape)))\n", - "/Users/sarahluw/.pyenv/versions/3.6.2/envs/my-virtual-env-3.6.2/lib/python3.6/site-packages/libpysal/weights/_contW_lists.py:29: ShapelyDeprecationWarning: Iteration over multi-part geometries is deprecated and will be removed in Shapely 2.0. Use the `geoms` property to access the constituent parts of a multi-part geometry.\n", - " return list(it.chain(*(list(zip(*shape.coords.xy)) for shape in shape)))\n" - ] - } - ], - "source": [ - "# recompute spatial weights for these observations then row-standardize\n", - "w_queen = Queen.from_dataframe(tracts_not_null)\n", - "w_queen.set_transform('R')" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "metadata": {}, - "outputs": [], - "source": [ - "# compute spatial lag\n", - "y_lag = lag_spatial(w_queen, y)" - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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      " - ], - "text/plain": [ - " med_household_income med_household_income_lag\n", - "index \n", - "48029181003 37832 45585\n", - "48201251000 95956 103259\n", - "48201312000 66250 39739\n", - "48453001850 60253 73079\n", - "48085030523 109949 120137\n", - "48491021100 60122 54787\n", - "48013960201 61125 52209\n", - "48029190504 42353 43969\n", - "48085031405 174462 93669\n", - "48485010100 19963 28442" - ] - }, - "execution_count": 54, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "col_lag = '{}_lag'.format(col)\n", - "data_lag = pd.DataFrame(data={col:y, col_lag:y_lag}).astype(int)\n", - "data_lag.sample(10)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 6. Spatial autocorrelation\n", - "\n", - "Statistical models typically assume that the observations are independent of each other. This assumption is violated when a variable's value at one location is correlated with its value at nearby locations. This is called spatial autocorrelation, and is common in the real world due to proximity-based spillover effects. Substantive spatial autocorrelation can be explained by social or economic theory that describes a spatial relationship. Nuisance spatial autocorrelation stems from data problems.\n", - "\n", - "*Positive spatial autocorrelation*: nearby values tend to be more similar (e.g. income, home values, temperature, rainfall)\n", - "\n", - "*Negative spatial autocorrelation*: nearby values tend to be more dissimilar (e.g. fire stations, grocery stores)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 6.1. Moran's I\n", - "\n", - "Moran's I measures *global* spatial autocorrelation: do values tend to be near other (dis)similar values. Values > 1 indicate positive spatial autocorrelation, and values < 1 indicate negative spatial autocorrelation." - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "metadata": {}, - "outputs": [], - "source": [ - "# calculate the statistic\n", - "from esda.moran import Moran\n", - "mi = Moran(data_lag[col], w_queen)" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.6191256645132304" - ] - }, - "execution_count": 56, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# show the I value\n", - "mi.I" - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.001" - ] - }, - "execution_count": 57, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# statistical inference: show the p value\n", - "mi.p_sim" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If we generated a large number of maps with the same values but randomly allocated over space, and calculated Moran's I for each of these maps, only 0.1% of them would display a larger absolute value than the one we computed from the real-world data set. Thus there is a 0.1% chance of getting the observed value of Moran's I if the spatial distribution of our variable is random. We can conclude that the variable's distribution is statistically-significantly postively spatially autocorrelated. We'll talk more about inference in the other notebook" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 6.2. Moran plots\n", - "\n", - "A Moran plot scatter plots the spatially-lagged values (y-axis) vs the original variable's values (x-axis). Moran's I equals the slope of the line in a Moran plot, which makes this all a bit easier to conceptualize." - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
      " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(figsize=(6, 6))\n", - "sns.regplot(x=col, y=col_lag, data=data_lag, scatter_kws={'s':1})\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "metadata": {}, - "outputs": [], - "source": [ - "# standardize the vector (i.e., calculate z-scores)\n", - "y_std = (y - y.mean()) / y.std()" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "metadata": {}, - "outputs": [], - "source": [ - "# compute the spatial lag of the standardized vector and save it as a series with same index\n", - "y_std_lag = pd.Series(lag_spatial(w_queen, y_std), index=y_std.index, name=col_lag)" - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
      " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# standardized moran's plot, ignoring outliers beyond 3 std devs\n", - "fig, ax = plt.subplots(figsize=(6, 6))\n", - "ax.set_xlim(-3, 3)\n", - "ax.set_ylim(-3, 3)\n", - "plt.axvline(0, c='k', alpha=0.5)\n", - "plt.axhline(0, c='k', alpha=0.5)\n", - "sns.regplot(ax=ax, x=y_std, y=y_std_lag, scatter_kws={'s':1})\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Notice the 95% confidence interval shading and the positive slope. Given the value of Moran's I that we calculated earlier (and its p-value), we can conclude that the slope of the line is statistically-significantly different from zero. This plot makes it visually apparent." - ] - }, - { - "cell_type": "code", - "execution_count": 62, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "m=0.6191, b=0.0201, r^2=0.6310, p=0.0000\n" - ] - } - ], - "source": [ - "# estimate a simple linear regression model (of the line above)\n", - "m, b, r, p, se = stats.linregress(x=y_std, y=y_std_lag)\n", - "print('m={:.4f}, b={:.4f}, r^2={:.4f}, p={:.4f}'.format(m, b, r ** 2, p))" - ] - }, - { - "cell_type": "code", - "execution_count": 63, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.6191256645132304" - ] - }, - "execution_count": 63, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# the slope is the same as moran's I, calculated earlier\n", - "mi.I" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 6.3. LISAs\n", - "\n", - "Local Indicators of Spatial Autocorrelation: are there specific areas with high concentrations of (dis)similar values?\n", - "\n", - "Moran's I tells us about spatial clustering across the data set as a whole. However, it does not tell us where these clusters occur. For that, we need a local measure. Essentially, we will classify the data set's observations into four groups based on the four quadrants of the Moran plot:\n", - "\n", - " 1. **HH**: high value near other high values (*hot spots*)\n", - " 1. **LL**: low value near other low values (*cold spots*)\n", - " 1. **HL**: high value near low values (*spatial outliers*)\n", - " 1. **LH**: low value near high values (*spatial outliers*)" - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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Gx5x4vCJ9I5P8+7ELvNbSB4AuTDuMphA0NKLACy9IAXKf/OQn532shZzFL1ZIo2w6EUX4+MY89mzMm/fxZrtHMym/cO/xQn0XwY4b6bnk79Lp9mKxOXA4Pbg9Xp7/oJtfvXMJh0uqZbcuP5m/vW0tO783+7g0haChEQWWLZs1oi9slooTOJr2ftl0crpjaFafRzhjmW+8f7j3eKG+i2DHjbQPhCiKvFxrZuOKVBJjY6jtHOanR89zaUBqX5oWb+Cvby5kR3k2Ol14AaWaQtDQiALbtm2L2rEu9yw+3Iqe4e4X7H3ZdLJnYx5bizIiFrCBY5nvPQp3/8v5XYRzLvk+ON1eRifd/Mur5/nTG1bR3GOnpqkXkMxDd2/I4y9uXE1iXAzxxhjSE4xhjUFTCBoaEbLYkTrRJtxaQOHuJ7/vcHmU3s9qxRCOgO0cGuepV1t5YHsRBWnxUZ+pR/odRus7l4/jcHn44eEWIDJT1K2lWdgnXJTnJ+PximxZlc7Tb19UzEPluUn87W1rKc5OwqDXkZ5gJCE2fDGvKQQNjQgJJgife+45AHbs/tiiK4v5Oidl5trURn7tcHrm7NR96tVW/ud9qSf8d+9Zr4xFTh4L59pmOse+M2aePNCEw+XhvhtWhjjCFNHyJcjHeXhHyYxJcsGwT7gYmXCxrTiT+m4bPzl6nra+MQCS42L4ws2F3FGZQ4xOR2q8gRSTASn3N3w0haChESHBBGFOTg6w+GGdcxnDbII/lGANtZ9aeJuMeuU+qY8z2xgf2F7k93cu1xa4rfr8yBXRglRGC3a96gJ+skKSzxGJ8lc/O+Hu43R76R+dZMLlYXDMyf99s41XGqRsZgHYWJDK399eTF6KicRYyTwUo59bEQpNIWhoREgwQXjTTTcBKGUPFtMhHA3zSqDwfnx/AyfbB2fMiA4k8D6pBfRsYyxIi+e796yf9n4k1xa47b5aM08ebMLh9LBnY56fslITTOnI1/L8qS7lMyBi5R9J3oQoigyNu7A5XLg9XvadMfP02xcZm5TMQyXZSdxQmM4Lp7tpMo+weWU6cQZ9WOMIhaYQNDSiyGK3TYzEXDTTtoHC+2T7IAfqrRFHB6mRI41ke/dcsqcjXc34bStM/Y0kAimwf7J8DdtKMiM2+8yE+p7vWpdL/6iUadxotvOTo+dp7R0FICkuhr+8aTW71uUy6fayNiuJPRvz5q0MQFMIGhpR4be//S0An/70pxd1HJGaVELN/ANNG5FmRMtMaz5j9DWfMejnHH8/2zWFOtaeDXmKkzvUOLcUpiv1hgCeP9U1zQEc7BqiQVV5Nl5RZENBKhabg+FxJ//55kUO1FuVbXZV5vBXHy0kJd5AUpyB9AQjlfkpURuDphA0NCIg1Gy2oKAg6secC8Fs3TMVvZNn/hV5KYoJJcVkCOrEnYvwm6n5jFoIR2umPZNJaaZVQbCub0BQB/BC5SYIAly3Kh2Xx8vvP+jml29dZNLtBaAoK5EHb1tLeV4ycQY9yxKNxMbMf0UQiKYQNC47V3LYZqgZ6NatW8M+RuD1R3OGHMzWPVPRO3nm73AFjwia79gChadaKMtjfHR3eUR5DjMxV5OdWpEGroTmU14iHCbdHvpHnUy6PDRb7fy0ppWWnhHl853l2Xzl9hJiY/SkJxpJjCCMNFI0haBx2VkKkThzJRqzw2i0bJxNMM10zGB2dpvDFdScMt/rnU+28OV8TkJ1fQs8b7h9mcNBFKVCdPYJN8PjTn751kX2n7UogU9luUl8tCiDuzbksSwxllSTAV24RYnmiKYQNC47S6U0w1wIJeB+85vfAPDZz3521mNES6nMFPkTjnkEpkfRBBJJ6exoVxdd6OdkLkI81JgiVV7jTjcDo04m3R4O1ln5v2+2YZ9wA1CYmcCDt62lMj+FeGMMyxKNGOYYRhopmkLQuOwsdiROOEQqLFavXh32sWcKxwz3vvjZ//NTpmUEz7av+m+4zDbOy1naOhwCs50Dmct4Q40p3Hvq9ngZHHMyOunmXM8IPz16niaLZB5KMOr58xtX8bGN+YqfIN54eUW0phA0NIIQqbC44YYb5nwudSijzeEKe3at2P+DZATPpNDma2cPFHrBnMPBonZmq3c0G+Hso94mMNs50uuRzzPbtUB499TmcDE05sTmcPLLt9p5+YxZMQ9VlWXx1zcXkpkUN+cs41DnVJLxwkBTCBoaQbicZq1Q4Zjh7BcsIxgWxv4eSujJ55KdwzaHi+8daOJAvZVdlTlBu4TNlEU8l/4GwbZ5YHsRTo+XwszEoIo2lDks8DzBIpAiuaey09jhdHO4oYdfvNHGsC+BUSfAZ65bwV9+VCpElx4/9yzjYKivJRw0haChEYRwZ3yyENn3vJSHcN99983pfPNRQMHGGs1s5ZlmxvI51CucmsYequst7K7M5YHtRUHzFwLHN9/+BsG2STEZ2Lomg29VN5KeYAy770Gw8FKHy8PQuJPbSrLYUpge8jjqZyIpNoahcSc2h4sLvaP89Oh56s12AEwGPZ++roDMxFi2l2WRl2qaV2JZKIUa6XOgKQQNjTmiFiIlJSUzbjvbDHi+9vLA40fDTxPuzFhe4Tyxv5GGbhsPbC/iG3dVKGMJ1SVstsqqoa4pWIE79baRVGwNtl3g2FJMBkwGPU++fgGArUUZfpFIwe7ZpNvDTUWZDDuc7D3ezounu/H67EO3lmRy/0dWcr5nlN3rc1kexL8RKaEUaqTPgaYQNDTmiL8QiazQWjhEYmeP9PjhHHum2Pxg255sH5yx+c1MYwwmuGYrr61+fyaBKJuBZrrW2aKp5FUC4syhvNesSuVvt0sJZAfrLfz7sQsMjUvmoRXp8fzt9iKuWZnGG+f6eOr1VjISY1keQrlEQtRMnKIoXlH/rr32WlEjNEj1G8PaduXKlSIgXrx4MeQ227ZtEwHxtddei84AP6QMjzvF5052isPjzrD3ee5kp7jh8VfE5052Rnz82c4X7NhzGWOwMXQMjgU9jvxsznR89TOpHk+o99XnLr/mhqDPqvpaZ7vG4XGn+LXnzojrvnkorPuuPse6bx4S//31VrGm0Sru+dmb4sqv7hdXfnW/WPJ/Dojf2d8gNltsonl4XJxwued9r+cCcFKcRb5qKwQNjRkId5b+q1/9CoDPfe5zQT8Pd6aqJnDWFyr6JZiZo6axhyf2N4bMUwg2o5yvI1oew7PvXpqx18B8s46DvZ9iMpCZFBv0OOprDSd09kC9ld2VuWHPtidcHsrzkvn8jatpstj5p0PNinno5uIMHti2hrzUeL8s49iY6NZBihaaQtD40BNu1c+ZfsAVFRWznme+fQpCRb8EO95sJpxgQnVLYTp3VuTM6DQNxrT7N0OvgRn3ixLHWvrYdP1UVJH6WmczrQT6EmbC6xUZHHdiG3dyvLWfX717iYFRJyD1M/7b29ZyS0kWqSYDqfHRCSNdaDSFoPGhZzbBqv4bimuvvXbW88w24w9GYOnlYMcJNrZQFUpnOueJtkEONkglrkM5TYPtr75/WwrTOd0xxEM7itmzMS/kvZBDU6vrLcDUfZdn1vYJV8h9Q41H5pl326nc3BNydSGvFIJFTwXzJZTlJfHM8Ut+CW5jk1KmcVvfKP/y6nk+6BgGIDZGx/Wr0/mgYwhRhOVppsuWZRwNNIWg8aFnNsEa7Th+OUrG4fTwwyMz99UNVFbziSAJdjw14Si/YPur9/vegSZeON3NZ2J00yKAAo9zoN7KjjIpZFXOE5jw9Up4+3w/60vXRnQ9Mp/eXDBjkp86eupAvVUxq8mfyQpC3q40O4n3Lg4C8MTHKxkYdTIwOskz717iuVNduH1a7MY1y/jirUWkJhg50zHMrvW5YSmDpVTsUVMIGvzkJz8hNTU16Gft7e2XdSyLQTSE/t69ewH4sz/7s1m3lQXNwztn76s7n+iR2YS3TKiQzUBsDhcOp4eHd5b47a++f8FaXwYK7W9+85tMuDyU9I1xscPLr9sGeHltJuV5yUyOSWUcNqxIDTqGwGd1wuWhuG+M9194k7aL7QBMerw8eaAJIKgPQx09BShmNfDvgCZvV5aXxK/ebuf+rSvpHBznjXN9/Py1C/SNTgKQlxrH39xaxNY1GaTFG0k2xVCemzwtPDaU4Fd3crvvI7P3d15INIWgwU9/+tPFHsIVz8aNG8PeVrbVbyvJDFpjR81cnNEywYR/JOGdgdQ09vDDIy08urs8pIAL1vpSPv+nviG9fvzxx6cd+/ev+78+0zHM1g3TxxDOs9rRPw6kh/RhqO9BMLOaupfEPdcuZ8Ll4W9uWyuZh462cvLSEAAGvcAfb1nBZ7esYFliLOkJRvSqaqRh+3yEgL9zJBorDU0haHDx4kVWrVoV9LNbbrmFY8eOXd4BXYFEohDUtvrkckPQH3GoUgqRKIdwlUk4q5Bgq4NwFUmgEhoedwYd0/OnuvjsbZtx2Xq4cW1G0GP9r6cOcLxXz2N3lQPw+P4Gdlfm8rVdZey+vYq333yDT123nE/nVYa1qgocm7qXhFcUuaUki76RCX79Xge/O9mJyyNpmetXp/M324sozEzEoBd441zfrFnCoe7zTJ3cIiEa5Uo0haChEQU8Hsn2rdfPXn4gnDDIUKUUAh24M1XzDDzOTI1yQn0mKybZ3/HwjpKQTu5wCTWmqvJsUuMN9Nmk18+f6lIcv7Kz+bWWXj7x0U3KOdW9nmN8s/OspDhuiVAgBjrvJ1weynKTOFRv4V9fa6XHLpmHspNj+Ztbi7h5bSbpiUaS4gxBmxEFm61HEkY7F9SmsNm65YVCUwgaGlHgmWeeAcLzIYQTBhmqlEKgAzewmmegIIo0lDRUNzfZ3xHYWS3cXgmhri0Ub5/v5z9OjyhlM2Rn85dvK+aPq6byKtQmn5+FdYXBka/T4xX5aHEGK5bF8639TYoz2aAX+PR1BfzJ9SvJSoolLd6oNKuZKafD4fJEVJp8PsjPSTjd8kKhKQQNjShwzTXXzGm/wNnhbA5e9fb3b13Jxf4x7t865YgMnH2HE0qqJnD/QGG374yZh3eUTBPmkZgrZqqaKlcBvXFtBkkZeTjcHh7OK+HrT+uV92er2RSYhxAOt5VlMe50szoznn97/QK/OdGhmIeuW5XGl7YXUZydTHqCEWOMf+TQTMUFg5UmD0W0oo3mE4igKQQNjSiwfv30evvhEFhRVDbNOJwev6b3wfZ7+o2LnOka5oP2YcpzU4DplUcjFQ6B26uF3fOnuvjh4SmnsnosDpcnqKKIhKrybFJNBvqGITlOKpgnO7FlX628coDQAnamPIRgTLg8jEy4cXq8/NV/n1KUUlZSLA/cuobtJVlkJMVG1KxmptLkoYhWyfL5mKA0hXCVMpPp4qmnniI+fv4FtTSmcLkkIWIwRDazU8fEV9db2LY2k9tKsnC4ZlYMchy/xyv6RacE660QiXCYSZiEUi41jT1BFUWkpJgMxBunfDDBznfj2gyy8gqoKs8OOaO+/4ZVM34uMzjm5KXabvLTTPz32+28fWEAkHoUfPq6Aj53wypyU+Pm1awmWIVWIGhi3Hxm9tFCUwhXKf/93/8d8rOf/OQnV6VCWMgEn9mO/etf/xoIz4egPt6WwnQe3lGCw+1hR2k2B+qtCAJsWpkW1GYvU1U+VX1zzwb/jOBoOBeDEUpZqAXZTPWWAq9/X60ZBGn84ZYET44zcE/p1Iol2L3ZVpJJisnAs+9cChnfPzLh4jfvXeInR8/j8YqK03rVsni+ckcJ161Kj2qzmsBcjGBlxaOZBDlXNIVwlSEVNQyPcJLOXn/99bkP5jKzEF3Cwj325s2b53S8R3eXK6aRh3eWsGllmiLkU0wGOofGaei2TXMKp5gMQZOu5M/m61yMhECzUqjYe/WzWdPYw5MHpeQxdZc4+ZkMTOpqb29XlEtativojHras+qb1DvcHuVYJoOe/tFJ3jzfx7PvTfkJEmNj+Lsda9lZkUNGYqzSrCbS8iLytc0WggrhlRW/3IStEARBqGN6qocNOAl8WxTFgWgOTEMjUhZyyT3bsSsrK+d8PPuEK2SiWiincCT9DGbLLwhX4Mk1fe7fupIm80jQWkCBUU0zRVE5nB4Qgvc0lltwwuw9D0KxrSSThm4biPDE/gZGJ92U5CTx89daefN8PyCZh9blp3DT2gx2VuRMa1YTzjmDzf4Dtw+W7wCE5ei/nESyQjgIeID/53v9GSAesAJ7gT1RHZmGRoQs1JI7lNBUvx8rSGGRcXFxEY+1prEnZCTQTHb7mQRVuOazSASeXNPnYv8YzT0jQTupnWgbVArWyWW3Q0VLhSrTUNPYw/46MxW5KZTlJSmz+0DTVLDieGpkZfqFmwu5aU0GjRY73z3QxITbC0BBmomBMSeFGQk8++4lCjMSpzWrCUepBtsmmJJbKvWKZiIShVAliqI6tq5OEIQPRFG8RhCEuTWS1dC4AggUmoHJWgAjdTVA+D4ECPAj7CzxK/ImEyosdUthetA6SMHGFigsZ6qgGsz8IZ9LXiF84tp8/nCqm/u3rqQiP8Vv3FXl2X7JYupMafl4wYSi+rocLg87y3Koae7lmeOX/JSO2jQl9y0I5Su5pTQTu2Mtdd02DjZYFT9BQqyeL9xcyB0VOdR2DrO1KIOb1mYGFfozhQUHJpxFo4z6YhOJQtALgrBFFMUTAIIgXAfIIQHuqI9MQ2OJECg0gxWnM8dfr2wf6ez80d3lmAy+yCBj8MYp0zKGd5ZgCtKUPZzCeTNVUFV/5nB6+M6BJnZV5vDY3RWkmAx89571ijAGqMhL4YdHWpRxy2W30xOM/Ozoedr6Rvn1iQ7l+KFKcKijrQ42WHl4RwlbizIoy0sCmOZDmS3b2+Zw0dY7xrFzfbx+rg+QzEMblqfSaLGRaIyhJCeZkpxkILjpZrZS34FKIBpl1BebSBTCXwK/FAQhEcldYwf+UhCEBOC7CzE4DY1wWeglucPlYV+tmT0b86ZlEXcOjfOrRpdS4TNcc06wWX4ogTEtY1iV8KQWrjMVzpttdSEfy+HySLkMbg8er0h1vYWtRRnKtWwpTKcsJ4nqOgsV+SlSlJSq3HSKycCheivtg+NUn7Uo55J9JVsK06fdI3nc929dqawsUkxSWYhg5rRQ2duTbg9W2wT/770OfvXOJSkSC1iXn8yXq4pp6xul2WonNkCZzib85XsceO9ClRgJdszLuTKY6+8hbIUgiuL7wDpBEFJ8r22qj38X9hk1NBaAhY4wevJAE15RpMFs42u7yvyE8FOvtvL/jp/HNengh398fdDZYLCZ5KO7y4M6GoMRqITUCU+BTs1Q/ojA8wZG8oAvj8G3Wnl4RwmP7Sn3ayxvc7h46tVW6s3DVOalsq0kkxNtg355DwDf/dQ6HvndWXZvyPFbBcgri09cm09pdpKyAjjW3MdLZ8wUZiaSnmAMet2hSDEZ+MSmfIbGnVQ39fDTo+e5NDAOSJ3L/vrmQj6xKZ9libFctyqdZQmxYflkgq1CAr+zwO9lpg53MxHtCc1cfw8RhZ0KgrAbqADi5EQNURSfiOQYGhoLwUyx8Grm8sOTZ82nLw0ptnGYMn88sL2Is6+9RN5QP3D9rCWmZ1MY4YxrtnpIs83+bQ4X+86Yg/Y+DhRygddRXWchO8lEg9mmFNcLXHFsWbWML922lm9VN5KbEo/JoGdLYTq7KqUEvIv9Y7x3cZBnjl+S6jD5QkSbrXaONvcq5w/nnoxNummxjvDU663UNEn76gS4e0Mef72tkNUZiUoYaYpJN2suRbj3OHCbcI4Z6nuO9oRmriaqSMJO/x0pquhW4D+BTwEnIjqbhsYCMVMsvJq5/PDkmP89G/LYWpQxTfCmmAz86Ev3zniM2X6gs41rps9DhTQGuw5ElPh/BXH6dqEcqbLTuLrOQkVeiqIgZxKyg2NOnjzQxNfvLFOK0anbUsJUiOj9W1eydU3GrDZ5ALfHS8/IBL9+t4O9x9sZd0rmofLcZB7asZYtq5cpq6nqs5YZFctsJp25mHwi6T0RrRDh+YwXIlshbBVFcb0gCGdFUXxcEIR/RgpF1dBYUsz045rLzCmw4FywH2ZJSUnIfQKLsAVTWKHGFY7dP/B84F8aQZ0voG7GsmdDnvRaJGS7SflY6vHKQt3vuEGQr/nZdy8p55TJT/VvpKPOt5jtnoiiiN3h5mCDhX9+5ZzSuSzFZOALH13NpzYXkJEYqzSrqWns4fH9DUqrzHASzBYqNHQuKw2ZyxGpFIlCcPj+jguCkAcMALnzHYAgCL8E7gJ6RVGMLLtHQyMIM/245jJzCvwhBvthjo6OApCYmBh0HzWy6WZwzMmz715SspJnWhnIyiCUoFcLPTnyR47YUecLqJuxyP6Cx/c30GC28cD2ommJZvJ45b9qoWmfcHGyXRpHqAJ8NY09bCvJVM4ZzFErrzzU5wr1XU24PJzrGeFfX23lcONUn+Y9G3L54i1FrM5MIDbG32Esr2pmWs0E+86iqSDCbVM6E5cjUikShbBfEIRU4AfAB0gLzf+Mwhj2Av8K/CoKx9L4kBPtWV6wTmHBfpjPPfccMJWHMNOPVxbEct9ftTM2kGCOzWCCvqo8m+Ot/bxYa6YwK1Gpj/TFTMkkI48/UMhuKUynPCeZ6joLTreX6noLx1v7eXBnsZ9ykPdR1wdqMNuUfgxf21UWMkrn4R0lmHxF66rKp6qx7qs1++VKzCQovV6RnpEJ/uvtdv77eDuTvuSy3JQ4vnpHCVXlOSTGThdn8vPwwPaiWctEBH5n0XQKR2N2fzkilSKJMvqW73+fFwRhPxAXEGk0J0RRfEMQhFXzPY6GBkR/WV3T6N9HGIL/MG+66Sa/1zP9eOVy0Q9VFc9YGjlQyMjbyTVw1LVwUkwGNq1Io7reQrPZzqaVafz8tVZFaajHrz7uibZBmqwj7F6XS2FWIvvOWjhQb8UYo6O63jLdzKKqD1SYmchd63MpzExUhLvDZ8dHkPwCwQr0mYx6ntjfSFVZll/J7FCF8Srzk3n23Q7eau3nYv8YAHEGHf/7liL+eMsKliVMNasJRG5g//U7y2ZtYK/+ziIp6R2q1IaayzG7jwazKgRBED45w2eIovhCdIcU9DxfAL4AsGLFioU+ncYVTCQ/vM6h8VlbUIZ7vKKiorDHWNM4VS5aLYAChaFayARG3MghpeqezHs25tFgtrH/rJlLA+N88ZYi7liXQ0Veil+egFppbilMp6osi8LMRO6ozMFk0CtCfUdZtp+ZxebrE/D1O8sAFLPUz19v9euo9uSBJkQRGrqlEF37hMuvQJ/imK63sHVNRsikr1fqrTyxv4G0eCOdQ7LFGtYvT+HRu8rZWJCKYbZqpHNsYK/+jmZbadY09lBdb2F3Ze6s/pSlTjgrhJlqFInAgisEURR/AfwCYPPmzeGX89T40DAXG+1Tr7bOaPKA8H/INpu0WE5JSZlxbKHq3gfzU1TXW9hWlMnxC/0Mjjn5+eutyufB9qsqz6YiP4XzPaOc7BhiaNzJHetypvVHCDRDHay3crDeSnqCkftuWKk4vR/eUaJE/Mjn+v4rzeyuzOWB7UWYjPppqxSbwwUivHdxgBdru6nIS8Fk1E/LjajIT6EiL8U/VNWXnHbd6jT6RyZpttqZdHsVZVCYmcAjt5dwc3HmtGY1oUw2c21gP9NEYKbvcynXKQqHWRWCKIp/Hs6BBEH4U1EUQxfh19BYQOZiKpJDHh/YXhTW/jPZiX/92+doNNv4zle/NGsdm2BKRm0OkpvLf+OuCo5f6OeFD7r55DUEjTIKFO4/PNzCF28pYmjcyYX+MZ56tZWv7Srz2zYwvl7uqxDMRxLoXA50zso+ln1nzGwryeRQvZVms53inESONvficEv+gttKspQVQk1jDz94pYVdlTlsK81kX60Zh9vD2Y5hDjdZ0QkCJy8N0dIjdUaLjdHxv7at4U8/soq0hOnNamYy2QSageYSthnYuyGc7/NyEy3fWTT7IXwZ0BSCRlSI9AGfi422IE0KfQzXXhyqZk5NYw8Ty4qpaWgj6UDTtNBGdUnomRrIyD0MntjfyK7KHL62q4wthekY9TrFrGVzuKQwThH2bPSPTpLPc8c66Z9sDpNnsfvOmKUObAY920ozFaexOiltpvsu1ylSO2drGqf6GjR023j+g26cHi+bV6Ty9V1lfnkPW4syKLg2XuUA78bp9nKwwYrL7cUriogifrWP1uWn8I27y9m4PJUxp4cXPuiedu8cTs+sJptQ318wAu+B+hrVq435+AOWSmZyINFUCHPqMScIwm+AW4AMQRC6gG+Iovh0FMelcQUS6QM+n1lauPbiYOGXSrG5HSV84uYUqust06p8qmPsgaAhrHKrTHU2b0V+CiaD3k/ByGU0gGmF8OTzyGYa9X77zph5Yl8jIiIGvY7THUMcbLBO6yY2k9ILzDt49t1LOJxTzvFtJZkUZiXySp2Fc72jivB0uDw0W+zKCiHFZGDTyjQONlgpzU2mNCeJty8M8G7bgFKaujAzgYd3lrB5VRpvnutnbVbStLHJmdYP7SjmG3dVzCpcZxPkoSrFypFRcu+GaKwIlkpmciDRVAhzsu2LovjZKI5B4yphIaMywrHpByNYcpnsTN2ca+SjK01+ymCmUhWB78mROI/uLldm4XIBO4fLo5R+cDg9PLSjGFPMdLu4/Hpw3MmTB5sYHHeSHm+U3hdBrxOoKsvm+tXLcLg9UshqwDROXmUULDPxD8+fVXIT1H2fT7YPUpGfwneqm/B4vTx2VwX33bASm8NFeryRH392E8da+hgcc7LvjBmTQc+R5h6MMTrFfIUIj9xRQkZiLD9/rZUmi2QeSjDq+ZMbVpKfauLGooxp91EOWe0cGuf0pSG8XpFms12pxhru9xeMmsYeKfqpNMsvzBgI2td6PkT7+Y6W2WrRVwgaGsFYCLtsqBngbCGiwZb2gXb2vXv3Mu70kH3t7YC/mWim0hLyZzaHyy9hTDbxPLyjBET/HryBxenUM/eq8myeeLkBr+il+kw3LT1jOJwe9mzM8xNqsvAOFEjyKkOuNXSxf4wf3LuBR3eXKzP8/WfNnO8ZYVtJJsdaenG4pBaV8n2Vy3nLK5mv7ypjd2Wu4nsQRZF/eqWJtVlJnO4YVmaSO8qzeXhnMWe7bHznQBN6QQABxZSXYjIoIav7z5hpsNjYWJBKTXOvUo11PqYYv+inogy/aK+ZGvFEQjQS1BaSaCqEt6N4LA2NiAk3OUie1YfThD7U0j5QyN9yyy0crLOEVXV0JuwTPqXl8iiOV3VEjzqEVDaZ3FmRw9GW3qkxN/eycXkatV3DeL2A4K945GsO5myVFURZXhJPvNxIo8XOibZBZV85iulUxxDXrkjj/+wuBySF9cVbixQlmBxnUMwsezbksa0kE4CK/GQO1VvxeOGDjmEAVqTHc0txBn9bVUxGYiw5KSZ0goDD5ZlmypNLbzeYbdy1Ps8vu3qm7yscQvlI5EY80ZjNz2V8l7PbWiTF7bKBJ4E8URTvFAShHPiIbO8XRfFvFmiMGhrTCKd5SSCBs/pwmtAHRv8E+1HaHC5ODcRw100byMqThJN9wsVtJVkMjjlnrBMks6/WzLcPNFKZm8y5vlEe2Vmq+BLUZiiAHx5ukZrjiFJdH6fHy8M7SthSmM6hOitVpVl8/ubVfNA+PFWzyEewe6T4MVSmqRNtg/zg3g1KiQx5BfCDwy3cXJTBtSvTONczMuVkFeF0xxBHmnuU8apXJP9zooPfn+zkaHMvvSNS7SGTQc9f3LSKnOQ4/vnIOTYUpPmt2NSrJhk5ke6u9XmKj0StcAN7OkdKoKIPfGbmK5zDNRWF23gn2kSyQtgL/Bfwf3yvzwG/BTQHsMZlJ9iPZLYfW7Afu9omHayOj7xPoPJQO1qferWVl95rZkdZNk98ZqsSmXKwwcrBBismo97PHBRUqAjg9UK9xc4nNuazZ2Meezbm+SmDJ/Y3cnNxhl8I54u1KdQ09XJ94TJOtA3y45pziCIYY3TK7FnNTP4MuSrpnRU5HGnuYXdlrpKfIa+sZCX1yM5SZdVS09gDgrQykWfS8nhPtg9ye2U2/3L0PG4RRRmU5STx089uZG1WEvYJN/HGmGnf20zhuaEEstqBP9vKLBzhHjiG+QrncE2hs/mgFopIFEKGKIq/EwThHwBEUXQLgq+zuIZGlAh3BhbsRyLb3iOJNZeTthp8fXchvNID6rpCB+qtJPWc5YVzDm5ev2YqMsUX3y/7AORjBxMqyixehG2lmdPszLJ9+8XabgRBYNPKNBq6bdR3D0u9gsUpBXe6Y4jqev/aRLLTNVRcflV5Nk/sa0AURVZlJlA+nMz+Osluru53sGdD3rSOZkoznbvKFQWxpTCdnWVZvHC6i9+f6sLta2ickxLHzrJsvrxjLcsSYpXvIZJS5DNtG4nwDLcK6lyPPx8CFd/l8jdEohDGBEFYhi+aSBCEG4B51zLS0FAT7gxstuqg8v6zKRi1SWim4meByka9X0V+Ct1d8cSpzBspJoMS36/ubqY+p/xXLqHxiWvz+e17nbx3cYA3zkvZyW19o0oOwgPbi3B6vJTmJIOIZNtenye99oV07NkolbTetCINh9uj1CbatCJtWqSMukGOyaDncGMPeSkmABqtdipykxWTVSinuPo+nGgb5FhzHz843Mwfb1lBndnOpFtSBHpB4LNbCnjkjlKSVd+D7AtR51XMxGzfZ9iZ5b6EusDyHPM5d7jbhEMk1xFN/0IkCuHvgJeBNYIgvA1kIjXJ0dCIGsFi/dUP+2zvhZrJQ3AFo/7hzWZiCDyWvJ/JoOc3jeM86nOwBmtLGcyh2zk0zvcONDHkcHKovod32gZoHxjHoBf41DXLabbYeeF0NwDfvWc9J9oGOdrcy9Y1GWwpTPcrWf3EfmmVU5Gfojhi92zMU3wNCEy/D3J4jygJ9KzEWC70j9HeN8Y37qqYtd+B+trklcIXbl5NSXYS//FGG75FAWuzEvn5n1xDcXZS0HsaLK9iLp3FIhGOctHCwPIcs+0z22Tlctr7F+J8kVQ7/UAQhG1ACdJ8pEUURde8R6ChoSJYrD9Md4AGvifbq7+2q2xaeedwnYwzZRHPlK9QVZ6NbbCfDRnCjD9QdRIaArz4QRcnO4bZuDwZo15g97pcrLYJSnOTufe6AuwTLhCgMDMRm8Pld37ZRyGvak62D7K/zozT7fUL09yzIU8qDeH08MVbi6QeDO9cUnwUsh/gqVdb6R2Z4IbV6Ty4s1gp9hduhNTNxRlsb83iP99sZ3DMCUBqvIHxSTef2rycui4b2clxfoLar+prgPNYXr0MjjlJTzDO+h2EU3E08DuT/4Y7sw7HXHQ57f0Lcb5Iooz0wC5glW+/nb5qpz+Kykg0NAIIJsxDOUXl+PHApX8kTsZgBebCqVmTYjKg667lrW74xKf/ZNr4ZMVSlpfEbSVZvNc+QE1jDyJww+p0Hru7nCbzSFDhZNTr+Nlr50lPMCqrkmA9GiryUnB6vNQ09Sox9PI1PXlQqj66YXkKZ7psiIg0mG2K8nz+VBcH6q1+kTvqcc8kNEVR5K3Wfv7xD/VcGpQa28fG6PjMlhUUpMQRFxuDw+nh0Zfqg2ZFy6uZab4f3+qi2WLnSHOPn50/mJ+oprGH/XVmKnJTwlL+c7HLh1MXab72/vn01p7vsSAyk9E+YAKoA7wR7KehMSdOtA0qCUGyMAj2AwgWPy4TyQxKdsoOjkuz6G2lmTy8s0RpSj/Tj2rHjh3YZzBzPL6/geKsJM50DROj07FrXS6lOcmYjHryU+Mpz51eJVWOgd9Rmu0XCeVwTSWAAUri1CM7SxVzkmyakpWq0+Pl9XO9VJVlYRl2UF03pTzlchly3SP1+WcyRwyMTvKjI+f4zYkOxTz00aIMHt1TTl2XTcm8VghIXZW/k7K8JP76VydptNiVc6lXL8YYnZ+dP1SEmVx470TbYES5HxC58Fwo01A0jzuXY0WiEJaLorh+9s00NKKD+kc+m9Nvptl7JBEsJqN+qpCZL1z0W9WN0+oGBZKYnsW/hshonYoQMoMosqsyhwd3FPPUq61U11umdUxTryh2VeZQmJXIDw+3cPrSENV1Fkpzkvj8jauVrmNy4tQ1q1J55vglv1LZIIWDVpVl8dXby0CAH7zSwu51U4lWx1r6eOmMmYr8FL9Cd6GUqcfj5fenuvjnw1P9jFPjDXztzlI+vbkAQRDITo6bvm9A72b5u/mH58/y7sVBVqXH++VtqJPh1KWyQ0WYfW1XGRX5KWEp8EAiFZ7zzXcIRTRNQHM5ViQK4aAgCDtFUTwc4bg0NObETDP/hUIdLlpVLiWYBdb2CdZM5/dv1PHSO+e487pSv2Y06uuoyE9Romn2nTHzYq2ZXZU5065NHdJ6sMHKw3lSZvXgmJMXa83UdtmIM+hp7hnhi7cUKbN7ub/DtSvS/MxJ6mY0VeXZfjkRAIjgFUVOXxpS+jvL4w4UjvXdNh7f18D77UOAlO/wmesKKMlO4s7KXKU0deC+MynWB7YXcbF/jDNdNn5cc04xj8n3IjBbeSblH3ie+YQxz0QkpsjFYi7mq0gUwrvAHwRB0AEupAWgKIpickRn1NCIgMsZgy0LD7VQlM02Jy4OcqF/jPM9o9zu6yymDpN0XjrN9kQbm1Z8RMkkDrR1y7X0ARxODx6vl9Lc5GmCSjZdOdweHs4rUc4jdyxrttj5/M2raTKPMDjmlGb3eSmKYG0w2zh9SRJSezbk+SlVpU6Sqr6/3GntQL2VTSvTpiXR7as1M+Rw8vb5fk5eGlJyCrauWcY39pRT323nW9WNxIXZGzqQgrR4/uNzm5Xw00D/UKj9gn1/gWXMw801iPQ5Wyjn8eWOUgokEoXwI+AjQJ0oilrXMo05sVB1WaJx3Jls0/vPmlmTkUC92cbZ7mFi9DoQpurjf/Lu3XwScMUm02C2KQlaoZzUJoOeGL1OCgsNgiygH7vLP5T1r24uVLbJT433JZMBwpRg/ccX6/jD6W5ePmvG4fKQHm9UxiMrKdnRfLpjiE0Fady/VTIVOZxS/SD52p/c38Dzp7txq7yGOclxfPm2tRj0AjkpJuJjY2Y1nwQTuIEtTNXmqpn2C0Ww1YTa7CiXBY/Gs7dQE5XLHaUUSCQKoROo15SBxlxQKo26pgTOYjjjZlIcoWzTcme1+7eu5INLw0qTGTkDWclIFgD6/MJB1cdT252T4wx+yWpq9tWa+UNtN7cUZ3K8tZ/BcSc/fy14+8wjTT18fGOeX70iy9AEXkDwQrPZztGWXsX8JI/H4fRwunOIA3UWDtZb+diGPMk8tWOq8N/f/+40R5v6lHQFQYA/uX4FX729lJdqzTy+v4Fj5/ow6nXUNPdGbD5RtzD97j0zuyfDUfgz+RY2r0pXSoyr72OkE4mFLjQ3m6JZ6PNHohDagNcFQTgITMpvamGnGuEgC+wv3lIUVWecuqZQOBVMZ1IcgT9G+dhm2zjPnepi1Onm2x9f5xeWaTLqGRxz8t3n3kIn6LhzSzm3lWYFLXt9rLlP6RL22N0VIX/4DrcHj0ek3myja2iCPR6vEprZOTTOd/Y1Yh2Z4MtVa9ldmcv9W1f6zf7P9Y6weUUat6/L4Y7KHLYWZfhlYtsnXDSYbXz+o6uljGanhwm31OZyW2km6fFGHvj1KY6d61fGtGpZPHetz+Wvbl5Dksmg1F461GBFrxP4xMb8iGe16hamMxFuCerZAguCFcuL1ESz2CadhT5/JArhou+f0fdPQyNsZGft6UtDc5pNhkL+gcg9AmarYBpsFqmedcnHlJO1DtRbWZuZgMsr8kp9D9evXibt5HMO33PtcqlzWHst+SkmjjQvQycIbF2TMf36BPB4RcVWLx9H3c4yxWTAFKNHEKDHPoleAJfbKyWzIc2qD/n8EI+92MDAmJPRSbfUv9jlUVYKaoUojyO5XCq6d7y1nxdOS60ut67JAFCK4llsE7T1j2G1TwCQFBvD13aVEqvX8UR1IwOjTr62q0w5j7xaCqfsRCByC9PZCFaCOlgXt3BqV81ULC8cAre/nKWpg50/2kSSqfw4gCAIib7XowsyIo2rEjkCRK6iGa0HOlQoYqjjB1sFqDNcHS4PTx5o4rbSLGqaethVmcvnb17N029cpDQv2a9HsBzJsq0kkz/6+B5uKc3kVx8Mk5NqChqVtGdD3pRpSYTvHGjC7fWy4XQK5/vGgKkYfIAhxyTHmvt4/Vwfx8730WC2cf/WldR1D9NkGaEwI4Ee+yQuj8/AL4Ze5cgKrrrewl/dVMj1q9NZtSxBKUz3Vx9dzW9OdHK8bQCAGJ3A/Tes5O93FpMYJzmXT10aUkKA5WgltQN+oQiWVSxPBG4ryVJage7ZmKdc67HmPsVpPtP4IvUFhKp+KrdAnYtiiESpLHSQRSSZypXAM0C673U/8DlRFBsWaGwaVxlzKRcwE8F+SJH+YGoae6ius1Cem0xZXhJPv3ER2U0mVxUtz03hnz+9UTmnLNCryqV6S0+92sqxThe9rhFqu2zou2302yc41TGM0+1la9H0WjnbSjN5r32AA2ct1JltfHLTcmWbFJOB+z6ykudPdXG+b4zd63NBhGpf9dF/u38zJ9oGGRxz8l77AFb7BA9VFSuKJPD61FVZd1fmkpZgpNFqJz/NxN/eWkTbwCjPvtuB0+c53lSQypOfXEdZ7lQAodqXsqUw3a8wXjBncDQJlp0s36vBMafSClR9rS+dMQNMy/GYK7N1zgvmnwiXxTZDqYnEZPQL4O9EUXwNQBCEW4D/C2yN/rA0rkaiPbsJ9UMK9eMN9n5VeTbHW/uprrfw9BsXOdLUQ0VuCsU5iVy/ehnbSjL9fBKBVUy/d6CJ6joLN2SJLE92MVGQQrNlhIzkWD65KZ/S3GRljA6Xhyf2N6DXSdFF6/NTOVRvZW1WEoWZidOur2CZiYwEI3dtyKV7UDLhVNdblGiZO9blcLSph/cuDrImM3FaHaZ9Z8xKiYttJZl+ju6Gbhsv1XbzanMvQ+NSOGtmYiz/sKuUT2zKxz7hnuaLUcfeqwvjBZpvIjHjzPW7VvsF5FpHMnJnOYS5m1YCn5VQz5p6HMCckuIiDa1dKk7lBFkZAIii+LogCAlRH5HGVUu4D3O424XKFg1WAnvfGbPiv1C/X9PYQ2leMgfqLYy53BRnJVFnHuZst42v3FGi+BHkfQLPU11vYfe6XAZOH+Y/j1h58pEvcbpDMq3sqszhxrXLaOsbVVYfggAVucmSWaOlDwGBlp6RaQlZNoeLf3iujgv9Y3x7XxMD404+f+NqynOSGRp38sMjF3l0dzk/uHcDPzl8DqfLy+P7pcV6VXk23zvQJGVGI/Lxjfns2ZCnHLtjYIzOIQfjLi/jLi86AT59XQFf31VGUlzo7nOB97vBbFN6N6gT6QIjmqLxnauFZuC2wUpzq2smzYV9tWaePNik1F8KxxQZblZ7sH0jCa1dKk7lNkEQHkUyGwHchxR5pKERFuE+zOFuFyxb1OZwMTjm5KNFGUoZBLnEslcU/aJh5KSlHaXZ7FqXy4E6KyCyLi+FeoudV+osnOsd9SvzoEYtJAa2ZLKpXjJdlOYms++smf11ZrqHHTRZRwA43GRleWo8Lb12jjX34XB5uL0imxXLTFiHJynLS+LZdy+BKEUadQ87SI83cGtZJqvSE3nv4gDvXhzkXM8In79ptTITL81L5keHz7FrXQ5leUl8/r9OUG+2saMsx68O0J4Nefzrq6384o0LTPjMQ6uWxdM/OsnmlemKMgi8NiVk2OmZWiHAtPBaOZJJHdEUre98tiq40cTmcPHexQHcHhGH2zPt/KG4HDkES8apDPwF8DjwAtKC8U3fexoaYRHuwzxbJJDa3BO4XU1jDz+uOYfbI3K0uVcxJ8i5AupoGHXS0ld2llCam0yz2c6nry/gR6+co948zJ71+UqGq3oM3cPjPPFyI393e7HyXlaWZ6p72J4K3msb4EhjD7vX5/KJa/M5cXGQHruDPRvyQYAfHWnB7RXZkJ/Cub5RjDE6Xqw14/F6ub0iB68oYp9w8avjHTy2p5z1y1PZf9bC4LiL50920d4/xpHGHoqzkgCRTQVpPHP8Eic7htEJUJybSJoplk0r04g16Kj60TE6fBVJlyUY+XLVWiZcHlosI2wpTJ8WbaW+p3ILTTn8NfC7AkiOm95bYj7feTS2nQs1jT3UNPUAIqaY4ImDwbgcWfXhnmOupqVIooyGgL8N+8gaGgGE+zAH2y5YCYJQzka57IMpZqrn754NedgnJJv/A9uLSI6T7MIPbC9SZrP7zkiF4i4NjnOuZ4RbS7Jxur3YJ1yKHfmJ/Y0cb+3nRPsgnUMOHvndWYYnXPSaO7lxbYafwHzrfB8uj5c0k0Ex/6xKjyc90chb5/ooykqi2WpX+ig/sL0Ip9vLgXoL65ensr4gleoz3TT3jHL60hAP7ixm2OHktyc66B4ap9vmYEN+Kud6RySz0MY8tpVmcr5nhHqLnfa+cX7R1MaK9AQazFIlUZ0Af7xlJY/cUcyRxl6+d7BZ6cEsN9cByRYum0xkZ3WgcJlpxq72K0T6bETDTj6fY8jPkByldCUyV9NSJFFGR4A/EkVx2Pc6DfgfURRvj2ikGhohmC2LWClBkJ/iVyso0Nmoth+rhdXJ9kElM3bzqnS//AUARPB4vdR3D/OJTctxur28cLobY4yO796zXnJAX+jn5dpuvKIkXHdvyCU32cQbB36PaEnjgS98HoD/+0YbrzRIPQ9qmnppHxwnK9GIdcTBfxxrw+0VMejg7g35bFqRpuQiPLizWIlKqmnsoW1gnMrcFA7UW9i0Mo3CjERcXthQkEaDxU5GkpGM5AxGJ9zYJ1wUpMXz9J9v4ZV6K3Xdw4w7vYoy2FSQytfuLOVsl43HX27k09cXKKWxZWe1rND2+aJ0AktWB/u+thSm8/COEr+ifmq/QnW9JaK+xbMJs2A+omD9EeZqVgp8hq5E5rqKisRklCErA5BWDIIgZEV0Ng2NGZgplNGvBIHT49d5TF3MLBD1D0OercorBPXnwFTYpm9maJ9wYYzRKeGW9gkXl/rH0QlwW2k2hhgdcQYdpzuGeNu1mhvXlijC6VTHECKQHm/gGx8r53B9D/dvXcnb5wc41TGEy+0h3hjDgzuLSY4z8MTLDeyvM3PXujwe3FnMvjNmhsac7CjNZlVGAo1Wu1/hNzmvQMp8FvGIIi6vl7R4I5X5yfzoyHkGfJ3LAK4pSOWXf34dR5t6+f6hJlxeOHauF6dH5Evb15IYG8O20kxl5bStJFPyZbg8PPFyg58zXkadFGgy6nl8fwMN3VLjHfU4gbD7Fgd+Z+F8HqoGlcPlUaJ+5O3USuNyJ5VdTuZqvopEIXgFQVghimIHgCAIK5kKPtPQmD9iwN8QbCvNxGTUK3WR5GJm4eQlbF4l1REKlsAlz4rlZKYUk8Evk/Ynh89xsmOIzStSuak4kycPNPFKQw96HXz8urVs31ioJLl9pDAdnQBjTheDoy6+e896bA4XzdaLHGvpo6osi6PNvawvSKWtd5TqegtujyQ4EWDfGQsiIga9jq/vKuNbH6ucFlXztV1lFGYm8tKZbprNds52DWO2TcL7U/cqIVbP2KSHs93D7D9jYc/GPO5oyaW6zkL/mIs1GQkgoJSFqMhLUZrvmIx6JXnu7g1508qCBArmwN4V6nGq+xTAdOGsZjZhpg71fP5Ul1K2RK1AAqN+YHpP6aUU/79UiEQh/B/gLUEQjiEtJD8KfGFBRqXxoUTukhWqLEBgmYrA2jThmhrUWaXy+3KGMuBXS1+eqbf3j5GTGodRr2NbqTR7/t/b1nCud4TirCRcw1Z+f/QU1Q3j7K7MJT3BiEAPtxZLKxPJhGSlrmsIEQFEEEWUSKZbijOx2ia4rTyLCacXt8dLaW4Sn7hmuV+2raK4fCUv2vpGudQ/Rl6qiY4hh3KtyXExbChI4WT7MHpBQBBQzD/Xr15GcU4ix5r7ONc7iilGz+7KXMUcJ9eEOtYsKa6aph42FaRxrKXPbwUXaPevyEuhIj9l2sw+HOE8FwKfh0AUf5LLw7aSzJAO8VArkat5BRGKSJzKhwRBuAa4wffWg6Io9s+0j4ZGJIQqCwCS4Aj8AQc6lQPNBPKPWC6zvLMym9LsJCWOX0aODPr6rjI/s4wcrjrp8uARYePyFB7bUw4i/PBICzesXsaRph4uZI9y+uhL3FGZzTfu+WOqyrN5Yl8DHhES4mI40TbI919pxuURMeoFPnfDSv70ptUkxsWw72w3pdnJWO0TnOmyUZabjNPjxQu09o6RHm/EPuHiiX0NSstNWXHJmc4mo95PGQCMT7q5vTyXm4uzONs5zPrlqWwryVRWMI/dVc7Tf75F6cx2umOIv9lepCif50918cMjLTy8o4RbSrIkv0Ktz68gMi0iSX3cUHkEDqeHwTHnrGa+UAQK6LByA4w+RRQkYzlwpREo+D+MK4hIVggAscCgb79yQRAQRfGN6A9LQ2P6DC5U9JH6R6ueicrKQi7mduLiIG39Y+SnmYLOFtWzcNkU8fVdZbx1vo8jjb00We00dEv1hG4ryeK9iwN4vCKNlhGMa67HsDpbOc6DO4ox6nWKv+K2hiwONfbg9IjUNPVSlpfC/VtXcrF/jNMdQ3hEkWtXSH0Jjjb1UJmfTHq8kdfP9fJSbTdvnO8nRujmq3eWcVtpFpNODycvDuIRYXTSgwBcuyKN2q4hvCLcuS5Xqe3zZms/t5RkKT2qA2tJPf3mRV443c1nYnRBQ3rl97aVZtJgtnHNqlS/+k9A0OMG0mC28WKtGUGAb32sMuJy03KUl9pBHY3cgFCC/3LkFSw1Ioky+ifg00ADILfLEAFNISxBltpydy7jmW0GB/5mAZvDpfx41Y3bv7R9LZ8EclJjsQ5P8uCOYr82mIFCRXZuf31XGffdsJI9G/K4yZfpXF1n8bV7HMbrlWb8To+IEJtAzYVR/vGFOr79yXXTKnneVJzJay29xOh1rFgWz+P7GijPTabRYmddfioNFjsfvyafJvMIT73WhtPtwSuiOKYBkk0GvF6Rg/VWpWsZSNFOX7q1iL/4aCG/e7+TZqudB3cUh5xFy6G4DqeHHx5p4Yu3FPGZ63TTylA7nB72nTErqwY5ERBQ6j/JvR3kc4Ry2MpZ3bsqcyjNSw67xINaCTywXWoXWl1vCeqgnmttq1CC/3LkFSw1IlkhfBwoEUVxcrYNNRafpbbcnc94Zos+CjQLqBu337A6nXuvK/CzNwfriSy3ikSAoTEnbs9UyWm5ftGeDXkYY3RU11nYVZkrje1sNwLgGjYjinCgHtYXpEq2clUi3J4NeTR023jhgy7ePN9PRX4y9eZhSrNTuL4wnZUZ8WwrkaJ8Xm/pZf9ZC4IAG5an8OWqtXx7XxPm4XF+efyiogwMeoGq0mw+UrSMW0uz2HfGTLPFTk1zb9Dy2ykmA1sK0/nK787QaLHzyB2lykopUJA7XJ6pqq4+P83guJPbSrKU7mrV9RZOtA0q99zmcCmZ1gj4dV5zuDw8srNUWbUEruJCTRTkcGNZCczUY3uuz9iHUfCHItIGOQZUzXE0li5Lbbk7r/EEKaSmFiCBNXZsDheFWYncsymfB3dOzZQdLsmG/ey7l5RZr7osw7cPNOL1wu2V2VJ7S6N+WuE2OZFNrkW0KiOBcz0jnHn1BE0WO/HL8jnbPcwr9T2IiFILywSjUnDtjdY+uocmGBp3cnu55Mg93TmEXqdj04o0RenE6EAQdOxen0fHgIO12Ylc6B/DYZd+fpmJRkYm3Bw730dVeTYn2gZ58kAToijVLgrlaH/q1VZFUW4ryeRYc5/fKkDJSt5RwtfvLFMKxNU09vDjI+cA2LQyLagDWfa5AHz9zjJF2dQ0+re2VD8LgePrHBrnJ4fPUZqbzL3XFfiFGwdGWUX1GVsgltpKfTYiUQjjQK0gCEfx75imZS8vQZbSrCfQATlTR7NgqKOPgkUKBdY0qmns4eevtU5bDTR02/hDbTc6QVBWE+qyDLeszeRIUw8r0uIVB/O+M2Z+eLiF20qyqK63UJqdyO4N+Rxr7uP7h5spzkyk3mLni5/6FCaDHtEQx9nOYbxeL14RznYO8+aFfqVu/0fWLMMyPEH/yCSrMhLIToqlfXAcj8dLi9WGyaCnpqmXOypzEUWRl053U+9LLAPIS4njlpJMfn+yk9vKc7hpTYYSEfTQjmJMMfpp5TnkvzaHi8LMRPZsyGV9firHmvv8VgGBjnv197OlMJ07K3MozUlWnOrq3sXyPqFKhKj/qp/NwM+eerWV5093YzxrVsqOzKVXwEyC+HIK6aW2Up+NSBTCy75/GlcIS2V2ov5RyOGdoeroz2YHlgWHuv584ApBHW3UOTTOibZBHE6P0g9AtmF3Do0r5aH3bMjjrfP9eES40DdGYWaiMlN+dHc5ZpsDp9tLbZedevMIX72zlN2VufzhdBduL8QlJPCXHy3k2XcucaSph7w0E70jkyDAF28p4mzXMKIosnF5KvFGPXjhXM8IlwbHiRHALcLBuh4evr0Mh9PDKw0W3mwdUO5LQqyeW4uzONrcQ6PZjssL/fZJtpVmKhVZH7urfCoaSNUcRjbnyG0od5Rm8+OaczxUVey3CgiF3POhpkkyRW0pTKfBbJtWlkJdGjzw/XDbXsrlO0pzk4OuIMJlpv0iOeZ8f0NLcdUyE5GEnf73Qg5EI/osldmJWmAfa+6T3gyRfBZszOrYe7ltpToHoaaxx2+FoI57bzDbpObxO0sUgSk7KvefNdNkHVFCJQ06KVC/0Wzjb6uKeHhHCQjS+H9y+BwGvbSyGJv0cLZzmEfuLGV00s2hBivWS220ZLlAiMMrivTaJ6nMS6GmqQeX28vRll5uK8mifWCMmsYeBEHgoSopEsmgF6hp6uUH966nyWLjnw41MzLpVu5JjE5gZ1k25fkpHG3uISfVxEagzjzMTw6f43CTlYrcFMVZHGj7l30C++ssLIs3MjzhQhSh2WrnsT0Vyn0PJoBtDinktfqsmd3r84Le73AJR7gWpMUrzYhg7gJVnSkduCKN5Jjz/Q0tpZV6OMyqEARB+J0oivcKglBHkJ+xKIqzN0XVWBSWyuxEbdIJTD4LJFiPA7VtWk4ak23R6iYwwZKOthSmU5Gf4pdf4HB6qCrL4nCjlVuKszje2s+WwnQe2VVKo8VOW/8Yzxy/xOZV6Ty+v4H9Z8zUm4fJTTbRNTyOxwtHGntIjI1h/fJUjjb30tlcy9HxSySvr+Irt5eCIJmLGiw2rCMTuDwi1pEJWs6PUlWezfWrlimJZdX1Fh7dXY51eJJ/fKkej89pHKMTcHtFVqbHc7Sll2tXprOrMlcyXeUkoRN0lOYlKyWu5baRD1UVTzOxPbyjhIrcZN69OEj3sINrV6ZxpKlH6aksC71AE9P3DjRRfdaCV4TSnGTFnxIYthsOcxGu4USazbRfsMJ7kQjppfIbulyEs0L4su/vXQs5EI3os1RmJ2rhvK/WjMPlH86oJliPA7VtOpgTU3akqitTqhWG3BjHZNQrVTwfqipm65oMJUdBLmC39/Nb+Mnhc+SlmRgcc7J1TQZHm3qoyEumyWzH4wUdsDY7kd+f7KQ0L5mHqoq5q+IjPP9BF0/sa+Qrd5SQHm/kjfP97KrMZVVGAmsyE1m1LIFzPaOsz5eikI619HGg3sqG/FSePNCkdC6T0Qlww+p0Hru7nCbziHTtAuw7a6HJYmdTQRp3VOaQHGeQajypSnnIQnRwzMltpVlsK81kW2mm5LDNS+aOyhylu9mx5j5FoQb2HThQb2X3+lw2FaQBzJgZHO5zMBfhOteZ+nwF+kL/hpaKWVdmVoUgiqLF9/fSTNsJgvCOKIofidbANK4e1LO17xxowun2YtALSiG0QMckTF/q33fDSjqHxv3KVztcHh6qKqbZYveroaOOHHryoH/kjVyvSF5plOUl0T3s4BPX5vP8qS7ldXW9Ba9XpDw3GZdHJCc5jhhBR53ZRm5yHFlJcdSJduq77cQIcLZ7mAs9ozg9Xs52DPPtT64D8BPSWwrTMQ9LGcWP729gW1EmxdmJvNnqn/BvihFwuEWyk+L4wb0bKEiLJz9VcpZfszKVPetzcXq8vHG+Xwn7lH0HsmCXZ/cv1nZLvaFXpGEy6Hns7grlfhdcG8/zp7r4weEWdlXmKApVHVklm9kAv+MHEq4Td67Cda6CfalMikKxVMy6MpFmKs9EXBSPpXEVUlWezestvRyst7A2O5kXa81U5Kf49SgOZcsGqbjc86e7cbq9bFqRxpMHm/j6nWU8uLMYY4xOMTPtqzXznQNNbC1Kpzg7kdtKs/nc1lVKPoBs+LQ5XDSZR2juGeEPp7o52GClNDuJdy8OEm/U4fSIbF6VxprMRJweLy09dlLiYmgfHMcteinPTUYnQIPZzvu1UvvKuMwCinOTFIF6qM4qdW8bd/Lfb13kf97v5C9uXElpdhL76yyKDdZk0PHlqrUcqrdyttMGgNXu4KlXW7l/60qeeLmRBouNnWVSe8qHqoq5pSSLLYXpPPvOJU53DnGkqYdv3FWhlJ44UG+lqiwbY4xOSkJT5QWoy32o4/zVkVfqsFE5iS0wskgmWk7cwOcgkgSzK5GlZpKKpkLQKp9qzEiKycD1q5dxtLmXVenxXOgb9Xtq1IIj2A+lNC8Z41kLTo9XMiEBCJKZSS6j8MD2It5rH8Dp9vBaUx9eoCA13s8pbTLqeWJ/Iw3dNh7YXsSju8spy0sCYGdlNm19o/SOSqWje+yTJMbGcLjRyrKEWCx2BxkJRrqGJjDqJ6XOaBcG+O2ZGhDguk2VNHTZ+NdXz7MuL5UGiw2PV+reFm/QIwL//U6HX6ZxXkosY04PekFHn20Sr3RZbFieRnWdhbda++kccrBxeQqluckcbLAqK5znT3Up+RN71uf6rbB2VeZQmJnIz19vZdOKND8Br77Pck6BvCpT+whm6pamJvD7Ugv1SIXeUps1R5uZekLPZf9oEk2FoKGhEOqhlQXylsJ0binNCuoIDvVDuXdzgVIq+vrCZUpJaJBKL1fXWTjfM8KZbhuCAMU5SbRYRrjQP8qhBouSbLalMJ2ynCRePtPFxf4xfnDvBg7VWXn+gy5GJ9yMu9ysSDOxNjsRkMo0VOSlcLpzELdH4OObcqntsHP9GmlFcv+NK2nv3Y1HhGaLnQazHa9X5Ez3sNQ3QS9QnJNEbecwR5t6FWWQlRRL78gkXi98ZM0yquvMdNsnAEiLN/DIrhL+cKqb35+SmvosS4zFZNDz9V1linmnqjyb4639HKi3smlFmpJs99SrrVTXW3hk5/RsZLXjvqaxR5n5H2vu49sHGtlRls31q5f5bbutJDNohrfMbIUJL6fdf6kzX4W3kAozmgphht5KGh82Znto1f13ZcKpgx+YtSqHpBZmJrKtOJND9RYQBLYVZ3G+dwQBaLaOoNcJ7PIVX6tp7KHBbENAx7sXB/nO/kaarSM4PSIu0UtlXiqNFjtZSXFKzZ6/u72Y7x9opt5ip2t4grPdNnLT4vj1ex0UZSRS1zOJToC1WYnoBQEEaOsfI96g41BjD6+19DHqCyVNio3B4/WSlxpH3+gk1pFJXqnvwagX2Lg8hcFxJx2DDv5wqpuv7SpTQlvjDXqlAqla2T64sxiAIYeUhe1wenix1kxJdiIOt8cvSQymHPcVeSl+lUd/d7ITt0fkUIOVoz4nPBAyxFSuIvvA9qJpymKmsE+ZUJOGq9U8JFNV7l9/K9JZ/kIqzGgqhPujeCyNK5xQD2047Q9lggkcdRiibDs/UGdFEOC20iwQBO6oyMEyPEHHoINEo45Rp5fynCScHi+/e7+TO9blUJGXwrsXBxGAjsFx2gfGyUg0siYjkf9sbmNdXqpSs2f/WTP/8FwdvSMTfGJjPqMTbpweL50D43z+plW8dNqMs68dgHPiKrmUD8XZibzXPsi4ryaSQS/wwC1FfGxTHo/8/ix1XUNU5qXQ0G1DrxNYl5/Kjz+7kZEJF0+83Mj9W6WeA4/cWUpibAyfuDYfY4yOt1r7eb2ll9fP9XL9qmUgSELb7fESo9dxZ0UOHq+XBotd6ndg0Cs5CXI9ozsrcnC4PPzstfPsrpQqo5pi9MToBWWFIPtAbivJoiwvaZpgf+rVVqUlqbqQn/p7Chb2GepZ+LAwW1nucPZfqPsVTh7CCDP4B0RRTPb9rY/iuDSucEI9tOosYnl2HypzOVDgBDqd5QiiqrJsjHodqzITONrcy/WFy7hmZSpPvNzIX99SyOH6HobGXew7a+GQz/5+W1k2E24PjWYbw75Wk7ZxFxf6RnF7oN5s4+3zAzg9XtZkJlLbZeOG1VJxtZYeO++0DdBsHcEtirT2jeG0tgKwqqiUZQlG6s12WnpGlWsRAI9XpK1vlKNNvZztsuH1gl6Ah3YU094/xuEmK98/0IzFNkFLr50P2odpMo8wOO7kpTNmRp1uXqnvwSt6EUU4VG/llXorX7m9lK/fWYbD5WHC7eacdZQvbV8LwLneEd67MMAbrf1KuQ+H06Mk68mNceQcEXmw20qkENWXznSjE3QAHG3xb6MpV0cNrJIa+H2r/87Ht3C5WUhb/VK99nDCTpMABEH4FmABnkF6vv8EyJ3vAARBuAP4KaAH/lMUxe/N95gaS5dp3bMCCtepyy4ECpxAZ6jD6cHh9tBslip8/k1eER/bkKdUDf2jzQVcsyKdW0uy+dnR87zSYCU7MRbLsIP/Ot7OQ1XFxAgCJzuGAXB7RS4OjKHTwS2lWbx8xkxdt42S7EQ2r0zjsbulCJs/nOqmf8yJAHh9/oCE8ltIitWzJjOBd9oG/WZQCUYd406pttGheovULQ2RvNQ4znTZWJOZyKaVUpz/y2fNuDwim1ekSZFDjT0UZyUiioAXBMFLbpKJuzfl0jHgoKapB5NBrzSF//vf1rLvrIV7NunYWpTBvx27gFcU+cTGfBCY5iDesyHPzwQnz1wbum0cqLciiiAKIqW5yWwtyvATYIElvtWECjWdj29hPsxFuC/kCmapmsUiMRndLYriBtXrfxME4Qzw2FxPLgiCHvg5sAPoAt4XBOFlURQb53pMjegT7ZlS4OxInVUrz/rlHIWv7SqjprEHClEyksvykvw6iB1p7mFHWbaiGOQkrScPNGG2OWjvG2PY18u3yzbB2xf6cXm8nO0aZltpJqc6hhGRdNP5nlEEwDrsoL5bCv9s6RnFZNDx9BsXKc1LJiFWT3q8gdEJF3euy8UYo6OuywZ6vV/9IZkxp5f81DjSE4zkp5iksNTmXtbmJJGVZGJVRgI/PNzCwztLKM1NptlipzQ3mZ+9ep70eCP1Zhs7ynJYX5CKxTbByY4hTrUP84N7N0jOXwGlZtOqzASMekGpBaQuNgf4mY5kgjWF2VKYTmFWotJtTa48Gi5LrenMXIT7Up3FLySRKIQxQRD+BPgfpN/OZ4GxeZ5/C9AqimIbgCAI/wN8DNAUwhIi2jOlwNmRunCdw+nhvfYBXqw1U5iVSFvvKAfqreyqlOLvH91dzjPHL/H8B90Y9Wa+crtUZK4wM1Gxh5flJfHYHxrweDw8f7KL9sFx9IKAXgfLU+O5sSiDRvMIhxutXOxPIjclDqttgrVZiYxMurHYJmg020iLNzA47qI8N4l4YwzV9Rb2nbXg8ngRkbKIP3XtcgZGnZzpHGZ0UvIVJMfFsDYzgQ86bazJTCAxNobbyrP5t9cvSKWyu4eJi9FxrFkKi71+zVRIaPfwOEebevj09QXsXpfLH2q78IqSk/rHNef4862rGBp30mC2caJt0G9Gf7DBysM7SvjuJ9f7JfSpma2pjPq7SY83Kt3WQimDwPLg8nFCCdO5lqKYiXAmLHMR7kt1Fr+Q6CLY9o+Be4Ee378/8r03H/KBTtXrLt97IRkYGKC2thYAj8fD3r17OXv2LAAul4u9e/dSXy+5MyYmJti7dy9NTVIdnPHxcfbu3UtLi5SgMzo6yt69e2ltley/NpuNvXv30tbWBsDQ0BB79+6lvb0dgP7+fvbu3UtnpzTk3t5e9u7dS3d3NwBWq5W9e/ditUox8d3d3ezdu5feXsn22tnZyd69e+nvlzJT29vb2bt3L0NDQwC0tbWxd+9ebDZpZtra2srevXsZHZVs0S0tLezdu5fx8XEAmpqa2Lt3LxMTUqhifX09e/fuxeWSZsNnz55l7969eDySoKqtrWXv3r3KvTx16hS/+tWvlNfvv/8+zz77rPL63Xff5Te/+Q1V5dk8urucJHsbv/3tb5XP33rrLZ577jnl9bFjx3jhhReU16+99hovvvii8rqmpoZ9+/Yprw8fPkx1dbUiHGoOv0KGrYnrVy1DEODA/mr+58X9VJVmUZiZyHZTJzHWeh7YXsQ91+RTFd9B8+l3eLHWzNmuYW6NbeempD6eOX6JUx1DxFx6n7aGU+SnxnFT0TISu0/QWn+KCz2j/NXNq5lseZNT75/AbJugIj+F/jM1XGw+i06A9QVp9Jx6hQRbO7eWZHG+d5RV/e+zPn4InQAlWSba609w549fZ+/xdkQEPGPD5Ah2tpVkUtfZz2hdDRdaW7j/I6v41IYsrnefRW+3YLFNMmCzM1pfw+SgmfcuDFFVlMQffvtrHnn6CO9eHOR7L5ykYOB9/rQyke1lmZxr72ao9gjn29rpH3Ny68pYek69QnmKm0d3l/OpikRu8tazJUfHPdcuZ3RQejZbL3Xz/KkuGs63BX32Xny3hW9VN/LrwyemPXs9p17h4VtXUlWeHfLZO3D6Et+qbuTxvQd56Ns/4dDZLmwOF//50uv0nHqFRKMkYt58933+5ps/wuZbqf3H80d46Ds/ZV+tmedPdfHam+8EffZkjh8/HvLZq2ns4as//x++/a9Tz3bgs/f+28cwmk8rCkN+9mQOHTrEoUOHlNfV1dUcPnxYeb1v3z5qamqU1y+++CKvvfaa8vqFF17g2LFjyuvnnnuOt956S3n929/+luPHjyuvf/Ob3/Duu+8qr5999lnef/995fWvfvUrTp06pbzeu3fvvOVeOERS7bQdafZ+2REE4QvAFwCWLVu2GEP4UCPPlI4f72BoHscZm3Tzbms/G33mDd2kG6NuagWyM2kAY4yOv77vZkxGPSOt41SucFCyMo3vv9JM8cTUgtSo17EqN4k32sdxe1I50milfHKMcaebwqxEPrkpn159K69emqR7eALz8ARjwxPokpI42tJL/+gkY5MeYnz59eOTLrqGHOjS4Ma1GaTEGshKNmF1uLnQN8auyhwyM7JxJKZy5pxIa58DT0IRTEjdZL0eN462k3TkFNFzxkJxRiJ2HazNTGRLYTopsYAIdoebGF+EZnpCLHk5STyyq4Tfvt/BydZ+Pr9zHb9tcvLlm/Noec+CEG/gv97twzEywoo0E5//aCG3O+PZkCHw1muXSDYZuKcoH6tVcgwn+0Jxq8+aGR2b5F+OnuP1Tidf3JxGbJDv5JaSLPRxSfSaO8AXDSUTb9Rza2kWNY09rIxxBdkbtpdlY4yNI2XCQpztEreVSaa/X7xxgR1Z48p277UN8vIZM7c29nDPtcu5fvUyOjdM+TU+u8pJfiTTUxVV5dl87oaVrE7ScmPniyCKM99EQRB+xsxRRnNukCMIwkeAb4qieLvv9T/4jvndUPts3rxZPHny5FxPqbGIyCGId1ZMmX/8ag8FFGeTkevy7D9rZmVGAuYhB4PjLjavSKXeYifdZECn12Ebd3LX+jzFxJSXFsdPjrTi8T3jKXF6VixL5Oa1GTT3jPBac68U/SNKjWf6RidxeyRnb/fwBCmmGGwON+W5SXQNO/iHO8potNp45p2OkNeoQ2o4npMcy/LUeM71jvCNPRVUlWfzp0+/R22XNANPjtNjn/BQmZ/M8hQTR5p60OvgnmsKlPpOcvewnNQ4rMMTPLhzqhd0MDNJ4H28syJHuReBNaOCba/OTpaPK39nwT4LZaqZaWzhbKuxMAiCcEoUxc0zbRPOCmEhpe/7wFpBEFYD3cBnmL8ZSmOJIttv5TIRgQ1WtpVkKk5PNSkmAw9sL+LExUHqu6XuYRkJBlISjEy6vJhdUgO/VenxpCcYWZuZSHWdhR1l2cToIV6vY2TSy+ikh9beURKMemo7hxFFSXgDFGUmsCzBSJ3ZjmV4gowEI/2+cNRz1hHSEow8ebCJkQk3wXD2SGZGY3YhAFb7JINjk9xzTQFbCtN54uUG6s02dAKIItgnpNl4g9lOo9mOV4Rko4HnPuiiMDORv7q5kKdebeWF09185roCHtxZ7JeTsa/WzJMHm3A4PUqEkbrUtdyU52+2F3Hv5uAOYfX2wcpaBHZQC/wsEt9SKHv85bbTawpoZsIJO/VrjCMIQqLv/dHge4SPKIpuQRD+BngFKez0l6IoNsz3uBqXD7VTUa7HH6ysNUz9+J999xIvnZkqbCcLt4eqiklPMAY9x1OvttI7MkF5bjKjEy72bMzjl2+1oxOkGX6CUY/F7uAXb7YhiiJ5KXHkpMZSlpOszMqzk+PISoqlwWzjxrUZnOkYwuZw4fLC2xcGuHHtMnRAaW4SGYmxvHm+n/REA6MTHvp8tY3UCECSb6bv7G0jxRTD//pMFS+dNtM1PEFOsokHthdxrKWPl8+aERBYvzyZ+m4bHi/kpcbRPzrJ2qxEYvQ66jpteIC9xy+CgJIY98D2oulJYD7F8t7FAeWeK2UmfF3U9p+1kBgbE5ajdS7tLtV/ZytMGA2iIcyjUWjvaiZsH4IgCJVIOQjp0kuhD/jcfAW4KIoHgAPzOYbG4iH/wO6syOElubR0iOxLdVlqYMoQ6St6crZrmJqmXo639vPgzmKONfcx5JjkWHMfzT129qzPpzAzkR/XnCPVZOT/7JbaTf7hdBeNlhFykmJJTzTSZBmhY2iCf3+9jdUZCcr5+0Yn6R+dIDZGT9fgOP1jkl1ch6RUzllH0QlSqQvEEURgYDSgRwFTq4r81Di6hiWHfuK6Km4pzeILNxfRMeDAYrPQN+rkRNsgiFICWmV+Kk98ooJ/e+0ChxqsfHxjPqcuDVFvHub2ilyWxRs52tJH9/AE36tuYlmikX+97xoK0uKn5WTs2ZBHQ7eNF2u7pTITBj0Ol0dRtPJ2929dqRSsU0cBqbvQqYmk3WVg74SZChNGg9mE+UJEG33YsqkjCTv9BfB3oii+BiAIwi3A/wW2Rn9YGlcK6rh1uTZOVXl20LIT6sqZ6sJ0ezbkYTLoGRxzcqjBystnpBLXrzRa8YhShu+ajARltg1S7sJ9N6zE5nBJM2rAOjLJwLhTkXEeEVr7phzRN6/N5HhrPyOTHsw2BznJsVjtk4qAt9onSU8wMDgW3IEKkjJIMOpxeryU5CSTkRRLra9cddfwOE+83MCxc318fGM+m1amUbDMxPcPtnBLSRbHLwzwzPFLFOck8kqDQMfQOI0WO9lJJo409rC1KAO9ryhfj32C3lEn//BcHUcfviXoWCryUijMTFQK9n3/QDMujxeH06MkjQX6bWDK3BPYhW4+zFaYMBrMJszDEd6Rju3DlosQiUJIkJUBgCiKrwuCkDDTDhofHpLjDIotG+B7B5qm1blRF/VSz0zluPXfvd9JRV4yLdZR0IFeJ/DRNRlc6B2lZ8Sh9GOWq33aHC6eeLmBHvskAnDz2gxMsXpeqe8JGgXRZLEz4ZbE/8iEJDStdsn/YNCB20tQZZBi0mNzSKuaBKOeu9bn8LuT3bze3EtKgoHMRANdrU3UWeFiXxEFaQl8/ubV5KfG87F/fYv2gXFKshOJi9Gx76yFW0syERFxubxsK87kSKOVHeU5dA6O4xEhVq9j97ocDtb18Hc71/L8qS6ls5t8P2Uz29fvlO7F9w408UqjBbcX3r3Yj8moZ8+GPD+FLWckK99FkC50c2U+SiBcs8xs51gI4X25fBxLxTQViUJoEwThUSSzEcB9QFv0h6RxJRFqVqY2caj9DA1mycwhAg1mqR/BibZBHE4PP645hyiKUtvJzHiMOqln8MlLQ9xenqs0gfnSrWt54uUGxlwejtRblRm+KIg0dtuJ0UFJbjKDI5OY7ZOYYnQ43F7Mtgm/sTdaRpT/d3n9PkIAKvKSWb0sgVcaLMr7k24Pb18YwFdJQlEg4sAlXCIkFpZxoX+Mf3vtAka9jq4hKfSyc8jBuNPDmowE1i9P5ZUGK8fO97FrXS6CoMMyPEFr3yhZiUaaLHYaLSPE6AXeOj/AgXorNxdncNc6KQGvc2ic9y4O4HJLyrWmsYfqOgvJcZIj/PXmPt65MOjnoFdXl5W/j1C+nstNtMwyV3Ii2VIxTUWiEP4CeByQM4/e9L2n8SHF5nAFbXAP/nVu1GaLA/VWdq3LBRGlqY1caO3rd5Yp/gTZnLFpRRqP3VWOw+nhB4db2FGaTXWdmdpOqeeBXFkU4I1zUtmIVenxlGQl8Ur/KKvS47k0OK4eGgL+7ovA1URSnB6n20tyXAz337iSM13DdAw5iDfocLq9dA9JikW9X1zFbVIPhZxE3jw3wLjTw4EWC4IABakmOoelyqv/uKeM7sEJvnJHKaYYPdesSqV7yEG9eVgpu33X+jxK85JBhAm3h7WZiRxr6WP3ulx+/norzRY7hxqsyD12qsqlrmf7znazeUUa20oySUswhnTwRlJx9nLwYTPLBGOp3INZ8xCWGloewtJhphh1NXLROodLqrZ5zcpUpS6Qutm77PS0T7j4yZFzlOYkc8c66fOCZSaerG5ifNLD+d7RoCYhAVieauLuTVIEUlVZFiA1pVdTmZfM0Ngk3bZJv/eNeoH0BKNiRgKpUc3Q+JQZKT1eyk0ozkkiL8WE2+PlfM8IvaOTlOYmc946QlKcgYq8ZI6d72ddfjIer0iTRerNkJ5gZMLl5vaKXDatSGNo3Mm/vHqeO8pz+N/b1/DM8UuK3+X5U108+lK9VNG1NBsEKM5OosFi43BDDwKwqSCNH392I8lxhqCx/+pOcaFyAp599xJPHmji67vKppW6uNwstnK6molWHoJ8sGLgYWCVej9RFLfPdYAaVzYzxagD00xF1XUWdq/L5b0LA+yrs3AP8FcfLSS53MD3DjRRXW9hcMzJ0aYeGi12Nq1I46lXWzlQb6UsJ0lx3sL0mX1eShz9o04+d+MqJpwenG4PdV02Lg2Oo/eFpsr7NVrsqDpYoteBxwtOj4jVPklKnAHbhKQE1MrAqBewOdyIQGvvKKMON902B14RhN7znDaLGHKKmRxz0js6yQ2r00lNMHCoXhLeXqB/zElGgoED9Vb2nTVTnpeMyyOCAE+/eZEDdVYlHFddKlxeFa1aZqPHPoleJ5CfYuJUxxBPvdrKd+9Z72dq6Bwa5yu/O0ODxcZXby+bWciKAX8XkaViOvmwEonJ6PfAvwP/CXhm2VbjCiWSGdpMMergH5JaXS91Hquut7CtOBODTmDM5ebZdy6BIJmP1OagzSvTGBpz8typTu6ozOV/37qG//XMKToGHQDEGgScLkmCiUBSXAx9I5O8dLqbCbcHjwjtPlORRyXoDHoBp8df8nlU/oONBSmsy0/mWEs/y9NNvHNBKmVtMuhw+BwNy9PisAxP0jnsUPYb6etCBxhypO5lSUY9tV3D3LI2kzsqc8Ar4nB5eOvCAJ/esoKOgXEO1Vu4sWgZazIScXq9VJ+14BFFnjvZicPl4caiZZzuGMLp8tLsW2FYhh3ctSGP0pxkeuwOahp7+cS108t/PfVqK+9dHORauYx2Uw8QPKFMbmu62OYKWDqmk8vBUlwNRaIQ3KIo/tuCjURjSRDNGZqcKHX/1pVSC8ZlJn70yjnu/8hKEo0xvFhr5lhLP7sqc/jK7SWApBj0AmwrzuRYSx8uL1zsH+PpNy/y+Mcq+MaLDXQMOZhwiQhAolHHiNOrNKOpN9tnHFOgMlBjitGREmfg2Xc7EYHyvBQl10BWBjoBLMMTJMYZlEJtACUfuZNEU4wyjnO9o7g8Ikdberl7Qx5Hm3v54i1FZCTFkmoykrrcyNHmXnKT4ynMSOLx/Q3cUprJibZBartsnOm2sSE/hdouGwa9wN0+JWAy6tlWIiWePf9BN06Plz+c6mbLKv8aX7JTvzAzkX99rZXdvvahEL7QXQyBdSU7hiNlKa6GwumYJtcX2CcIwgPAHwDFyCqK4uACjU1jEZjrDC3Ywy337t28Kp17rl3O3/+ulncvDvLw786wZ30e20oysA47ONxoxRijU4SYw+mh2WqnyTpMeryBerOderOdt8/30zc6SX5KHN22CURgxOkfHmTQ+UcMxcYITLqnKwEBiNGDS7XWdbi9HDvfr7zuGBzDPOwfmWTSw5gbP2WQFm+gf2yClIQk5b1hh1uqkwTkJMfx6O5yHC4PB+qs7Dsjle3+1scqlQzvR24v5b22AQZ9JipRhGVJsdxzTT6lOcl+/QieP9VFdb2F2yukTnHyfVMLcNmpb3O4SPc5mEMJ9VCCqaaxhyf2N3KyfTBoLSSN+bEUV0PhrBBO4R/M8RXVZyJQGO1BaSwec52hBXu45RVCWV4Sz75ziTFfv4COQQf/fuwCep0OnU4kKymO5z7oYtTp5pE7Snnq1VYON1hJj4+jyzZllrGOSPOQwPBRgGSTDrvDqzykMoHKYFm8noFxDyJTyiAuRsDrS4BTLyDO9YwSEI3KmKqUkU6AVJPUKGe0s5nTnZCwvJTK/BSazDYS4qQkN6t9gi9sW8Pv3u+kJCeRZotdSQZ79p1LfPtAIzeuyaDZYlcS01p7RoiP0fPYngoAv5l6sJIT8jaBgj3Y9xnYtjSUYJKjl6rrLYpSjyZL0WRyOVmKq6FwahmtvhwD0biyCfZwn2iThMnF/jFqu4bxeEUlaqcsL5mPbcyn2Wzn5TNmXF6RQ3VWOgfGabDYKMlJpslsRxQh3qBj3Dftlx3EgU5lh68MtdMbPJRURhR0yC4weTXxR9ctp67TRm2Xv7kpRi8loo1MSglcMQH+B68IY043ezbm81r3CTqHHFRuuZ6f/fE1/OTIOfafNbM8LY7hMSf/+GIdNY1SX4xPbFquJNad7hjC64VXW3oRRViRbmJlegKxMTqOne9TqpGqBbj6Xqvbjm4ryVQivgI/88s5CHAiz1R47mu7yvwS2qLJUjSZfNiJJMooHvg7YIUoil8QBGEtUCKK4v4FG53GFYuco7CjLJsjjT1U5iZzptuGw+nhhtXpPHZ3OU3mET5/82qcXi8ulxd0cLi+By/Q2S85hPNT4+i1Tyg1hDwiJBh0TLi9xOjAt+jApdIAoZSBXpASyTISDLi9KG01j9T3KlFFaiZcIhMuD+kJBrKT4jjfO0qcHiZUZqb0BCOrMuL588/dx89ePc9tZdmcaBukNCeZl2u76R6aoGtoAoNO4I51ORh1kolHLi19pLmHPetzSUswUNPYy5qsRA41SH6Uj2/Kp6o8WxLqqgsLLCT35EFfCQpfDSm54ZDD6Zn2GTCrEzlw5r5Qwnopmkw+7ETiVP4vJPORXLuoGynySFMIVwHhLt9nqnWvziWoaezhh0daeHhHCZsK0nC4PWwryaK9f4wHdxZzom2Qb1U3cltpFkebenloRzHNZjs6QZp52ybd6ARJ4FpsE3iBxFg9K9LjlQxjzwyxbiaDnj3rs3nhAzOy1Uie3PePuUiNlx59o37KFCWvLARgeUosnbZJEgxgG3cpGckekBLifMey2Cb5l6OtbMhP5Su3lwLw6Ev1PFRVzG1lORxp6qEkK5E71+fQ3jcuRfsIUv7ANatS2VGazarMBM5ZR+gZmeCezcuxjbuoN9vYVJBGismgCPAthek8+84lTncOcbjRysn2QR7YXqQk9MmCVV0zSv5sS2G6X8vKmYR8uDP3+Zp8oqFsPuxmp2gTiUJYI4ripwVB+CyAKIrjgiAEmmw1rlDCFQIzZb7K4aWyoFInrH3/cDPlOck0Wu1sLcpQYuzfuzCAVxR5pc5CS+8IpXmSqSgtIYb+UTd13XZykmPRI/DgziJ+dvSCMhZTDDgC2hMkx8Vgn3DjcHk4WN9DEH8yBj0Mj0tOX3WTMBGUFUCfrxeCV9ThEac8CcsSYphwexmd9JJo1DHu9JKZGMtbx48z0JbEH39sB6IIr9RbSYmPkQrzZSaSlxLPL95oIysxjuqzFvadMbNnfR4H6i043V50goAgQJrJyI8/u5GnXm1lW2kmMCU4nz/VxZMHmxBF2LA8hQP1ksNeXUMKgpe1/r9vtvGDQy0Mjjv5q4/O7PYLZ+YuNy2qrpeS/hbL5KOZnaJLJArBKQiCCd/CVRCENaiijTSubMIVAoGlKtTvbSvJxOn28mKtmcLMRKW3QVleEpkJsdSZbazLS5FaSpoMmAx6jrX2sXG5VLJhZ1kOLb0jeEQYn5yS5Fb7JAa9wL+/fpGOIYdi+w9UBgY92H0NbPQCjEx6iNMLTPiWBrE6mPTCmoxERifdSulqNbI5aMIthbVmJRm55CtVoROgX+VVHnN6idELpJgMeEf6aB7to9lspyI3mZMdQ6zLT0YvCBTnJk05aOsslOYm0WC2syojgV2VuRyot1JVnsX6/FQAjrX0KdFZBdfG+31HDqcHh1vyadylKl6nJtjMu9lsx+nx0jxLWG6o/QOpaezhQL3VL5x1MdDMTtElki6m3wAOAQWCIPwaOAo8siCj0rjsyEJgpmW3bAYyGfTKdur3CtLi2bQyDUGAZoudb1U3su+MmSdebuRC/xiZCbGc7hziJ4fPYXO4qCrP5ht3VfCDezfwjT0VlOYl0+JLwHK6PRj1UwvQ20qy+Luda0kzxWAyBp/HyFFDMarM5AmVE3jSN9Fv7hlVIpX0AWtcQfX3rnW5JMRN3Y/SnCRykqTXcXq47yMFlGQncq5nhJTKbWy+bQ/7zlrYuDKFjQUpuD0ihhhp1g9QkZ/Cl7avpSAtnhidjvYByXz27Y9X8u2PryM9wcgPj7SACA/vKMHh8viFt6aYpIqyphg9P645ByJhm0ke3FnMZ32d12ZC9j+ozxuMqvJsHrurfNHDUcN5bjXCJ+wVgiiKRwRB+AC4Aen38mVRFPtn2U3jKiLYbCzwPbm3wZbCdLYWZeBwemiw2LhhdTo3rs3gX2rO8/JZyUn64M5iHC4Px5r7uGZVKo/9oQGvKM3MTbF6RnzTdb0AqzMTeOS5OsZUNp40k4Gi7AQazSN+78tmosBoo2XxBgZ8cf5elV9B9lvg274g1UTF8mTsEy6lBpEIrEiP59bSTP7t9TZEQaCpe5RG8wheID8ljvzUOOq6bdReGqbRbMfpEblhdTp7NuZJitPX5/jY+T42LE/hSFMPW9dk+GV7O5weZeA/PNwSvNmQT2s5XB4/v8BMqIsNzkS4JpilGDIZLprfITSRRBndCNSKolgtCMJ9wNcFQfipKIqXFm54GouN+scT7DM5rDEQudyyzeFSIlrsEy6ONfdR2zXMC6e7qTfbON87il4nsCE/hVMdw6TFSxnAijIAspJj+cUbbcqsPzFWz/LUeJp7Rvigw4ZHVZhIDkvVCWDUCX4rBJvDRX6ykSGHhwnfcmLTilRuKFxGc4+dLl9ZDL1OoKZhyv+QHm/APuHicGMPpYNJ6HxF5s50DysKp73+FDfEr+SeTcWsykyQHOgDYzy4o9gvd2BLYToV+SkMjTnJTzP59ZVOMRkwGfWKQ1gdQqpGVroOlyfq9vNITTBLUbjONibN7xCaSHwI/wZsEARhA1L46dPAr4BtCzEwjaWB+scTGA8fGPJYVZ4tORrrphzLciXTmsYezMMOznQNEW+UHL9ymQc8IuMuD8tTTfSMSMXiEmJ1jE168SBF8qgZnfTQ0jNCcVYi4y4P3UMORCT7590bc3G74VCjVckZMPryB9widNslZ3GMIK0kPugcRkDgTPcwLo/ot1oASDXF8GBVEf90sJkxl0iLVVJgq9ITaLZKqwcBKEsTqUz3oluRxrcPNLK7MpfH7q5QBJLfjFqEn716Hr1Oh1Gv8zO7KH2RSzKVTnOh2FaS6dfzIBpEOvNfisJ1tjFpfofQRFrLSBQE4WPAz0VRfFoQhM8v1MA0lgbqH8++WrPU2P2C1HdgW0mmX1jj9w40sf+smaykOPbXSWahgw1WbivJorreQrrJgMsLsb6YzcRYPeNODznJcTT6TDNrshJo7R1jbNI/R1hdsRQkE459woXVPkleShxmX2jqH05bWJFu4gsfLaS5x87Rpr6g9Yvk2b8owgcdQ6zNTsTmcJFo1IMgKK03Rybc/PKtS4z5Eh1uKc3k1uIsrlmVijFGx/CEi2PNvcQUfYTJ5fng9uD1SjWZ5GiqwNmqw+1BFKEoK4EXa7uV6qbgX+5D7VBWI5eU2FWZs+g2/KUoXGcb05Vs7lpoIlEII4Ig/ANSp7SbBUHQAUtjjaixYKh/PHs25tFgtvGH2m6OtkiN3e/7yEo6h8Z56De11FtsVOalcK5nhLvW5fHA9iI2r0pncNzJvrMWpc/AqC+bbHTSQ4xOOofZV5uoe9AxbQzp8TE43R5Gnf6CfcCXP5AYq/d7v2PQwb+/foFYg2TLSoqb8kfIyBFHICW8KasVJB8CSDWKRifd5KfFMeyYZHTSyw2rl3HfR6TG9UdbevloUQYeEeq67TRbRrijMoftpZkY9Dq2FKYrrS4dTo/iS5hwehAEyE8xcaFvDET/XI5Hd5dPyxtQE25JicthzomWcI3mWDWBP3ciUQifBv4Y+LwoilZBEFYAP1iYYWksReRSBhV5KX6JUD85fI6THUNKldK1WYlKk5eCa+MlP4JBz9CYk3M9I5yzjtDSKwnguBg9F/qmWlk63FMrA9l8MzgeEF+KtGJwiRBnEOgdcU773AOM+2b1QpBchElfiYtg5bBz0+JYlmgk2WTg7dZ+3rs4iCiCTidQfdbMsMNJR/84X7y1SBLuWJjorGN5RgI1TTrcXqmm0vWrl4Evie299gFOd0hlqIuzk9DrdFy7Op0dFTnS6stXX0huUiM3H4LpZo9wS0osRXNOKK6ksV7NRBJlZAV+pHrdgeRD0LjKCZy9qROhbA4XTo9XKs1QmUNagpGn375IRV6K4kxOMRnYVpLJ9w82A5CRaKSlVyo3Per0kBirxxkk7dgbIMhNBjDo9VKVU9+MXyovMRUiaRD8y1gA2Cc9ftnFMiJSOex0nyNb1gsn24fQ66QdBCAv1YR12EGyyUBtl43aLl+jHgGMeh1Gg47sBC9/tD6VZRVlvHdhgJrmXk53DvHgjmJOXxri5bNmdILI8tQEWqx2Pr4xj3s3T1UwDawvFKnZI9gMeymac0JxJY31aiaSKKMRph5bI5K5aFQUxZSFGJjG5WOm5bqckSr3Pw4UQt870MSxc3186trlPLC9iGPNfTy8U4qh/86BJo639vPY3RU89Wqr0soyPy0OgOXpJtr6xxUTUqBDN9EooNfpsfmSzVweybGtJs4gMKHSAMEyk8tzk7jQN4bT7Q1a52hw3EWcQYfH5cVk0OHxeinPTeK28hza+8c43NhDXmo8FvsECUY9Y04POgHOdA7TP+ogOdbIaO41LN9YKTWc2ZCH8eUGqussIEJpXjIH6q1kJ8XSOzLBXevzFNu/fO+3lWbOq0nNbJVOIzXJXO7oIc3MszQIOzFNFMUkURSTRVFMBkzAPUiRRxpXOLIwqWnsCfrZ/jozazMTMQ87ePbdS9gcLmwOF0+83MALp7vYVpxJYWYi3z/UzHcOSELJFKPH7fXyh9Pd/O79Th7YXsSe9bncVpqFXAnifO+YX8ioWlrrBRh1iooyAHB7ITlOT3lukhLp6nKJJBj9E8rykmMpyoonI1ESZKnxMSQY9X7KICUuBkGY2m/CV031ulVp3HNNARcHHKSZjGwqSGNnWTYW2zhOtxeny8Oq9HhKs5PoGHKgE3T0jjqpyEtRhHmKycCmlWl4vVBdb6bZbGdXZQ4D45N+ykB974+19IX9nQSjqjw7ZJhquMdTJ6VFen6Nq4NIfAgKoiiKwIuCIHwD+Fp0h6RxuZlpuS47MF+sNXO2e5gYvQ6TQXLiHqi34vVK/XsP1lsQRSmjqr1vlNpOG4UZCbT0jHKqY4gJl4fWvlHaB8ZwOP39BBmJRnpHnKQlxiAIAi6XyMjkdL8BSKaiFWnxDI+7MNsmiDXo/ZLSTEaBMZcXs30qVPX4hSG/Y8QbdOSlmhi1jiACeclx9IxIUUrrlqcQZ9CzNjOBoXEn//nWRarKsthZnktNcy8Vecmc7x1lXUEKLb2jTLhFVqXHc/uyfj549y1uvfVWQIrAur0tm/b+MarrLPz9zhIl6kg945bvucPpn1Mw03cSbPY+2ww7HJOMepUhh7+q8yQ0rn4iMRl9UvVSB2wGpheD0bjiCCZM1ELna7vKqMhPweH0+Jk1BsecvNJgpb7b5utLLMXxH27qoWPQQXq8QXLaOt3885Fz086bk2TENuEhOzmOvhEnA6OSEogJMB1lJRmJN8TQPjiOCLyimrU6XB6SYnWM+EKGxpwioPIp6ARcvoNlJRkx6HUkxMbQZB0h1RTDmNPDZ24ooL1vnOp6Cx0DDg7WW3F7RdyiyNqsRI409vCl7WtJjIvh/q0raTKPUJaXhGVogtquIXpGJmi+1ENazJRiOtE2yNHmXtweERCVhjih7r06gW+278Th9EglLgjfARuOSUatNGoae2YNf9W4+ohkhbBH9f9uoB34WFRHo7FkUHfVkjNj92zIA6a6d6UnGDnXO0Jlfgr13TZKc5Jo7hlhz/o8DtVbsQyPkZ0Yx4lLQ0HPEWvQkyRKdY8QpMb0DpcHt9QaQelW1jfiRBCmIolEoCQ7kR7bBLYJt6IMcpJicYte+kenFEKsfqqlZp8vGkkvSCuT0Qk3Op1AXEwMm1amsWlFGg6XhyNNVikT2mJHEAR2VeZytnOYI809ON1eHru7gprGHpp7bOQmm6hYnsKf3raVJvMINodLyUxWF6IDlM/UqBVvOAXlZsting9qpRFsRRGy4Y7GVUMkUUZ/vpAD0VhiqKJe1KYEQPl/dTmGYy19OJwePmXQs2djHndvzOOP/v0duoK0u5Sx2hxMeqTOZaIoVSfNSY7Fap/0a10pAokGndI/OSVOj8cjMjzhb1ayjkxSuCwel0fE5iuFOuoS/WoaiUBGUiwbC1LJTDJS09TH02+1MTTu5G9uXUt7/xh/v6OUO9blcKy5T3EyfLu6EafbS3W9mU0r0kCAosxEarvsWO0TpJkMHGyYcryro7HkENLAukTqEtLq1VcoQRuqdeZCEGxFEawZj8bVRSQmo+XAz4AbfW+9iVTgrmshBqaxuAR21XK4PDicHraVZk6bnSbHSaWsf3i4hX/cVcbRph4efale6aGsFwQ8vphPAYjxla9Wup2ppL/VLgn1tgGpY5oOMBn1jKj8BLYJD7aJsaDjlvfLTYnFaptEMthI5KTEYbFN0GOf5LWWXlLiDPSOSqsGnSCVnT7VMcRnYnQUpMUriWRbCtN5rWUZrzb1sWVVOu+1D1DT2Mu2kgyMllFur8ymyH2RIqeFwfGiaSuBUPZ7dQlpBGaNw1/sSBx18T0tPPTqJNKOaf8P+CPf6/t87+2I9qA0Fh+1bVuONPnhkRY/W/iz717iyQNNDI45MRn1/NE1y/np0fN0DvlnG3tEEb0AazITae0bReezB2UlGYmL0WO2TeBWOQ36x6Ycwl7wcxrPRGaSkSSjnrYBB7src1mVkchrLb281txLUVYiBcvi0YnQbZ/AZNAzOO4kJzmWFJOB5WkmSnOTWbksnvu3Solhg+NOfnT4nBQhNOJEBDoGHPS0D+Hxily7Ip0dZVJi2ff+/QzvtVpp1J0jPd4YluAOVBTB6hItpeJxgTkoGlcfkSiETFEU/0v1eq8gCA9GeTwai8BMQkexW+8IYrcWQRRFDpy1UG8e9pvpJ8bGMOqLFMpINLBtbRbDEy7O9Y4iBxClmgyc6x1jVXo8nUPjSpXSjMRY7BPSTF/w/UcQUWb78TEw7ob0BD2DYx7FJGSK0VORn0rbgINDjT1U5jkozUnCNu7iTPcw53pHWZ4WR35yHGa7VCrDoNPxqc0F/PjIOd5pG+RbH6ukyTzCt6ob+WhRBk63l/11Zm4tzWazTuCRXaWcs46CKK2i5Pv1tf91HxU3mKfNnmfqLBY44w+mRCLJ4F1KykPjyiQShTDgK3v9G9/rzwID0R+SxuVAXTvnqVdbgyaewXQzgbqPsiiKVOancLJ9SDHLyEXoxnxSXwA2FKRRXWdROqjJjE56uK0kE4fbw8DYBCOTXj5alMHAuGTGSY83MDjukhSPar9xN+xZn0txTiI/qWlVchm6hh2szkggI8FI15CDriEH0INegLLcJOwTbjp8tZLk/IPOYQfNZjsPVRXTbLVTsMzEb0908sVbigA40mQlJ8nE8dZ+vrGngi2rlrFl1bJp91M9e5bj+eVoncDOYoGCe7Y+1eE6kLXyDxrzJZKOaX8B3AtYAQvwKUBzNF+hyMLjqVdbqa63sKsyJ2Q7RpNR8g/UNPYowu7pN9v45v5G3lcpA4DCzARgSoBX5iezcXkqLo9X6VImY7ZNcKbLxvELg0qkUIPZzsXeETISjGwrzsBXn474GIGirARl387BceIMMeyqyFHe84rwxvl+hsadxBt0fu+39IyyZ30eqSZpDrQmM4Gy3CTKc5JY5RvzgTorj/zuLC980E1b3yj3XlfA7so8rPYJthVn4nD6dzBTc/jwYQ4fPqzc22/uq+evf3WSsrykaZ3FApO+giWBye+daBsMuyPYbMlpGhqzEUmU0SXg7gUci8YCMVOdmy2F6UqRtHCiW6rPmvnmyw24vKJSGyg5LoaRCalpvfy3LDeJ872jtPaOkZ9qpzgniYHRSfpHnQhIqwi9ADaHE70ACUY9CXEGbOMTxBuN9I85+UOtRRnDpzYX8Ffb1vD9g81c7B+jrttGg8XOHRU53H99Ab892YXTIyo1i5weL+vykslKjqNraJzWvjEu9I8q/oiBESdDEy50Apw/OsodFTl4vCKdQ+NcuyKN+7dK/R5Kc6WyE5bhCX5wuCVkPoFLVU+pqjyb35/s5L2Lgzxz/JLSqSzUrF/9Xcgri7nU9llsp7PGlU/YKwRBEDIFQfi6IAi/EAThl/K/hRycRnQINgOVhUdBWvysM9AUk4FPXpPPG+f6+EnNeZweSRmkmgzctGYZIxNulqeZ8CJFCYlAboqJm4syyEw0crjBSpNlhN4RJ15xqq+BR4SsxDhuKlrG6KSH1RnxJMZJygCmIpIA3rs4xFOvtvK/b11DSU4SggBur8jhRiuvNPRIReoSDHy0KIPy/GRidDru3pRPqsnA+d5RPF4Rl8tLdlIsALYJF8tTYiX/gyiyviCVPRtyMcbo+Pg1+XxwaZhHX6oHAT6+MY+WXjtVpVkMjjl59p1L01YKu3fvZvfu3cr9+sG9G/jMdQU8sL1o2vcQOOuXv4sTbYPK96T1CtZYDCLxIbyEFGpag1RdWOMKYb6VJFt7R3jspQaO+xrj6HUCW9cs4xt7yjlYb2XC5eX6Nen84o023F4RHfBqcy+Az3ELXm/wY3fbJ/Ag4gWOXxj0+0xAql8E0D00RkvPCG9f6GdwdJJUkxH7hJPcZBPtg+NkJRoZGnNy7Hw/xhgdBakmhsecvHzWjIDAuvwkBsaddA1PIAB6vUBGUhz94y52V+Zy7+YCALauyVCaAYHkqJZLTcsd44CQKwX1aiywh/Fs34NW8VNjsYlEIcSLovjVBRuJxoIRiSlBLdCMeoEfHznPfx2/iMs3rS/KTMBqn+DjG/M502njqddacXm8uL1e/va2tQD8z4kOuocnSDHFYHO4/aKPYCpySDY5jTmnEsxidJISkIV2kkGHbcLDqC8prWPQQX5qHN3DE6TFG8hKNuIVvXz1zlK+8XIj/aNOjHqBC/1j7DtrxuOVymnE6nW09I6yeWUa169O5722QZqsw2xcnsaDO4sB/MxqezbmqQaLEoILUt0hh9ND59A4J9oGqSrP5p1jR6Vryazkif2NnGwfnNbNbLbvYaFNPloUksZsRKIQ9guCsEsUxQMLNhqNRSOwTk5t5xA1Tb1YfI7grKRYHrurnPUFKfzbaxeUomcVuSl80DFEbZcNp8fL6IQby7C0j2PSTbxBz7iqZHVRZgJlOckcabKyOiMR+6QL85C0/fI0EyvSTLzTNsg1BclcHHBIUUZAVqIBh9vLyISHGEGS0kPjLk60DwPwzDsdOJweEo16dpRncfqSDYvdgV4nUJGbzNmuYfLS4vni9jX8x+tttPSOsDYrmVMdQ7z4QTfmYYdfaKi64b3JoFeise7fupJnjl+iut5Cg9mmZCfL7u5wu5ktBloUksZszKoQVH0QBODrgiBMIlUP85lfxeSFHaLG5UCuXXT/DSvITIzlmXc7ACkvIEYv8OXb1rJhRSoP/aaWBoudivwUEKHeYmNtdiLnekZptEx1PovRCSSaYhgcc02FjyIlmd1/40rq/n97dx5eVX0nfvz9ySWQhCUkEklYQoypWUHECEodQQXKIu6tWrXjzDx1pvXXdub3o7ZV27q1T/enM53ajl1+Vss4+kPQQhBZXKh1K0iALARZgpCdJgRCEnJz8/39cc69JCHLTc4JJzd+Xs/DA/fm3Hs/J9znfM75fr/n86lo5KOaU4yPjT47EexvZ0d5PVECVSfbQq8BqO1Un+hEy9m6Rtkp48lIGse982ew6sXdfFzfwrpdVfgEluelMC/9Ahpa2th9rJHyvzXz5PpSDh4/TX5qAu0dHfgDhrfK6viorqnL0lCwl9zad2j/fMt+1n5YweHjpymtPsXi7MmkJ41j1ZJM+4z77AE2nG5mXtAhKdWffhOCMWZ8OG8kIrnGmGLnISkvnPEHaGvv4Ldvl4fW9V+Rlsg3lmVy5Hgzc9MT+bfnd7Hj4xP4gneCCQhCiz9wTuOZKIETp62DeKDDkBDno6E5QFVjK1/8w45QraGGbgf9qQkxVDeeIW60VWI7xie0dmtxeWnqRJLGxZB2wVgSxo5m5aVWiYnglQl2ePMuvoB7rpzBH989wphRQt6UiTy4PJMX3j/KaX87b+yrJT81gcdvyaW08tQ5QynxsVZJjicKSnhgYQa3XgZpSXHcMMsaTvrJljK+vSLnnOGX4braZ7jGpYaPQfVD6MVzwBwX30+dJwV7Kvnt24dD5SOSxo/hW8uyuOWyqZxsbefI8WbeKqtjb2UjIrAw60IQmDNjIrlTJrDnaEOok1hQ5z7Fjd2K0AWTQVy0MCZ6FPPTE3m/vN5qaD8hhoqGVqKihEljrYligPExPiaNHcPoUVHERUfzT393Eb/bfpin3jwYumv4q40ZbNhdxclmP7fmT2NBZhIv7TxmdyObxdz0RD44VM9lMxJ4sqAEY4Sb50wlJyWenJR4O7au4+zdS0I/UVASWjLaudZTQUEBQGilkVKRyM2EIP1vooaTg3VNPLa+mO37jwPW6qG756Xy9SWZjLfPetcXVvL9V0v50oKLmTllIkWVjcRF+3iyoIS8lAkUVTYSMNDRHiA1IZaKEy0YYMwoq7WlYK3ISZ4Qw8Hjp4kS6/4Dfwc0+w3Nfj8Vja342w2tfkPhsUbGRkdxsLaJgLHuT2j1B2g+E+BwazOCdZNZxYlmPqptoqMDdn3cwMrZU5gSH8fH9S0EOjqYGDs6dNPdd2/I5bbLp4Wqjq5anMkjy3NCZZw76z7O3ltJ6O5n29HR7k3SDmbyVyeMlRvcTAg9tatVw9DpM35++cZBfvf2Yc7Y6zp9UcJXr8vga4su6bqxnebL/3baGmeflUKbv4OODiiqOsmKWVOoO3WGP390nLSkOC6dNpFNJdW0BwyjfEJ8TDRn2gPcOTeVfVUn2V9zir2VJ8++vcCeo42hctdtAUNbwDAtPoYon/BxfQtRwJK8yURHRbGzvJ5K+16H/7M4k4K9lRTstUpSr5w9hXcOHqdgj/VcaeVJZk6bSPaU8by081iXG8LCuQmvu76GXJYsWRL2778/g5n81Qlj5QY3E4Ia5owxvFZczfcKSkMVSS8YO5oHrs1gQswoFncqAxFshtLSHuChZdksyEpi/sWTaGkL8KPN+1g5K4WslAnERvt4YcfHGGD7/r+xNHcyX73uUxQea2BH+QmOn24jNTGWpTOT+dwV03nk5b0UVZ4MnT1cPGksB+pOd+lZAJB+4ThqTrUSJXBt1oX88LZLiY+NpqSqkcf/VMJ3bsyhtPIUZdVNVnc1sQ7Y31mZy2hfFC8XVtLeYSiuPMlz7xzh1eJqvr0ip9+DpRfj7H0NU4VLJ4yVG9xMCG39b6K8crC2iScLSnjDbuYeJXDnFal8/TOXkDB2zDnbry+s5PENxfiionjy5jwmxERbK278AR78TFZoIveJghKuTEtkb4V11r+puIbjp9ooPNYQuqms8kQLb+2ro7iykTdKa5g2MZaAMVQ2ttIWsLudxccQZaC2qZX42NG8c+A47XaGiIv2sX53JRhYkJXEZ/OnM3ViHONjolk+M5ms5AmsvHRK6MD65esyurT8XJCZNOSrftavXw/AypUr+9nyXH0NU4VLJ4yVG8JZdtrnRLEx5kP77ysH8sEi8lngUSAbmGuM2TGQ16vwNLW28+u3DvLbtw/Rat8hNmtaPI/dmGt1/uqNWMNIy2dajdZ/sLGUlwsrEYGHlmWztaSG6RfEkjV5PHfMm84H5fWhZaIfHm0gvdOZf3ZKPC3tATYWVTNpXAzl9c3kpIxnSnwMD1yfwS+3HWTX0QYCBsaN9nH8dBsJsaNoaGln6sQY/O0dPLa+mFFRQnHlVF4trqbFH2DXxw1s3FvFZakJxMdGh+YIvr0ih3uunBFKEBNihv5gGRsbO+jX6tm9Gi7CuUL4qf13DJAP7MYaWZ4F7ACuGuRnFwG3Av81yNerfmy2h4eO1Fu9BRLiovm3RZdw97xUfL6+y1gtyEyiuGIqX74ugw8O1YcqogbbRz6+oYSE2GjK65tpDxjaAgES4qJpaPbTYaDVvhnNAB/VnAIDy/OSGTvGx+//Us5HtU34ooTNRTV8ZmYyeytOEOuL4kx7AJ/A569Mpb7JT/qF4/jpa/sIdBjmp19A05l2/unqNHYdaWBjUbW1RNa+kpibnsiy3OTQnEGLP8BPNvffjN6NCdlFixYN6nWgZ/dq+AjnPoRrAURkLTDHGLPXfpyHdYY/KMaYUvt9BvsWqhd1p87wxIYS/rTbqscTJXDbnGk8uDSTpPExYb3HB4fqQz0SvnxdBg9+Jsvqi4CVLHYdaeDlwgp8As1t7VybNRl/ewebiq0CetdmJ/HWvuNUNLSwMDPJalJfWs2ksWPAwOQJY8hLiadgbxWLsyeTPWUChUcbSU2I5d6r0qyexmV1lFU30tFh6DCwt+IE9c3t5J+YyP66plCCCpaZ+OBQfejO4VeLq3lgYQbLcpNDd1X3RidklbIMZA4hM5gMAIwxRSKSPQQxnUNE7gfuB0hNTT0fHxnROoxhW6l1YM6dMoHvrMxh3kXnNnXpS7AEw4a9lfb7xPOzLftpaw/wTX82l81IYGNRFZPHx1BSfYoDdaf5ynUZFFU0UtXYSt2pNmpPnQERoqOi2LKvmjPthmN2KYxjDa1clppATsoENpdW8+n0SQiN1JxsJXHsaD44VM/3N5bSbCehMT44fSbAqChhQVYSd82bERpiCZ7dB5v5tLQHeCApg31VJ9m6r5b8tESmXx7X5752/nswXn75ZQBuvvnmQb+HUl4bSELYIyK/Bf5oP74b2NPXC0RkK5Dcw48eNsa8Eu4HG2OeBp4GyM/P1+Wt/Zg8IYZHbsih1R/g7rmpjI729bptb8Ml8bHRfHO5le83FlWTOzWe3JQJ7Pi4gX1VJ/nXJZdQXDGVJXmT+eW2gyzISiIhbjRVja0EDDS3trM4ZzKvFddwSco4IJmCvVatoPFjopiWOJZLLhzPW2V15KbEc/HkcUSX1bEk1zqwn2z1sywvmarGFt47VM+989NISxwXuncgGGtw3gCss/vY0T5+sqWMZbnJbNlXc045ip64MWQTHx/v6PVKDQcDSQj/AHwJ+Jr9eDvwq75eYIwZ/MCqcuSuueFdSfU1XBJMCsEVOgsyk3jq9QOheYXg8MyB403cFZ/KopzJPL39IB/Xt9DQ4ic+bjQGQ8yoUcxLv4Ct+2rs5vZ+yqpP8fl5M1gxM4WNRdUkjR8DGGZNm0h8bDRbS2rYWlrLoqwLefymvC5JoLPuZ/f9Nf4Zqhu4rr32WtfeSymvDKRjWquI/BrYaIwpG8KY1HnU03BJT+viu9f4n5ATTf3pNvZUnOCBhRmhbX997+V8Z10RkybEsKWkho4Ow87yei6/KJEvL8zg1b1VNLb4yZ4ygQWZSay8dAr5aYnUn25j275aYqN9NLb4afEHWJR1IVv21TA/Y1LYB+/OZ/s9DRMF77xuaQuEeiArpSwD6Zh2I1AIbLIfzxaRPw32g0XkFhE5hrVKqUBEXhvse6nB66kzVzg9f+NjozlU18SG3VUcqmsKvT4nJZ675s3g3YN/I29KPCKwpbSGn23ez1v76yipPsX0xDg+rrd6CQQtnZnMEzflsSAriR9sLOXHr5Vx2YwEvntDbp9DPj3F1ifp9rdL1q5dy9q1a919U6XOs4EMGX0XmAu8CWCMKRSRiwb7wcaYdcC6wb5ehWcwQyS9DcMsypncpS9wetI4bp0ztUubyOB2wbLR8WNH8XppHRdNiiM+ZhTRPuG2OdNIiBtNiz/A+t2V/GRzWegu4pd2HqOgqIoVeSm9DhP1FWt/Vl46hdhon6MJ5J5ccMHAJu2VGo7CvkIA/MaYxm7P6QTvMDfgM2i6Dru8tPMYYM0vnGz188/P7uCx9cU89foBfvnmAS6bnsAHh+rP6TG860gDP91cxkfVTRigtPoUfzl0nNvnTOML89Osyd/NZWBg1ZJMWtoCNLb4mZueyIq8FL58XUZYCWygvYeHqlfxggULWLBggavvqdT5NpArhGIR+TzgE5FPAV8F3hmasJRbBnoG3fmKYmtJTZd2kD/fvJ/3DteTlhjHvfNnhMb+u4/Jby2poaCoCn/AUNt0hrTEOCobm8lLmRg60AevIjCAsXoLxNo9EF4tru53qahSyn0DuUL4CpALnAH+G2jk7IojNUwN9Ix4a0kNj20o5gcbS5mbnsjyvGQKiqrYWlJDVsoEooCKEy18WH4idBXRHuiwDu62RTmTeWR5Dt9clsUjK3J47ovzuG3OdD6qawrNGwSbz/xkSxkIrFqcSYs/0KUiaSRZs2YNa9as8ToMpRwZyBVCjv1nlP3nJuBGrBIWKsJ1nhtYkWctBc1PS+yy7PRkq5+CvZUUV56kpd1KALGjfYzyRYFYw0vBuYp7rjpbSwisG9typ8YzNz0xtF1PzWdio30RebdwcnJPt9soFVkGkhBWA6uwahB19LOtijDB5ZgPLcs+pydwS1sgVG20rPqU1fRmlDW8E5ykbWkLnHM/Q/Agvyw3OVR++oND9V2266n5TCS6+uqrvQ5BKccGkhDqjDHrhywS5YngWXzwjD/YV6DzpPL3Xy0F4KHl2Ty8IifUshLObtvY4u/SUhJ6vkms+8+CtMCbUt4TY8JbKCQi1wN3Aduw5hEAMMac18XX+fn5ZscOrZTtls5tJYMH9O539q4vrDynZITq6oUXXgDgjjvu8DgSpXomIjuNMfl9bTPQ0hVZQDRnh4wMoHfjRJCe7kIOrvYJPtd9m+DqocYWf5d5Au3je9b06dO9DkEpxwaSEK4wxmQOWSTqvOipO1dstM+a0B1tTej2Vt+o+/NaNvqs+fPnex2CUo4NJCG8IyI5xpiSIYtGDbmeJm/7ujO5r9cOZCJYryaUGv4GModQClwMHMaaQxDAGGPO67JTnUOITJ3bW47Eq4nnn38egLvuusvjSJTqmdtzCEsdxqMixFCczUf6stL+XHTRoMt6KTVsDKT89ZGhDEQNvXAP9EMxNzDSl5VeeeWVXoeglGMDuUJQES7cA/1IP5tXSvVME8InSLgH+pF+Nj8U/vhHq7PsPffc43EkSg2eJoRPEKcHel0p1LvMTF2RrSKfJgQVNr3voHdXXHGF1yEo5ZgmBNVFX1cBOreg1Mg2kH4I6hOgrw5rQ9VtbCR49tlnefbZZ70OQylH9ApBdaFXAYOTm5vrdQhKOaYJQXWhK4wG5/LLL/c6BKUc0yGjEUZEEJGwtk1LS0NEKC8vH9qglCK872Zv30n9rp4fmhCUcsEzzzzDM88843UYSjmiQ0ZKuWD27Nleh6CUY5oQlHKBJgQ1EuiQkVIuCAQCBAIBr8NQyhG9QlDKBc899xwA9913n7eBRIBHH32015+dOHHivMWhzqUJQSkXzJkzx+sQIsZjjz3mdQiqF5oQlHLBrFnntXFgROurS2NaWhpHjmjrFa/oHIJSLvD7/fj9fq/DUMoRTQhKuWD16tWsXr3a6zCUckSHjJRyQX5+n73LlYoImhCUckFeXp7XISjlmCaEEaqv5Y9PPfUUcXFxocerVq1i3LhxPW77+OOPk5qa6nZ4I05raysAMTExHkcysul3dWhJXzP+w1F+fr7ZsWOH12EMW+EUtmtoaGDixIlhrejYtWuX3oUbhmAdI70PoXfB72Y4q4wOHz5MWlraOc/3Rb+rfRORncaYPsc29QphhBlIgtfKke6ZN2+e1yEMe+F8N3v7Tup39fzQhKCUC7Kzs70OQSnHdNmpUi5obm6mubnZ6zCUckQTglIuePHFF3nxxRe9DkMpR3TISCkXXHXVVV6HoJRjmhCUckFmZqbXISjlmA4ZKeWCpqYmmpqavA5DKUc0ISjlgjVr1rBmzRqvw1DKER0yUsoFV199tdchKOWYJgSlXJCRkeF1CEo55tmQkYj8WET2icgeEVknIhO9ikUppxobG2lsbPQ6DKUc8XIOYQuQZ4yZBewHvuVhLEo5sm7dOtatW+d1GEo54tmQkTFmc6eH7wG3exWLUk5dc801XoeglGPDZQ7hH4EXvA5CqcFKT0/3OgSlHBvShCAiW4HkHn70sDHmFXubh4F2oNf+gyJyP3A/oPXO1bDU0NAAQEJCgseRKDV4Q5oQjDGL+vq5iNwH3ABcb/qojWuMeRp4Gqx+CG7GqJQbXnnlFUD7IajI5tmQkYgsBR4EFhhjtEykimgLFy70OgSlHPNyDuE/gTHAFruT0nvGmH/xMB6lBq1zdy+lIpWXq4z0Th41Yhw/fhyASZMmeRyJUoOntYyUcsGGDRvYsGGD12Eo5chwWXaqVES7/vrrvQ5BKcc0ISjlgunTp3sdglKO6ZCRUi6ora2ltrbW6zCUckQTglIu2LhxIxs3bvQ6DKUc0SEjpVywePFir0NQyjFNCEq5YOrUqV6HoJRjOmSklAuqq6uprq72OgylHNGEoJQLNm3axKZNm7wOQylHdMhIKRcsXbrU6xCUckwTglIuSE7uqcq7UpFFh4yUckFFRQUVFRVeh6GUI5oQlHLBli1b2LJli9dhKOWIDhkp5YLly5d7HYJSjmlCUMoFF154odchKOWYDhkp5YKjR49y9OhRr8NQyhFNCEq5YNu2bWzbts3rMJRyRIeMlHLBDTfc4HUISjmmCUEpF2jrTDUS6JCRUi4oLy+nvLzc6zCUckQTglIuePPNN3nzzTe9DkMpR3TISCkX3HTTTV6HoJRjmhCUckFCQoLXISjlmA4ZKeWCQ4cOcejQIa/DUMoRvUJQygXbt28HID093eNIlBo8TQhKueCWW27xOgSlHNOEoJQL4uPjvQ5BKcd0DkEpFxw4cIADBw54HYZSjugVglIuePvttwHIyMjwOBKlBk8TglIuuP32270OQSnHNCEo5YJx48Z5HYJSjukcglIuKCsro6yszOswlHJErxCUcsG7774LQGZmpseRKDV4mhCUcsHnPvc5r0NQyjFNCEq5IC4uzusQlHJM5xCUckFpaSmlpaVeh6GUI3qFoJQL3n//fQCys7M9jkSpwdOEoJQL7rzzTq9DUMoxTQhKuSAmJsbrEJRyTOcQlHJBUVERRUVFXoehlCN6haCUC3bs2AFAXl6ex5EoNXiaEJRywd133+11CEo5pglBKRdER0d7HYJSjukcglIu2LNnD3v27PE6DKUc0SsEpVzw4YcfAjBr1iyPI1Fq8DxLCCLyBHAT0AHUAvcZYyq9ikcpJ+69916vQ1DKMS+HjH5sjJlljJkNbAC+42EsSjni8/nw+Xxeh6GUI54lBGPMyU4PxwLGq1iUcqqwsJDCwkKvw1DKEU/nEETke8AXgEbgWi9jUcqJYDKYPXu2p3Eo5YQYM3Qn5iKyFUju4UcPG2Ne6bTdt4AYY8x3e3mf+4H77Yd5wEi+JXQScNzrIIbISN430P2LdCN9/zKNMeP72mBIE0K4RCQV2GiM6fc2TxHZYYzJPw9heWIk799I3jfQ/Yt0un8eziGIyKc6PbwJ2OdVLEoppbydQ/iBiGRiLTs9AvyLh7EopdQnnmcJwRhz2yBf+rSrgQw/I3n/RvK+ge5fpPvE79+wmENQSinlPa1lpJRSCojQhCAiT4jIHhEpFJHNIjLF65jcIiI/FpF99v6tE5GJXsfkJhH5rIgUi0iHiIyYFR0islREykTkgIh80+t43CQivxeRWhEZccu9RWS6iLwhIiX29/JrXsfkJhGJEZEPRGS3vX+P9bl9JA4ZiciE4J3OIvJVIMcYMyImpUVkCfC6MaZdRH4IYIz5hsdhuUZEsrEWEvwXsMoYs8PjkBwTER+wH1gMHAP+CtxljCnxNDCXiMg1QBPwbDhLwyOJiKQAKcaYD0VkPLATuHkE/d8JMNYY0yQi0cDbwNeMMe/1tH1EXiGM5LIXxpjNxph2++F7wDQv43GbMabUGFPmdRwumwscMMYcMsa0Af+DtZR6RDDGbAfqvY5jKBhjqowxH9r/PgWUAlO9jco9xtJkP4y2//R6vIzIhABW2QsROQrczcgtjPePwKteB6H6NRU42unxMUbQQeWTQkTSgMuA9z0OxVUi4hORQqyq0luMMb3u37BNCCKyVUSKevhzE4Ax5mFjzHRgNfC/vI12YPrbN3ubh4F2rP2LKOHsn1LDiYiMA14C/rXbCETEM8YE7KrS04C5ItLrsN+wbZBjjFkU5qargY1Aj3WQhqP+9k1E7gNuAK43ETjJM4D/u5GiApje6fE0+zkVAeyx9ZeA1caYtV7HM1SMMSdE5A1gKb3Ugxu2Vwh9GcllL0RkKfAgcKMxptnreFRY/gp8SkQuEpHRwJ3AnzyOSYXBnnT9HVBqjPmZ1/G4TUSSgisVRSQWa+FDr8fLSF1l9BLQpeyFMWZEnJGJyAFgDPA3+6n3RsoKKgARuQX4BZAEnAAKjTGf8TQoF4jIcuDngA/4vTHme95G5B4ReR5YiFUNtAb4rjHmd54G5RIRuRr4M7AX63gC8JAxZqN3UblHRGYBf8D6XkYBLxpjHu91+0hMCEoppdwXkUNGSiml3KcJQSmlFKAJQSmllE0TglJKKUATglJKKZsmBKWUUoAmBBVBROTNvkpmi0hTbz8bSiJSLiKTBrD9fSLyn738rNd9EJEpIrJmMDEqFY5hW7pCKdWVMaYSuN3rONTIpVcIakiJSJrd8OcZEdkvIqtFZJGI/EVEPhKRuSIy1m7C8oGI7AoWwRORWBH5HxEpFZF1QGwYn/c9uxnIeyIyuVMMr9tNh7aJSKr9/DMicnun1zbZf6eIyHaxGjAVicjf2c8vEZF3ReRDEfl/dkG0oK/Yz+8VkSx7+0QRedn+3Pfsu0a7x3uR/Z57ReTJMH6XRfa/7xORtSKyyf49/qjTdkvtWHaLyLa+YhGRR0XkDyLyZxE5IiK3isiP7Hg22XV+EJHLReQtEdkpIq+J1UdAjTCaENT5kAH8FMiy/3weuBpYBTwEPIzVFGgucC3wYxEZC3wJaDbGZGMVL7y8n88Zi1Xq41JgO/BF+/lfAH8wxszCKob4H/28z+eB1+wKkZcChfaQ0CPAImPMHGAH8L87vea4/fyv7P0CeAzYZX/uQ8CzPXzWvwO/MsbMBKr6iau72cAdwEzgDrG6fyUBvwFus38Pnw0jlouB64AbgT8Cb9jxtAAr7KTwC+B2Y8zlwO+BEVOaQ52lQ0bqfDhsjNkLICLFwDZjjBGRvUAaVnXQG0UkeCCNAVKBa7AP3saYPSKyp5/PaQM22P/eiVXIC+Aq4Fb7388BP6JvfwV+bx8IXzbGFIrIAiAH+ItVD43RwLudXhOskrmz02ddDdxmx/+6iFwgIhO6fdang9vYsf2wn9g622aMaQQQkRJgBpAAbDfGHLY/N9jYpq9YXjXG+O3/Dx+wyX4++P+TCeQBW+x99zHw5KUigCYEdT6c6fTvjk6PO7C+gwGsM9oundTsg89A+DuVCw/Q//e7HfsqWUSisA7yGGO2i9U2cgXwjIj8DGjAai5yVy/vFdyncD63u8EWFOv8ex3M53Z5H2NMh4h0/h0G/38EKDbGXDXI91cRQoeM1HDwGtYYvACIyGX289uxhm8Qq6nHOWPwYXoHqyQ1WB32/mz/u5yzw1A3YrUXRERmADXGmN8AvwXmYLUz/bSIZNjbjBWRS/r53D/bn4eILMQaVurefOUv3WJz6j3gGhG5yP7cxAHE0psyIElErrJfHy0iuS7EqoYZvUJQw8ETWKWj99hn6oexGgT9Cvi/IlKK1et25yDf/yv2+3wdqAP+wX7+N8ArIrIba5jktP38QuDrIuLHai7/BWNMnViNi54XkTH2do8A+/v43Eexhp72AM3A3/ewzdeA/xaRbwCvDG73zrLjvB9Ya/8ua7GGzsKJpbf3bLMn3/9DROKxjhs/B4qdxquGFy1/rZRSCtAhI6WUUjYdMlIRR0Tex+oq19m9wZVMkU5EZmKtOOrsjDFmnhfxqE8OHTJSSikF6JCRUkopmyYEpZRSgCYEpZRSNk0ISimlAE0ISimlbP8fQejarLdfXJkAAAAASUVORK5CYII=\n", - "text/plain": [ - "
      " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# standardized moran's plot again, from subsection above\n", - "fig, ax = plt.subplots(figsize=(6, 6))\n", - "ax.set_xlim(-3, 3)\n", - "ax.set_ylim(-3, 3)\n", - "ax.axvline(0, c='k', ls=':', alpha=0.5)\n", - "ax.axhline(0, c='k', ls=':', alpha=0.5)\n", - "ax.text(1.25, 1.25, 'HH', fontsize=20)\n", - "ax.text(1.25, -1.75, 'HL', fontsize=20)\n", - "ax.text(-1.75, 1.25, 'LH', fontsize=20)\n", - "ax.text(-1.75, -1.75, 'LL', fontsize=20)\n", - "ax.set_title('Moran Plot')\n", - "sns.regplot(ax=ax, x=y_std, y=y_std_lag, scatter_kws={'s':1})\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 65, - "metadata": {}, - "outputs": [], - "source": [ - "# calculate LISA values using the queen spatial weights\n", - "from esda.moran import Moran_Local\n", - "lisa = Moran_Local(data_lag[col], w_queen)" - ] - }, - { - "cell_type": "code", - "execution_count": 66, - "metadata": {}, - "outputs": [], - "source": [ - "# set the statistical-significance threshold (alpha)\n", - "alpha = 0.05" - ] - }, - { - "cell_type": "code", - "execution_count": 67, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "False 3183\n", - "True 2019\n", - "Name: significant, dtype: int64" - ] - }, - "execution_count": 67, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# identify whether each observation is significant or not\n", - "# p-value interpretation same as earlier with moran's I\n", - "data_lag['significant'] = lisa.p_sim < alpha\n", - "data_lag['significant'].value_counts()" - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "LL 2732\n", - "HH 1550\n", - "LH 512\n", - "HL 408\n", - "Name: quadrant, dtype: int64" - ] - }, - "execution_count": 68, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# identify the quadrant each observation belongs to\n", - "data_lag['quadrant'] = lisa.q\n", - "data_lag['quadrant'] = data_lag['quadrant'].replace({1:'HH', 2:'LH', 3:'LL', 4:'HL'})\n", - "data_lag['quadrant'].sort_values().value_counts()" - ] - }, - { - "cell_type": "code", - "execution_count": 81, - "metadata": {}, - "outputs": [], - "source": [ - "data_lag_subset = data_lag[data_lag.index.isin(df_merged.index.values)]" - ] - }, - { - "cell_type": "code", - "execution_count": 85, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(5202,)" - ] - }, - "execution_count": 85, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "lisa.q.shape" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### Now map the tracts, colored according to their LISA quadrants, to identify clusters:" - ] - }, - { - "cell_type": "code", - "execution_count": 69, - "metadata": {}, - "outputs": [], - "source": [ - "# merge the original tracts and LISA quadrants data together\n", - "tracts_lisa = gpd.GeoDataFrame(pd.merge(tracts, data_lag, how='left', left_index=True, right_index=True))" - ] - }, - { - "cell_type": "code", - "execution_count": 76, - "metadata": {}, - "outputs": [], - "source": [ - "tracts_subset_lisa = tracts_lisa[tracts_lisa.index.isin(df_merged.index.values)]" - ] - }, - { - "cell_type": "code", - "execution_count": 75, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
      " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# create figure and axis then draw the basemap of tracts\n", - "fig, ax = plt.subplots(figsize=(9, 9))\n", - "tracts_lisa.plot(ax=ax, facecolor='#999999', edgecolor='k', linewidth=0.1)\n", - "\n", - "# plot each quandrant's tracts (if significant LISA statistic) in a different color\n", - "quadrant_colors = {'HH':'r', 'LL':'b', 'LH':'skyblue', 'HL':'pink'}\n", - "for q, c in quadrant_colors.items():\n", - " mask = tracts_lisa['significant'] & (tracts_lisa['quadrant']==q)\n", - " rows = tracts_lisa.loc[mask]\n", - " rows.plot(ax=ax, color=c, edgecolor='k', linewidth=0.1)\n", - "\n", - "ax.axis('off')\n", - "fig.savefig('clusters.png', bbox_inches='tight', dpi=600)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Gray tracts have statistically-insignificant LISA values. In red we see clusters of tracts with high values surrounded by other high values. In blue we see clusters of tracts with low values surrounded by other low values. In pink, we see the first type of spatial outliers: tracts with high values but surrounded by low values. Finally, in light blue we see the other type of spatial outlier: tracts with low values surrounded by other tracts with high values." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - }, - { - "cell_type": "code", - "execution_count": 77, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/sarahluw/.pyenv/versions/3.6.2/envs/my-virtual-env-3.6.2/lib/python3.6/site-packages/geopandas/plotting.py:681: UserWarning: The GeoDataFrame you are attempting to plot is empty. Nothing has been displayed.\n", - " UserWarning,\n" - ] - }, - { - "data": { - "image/png": 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HggBoc+66665rNm/eXJObm9t+9erVtWnvgeZAEABtzpo1a3a98sor36upqbln4cKFz6e9B5oD9xAAbU5Fxfn/kJu7fveIEbVf8AmG8DpvXQy0Od27H3/s9u2rH9+6tboh7S0t1eTJk8/r0KHDK3PmzHkgk8k0pr2HfScIAHjfLrjggnHdunW7PD8/v6yuru73a9asuXzOnDlL097FB+ceAgDet2uuueaBW2+9dfS2bdvu79+///Q+ffrMTHsT+8Y9BAB8INXV1Y0jRow4b+zYsTsymcz2tPewb1wyAGCfJEmSHHbYYZ0WL168Me0tfHCCAABwDwGQvmOPPbbglFNOKU17B7RlggBIXUlJyUc7deo0/bzzzjsu7S3QVrlkADQLY8aMGbBjx46tCxcufDXtLdAWCQKg2Rk1alRx//79R86dO/e3aW+BtsIlAyB148eP71lVVZUb8fod60VFRR9rbGw8Ne1d0JYIAiBVxx13XFlBQcGcIUOGHF1Z+bnDKypOPb1bt26Tc3Jydqa9jQNj6tSpA2fOnHlE2jvaOkEApObCCy88sV+/fl9uaGj49q233vr7hoaaTe3alaxYsGDB9GeeeeZ7ae/j/Tv88MO7z5gxY9qer0eNGlVy+eWXPzJp0qTR7/RrMplM5c6dOzccmIW8E+9UCKQmLy9v4ObNm395zz33PNqx49Cc4uLDjtq+fduINWtWPB4RNWnv4/2rr6/flJube2xE/CIiYtWqXuMfemjJS0my4al3+jXz5s2bf8AG8o4EAZCaH/3oRz/b8+NOnYaOzWR6fqqpadHFaW7ig6usrMwaMWLEZRUVFadMmjSp6o477niuY8fDS1as2F26efMdTqSbOa8yAJqdJEmSjD+cWqzRo0cPWr58+apXXnnF5xu0IIoNaBb69j3nmO7dZz6UJElW797n/EtZ2bSPp72JD+aRRx556ZVXXtleUFBxcMeOQ3Pe/NxfThFa1N89FRU/vKey8mvT096xv7lkADQLmzdXL0iSXadnMpnGiPhm2nvYd7m5PV/Ky7tgda9eX1y4evUPJ0RElJSUdOnZs+fXI+LSlOe9J2eeeeaRETm/bmjofEjaW/a3FlVpQOt12mmVh55++vDjjjnmmN5pb2HfJEnSbuLEiT2PPLLoiwUFi36Yk1MwMCKiT5+ZZy5b1qndvHnzWkQMREQMHjz4PwcPXv/YihVXfjXtLfubIACahT59+nxp+PDhtx1zzDEt5i+Ltuqwww4r+frXv37T6NGje771uU996lOTx44de3zXrl1/EhFPDBq09J6JEzdeHRHR0ND/8tzcium9es389sCBV93eteuRzf7/utetW7epoqLiS716ffr/pr1lfxMEQOouvfTSK3v06HFGRER9ff3StPfw7p5++un1jz/++Pd27NhRExHx+YqKbydJkkRENDQ0VO4s6HJwJpP5dpcuXQ659957X1y2bNmjhx564fcLCjb8uKlpy9pduzK9mpp2tHvttf95Lt3fyd7t2LHjrmXLls0dOnT1f6W9ZX9zDwGQurVr1z6Ul5c3bOPGjS9s3rx53YwZM8bn5OQcfv/9939/zZo1u9Lex9/6yytAnt/zdX2SPLXnVSE5OTl3bnl1fdXPf/X7xSeccEJ9RMT8+fOXnHTSSePXrRvwsXbturcrLt7y5aVLv/JsxFdS+h28d9dff/21aW84ULzsEGhWTjvttIFDhgz5U0lJSdc//OEPI++8886n097E3p1wwgkf+d3vfvfnUaNGlW1Nciuee+yPf3zz85WVlXk7dnzylogOxTk5O55Ztux7l6S1lbfnhABoVgYPHnxGRUVFSVNTU3To0GF4RDyd9ibeXd++fYsmTJhwWUSc2b59+5HFpRUDI+JvgmDp0qU7M5nvv/GBVSUlF/42N3fnw6tWXf+dA72Xt+ceAqBZKSwsPCki4tVXX73n5ptvviHtPezdjh2jZ99+e+f+EREPP/zwHffffP333/pzxo8f/8bnG3TufNhBeXn92m/c+MgPD+RO3p0gAJqNY489trSoqOjIxsbGeOGFF77l3QpbhrKyhuqOHTs/8k7PDx8+vOOmTYWHzpw58/iIiHbt+tW2a/fcV7ZurW44cCvZG0EANBvl5eXDcnJycjZt2rRs3rx5C9Pew3tTXPzaDX373n9DRETv3p86/q3Pb9585nWrVw/ov2nTplcjIjZsuD2zfPlN7xgQpMM9BECzcdNNN913/vnnT8jPzx+Z9hbeu4ceemjlnh9nZXXIeevzu3Y9/YskaVzy7LMDzi4pOWVFVVV9Q21t7QNLlizxiZbNiFcZALDfzZo169i1azcX/eY3t9/z5senT58+Y+7cuXPS2sVfOSEAYL/Lycnp1Ldv9y5vfXzjxo2L09jD33NCAAC4qRCAA2/KlCnDL7nkkivS3sFfCQIADriFCxc+s2HDhjV7vp7xtSuumfrlb3tlSYrcQwDAAbdixYrdEfHTiIgkSZLPXHnjiUmm0asOUuQeAgDAJQMAQBAAACEIADiAKisrC97t+eHDhxdUVVXlHqg9/JV7CAA4YGbMmHHdnDlzznvr4+PGjTv46KOPvryxsbF89+7dW9q1a1ezffv2dVdeeeWlPuTqwPAqAwAOiMrKyqz6+vpvvN1zDz744AtHH310QVlZ2ciIiF27du2qrq7+7ahRoyoiYtkBHdpGCQIADojq6urGiFj3ds9lMpnMGWeccWlTU9OGoqKik9u3b1/04osv/uqxxx4TAweISwYAREREeXl59sqVKxvTPKKfOHHi0JKSkhENDQ09N23a9FBWVtaRd95554/S2tOWOCEAIM4444xB55577jW1tbW3RcTVae14/PHHX/zIRz6S/fDDD98QEdGvX7+n09rS1ggCgDYsSZLkoosuOnvUqFH/VlBQ0LWurq46zT3r16/fGRFvfALismXL6pMkSfr3v/mxrKysnVu23PDJtWt/syHFia2WIABooyZMmFB+2WWXXdG7d+8Z7dq9/ir0TCbTI+VZf+P1GJiz4OWXzzgyol10797pfz/72UvnZjI7H/vJT35yU9r7WhPvQwDQRh1xxBF39OnT540YiIjYunXrAylO+jsDBvz0jpdfnjoqol20b782U1y8bE2PHl0Pr6urc0rwIXNCANBGZWdn/90bABUUFHRMY8s7aWpa/LUhQ26ora19eliSvPhahw611955Z9ZdixYtakp7W2sjCADarpw3f5HJZGLp0qXPpjXm7bz88nUvRsR5Y8aMGbxp06ZXnn76ma1pb2qtvOwQoA2qqqrKnTlz5ivFxcUHvfnxrVu3bl22bNl/XH311d9MaxvpcEIA0AaNHDly7JtjoL6+fvvy5cu/+9xzz92TlZXl74Y2yL90gP1k+vTpV8ydO/dt36o3bQcddFDlnh+/9tprz1ZXV19w/fXXP5bmJtIlCAD2k3Xr1t2Y9oZ3UldXt2rdunUPbNmy5cEnn3zymocffrgu7U2kyz0EAID3IQAABAEAEIIAAAhBAACEIAAAQhAAACEIAIAQBABACAIAIAQB0AYkSeLPOtgLn2UAtFpTpkw55pBDDvnxueee+8OImJP2HmjOfJYB0KpNnjx54rZt2xbcd999tWlvgeZMEAAA7iEAAAQBABCCAAAIQQAAhCAAAEIQAAAhCACAEAQAQAgCACAEAQAQggAACEEAAIQgAABCEAAAIQgAgBAEAEAIAgAgBAEAEIIAAAhBAACEIIAWq7R04qkVFZ/5TNo7gNZBEEALlZMz+svt2mVWpb0DaB2STCaT9gbgAyosrMzr1u3YScuXX/eLtLcALVt22gOAD27btqU78/O7LUh7B9DyOSEA2qxu3Y4u37lz65otW57ZlfYWSJt7CIA2q7Bw7OWdOo2YkvYOaA5cMgDarJycjVft2rVrZ9o7oDlwyQAAcMkAICIiSRJ/HtKm+Q8AaPNOOeWUyf/4j/+4cNiwYV3S3gJpEQRAm3fvvffeUVtb+90lS5a8lvYWSIt7CAAAJwRAyzFjxoyqSy65ZPbQoUML094CrY2XHQItRm1t7au5ubkPPfPMM1vT3gKtjUsGAIBLBgCAIAAAQhAAACEIAIAQBABACAIAIAQBABCCAAAIQQAAhCAAAEIQAAAhCACAEAQAQAgCACAEAQAQggAACEEAAIQgAABCEAAAIQgAgBAEAEAIAgAgBAEAEIIAAAhBAACEIAAAQhAAACEIAIAQBABACAIAIAQBABCCAAAIQQAAhCAAAEIQAAAhCACAEAQAQAgCACAEAQAQggAACEEAAIQgAABCEAAAIQgAgBAEAEAIAmAf9e//b3f073//88OHn3dBeXl59p7HO3U6rGjQoJ/cXF4+rSLNfcB7IwiA96Sy8v8c36PHI1sGDbpxfknJFcvKyk6vjIjIy8t5oa6uqM+YMb1+8NnPfnbLxRdf/C8RET16nPWtpUvPnpabe9DgVIcD74kgAN6Tdu3yyjp1enXha68dcvz69V+tyMn5h7sjIp5//vOX1dR8tGjt2hdO2rRp09U7dux4OiJi27ZHr+zV67v31tfH9NLSj5WmOh7YqySTyaS9AWhBkiRJKisvOy8rK+e155//5m1vfX78+PHlNTU1axYtWrQrIqKi4uxDs7M791u69D/vOPBrgfcqe+8/BeCvMq//X8S1ERETJ07s2bVr16obbrjhgT3Pl5WVjV2/fv1tEbErIiInp0vviGO/NGDApQe9+ur8n2/dWt2QznLg3bhkAHxgjY2Nu7Kysor3fD1gwD9996GH2tUtXrx4y57H2rXLqc9ksttv2DDrqsbG23aUlp58fDprgXfjkgHwoUuSJIl44zThDSUlxx+2fv3vn37r40D6XDIA9sngwV85MUl6T6itveOrNTUP74iIOPnkk4/s2bPnuIj41pt/7rp1Dz+VykhgrwQBsE8ymaFfeemlKSf27j3k9MGD76zetWvlhuzsyTnPP/+Hm9LeBrx37iEA9kl9/cIX+/e//Nt1dbs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      " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# create figure and axis then draw the basemap of tracts\n", - "fig, ax = plt.subplots(figsize=(9, 9))\n", - "tracts_subset_lisa.plot(ax=ax, facecolor='#999999', edgecolor='k', linewidth=0.1)\n", - "\n", - "# plot each quandrant's tracts (if significant LISA statistic) in a different color\n", - "quadrant_colors = {'HH':'r', 'LL':'b', 'LH':'skyblue', 'HL':'pink'}\n", - "for q, c in quadrant_colors.items():\n", - " mask = tracts_subset_lisa['significant'] & (tracts_lisa['quadrant']==q)\n", - " rows = tracts_subset_lisa.loc[mask]\n", - " rows.plot(ax=ax, color=c, edgecolor='k', linewidth=0.1)\n", - "\n", - "ax.axis('off')\n", - "fig.savefig('clusters_subset.png', bbox_inches='tight', dpi=600)\n", - "plt.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.2" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/data/data-pipeline/data_pipeline/ipython/experiment_4_texas_example/spatial_analysis_1_12292021_cal_enviroscreen_economic-lisa.ipynb b/data/data-pipeline/data_pipeline/ipython/experiment_4_texas_example/spatial_analysis_1_12292021_cal_enviroscreen_economic-lisa.ipynb deleted file mode 100644 index 8a4d3e51..00000000 --- a/data/data-pipeline/data_pipeline/ipython/experiment_4_texas_example/spatial_analysis_1_12292021_cal_enviroscreen_economic-lisa.ipynb +++ /dev/null @@ -1,2187 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Spatial weights and ESDA (Exploratory Spatial Data Analysis) - TX, ACS 2019\n", - "\n", - "ESDA offers direct parallels to many of the aspatial descriptive statistics that are already part of most quantitative research projects: The geographic mean and spatial standard deviation, for example, tell us where the center of our study area is how compact our population is within that area. \n", - "\n", - "I use ESDA to generalize about our observations and to identify outliers and discontinuities that may disrupt our Score L model or suggest hypotheses about relationships we may wish to explore further as it pertains to health disparities and educational disparities.\n", - "\n", - "This is very pertinent to Score L as we make strict assumptions about distributions - for any thresholding approach it assumes that the data represents no measurement bias and no systematic error. Everything is homogeneous and is treated equally.\n", - "\n", - "ESDA can also be important in confirmatory statistical analysis as spatial autocorrelation can bias outcomes in many spatial and aspatial statistical models. \n", - "\n", - "Testing for spatial autocorrelation and locating clusters of correlated residuals will be important in explaining why certain scores are associated together.\n", - "\n", - "\n", - "For example, in section 6, I use LISA's to identify geographical clusters of values or find geographcial outliers. They are a very useful tool that can quickly return areas in which values are concentrated and provide suggestive evidence about the processes that might be at work - namely median income. I will be extending this to educational attainment and job opportunities in a given radius - indicating economic mobility." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.9.0 2.3.0\n" - ] - } - ], - "source": [ - "import geopandas as gpd\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import pandas as pd\n", - "import pysal as ps\n", - "import seaborn as sns\n", - "from scipy.stats import stats\n", - "\n", - "np.random.seed(0)\n", - "print(gpd.__version__, ps.__version__)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Load the tracts data set" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(5265, 38)" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# load geojson, display shape\n", - "tracts = gpd.read_file('census/census_tracts_data.geojson')\n", - "tracts = tracts.set_index('index')\n", - "tracts.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['STATEFP', 'COUNTYFP', 'TRACTCE', 'NAME', 'NAMELSAD', 'MTFCC',\n", - " 'FUNCSTAT', 'ALAND', 'AWATER', 'INTPTLAT', 'INTPTLON', 'total_pop',\n", - " 'median_age', 'pct_hispanic', 'pct_white', 'pct_black', 'pct_asian',\n", - " 'pct_male', 'pct_single_family_home', 'med_home_value',\n", - " 'med_rooms_per_home', 'pct_built_before_1940', 'pct_renting',\n", - " 'rental_vacancy_rate', 'avg_renter_household_size', 'med_gross_rent',\n", - " 'med_household_income', 'mean_commute_time', 'pct_commute_drive_alone',\n", - " 'pct_below_poverty', 'pct_college_grad_student',\n", - " 'pct_same_residence_year_ago', 'pct_bachelors_degree',\n", - " 'pct_english_only', 'pct_foreign_born', 'state', 'county', 'geometry'],\n", - " dtype='object')" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# what variables are present?\n", - "tracts.columns" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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      STATEFPCOUNTYFPTRACTCENAMENAMELSADMTFCCFUNCSTATALANDAWATERINTPTLAT...pct_commute_drive_alonepct_below_povertypct_college_grad_studentpct_same_residence_year_agopct_bachelors_degreepct_english_onlypct_foreign_bornstatecountygeometry
      index
      48001950100480019501009501Census Tract 9501G5020S4833066197864313+31.9714680...84.016.330.64775.091.24495.00.448001MULTIPOLYGON (((-95.69702 31.97597, -95.69699 ...
      48001950401480019504019504.01Census Tract 9504.01G5020S16549991296734+31.7346372...100.00.034.24838.075.24800.01.648001MULTIPOLYGON (((-95.84791 31.75753, -95.84806 ...
      48001950402480019504029504.02Census Tract 9504.02G5020S724720392627857+31.8000515...100.021.533.87506.069.77501.01.048001MULTIPOLYGON (((-95.98357 31.78925, -95.98354 ...
      48001950500480019505009505Census Tract 9505G5020S2313205299223+31.7878850...85.119.428.24423.084.04178.00.748001MULTIPOLYGON (((-95.68769 31.81041, -95.68741 ...
      48001950600480019506009506Census Tract 9506G5020S20653883329641+31.7502049...79.917.439.35109.071.24890.00.048001MULTIPOLYGON (((-95.70774 31.72559, -95.70765 ...
      \n", - "

      5 rows Ă— 38 columns

      \n", - "
      " - ], - "text/plain": [ - " STATEFP COUNTYFP TRACTCE NAME NAMELSAD MTFCC \\\n", - "index \n", - "48001950100 48 001 950100 9501 Census Tract 9501 G5020 \n", - "48001950401 48 001 950401 9504.01 Census Tract 9504.01 G5020 \n", - "48001950402 48 001 950402 9504.02 Census Tract 9504.02 G5020 \n", - "48001950500 48 001 950500 9505 Census Tract 9505 G5020 \n", - "48001950600 48 001 950600 9506 Census Tract 9506 G5020 \n", - "\n", - " FUNCSTAT ALAND AWATER INTPTLAT ... \\\n", - "index ... \n", - "48001950100 S 483306619 7864313 +31.9714680 ... \n", - "48001950401 S 16549991 296734 +31.7346372 ... \n", - "48001950402 S 72472039 2627857 +31.8000515 ... \n", - "48001950500 S 23132052 99223 +31.7878850 ... \n", - "48001950600 S 20653883 329641 +31.7502049 ... \n", - "\n", - " pct_commute_drive_alone pct_below_poverty \\\n", - "index \n", - "48001950100 84.0 16.3 \n", - "48001950401 100.0 0.0 \n", - "48001950402 100.0 21.5 \n", - "48001950500 85.1 19.4 \n", - "48001950600 79.9 17.4 \n", - "\n", - " pct_college_grad_student pct_same_residence_year_ago \\\n", - "index \n", - "48001950100 30.6 4775.0 \n", - "48001950401 34.2 4838.0 \n", - "48001950402 33.8 7506.0 \n", - "48001950500 28.2 4423.0 \n", - "48001950600 39.3 5109.0 \n", - "\n", - " pct_bachelors_degree pct_english_only pct_foreign_born state \\\n", - "index \n", - "48001950100 91.2 4495.0 0.4 48 \n", - "48001950401 75.2 4800.0 1.6 48 \n", - "48001950402 69.7 7501.0 1.0 48 \n", - "48001950500 84.0 4178.0 0.7 48 \n", - "48001950600 71.2 4890.0 0.0 48 \n", - "\n", - " county geometry \n", - "index \n", - "48001950100 001 MULTIPOLYGON (((-95.69702 31.97597, -95.69699 ... \n", - "48001950401 001 MULTIPOLYGON (((-95.84791 31.75753, -95.84806 ... \n", - "48001950402 001 MULTIPOLYGON (((-95.98357 31.78925, -95.98354 ... \n", - "48001950500 001 MULTIPOLYGON (((-95.68769 31.81041, -95.68741 ... \n", - "48001950600 001 MULTIPOLYGON (((-95.70774 31.72559, -95.70765 ... \n", - "\n", - "[5 rows x 38 columns]" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# inspect the first 5 rows\n", - "tracts.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "calenviron = pd.read_csv(\"merged_df_score_ejcreen_2272.csv\", dtype={\n", - " \"FIPS_tract_id\": str\n", - "})\n", - "\n", - "calenviron.set_index(\"FIPS_tract_id\", inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "census_acs_2019 = pd.read_csv(\"../../../data_pipeline/data/dataset/census_acs_2019/usa.csv\")" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "# calculate pop density in persons per sq km\n", - "tracts['pop_density'] = tracts['total_pop'] / (tracts['ALAND'] / 1e6)\n", - "tracts = tracts.replace([np.inf, -np.inf], np.nan)\n", - "tracts = tracts.dropna(subset=['pop_density'])\n", - "\n", - "# project spatial geometries from lat-long to a meter-based projection\n", - "utm_tx = '+proj=utm +zone=18 +ellps=WGS84 +datum=WGS84 +units=m +no_defs'\n", - "tracts = tracts.to_crs(utm_tx)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "df_merged = calenviron.merge(tracts, how='inner', left_index=True, right_index=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(206, 75)" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_merged.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "tracts = tracts[tracts.index.isin(df_merged.index.values)]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1. Exploratory analysis\n", - "\n", - "Let's see what we've got with a couple key variables of interest." - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "count 206.000000\n", - "mean 1813.040557\n", - "std 1260.605954\n", - "min 25.235368\n", - "25% 1054.963496\n", - "50% 1634.745190\n", - "75% 2347.165295\n", - "max 8705.497048\n", - "Name: pop_density, dtype: float64" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# descriptive stats\n", - "tracts['pop_density'].describe()" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "count 206.000000\n", - "mean 41346.922330\n", - "std 12894.864314\n", - "min 15365.000000\n", - "25% 32067.750000\n", - "50% 40246.000000\n", - "75% 48517.250000\n", - "max 90215.000000\n", - "Name: med_household_income, dtype: float64" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# descriptive stats\n", - "tracts['med_household_income'].describe()" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "# create a function to choropleth map a df column\n", - "def map_variable(df, col, scheme='quantiles', k=10, cmap='plasma', figsize=(10,10)):\n", - " values = df.dropna(subset=[col])\n", - " ax = values.plot(column=col, scheme=scheme, k=k, cmap=cmap, figsize=figsize, legend = True)\n", - " ax.axis('off')\n", - " plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([False, False, False, False, False, False, False, False, False,\n", - " False, False, False, False, False, False, False, False, False,\n", - " False, False, False, False, False, False, False, False, False,\n", - " False, False, False, False, False, False, False, False, False,\n", - " False, False, False, False, False, False, False, False, False,\n", - " False, False, False, False, False, False, False, False, False,\n", - " False, False, False, False, False, False, False, False, False,\n", - " False, False, False, False, False, False, True, False, False,\n", - " False, False, False, False, False, False, False, False, False,\n", - " False, False, False, False, False, False, False, False, False,\n", - " False, False, False, False, False, False, False, False, False,\n", - " False, False, False, False, False, False, False, False, False,\n", - " False, False, False, False, False, False, False, False, False,\n", - " False, False, False, False, False, False, False, False, False,\n", - " False, False, False, False, False, False, False, False, False,\n", - " False, False, False, False, False, False, False, False, False,\n", - " False, False, False, False, False, False, False, False, False,\n", - " False, False, False, False, False, False, False, False, False,\n", - " False, False, False, False, False, False, False, False, False,\n", - " True, False, False, False, False, False, False, False, False,\n", - " False, False, False, False, False, False, False, False, False,\n", - " False, False, False, False, False, False, False, False, False,\n", - " False, False, False, False, False, False, True, False])" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tracts.index.isin(tracts[tracts['med_household_income'] < 20000].index)" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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      " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# map a variable\n", - "map_variable(tracts, 'pop_density')" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
      " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# map another variable\n", - "map_variable(tracts, 'med_household_income')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "It looks like these two variables might be negatively correlated?" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "r=0.0284, p=0.6851\n" - ] - } - ], - "source": [ - "# calculate the correlation coefficient and its p-value\n", - "x = tracts.dropna(subset=['pop_density', 'med_household_income'])['pop_density']\n", - "y = tracts.dropna(subset=['pop_density', 'med_household_income'])['med_household_income']\n", - "r, p = stats.pearsonr(x=x, y=y)\n", - "print('r={:.4f}, p={:.4f}'.format(r, p))" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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      " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# scatter plot them with matplotlib\n", - "fig, ax = plt.subplots()\n", - "ax.scatter(x=x, y=y, s=1)\n", - "ax.set_xlim(left=0)\n", - "ax.set_ylim(bottom=0)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "m=0.2908, b=40819.7676, r^2=0.0008, p=0.6851\n" - ] - } - ], - "source": [ - "# estimate a simple linear regression model with scipy\n", - "m, b, r, p, se = stats.linregress(x=x, y=y)\n", - "print('m={:.4f}, b={:.4f}, r^2={:.4f}, p={:.4f}'.format(m, b, r ** 2, p))" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "m=0.0103, b=10.5078, r^2=0.0009, p=0.6622\n" - ] - } - ], - "source": [ - "# estimate a log-log simple linear regression model with scipy\n", - "m, b, r, p, se = stats.linregress(x=np.log(x), y=np.log(y))\n", - "print('m={:.4f}, b={:.4f}, r^2={:.4f}, p={:.4f}'.format(m, b, r ** 2, p))" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/sarahluw/.pyenv/versions/3.6.2/envs/my-virtual-env-3.6.2/lib/python3.6/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n", - " FutureWarning\n" - ] - }, - { - "data": { - "image/png": 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\n", 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      " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# plot the log-log regression line with 95% CI using seaborn\n", - "fig, ax = plt.subplots(figsize=(6, 6))\n", - "ax = sns.regplot(np.log(x), np.log(y), marker='.', scatter_kws={'s':2}, ax=ax)\n", - "ax.set_xlim((3, 11))\n", - "ax.set_ylim((9.5, 12.5))\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2. Spatial weights matrix\n", - "\n", - "Spatial weights define the spatial connections among our units of analysis (tracts in this case).\n", - "\n", - "### 2.1. Contiguity-based weights: rook contiguity\n", - "\n", - "Using rook contiguity, two spatial units must share an edge of their boundaries to be considered neighbors. This isn't terribly common in practice (since queen is usually more useful, but it's worth understanding)." - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [], - "source": [ - "# get the tract labels (GEOIDs) and pick one to work with later\n", - "# tracts_subset = tracts[tracts.index.isin(df_merged.index.values)]\n", - "labels = tracts.index.tolist()\n", - "label = labels[5]" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 187 ms, sys: 9.43 ms, total: 196 ms\n", - "Wall time: 197 ms\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/sarahluw/.pyenv/versions/3.6.2/envs/my-virtual-env-3.6.2/lib/python3.6/site-packages/libpysal/weights/_contW_lists.py:31: ShapelyDeprecationWarning: Iteration over multi-part geometries is deprecated and will be removed in Shapely 2.0. Use the `geoms` property to access the constituent parts of a multi-part geometry.\n", - " return list(it.chain(*(_get_boundary_points(part.boundary) for part in shape)))\n", - "/Users/sarahluw/.pyenv/versions/3.6.2/envs/my-virtual-env-3.6.2/lib/python3.6/site-packages/libpysal/weights/weights.py:172: UserWarning: The weights matrix is not fully connected: \n", - " There are 157 disconnected components.\n", - " There are 124 islands with ids: 48025950500, 48029140300, 48029152000, 48029152201, 48029160901, 48029170401, 48029180602, 48029181819, 48029181823, 48037010700, 48039661200, 48041000201, 48061012508, 48061013104, 48061013206, 48085031709, 48085032003, 48113001502, 48113003902, 48113006100, 48113006200, 48113007914, 48113008703, 48113009201, 48113012206, 48113013717, 48113013725, 48113015404, 48113015900, 48113016202, 48113016610, 48113016803, 48113016902, 48113017704, 48113019040, 48113019206, 48121020504, 48121020601, 48121021637, 48135003100, 48139061600, 48141002900, 48141003502, 48143950500, 48167721900, 48167722200, 48167723000, 48167724700, 48181001300, 48183000900, 48201220400, 48201223001, 48201230600, 48201231300, 48201231500, 48201251200, 48201312300, 48201312900, 48201313300, 48201320602, 48201323701, 48201331602, 48201332500, 48201341301, 48201350500, 48201422301, 48201450400, 48201452100, 48201453401, 48201453502, 48201520500, 48201522000, 48201532001, 48201532100, 48201541800, 48201550302, 48201550402, 48201551500, 48201552700, 48209010100, 48209010702, 48215020101, 48215020723, 48215020804, 48215021403, 48215023514, 48215024203, 48245002400, 48273020300, 48277000800, 48303001708, 48303002001, 48303002500, 48319950100, 48321730201, 48329010106, 48339690300, 48355001100, 48355001300, 48355002704, 48363000900, 48375014700, 48423002003, 48439100201, 48439101301, 48439102401, 48439106507, 48439110302, 48439111523, 48439123400, 48441010900, 48453000402, 48453001818, 48453001915, 48453002201, 48453002307, 48453002314, 48453002402, 48469000501, 48471790500, 48479000107, 48479001300, 48479001900, 48485011000.\n", - " warnings.warn(message)\n" - ] - } - ], - "source": [ - "%%time\n", - "\n", - "from libpysal.weights.contiguity import Queen, Rook\n", - "from libpysal.weights import KNN, min_threshold_distance\n", - "from libpysal.weights import DistanceBand, lag_spatial\n", - "# calculate rook spatial weights\n", - "w_rook = Rook.from_dataframe(tracts, ids=labels, id_order=labels)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2.2. Contiguity-based weights: queen contiguity\n", - "\n", - "Using queen contiguity, two spatial units need only share a vertex (a single point) of their boundaries to be considered neighbors." - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 267 ms, sys: 7.2 ms, total: 275 ms\n", - "Wall time: 274 ms\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/sarahluw/.pyenv/versions/3.6.2/envs/my-virtual-env-3.6.2/lib/python3.6/site-packages/libpysal/weights/weights.py:172: UserWarning: The weights matrix is not fully connected: \n", - " There are 153 disconnected components.\n", - " There are 117 islands with ids: 48025950500, 48029140300, 48029152000, 48029152201, 48029160901, 48029170401, 48029180602, 48029181819, 48029181823, 48037010700, 48039661200, 48041000201, 48061012508, 48061013104, 48061013206, 48085031709, 48085032003, 48113001502, 48113003902, 48113006100, 48113006200, 48113007914, 48113008703, 48113009201, 48113012206, 48113013717, 48113013725, 48113015404, 48113015900, 48113016202, 48113016610, 48113016803, 48113016902, 48113017704, 48113019040, 48113019206, 48121020504, 48121020601, 48121021637, 48135003100, 48139061600, 48141002900, 48141003502, 48143950500, 48167721900, 48167722200, 48167723000, 48167724700, 48181001300, 48183000900, 48201220400, 48201223001, 48201230600, 48201231300, 48201231500, 48201251200, 48201312300, 48201312900, 48201313300, 48201320602, 48201323701, 48201331602, 48201332500, 48201341301, 48201350500, 48201422301, 48201450400, 48201452100, 48201453401, 48201453502, 48201520500, 48201522000, 48201541800, 48209010100, 48209010702, 48215020101, 48215020723, 48215020804, 48215021403, 48215023514, 48215024203, 48245002400, 48273020300, 48277000800, 48303001708, 48303002500, 48319950100, 48321730201, 48329010106, 48339690300, 48355001100, 48355001300, 48355002704, 48363000900, 48375014700, 48423002003, 48439100201, 48439101301, 48439102401, 48439106507, 48439110302, 48439111523, 48439123400, 48441010900, 48453000402, 48453001818, 48453001915, 48453002201, 48453002307, 48453002314, 48453002402, 48469000501, 48471790500, 48479000107, 48479001300, 48479001900, 48485011000.\n", - " warnings.warn(message)\n" - ] - } - ], - "source": [ - "%%time\n", - "# calculate queen spatial weights\n", - "w_queen = Queen.from_dataframe(tracts, ids=labels, id_order=labels)" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# find the neighbors of some tract\n", - "w_queen.neighbors[label]" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 38, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# this is a raw contiguity matrix, so weights are binary 1s and 0s meaning neighbor/not\n", - "w_queen.weights[label]" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0" - ] - }, - "execution_count": 39, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# how many neighbors does this tract have?\n", - "w_queen.cardinalities[label]" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "count 206.000000\n", - "mean 0.553398\n", - "std 0.701610\n", - "min 0.000000\n", - "25% 0.000000\n", - "50% 0.000000\n", - "75% 1.000000\n", - "max 2.000000\n", - "dtype: float64" - ] - }, - "execution_count": 40, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# convert cardinalities to series and describe data -- looks right-skewed\n", - "cardinalites_queen = pd.Series(w_queen.cardinalities)\n", - "cardinalites_queen.describe()" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
      " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# look at the distribution of number of neighbors\n", - "ax = cardinalites_queen.hist(bins=20)" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "206" - ] - }, - "execution_count": 42, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# number of observations\n", - "w_queen.n" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.5533980582524272" - ] - }, - "execution_count": 43, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# average number of neighbors\n", - "w_queen.mean_neighbors" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0" - ] - }, - "execution_count": 44, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# min number of neighbors\n", - "w_queen.min_neighbors" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "2" - ] - }, - "execution_count": 45, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# max number of neighbors\n", - "w_queen.max_neighbors" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "117" - ] - }, - "execution_count": 46, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# islands are observations with no neighbors, disconnected \n", - "# in space (can cause modeling problems)\n", - "len(w_queen.islands)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### Plot a census tract of interest, along with its neighbors:" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 3.3. Distance-based weights: *k*-nn\n", - "\n", - "Find the *k*-nearest neighbors of each tract" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 34.4 ms, sys: 2.39 ms, total: 36.8 ms\n", - "Wall time: 35.6 ms\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/sarahluw/.pyenv/versions/3.6.2/envs/my-virtual-env-3.6.2/lib/python3.6/site-packages/libpysal/weights/util.py:1073: ShapelyDeprecationWarning: The array interface is deprecated and will no longer work in Shapely 2.0. Convert the '.coords' to a numpy array instead.\n", - " data = np.vstack([np.array(shape.centroid) for shape in first_choice])\n", - "/Users/sarahluw/.pyenv/versions/3.6.2/envs/my-virtual-env-3.6.2/lib/python3.6/site-packages/libpysal/weights/weights.py:172: UserWarning: The weights matrix is not fully connected: \n", - " There are 2 disconnected components.\n", - " warnings.warn(message)\n" - ] - } - ], - "source": [ - "%%time\n", - "# k-nearest neighbors finds the closest k tract centroids to each tract centroid\n", - "w_knn = KNN.from_dataframe(tracts, k=6)" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['48029160901',\n", - " '48029152201',\n", - " '48029140300',\n", - " '48029171000',\n", - " '48029171100',\n", - " '48029170401']" - ] - }, - "execution_count": 48, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# they all have exactly k neighbors\n", - "w_knn.neighbors[label]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 3.4. Distance-based weights: distance band\n", - "\n", - "Here tracts are considered neighbors of some tract if they are within a given threshold distance of it. Distance band weights can be specified to take on continuous values rather than binary (1s and 0s), with these values being the inverse distance between each pair of \"neighboring\" units. The default (linear) distance-decay exponent is thus -1. If we set it to -2, we get gravity model weights." - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "189497.5691849163" - ] - }, - "execution_count": 49, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# first determine the minimum nearest neighbor distance so each unit is assured of at least one neighbor\n", - "x = tracts.centroid.x\n", - "y = tracts.centroid.y\n", - "coords = np.array([x, y]).T\n", - "threshold = min_threshold_distance(coords)\n", - "threshold" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 50.2 ms, sys: 3.37 ms, total: 53.5 ms\n", - "Wall time: 52.4 ms\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/sarahluw/.pyenv/versions/3.6.2/envs/my-virtual-env-3.6.2/lib/python3.6/site-packages/libpysal/weights/util.py:1073: ShapelyDeprecationWarning: The array interface is deprecated and will no longer work in Shapely 2.0. Convert the '.coords' to a numpy array instead.\n", - " data = np.vstack([np.array(shape.centroid) for shape in first_choice])\n", - "/Users/sarahluw/.pyenv/versions/3.6.2/envs/my-virtual-env-3.6.2/lib/python3.6/site-packages/scipy/sparse/data.py:117: RuntimeWarning: divide by zero encountered in reciprocal\n", - " return self._with_data(data ** n)\n", - "/Users/sarahluw/.pyenv/versions/3.6.2/envs/my-virtual-env-3.6.2/lib/python3.6/site-packages/libpysal/weights/weights.py:172: UserWarning: The weights matrix is not fully connected: \n", - " There are 3 disconnected components.\n", - " warnings.warn(message)\n" - ] - } - ], - "source": [ - "%%time\n", - "# default alpha=-1 and gravity weights alpha=-2\n", - "w_dist = DistanceBand.from_dataframe(tracts, threshold=threshold, binary=False, alpha=-1)" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "31" - ] - }, - "execution_count": 51, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# how many distance-band neighbors does our tract have?\n", - "len(w_dist.neighbors[label])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### Map the neighbors, colored by weight from nearest to furthest:" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
      " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(figsize=(6, 6))\n", - "tracts.plot(ax=ax, facecolor='k', edgecolor='gray', linewidth=1)\n", - "\n", - "# get the tract of interest and its neighbors/weights\n", - "tract = tracts.loc[[label]]\n", - "weights = pd.Series(w_dist[label])\n", - "neighbors = tracts.loc[weights.index, ['geometry']]\n", - "\n", - "# plot the tract's neighbors in blues by weight\n", - "neighbors['weights_scaled'] = weights\n", - "neighbors.plot(ax=ax, column='weights_scaled', cmap='Blues_r', edgecolor='gray', linewidth=1, scheme='Fisher_Jenks')\n", - "\n", - "# plot the tract of interest in white\n", - "tract.plot(ax=ax, facecolor='w', edgecolor='r', linewidth=2)\n", - "\n", - "# zoom to area of interest\n", - "xmin, ymin, xmax, ymax = neighbors.unary_union.bounds\n", - "ax.set_xlim(xmin, xmax)\n", - "ax.set_ylim(ymin, ymax)\n", - "ax.axis('off')\n", - "\n", - "ax.set_title('Neighbors of tract {}'.format(label))\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 3.5. Standardizing weights\n", - "\n", - "A spatial weights matrix with raw values (e.g., binary 1s and 0s for neighbor/not) is not always the best for analysis. Some sort of standardization is useful." - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{}" - ] - }, - "execution_count": 53, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# inspect the neighbors and weights of our tract\n", - "w_queen[label]" - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'O'" - ] - }, - "execution_count": 54, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# check the current transformation of the weights matrix (O = original)\n", - "w_queen.get_transform()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Typically, we want to apply a row-based transformation, so every row of the matrix sums up to 1." - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - 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"('WARNING: ', '48453001818', ' is an island (no neighbors)')\n", - "('WARNING: ', '48453001915', ' is an island (no neighbors)')\n", - "('WARNING: ', '48453002201', ' is an island (no neighbors)')\n", - "('WARNING: ', '48453002307', ' is an island (no neighbors)')\n", - "('WARNING: ', '48453002314', ' is an island (no neighbors)')\n", - "('WARNING: ', '48453002402', ' is an island (no neighbors)')\n", - "('WARNING: ', '48469000501', ' is an island (no neighbors)')\n", - "('WARNING: ', '48471790500', ' is an island (no neighbors)')\n", - "('WARNING: ', '48479000107', ' is an island (no neighbors)')\n", - "('WARNING: ', '48479001300', ' is an island (no neighbors)')\n", - "('WARNING: ', '48479001900', ' is an island (no neighbors)')\n", - "('WARNING: ', '48485011000', ' is an island (no neighbors)')\n" - ] - }, - { - "data": { - "text/plain": [ - "{}" - ] - }, - "execution_count": 55, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# transform the queen weights\n", - "w_queen.set_transform('R')\n", - "w_queen[label]" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'48025950500': 0.008232753933109464,\n", - " '48029140300': 0.08263783371911909,\n", - " '48029152201': 0.08267825082420831,\n", - " '48029160901': 0.10949849461731447,\n", - " '48029170401': 0.05902015079093859,\n", - " '48029171000': 0.06568872299207794,\n", - " '48029171100': 0.06533160833565992,\n", - " '48029171902': 0.05030550106153124,\n", - " '48029180602': 0.04761365262659351,\n", - " '48029180902': 0.04313386942604558,\n", - " '48029181705': 0.044247647578708686,\n", - " '48029181725': 0.046001174539673596,\n", - " '48029181819': 0.0320305944033456,\n", - " '48029181823': 0.030216202264941996,\n", - " '48029190901': 0.041381063926280305,\n", - " '48029191003': 0.04251954627070352,\n", - " '48029191202': 0.03716314849666733,\n", - " '48209010100': 0.011837514970117797,\n", - " '48209010702': 0.010929474202731253,\n", - " '48319950100': 0.005859739246132039,\n", - " '48453000203': 0.00751191909112949,\n", - " '48453000204': 0.007562361373592427,\n", - " '48453000402': 0.007590553418257023,\n", - " '48453000500': 0.007587744087148726,\n", - " '48453001818': 0.007241373146354116,\n", - " '48453001915': 0.008225440957594428,\n", - " '48453002201': 0.007250567605666212,\n", - " '48453002307': 0.00799504254960194,\n", - " '48453002314': 0.007897015057350196,\n", - " '48453002402': 0.00824010166185764,\n", - " '48469000501': 0.006570936825547536}" - ] - }, - "execution_count": 56, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# transform the linear-decay distance-based weights\n", - "w_dist.set_transform('R')\n", - "w_dist[label]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "PySAL supports the following transformations:\n", - "\n", - " - O: original, returning the object to the initial state\n", - " - B: binary, with every neighbor having assigned a weight of 1\n", - " - R: row-based, with all the neighbors of a given observation adding up to 1\n", - " - V: variance stabilizing, with the sum of all the weights being constrained to the number of observations\n", - "\n", - "**It can take a long time to calculate a weights matrix for a large data set.**\n", - "\n", - "Once you've created yours, you might want to save it to disk to re-use in subsequent analyses." - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "metadata": {}, - "outputs": [], - "source": [ - "# save your matrix to disk\n", - "import libpysal\n", - "f = libpysal.io.open('tracts_queen.gal', 'w')\n", - "f.write(w_queen)\n", - "f.close()" - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/sarahluw/.pyenv/versions/3.6.2/envs/my-virtual-env-3.6.2/lib/python3.6/site-packages/libpysal/weights/weights.py:172: UserWarning: The weights matrix is not fully connected: \n", - " There are 153 disconnected components.\n", - " There are 117 islands with ids: 48025950500, 48029140300, 48029152000, 48029152201, 48029160901, 48029170401, 48029180602, 48029181819, 48029181823, 48037010700, 48039661200, 48041000201, 48061012508, 48061013104, 48061013206, 48085031709, 48085032003, 48113001502, 48113003902, 48113006100, 48113006200, 48113007914, 48113008703, 48113009201, 48113012206, 48113013717, 48113013725, 48113015404, 48113015900, 48113016202, 48113016610, 48113016803, 48113016902, 48113017704, 48113019040, 48113019206, 48121020504, 48121020601, 48121021637, 48135003100, 48139061600, 48141002900, 48141003502, 48143950500, 48167721900, 48167722200, 48167723000, 48167724700, 48181001300, 48183000900, 48201220400, 48201223001, 48201230600, 48201231300, 48201231500, 48201251200, 48201312300, 48201312900, 48201313300, 48201320602, 48201323701, 48201331602, 48201332500, 48201341301, 48201350500, 48201422301, 48201450400, 48201452100, 48201453401, 48201453502, 48201520500, 48201522000, 48201541800, 48209010100, 48209010702, 48215020101, 48215020723, 48215020804, 48215021403, 48215023514, 48215024203, 48245002400, 48273020300, 48277000800, 48303001708, 48303002500, 48319950100, 48321730201, 48329010106, 48339690300, 48355001100, 48355001300, 48355002704, 48363000900, 48375014700, 48423002003, 48439100201, 48439101301, 48439102401, 48439106507, 48439110302, 48439111523, 48439123400, 48441010900, 48453000402, 48453001818, 48453001915, 48453002201, 48453002307, 48453002314, 48453002402, 48469000501, 48471790500, 48479000107, 48479001300, 48479001900, 48485011000.\n", - " warnings.warn(message)\n" - ] - }, - { - "data": { - "text/plain": [ - "{}" - ] - }, - "execution_count": 58, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# read a matrix from disk (notice its transformation)\n", - "w_queen = libpysal.io.open('tracts_queen.gal', 'r').read()\n", - "w_queen[label]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 4. Spatial lag\n", - "\n", - "Spatial lag tells us about values near some spatial unit. Calculate spatial lag of the `med_household_income` variable. If the spatial weights matrix is row-standardized, then the spatial lag is the average value of an observation's neighbors." - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "metadata": {}, - "outputs": [], - "source": [ - "# pick a variable to investigate\n", - "col = 'med_household_income'" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "metadata": {}, - "outputs": [], - "source": [ - "# first drop null rows\n", - "tracts_not_null = tracts[[col, 'geometry']].dropna()\n", - "y = tracts_not_null[col]" - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "('WARNING: ', 0, ' is an island (no neighbors)')\n", - "('WARNING: ', 4, ' is an island (no neighbors)')\n", - "('WARNING: ', 5, ' is an island (no neighbors)')\n", - "('WARNING: ', 6, ' is an island (no neighbors)')\n", - "('WARNING: ', 7, ' is an island (no neighbors)')\n", - "('WARNING: ', 8, ' is an island (no neighbors)')\n", - 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Use the `geoms` property to access the constituent parts of a multi-part geometry.\n", - " return list(it.chain(*(_get_boundary_points(part.boundary) for part in shape)))\n", - "/Users/sarahluw/.pyenv/versions/3.6.2/envs/my-virtual-env-3.6.2/lib/python3.6/site-packages/libpysal/weights/weights.py:172: UserWarning: The weights matrix is not fully connected: \n", - " There are 153 disconnected components.\n", - " There are 117 islands with ids: 0, 4, 5, 6, 7, 8, 12, 16, 17, 21, 22, 23, 28, 29, 30, 34, 35, 36, 37, 38, 39, 43, 44, 45, 46, 47, 48, 51, 52, 53, 56, 57, 58, 59, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 77, 79, 80, 85, 88, 89, 90, 91, 94, 95, 96, 97, 99, 100, 102, 103, 104, 105, 106, 110, 111, 114, 115, 118, 120, 129, 134, 135, 136, 137, 138, 141, 142, 147, 152, 153, 154, 160, 163, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 181, 182, 186, 188, 189, 192, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205.\n", - " warnings.warn(message)\n" - ] - } - ], - "source": [ - "# recompute spatial weights for these observations then row-standardize\n", - "w_queen = Queen.from_dataframe(tracts_not_null)\n", - "w_queen.set_transform('R')" - ] - }, - { - "cell_type": "code", - "execution_count": 62, - "metadata": {}, - "outputs": [], - "source": [ - "# compute spatial lag\n", - "y_lag = lag_spatial(w_queen, y)" - ] - }, - { - "cell_type": "code", - "execution_count": 63, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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      " - ], - "text/plain": [ - " med_household_income med_household_income_lag\n", - "index \n", - "48113015900 29773 0\n", - "48439110302 42670 0\n", - "48029152000 40625 0\n", - "48029190901 39583 49821\n", - "48441010900 38513 0\n", - "48355001100 18717 0\n", - "48167724101 44583 35057\n", - "48277000800 25491 0\n", - "48201341301 50563 0\n", - "48061013903 24472 29986" - ] - }, - "execution_count": 63, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "col_lag = '{}_lag'.format(col)\n", - "data_lag = pd.DataFrame(data={col:y, col_lag:y_lag}).astype(int)\n", - "data_lag.sample(10)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 6. Spatial autocorrelation\n", - "\n", - "Statistical models typically assume that the observations are independent of each other. This assumption is violated when a variable's value at one location is correlated with its value at nearby locations. This is called spatial autocorrelation, and is common in the real world due to proximity-based spillover effects. Substantive spatial autocorrelation can be explained by social or economic theory that describes a spatial relationship. Nuisance spatial autocorrelation stems from data problems.\n", - "\n", - "*Positive spatial autocorrelation*: nearby values tend to be more similar (e.g. income, home values, temperature, rainfall)\n", - "\n", - "*Negative spatial autocorrelation*: nearby values tend to be more dissimilar (e.g. fire stations, grocery stores)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 6.1. Moran's I\n", - "\n", - "Moran's I measures *global* spatial autocorrelation: do values tend to be near other (dis)similar values. Values > 1 indicate positive spatial autocorrelation, and values < 1 indicate negative spatial autocorrelation." - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "metadata": {}, - "outputs": [], - "source": [ - "# calculate the statistic\n", - "from esda.moran import Moran\n", - "mi = Moran(data_lag[col], w_queen)" - ] - }, - { - "cell_type": "code", - "execution_count": 65, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.633274894004128" - ] - }, - "execution_count": 65, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# show the I value\n", - "mi.I" - ] - }, - { - "cell_type": "code", - "execution_count": 66, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.001" - ] - }, - "execution_count": 66, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# statistical inference: show the p value\n", - "mi.p_sim" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If we generated a large number of maps with the same values but randomly allocated over space, and calculated Moran's I for each of these maps, only 0.05% of them would display a larger absolute value than the one we computed from the real-world data set. Thus there is a 0.05% chance of getting the observed value of Moran's I if the spatial distribution of our variable is random. We can conclude that the variable's distribution is statistically-significantly postively spatially autocorrelated. We'll talk more about inference in the other notebook" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 6.2. Moran plots\n", - "\n", - "A Moran plot scatter plots the spatially-lagged values (y-axis) vs the original variable's values (x-axis). Moran's I equals the slope of the line in a Moran plot, which makes this all a bit easier to conceptualize." - ] - }, - { - "cell_type": "code", - "execution_count": 67, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
      " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(figsize=(6, 6))\n", - "sns.regplot(x=col, y=col_lag, data=data_lag, scatter_kws={'s':1})\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "metadata": {}, - "outputs": [], - "source": [ - "# standardize the vector (i.e., calculate z-scores)\n", - "y_std = (y - y.mean()) / y.std()" - ] - }, - { - "cell_type": "code", - "execution_count": 69, - "metadata": {}, - "outputs": [], - "source": [ - "# compute the spatial lag of the standardized vector and save it as a series with same index\n", - "y_std_lag = pd.Series(lag_spatial(w_queen, y_std), index=y_std.index, name=col_lag)" - ] - }, - { - "cell_type": "code", - "execution_count": 70, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
      " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# standardized moran's plot, ignoring outliers beyond 3 std devs\n", - "fig, ax = plt.subplots(figsize=(6, 6))\n", - "ax.set_xlim(-3, 3)\n", - "ax.set_ylim(-3, 3)\n", - "plt.axvline(0, c='k', alpha=0.5)\n", - "plt.axhline(0, c='k', alpha=0.5)\n", - "sns.regplot(ax=ax, x=y_std, y=y_std_lag, scatter_kws={'s':1})\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Notice the 95% confidence interval shading and the positive slope. Given the value of Moran's I that we calculated earlier (and its p-value), we can conclude that the slope of the line is statistically-significantly different from zero. This plot makes it visually apparent." - ] - }, - { - "cell_type": "code", - "execution_count": 71, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "m=0.2736, b=-0.0257, r^2=0.1591, p=0.0000\n" - ] - } - ], - "source": [ - "# estimate a simple linear regression model (of the line above)\n", - "m, b, r, p, se = stats.linregress(x=y_std, y=y_std_lag)\n", - "print('m={:.4f}, b={:.4f}, r^2={:.4f}, p={:.4f}'.format(m, b, r ** 2, p))" - ] - }, - { - "cell_type": "code", - "execution_count": 72, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.633274894004128" - ] - }, - "execution_count": 72, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# the slope is the same as moran's I, calculated earlier\n", - "mi.I" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 6.3. LISAs\n", - "\n", - "Local Indicators of Spatial Autocorrelation: are there specific areas with high concentrations of (dis)similar values?\n", - "\n", - "Moran's I tells us about spatial clustering across the data set as a whole. However, it does not tell us where these clusters occur. For that, we need a local measure. Essentially, we will classify the data set's observations into four groups based on the four quadrants of the Moran plot:\n", - "\n", - " 1. **HH**: high value near other high values (*hot spots*)\n", - " 1. **LL**: low value near other low values (*cold spots*)\n", - " 1. **HL**: high value near low values (*spatial outliers*)\n", - " 1. **LH**: low value near high values (*spatial outliers*)" - ] - }, - { - "cell_type": "code", - "execution_count": 73, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
      " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# standardized moran's plot again, from subsection above\n", - "fig, ax = plt.subplots(figsize=(6, 6))\n", - "ax.set_xlim(-3, 3)\n", - "ax.set_ylim(-3, 3)\n", - "ax.axvline(0, c='k', ls=':', alpha=0.5)\n", - "ax.axhline(0, c='k', ls=':', alpha=0.5)\n", - "ax.text(1.25, 1.25, 'HH', fontsize=20)\n", - "ax.text(1.25, -1.75, 'HL', fontsize=20)\n", - "ax.text(-1.75, 1.25, 'LH', fontsize=20)\n", - "ax.text(-1.75, -1.75, 'LL', fontsize=20)\n", - "ax.set_title('Moran Plot')\n", - "sns.regplot(ax=ax, x=y_std, y=y_std_lag, scatter_kws={'s':1})\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.2" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/data/data-pipeline/data_pipeline/ipython/experiment_4_texas_example/spatial_analysis_1_12292021_score_l_economic-lisa.ipynb b/data/data-pipeline/data_pipeline/ipython/experiment_4_texas_example/spatial_analysis_1_12292021_score_l_economic-lisa.ipynb deleted file mode 100644 index 76a1f58e..00000000 --- a/data/data-pipeline/data_pipeline/ipython/experiment_4_texas_example/spatial_analysis_1_12292021_score_l_economic-lisa.ipynb +++ /dev/null @@ -1,2103 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Spatial weights and ESDA (Exploratory Spatial Data Analysis) - TX, ACS 2019\n", - "\n", - "ESDA offers direct parallels to many of the aspatial descriptive statistics that are already part of most quantitative research projects: The geographic mean and spatial standard deviation, for example, tell us where the center of our study area is how compact our population is within that area. \n", - "\n", - "I use ESDA to generalize about our observations and to identify outliers and discontinuities that may disrupt our Score L model or suggest hypotheses about relationships we may wish to explore further as it pertains to health disparities and educational disparities.\n", - "\n", - "This is very pertinent to Score L as we make strict assumptions about distributions - for any thresholding approach it assumes that the data represents no measurement bias and no systematic error. Everything is homogeneous and is treated equally.\n", - "\n", - "ESDA can also be important in confirmatory statistical analysis as spatial autocorrelation can bias outcomes in many spatial and aspatial statistical models. \n", - "\n", - "Testing for spatial autocorrelation and locating clusters of correlated residuals will be important in explaining why certain scores are associated together.\n", - "\n", - "\n", - "For example, in section 6, I use LISA's to identify geographical clusters of values or find geographcial outliers. They are a very useful tool that can quickly return areas in which values are concentrated and provide suggestive evidence about the processes that might be at work - namely median income. I will be extending this to educational attainment and job opportunities in a given radius - indicating economic mobility." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/sarahluw/.pyenv/versions/3.6.2/envs/my-virtual-env-3.6.2/lib/python3.6/site-packages/geopandas/_compat.py:110: UserWarning: The Shapely GEOS version (3.9.1-CAPI-1.14.2) is incompatible with the GEOS version PyGEOS was compiled with (3.10.1-CAPI-1.16.0). Conversions between both will be slow.\n", - " shapely_geos_version, geos_capi_version_string\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.9.0 2.3.0\n" - ] - } - ], - "source": [ - "import geopandas as gpd\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import pandas as pd\n", - "import pysal as ps\n", - "import seaborn as sns\n", - "from scipy.stats import stats\n", - "\n", - "np.random.seed(0)\n", - "print(gpd.__version__, ps.__version__)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Load the tracts data set" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(5265, 38)" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# load geojson, display shape\n", - "tracts = gpd.read_file('census/census_tracts_data.geojson')\n", - "tracts = tracts.set_index('index')\n", - "tracts.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['STATEFP', 'COUNTYFP', 'TRACTCE', 'NAME', 'NAMELSAD', 'MTFCC',\n", - " 'FUNCSTAT', 'ALAND', 'AWATER', 'INTPTLAT', 'INTPTLON', 'total_pop',\n", - " 'median_age', 'pct_hispanic', 'pct_white', 'pct_black', 'pct_asian',\n", - " 'pct_male', 'pct_single_family_home', 'med_home_value',\n", - " 'med_rooms_per_home', 'pct_built_before_1940', 'pct_renting',\n", - " 'rental_vacancy_rate', 'avg_renter_household_size', 'med_gross_rent',\n", - " 'med_household_income', 'mean_commute_time', 'pct_commute_drive_alone',\n", - " 'pct_below_poverty', 'pct_college_grad_student',\n", - " 'pct_same_residence_year_ago', 'pct_bachelors_degree',\n", - " 'pct_english_only', 'pct_foreign_born', 'state', 'county', 'geometry'],\n", - " dtype='object')" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# what variables are present?\n", - "tracts.columns" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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      STATEFPCOUNTYFPTRACTCENAMENAMELSADMTFCCFUNCSTATALANDAWATERINTPTLAT...pct_commute_drive_alonepct_below_povertypct_college_grad_studentpct_same_residence_year_agopct_bachelors_degreepct_english_onlypct_foreign_bornstatecountygeometry
      index
      48001950100480019501009501Census Tract 9501G5020S4833066197864313+31.9714680...84.016.330.64775.091.24495.00.448001MULTIPOLYGON (((-95.69702 31.97597, -95.69699 ...
      48001950401480019504019504.01Census Tract 9504.01G5020S16549991296734+31.7346372...100.00.034.24838.075.24800.01.648001MULTIPOLYGON (((-95.84791 31.75753, -95.84806 ...
      48001950402480019504029504.02Census Tract 9504.02G5020S724720392627857+31.8000515...100.021.533.87506.069.77501.01.048001MULTIPOLYGON (((-95.98357 31.78925, -95.98354 ...
      48001950500480019505009505Census Tract 9505G5020S2313205299223+31.7878850...85.119.428.24423.084.04178.00.748001MULTIPOLYGON (((-95.68769 31.81041, -95.68741 ...
      48001950600480019506009506Census Tract 9506G5020S20653883329641+31.7502049...79.917.439.35109.071.24890.00.048001MULTIPOLYGON (((-95.70774 31.72559, -95.70765 ...
      \n", - "

      5 rows Ă— 38 columns

      \n", - "
      " - ], - "text/plain": [ - " STATEFP COUNTYFP TRACTCE NAME NAMELSAD MTFCC \\\n", - "index \n", - "48001950100 48 001 950100 9501 Census Tract 9501 G5020 \n", - "48001950401 48 001 950401 9504.01 Census Tract 9504.01 G5020 \n", - "48001950402 48 001 950402 9504.02 Census Tract 9504.02 G5020 \n", - "48001950500 48 001 950500 9505 Census Tract 9505 G5020 \n", - "48001950600 48 001 950600 9506 Census Tract 9506 G5020 \n", - "\n", - " FUNCSTAT ALAND AWATER INTPTLAT ... \\\n", - "index ... \n", - "48001950100 S 483306619 7864313 +31.9714680 ... \n", - "48001950401 S 16549991 296734 +31.7346372 ... \n", - "48001950402 S 72472039 2627857 +31.8000515 ... \n", - "48001950500 S 23132052 99223 +31.7878850 ... \n", - "48001950600 S 20653883 329641 +31.7502049 ... \n", - "\n", - " pct_commute_drive_alone pct_below_poverty \\\n", - "index \n", - "48001950100 84.0 16.3 \n", - "48001950401 100.0 0.0 \n", - "48001950402 100.0 21.5 \n", - "48001950500 85.1 19.4 \n", - "48001950600 79.9 17.4 \n", - "\n", - " pct_college_grad_student pct_same_residence_year_ago \\\n", - "index \n", - "48001950100 30.6 4775.0 \n", - "48001950401 34.2 4838.0 \n", - "48001950402 33.8 7506.0 \n", - "48001950500 28.2 4423.0 \n", - "48001950600 39.3 5109.0 \n", - "\n", - " pct_bachelors_degree pct_english_only pct_foreign_born state \\\n", - "index \n", - "48001950100 91.2 4495.0 0.4 48 \n", - "48001950401 75.2 4800.0 1.6 48 \n", - "48001950402 69.7 7501.0 1.0 48 \n", - "48001950500 84.0 4178.0 0.7 48 \n", - "48001950600 71.2 4890.0 0.0 48 \n", - "\n", - " county geometry \n", - "index \n", - "48001950100 001 MULTIPOLYGON (((-95.69702 31.97597, -95.69699 ... \n", - "48001950401 001 MULTIPOLYGON (((-95.84791 31.75753, -95.84806 ... \n", - "48001950402 001 MULTIPOLYGON (((-95.98357 31.78925, -95.98354 ... \n", - "48001950500 001 MULTIPOLYGON (((-95.68769 31.81041, -95.68741 ... \n", - "48001950600 001 MULTIPOLYGON (((-95.70774 31.72559, -95.70765 ... \n", - "\n", - "[5 rows x 38 columns]" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# inspect the first 5 rows\n", - "tracts.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "score_l_df = pd.read_csv(\"merged_df_score_l_2794.csv\", dtype={\n", - " \"FIPS_tract_id\": str\n", - "})\n", - "\n", - "score_l_df.set_index(\"FIPS_tract_id\", inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "census_acs_2019 = pd.read_csv(\"../../../data_pipeline/data/dataset/census_acs_2019/usa.csv\")" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# calculate pop density in persons per sq km\n", - "tracts['pop_density'] = tracts['total_pop'] / (tracts['ALAND'] / 1e6)\n", - "tracts = tracts.replace([np.inf, -np.inf], np.nan)\n", - "tracts = tracts.dropna(subset=['pop_density'])\n", - "\n", - "# project spatial geometries from lat-long to a meter-based projection\n", - "utm_tx = '+proj=utm +zone=18 +ellps=WGS84 +datum=WGS84 +units=m +no_defs'\n", - "tracts = tracts.to_crs(utm_tx)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "df_merged = score_l_df.merge(tracts, how='inner', left_index=True, right_index=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(104, 75)" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_merged.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "tracts = tracts[tracts.index.isin(df_merged.index.values)]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1. Exploratory analysis\n", - "\n", - "Let's see what we've got with a couple key variables of interest." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "count 104.000000\n", - "mean 2607.677071\n", - "std 2474.100685\n", - "min 36.175061\n", - "25% 997.242613\n", - "50% 2072.942692\n", - "75% 3287.516789\n", - "max 15581.109178\n", - "Name: pop_density, dtype: float64" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# descriptive stats\n", - "tracts['pop_density'].describe()" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "count 103.000000\n", - "mean 34038.737864\n", - "std 8177.985053\n", - "min 11912.000000\n", - "25% 28953.000000\n", - "50% 35990.000000\n", - "75% 39517.000000\n", - "max 53516.000000\n", - "Name: med_household_income, dtype: float64" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# descriptive stats\n", - "tracts['med_household_income'].describe()" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "# create a function to choropleth map a df column\n", - "def map_variable(df, col, scheme='quantiles', k=10, cmap='plasma', figsize=(10,10)):\n", - " values = df.dropna(subset=[col])\n", - " ax = values.plot(column=col, scheme=scheme, k=k, cmap=cmap, figsize=figsize, legend = True)\n", - " ax.axis('off')\n", - " plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([False, False, False, False, True, True, False, False, False,\n", - " False, True, False, False, False, False, False, False, False,\n", - " False, False, False, False, False, False, False, False, False,\n", - " False, False, False, False, False, False, False, False, False,\n", - " False, False, False, False, False, False, False, False, False,\n", - " True, True, True, False, False, False, False, False, False,\n", - " False, False, True, False, False, False, False, False, False,\n", - " False, False, False, False, False, False, False, False, False,\n", - " False, False, False, False, False, False, False, False, False,\n", - " False, False, False, True, False, False, False, False, False,\n", - " False, False, False, False, False, False, False, False, False,\n", - " False, False, False, False, False])" - ] - }, - "execution_count": 47, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tracts.index.isin(tracts[tracts['med_household_income'] < 20000].index)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
      " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# map a variable\n", - "map_variable(c, 'pop_density')" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
      " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# map another variable\n", - "map_variable(tracts, 'med_household_income')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "It looks like these two variables might be negatively correlated?" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "r=0.1018, p=0.3061\n" - ] - } - ], - "source": [ - "# calculate the correlation coefficient and its p-value\n", - "x = tracts.dropna(subset=['pop_density', 'med_household_income'])['pop_density']\n", - "y = tracts.dropna(subset=['pop_density', 'med_household_income'])['med_household_income']\n", - "r, p = stats.pearsonr(x=x, y=y)\n", - "print('r={:.4f}, p={:.4f}'.format(r, p))" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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      " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# scatter plot them with matplotlib\n", - "fig, ax = plt.subplots()\n", - "ax.scatter(x=x, y=y, s=1)\n", - "ax.set_xlim(left=0)\n", - "ax.set_ylim(bottom=0)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "m=0.3366, b=33152.6311, r^2=0.0104, p=0.3061\n" - ] - } - ], - "source": [ - "# estimate a simple linear regression model with scipy\n", - "m, b, r, p, se = stats.linregress(x=x, y=y)\n", - "print('m={:.4f}, b={:.4f}, r^2={:.4f}, p={:.4f}'.format(m, b, r ** 2, p))" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "m=0.0172, b=10.2724, r^2=0.0035, p=0.5508\n" - ] - } - ], - "source": [ - "# estimate a log-log simple linear regression model with scipy\n", - "m, b, r, p, se = stats.linregress(x=np.log(x), y=np.log(y))\n", - "print('m={:.4f}, b={:.4f}, r^2={:.4f}, p={:.4f}'.format(m, b, r ** 2, p))" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/sarahluw/.pyenv/versions/3.6.2/envs/my-virtual-env-3.6.2/lib/python3.6/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n", - " FutureWarning\n" - ] - }, - { - "data": { - "image/png": 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\n", 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      " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# plot the log-log regression line with 95% CI using seaborn\n", - "fig, ax = plt.subplots(figsize=(6, 6))\n", - "ax = sns.regplot(np.log(x), np.log(y), marker='.', scatter_kws={'s':2}, ax=ax)\n", - "ax.set_xlim((3, 11))\n", - "ax.set_ylim((9.5, 12.5))\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2. Spatial weights matrix\n", - "\n", - "Spatial weights define the spatial connections among our units of analysis (tracts in this case).\n", - "\n", - "### 2.1. Contiguity-based weights: rook contiguity\n", - "\n", - "Using rook contiguity, two spatial units must share an edge of their boundaries to be considered neighbors. This isn't terribly common in practice (since queen is usually more useful, but it's worth understanding)." - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": {}, - "outputs": [], - "source": [ - "# get the tract labels (GEOIDs) and pick one to work with later\n", - "# tracts_subset = tracts[tracts.index.isin(df_merged.index.values)]\n", - "labels = tracts.index.tolist()\n", - "label = labels[5]" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 93.6 ms, sys: 6.18 ms, total: 99.8 ms\n", - "Wall time: 98.3 ms\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/sarahluw/.pyenv/versions/3.6.2/envs/my-virtual-env-3.6.2/lib/python3.6/site-packages/libpysal/weights/_contW_lists.py:31: ShapelyDeprecationWarning: Iteration over multi-part geometries is deprecated and will be removed in Shapely 2.0. Use the `geoms` property to access the constituent parts of a multi-part geometry.\n", - " return list(it.chain(*(_get_boundary_points(part.boundary) for part in shape)))\n", - "/Users/sarahluw/.pyenv/versions/3.6.2/envs/my-virtual-env-3.6.2/lib/python3.6/site-packages/libpysal/weights/weights.py:172: UserWarning: The weights matrix is not fully connected: \n", - " There are 91 disconnected components.\n", - " There are 81 islands with ids: 48027023202, 48027023500, 48029110800, 48029130600, 48029150800, 48029160501, 48029170101, 48029180702, 48029191304, 48037010500, 48061013401, 48061013802, 48113003700, 48113006002, 48113007201, 48113007821, 48113007823, 48113007827, 48113008900, 48113009000, 48113009202, 48113009610, 48113010703, 48113010903, 48113011104, 48113011300, 48113012210, 48113014103, 48113014308, 48113014407, 48113018141, 48113019034, 48121021739, 48141000301, 48141001001, 48141001115, 48145000400, 48167724200, 48167725900, 48185180400, 48201233103, 48201240100, 48201310800, 48201311800, 48201312200, 48201312800, 48201313800, 48201341201, 48201420500, 48201421201, 48201432002, 48201432901, 48201453403, 48201453601, 48201454400, 48201521200, 48201534001, 48201550301, 48201550401, 48209010302, 48225950400, 48303000202, 48303001702, 48309002700, 48339693800, 48347950900, 48355003305, 48423000201, 48439102301, 48439105204, 48439106001, 48439106600, 48439121704, 48439121903, 48439121906, 48453001811, 48453001863, 48453002315, 48453002419, 48479000105, 48479001717.\n", - " warnings.warn(message)\n" - ] - } - ], - "source": [ - "%%time\n", - "\n", - "from libpysal.weights.contiguity import Queen, Rook\n", - "from libpysal.weights import KNN, min_threshold_distance\n", - "from libpysal.weights import DistanceBand, lag_spatial\n", - "# calculate rook spatial weights\n", - "w_rook = Rook.from_dataframe(tracts, ids=labels, id_order=labels)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2.2. Contiguity-based weights: queen contiguity\n", - "\n", - "Using queen contiguity, two spatial units need only share a vertex (a single point) of their boundaries to be considered neighbors." - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 76.1 ms, sys: 4.81 ms, total: 80.9 ms\n", - "Wall time: 79.4 ms\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/sarahluw/.pyenv/versions/3.6.2/envs/my-virtual-env-3.6.2/lib/python3.6/site-packages/libpysal/weights/weights.py:172: UserWarning: The weights matrix is not fully connected: \n", - " There are 87 disconnected components.\n", - " There are 75 islands with ids: 48027023202, 48027023500, 48029110800, 48029130600, 48029150800, 48029160501, 48029170101, 48029180702, 48029191304, 48037010500, 48061013401, 48061013802, 48113003700, 48113006002, 48113007201, 48113007823, 48113007827, 48113008900, 48113009000, 48113009202, 48113009610, 48113010703, 48113010903, 48113011104, 48113011300, 48113012210, 48113014103, 48113014308, 48113014407, 48113018141, 48113019034, 48121021739, 48141000301, 48141001001, 48141001115, 48145000400, 48167724200, 48167725900, 48185180400, 48201233103, 48201240100, 48201310800, 48201311800, 48201312200, 48201312800, 48201313800, 48201341201, 48201420500, 48201421201, 48201432002, 48201432901, 48201454400, 48201534001, 48209010302, 48225950400, 48303000202, 48303001702, 48309002700, 48339693800, 48347950900, 48355003305, 48423000201, 48439102301, 48439105204, 48439106001, 48439106600, 48439121704, 48439121903, 48439121906, 48453001811, 48453001863, 48453002315, 48453002419, 48479000105, 48479001717.\n", - " warnings.warn(message)\n" - ] - } - ], - "source": [ - "%%time\n", - "# calculate queen spatial weights\n", - "w_queen = Queen.from_dataframe(tracts, ids=labels, id_order=labels)" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 51, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# find the neighbors of some tract\n", - "w_queen.neighbors[label]" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 52, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# this is a raw contiguity matrix, so weights are binary 1s and 0s meaning neighbor/not\n", - "w_queen.weights[label]" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0" - ] - }, - "execution_count": 53, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# how many neighbors does this tract have?\n", - "w_queen.cardinalities[label]" - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "count 104.000000\n", - "mean 0.326923\n", - "std 0.565038\n", - "min 0.000000\n", - "25% 0.000000\n", - "50% 0.000000\n", - "75% 1.000000\n", - "max 2.000000\n", - "dtype: float64" - ] - }, - "execution_count": 54, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# convert cardinalities to series and describe data -- looks right-skewed\n", - "cardinalites_queen = pd.Series(w_queen.cardinalities)\n", - "cardinalites_queen.describe()" - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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      " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# look at the distribution of number of neighbors\n", - "ax = cardinalites_queen.hist(bins=20)" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "104" - ] - }, - "execution_count": 56, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# number of observations\n", - "w_queen.n" - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.3269230769230769" - ] - }, - "execution_count": 57, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# average number of neighbors\n", - "w_queen.mean_neighbors" - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0" - ] - }, - "execution_count": 58, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# min number of neighbors\n", - "w_queen.min_neighbors" - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "2" - ] - }, - "execution_count": 59, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# max number of neighbors\n", - "w_queen.max_neighbors" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "75" - ] - }, - "execution_count": 60, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# islands are observations with no neighbors, disconnected \n", - "# in space (can cause modeling problems)\n", - "len(w_queen.islands)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### Plot a census tract of interest, along with its neighbors:" - ] - }, - { - "cell_type": "code", - "execution_count": 62, - "metadata": {}, - "outputs": [], - "source": [ - "# fig, ax = plt.subplots(figsize=(6, 6))\n", - "# tracts.plot(ax=ax, facecolor='#999999', edgecolor='w', linewidth=0.5)\n", - "\n", - "# # plot some tract of interest in red\n", - "# tract = tracts.loc[[label]]\n", - "# tract.plot(ax=ax, facecolor='r', edgecolor='w', linewidth=1)\n", - "\n", - "# # plot the neighbors in blue\n", - "# neighbors = tracts.loc[w_queen[label]]\n", - "# neighbors.plot(ax=ax, facecolor='b', edgecolor='w', linewidth=1)\n", - "\n", - "# # zoom to area of interest\n", - "# xmin, ymin, xmax, ymax = neighbors.unary_union.bounds\n", - "# ax.axis('equal')\n", - "# ax.set_xlim(xmin, xmax)\n", - "# ax.set_ylim(ymin, ymax)\n", - "\n", - "# ax.set_title('Neighbors of tract {}'.format(label))\n", - "# plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 3.3. Distance-based weights: *k*-nn\n", - "\n", - "Find the *k*-nearest neighbors of each tract" - ] - }, - { - "cell_type": "code", - "execution_count": 63, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 17.6 ms, sys: 2.24 ms, total: 19.8 ms\n", - "Wall time: 19 ms\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/sarahluw/.pyenv/versions/3.6.2/envs/my-virtual-env-3.6.2/lib/python3.6/site-packages/libpysal/weights/util.py:1073: ShapelyDeprecationWarning: The array interface is deprecated and will no longer work in Shapely 2.0. Convert the '.coords' to a numpy array instead.\n", - " data = np.vstack([np.array(shape.centroid) for shape in first_choice])\n", - "/Users/sarahluw/.pyenv/versions/3.6.2/envs/my-virtual-env-3.6.2/lib/python3.6/site-packages/libpysal/weights/weights.py:172: UserWarning: The weights matrix is not fully connected: \n", - " There are 2 disconnected components.\n", - " warnings.warn(message)\n" - ] - } - ], - "source": [ - "%%time\n", - "# k-nearest neighbors finds the closest k tract centroids to each tract centroid\n", - "w_knn = KNN.from_dataframe(tracts, k=6)" - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['48029170101',\n", - " '48029110800',\n", - " '48029150800',\n", - " '48029130600',\n", - " '48029180702',\n", - " '48029191304']" - ] - }, - "execution_count": 64, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# they all have exactly k neighbors\n", - "w_knn.neighbors[label]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 3.4. Distance-based weights: distance band\n", - "\n", - "Here tracts are considered neighbors of some tract if they are within a given threshold distance of it. Distance band weights can be specified to take on continuous values rather than binary (1s and 0s), with these values being the inverse distance between each pair of \"neighboring\" units. The default (linear) distance-decay exponent is thus -1. If we set it to -2, we get gravity model weights." - ] - }, - { - "cell_type": "code", - "execution_count": 65, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "210475.92289934136" - ] - }, - "execution_count": 65, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# first determine the minimum nearest neighbor distance so each unit is assured of at least one neighbor\n", - "x = tracts.centroid.x\n", - "y = tracts.centroid.y\n", - "coords = np.array([x, y]).T\n", - "threshold = min_threshold_distance(coords)\n", - "threshold" - ] - }, - { - "cell_type": "code", - "execution_count": 66, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 26.4 ms, sys: 2.9 ms, total: 29.3 ms\n", - "Wall time: 27.9 ms\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/sarahluw/.pyenv/versions/3.6.2/envs/my-virtual-env-3.6.2/lib/python3.6/site-packages/libpysal/weights/util.py:1073: ShapelyDeprecationWarning: The array interface is deprecated and will no longer work in Shapely 2.0. Convert the '.coords' to a numpy array instead.\n", - " data = np.vstack([np.array(shape.centroid) for shape in first_choice])\n", - "/Users/sarahluw/.pyenv/versions/3.6.2/envs/my-virtual-env-3.6.2/lib/python3.6/site-packages/scipy/sparse/data.py:117: RuntimeWarning: divide by zero encountered in reciprocal\n", - " return self._with_data(data ** n)\n", - "/Users/sarahluw/.pyenv/versions/3.6.2/envs/my-virtual-env-3.6.2/lib/python3.6/site-packages/libpysal/weights/weights.py:172: UserWarning: The weights matrix is not fully connected: \n", - " There are 5 disconnected components.\n", - " warnings.warn(message)\n" - ] - } - ], - "source": [ - "%%time\n", - "# default alpha=-1 and gravity weights alpha=-2\n", - "w_dist = DistanceBand.from_dataframe(tracts, threshold=threshold, binary=False, alpha=-1)" - ] - }, - { - "cell_type": "code", - "execution_count": 67, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "15" - ] - }, - "execution_count": 67, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# how many distance-band neighbors does our tract have?\n", - "len(w_dist.neighbors[label])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### Map the neighbors, colored by weight from nearest to furthest:" - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
      " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(figsize=(6, 6))\n", - "tracts.plot(ax=ax, facecolor='k', edgecolor='gray', linewidth=1)\n", - "\n", - "# get the tract of interest and its neighbors/weights\n", - "tract = tracts.loc[[label]]\n", - "weights = pd.Series(w_dist[label])\n", - "neighbors = tracts.loc[weights.index, ['geometry']]\n", - "\n", - "# plot the tract's neighbors in blues by weight\n", - "neighbors['weights_scaled'] = weights\n", - "neighbors.plot(ax=ax, column='weights_scaled', cmap='Blues_r', edgecolor='gray', linewidth=1, scheme='Fisher_Jenks')\n", - "\n", - "# plot the tract of interest in white\n", - "tract.plot(ax=ax, facecolor='w', edgecolor='r', linewidth=2)\n", - "\n", - "# zoom to area of interest\n", - "xmin, ymin, xmax, ymax = neighbors.unary_union.bounds\n", - "ax.set_xlim(xmin, xmax)\n", - "ax.set_ylim(ymin, ymax)\n", - "ax.axis('off')\n", - "\n", - "ax.set_title('Neighbors of tract {}'.format(label))\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 3.5. Standardizing weights\n", - "\n", - "A spatial weights matrix with raw values (e.g., binary 1s and 0s for neighbor/not) is not always the best for analysis. Some sort of standardization is useful." - ] - }, - { - "cell_type": "code", - "execution_count": 69, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{}" - ] - }, - "execution_count": 69, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# inspect the neighbors and weights of our tract\n", - "w_queen[label]" - ] - }, - { - "cell_type": "code", - "execution_count": 70, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'O'" - ] - }, - "execution_count": 70, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# check the current transformation of the weights matrix (O = original)\n", - "w_queen.get_transform()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Typically, we want to apply a row-based transformation, so every row of the matrix sums up to 1." - ] - }, - { - "cell_type": "code", - "execution_count": 71, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - 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] - }, - { - "data": { - "text/plain": [ - "{}" - ] - }, - "execution_count": 71, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# transform the queen weights\n", - "w_queen.set_transform('R')\n", - "w_queen[label]" - ] - }, - { - "cell_type": "code", - "execution_count": 72, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'48029110800': 0.18978185729300112,\n", - " '48029130600': 0.12319529619318503,\n", - " '48029150800': 0.1370487572692595,\n", - " '48029170101': 0.28791347498098213,\n", - " '48029180702': 0.10515753230504479,\n", - " '48029191304': 0.07700863495815723,\n", - " '48209010302': 0.01353957165348076,\n", - " '48453001804': 0.008184678306329738,\n", - " '48453001806': 0.008087552480183932,\n", - " '48453001811': 0.008186327368227162,\n", - " '48453001819': 0.008052183129697358,\n", - " '48453001822': 0.007973296124783142,\n", - " '48453001863': 0.007616550406552777,\n", - " '48453002315': 0.00891808387476705,\n", - " '48453002419': 0.0093362036563483}" - ] - }, - "execution_count": 72, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# transform the linear-decay distance-based weights\n", - "w_dist.set_transform('R')\n", - "w_dist[label]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "PySAL supports the following transformations:\n", - "\n", - " - O: original, returning the object to the initial state\n", - " - B: binary, with every neighbor having assigned a weight of 1\n", - " - R: row-based, with all the neighbors of a given observation adding up to 1\n", - " - V: variance stabilizing, with the sum of all the weights being constrained to the number of observations\n", - "\n", - "**It can take a long time to calculate a weights matrix for a large data set.**\n", - "\n", - "Once you've created yours, you might want to save it to disk to re-use in subsequent analyses." - ] - }, - { - "cell_type": "code", - "execution_count": 73, - "metadata": {}, - "outputs": [], - "source": [ - "# save your matrix to disk\n", - "import libpysal\n", - "f = libpysal.io.open('tracts_queen.gal', 'w')\n", - "f.write(w_queen)\n", - "f.close()" - ] - }, - { - "cell_type": "code", - "execution_count": 74, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/sarahluw/.pyenv/versions/3.6.2/envs/my-virtual-env-3.6.2/lib/python3.6/site-packages/libpysal/weights/weights.py:172: UserWarning: The weights matrix is not fully connected: \n", - " There are 87 disconnected components.\n", - " There are 75 islands with ids: 48027023202, 48027023500, 48029110800, 48029130600, 48029150800, 48029160501, 48029170101, 48029180702, 48029191304, 48037010500, 48061013401, 48061013802, 48113003700, 48113006002, 48113007201, 48113007823, 48113007827, 48113008900, 48113009000, 48113009202, 48113009610, 48113010703, 48113010903, 48113011104, 48113011300, 48113012210, 48113014103, 48113014308, 48113014407, 48113018141, 48113019034, 48121021739, 48141000301, 48141001001, 48141001115, 48145000400, 48167724200, 48167725900, 48185180400, 48201233103, 48201240100, 48201310800, 48201311800, 48201312200, 48201312800, 48201313800, 48201341201, 48201420500, 48201421201, 48201432002, 48201432901, 48201454400, 48201534001, 48209010302, 48225950400, 48303000202, 48303001702, 48309002700, 48339693800, 48347950900, 48355003305, 48423000201, 48439102301, 48439105204, 48439106001, 48439106600, 48439121704, 48439121903, 48439121906, 48453001811, 48453001863, 48453002315, 48453002419, 48479000105, 48479001717.\n", - " warnings.warn(message)\n" - ] - }, - { - "data": { - "text/plain": [ - "{}" - ] - }, - "execution_count": 74, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# read a matrix from disk (notice its transformation)\n", - "w_queen = libpysal.io.open('tracts_queen.gal', 'r').read()\n", - "w_queen[label]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 4. Spatial lag\n", - "\n", - "Spatial lag tells us about values near some spatial unit. Calculate spatial lag of the `med_household_income` variable. If the spatial weights matrix is row-standardized, then the spatial lag is the average value of an observation's neighbors." - ] - }, - { - "cell_type": "code", - "execution_count": 75, - "metadata": {}, - "outputs": [], - "source": [ - "# pick a variable to investigate\n", - "col = 'med_household_income'" - ] - }, - { - "cell_type": "code", - "execution_count": 76, - "metadata": {}, - "outputs": [], - "source": [ - "# first drop null rows\n", - "tracts_not_null = tracts[[col, 'geometry']].dropna()\n", - "y = tracts_not_null[col]" - ] - }, - { - "cell_type": "code", - "execution_count": 77, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "('WARNING: ', 0, ' is an island (no neighbors)')\n", - "('WARNING: ', 1, ' is an island (no neighbors)')\n", - "('WARNING: ', 2, ' is an island (no neighbors)')\n", - "('WARNING: ', 3, ' is an island (no neighbors)')\n", - "('WARNING: ', 4, ' is an island (no neighbors)')\n", - "('WARNING: ', 5, ' is an island (no neighbors)')\n", - 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Use the `geoms` property to access the constituent parts of a multi-part geometry.\n", - " return list(it.chain(*(_get_boundary_points(part.boundary) for part in shape)))\n", - "/Users/sarahluw/.pyenv/versions/3.6.2/envs/my-virtual-env-3.6.2/lib/python3.6/site-packages/libpysal/weights/weights.py:172: UserWarning: The weights matrix is not fully connected: \n", - " There are 86 disconnected components.\n", - " There are 74 islands with ids: 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 18, 19, 20, 21, 22, 24, 25, 26, 27, 28, 30, 34, 35, 36, 37, 40, 41, 42, 43, 44, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 66, 70, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 84, 85, 87, 88, 89, 90, 93, 96, 97, 98, 99, 100.\n", - " warnings.warn(message)\n" - ] - } - ], - "source": [ - "# recompute spatial weights for these observations then row-standardize\n", - "w_queen = Queen.from_dataframe(tracts_not_null)\n", - "w_queen.set_transform('R')" - ] - }, - { - "cell_type": "code", - "execution_count": 78, - "metadata": {}, - "outputs": [], - "source": [ - "# compute spatial lag\n", - "y_lag = lag_spatial(w_queen, y)" - ] - }, - { - "cell_type": "code", - "execution_count": 79, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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      " - ], - "text/plain": [ - " med_household_income med_household_income_lag\n", - "index \n", - "48113010903 30313 0\n", - "48201432002 33478 0\n", - "48029110800 28378 0\n", - "48201240100 30945 0\n", - "48201550301 30236 50941\n", - "48303001702 36569 0\n", - "48113007819 32478 30445\n", - "48201454400 44526 0\n", - "48201313800 28885 0\n", - "48167724200 38906 0" - ] - }, - "execution_count": 79, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "col_lag = '{}_lag'.format(col)\n", - "data_lag = pd.DataFrame(data={col:y, col_lag:y_lag}).astype(int)\n", - "data_lag.sample(10)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 6. Spatial autocorrelation\n", - "\n", - "Statistical models typically assume that the observations are independent of each other. This assumption is violated when a variable's value at one location is correlated with its value at nearby locations. This is called spatial autocorrelation, and is common in the real world due to proximity-based spillover effects. Substantive spatial autocorrelation can be explained by social or economic theory that describes a spatial relationship. Nuisance spatial autocorrelation stems from data problems.\n", - "\n", - "*Positive spatial autocorrelation*: nearby values tend to be more similar (e.g. income, home values, temperature, rainfall)\n", - "\n", - "*Negative spatial autocorrelation*: nearby values tend to be more dissimilar (e.g. fire stations, grocery stores)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 6.1. Moran's I\n", - "\n", - "Moran's I measures *global* spatial autocorrelation: do values tend to be near other (dis)similar values. Values > 1 indicate positive spatial autocorrelation, and values < 1 indicate negative spatial autocorrelation." - ] - }, - { - "cell_type": "code", - "execution_count": 80, - "metadata": {}, - "outputs": [], - "source": [ - "# calculate the statistic\n", - "from esda.moran import Moran\n", - "mi = Moran(data_lag[col], w_queen)" - ] - }, - { - "cell_type": "code", - "execution_count": 81, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.3657174069181714" - ] - }, - "execution_count": 81, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# show the I value\n", - "mi.I" - ] - }, - { - "cell_type": "code", - "execution_count": 82, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.047" - ] - }, - "execution_count": 82, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# statistical inference: show the p value\n", - "mi.p_sim" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If we generated a large number of maps with the same values but randomly allocated over space, and calculated Moran's I for each of these maps, only 0.05% of them would display a larger absolute value than the one we computed from the real-world data set. Thus there is a 0.05% chance of getting the observed value of Moran's I if the spatial distribution of our variable is random. We can conclude that the variable's distribution is statistically-significantly postively spatially autocorrelated. We'll talk more about inference in the other notebook" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 6.2. Moran plots\n", - "\n", - "A Moran plot scatter plots the spatially-lagged values (y-axis) vs the original variable's values (x-axis). Moran's I equals the slope of the line in a Moran plot, which makes this all a bit easier to conceptualize." - ] - }, - { - "cell_type": "code", - "execution_count": 83, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
      " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(figsize=(6, 6))\n", - "sns.regplot(x=col, y=col_lag, data=data_lag, scatter_kws={'s':1})\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 84, - "metadata": {}, - "outputs": [], - "source": [ - "# standardize the vector (i.e., calculate z-scores)\n", - "y_std = (y - y.mean()) / y.std()" - ] - }, - { - "cell_type": "code", - "execution_count": 85, - "metadata": {}, - "outputs": [], - "source": [ - "# compute the spatial lag of the standardized vector and save it as a series with same index\n", - "y_std_lag = pd.Series(lag_spatial(w_queen, y_std), index=y_std.index, name=col_lag)" - ] - }, - { - "cell_type": "code", - "execution_count": 86, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
      " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# standardized moran's plot, ignoring outliers beyond 3 std devs\n", - "fig, ax = plt.subplots(figsize=(6, 6))\n", - "ax.set_xlim(-3, 3)\n", - "ax.set_ylim(-3, 3)\n", - "plt.axvline(0, c='k', alpha=0.5)\n", - "plt.axhline(0, c='k', alpha=0.5)\n", - "sns.regplot(ax=ax, x=y_std, y=y_std_lag, scatter_kws={'s':1})\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Notice the 95% confidence interval shading and the positive slope. Given the value of Moran's I that we calculated earlier (and its p-value), we can conclude that the slope of the line is statistically-significantly different from zero. This plot makes it visually apparent." - ] - }, - { - "cell_type": "code", - "execution_count": 87, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "m=0.1030, b=0.0006, r^2=0.0343, p=0.0611\n" - ] - } - ], - "source": [ - "# estimate a simple linear regression model (of the line above)\n", - "m, b, r, p, se = stats.linregress(x=y_std, y=y_std_lag)\n", - "print('m={:.4f}, b={:.4f}, r^2={:.4f}, p={:.4f}'.format(m, b, r ** 2, p))" - ] - }, - { - "cell_type": "code", - "execution_count": 88, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.3657174069181714" - ] - }, - "execution_count": 88, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# the slope is the same as moran's I, calculated earlier\n", - "mi.I" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 6.3. LISAs\n", - "\n", - "Local Indicators of Spatial Autocorrelation: are there specific areas with high concentrations of (dis)similar values?\n", - "\n", - "Moran's I tells us about spatial clustering across the data set as a whole. However, it does not tell us where these clusters occur. For that, we need a local measure. Essentially, we will classify the data set's observations into four groups based on the four quadrants of the Moran plot:\n", - "\n", - " 1. **HH**: high value near other high values (*hot spots*)\n", - " 1. **LL**: low value near other low values (*cold spots*)\n", - " 1. **HL**: high value near low values (*spatial outliers*)\n", - " 1. **LH**: low value near high values (*spatial outliers*)" - ] - }, - { - "cell_type": "code", - "execution_count": 89, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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      " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# standardized moran's plot again, from subsection above\n", - "fig, ax = plt.subplots(figsize=(6, 6))\n", - "ax.set_xlim(-3, 3)\n", - "ax.set_ylim(-3, 3)\n", - "ax.axvline(0, c='k', ls=':', alpha=0.5)\n", - "ax.axhline(0, c='k', ls=':', alpha=0.5)\n", - "ax.text(1.25, 1.25, 'HH', fontsize=20)\n", - "ax.text(1.25, -1.75, 'HL', fontsize=20)\n", - "ax.text(-1.75, 1.25, 'LH', fontsize=20)\n", - "ax.text(-1.75, -1.75, 'LL', fontsize=20)\n", - "ax.set_title('Moran Plot')\n", - "sns.regplot(ax=ax, x=y_std, y=y_std_lag, scatter_kws={'s':1})\n", - "plt.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.2" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/data/data-pipeline/data_pipeline/ipython/experiment_4_texas_example/spatial_analysis_1_12292021_score_l_economic.ipynb b/data/data-pipeline/data_pipeline/ipython/experiment_4_texas_example/spatial_analysis_1_12292021_score_l_economic.ipynb deleted file mode 100644 index 2b47c062..00000000 --- a/data/data-pipeline/data_pipeline/ipython/experiment_4_texas_example/spatial_analysis_1_12292021_score_l_economic.ipynb +++ /dev/null @@ -1,2281 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Spatial weights and ESDA (Exploratory Spatial Data Analysis) - TX, ACS 2019\n", - "\n", - "ESDA offers direct parallels to many of the aspatial descriptive statistics that are already part of most quantitative research projects: The geographic mean and spatial standard deviation, for example, tell us where the center of our study area is how compact our population is within that area. \n", - "\n", - "I use ESDA to generalize about our observations and to identify outliers and discontinuities that may disrupt our Score L model or suggest hypotheses about relationships we may wish to explore further as it pertains to health disparities and educational disparities.\n", - "\n", - "This is very pertinent to Score L as we make strict assumptions about distributions - for any thresholding approach it assumes that the data represents no measurement bias and no systematic error. Everything is homogeneous and is treated equally.\n", - "\n", - "ESDA can also be important in confirmatory statistical analysis as spatial autocorrelation can bias outcomes in many spatial and aspatial statistical models. \n", - "\n", - "Testing for spatial autocorrelation and locating clusters of correlated residuals will be important in explaining why certain scores are associated together.\n", - "\n", - "\n", - "For example, in section 6, I use LISA's to identify geographical clusters of values or find geographcial outliers. They are a very useful tool that can quickly return areas in which values are concentrated and provide suggestive evidence about the processes that might be at work - namely median income. I will be extending this to educational attainment and job opportunities in a given radius - indicating economic mobility." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.9.0 2.3.0\n" - ] - } - ], - "source": [ - "import geopandas as gpd\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import pandas as pd\n", - "import pysal as ps\n", - "import seaborn as sns\n", - "from scipy.stats import stats\n", - "\n", - "np.random.seed(0)\n", - "print(gpd.__version__, ps.__version__)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Load the tracts data set" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(5265, 38)" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# load geojson, display shape\n", - "tracts = gpd.read_file('census/census_tracts_data.geojson')\n", - "tracts = tracts.set_index('index')\n", - "tracts.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['STATEFP', 'COUNTYFP', 'TRACTCE', 'NAME', 'NAMELSAD', 'MTFCC',\n", - " 'FUNCSTAT', 'ALAND', 'AWATER', 'INTPTLAT', 'INTPTLON', 'total_pop',\n", - " 'median_age', 'pct_hispanic', 'pct_white', 'pct_black', 'pct_asian',\n", - " 'pct_male', 'pct_single_family_home', 'med_home_value',\n", - " 'med_rooms_per_home', 'pct_built_before_1940', 'pct_renting',\n", - " 'rental_vacancy_rate', 'avg_renter_household_size', 'med_gross_rent',\n", - " 'med_household_income', 'mean_commute_time', 'pct_commute_drive_alone',\n", - " 'pct_below_poverty', 'pct_college_grad_student',\n", - " 'pct_same_residence_year_ago', 'pct_bachelors_degree',\n", - " 'pct_english_only', 'pct_foreign_born', 'state', 'county', 'geometry'],\n", - " dtype='object')" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# what variables are present?\n", - "tracts.columns" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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      STATEFPCOUNTYFPTRACTCENAMENAMELSADMTFCCFUNCSTATALANDAWATERINTPTLAT...pct_commute_drive_alonepct_below_povertypct_college_grad_studentpct_same_residence_year_agopct_bachelors_degreepct_english_onlypct_foreign_bornstatecountygeometry
      index
      48001950100480019501009501Census Tract 9501G5020S4833066197864313+31.9714680...84.016.330.64775.091.24495.00.448001MULTIPOLYGON (((-95.69702 31.97597, -95.69699 ...
      48001950401480019504019504.01Census Tract 9504.01G5020S16549991296734+31.7346372...100.00.034.24838.075.24800.01.648001MULTIPOLYGON (((-95.84791 31.75753, -95.84806 ...
      48001950402480019504029504.02Census Tract 9504.02G5020S724720392627857+31.8000515...100.021.533.87506.069.77501.01.048001MULTIPOLYGON (((-95.98357 31.78925, -95.98354 ...
      48001950500480019505009505Census Tract 9505G5020S2313205299223+31.7878850...85.119.428.24423.084.04178.00.748001MULTIPOLYGON (((-95.68769 31.81041, -95.68741 ...
      48001950600480019506009506Census Tract 9506G5020S20653883329641+31.7502049...79.917.439.35109.071.24890.00.048001MULTIPOLYGON (((-95.70774 31.72559, -95.70765 ...
      \n", - "

      5 rows Ă— 38 columns

      \n", - "
      " - ], - "text/plain": [ - " STATEFP COUNTYFP TRACTCE NAME NAMELSAD MTFCC \\\n", - "index \n", - "48001950100 48 001 950100 9501 Census Tract 9501 G5020 \n", - "48001950401 48 001 950401 9504.01 Census Tract 9504.01 G5020 \n", - "48001950402 48 001 950402 9504.02 Census Tract 9504.02 G5020 \n", - "48001950500 48 001 950500 9505 Census Tract 9505 G5020 \n", - "48001950600 48 001 950600 9506 Census Tract 9506 G5020 \n", - "\n", - " FUNCSTAT ALAND AWATER INTPTLAT ... \\\n", - "index ... \n", - "48001950100 S 483306619 7864313 +31.9714680 ... \n", - "48001950401 S 16549991 296734 +31.7346372 ... \n", - "48001950402 S 72472039 2627857 +31.8000515 ... \n", - "48001950500 S 23132052 99223 +31.7878850 ... \n", - "48001950600 S 20653883 329641 +31.7502049 ... \n", - "\n", - " pct_commute_drive_alone pct_below_poverty \\\n", - "index \n", - "48001950100 84.0 16.3 \n", - "48001950401 100.0 0.0 \n", - "48001950402 100.0 21.5 \n", - "48001950500 85.1 19.4 \n", - "48001950600 79.9 17.4 \n", - "\n", - " pct_college_grad_student pct_same_residence_year_ago \\\n", - "index \n", - "48001950100 30.6 4775.0 \n", - "48001950401 34.2 4838.0 \n", - "48001950402 33.8 7506.0 \n", - "48001950500 28.2 4423.0 \n", - "48001950600 39.3 5109.0 \n", - "\n", - " pct_bachelors_degree pct_english_only pct_foreign_born state \\\n", - "index \n", - "48001950100 91.2 4495.0 0.4 48 \n", - "48001950401 75.2 4800.0 1.6 48 \n", - "48001950402 69.7 7501.0 1.0 48 \n", - "48001950500 84.0 4178.0 0.7 48 \n", - "48001950600 71.2 4890.0 0.0 48 \n", - "\n", - " county geometry \n", - "index \n", - "48001950100 001 MULTIPOLYGON (((-95.69702 31.97597, -95.69699 ... \n", - "48001950401 001 MULTIPOLYGON (((-95.84791 31.75753, -95.84806 ... \n", - "48001950402 001 MULTIPOLYGON (((-95.98357 31.78925, -95.98354 ... \n", - "48001950500 001 MULTIPOLYGON (((-95.68769 31.81041, -95.68741 ... \n", - "48001950600 001 MULTIPOLYGON (((-95.70774 31.72559, -95.70765 ... \n", - "\n", - "[5 rows x 38 columns]" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# inspect the first 5 rows\n", - "tracts.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "score_l_df = pd.read_csv(\"merged_df_score_l_2794.csv\", dtype={\n", - " \"FIPS_tract_id\": str\n", - "})\n", - "\n", - "score_l_df.set_index(\"FIPS_tract_id\", inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "census_acs_2019 = pd.read_csv(\"../../../data_pipeline/data/dataset/census_acs_2019/usa.csv\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "cdc_places = pd.read_csv(\"../data/dataset/cdc_places/usa.csv\")\n", - "\n", - "cdc_le = pd.read_csv(\"../data/dataset/cdc_life_expectancy/usa.csv\")\n", - "\n", - "# all CDC datasets\n", - "temp_cdc = cdc_places.merge(cdc_le, on = \"GEOID10_TRACT\")" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "# calculate pop density in persons per sq km\n", - "tracts['pop_density'] = tracts['total_pop'] / (tracts['ALAND'] / 1e6)\n", - "tracts = tracts.replace([np.inf, -np.inf], np.nan)\n", - "tracts = tracts.dropna(subset=['pop_density'])\n", - "\n", - "# project spatial geometries from lat-long to a meter-based projection\n", - "utm_tx = '+proj=utm +zone=18 +ellps=WGS84 +datum=WGS84 +units=m +no_defs'\n", - "tracts = tracts.to_crs(utm_tx)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "df_merged = score_l_df.merge(tracts, how='inner', left_index=True, right_index=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(104, 75)" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_merged.shape" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1. Exploratory analysis\n", - "\n", - "Let's see what we've got with a couple key variables of interest." - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "count 5253.000000\n", - "mean 1283.221970\n", - "std 1360.394266\n", - "min 0.000000\n", - "25% 198.253540\n", - "50% 1030.673949\n", - "75% 1937.892611\n", - "max 23721.665788\n", - "Name: pop_density, dtype: float64" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# descriptive stats\n", - "tracts['pop_density'].describe()" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "count 5202.000000\n", - "mean 64130.328720\n", - "std 33451.776234\n", - "min 8907.000000\n", - "25% 41998.000000\n", - "50% 55771.000000\n", - "75% 76667.000000\n", - "max 250001.000000\n", - "Name: med_household_income, dtype: float64" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# descriptive stats\n", - "tracts['med_household_income'].describe()" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "# create a function to choropleth map a df column\n", - "def map_variable(df, col, scheme='quantiles', k=10, cmap='plasma', figsize=(10,10)):\n", - " values = df.dropna(subset=[col])\n", - " ax = values.plot(column=col, scheme=scheme, k=k, cmap=cmap, figsize=figsize, legend = True)\n", - " ax.axis('off')\n", - " plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
      " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# map a variable\n", - "map_variable(tracts, 'pop_density')" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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UMplsYFZWltphYtVba2ybzYYnn3wy6t13363qzf07zVFACMHYsWMTlUpl6t///ncZYHeSdAQoAKBQKMx1dXXXzTaaDRJYWK4v0+Fej8QXwDwAmwE0t66au6911dylravmuv3QZ2FxsP/zksCuKwhdMRts1P7PS7zytnnkyJHL9fX1F81mM7Vz5063ge3y5cubw8PDLQMGDFA+9thjUUOGDNF2tkh219czzzwj53A4zCOPPNLN9MlqtZKCggLxY4891lRQUJAvFotpZ9sSFEWh80qAJ3C5XBQWFuZXVlbmnDt3zuf06dNCoPfW2G+88UbIHXfc0RYfH98nYy2r1UpOnz7t+9///rfs5MmTRbt27Qr89ttvJT1f6V1YMSUWluvLrF605QG44+rXv1tXzT0BYAeAHdJXtxZdh7Gx3AS01Bk8eov0tJ0niMViZsaMGW3bt28PmDVrltpVOx6Ph/Xr13e8SQ8ePDhFqVReY6vsrK/33nsvaN++fQGHDx8udpgtdSYmJsYsk8nMjrf8+fPnq15//XU5AAQFBVkrKip4CoXCUlFRwZNKpW4dHV0RHBxsGzdunGbnzp3+ERER1q7W2FOnTk28OhbLDz/8cAUA2tvbqd27dwcGBwfbTpw44Xv69GnfDRs2hOr1espisVC+vr62Dz/8sMaT+0dGRppHjBihCQsLswLA7bff3n7mzBnxQw891Np55aCiooLfeWXB27ArCSws14nWVXNFAKb2o4uRAF4HUNi6am5R66q5b7SumjumddVcr2ass9zYBIWJPHpAeNrOFe3t7VRFRQUPsCfV7dmzxz8lJcXg7hqNRkM5tgG2b9/ux+FwmMzMTKO7vrZu3er37rvvynfv3l0ikUicpu1HR0db5XK5+eLFiwIA+OGHH/ySk5ONADBlypS2devWBQHAunXrgqZOndoGAAcPHhTPmjUrxt14a2truY5qCq1WSw4ePOiXmppq7Is19nfffVdWV1d3qaam5tJLL71UPXv27BZPAwQAmDVrlrqwsFCk0Wgoi8WCo0ePStLS0owKhcLi6+tLHzhwwIemaXz55ZdB99xzT5un/fYWdiWBheX6cTsAcY+tPCMJwF+vfjW2rpq7C8C3AH6UvrrV7Qc1y83NbfcnqDY8dy7K3ZYDX8Shb7s/oV9W0Wq1mpo+fXqC2WwmDMOQ0aNHq59++ukmANi0aVPA008/Ha1SqbizZs1KTE1N1R85cuRybW0td8qUKUkURTFyudzy1VdflfXU18qVK6PNZjM1adKkJAAYMmSI9quvvqosLy/n/fGPf1RkZ2eXAMDatWsrFy1aFGc2m0l0dLRp8+bN5QDw0ksv1c2aNSteoVAER0REmLdv334FAMrLywUikcit7XFVVRXvgQceiLXZbGAYhtxzzz2t9913XzuAXltju+ORRx6J3L59u9RoNFIymSxj0aJFzWvWrKntbI0dEhJiW7ZsWcPgwYNTCSGYPHly+4IFC9oB4IMPPqj405/+FGs0GsnEiRPV9957b7e8DW/BWkWzsFwnWlfN/QzAg9f5NnoAP8AeMOySvrq1R1thlhsHT62iHdUNrs7f+1R6zUOvD70lnSAdLF26NHLJkiUtI0aMuOWD6t5YRbNBAgvLdeDqlkAdgJBf8bY0gBMAtgP4VvrqVtYm9wbH0yABsAcK335QENZ5RYEv4tD3PJZad6sHCCzX0psggd1uYGG5PozBrxsgAPYco9EARpsM/BU/jXy1HSDfA9gF4OikE8/2KYGL5cbgodeH1s//64DG/Z+XBLbUGXhBYSLLbfcnqCRSAWsVzdJn2CCBheX6MPO3vHlzVWgJQMYDSAXwFIDWn0a+thP2aokfJ514Vvdbjo/l+iCRCuhZy9NYq2gWr8EGCSwsXuaqT8PM33IMqkapb5dDUgB/vPpl+mnka/sBfAdg16QTz9b+2uNjYWG5MWCDBBYW75MBuwzzb4LNwtFYzdwMN00EsIs8TQeAn0a+dhr2PIZdAHInnXiWTVRiYWEBwAYJLCzXg5m/5c1b6oJyATKqF5cMu/r1KoCKn0a+tgv2VYafJ5141nw9xsjCwnJjwIopsbB4n5m/5c1baoP7sxKgAPAYgH0AWvYPf/0/6+UbHlgv3xDqndGxXE90LSZO9r+Kgnf+9UJY9r+KgnW/klX0sWPHRAMHDkxJSUlRpqenpx48eFAMAE1NTZzbb789PikpSTlgwIBUh8SxA6vVitTUVOXEiRMTHMdeffXVkOjo6HRCSGZdXZ3LF9nLly/zx4wZkxgXF5cWHx+fVlRUxAeAwsJCfkZGRkp0dHT69OnT44xGY69t2p3ZOzuYNGlSQmJiYlrnY//85z9DY2Nj0xISEtIeeeSRSMDuLyEUCoekpKQoU1JSlAsXLuzoy2g0kvvuu08RExOTHhsbm7Zx48aArvfZvn27X1paWmpSUpIyLS0t9bvvvuuQZP41raLZlQQWFi/SumpuDIBBv9X9GZrYTHphupe687VaOfEAFgNg1ss3nATwPeyrDJf+VP8guy3xO2LXMxflR96/HGbpVAK55++XosYuS6y7642BXimB/Pbbb69IpVKapmlMmzYt/rPPPgt8+OGHVU8//XTk//t//6923rx56i1btvg/88wzUadOnSr6+9//HpaRkaH/8ccfr5w/f1746KOPRh8/frzY0d8rr7wiS0hIMGi12o5gJisrSztnzpz2SZMmJbsby6JFi2KfffbZulmzZqnb29sph3zzypUrI5ctW9bw8MMPqxYuXBj97rvvBj/zzDNNvZmnM3tnAPjPf/4T4OPjY+t8bOfOnZLvv/8+ID8/P18kEjE1NTUdz9WoqChTYWFhftd+nn322bCQkBBLeXl5rs1mQ2NjY7dncWhoqOX7778viYmJsZw+fVo4ffr0pMbGxhzgF6voiRMn6iZMmJC4detWv3nz5rmUx+4P7EoCC4t3mflb3ry92f8SQLzmJqnRiBwf3gR2meh/ALgIoHy9fMMH6+Ub7lwv3+AtVUmWPrLrmYvyg28VRli6qC5aDDbq4FuFEbueuXhdraIJIWhvb+cAdjtlmUxmBoCioiLh7bffrgGAwYMHG6urq/lVVVVcALhy5Qpv3759/n/+85+v0YEYM2aMITk52e0219mzZ4U2mw0Orwd/f39aIpHQNE3j+PHjEoej5JIlS1p27twZ0Js5urJ3bm9vp9577z3Ziy++WNf5+EcffRTy17/+tc6h5hgREdFjqfHmzZuDX3nllXoA4HA4cPgzdGbMmDGGmJgYCwBkZmYaTSYTZTAYCGsVzcJyY9MbQyev01QV6rW3CYaBVasVpbk4HQ3gUdhXFlrWyzd8v16+4dH18g0Kb92fxTN0LSbOkfcvu3UNPfL+5TB9q/m6WUW/9957Vc8//3ykXC7PeO655yJXr15dAwDp6emG//73v4GA3Tuhrq5OUF5ezgeAxx57LOrNN9+sdmbg1BP5+flCPz8/2x133BGfmpqqXLp0aaTVakVDQwNXIpHYeDy7/1FMTIy5oaGB30N3Hbizd165cmXE8uXLG3x9fa9Z2y8tLRVmZ2dLMjIyUoYNG5acnZ3dETRXV1fzU1NTlcOGDUveu3evLwA4vCFWrlwZrlQqU6dNmxbnCJxc8Z///CcwLS1NLxKJGNYqmoXlBqXovkdD2uoCabOen/dbCJkyDKBT+8Z5qz+api7RDOWJNa0QwJ0APoB9heHSevmG19bLN4xdL9/AbmleZ85sKg/suoLQFYvBRp3ZVHbdrKLfe++9kNdee62qvr4+59VXX6164IEHYgDg5Zdfrmtvb+ekpKQo3333XVlKSoqew+Ewmzdv9g8ODraOGzdO35cxWK1WcubMGd933nmnKicnJ7+8vFywdu3a4P7OzZW987Fjx0RlZWWCxYsXt3W9xmazkdbWVs6FCxcK33zzzaqFCxfG0zSN6OhoS1lZWU5BQUH+mjVrqh544IG41tZWymKxkIaGBt6YMWN0+fn5BSNGjNA9/vjjUa7GdObMGeHzzz8f8cknn1T0d359gf0DZmHxFgw1w2IQTGg3CAAwjVyBpVjkpxcKfIwZhILHbzN9xaARXwFD4r3Vn04n6LYn6yHpV7/+BkC1Xr5hn19w2/ZZT239ifvAUdZbwsuoPbSAVtcZr5tV9P/+97+gzz77rAoAlixZonriiSdiAPv2xNatW8sBgKZpREVFDUhJSTF9+eWX0h9//DEgIiLC32QyUTqdjrrnnntiv/322zJP7h8dHW1OSUkxKJVKMwDcfffdqhMnTvguX768WaPRcCwWC3g8HsrLy/mOrQ9PcGXvrFAozLm5ueKIiIgBVquVtLa2cocPH5586tSpIrlcbp47d24bRVGYOHGinqIopr6+nhseHm4ViUQ2ABg3bpw+OjralJubKxw7dqxeKBTSixcvVgHAH/7wh9YvvvjCaYBz5coV3ty5cxPWr19flpaWZgIAhUJhYa2iWVhuTO755X9JqNXEH6tpChjaXC4zt1YGn9C3+RyjbeS6JBcBQFN1aLU3+1NrxN7YOggEsCB8SPFSAI3WjWOOWzeOec66ccxw68YxrOW1F/Dz0ALaL0x43ayiQ0JCLLt375YA9kQ+hUJhBOxL647qgn/961/Bw4cP10ilUvqDDz6oaWhoyKmpqbm0cePG0pEjR2o8DRAAICsrS6dWqzm1tbVcADh48KCfUqk0UBSFkSNHajZs2BAIAJ999lnQXXfd1QbYnSofe+wxlyZYgGt752eeeaapsbExp6am5tKhQ4cKY2JiTKdOnSoCgBkzZrQdOHBAAgA5OTkCi8VCyeVya21tLddR/ZGfn88vLy8XJCcnmyiKwuTJk9u///57CQDs3r3bLzExsZvpVHNzM+fOO+9MfOmll6rvuOOODoXUX9sqmg0SWFi8QNGCZT4A7nB+lvjarNyRulbJ6JaKUHFzRcg5bbPkkM3CqXPevm+0N/sHeasvhkG5xcLzSn6BjqbbwzNKRPgl+fFlACcB1Fs3jvncunHMfdaNY6TeuNetyNDFMSqeiOO2Bo4n4tBDF8d6xSo6KSlJqVQq04KDgy0Oe+ePPvqo4plnnolMTk5WPvfccxH//ve/KwDgwoULwpSUlLSYmJj0ffv2+X/88cfd9vq78sorr4TKZLKMhoYG/sCBA5Xz589XAMChQ4fEjv/ncrl4/fXXqydMmJCUlJSkZBgGK1asaAaA1atXV69du1YeHR2drlKpuMuXL28GgJKSEoGfn5/N9Z37xl/+8pfmsrIyQWJiYtqCBQviPv744zKKovDDDz/4pqSkpKWkpCjnzp0b/84771TIZDIbAKxZs6b65ZdfDk9KSlJu3rw56L333qsGgC+//NL/iSeeCAeAN998M7SyslLw2muvhTvKKB2VEx988EHFI488EqNQKNJjYmJMrFU0C8vvnKIFy2bCrlrYOwhdzBeb68T+OjlXYEkGAJNWeIYntESCMFwOl/Zon9Vi4jbnHc0IAkiva8KdYTDwD9U3SMf3t59Kq6VknamJqXpvt5wQuMtvsAE4BmD31a9L3AeO3vIfTp66QDqqG1ydn/h0So23yiBvVO65557Yjz76qCo8PPyWNzpjXSBZWH59+lbVwFBJZp0wyawTAmBquAJLidXMBRgilUY3tXnaTUttSBFAxvRpDE5Qa8T9LqPcb9Qd+96kTb9zYGNZDwECAHAAjLv69RqAGuvGMXtgDxgOcB84et22aW4GHAFAV50EnohDe1Mn4UamN9sZLL/ABgksLP2kaMEyLoAZ/e+JRFhN/Ktvg4y6vVZaKvQzlAgl+jSKw/i7u7K1LshrSWkMA7VeL+izIBPDMMixmi58b9KmAPB7cFxFWx+6iQDw0NUvq3XjmMMA9liN3N0VPw/IT/76/Vt+laErd70xsH7SM6mNZzaVBarrjDy/MKFl6OJYlVjKZ62iWfoMGySwsPSf8bAn6HkR4nc1jwG6Vl8boZiLAh9ju8hfF8Pl266RiqVtxGQ28gd4685WKycfICP7cq2atjVvNqjzCq3msbCvDmBscnOPuQ2b103Jjk2qEY6cmDvCyWkugIkAJpq1wrsAxBQtWLYPduno/clfv3/d9mNvNMRSPj3+iWTWKprFa7BBAgtL/5l5fbsnHIYmA40aMYwaMUCYK3yRqVrkrw/hCc3KtkbpJYAM9dbdNFpRn5K7GIbBm9qWah3DZDmOJcs1lQIuE+PuOpNO0GxsCLTuLwrnjJyY6/Ye6upgCexCTn+++mUtWrDsOIA9APYCuJj89fvsmzMLi5dggwQWln5QtGAZwa8txcyQeLNeGG/WCwEwTc2N0hoANIA0AD796poBo9WIUnp7nYmh9Wu1qvM6hrkmL+LB8eXlsD/UXUJbOKZEZZUp0Mh1K6zDMNDrG/27KkBy8Usuw6sAmooWLNsPYBuAQ8lfv9/Yy6mwsLB0gg0SWFj6x1AALtXSrjcMQ0JMetEIAHIAZgBnAegBJF491tv+8m00x5UUczdohmGOmg0ntxk1GQC6JU7eM6TOt6c+RAH6iCHTzvSo7Ggz8vIYmhrWQzMu7PkMGwH4FC1YdhbAj7CvMhxL/vr96yY6w8JyM8LqJLCw9I+7f8ubm038IoA4ggE+gEzY36rlAIrNZs5emibdXOhcodcLnJbbMQwDHU23t9K2epphwDAMTAyt/0inOrzNqBkKoJvJk4/Aqg/1M3mUACn2MfmJfUxuKyq0DQFGD7q6BHuOiGNFJRN25cefATQXLVi2vWjBsv8rWrAs1pNx3WgYVUZO7rq84FMvnw7LXZcXbFQZr4tglTO7ZAB44YUXZF0tnnft2iVJSUlRJiQkpA0bNqzD2TEiImJAUlKS0mEv3bkfZ9bLnSkpKeGNGDEiKT4+Pi0hISHtH//4R4eVeUNDA2f06NGJCoUiffTo0YlNTU29+h64Gperfs+fPy8cNGhQCp/PH/L888/LOvd17733xkil0oFdv1fLly8Pd9xjzJgxieXl5U4Tj6+nHbansEECC0v/+E0NnXQaH3elbUkNjYEDKiplysqqkGaVyveoxcI5yTDQubpArRF3MwpiGAYb9e3Zf9c0cf+haZY/qW5sf1Ld2Pw3dRMpsVnGw8WK5OyhNXmEeE+OWl0VHONBM3dGR36wbw19CGC1F4b0u+LUP07Lvx78TcbJF04pLn2YG37yhVOKrwd/k3HqH6e94gDpwJldMmB/cB84cMAvLCysQwa5ubmZs3z58uidO3eWlJSU5O3YseNK52uys7OLCwsL83NzcwscxzpbL5eUlOQ999xz3X7HeTweVq9eXX3lypW806dPF6xfvz707NmzQgB44YUXwiZMmKCpqKjInTBhgub555/v9fydjctVv6GhodZ33323cunSpQ1d+1myZEnzd999d7nr8RdeeKG+uLg4v7CwMH/atGntq1atcvp7u2jRotinnnqqobS0NO/cuXMFDo0Hhx12ZWVlrr+/v/Xdd9/tt2+FK9gggYWljxQtWJYAex7Ab4ZRLwx1dY6mSaHVyo0AAJuNE9zW7jumuiZkRHmFjN/Y6H/OYOQdYhh0KODZGNTvUZu7rSRsNWoO5VhNWfjl7dyfAYIBiNyNbfHYSlOfJuUEhiblFp3Qk22dWg+73Nuf8fzeOPWP0/JLH+RG2IzXGj3ZjDbq0ge5Ed4KFFzZJQPAsmXLot56661q0knP69NPP5VOnz5dlZiYaAY8s1H2xHpZoVBYxo4dqweAwMBAOj4+3lBZWckHgL179wYsXbq0BQCWLl3asmfPHq9UHrnqNyIiwpqVlaXn8XjdynKnTZumDQkJ6TZ+h+U2AOh0Ooo40UC7nnbYvYENElhY+s5vuopgtXIaGYZKdXWeopgURXT9ldBQVbaAb+70NkN4Or1oSH190PjyCllUTW1QqUYjyr7czj37vVE34pn2xsK9Ru3RZpu1tspmuXLabAjv/egYDIxq95rZlFHlU+lhU7d6Ep34oa9j+b1hVBk5+esL3FpF568vCDO1mfr9ee/KLvmLL74ICAsLs4waNeoaD4Li4mKhSqXiDh8+PDktLS31/fffv0Y6fPLkyYlpaWmpb7/9dsebsDvrZWcUFRXx8/PzxVlZWVoAaGlp4SoUCgsAREVFWVpaWnqde+dsXN7otzOPP/54hFwuz9i6dWvQW2+91S24vV522L2FDRJYWPrOzN/y5jqNT3FPbSgK8T5iU5a/v87FGzaB2cyLa27xzxK0BYe+7ReqXekrVVkYhrynU9H/0rb6WoCE3o5tsKKthMth3D64eoO6OsiTqg0dPFvZKU7++v3y/o3o90PJN1cCu64gdMVmtFGXvynp1xu1K7tkjUZDvfnmm/K333672++Y1WolOTk54v3791/ev3//5bfeeissJydHAABHjhwpzM/PL/jhhx8uf/LJJ6F79uzxBVxbLzujvb2dmj17dvzrr79e1fnt3AFFUXD2lu4OV+Pqb79dWbt2bU19fX3O3LlzW956661uK4LXyw67t7BBAgtLHyhasEwGYNRvOQaDViTwtK1YbEoNC2s5TAjjKvnPDCCdEOIv43BHzRBJRr8kCY54xS+kcaHILzuaw+22r+qOh7LKa3rT3h0MA6OuIcCTBMh8AJ4oT+7r55B+V+gb9B6pbXrazhWHDx/2ddgljx8/PqW8vFwwfPjw5IKCAkF1dbUgIyNDGRERMaChoYE/ZMiQ1MrKSm5kZKR50qRJaj8/PzosLMw6YsQIzZkzZ8QAEBsbawHsy/XTp09vO378uA8AuLJe7joek8lEpk+fHn/vvfe2/vGPf2xzHA8KCrI63CorKip4Uqm0V14NrsbV335dsWTJktZdu3Z1C+A622HzeDzcfffdqnPnzollMpnVYYcNoNd22L2FDRJYWPrG3bC7Gv4m0DQx2mwcpw9OirIZgGu3RwlBqIBviWMYCF10mYsuOQaEECIm1IBhfFHWCt+gxLf9Quue8JEeHsETnuaDdLO27cyUAQ1eU6C0mbh5DE11Dohc+Th4Uv0A3GRBglgm9qis09N2rnBllzx8+HBDa2vrxZqamks1NTWXZDKZ+dy5cwXR0dHWuXPntp04ccLXYrFAo9FQ58+f9x0wYIBBrVZTKpWKAuzukgcPHvTLyMgwAK6tlzuPhaZpLFiwQJGUlGR88cUXr0kYnDJlStu6deuCAGDdunVBU6dObQOAgwcPimfNmhXjbo7uxuWq375w6dKljt/nb775JiA+Pr7b31Nf7LCvB6xOAgtLH5BGNY20GPnZhnZxlNXMjfu14wWDXpTrSmUxIbPoXFlOQrzFxL8mWc1k4l0BiCunQG1P9+QQEqbg8sIUXH/MZxizhqHPnrcYdcfMhthG2taRVOgvMqsDfSxeS+jUN/k3A6gAUINfPrM4sJc3dsYTa2sz7OWQNw0J8+JVZ147G+Vuy4Ej5NCJ8xL6ZRXdF4YMGWK87bbb2lNSUtIoisL999/fNGzYMGN+fj5/1qxZCYB9e2HOnDktc+fOVQN26+X58+fHJCYmpvF4PNphvVxeXs774x//qMjOzi758ccffXfs2BGUmJhoSElJUQLASy+9VDN//vz2l156qW7WrFnxCoUiOCIiwrx9+/YrAFBeXi5wJEO6orq6mutqXK76rays5A4bNkyp0+k4hBBm3bp1soKCglypVErPmDEj9sSJExKVSsWVyWQZf/vb32pXrFjR/NRTT0WWlpYKCSFMZGSkef369RWA3Q77gw8+CNmyZUtFZztsABgwYIC+sx32/Pnz41955ZWItLQ0vcMO+3rAWkWzsPSS1lVzJQCaYdclAMOg0mLglxvU4gCzXpAGkOtSm96ZxtqQIxYzf2zX4wwDaI28vYH+ukyrmR9yzTVN/md1OlHXB6uDKvRDFMrKMOVlNkvlMbPed9SYAv0/5+d1G1tfqTqactmsESWgeySWDXuwkAB7PoIniZI/JX/9/mRvje1646lVtKO6wdX5AY+l1wx/btgt7QS5dOnSyCVLlrSMGDHC7SrYrQBrFc3Ccn2ZCvxS/08IovliczRfbAbDQGOzcHKNGhFj1IjTGZrqt+VyVxgGsJh5Sc7OEQL4CCwTrWa+oMs1NG2jXOUwlAHol7gQl5CYRC4/JpHLx8RoyxH1ZVkxV2yu4/nrpVwfUxohfdvaZGhUmzXCRBenHR4RWgBSD7u8qbYaHDgCgPz1BWGdVxQ4Qg6t/FNq3a0eIADAunXrqn/rMdyIsEECC0vvmenqBCGQcPm2Ub5BWvhItTaGJhdMWqHaoPaJtVm4XpFvNpv4BQBxU/qIbsEAIaDkclW6Ti/IbmwMyOryUl6FfgYJv8DAX6pJAUMFW3XCJKtOCIBpp3i2fJ6fgeb561MoLh3UYzdXMbX7lAGkm+JeF3qUfu7ETVP62JXhzw2rH/h4RuPlb0oC9Q16nlgmtiTOS1AJAgSs4RVLn2GDBBaWXtC6ai4PwHRP2hICDuEwg0T+Boj8DWBolFqM/CqDWuxv1gvSAdKnvz+t2rcRgMsgwRUMA1okNEURAhPDXBNIeG21I0jeWkBI17ERf9rCHWVqkcDUIgEoOp8rNjXxA/ShHJE51V0lmbo6yFWiZV9oAJDjxf5+dwgCBHT6w2msVTSL12CDBBaW3pEFzwV7roFQiOOLzXGObQnaysk3akRWo0aUTNs4Htc/mwwCWc+tuqPTCQ83Nft3XUXQAPDIX8ET4lIqeg5gaEpp1Ypg1YoAMM2UwFrE8zNQPIk+leIyAY5mDAOztt6j0kdP+ZG1kWZh6R1skMDC0jvu8UYnhEDC4dlG+Ei18JFqQdMk36wXNBnaxXKriZfsqlrCauU0MAzVayvnq9dynPSbB2BkX/pzhiyyKaTnVp0hwbSJF2xq4sHUJKFBMZe4PqZWvr9exhBay9g4Tis4+shNmY/AwnI9YYMEFhYPaV01l+A6qSxSFKMU+hoh9DWCYdBgNXMvG9VivkkrTGcYqkOWVqfxuQygTysJvr4GgaqtmyNzN6OevuIr0ZZxedZeb4P8AqFAkwFWjQhWjQgMg0PiQM1Rk1ZE2SycVIAE9HOIP/bzehaWWw5WTImFxXMGA+gpia7fEAIZT2AdKwlRDw+KaeQGRTee8w1uP8ThWcoNWqHHKotd4XLpYSEhbdldDic7bdwrGMTGVx0aNelsLSHeE4wgBJRPoG6MNKp5VHBsg7+/vDWPLzZmg9CFfejufPLX73dz6bvZsLQbOJWbTwWXrP0prHLzqWBLu8Gr5bjDhw9PjomJSU9JSVGmpKQoa2pquABgMBjI9OnT46Kjo9MzMjJSHJbG9fX1nBEjRiSJxeLBixcvju7cl8O7QCwWD+58/M033wxx2ChnZmYmO9wdu9Lc3MyZOnVqXGxsbFpcXFza/v37fYDrZxW9cuXK8NDQ0AzH3Lds2eIPAB999JHUcSwlJUVJUVTmsWPHRCqViup8PDAwcOCSJUuiAGDPnj2+SqUylcvlZjpEkZxx+PBhcVJSkjI6Ojr9gQceiHLIU/d3jr2BDRJYWDzHK1sNvYEQ8CkuPUTkZxgvjW6Sp9xxThsQ0ZQH0jeBE18fY1ZQUPuhq/8sgN3NsddwuVZ1WETjidS0K9kjxly8FBHVOF4oMXj78yTO8T+EgPDF5jR/eVtWSGxjijS6sdknUHOMw7MeAxhPhGRu+lWEkvcPyo/e/X5GybsHFJVfngwvefeA4ujd72eUvH/Qq1bRmzZtKi0sLMwvLCzMdzg0vvvuu8H+/v7WysrK3GXLljWsXLkyEgDEYjHz8ssv17744ovdyg9nzpzZdvLkyYKuxx966KEWh43yypUr65944gmnVUEPP/xw1B133KEuKyvLy8/Pzx80aJARuH5W0QDwyCOPNDjmPn/+/HYA+L//+79Wx7FNmzaVRUREmEaPHm0IDAykHccLCwvzw8PDzffee68KAOLi4swbNmwonzFjhtsk00cffVTx0UcfVZSXl+eWlpYKt27d6uetOXoKu93AwuI5M3/Lm/Mkxkt+8vaJQYomMAxadK2SwvqCaE5DUWS6zcLzuAzQT2IYbzLxTmu1Yj08rJLgC8yN4RGNRapWvyAfX0N7RFQDTyCwdMplYBgOz9aPrYZulAOIcXWSw6WDxYG6YHGgDgwDWI28QoNG3GDWC6QMTZROBK1u2tJHwB4gVH5xopuYEm2yUo7jCcsmXjethF27dgW8+OKLtQDw4IMPqp555plomqbh5+dHT5kyRVtUVNRtBWzy5Mk6Z311NmrSarUcZ0ZKLS0tnJMnT0q2bt1aDgBCoZARCoU2wG7pnJ2dXQTYLZ2zsrKSYVfrvO5s2rRJOnPmzG7Kljk5OYKWlhbelClTtACQnJxsBuxGUa6oqKjgabVayvF9WrRoUcuOHTsC582bp/4158gGCSwsHtC6am4sgIzfcgz8AJ3J8f+EIMg3SDMmYWwe4sfkWSwG/vmm0nBNbW5MrKHNt0c9huAgtcJg4DX4Sgz5EVENTapWfwFDE0qvF/qp231TCGFsDENxAIDPNzdnDs9t53CYcZHRzlfseSJzPiEeOTB6SiXcBAmdIQTgiSwpPFF7CgDQNqIx6YQFRo3IZDXxogGEAOSIF8f2u8LSbuBU//eMW8fN6v+eCVMsHtnI8xP1u7rjoYceiqEoCjNmzFC98cYbdRRFoaGhgR8bG2sGAB6PB19fX1tDQwM3LCysTyZIr732WsiHH34os1gs1I8//ljU9XxRURFfKpVa77333pj8/HxxRkaG7pNPPqny8/OjvWUVTQjBgw8+2PTUU091rFStX78+9Ouvvw4aOHCg/sMPP6wKCQm5Jqfn22+/Ddy2bVtJ1/42bdokvfvuu1vdBQVdqaio4IWFhXX4bSgUCnNdXR0P8L5ttTvY7QYWFs/41bcaroUBJbA6rWogBDy+2Dw4Ir18/LAFP0eNfWh3afqdJ7ODFPUXCUU7TUwkhKFGjMkJHTDoslIapM6KT6wamZBcOTxjcHHKmKyz7ROmHzsbE191KH1g8aFhoy7xORzGleohAEDkp/e2drwn1tBOoTiMRORnGB4Y0TouJK5BIY1q3pn89fumnq+8ManbfSmQNlndfpbTJitVvzu336ZbW7ZsKS0uLs4/fvx44bFjx3w//PBDj4WxesOzzz7bVFVVlfviiy9Wv/DCC90CIKvVSgoKCsSPPfZYU0FBQb5YLKafe+65bkvu3rSKXrFiRWNFRcWlgoKCfLlcbnn00UevCcZ/+uknH5FIRA8bNqyb0dj27dul999/f2uvBuIh3rCtdtv/deuZheXmYuZveXOO2JxHiGf5AxSXjpNGN2WlTTszcOyfd+uGLjh4ImrI5aM8kanjQ4rLsxZRVNcqCQaygWXZiqz8wqD0KjL+T/sSYocWcwjpWWyJJzKH9nZObjDAi9oNHJ7tuLf6+j1ibtZ6ZAFt8rCdOxw2yoGBgfT8+fNbT5065QMAMpnMXFZWxgcAi8UCrVbLkclk/bZS/vOf/9z6448/BnQ9HhMTY5bJZOZJkybpAGD+/PmqixcvioHrZxUdFRVl5XK54HA4WLZsWdOFCxeuCWS//PJL6ezZs7sFAsePHxfZbDYybtw4fW/GoVAoLI6Vg6tz4TtWFq6XbbUz2CCBhaUHWlfNDQYw7rccgyBQ2ycVPULgJw7QjYwdXjRm5OIfA0cv2Zufctu57NDkKhUI09S5Lc/HVOkb1pZlpWi///fybK1KI6oWh6h7TJCkOLZmimK8mY+QC3SXlu4HN3U+Aj/Y1yMLaIGH7VxhsVhQV1fHBQCTyUR2797tn56ebgCA6dOnt3322WdBALBhw4bAUaNGaXqztN6ZzjbKW7Zs8VcoFN1WgaKjo61yudx88eJFAQD88MMPfsnJyUbg+llFOx7KAPD1118HJCcndxhF2Ww27Ny5M3Dx4sXdgoTPP/9cOmvWrF6vIigUCouvry994MABH5qm8eWXXwbdc889be7meD1gcxJYWHpmOn7TgJoBR2yO6W8vhIBw+VZlaEKNTXbXJR0oxs9YE1DYdiGqQV8ulYYOqFAxDMJq8qMrp8S2hTbkRjb60JweH/5CiaEIfayScEG35dp+UA2gLyWTNwxhdw5Qlf47O8rdlgMl4NLyO9P7ZRVtMBio2267LdFisRCapsm4cePUK1eubAKA5cuXN8+ZMyc2Ojo63d/f37Zly5YrjusiIiIGaLVajsViIfv27QvYvXt3cWZmpvGRRx6J3L59u9RoNFIymSxj0aJFzWvWrKlds2ZN6OHDh/24XC7j7+9v3bhxYxmAa6yiAWDt2rWVixYtijObzSQ6Otq0efPmcsC1pXN/raKXL18emZ+fLwKAyMhI84YNGyoc1+3Zs0cSFhZmViqV5q59fvfdd9KdO3de7nwsOztbPG/evAS1Ws05cOBAwD//+c/wkpKSPABISUlRFhYW5gPABx98UPGnP/0p1mg0kokTJ6rvvffedndzvB6wVtEsLD3QumruNgCzfqv7UwJLqa+iOa7nlh7CoS9SKa0Dux626nkq7eXQPFolaK88lOYjFhsneLLVGRjZdJzLt43y2viAWgDhXurrM+mrW//kpb5+dTy1inZV3eAg+g8ja65ndcONAGsV/QusVTQLi5doXTVXBGDKbzkGfqCuCp00A/qNxNzu7DBXbAkMGFgzFgACs0phqA4saL8Y2ai7EhLEWCklQJy8qTK0l0sfS+HNud7kWw0OHAFA9X/PhHVeUaAEXDry3qF1t3qAALBW0X2FDRJYWNxw6uDg8SmDLl+QBGhTCUG/s8P7As/H6NUMciI19KgaSQggjlKliqNUqdojsfnmeonGqBUVGDUim83MTQJICNBR+uhNE6ZqeC9IYADs91Jfv3sSlk2sVywe2Vi/OzfQ1KzlCYJ9LfI701XeKHtkuXVhgwQWFjfUlIXPqSkLHw3CMCFhzXnxyvLm0PDmSA6Xjv817k84tlZQjBf1B5gaCG0eP4StKuERa5PvWIrDQOyvHyn219vFi0y8IoNaXC+U6M3wYiUCAI9FoTzgrPTVrbeUbTLPT0RHLRh2S82Z5frCBgksLC5YL99AAZgBAGAIaaoNSWuqtZsc+vprq+KV5WXhMXW+AqE5nRDwr8cYeP76AkIwxmsdiqylhMDl3nVXzBXSbmVzhAA8oSWZJ2xPbqkMPscXm08KJXoLl2+NJwRuRX16wAh4VZDplthqYGG5nrBBAguLa4YDcKqJrm33jbp4PD3q4vF0cLhWfWRs3cWY5EpjQFB7DMVhelQ89BS+v8Grf6Mk0CjytC1joQrMZdIRrs7brFQzbeUOMaq5MKrFABhw+dYrQj99tcDH6E8oJo0Q9KY2Px/AkF607wk2SGBh6SdskMDC4pq7PWlks3LFFZejhlVcjgLAQBKgrYxJqqqIjKsVC0SmjF4+KH+BMCbCtQ3o07VOYUzwN3n+ps6l3a44GDWiYnQqfdRpxeWSIHWottk/XtvsDxDGKBAbLwklBi1PaA4jFNyqNgLQejy2ntEBuKlFlFhYfg1YMSUWFtf0QYqZQNMmib50Sjluz9e3ZX7/5R3GnBPKE2qV7zGGgbo3PfEkhkuEQNz7MbiAS18iFDxeSbDU+J+HG+dnk1bUcVKvE1Y2M/zLUYtO5XU0YIjQpBMNaa+Xjm8ulye2VAbX69t8DtssnJMMA42TLr3pZHdQ+urWbjXrNzs2jY7T+v1PwY1fbg9r/f6nYJtG96tYRb/33ntBgYGBAx3H16xZEwwAO3fulHS2SxYIBEM+//zzAADIzMxMdhwPDQ3NuO222+IB4Pz588JBgwal8Pn8Ic8//7zM1VgKCwv5GRkZKdHR0enTp0+PMxqNBHBtW+0pRqOR3HfffYqYmJj02NjYtI0bNwYAwIsvviiLj49PS0pKUo4aNSqpuLi4o9/Lly/zx4wZkxgXF5cWHx+f5rjnnDlzYiIiIgY45nns2LGOv79du3ZJUlJSlAkJCWnDhg1zatl+vebYG9iVBBYWJ6yXb0gAoOxvPxYzT3IlP3bklfxYEIq2hivqz8WllmuloW2JFMW43b/nB+g9EhWibaRc2+xfJRQZeWAIl+9vGOq0ob/J4/pwhoHakBPuchWDYcDYLJwOL4kD5QGXfyoPUL+Yezf5bGQLnOkr0FauXNcqketaJQAYK19suiiUGNr4IlM4CAIJQZKn4/OAW26roenLHXLVvuwwxmzpePlr3rIzKnBKVl3IopleK4HctGlT6fjx47tJDM+YMUO1adOmyi7HNDNmzMgHgIaGBk5SUtKAmTNnqgHg7NmzHcZNU6ZMiZ8xY0YbAISGhlrffffdyq1bt7qtJlq5cmXksmXLGh5++GHVwoULo999993gZ555pqmzbfXHH38cuHLlysjvv/++1NP5Pfvss2EhISGW8vLyXJvNhsbGRi4AZGZm6p988skCiURCv/HGGyErVqzo6HfRokWxzz77bN2sWbPU7e3tVGe1yVdeeaX6wQcfvEbIqrm5mbN8+fLovXv3Xk5MTDQ7gq1fa469gV1JYGFxjkdbDb2BoSluTVn4kMO7R4//duO0sEPfjyqsKZNnWy2cYietQQksTt8uutJaGVpxYfvYcS3FEVaKa3O5UkACjbGejtXWIr7AmLhSV+ctBn4+QDo+xEfFNw4cmVol+2h4q4fy1YRr1gsHqhsCs5rL5YktFSGXWquDjurbxMdpK+UNI5wfvdDHDUPTlzvkrTv3R3QOEACAMVuo1p37I5q+3OHNVZo+8fnnnwdmZWW1SySSa0oyW1tbqePHj0sWLlyoAuyeCVlZWXoej+dS6Y+maRw/flziePguWbKkZefOnQGA3bZ6yZIlLYDdtvrYsWMSmva8CnTz5s3Br7zySj0AcDgcOJwsZ8yYoXGMfezYsdq6ujo+AJw9e1Zos9kwa9YsNQD4+/vTXefYlU8//VQ6ffp0VWJiotkx519zjr2BDRJYWJxznV0fCVoapCmnDmZm7fx8atKP/8uqKi+OzLaYufkMA4Zj1x8IcdeDVWHINuiER2ouxYYCDMQSfQxHbHaec0CYciKge9RHcGAqDXJbimjQiK9RAQwW0sHz43RjhRxG0BdDOobmCGxm3hhdq9+olspQaVNZaFF7fUC2Sc+/xDBw6mTphioA3eyFb1ZsGh1HtS/b7aqUal92mE2r88rn/UMPPRSTkpKifPrpp8M6P5j27NkTkJSUpJw6dWpcSUlJtzycrVu3Su+7775uAeBXX30VOHr0aLVUKvX4KdfQ0MCVSCQ2Hs9+m5iYGHNDQwP/6jmnttWe9Nvc3MwBgJUrV4YrlcrUadOmxVVVVXW7dt26dSG33XZbOwDk5+cL/fz8bHfccUd8amqqcunSpZFW6y/P/JdeeikiKSlJ+ac//SnKYDAQACguLhaqVCru8OHDk9PS0lLff//9bloo12uOvYUNElhYurBeviEIwNhf857adt+o80cGZu36Yopyz9eTWwyB5suQGk6Aop3t3YMB02oa1TY+72KarbE6mHcoL+Ln3LzoOp2Bl+v0BmJLpdPjzvpmYLDU+blNcLTo+W4DmD5w7VYDQyWb9cIsdb10QHOZTN9SGXxK2yI5bDVzqjzo6wfpq1tvGb359kMnA7uuIHSFMVuo9kOnrptV9Lx589oqKysvFRcX50+ePFn9hz/84ZpVq4qKCl5RUZFo9uzZ3fJyvvnmG+mCBQuui41yb7FYLKShoYE3ZswYXX5+fsGIESN0jz/++DXVSh9++KH04sWL4pdeeqkesNtWnzlzxvedd96pysnJyS8vLxesXbs2GADWrFlTU1pamnvx4sUClUrFcdhZW61WkpOTI96/f//l/fv3X37rrbfCcnJyvGlq5jXYIIGFpTu/qaGTySAMlg6qTqPCdCNJSquIJLdcIGHaQxBYywEGDAN9rkF0ggFU26HV/aUQV3L0NtXXJ2OaF65YlLThP2N/1pYHHzWpxCcYG1EDAAk0Sjy9P2Pk5oGmXH5g0VaqlWGIN6WYr6mS6A6R0FbucEO7zzhVdUhUU6msUlUjPWxQi07RNHFWEXFL5SNY29o9qp6xqTxr5w5XVtFyudzmME9asWJFc15e3jUJt5s2bQqcOnVqm0AguCZ4q6ur4+bk5PjMmzfPqVS4K2QymVWj0XAsFruxZXl5OV8mk5mvnuuzbbVMJrMKhUJ68eLFKgD4wx/+0Jqbm9sxlx07dkjefvvtsN27d5c45hsdHW1OSUkxKJVKM4/Hw9133606d+6cGLA7OVIUBZFIxCxZsqTl7NmzPoDdIGrSpElqPz8/OiwszDpixAjNmTNnxF3Hcj3m2FvYIIGFpTtez0foDRK5qpYrsCYAACHgEi4ziEiN46mEthiibKlsl5gOvPFVSsjS9D8ck+xPNQiNIt1xbgvVYKYNDw9oOnf3kIp02sxLMDX5h2muyHw0paGnGV+zx6qI5hp/twmORq2oEE5TE/tML5PqSLTVxB+nbfYf3lIeKmouD81RN/pnmw38gqtbEwe8OLbfPdwAf48soDmBnrVzhTur6M42yl999VVAXFzcNUm3W7dulS5cuNCZjXLgpEmT2sRica9WfiiKwsiRIzUbNmwIBIDPPvss6K677moDXNtWl5WV8UaNGuU2OZaiKEyePLn9+++/lwDA7t27/RITEw0AcPToUdHjjz+u+Pbbb0s65xBkZWXp1Go1p7a2lgsABw8e9FMqldd8X2iaxrZt2wJSU1MNADB37ty2EydO+FosFmg0Gur8+fO+AwYMMHQdS2/neD1gqxtYWDqxXr5BiN/Y0Cl52vkrcOGCSAiiA3wsFRtWZ9dotyfhfHE4IxRGknMtXKtCG+DPg1HXUhe8RR5TfxSAAiCvEb6NpjieazWYy4LcJjgaNcLeTahn+iHFTDgMTTJMWhFMWhEAJjv56w9uKVli//EjVM1bdka523IgfB7tP374dbOKfvPNN0P37dsXwOFwmICAAOvGjRvLHdcVFRXx6+rq+HfeeWe3rbOtW7dK//rXv9Z1PlZZWckdNmyYUqfTcQghzLp162QFBQW5UqmUzsrKSvjPf/5TERMTY1m9enX1/Pnz41955ZWItLQ0/fLly5sB17bVVVVVPA6H02MwsmbNmuqFCxfGPvXUU5ygoCDrpk2bygHg6aefjtLr9Zx77703HgDCw8PNP/30UwmXy8Xrr79ePWHChCQAGDBggH7FihXNADB//vzY1tZWLsMwRKlU6jdt2lQBAEOGDDHedttt7SkpKWkUReH+++9vGjZsmBEA+jPH6wFrFc3C0on18g3TAOz+Lccwb8P7Z3yCtc7LGAFYVD4nrKkaVd1HA41GA5eQqtBIDoeO0ekFuRTXqpcrmuR8kWkwIeQUgBGC5IYjImWjRzkWjI2Ut3+XHuPyPAOmuUymAojLyodeYoRdjMFb+7GvJX/9/iov9fWb46lVtKO6wdV56YzbarxZBnkj8uqrr4YoFArzokWLerW1cTPCWkWzsPSd33SrgSswG8RBWnf6BDauwGTBz8EkZkKxv6ogQtBaFzwUAHx9TBMAQF0faIqZnFMPmrJZ1KJj/GhVqKf3tzb6VgCIcXXeYuQXAKTf+hGdYKWYvYAjAOiqk0D4PNrbOgk3KqtWrWr6rcdwI8IGCSwsV1kv30DwGwcJilFFOVctqZ3unRICDiOyjjRUhLTQBRFyimvd5xfddEjX4C+2mXhpABFRPFshh0cPBOgwToimEBV+ibSPJZdIjS3wNYcRyrVokbki0N/d+Axqsbc/aFkpZi8RsmhmvfSe2xvbD50KtKnaeZxAf4v/+OEqjq8PaxXN0mfYIIGF5ReGwEUuwK9F6l3nzAAUAM4AMAOwAggB0FFNQLcLT9F6/hgA8JG3R/mLzcoQZTVoGzHqm/wu+IS2d16JaABICnT8dEZnV25luLZGEmgsRoCJDx6d7pB+ps3UJUu9n1uvCG+XPpZUhlgCJPrSoABdnBdSIY8kf/2+yQvDumHh+PrQ0jsn3lI5GSzXFzZIYGH5hd90FQFgEJRQHw/7/nznnIRm2CsA5ABgKgu6moTItHFEpmQANoBwKA4j9JW3D+rSaXfteysnlGnyCUWTD0AYM+NnOksCjTrNzwnBYIhLrX/aRqm8WfqoN/CafzqRMhlgGA6HvsyhaKMyoa59iLJyOJfD9EWL/qC3xsbCwmKHDRJYWK4SE1edWF8bXGM0Ct26H14vghPrSigOk+DsFDqVCVoafKMBgCvXXKBSW4NAMTKmJMAImohh5XTWG2gEkAJ3MISPdmEm0y6EJKYZNhOn0tLuU27RCsWMlRoAkI6EQqNGWAiQUf2aZCeKy2VX9REIsdk4iTYbBxcKopFTFFmx8K5TvmKhpZsKXQ/ckvkILCzXEzZIYGEBULRgmSIyGvdFRjfAZiMlbSq/mtrq0JD2NonSnROiN0m961wNAGdBAgDI2xslP+QfSRIOmphvEwYYuIRgQsfZuPYCUEwAUyRth41y5BUUA/A4aREAOAJbNCdUHS0MVYOhidGqFZw1t4t1NiM/2qgVeXVvu6QyxGlpFU1TiqPn4rNvH12Y1YvumgFc9M7IWFhYHLBiSiwsdmY4/ofDYRKCgtuzBgy6rByTda5pYGbBEVlY82mKsnnsotgdpq6nFlHDL7stKyw9HyPY/cEdtMXK9SPkWoVCwmFSCYEfCdbndDrcr7JCQjFCnp8x0yeqdbwkvj7m1JEMaUVZ2FG9XnCcYdBjWZ47GAZQqX3iXZ0vrwlyFSy5Yk/y1+/f8gl6tF7DMR7ZFazfsynMeGRXMK3XeNUq+ma3UVapVFRna+vAwMCBS5YsiQKAPXv2+CqVylQul5vpEDhywOFwMh3XTJo0qeN3l6ZpPP744xExMTHpcXFxaa+88so1QXt2drbYWX8ODh8+LE5KSlJGR0enP/DAA1EOr4yGhgbO6NGjExUKRfro0aMTm5qavPpz7gy7ksDCYsepoRMhCJFI9CGS5AokJFWYzCbemcaGIGNdbUii2cR36XXfGYHAtDswqJ1bXxvq0oRH6K9T8X1Mbv0SBk+5lDXojktqQuDnspHEwkUDAMAEwGOVxZ5oKJcXGPTi1KoKMaoqwgEwkPjpiuThTfXSoPZALteWRgg8/qBqavUtYRjiMhBgGBJktRFzL3IT9np675sV/d7P5cbje8JgMXe8/Ol//CpKOGpanXjq/V4pgbzZbZQDAwPpwsLCfMe/09LSUu+9914VAMTFxZk3bNhQ/vrrr3f7uxcIBNdc52Dt2rVB1dXVvCtXruRyOBx0novVasUzzzwTOWbMGJe6DY8++qjio48+qpg4caJuwoQJiVu3bvWbN2+e+oUXXgibMGGC5tVXX728atUq+fPPPy//6KOPajyZY29hVxJYbnkOjvqnv81GZfTUjhAIBELL0ChF/djhoy7JRo09X5isLM3289cUAq5FyUwmgW99bagAcP32nTz1Qj4hPf89ug0QAIBDO/TfLwFwaRvdW4pOpDZ2GQk0at/ky4WxWSePDso4cWSQ4XJR9Cl1u89hmkaPH1YFV8J6asO3Wjm9Wbm5paSYu6Lf+7nceOjbiM4BAgDAYqaMh76N0O/93CtW0beSjXJOTo6gpaWFN2XKFC0AJCcnm0eMGGHojfzxp59+GvqPf/yjjsOxx8+d5/Lqq6+G3nPPParg4GCnngsVFRU8rVZLTZ48WUdRFBYtWtSyY8eOQADYu3dvwNKlS1sAYOnSpS179uzpt3mXK9gggeWWh2GoqfVVcmFDdehx+qohkidwuHRKSKgqK2NwccqYrHP1GYMLDweHtpwBmK5leOMBZMKNiVHi7Tn9Nt8BADDEoc9vdNuul9QUR7pNIrTZOL4NdSHDc86njDt2aEjEudPK8trq0EMmE+8Mw6Dbw76iTupWjwEgVJtG1ODsjMnM7fozykn++n2nbW8FaL2GYzy+x61VtPH4njBar+3X5/2tZqO8adMm6d13393qSVBgNpup9PT01IEDB6Z8/vnnAY7jVVVVgs8//zwwPT09dfz48YmXLl0SAEBZWRlv586dgX/9619d6o5UVFTwwsLCOvw2FAqFua6ujgcALS0tXIVCYQGAqKgoS0tLy3XbFWC3G1hYgBkA8bNaeaPqqsLqfSS6Czy+ReQj0Q/ztANCIPfz18n9AzUXAiJO2yqvRJwsL1RY21slKQAJght/Ag7PavYNbXe71eAxXFoJvrUKZm5v9/RdYtQKVRYjvxcqiwR6nSimtCQqprQkCoSizdKg9vPysGa1n78m3GYj4UYTr8f57vo5gx8lV2WnxtdJfziqlFCEMVIUbTFbuGk8rq1gUEq1arCyajSAH/sxvRse87nswG4rCF2xmCnz+Z8DhWPu6rOGQmcb5U8//bT6xRdflD3++ONRO3bsKHO0cdgor1u3rgj4xUb55MmT+YmJiea77rorfu3atcErVqxoXrNmTU1UVJTFZDKRRYsWKZ577jn522+/XeewUT58+HCxTqejRo4cmTJ+/HhtRkbGr6qBsX37dunGjRvLem4JXL58OSc2NtaSn5/Pv/3225OHDBliSEtLM5nNZiIUCpnc3NyC//znPwEPPPBAzNmzZ4seffTRqNdff73ascLQHyiKAvGq39q1sEECyy3NTyNf4wK485cjRK7T+MoFImO2j0Tf6/4EPkY1l2cTx6VUjohLqQRDg2msDc69kh/T0lwXFGazcbupHSpGFV0iBJn9mUfH6AnEiG0vYoqCBnujPwAoOZtYAJDRfb2eoSl+S1Pg4JYm+4qoFdZDAOHAXjaSDMDpKgVNUzEVtUExFbVSNUD8aACw2T9ULVZu6uncGJwviCqOkLXtdprZdotAa1o9WoWiNap+rVY5s1H+4osvOlbHHDbKhw8fLnJmowwAd999t+rEiRO+gN1GGUCHjfLq1atlgN1GOSgoyOrn50f7+fnRDhvlzkFCZxtlHo/n1EY5Pj7e0lcb5ePHj4tsNhsZN26cRx8CDgttpVJpHjlypObUqVPitLQ0k0wmM993330qALj//vvbli1bFgMAOTk5PosXL44DAJVKxT148KA/l8tl7r///jZHnwqFwuJYOQCAiooKvmNlISgoyFpRUcFTKBSWiooKnlQqvS420QC73cDCMhZAt/08s5EfqtOIT1+1HvYYkb8+uvO/CQUii2xOH33Hmay7/7gvacq8Aw0pg4qP+PjpTgCMGgBS7jyv69cMuqLjebxl4glXzrl11+017XohATAGwGjYA4RCANmwlzA6sTMmLvMwrDaOoqI26IRXB3iDQUmkHllAU5LAfllF3+g2yp5YRTv4/PPPpbNmzepmbe2MpqYmjmOrpK6ujnvmzBnfjIwMAwBMmzatbe/evY7vl0ShUJgAoKam5pLja9q0aarVq1dXdg4QAHuQ4OvrSx84cMCHpml8+eWXQffcc08bAEyZMqVt3bp1QQCwbt26oKlTp15zrTdhVxJYbnWcqiwyDJXa1hIIhiEnff10Izzriqng8Kwx7lqIfY2y1CGXZalDLoOmia2uUnZBzLGYGCspIlzGKy/ETIvIa9LStI3QbQ0B3tkKuYrKwFV0OZSCX0Sf9LAHC3oAkQDieuju6Gp6fu+XfG4i+EOyVPofv4pyu+XA49P8wRP6ZRUN3Ng2yp5aRQPAd999J925c+flzseys7PF8+bNS1Cr1ZwDBw4E/POf/wwvKSnJu3DhgvCxxx5TEELAMAyeeOKJ+szMTCMAvPzyy/Vz586N/fDDD2VisZj+5JNPynu6d0pKitJRKfHBBx9U/OlPf4o1Go1k4sSJ6nvvvbcdAF566aW6WbNmxSsUiuCIiAjz9u3bWatoFhZv89PI1wjsgkMu9u8ZfUhYUx1fYHFZz98ZntB0KCBcNb6v4yEic4NAobrMi2rjUj5mZY+VDE5gGNQz+UFybwlAVRdGXTqwcYpbP4fewDCoLGgSR/fcsoN6ACUAOLAHEl1XfVatpue/5q3x/d7w1Cq6o7rBBcLx99R4qwzyRoW1iv4F1iqahcUzUuBa4RAAETfVhcaDMKUCganaL0At5wstLpcrRX56SX8Gwxj4MmOhTGYslKFFJa74z+aJ5cqxRarRc06FR6XVJHqUm2TgXgaIV8rdAKDoRKpHS66eYrBQFQB6EyTIr34B9jrTfABNAPwBKMFKMQMAHAFAV50E8Pi0N3USbmRYq+i+wQYJLLcynhk6MSTOZBTGGY3mo66DBEbD9zF57Y37/LmESoNaPO7s7sE4u3swCEXXR6bUlgy/5yx/8B2X0gVis9jZdUyr0GvaCABQdyXcq66YbUZuf1QgCeyBgYNqAOf7N6KbB/HU++uF42c1ms//HEhrVDxKEmjhD56gosS+t7wSJUvfYYMElluZXrk+atokGXqt+KTYR28RS/QJXK6t442dw7XlEYKRXa9pV4saDv6cUTh92pmRPJ7N4wdkfmHUNQ97hqbkVfmR8qr8SPzvtbvNEqn23MDbcrWj556KC1G0RAIAw8AAtcBrgYpW5Vtvs3ATux5nGFh1ZuoYj8PwBFzGY8MnhoFVbeL0opSyR46tpuezD8BOUGJfuj9ljiwsXWGDBJZbkp9GvhYCoJeOhkRis3JHaNr9oGn3AyF0MV9oruPzzZzA8GanNdwnTyYX5eVHZymiG08NHlQ23JO7WCwck0YjciOpTPiaVsmQI9+MwpFvRoHDs5bHZFRWDp6Uqx8UXzWJx+9VQYZLik+mlOCXpf7OUAYrRRmtsAq4nlde2Rjk0QwZ6JXB2dnvxb5YWFicwAYJLLcq09HP7D6GoZJMBmGSUS9k8i7FtYBCsTyysS4+rSxMGtqWxDCEPn02IR0AuFybx6opRcURuQDxWDfBZuHGXDkbF3PlbBy2gtH7S7UXBw4rNg7PyosNkbdF9mVuAFB6Md6pbwIhoILF1pGE9O7zQ2vitPV1LC64pUWUWFh+DdgggeVWZUbPTTyDpkmujeYOAI3gmvLwpJrycABMm9pCTgPkdgA4eizVnKasYiiK6TEwOXc+rh+6CUTc3ioZdmhfJg7tywSPb8mNiq1vHT4+30c5uDRVILQ4zWXois1KWXQqX5elj70NEABAZeC6lQ7uJSWr6fnlXuyPhYXFCayYEsstx08jXxMAmOKt/vR6oZMKABLgy0VCpJ8pG2BsLa1+o15/a07L+g2Tj5QVRZ62WjlOvRUYBqipDYr11tgsZp62tChq/NefTMl8/tFH+C//5c8Xtm6YnF1VKitxV/1cla/IBYiPt8bBMGg2WClvqjKxWw1OYEztHDrv62DbmQ/C6LyvgxlTu9cshG8VG+WXXnopNCEhIS0xMTFtxowZsXq9ngDAvHnzFMnJycqkpCTl1KlT49rb2ykAeO+994ICAwMHOua4Zs2aDhXKcePGJUokkkETJ050WkX1wAMPRInFYrfqqJcvX+aLxeLBzz//fIf75NatW/1iYmLSo6Oj01etWuW1aiZnsCsJLLciYwDUAPDKQ0utETtd0qcIYn0FNuInsF2kGcLTmql0f5sw4MLRgekXjmYYRD6GU4qkaktsckWKUGwOAoCmZr9ymqZivDGuq3RKGiBcnVY06PThNJw+nAZC6IawqObLIybk8gcNL1YKxeYOf4mikykaL44BJhspAohLg6s+wG41dMF25kM5U7A1DDYTBVz1JT33cRRJnVvHGfpov0sgbwUb5bKyMt7HH38sKyoqyvX19WXuvPPOuE8//VT6l7/8peXf//53lVQqpQHgoYceinzjjTdCX3311XoAmDFjhmrTpk2VXft76qmn6nU6HfXJJ5+EdD136NAhcVtbW4/P4McffzwyKyur4/tgtVqxYsWK6H379hXHxcVZBg4cmDpnzpw2h4CTt2GDBJZbkTmwBwg1AEpht1ROByDsbUcMgxqzmetSbIlhoAv3MwdQBHEMAxUhuJqQSEQGnXh44fkkFJ5PZHh866WBo3LNeSUyHYCY3k/JJS51IBiGktVWhsq2b5qE7ZsmWsW+xospGeVtQ8fmhzaWy/ucy+CMNgPXm6uWNICDXuzvhsd25kM5k/tldzElm4licr+MsAHwRqDgwJmNMmCXS/aUTz/9NHTz5s2l7myUz5w543Q1q7ONMoAOG+V58+ap9+7dG5CdnV0E2G2Us7KykoGe7csd2Gw2otPpKIFAYDMYDFRkZKQFABwBAk3TMBgMlCemSvfcc49m165d3fRTrFYrnn766chvvvmmLDU1NcDV9Z9//nmAQqEw+/j4dFTx/Pzzzz4KhcLk8MOYPXt269atWwMyMzOvixYGu93AcktxVWXxrqv/jAAwDsDQq/8+A+Aw7Cp/HmEy8Urd5T9yKKRRxC4tTEh3jwg7hFjMvAFnsgdnXrwY59ItkkGv1VFdVSc4GwNXrxUNPHcsNevjN+ekFtT7BtZreMeNFnKUYdBvG+Z2U3djq35wZjU9v98SwzcLjKmdwxRsdZvvwRRsDWNMaq993t+sNsqxsbGWxx57rD42NjYjNDR0oEQisc2ePbvDC2Xu3LkxISEhA0tKSoR/+9vfGh3H9+zZE+DYhigpKenRSOu1114LvfPOO9sc43RGe3s7tXr1avmbb75Z2/l4VVUVPyIiwuz4d2RkpLmmpsZpkrE3YIMElluNDDhX/BPCHiyMg/3BWgy76VAe4PrprNaIPUoE9AQbDb2VJi5LH+uJ5VAhMZztRbDg8dtTt7EwJLDVwBtVqhKNKWgSyS638HOy23GoyoTzNgbmnnv4BZpGoY0mTp0e+wibj9AJpmRPoGOLwSU2E8WU7HERpPae7du3S++//36P1DgvX76ck5ubW7B58+bSv/3tb1F5eXkCAOhso/ynP/2p6YEHHogBgN/SRrmpqYnz/fffB5SUlFyqr6/P0ev11Icffih1nN+6dWt5Q0PDxcTERONnn30WCADz5s1rq6ysvFRcXJw/efJk9R/+8Ae3OUXl5eW8HTt2BK5atarRXbunn346fNmyZQ3+/v6/qRYIu93AcqvhaVVDEn7JWWiF3amQgl3xzw+wixfp9UI3ega9Q23i5AFkmKvz7bCJbYCvAXSxGBxP3sy99FAgaLDB0MCQ8Q0WAGCMQgpnI/jQxQkRGcBBnLvPYZ2FasAvBk7egM1H6ARjaPHIAtrTdj1xM9so79y50y86OtoUHh5uBYCZM2e2HTt2zPfRRx/tCIi4XC4WLVrU+uabb8qXL1/eIpfLO4RJVqxY0fzyyy+73ao7ceKEuKKiQhgTEzMAAIxGIxUdHZ1eWVmZ27nd2bNnfb7//vvAF154IVKtVnMoioJQKKSHDx+u77xyUF1dfc3KgrdhVxJYbjV6pbJ4FSnstsYjAVwCcAHAIauVOs4w6I/M8DWEh6ibhybWZ4f46y/Z6xyuhQKxyBlem4cBghqA19wbVcTaaVmUCI00ybxiJON/bCNxW1tQ+2Mbjlw24ISJRjeb6jYD199b44DdHfK4F/u74SGiII8soD1t1xM3qo3ywYMHxbNmzYpxN96YmBjzuXPnfDUaDUXTNH766SdJamqqkaZp5ObmCgB7TsL27dsDEhMTjcAvltcA8NVXXwXExcW5TSBcsGBBe3Nz80XHHIVCId01QACAs2fPFjna/PnPf25cvnx53apVq5qysrJ05eXlwsLCQr7RaCTbtm2Tzpkzp83JrbwCu5LAcsvw08jXZABcvql7gA3AAFxdSeDxaMQoGmpMJl6pWiMW6/XCdIYhfQwaGMiCNBmEICI0QG/ZdzZGz+PadEYzJ9SR8xDN8MN5IN2ypF1QAMBDi+ueRsbAAMblEioDEq6yIlxlBc7rGJpHkCfjoTlOiKAQLqK0Zo7XVlsAHFpNz3eqbnmrQhKmqXDu4yi3Ww4cAU0Spnklj+NGtVEuLy8XiEQit3t1kyZN0s2YMUOVkZGRyuVykZaWpl+5cmUTwzBYvHhxrFarpRiGIampqfqNGzdWAMCbb74Zum/fvgAOh8MEBARYN27c2DGPzMzM5NLSUqHBYODIZLKMDz/8sHzOnDndAmkHX375pf/p06d93nnnnVpXbXg8HlavXl05derUJJvNhoULFzYPHTr0ulQ2AKxVNMstxE8jX1sCYH0/urgIwKWsMMPAaLNRl7Q6kVGjEcVbrVyPzZH4fEtpRHhLnOPfVhsp4FBMDMOguVktKs2vDIq3WCmx2crt2B8V8q2NAT7GGi6HtrbphKEGEzc0yM9QnByp0l6uCbDUq3wn9HWinTGALrtCmfqq3dDOZ0iBPzjWAIYTKwDl0s7YQ55aTc9f3c8+bhg8tYp2Wd1wFZK+qMab1Q03IkuXLo1csmRJy4gRIwy/9Vh+a1iraBYW5/RXZbEeQBvsJYqKricJgZDLpYcF+OsQ4K+DxUqdPVsU1ibmItiXb0snBC4zsSQSfTWAjiCBy2FSr/YZFRpgiAoNqAbDQKM18I5pjXyLv49JJOJbUwnBYABgGDCEgOBqENOuE3jtgaAi1ioAfQ0S/M2EGdkEK5qIFRRhih+ac7E+72eluL3RTwmQ3iZ+skmLTuAMfbTeBnsVwzUrChwB7S2dhBuddevWVf/WY7gRYYMElluCqyqLt/WzmyT88rCsAFAJQAx7xUS3pDAel87UW6j6Fh1PDjBqPofJCxBaGX+hNYnHwTXCQj5iU7da6q4QAolEbBktEXffWr4aIAAAbDQpMZh5LvUReosaNq8pL/oF6HWzntwzftaTe2A1cyyFxxMvntk1uK3kTFyoSc9PAdymojfBnhPC4gTO0EfrmQF/aGRK9gQyhhYeEQVZSMI0FRH4sU6ZLH2GDRJYbhUmAHCpQeABlbj2bVqBX1YT2gA0oouCo9VGCg1m3tWsfuJntpFRjTo+GnV8hAXq9iSHt4nVGnGIycSNoijaa/v2LWphDdyIKPUGGozJaq/o8AojxpZ07Mdy+TZeelbhwPSsQgCAtk3cduGHAYXn9gy01hSHKWgrJ6rL5QdYa2j3EIEfTdLms1bRLF6DDRJYbhX6u9VQDuf6CgAQAKDbvnFjm9hl6V9EsCbcx8c00MfHBIZBCyEdb8ip6GfpYmWTn9eqCTSw5YFgiLf6u2PGRZeiP74B+oCx806OHDvvJACgqSKo+vSuweU5P6VxW6qlSoCwWw0sLL8ybJDActNzVWVxej+7cbcKUQD7w/0ayur9XTwQGW2Iv76jPJEQBMFeYgnYhZvyYA86Qp316w6Gga6pTey10sdWYtN6qy9CmJaUtBqPlRdDFC2Rdz62P/LOx/bDZqVspedifgTme2s4LCwsHsDqJLDcCqShf34IegDutgO6rSIwDBradAKnD0Qh31pAUS4DdAL7eLNgDxBaAByDXRugraeBGs2cfJohXhHNAQA9aI8rNHoiIrqluBfS/tfA4dKlyY/t6Wagw8LCcn1hgwSWW4FZ/bw+F4ArbXQb7ImLHdS2+GQXVkmvuPJ0CJfqelPT7FhlGAXAH/ZVhmzYVy+6Uafy9Vq9tBl0PUO8k9sAABNuz+9PPsEBb43jZoaxtnGY+o3BdNWbYUz9xmDG2uY1q2gAWLdunTQpKUmZlJSkHDduXGJdXR0XAJYvXx6elJSkTElJUY4ZMyaxvLycBwC7du2SSCSSQQ4b5aeeeqpjde3ee++NkUqlAxMTE52ufL3wwgsyQkim4x7OaG1tpWQyWcbixYs7tgJdWUi74vz588JBgwal8Pn8IZ3tmAHXlsyubK6/+OKLAMf3IT09PXXfvn0dK5Br164NUigU6QqFIn3t2rUdMuVGo5Hcd999ipiYmPTY2Ni0jRs3BnQd4/bt2/3S0tJSk5KSlGlpaanfffddR6Jzb+fbW9gggeVWYGY/r3cneVoF+0oDAMBk4Zw/fyU0q7Q+YLSrC6JCNH0tJ+y6ytAMYC/sqwztAFDVKOlWmtlX2mC74q2+AOC2O3NdumV6AJuP0AN01Vty5kJWBlP1mgL168OZqtcUzIWsDLrqLQ9NvtxjsVjw7LPPRmVnZxcXFxfnp6WlGd56661QAHjhhRfqi4uL8wsLC/OnTZvWvmrVqo5gYOjQodrCwsL8wsLC/LfffrvOcXzJkiXN33333WVn9yopKeEdOHDALywszK3c8JNPPhkxfPjwa2zNHRbS5eXluaWlpcKtW7f6uesjNDTU+u6771YuXbr0GiMzhyXz7t27i4uLi/P+97//Sc+ePSsErrW5Li0tzXvwwQdbAWDGjBlqx1zXr19f/sgjjygAoKGhgfPGG2+Enzp1quDMmTMFb7zxRnhTUxMHAJ599tmwkJAQS3l5eW5JSUmew1mzyxgt33//fUlxcXH+xo0byx566KGOz5Dezre3sEECy03NTyNfiwH6nXjn7m06BkAYgGKGwcFj+eGh7lwhAabCRhOzlzTMgmHPlRgFQMIwyE2MaC0L8jPkO5N17i1txOa1t1Aez1omC2vv68OKBvCTt8ZyM0JXvSVH/acRYIzXfqYzRgr1n0Z4I1CgaZowDAOHZLFarabCw8PNwC82ygCg0+k8slGeNm2aNiQkxKmvwrJly6Leeuutanf9HD58WNzU1MS7/fbbOypmOltIUxTVYSHtbhwRERHWrKwsPY/Hu+ZvprMls1AoZByWzIDd5vof//hHXVeba39/f9rhjKnRaDq+Dzt27PAfP368WiaT2UJCQmzjx49Xb9u2zR8ANm/eHPzKK6/UAwCHw0FYWFi378mYMWMMMTExFgDIzMw0mkwmymAwkL7Mt7ewQQLLzc7Mfl5fDM/slpNMFo6/3sTrQVGQVBzJi4zbczq25lh++OGqJt/TVhtxpwBXQzO4cFkl+OFik+gQ090C0vFGQRGC9PAgfdbIlDrlncPK2rIGVB1PjGg9IuRbe231zIChzWB6lTTpjpT02qp+XH6WGlbEWkO7gLG2cdDwhVuraDR8EcZY2/v1eS8QCJg1a9ZUDhkyJE0mk2UUFxeLnnjiiY58nMcffzxCLpdnbN26Neitt97qkBU+f/68b3JysnL8+PGJZ86cEfZ0ny+++CIgLCzMMmrUKJd/FzabDU8++WTUu+++e83vlTsL6d7izpLZlc01AGzatCkgNjY2bc6cOYkff/xxOQDU1NTwIiMjO/qKiIgw19TU8JqbmzkAsHLlynClUpk6bdq0uKqqKrcFBf/5z38C09LS9CKRiPHmfF3BBgksNzv9zUeo67mJnSt1/vUASntoJgYABiRCpRWOyykLHbbvbAxn//nos7nlQYfOXwnJbtfxO/dR2q4RanaXBlI5zWJ5Z9Gkq/dyGpQQgkBfkWVUUkTb2MmDKmVTM8suD0uqy5YH6i5QhOnR6EcLOh8EXiulvO3OHFE/LmfzEdzRvCOw2wpCVxgjheb+vWGaTCby8ccfh5w8eTK/oaEhR6lUGjpvK6xdu7amvr4+Z+7cuS2ObYjRo0frKioqcoqKivIfe+yxxjlz5rjNcdFoNNSbb74pf/vtt116FwDAG2+8EXLHHXe0xcfHe8W0qre4srkGgMWLF7eVlZXlff311yXPP/+825cGi8VCGhoaeGPGjNHl5+cXjBgxQvf444931Qfp4MyZM8Lnn38+4pNPPqnw4nTcwgYJLDctP418LQTA2H52E9xTA4axf1U0+A+EXVq5CsAhAGcAdDYjssCpMBHhmyzczIpG//G1LZKsika/zvKxDG3lqpNE4E+PbevqvOexzCyHwySGBhiyMhMbBk0dWmaZNKjijDK6+ZBEZC5Ht8UJQEWsXhTkYSzjJhX2uSyztTbg+JPUlownqS19cfC86WEsjZ5ZRXvYzhUnTpwQAUBaWpqJoijcd999rSdPnuymxrlkyZLWXbt2BQL2bQh/f38aAObPn99utVqJu0TEgoICQXV1tSAjI0MZERExoKGhgT9kyJDUysrKa645ceKE7/r160MjIiIGvPDCC5Hbtm0LevTRRyPcWUj3lqioKLMrS+auNtfFxcXdguBp06ZpKysrBXV1ddyIiAhLdXV1R181NTX8iIgIi0wmswqFQnrx4sUqAPjDH/7Qmpub61Sq/MqVK7y5c+cmrF+/viwtLc0EuLfM9hZskMBy02I0c+5E/37HW9CD2qCNJqa9Z2Ku/HQx+jQD4nhriAIwHsBQ2KsfHEJJebi6kuCO6BB157LD8dJA7aDIkHZLoMDWdSw9Sjk7gxCIRXzb0Fi5evz4AdUx04aV1cTLVXsAnMDVMkst6B6DI0/xD9DnicSW3no0ALAHX6/PeeIT2M21vn2S2jLRW+O6WSC8UM+soj1s5wqFQmEpKSkR1tbWcgFg7969fklJSUYA6Lzc/s033wTEx8cbAKCyspLryLY/ePCgmKZpyGQyp3kIADB8+HBDa2trh42yTCYznzt3riA6Ovqaa7777ruyurq6SzU1NZdeeuml6tmzZ7d8+OGHNe4spF999dWQV1991VMXVbizZHZlc52bmytwzPfIkSNis9lMZDKZdebMme3Z2dl+TU1NnKamJk52drbfzJkz2ymKwuTJk9u///57R19+iYmJ3bZZmpubOXfeeWfiSy+9VH3HHXfoOv9MXM3XW7BiSiw3LQcuRM+lCMqkEkNVjEztH+KvT6co1yZLTigEMMbZiYY2cY6/2BR+sTQkn2ao8UYz5SpzXwy76uI52IOOHsmrCK6LCtGopBJjmK/IEkkIIgaEGLouWxpgr3ToNxRBRKtWVApgJOxeUQWRDL9ZBesFLWglQ1yWf3rEyHEl7X29tqYorJShqbhOh7Y/SW2JWU3Pb+vPmG4qgmeqUP2vKLdbDkRII3hmv/I6YmJiLE8//XTd2LFjk7lcLhMZGWn+6quvygDgqaeeiiwtLRUSQpjIyEjz+vXrKwDgiy++CPzss89CORwOIxQK6U2bNpU6EvtmzJgRe+LECYlKpeLKZLKMv/3tb7UrVqxw6Xh56NAh8QcffBCyZcsWt0vtriykCwsLRWPGjOlWOVBZWckdNmyYUqfTcQghzLp162QFBQW5UqmUdmXJ7MrmevPmzYFbtmwJ4nK5jFAopD///PNSiqIgk8lsTz/9dG1mZmYqAPz1r3+tlclkNgBYs2ZN9cKFC2OfeuopTlBQkHXTpk3lwLW20W+++WZoZWWl4LXXXgt/7bXXwgHgwIEDxREREVZX8/UWrFU0y03Jk9QWCeyGQIJfjjJqH6ElLzJYy0SFaFIEPJvU1fVXOQ575cA1MAyY3adjWwASDDBmgHj0EOXzrGfMFu5QT+cwMqW2IMjP6Cp58CyATE/76ondp2MrGIZ0K59kwBisQL4aNl0bsYYbwCS4Ld5wwr8+/c/llLTaxK7HW1t8Wg7tT708aWpeqp+/wWn+w/a3ph86tnXE+C6H/7Sanv9Z70ZxY+KpVXRHdYMr5A/VUFFP39JOkBMnTkzYs2fPFaFQeMs/9HpjFc1uN7DcrEzDNQECABA/nZE/qqhaOnr/+ejAfWdj8s6XhGa3agRFTmJlGi7e1A1mboNUYswHmOMAMTlr0x2maayyBjGy9hMetm+RSlwGCACgc3OuV+iM3BpnAQIAEBARDyQzCNzx8YwwIY0RNiXQgmPBDPcYl0FTT31TFN2QrOweIACARi3UrXvn9pFlJSEuk9TyDqU426Z48Ulqi/ts/lsMKurpesgfqgERXqukQ4Q0GyDYOXjwYAkbIPQedruB5WZlpvvThFhtJK221Re1rb4AmAY/sflyVIiGFxGkSeNxmVJ0UVJ0IBZY5ekxzeZDl6KiYU9GPAv7QzsW9nyEbkhE5mKRwDZGGd1iiJG1n6xullirmiTJJgvX6d6/j9BSRAhcCjK5uk9fqGqSlMFFlURXCEiIECREzlCQgweaYUqMoOtUxMZvhy2VJrhGyCUmvqmEEMic9XVov7KSEEaSPqjKqQmWzUrZ2hv9nAVKUQBeA/CAJ2O+VaCinq5nwh5uRPOOQMbSyCO8UAuCZ6oI1591zmTpM2yQwHLT8SS1hY9eGzoRmVovkOVVCJBXEWSdOPRyZaDY2mYyCqIA0k0hsbzBvxx2V0gerl32r4D976oK9qTFTADCiGAtBQCEQOQjtI5IjlQhOVIFq40UNqtFDRWNfkEt7SIlA0IBgM7IY/adicmPCNY0p8e0XLPczjCoBRDrgV6NR9S1+vY5650CSRCDkyBmOAgHQ+sY+sdyyswHEAIgdeKUXJerlUNHXQktygvPaW6UJMvC1N20KC6fissHyAAXl495ktpCVtPz2TfDThCuPw35H1u89KvBwsIGCSw3JZMA9EOalHCjI5tjBHxbOgBYLNwqncanzKAT+dA2Kg0gwvpWH1eVBY5le8eb+WGAGRsZrE121pjLYVLkgfoUeaAeDIN2nYlbUNsssVU1+4YYzbykikY/tdbIOxQdovGVBerTOBQjMJu5pbV1QYzE13AlKEg9vj/BAs2A1pu4XhFNIiCUAbQQwLirh9q//WYoTEbe0dunX0oIlauvWVFITa9NeuWdLS5dIU/vGtK15LMzCQAeBfBBvwfOwsLiEjZIYLkZmdmfi7kcm4HPs3UsgfN41qgAaXtUgLQdNE2MBp3wbFCgVFvf7FfNMFRkD92N4XNtBwQ822093ZcQ+PsKrSOTIlVIjFAx+88rVGYrJ7BFLR7fohYDYIwCnu1MbJCeokAiNFpxhNXKyQ4NbcukKMadlbVLWtSifIC4c7jsFW3E1jm3wb+50X/UF5+Oxxefjgefb7mSOqCm5o67cnxGZRUrRSKLW4Gly6fjehL/ee5Jasum1fR8TQ/tWFhY+ggbJLDcVDxJbaHQzyAhOry1gBDnfg8UxQh9JIbMGRMvgWGA1nafsvySsKrCUrmEsT9suy7dUwG+pl4v59MMKTVbOV3KKonQZOEMYaxcrSPl2GAUZFVUhlopiskRiUwqfz9dFI9nCzWaeOUioTmVEPcln5WNEq+JJtFgKk2EiXZ13mzmxV88GxN/8WwMAMbi52+4OHRkadtt03NCMgZXpnC4TMfWhEnP1xvUIrcaFQBkAPY/SW2ZvJqe3620jYWFpf+w1Q0sNxsjAeeJcp6SEN3o0ZspIUBQgC52TGZJCgMMBmAEcBLAEQAd2eTRIZqA3o5BpRXUODtOEeRzqK5bKYRL01SGTifKqq0LjquoDDU0NEjTyytkhsqqkNP1DYHZKpXvEYuF062KoFkt9ppokgF0L6RiCU/dLh740770rFV/Wai8a9wz2j/OevTUuncmHyq9HFqWdyi5ACCevMQMB3B75wNPUluCn6S2LHiS2tJtI6Z11Vxe66q5M1pXzb0pt+1pRsUx2dYHG6yvhZls64NpRtVvky5Xls4NDQ2c0aNHJyoUivTRo0cnOlwNAbtFdEpKijIhISFt2LBhyQCg1+vJgAEDUpOTk5UJCQlpK1as6BANKyws5GdkZKRER0enT58+Pc5oNHb8fD799NPA+Pj4tISEhLQZM2Z0yw/SaDTUhAkTEmJjY9MSEhLSHn300Y4kXIPBQKZPnx4XHR2dnpGRkVJUVOS2XLmkpIQ3YsSIJMf9/vGPf4Q6zq1cuTI8NDQ0w2F7vWXLlo6y3WeffVYeHR2dHhMTk/6///2v4+/TldW0q/nu2bPHV6lUpnK53MwNGza4XElzZQ/t7mfSV9gggeVm457+dhAWou5JP+EaWtt8Ll91fpQAGAG7FLQcwGWh2PhT6uBSK+HY3FredqWs3j/A2XEfvs2DN39yVVWO+NpsnGEGgyCrrd13bHVNSHhZuayitk56uLHJP7uqVnrAaiNeM3FqI7YezXtcQ/wa6/2H79gyfPxji/8Uu/6lmb3ZQvj8SWrLhiepLU9e1cdYCWAzgKonqS3PdWn7AoCtgPd8KX4vGKyvyzWWMRlG2z8VZvqTcKPtnwqNZUyGwfp6vxwgXVk6v/DCC2ETJkzQVFRU5E6YMEHz/PPPywG7OuDy5cujd+7cWVJSUpK3Y8eOKwAgFAqZI0eOFBUVFeXn5eXlHzhwwO/AgQM+ALBy5crIZcuWNVRWVub6+/tb33333WDAruS4evXqsBMnThSWlJTk/fvf/3ZqFPbkk082lJWV5eXm5uafPHnS95tvvvEDgHfffTfY39/fWllZmbts2bKGlStXut0e5PF4WL16dfWVK1fyTp8+XbB+/fpQhz00ADzyyCMNDivo+fPntwPA2bNnhdu2bZMWFRXl7d27t/iJJ56Itlqtbq2mXc03Li7OvGHDhvIZM2a4/Tt3ZQ/t6mfSH9gggeVmo19BApdjM/J5Vqclea4oLJW7euNNTBtc6pc+9Uzm2If22Ebc/+PZhHE5hyShqsvO/BIcMAxam9rFTjUaAoXWoN6MrTtEYTLxx+l0oqzGNrEvrlZT9BcGjK0dNq8FHEKaG95zqw58YC+HfBt2Welnrx6PAPDS0dlPPNW6aq7jZ6QEwAfwhndG+vvAYH1dbqY/jgC6qi4aKTP9cUR/AgVXls579+4NWLp0aQsALF26tGXPnj2BAPDpp59Kp0+frkpMTDQDv9goUxQFh4+D2WwmVquVEEJA0zSOHz8uefDBB1UAsGTJkpadO3cGAMAHH3wQ8uc//7kxJCTE1rmvzkgkEnrGjBkawB6IZGRk6KuqqvgAsGvXroAlS5a0AMCDDz6oOnbsmMTx1u0MhUJhGTt2rB4AAgMD6fj4eENlZaXb1YetW7cGzJ49u1UkEjEpKSlmhUJh+vnnn31cWU27m29ycrJ5xIgRBocqpTPc2UO7+pn0BzZIYLlpeJLakgLAaRWBp0TKVYWEdMsrcAnDgC6tCnF5zyGjC/WAvfRR4GPKDE+rHD949tHEcQ9/3ySU6A4AOArgGivnulafSwxDnCwTMmofPt3TPr3HtBk5XjOCoYF8mqBPyZOd4TGkgUejlgJxWfXQA9d8phFCMynJ1c8A+Hfrqrlz8Isr6OLWVXOLW1fNdSr0dCNBMyqOmd7kVlzKTG8Ko5k2r37et7S0cBUKhQUAoqKiLC0tLVwAKC4uFqpUKu7w4cOT09LSUt9///2OwNZqtSIlJUUpk8kGZmVlqSdNmqRraGjgSiQSG49n/7OLiYkxNzQ08AGgpKREUFxcLBwyZEjKwIEDUxxvzK5obm7m/PjjjwHTpk1TA0BDQwM/NjbWDNhXCXx9fW0NDQ0e5eIVFRXx8/PzxVlZWR35LuvXrw9NSkpS3nvvvTGOpfyamhp+VFRUx0pheHi4uaqqiu/KatrdfD3BnT20q59Jf2CDBJabidn97SAhuqlXuueEgLr/nhOce6ecPRYhU+V1XSFQxNc5fSNmGGIwanwmw+4NIYPd9vmQMEh9stHIdfpByKOYAkK88zfLMIDRSnXb3+0rWtDuyhU9QsCQyiRGEBgLQY43xgQA0VHNRYQgGMCfYN9mcCAEkIh+Jrn+HrDQ2wK7ryB0xUjZ210fKIoCuVqLa7VaSU5Ojnj//v2X9+/ff/mtt94Ky8nJEQAAl8tFYWFhfmVlZc65c+d8Tp8+7XaLymazkStXrgiOHz9etGXLltJly5bFNDc3O91nt1gsmD17dtzDDz/coFQqe7W915X29nZq9uzZ8a+//nqVVCqlAWDFihWNFRUVlwoKCvLlcrnl0Ucf9Zqg2fWg88+kX/14YSwsLL8X+p2PEB7aFtDbawiBJNBfP/rO8bkxd024dGjOHeeOpiXWnJDJWy/z+LYEZ9doGgLLuxyKAzA+cmI+s/x/7w58+cA/8/74xlfZaeMLLnJ4VgsA+AttXnvzN9lIBX5xrew3KmLtVR6HMwIYTgUB4fNB3Q7gvBeGhfT0isYemixrXTX3uj08fw1oxjMLaE/beUpQUJC1oqKCB9jfbqVSqRWwvzFPmjRJ7efnR4eFhVlHjBihOXPmzDXy2sHBwbZx48Zpdu7c6S+TyawajYZjsdh/vcvLy/kymcwMAGFhYea77rqrTSAQMCkpKebY2FhjXl5eF7l1OwsXLoyJi4szPv/88x0/c5lMZi4rK+MD9iBCq9Vy3LlQAoDJZCLTp0+Pv/fee1v/+Mc/tjmOR0VFWblcLjgcDpYtW9Z04cIFHwCIiIgwO7Y3AKC2tpYfFRVldmU17W6+nuDOHtrVz6Q/sEECy03Bk9SWCNgz3fsMh6LNAn7v8hE6Qwh8wkPbxwcF6MaMGVw68o9//Mmlt0FtvsLH2fGQQRUJhIAS+ZrS0icUZj3w1uaBrx1+2fL3XW+dFUsMOppBtwSyvtBm4FZ6ox8AYMDotOj/Nog/OI4PVA6AeABl/e0zIa6+py2QaMB5ueuNAkU8s4D2tJ2nTJkypW3dunVBALBu3bqgqVOntgHA3Llz206cOOFrsVig0Wio8+fP+w4YMMBQW1vLdawCaLVacvDgQb/U1FQjRVEYOXKkxpHN/9lnnwXdddddbQAwe/bstuzsbAkA1NXVccvKyoTJycnd/FL+8pe/hKvVas769euvSWycPn1622effRYEABs2bAgcNWqUhqIolJWV8UaNGtVtS4umaSxYsECRlJRkfPHFF6/ZBnQ8fAHg66+/DkhOTjYAwJw5c9q2bdsmNRgMpLCwkF9eXi6cMGGCzpXVtLv5eoI7e2hXP5P+wAYJLDcL/V5FiJCpCgnpagplR6vnN9Y3+zkzgnKJb5iqI0Oq6mjyUaPKpxgAGAa2lnJZt8REQtHVfD9Dt5JEQiD2CdCFVDb4TSlsEicWNwubm3Tc740WcpRhUOf5iH5BY+I4nWdfsAIF6EGPoUcY0DxcU2nhB7uOS5+3MSjKZvXxMXoSvKzq6z1+D/Co2SpA2IM/g5C2t+s9M2bMiB07dmxKWVmZQCaTZfzrX/8KBoCXXnqp7uDBg34KhSL9559/9nvppZfqAGDIkCHG2267rT0lJSVtyJAhqffff3/TsGHDjFVVVbxx48YlJyUlKQcPHqycOHGi+r777msHgNWrV1evXbtWHh0dna5SqbjLly9vBoDZs2erpVKpNT4+Pi0rKyvp5ZdfrpLL5TYASElJUQLAlStXeGvXrg27fPmyMC0tTZmSkqJcs2ZNMAAsX768WaVScaOjo9PXrl0rf/vtt6sBoKqqisfhcLr9Nf/444++O3bsCDpy5Iika6nj8uXLI5OSkpRJSUnK7Oxsvw8++KAKAIYOHWqcOXNma1JSUtrUqVOT1qxZU8Hlch2VEpVTp05NSkxMTJs5c2arw2ra1Xyzs7PFMpksY/fu3YErVqxQJCQkdHxOOOYL2O2wH3nkkRiFQpEeExNjcthDu/qZ9AfWKprlpuBJassP6FIv31smjijMTlQ0ZTk79/OpxOyWNt8of4HtcojEJM4YXDTO/XYfg7g7LtQQyi7P3FIcdkQaXz+CcBiexcS9dHzD1G6eBBTfUjj6lW8iuSJLt7ffytyIw2v/tHRcp0MnAIwEGAg4THmAyFIhFNhEYgophLiXpGYY2AqaRHqAuJKW7hUtsB6qoyxd7Zx7hQ9D5ccyAmcP9EuwJ6N6nNjlID6u7tKCeUdceT905hHpq1vX9bb/XwNPraJ/qW5wDp96uEbE/dst7wTp4NVXXw1RKBTmRYsW9SoH6WahN1bRrOIiyw3Ps4IvAwDuxP72ExHa5vLhOi6zZDRFMe0MQ6LOn1a295QPZKNJRV2+oj40scaPK7BKgpLqxjrOqSpDnb4d02ZuBMW3OpUqPrd3YNf95KsrAQQmG4mp1fKNBXpjCgFDh/CQHy9Ek4wHKY9A2VV1UWum3Bkn9RoVsfY7tyGQ4bjamhkA4DiAUb3tM2NAuaerEF5L4PytEHH/Vg+rvYrh2iRGIc2nFtexAcK1rFq1qkebcxY7bJDAcsPzx5nH77TaqNKahoC6vJLwoNrGgDSG6V1aL0UYi6ohJEjLtxz29dOKRWLDABDwCLGrJHE4DA9AGyEMyRyR1+MDS9vmV1lfGTGu7FRKbsZdJ4Q1ubE1YakVcj+5KrmtMdACMEaAFML+pnw/AAil2gKKw3TLq2AYMOd/yOi8FG8AcM12hR50A4AUBoRqtEDZeHX3mUsYbRQfBQohDEFcRHII4tqM3H5XInSMDUyjEUx8zy3d4wuOO3GjUQCyAThd5XFFjKIxwMOmN4X3g4j7t3oB80ijhd4WSDONPIqEWnjUbBVFAliraJY+wwYJLDc8hGAmj0snxUS0JsVEtIJh0NqmFhcWlslIcblMaTLzelTXC/IzFAJkgMXMj1Y1S6ECowOg5/Ks+RJ/jUAkNgwgFK6pVPALVWXbbBxiVIvjbBZO5FXVRQAAN1hngtoXVhMv/vy2cSIACY3FkQWU0PTTmNe+nhheeyk/+4V5xT5iUzlF2bf8QgZVdEvIAgCzgV+kbxd3Tqg8g1+cFgEAGmJzmmNgZYhvmQnDyq72LKaY+jgb0QM4BruTYqiz6zzFBOYySP/6IAyMHLvIkTuyYB/zaE/65HKtRpHI7Km4U6GH7X73UCSAFnCWeM2Pg4WFDRJYbmiKFiwTApjW+RghkAb660ePGlSGkQPLbCYzN6eyVqrKuxIe0dTqm9D5Ye4gPEjb5e2a+ACA1cIb7QgaOFzbBZGPweznrx5GOAzD9zGNIARCsb8eDAMtbaOKTFqh3qAWJwz8y49DrXr+pa/vXybnUkzpgDG5TS1VocacOon4389M+em1x04GnmriR02ONc5z3DE0s1Revzs9m+JbSehthR17/KXnFQ0AHEGCBU6y8cMZ/kgLTDkaQme4+34ZbcSXYag7CDq2IEoBVAMQXb1Hr/IU2oit34XYfuDkExBPKgyGAiiCXXwqEPatCKckxNcXEILBHeM0ctTvnwy7GBtoFNyd1JomEdKdq0umtq6aWwWgTfrq1uI+TuN6QdM0TShHJMnC0k9omiYAPF5dYoMElhudyYBrpT9CwBEKrBlJsY1Iim0ETaOuWSUpKSiVC69UhaRZrRwxAAT7GXp4OBIfm5U7XNsugV+Ial9AaJuYkF/e5gmBL4dLZ4oD9JDEN+YQggyejzlg/ucf5NZdVNSd/nys+IdiqcaHa23PLUmwfdIcZHlfrU6StGPfcH9MIRRd7RvVmiiJbu2mAHh296DOY8sHMLBrGwaMSQu6R7VJP3AKCMiwTofirn4B9g+OXAAtsD+EU9Hd1fIa2mDtqzJiBwEMx9Plfj7sSYyOeVYC0AEIAXBNVUh6WoXa8f9mG7HO2pyaV98mGiilOM1f/+hz4r+P/zyaz7OJKHuI89DVr52tq+ZqAKyHfXtDCoCSvrr1mlK4X5ncpqYmZUhISDsbKLD0F5qmSVNTkz/sf+cewQYJLDc6M3vTmKIQFhqkCQsN0mD80Mtmg4l3rrxGqvEV0sFWCw/OVhmuhbEEhbeM4XBpl4EJP7qtDQAYBma+jyk9avhl4dHNw6pS5S0WviaQFENk215uZMDYaky03R7ZP6L5MqxUAHjX9sswMOcfSe68FN/m7J40UMwQ12/WDgIZjsHNaQpAeqd/G2DPmdDArgqZjE7fIBrMZStBv2WNxaD66trpsKVuAZADoGMVRRHdFAQANhrMA9sTj0ksImqpX0i9gJCk7S0wPPjOVGr53efOj0iuG9wpe2XG1f/eA0ALu6vnQc0b03ZIntnzbR/H2C+sVutD9fX1n9bX16eDLVln6T80gFyr1fqQpxewQQLLDUvRgmUcAHf39XpCwBcLLUOU8Q2XAKQxDGosJl6pTusjNOhE6QxDdas04PKsuRwuPdhJdx3w5BofAKqmg8m5oohWLT9I5z9pUo5Ob+OiJC9WiP0Kw2iTXF5C6ehMv9ZQgCAsskHcvkspIjxbDjdEp+KFq315Ye1peq2oyGLkd145cGrU00isWmfHuyIC5dYFr1vza7c22mDfv7cAUOhB1wH9CxI4DNqofvptAAiCPVA4BEAuFJrrBALLGACgCMiXc4rHtzYGFJ895NMK4ATd4hMMEMHg+MZUF+mtPle/ADB/FARpx1s3jtnPfeCorp/j7DWZmZmN6MfvOAtLf2GDBJYbmZHoZ+LdVVoBgBBE8IWWCL6wDQFBbWaaps4b9UKNTuMTaTHz4gCo5XG17lXrKNpMBFYLAEnIxKJU0MRIOAwVdGdBe7C/mWNe7Wd8YjTFOV8UGSFTi0xJmSWN1RfjaZ7AEgoQDmPhZlhq/WGp9QcQab5YFNlZVrgZLh6ovgxlbulhEYTHkAYKJM59K7cEwP49BwDcrqwtMxPmSEGriJPfKkrUWjjdhKB6wh+cQgIysueWPZJ09QvxcXUaQuyfbY4gIEjWlnTHvT8DAGRlIbntOv4ZijDdtm26QZjLFJdOArDPunHMbO4DR3uSeWZhualggwSWG5mZXuqnmwkTIeBzOPRgH4kePhI9GAZ1NE0V+fjqRbSNqCkO41RTgSvTFBBif5ASgmBcFXXjaLmXGZXAPzErzxpv4Vgn823RNitVSXHozOjBJSW68lAnb+SEn3c+rrO7HwfAWQCZXVuqic2pvkJnAsApgX3boN9QhLEE+liGEQKx3EeDiVEaWGiU1Gn5tbktIlGRSphqpqkeXSEDGK5XpYIBYEBapdtVlYGxTenuzneGI7TUwx58jAHwL+vGMRcB7OU+cNRrJlQsLL9n2CCB5YakaMEyAi9IMQOohwdL5oQgjMOhw7QtftC2SGhCMZf4YlOrUGII5QnNHcvWjnyEbrSIRMTCGQ4CcPg2AACHS0cDgE0raiIE3RIAGQbaisrQzmV8gXCSSMiAsbXD1mO5nz/D6b8l3FWC/fQFhOCaSgoehYRoP3NCtJ8Z02LabSYbuVShEbTmNosCytQCpY0h3cYuBFF4a0wOIiObvbG6BADg+Rk6B18Lr349Y9045nbuA0fPdW5r3TiGcB84yiYXstxUsEECy41KKvq5H36VErjY53cNoRiaDDBpRTBpRQCYZg7PWizwNTIcmgi66gczGt4lWDjdRJL0NYHZtIXDo63OhYQ0GlEhTVNDOx3SwomeAA3k0z0lLTKAAKTfokcOIkO0bn0ACAFHyGUGJAcakRxoBMNAr7NQOVfaBdrcFpGsRstP5jFULQUS7a6f3iIUmNV8vrW/OQ5XYRier9FZX1IAJ60bxyy4+v8XACwFMN66ccwHADZwHziqdnIdC8sNBxsksNyozPRSP16wzyXBNgsvWNfKU//w6FIfDs92WZZWXRc3Pl+kGFWUylUJnCocWvV8ORjK5QOt5EqYvsuhAgDDurb7/+z9d5Rc13UljO/7cuXQ1TnnbmSQSARAQCJFUaKCbVke0kFjejnOmOMkz5jS2OOfv28+ifaYtsem7XGm7bFMWbIlU5FZSARAEDl0zrm7cnr10r2/P153o0N1d3WjQYJU77VqEex64b6qV+/ue84++6RgremgqID0kzvTIyxCkVddV2toQuB0S/T+3cUqdherYAzJtM4Pz0Td/UPTnqqsJtavXVmyNlpbx7oIWf4ZbQiE9RCerVTiKQD4ap6//xGAYwB+eFPGsIUtvMvYIglbeK/iBzfhGCbWdvorGFpO6gDjDlo61zx+ub55/HI9Tv/vxyxR0YMVzWMnmu7vCZY3jbXzAhUYA8BI0cL9v3Wh/tRj+waOEGKXul29Vle+5BRZADryNDviGZLWKo2d/EwYxW0/hDsCz9GsyNNC3QzzghB4PbL1gKc8AcGULpqm6dQt0pfICSyR4+sNyi3TiRSC7duGVyvxXBd4xZjsmXRFGksz27k1mmbNgTEwQvBx8/kj24Qnz9xa+n7XE0/tBBBpfeG58c0a5xa2cDexRRK28J5D1xNPVSDPinoDuAFgzyYcBwAwMx3MI8IjvJGTdw5db8DQ9QYALOvwZq9WNI7FKitmaoIl8dDCMrxoWrlU5MntoxTh8Yng0nTKcdjmQQIW1Mz7IRz3MX74JnLelRbjXnDKHV7ePMoCmVuEYN/aW64NxkAti2slBG5ZYKUlbgMlbgOUoac77KikjDjXc7zKisjm6RE8qvLb/7xD/qdfuLCmKHQO3/6XI286nJr1wY+//RSA/7zwva4nnvokgC8BELqeeOqp1hee++vNGusWtnC3sEUStvCeg68s+oiuSm/mUo52RvnAHRxqU/PGkxOhAnL+xKkmXff3XW5B3+UWACzq8ma6K+smzQebR+u8nlw1AMRinh6A5CspdCGPpSoBqalg4slxkqdlMwMVQdqW/X2DqAqll6ZBNoxcTuoEyLJoDgGaWkMq0yzyZiQjyglNuJ+AaQyQV0pLKLKeFEVr0/QIgifX+k+/cEEhpLCUFGOYbg+mtvGKNpGLuVrI80dKhSfPTAHzxPZ/YN5/AX/e9cRThwC8AuDN1heeG5ndTgFQ2vrCc0Obcx1b2MKdYYskbOE9B8mpPyE59cOuYNqIDJakGVu91M5XHj1JwJCc9rdRi1+40iy4xXHWJKpTYCuuKKlF+rWcvIFwPglmku5D3dea0H2tCYSjw4GixJDB63GOsBRlZKld9NhK43avEC1wgeskeSbijSLgzi1Ng2wY6YySt2XvbPdNogjscLlXH/QZ5kmegDMpsaJZIZQx+O1L92lvH+kkBMsEohsCYT3cynqEvNBTSgezeNnMOEtmustDVQd7v2Y+f+QtxvBBXtpBLV3cs2BzAcBPz77e7nriqZ+F3ZPi3wEYXU88dbT1hee2KiW28K5jy+ZzC+8pRD//aS+AhwCAUdK5FkEghGZFRT8kOoxjwZoZlyDrPQADgHEABav9f+tC8PyNa43fW+n9ZNI9WuixVgOjXE10JvBgarL0/taQ6mwNZW9W+7QTHsm8SsB0AH0r7ZsGzZuPDzA+70S8EUiiGeF5thlVJQAAVZVda23DEdS5JXrMIdKjHtk6Xu3XXCJHxwG2aBKVJDOs63wXY7jjyXXWH2FdSA4XO2CbTQX+5GxFtP7XPrLzD77TdL9hcPwSgrAU+wBchn1P7ofdh2Lr2byFewJbkYQtvNfwYcwK9xgjGi8ag5Yh1C0OQTPq9GfO5lKOVsmpDcyp3QmBy18RbQAQToe9V3IpZ0HiuGev+06m4z4lrvn29nbXnGxqGV4W0p+cCK1pHLQODACoJwTgCbZ7ZAse2QJjUC0GV1rjT8RzQknW4NpuewoCMWLmvR43eP9mDawymO4G8MBmHGtOj7De/TiCuqai3HDW4E6lNF6KquIhADj/Vmvr+bdaGx2KlmhrG+3a3j6Sq6iIlouitW5SI3rUNTUcjEEDwNSI561Yf2mxnnTORWusi2HlSionbvufL7Yd/aG22MsAlkU+VsAlAJ9qfeE5a71j3sIW7ga2SMIW3lNgDB+amxZ5ge4LVkfAGIaNnDQoykapZfIRUxcMxZ077gqmwdjifDIh4GGv1IqWHz0/FJ5xxxRHGiDy5Hjovrr6saQgWvNqd8aQi4b9hU4ChWAEQP3SPxICh0Bwv99hwe+wwBjihkU6kxpvxnJ8kUrZsooDwqDym1jBUVGU2bTJK6eJXQDZUJUEIahxSbTGKdKwScnFpCY0YjYypOZk3+UrjQcuX7EDRQ6HFm9vHe1qbx/RKsqjZaJoNc+mM1YAY4I7t2aq4ca3D/QkJoLB8qrJwwC5/SzlrKt/enjm5JFvVn0AIKV/+eJ218+0FtroEie29AhbuJewRRK28J5B1xNPcUDpJ0XF6FC82WnJqZVwHGsnBDWSQ68BAEEyIUjm/D6EYJlREWMwtbRS8KT+i9uSR8c8M2fHRkovWhbvEERr0aSr6+KNJaZHd4o1Q/AAQAj8ksAOhQQTIZeJRoaRmImhIQ3CsIY2nRG/F/wtArLMxnmj8Dq1us06VjrtmIFtirVhEIJQpVd3WglyMqPzH8m3jarK/ktXGg9eWkAaWlvGurdvG87lJQ2E9XDC2nqEuv1d0x3f2ReZjWL1wm6G5aYWz785rewDUAcAD1Wo1eu4pI93PfHUP7a+8NzldeyzhS3cNbyrJOFR4fmaPi7xX2c4teSgWfo1AvLtl8wnt5zKtrAS9gKk3MhJ5UZOagcAjrdmFI/aLXtUnhesbaSAenZGyQ3GuD3rOXFl9fQDldXTS7LgNgxNSlZWhM+k0g4unVZaKeXXZTS0BDo2uPLnCKqLRFQXicBeF5jJWEdct0ZymsmnNb6dgch3MC64FH2U47CeLpKrQlXlgksLVwMhcLpES87ofKHn9V+52nDgylVbZ+pQtERry1j3tm0jamVFpETxaVPAcpvshTCy0nl1yqcVlUbnolKlACKXRgJnB7Is/Uyn68cA+F2Eu1XvNtfzfZqwO21uYQv3BN7tSEKsgrrqc8QkBOSfAeh/9mO/+Fe/8KuvXgbw79z+rvC7PL4t3Fv42NI/UIsvzsbdxdm4GwAzJWfusrc0kSQElQCa8h1EyygbJqL5Wgtn0846STIbioIpFAVToJR05nLSVCrl8GdVeftcKDplYcxiMHw86lZoUQwANwGs2oq6wHESkaC9WGEiFL2JMWgWw+W0xifjOaF4Vs+wLnFcRTAzBGwOSWAMzLK4dVUPrIaUzm+44kLNyb4r1xr2X7lmk4bjD9yc3neo54K7PKYqvkyICLR1Nk01j+RIyEqNFX0Utgr2bQAKALm9JGX8wZkiTmBcR73l7d7mhmuV7zofWgD8ZNcTT/116wvPmWtuvYUt3GW82yShViMW72LiXHhV6rpZsQvAL1KKP6TPH3515mbNt5IjoZdaX3huU9TjW3hPYxlJWAwi6FnHXiOn3pIcehPs5k29sK2XtwNwA4CadG7aapgxTMy2kZ4Hx7E2p1Nrczo1MIaUafK3plJK/Dv9nvsBlP5QEGmRYCWhY8HJ6wIwg1miRAhkgWDvAj1D0qCkK6XxajwnVGkmaVjLFrm8KL1pDaI0TewByKaQBMaQVI31CyBXQnVxakdqrCiYGrNlK4S3ss7iZKe7PJZyBNJ+TrSak2PBdsbAKOUuG5qY1TVJ1zUhNjJRpO8gzKrT/W5ChMC2srHMOk/PA/hzAL/T9cRTtwB4AXy89YXnJjbr+rawhfXgXSMJ9EJr8MGHfvBTf3+i3FdpeeZXfLJiMAAgFtdJGSlKT/p/H8Bfdz3xixe8pfF/lF3ay8EvfLXr3Rr3Ft4ddD3xVDEKdFlklMw5H5bhdvMmE8AVShG1DH7TmgoZutgHkBVXsYTAI4rWwYpAZsIvO7VWv3q2KqSzVMrpVVV5G2NkqcXyhuyIV0APgOIVxuWVeLa/yGmiyGmCMczoFulLabwZzwk1ukVqllSMwK0Ym1b6mM4ocy2Y7xgmJV0A2ZR+DTxn9TkVY1FpLLN4Z2YycF9mcta3izCd8NZFuWkmO/Z2S8jlTVuNR69Wjr/dQDRNUvcXJTM3Zkp3yYwLH6yKLvW5KBQlsy/Adtp8YaPXtIUt3AnezUjCz3z+//v670Se/Mw3hydkVzrpKAaAH3zigh8AWFxWLU3gqSH4AUDxqqrs0v4YAKKf/3QHgK/Nvi4Gv/DVLdOR9z8+goI6ALG05NTyiRIFAHs4Diiun4Zl8JO5lKM3l1ZEavLtACnIm38psmlnQb+htMGN7ShSc0cq0g8CgNOhgzHkLMpdUrNyKpVyVGi66AFI3hTJBlHw74IQFMsCK5ZnRZCMYSxncgPxHC8mckK726HPEFK4r8RaUFV502yi0zq3af0aQoH02v4ZjEjM5PfQGXfHgc9/rYF3GH6WFboNcKYRyLDXX2wTasEXA0yqCGbnhLMx2K2+14u/BPDlDey3hS1sCt5NklBCTe7UD+yMeQ5+7e9jE4Jr+pe2P5U5cLh3FwBM3yxNjPeUMzdDxhVIX3YF0wtr09tnX58HMBr9/Ke/DuDrAE4Gv/DVPP75W3gf4LECt9MBrCnQ40WrzBVMl82WSVp6Vr6WSzliuiqVgxWWK2cMVM04ClLneyW670hFetHfCIEi8PQ+j0eFx6MilxO/NzFZtM621atiw4SDEFQ6RFrpECnK3Ab1B5KnAIzCFum1AyhMJZgHjAGmyW8gisAgCtaIZXF+UTQnNV1qBoBETsgbLdkImmpmljXPyg/iEVyaxkmmmxAQ4jJbiz/Y01r8wR58+affRHgkOHbzX/ddIxQfnd3hOmySexSFt7tkAKrXcl7seuKpEIB/BTAI4JdbX3guXuDxt7CFNfFukoTXTAt7VH88hbCDFe1LDvzXP/3XGY5DvZaRbv3l/3w8N6xywm88eeKKK5g+uspxqgA8BeCpL/7Lge9e+73np2D/YF55yXwy945cyRbuKrqeeIqHbaJUAEgwG3edcQUyRwo9PiHgZZe2S3ZpAADL5MK5lKNbSztgGXwbQPJWKzBGOihdbg+8UWSyyh1VHyxBPzap6yMh4DzuXDEwb3mchq3Az8JOj6yLjGia2F2IHoEQmq2siNyYCfv8smREASAYTB20LO4yIUwYHimxJMm4kTUcO9d3RSujvjpc8LXwDoNyAhOZRUzCs/lnKSFAcU20sqki2qeOBgDbIKkVdgXEOQA7ALgZQ45SMsIopwmitSPPKX4PwP9e6fxdTzzVBlsw+Yew21MfA9DY9cRTH2p94bmtZ98WNgXvJkk4KUj0906+1hT1mpzQIFydaTSZoA37T7z06vZUp66Jv/fzb5a7PerBQg5GGXB9qPh+2DnYnwSQDiq/+yWTsFcBfCepPp1e/QhbuIdxCEDBZYXZuLvU6c/krUQoBLxAQ65AJuQKZMAYmJGTbqkJ57CRkyoYJdvmqhVyWWVTq28yaWXT1P6wV/2bQhIARgWeLmwQ5QYWdYGcga1/oLD7SiwzglqIzBp6BJ63phyKPuLzZTRRtI5UlEfn3A0NQkAEgd7HGIbLy6JnwFEnRv2bYmHMc1avUzEKJgmZ/uLjvf/7oV5mchVEoP2upumJ8o/dOD73fm7SOxcVsmATBAA4RCk6um7Vq4mEp8E0hOaqmsk3a+vH02S5mHUXgMe7nnjqz1pfeE6f+2PXE0+JAJ4A8CuwvRkW4ghs2/JvF3odW9jCanjXSAK3vyuDC63PFO8a/OSVN7aR0Td3FHG+uFFaGuP/4USd+cWfPVnldhsFG9R0jBR1MHbbvS0LM2oS9nMAfg5Azut45iUA/wbgG0n16djmX9EW7iIKTTXYYKQpPlZ02uHLypIrt53jmJNSkuY4tm7rZEJAJIe+TXLoEoAmSklGSyuXtbRDVacDBYam1walpNOi/KZ1agSwKR4EAKAoehchq5oeFWOxQHISdo8JApuoLEqhZFV51YiJKFhRl0vNSJI5P+ESAhkL0kiEoEZRjJqRGfeJwq9kdYQCmQmsMyrCTL5p9r8t6c6yhoHRwNtln7jmFf2ZUmZyzbD7MSxy9+Q4tMuycU4UrNTOPd1nnc5cNSHIp9FoBfBRAB/reuKpT7a+8Jza9cRTQdgRho9gSaMvSsmgafJTkmRuGTFtYdNAWD53mHcQNa7f3a7D+sM/qHDX3Rz3RJKexMQn28Pe+w51PrzaSpBSQjmOza8gnvnKgRNXB0rmHyr9XOLkFK8ub5trq9xfh00Yvp5Un57avKvZwt1A1xNPXQawZ2N7M53w9CazuBoQxERFH/MUJ9p4gZauve88wrCtnJdBU6XJ2GRRX2wqQLSs0gqQgu2eFyKblU9MTQeOr71lQbAAqMCKZZbrQigUP+Fx5+5kbMMAhgCIjKF5cKgUa31OZaXRkw6Hnu/3uwjnO8tuhJPOfKH6dePwnr5zO1rGD630fjziO+kNJO8rjGzSswBXCjsduoxMMoYcgCwh8xGyHOwUTgK246YfiwnLddh+DEeQJwpjmVznW2d3VYMwk+foz+u6dA7AY5KiJT8z+Av/tPZ4t7CF/Hi3fRIwnPmNmw83/nfx4Ic6I//yF8dnPvpIJ5uJlIq6LvyaLJtJ2D+c07mcmLpytf5GMJhyNTdN7Pq9Z39o2AhGb7a1jvse2Tu47eZQqG7hcSNcbpkd7ywE2PntDwP4c6/jmTOYrZRIqk8P3MVL3cIG0PXEUxXYMEEAACIxi7fNiRiKDFVuAiPr7fDXgxVIguzQy8rqJ8rK6ifAGJBJuLqiE6HJZMTnMXVhOwp0OUylHP51jmk1dMDOe28KHA59o2V8c6iZfcFi6HstTqxaGTfLJTicHFrzuWRKkrnmORlDNpJybFr0ZS09gsuTqZkcLRtQHLmo050tkhWtzTSEcQCWKJlLUiycjlXSPbORg4XRAwWL7/O3luyyc/aVFyPDZVHLsiNR1oJySX9p7JT5/JGA8OSZ51a7ti1sYSW86yQBANyS+bUn/m7v0RExws2caMJP7p+Mf+mrh8fDJrn5X3/yxAEAOP1m+5Xzb7U++CM/fObq2Hiw27L4lo6w27gVaZn8tzeb/QvFZQasmAlWiJhpTm18FMCzXsczV3C7tPJGUn16q7Ty3UdeP/6NgyWNnDjK8ZafcHlDvPlQUFMjQgC3P9Pq9mdaAYBaREtGfJcj46FkJu4upzS/op8xZLOqvGlNmABENutAhDCd5+imjW08I41FTHIsYqIFGYCAsaCArloZUxUSHI5Z0sBxdE0Nim7ynYyRpTn5DYHnaL/Loa+q4RAlsw4M3WrGeVzNOHQAWYDUAMxwurMnBNEU1bSDeQIpt8OZ20i540IULDyklIyMDpcfzvdezY5BAcCqvSOin/90EYBU8Atf1Vfbbgvfn7gnSMIbg4G/A9FGQDB8XaW/8tevN4V+pi32mVoqGT1vN6o5k/83LSOJFcXxk2+8vosIgqXmYA0wgmYAdKm97BSn3gJBwer2Bdgz+/odAL1exzNfh00YziXVp+mdXeUWNoiPrr1Jfgiy3sMY4a1FjojEm5rx70vNMI3j6UXJpWXcRcmjhGA18duGzJc4nsn+kvhef0kcAGDoQiw2HTiXiPo8mYi3CYyUAYBlcR3YxCZMWIfIcy04FK2DEOzerON1RhdXcDAQEjHRGjHReuk2aehu470jO0vS016JtuRr0gUAkaSyae6URYH0GAoQekqyPqnllBaASJhPIxAxm3bNp2Oi0wG9onaCbVQ4O4uS1d4cizsGBiLu6aONMwcJYYJdLbn8hDXbhoIA/njh38znj3w82V1uBL/w1Zein//0YwC+BeCfAPxEvnNFP//pBwDsHbxRH4lPB+seOve5393gNW3hPYh7giQk1adTsCdjeB3P/LcMMX/vpY5S3+9/9tviyLntrLh2wqzbOQSYfDoX8RwASKinu/qNsbESQ6dIJS2cnDIgJEx4Jg2yc5pTN+O6mgD8+uxr0ut45t9nx/hGUn16i3G/A5hVcT+y0f150UoCYNYS22QbRKYWf38u6Rj2hJKrEYQxbJAkLIUomYGqXf2eplD6KGNAesbXM3GrdnykuzIDMK3Q1MQaSGMTW0N7PGp8s44FAN0xZdWQ/ixpaDkz5Wo5M+UCAWMVbqNrW1CdavRpileyWudIw1jEfaer9Xk0Vs+Ia28FBIujeyZGyqMrlcUCgOzQuwhZOTWwFgihSW9pfMoyhOl0xJtXl9E97Z3ULY4Ctl9HPoJAODrmKUoVw9ZhAQDM5480AniKk4ym6Oc//RyAH5p961OrDMkB4E9zGcebsCsutkjC9xHuCZKwEEn16YkHxL/86kUu/uQf/Nsu64AVdKhAukaL1nMcm298E5kOBgWCFoEHnDxQNsvpJ3TrxNmstSkWrQtQBuDnZ19Jr+OZb8EmDN+dJThbuDs4DORfRRYCLe1Yc3UuyMYQVicBA1iiIr8TSD61EbBTE56SRLOn5FpzyweuwTR4bayr+krvxebEVH95saGJ627ANIsOFGhfXQgURV91RbsemBT9GZNfV1kmAyFjaal1LC21vgI70lDuMrq3BXJjMwmHDFvot+F7ZA4NBfgjGLowGAsHRgGymm8LnK5s9E7GIrm0HsmpHwd0ZGLuOKOcf+k2D28bq+MFK2bkZNiaDsaWth/zhhJDsH9DHebzR16G7dfwiwBqlNLEqexI6A8XbC5HP/9pLviFr+aLmAYBIJeVqwFs9ZD4PsM9RxIAwMukqxpE/cJQUdVnfvZ8pKk8quRm/MnUtM8AiMgYmGnmN7EZguYEgfNuDg/Aj86+NK/jmVdhuz1+Y6tSYtOx4VRDoZCc2loTcUErzIJA2CAnWnX53hJES67dMbindscgAEBXpdTg9frO/stNanikuNIyhUItkTfNopjjaJIQtmnCwOmsWFBIfzUwEDKekVpSaUWtZdxu2HH2LgBTsFe8rcDa7cIXguPooMuh1614TgotOhM8l1OV+wGy4nZzkBXNtdY2q+7vyqUBu3EVo8S/8D1BMvoUb3ZM8ag7dFWa0Sekmb6emn6ALPOTqWobntPSBAA8Pvuyj+MwHvQ0TQym+soqwcg1AC9j1sY7+vlP7wPwM8EvfPUXZlMNf6bnxGkwrhrAlTu5ti2893BPkoSXzCcHvY5nJvbqQflcZ7m3OZguUty5XbIrN5aJuofUhOswIWycMbJsBXhay2rv4FBl2J0JPwaAeR3PnAXw7wD+Pak+vdWE6s6xPn+EDSAbcx/OpRxvOf0ZonjUPYQsIgUUWNkfIJmVrpiqnOI5xkuSKTlcuVV9PXjZGAFQV8i4JIfuaTnQtb/lgH0bZZPOcP+Vxp7Bqw1WdKKonlFupejGpjWIcrlyHYSgIDOzQtAVUzbteeNnfHz2nwQ2MZjrAslgV0RNo0DSUOTPjGKF74VSkpkcLRtglCuwBJSB4+kdNcISFX0ujeIO1U8NgRFNz0oTmipJ7qK0j+PYMQCQnfpxk8frk+MlD+U7Tv2u/lXvBcKhTnBq182M8j+DX/jq1xe8dQnA6ejnP/3PsNMQUmLGfw62TqLl9UNf9AG4H0D4oXOfu7bwmH9T9ncvAPjKT0/+1L9u4NK3cA/iniQJALDDDEZlIn7qtRPtqYfrwsPlZYkUr2iCuwiHRWfu8kLjpDlQxtg4NQvy0r8LILBDe4cB/K7X8Uw3gG8AeBHAm0n16a3e8OuAeba9quTDZWryRsUpbcpXzSyu7u6ciRBqCgfSYR/SYW9SkMzzDl/GIbm0No5jQ1ghv29aZPgfXt9mjfZV0oMePHjwobdPOFyrC9JFb3bDmgOnNxvacex6aMex62AMiI0VXciMBY3wRMgc6qkIxMKBZo4ghTvo17AUbpe6qdqbjqgjfzSEYcwPvjcOay/ylEMuBWEYdINfKcVAALTNvgCb6M2RBids0rCovLKhKpw3msQY2MRw+RRACi4nFSWjn5A7iZYwg3CsFbDtsAHUgjDIbq1FdmsnseR+nBguXeHzYvFgRWTN6JPkz0TNjLITdjQU0c9/WoAtZKzGAoKcjPjm7gUnbNv7bYxB+ofqv3ziP4783KsA8Ddlf1cNO1rx2N+U/d3HADzz05M/1V3wpW/hnsQ9SxI8TOoSKAYbIKdf/MZB36MfvmxqkqHVBVPXx4fKDYAsK1/rNvWbbBPrw+8QLQA+O/uKeB3PfBN2lOGVLYvotcEJ9KO+neMHfDvHAQB6zDGWuFbVn+4qU8yUvB0gdyGlRLymLh5NzfiBGUYVb3bcE0rlJQk//9yHA1lNrCkW2EkA4EVrDS07s0RPblMILCGAQzRUIZA+5gukUdc6NPbM//qUSECiHtm6VebWdwg8NmTqtBCybKxaOrcemBQDKYPPa9fsABmoYtLxYtCREejXc4TlrUxSGDlTxATJD34XWaU99xJwWE4aOmBbSTsB1tJQPZN3UtdU+TpAdhV4HnuMztw47iClIjr0zlVEjyUArsEWDwIAOI7lrbpyerO9hMOqkS0AYIwAi5t1EQAfAhZX+2STroUi0YcBIJF0njIMsRYA/qbs7wiAz0uKdrmidmp0qLv6FGNky3fmfYB7liRIjLzmJfwvaYx5n0lGm7hvHrqZUkWPZnHVZ5SJC81S9uR9ouJ1EbKLEFvgdUrPblp9+CajCHY/iZ+ErWN4ba8R+jcFwndeMp8cf5fHdq9iUapBCqiVxcd7KouP94CanJ7pLb6UuF6Zzo35q5i1PjFcYSCc5NBX7C4ocNTUYM18DzEWyHnPpN7YJT/x+Pd6JHGFUDPHOgjPNo3AmhllniTxPKssL4/3TkwEW5Oa0JrUeMYRdDhFOu1XTJ9btrZzZH3aCkEwxwkpLDVSCKay4ihW6OngYzwPADK46iomWTGYp7Kg8DCeIwBxgS9OEHOylIkHObv0cNFEuU5wuN1FFhJPB29calc93nR3cWnMWRSKtwqC5QEAyjgTYOZcr45CoDhzd1T4KLtyq4ke22A7L86jtHpaHexerrstaxzPFHI+qgk8gB+Jfv7TvxP8wlcZ7CjLIoJAKTEtk190XzMGMx533wdg199V/PVnAL4MYE3HHjs77vFnhvceuf6VrY687w/csyShhSkpBtbUQVJXGIfouJAN7Aoqe0zKRv4ipd1/I6cFv5ZLwUVI7IjkvHVYcijdpr5pK5+7CJkw7JDBPwYAjwrPXwDwFQD//JL55Oi7O7R7A/RCq4TZ1Uo+cAKVPG1T93nabJ2oHnOMJa9V9ae6ShUzpWwDyB0Jx2wwVXJqK5YSCgI1ZPAl7WbwaGeaoDst8frr29/6mUev59/eqW8agWUMJjO5RWNrrJ+cmpgIzqYaCKEM7Wmdb0/rPACWFTh21SNbWb9iVSgCbVqrht/tVvuxifqGjqgjL0khDP0+CPMmUwq4unIm1S3dzsmkhZPUpvVe8SnWqGXxR+MxX3s85kMPGOV5esvrS80Ul0ZdlBKN4wp/Tjo8GWppogGQDQleJae2KEL2tX8/eOL4sRtNwUBmToOySJhaXjP1YG3zyIVsRnHOjBfPi7lrtg8W1LuDCNSCncL4++jnP/08gF9euk026eoFyFIBK88YkQAmfezHX9k/2F19baSv8qzHnzkKO1VxKvr5T3+6M+yYOPyX/5gtZCxbuDdxz5KEZ+nj2gPi/zlngSkApt/UVbZLVNBt6XEscBDLMBZ4WcsceVnLDGOT6tnvNjxMGiSYF13un3397qPC86cAXAXwTQAnXjKffCdFmPcSDmNJ3ng1SAG1MnS8pzJ0vAfUJEZ2IHQlca0qoY4GypmZ3+VwLRCedhCyrMPePP70P71WwhgY7PD1XK+EFWvjRW/Wv5Fx5IOZkW8tDYMnks5V9A7EaVKyL6ZyiKkiABaWedbtVUzmU6xGiWdlS/dwOXOb0llxDh3R5R0ueYYrzUxpEEDWVY2ATSh5nINHXpomIpxl8dtiUT9iUT8AZnAcuy7LetTtyrmdTq11pd4NgmREgpXRBxmDxii5YqhSIpd2+HRVagXjCpi0F4seTZMzunsqKwcGS8O/9ssv5hWqEgLuvgev7QeAvlu1J6+f33aUMWJ6ecNPdX6Mk6xVy3dfu1zLiinpbqmKfwbAZ/Jtk5jxz+B2umbuvMTjzZw9/NHzbYJolTRtHzzUtH3Qvgq7GHMPgN63x9z/cnhBVcUW3nu4Z0kCAJQwidVTZ70E0uswpJK3KE50UjPDMbRQgqUPxUG8R0hCKXXkmwAJbveE/y8AUo8Kz78EW/z47ZfMJze1LfE9jg1XNXACE93NM3vczTMAACOpTCVvVPSmOsp4I+5sAxaXlK0E2amtqRshBAR2ieQaq0aWFVaJSqwXRsK5bCX9gWM32PUbtchnqrMcJKRZJDSTkTCTYXGZpz1BpznhlqhL4FgbIcwpitbmjdci3dk8ZM0DPrEBgmBgyYR1J5B5tsYzg4iUkp2qqkBVFQCMEsI6ZdmYdjo1yeXMNQgCLQEAXyjeB6CIEMiEZ3tktwbZrYExWIyRW2ZODGsZRdEycjOj/DIjKEEyF4keBYGKP/8z33V/6zv7FqYkV/y8GrcNHatpGrv1vRePZHie7U8PFo85ymNXBLe2J1/kKBxzzvzlvzxQSTRB+HhF7jvHHrpSU1IZXlZaXt4wRktqJi/GpoLZycHyPdQUPACw/b5uzunKzftoMAYYqnR9aDzITp3elty+v0t642K18Uurf8AFw3z+iFt48kx67r+F7tf1xFPSwlbbW1gf3vUukKvhs9yXPw3gKznQoV5Oq537OwMzLLCuJNEjYS7niRGtjRLWi43nKd85MKQPmqUyt75wJAVwBnalxDdeMp98X5dX0gut13EXBKiMgmWHgzeT16oi2aFgiOpC+0qGRf6KSLeoGBuKQiwF4a2LnsbpTbNdTvaW3gLllk3ib55rPfXG93Y9uM7DnQMw3/mQgJlu2exoqopGQz41IPJ0GyF3tpgYTkon/7m7aJlzYA2VrnnBr/c3ex2rRGzWA0XWp9pKM0Jt82iXnpPEieHSFl2TNhClYMOiaI7sOXw9W9U4UZBDKKPoNw1hXM/K0DJKlaULda5A+rQzkFnVqAl21OotAA+stMH4UMl3K2qn53ueEME876yMlXGSWUuIPZlTTez98rf29H7zRNt+R9p961CRKX3kh0+U+oKpujyHHMRsiWg67j7Zd6X5oCAa19oP3WjkeEzBrhyBmlSkdNj/gDOQeo136KX/z189NPivRmwn5a2rBiUJAENJ9enfWu3izOePyLD9UX4WwAUAOuxqigrY7pBfBfD/A/AKgGeEJ8/MT2BdTzxVAiDU+sJzt7qeeMoFm1DdD+D/AfAvrS8898xq595CftzrJEGEfZPgFlE76Ao97XVY4YvCTBCr++/fE1AYf3avWbziD7xA9MCOMHwDwJmXzCffNwIheqG1CsDIO3EuSxWSqc6yjuTNSkObdjeCcbOKeRYJ1U8V3aH3/jxEX/akozSxZtvjQkAtkkj3lXqXuusBAGNIP/uHPwhNF9fTIvo07AZnecFzNFMezNyqCqVUv1ur4jm2bpHodwd9b10NOw8se4NBI8CQE9xkLZMOc4UJBE8A2JSW2vv3dZ/74LEbPkG02ggBYQzMsvieeNg3efq7Bw8zyq2LHB1+9PyN0srwhsgtY4iB4QbhUCjJOwPk70/DGC4SgjyklI0SnoaZxQUAUgsApkVGn/2zx241OcF/8BOnqoMlyaXEOA67bTUAgFIyTAgrAqATAhdm+1cwBjYzWHKVMG6PIBmnTZPnX3x5T7alPuyurQh7X5sRMhfDcuaH69Of+uGX/nBenGk+f8QjPHkmteD/dwN4FSt0XV2CzwH43b7v7t0PW8z6PwH8CoA/BfAQ7HJgCrtsM936wnMFpzA/y325HUDns/Txe3eCfIdwT6cbZhEFEHSBm06B5iUJM5zaDYK8XdDuNZTSTancawbwa7Ov+KPC89+FTRi++5L55B1Zwt4DuOsui3PgHabXv3f0oH/vKBgDtGlPf/J65agx6ksRgodgm/HcMURvdlnOf6Mwk44OgBzK9x4hcDc0TF7u6Kzem+/9FZC34mAOFuVco2HP/tGw/Xx1SMZkZSjdV1GUJm7FaCYEK1aAzKE7vlyPYA8YMgNaMowGCyQIwDq0KmuhrXVMFyVr/plCCIggWC2hsmjLx37slVtnXjogxML+ZhTUqompxWWRDadBCEEABDxsbUsh9919ACZhW8YvGgixG9/lO0sVs/iqhX8ReFa1pywzmk0596Ys6e3q2pkZddJXSjWxaTZ11QfcJhwcN5+e6Zodw9z4yUwkkO29Ud9NeEtp3N6buL8p8jBAkIr7Tn28ZubBj9dkAeA3YT+3YD5/ZBeAC+bzR34XwG/PRgWaUBhBgJaRfmbsTLsLwC8AOAvbSIsHkC/DMTj5qz/6QclhzAS/8NUbqx33s9yXfwnA/wLwlc9yX/7VZ+njM4WM5/2KezqSAACf5b58KQuz5jqXvNBI3dwUsR5iS8Kfl4WZczli5X1w3lNgsPaZJRkR3HrzsIXCgr3C+Obsq/Ml88l7+wteAnqh9WsAfvDdOj9jYImv74gBxA3gFuzeAKXYcB6cRTzNk5sWlUgPFZ2mmrTiyv/cWy2nXnt9d6Gr0UEU6ACZHww+l9ZbHUqNlwayTkWytgGLLdENi3T/weWyVdM2IiNvtTJleaRhOSzYLZQ3oXoF+G+f/bduUbRWHRtjSOaySvdbb+z1RKeDrSttJyna5Y/92KvrIWcrnc/87pcfvuT2pXOV9RNyRc1Uo+LUVpo0L8KeqBfeXd2wPVrWhd7u6m/veezCRznONk5gDBPM4iYyQ6EIs/hHAED0Zc5TXfBaqmQAJIYlEZ2bb7ec7Lne+MBzqfjZYaaXf3W3lWUgJFAUow5Xbg8AUEo6J4YrHn3o3OeGjb878hwh+MXZ3V+D/f0eB5bpzZbBMrmR7/zth7rba2ZWrIJaCEHWT/krog8Sgq/D/oyeni35nMdnuS8rsBvK/TJuV1fdAvDss/Txvy3kPO9H3PORhH+Shl/PEuuzhOHD+/TAsMz4M0NEn785KZiRg5W3j8O9BgHkughuz108BY/b4sffA9D/qPD8N2A7qJ2816sl6IVWEauUPr4TsCKuTmDezXPPgreisFdPFoBGAAWZ+XCi1U3Iyvnj9WA2l1y32jbXb9RWrfb+EgzjjkgCQSKjNCUyStNkPHvjYOukBOAGgAjsFX/bRFacxBqTlglWzMBA1hZddmGTOlzKsp4SBGtN+2RC4JUVrSQR9a4aMfF4MzOGJoRF2SxoFbwSMknnVC6rHMhlFYQnQrj65k7IivbWoWOXDdmpMcmVqxAks2GWdLbDFnJKCw4xhQ2QhG2HbnnnCAIAEIJyItByKZD+th7zxJnF+aVAhvGS1c4YZqgmJrWo67KZVnYDhKMMOD3qUc9lst/utfSPfKw6e668JrYsLWTo4jSA+18/9MXtntIjD+z9oTNzbxX8u7csMva/Pv8Z4cFdA9LaWwOKJ3vSHUoemf3MfnD2z5+Jfv7TL8Ju4JaMx507gY89C+C3gEUmVNsAhD7LffkJADeepY+vGoV4P+KeJwlZYn0OQAkj+MxZITJ11AwdL2bCmRliHgGAKMldxyqlavcSgkxJvsOnbIDNin8ZQPpR4fmXYUcYvvOS+eTkOzyWQnAEmxhO3gi0geA08vdrCGKxWGwQtnZCnt0+77gFj2rl+/tGQA1+BCAreoGYJjc4Pe1bNX2wBJvmWlkdSkVgP08W5uTNMqfuP1ycffnatLs8A9q+NAoIAByQKoAgALZL4qaguWmiO3/efjlyqjxlmUKNHc3OP06ekZKbZ3aHCEf7HZ7sWN32gWZJ0dedZhobLB/Ckq6jWk6q0TKK09JFbzbuBsDiHE97RYeekV2qJrtzDy4osdzQM50TaD4hNZOD2QelQFa2stJJTrCqAIAQFPOKUeysiIMxxJjJ94z3l0SnO+uOBmG9+YuS/+Inq7th6MKAIJp1ZMGXm8sqIoB/A4DUlB/UIkMcz2rznHsZzn1vx8kT37m/KZtWvDlV9hT5M4nV92AD7lAyrHjUY0sjeYyhnFrcZ2ZGSy4nZvzsRne5CaB3hQP9LoCnARTDJsHfV7jnSUJSfdrwOp75H14qlpiUK1FBz7gYZ8wAMRAEcsRSwRAFwYr93e8VlFrOTWs5vAG4YTdr+RQA/FnJ330jUJw68/jzf/oGgLe5/V157V3fYdz1hk5rwZxyF3of1eH2KpwCuAkgDFvktQ2zZZGSV13PpL0qjIRzCAs8QpaCEDZ4/NjN0ZOntt3HGLcWATCwSatyAAh5VX+ePwsSj12i6ojXMXknA8uYDFeSsLJxYlaoYE0ggAd8oeZIm5JmAID2tpGCS+gUp1ax7/jlE5V1Ew2xsH9warSYG+2vqMukXLO/Z6Y5HFobADDKNWQT7oaei60Xth2+XrbeNNPEUFm+32FRx82Gszv39MyKX4mfWvw+Le2AllZo3Y5rKY5ng1ZOnDHSCjVTSoRZ/DpsuRkTPWo+YtwHoIkQQHDpeYW3hCBAROtAUVX0SkvjZL/TqXk+ePy6oCU95khnbT3AksXlM2FJNhoAQM06FvweCLS0Y8Lhy65JEkyDH/z3fzp+hFq2O6fAWzlhjUZaomKMOLzqsnEzBtb9dtsZNeU8AJAjADAdd51cYwj/H4B/Xmuc70fc8yQBAJLq04OPCs//NwDP9XHaXL71BIDjVdR9JEn0kwmS/ya+Z8Aw4ULB7X7vKooE1i1z+EQu4dx9+UtHPrH3x8400wut34GdlniZ29+1BkO/a3h0Mw7CGED7fN+FyxC5suzDhT6oqc4lmcFvZOLkACxMeakArpkGF07H3Y2e4kSeWoT1w0yv3kXRMATH0cMdB9++2PhWJuPggVVXyrcA7Lb/yZJO2eyQBEtXJJNGUw6fbnKVAsdmzDyllkvBczQnCnTF7WIppRIACIhLBPYVQUARE8DAwjpjPQIIhR2VWc0xleJ2p8c7RnVVZJlPwUrgOFZZ3TheCQBFpbHqotIYtt3fDUrJWCLqHei7WZfhLGHRvWto4jZDE2ckxVhT2LkQiagnz0KCiMmEe4WJlHBj51pvVR7s3i449XbBqQMlSTCKQTMrdxoJZ6WZkbcBhM+/PwBgBGS5oRZsYWRBDcMEIPH4j5yZTy+4XJoVmwx2pOOe9lg4cLOkYrohm3aesExhUQpCTbgSN1/aN1jePjxcsX2wHQRuQhYLNxlD6it/93CEWnzd3N/KixM9q/S4sMckLS/6Ygzs1ps7LxpLdD3JrLTW4oAH8MOf5b7MYJdkXn2WPj64xj7vC7wnSAIAvGQ+ee1R4fmfhf1w42C7FPYwsOYkWbkX/L0CB4SBdTSluavY6cIkgBZq8jXdL++e3vtjZ0pwu7eESS+0noFNGL4N4Ba3v+uuix/phdYK3KHPBbOInjlfc9ac8hwlAi1iFvGBkTRxGLekqoQm1UUbeLe+YjTHGPV3AGQzWiM7ANyvZR2nbvzb3iYQNu0KpHpKmsdISctoi+xaUYi2IhgDowa/ongypwnpZ//oB9qdTu0ytTgHbC+BCIB+2DoKtvAlCWZEN/ntABHqyxJXt9VE58WOlIGCgVFGXOc6y08nMnIbQFYcc4k/20EI8or2spowSRmXlxwTkJC8+LhjAAYUgTKTosWkXOmC97qxSSZKPG8ZiqLfMeHgOFYZCCUqG1pGTgx33F4gE0KHq1qHpyTF2L+e4+WyUoTS/E2wGONq0ylnn9uTbSQc7WP09mcqOHR9aRaEcKgT3dqI6NZ2MoY0M7kOM62oetJZRTWhYWHaRApkhgiX14iuYGqrR9wLvysQAr7l02cHr/z9Q/WmIT6QSnjOpOLLMnIXu97Y3WyoSl3fmR11fWe2gxCWCjVOfNsdSnoYBUlOBr3R0SLXlXPNiwhvXWVkzegTL5pyMuWYGYhJ0yUSC5eXJY/3X206aWjSIqJCKSzT4gpp7+2A7Ur5GQC/CuCPCtjnPY/3DEkAgJfMJ7seFZ7fDrvk5ScZmDLKpU8ygns7igAgRJc1rXxXwIEZxcJtBp6Lu0piQ6GhQG14bqUiwFYYH4ctfhyiF1q/DZs0vMHt77pbPux3FEUwptw3MudqXaDccQBgJj//gGaqdEDrKYbWUwxwdJAPZIfluphLrEhsJwKb/2L04cCmurJFxkP2sRkpyUS9JQPnvRg43w5OsHq8ZdGJstYRV1Hd1HZetNa8Oayc2JXHP38eN2/V3ATIwWxWWThZF82+lmFv4/Qtv1szMzlxWJHMRbX9HAEHAnBgzqPbx48aJkmfvF79ds4Q8nYVrCxK59XaFJdPn87pvN4cl0cHRkM7TItf6zorAVRW+bTzEs9KGcNwziTDSU3g4iqfsRi3KSShtmami5DNM+uKTIRKFv2BwCiqiMzff9cGQjfOdVVEP3W4uzXkzZUuO8AsJkZK+7DC9wUAhsGrklt9s/po52E9LQ+Pnm3NMIsLlO4e3MuLVr6KqVrALo0lIt0vBbKQAlkwhmmqC71mWqFGUqkXvSuG+wtKjzKGrJUTF02yvE/t9+we/+i+33jx7Fv/84ceSMW9RwCcWrJrVssqFZpFrik8ayOESIyRyzO9lY/N9N4+dTIrnQHIIqJZURJfU7T45a8dTp2aEW+8xoeD//TBySoxp1zLJFzLyEA8I/cCZBlppGB93JLzLsBHPst9+dqz9PHX1xrHex3vKZIAAC+ZT3YC+JVHhed/+4Iw/S2LsHueIABACXVsinvfnaJBwSVCML9aphZfk4s7b2HlrGAtgP80+9LohdbvwY4wfJvb37WS0Gcj2JA/AjM5NXO29oIZdj2Yz2BoGShXZ0XcddmIG7hYpRPZvCSWJ9NSfbTGijvqNjKGvONiMOLTgbwheGryzfHR4ub4aDEApouKfjlYO5Us3zZc7imJt+S7CiPhnMIqK+krVxrWYajFkkXeXDshID6XvqY5kigw9/FdI9u+d636omYIy1IYQU9u2cTmdGXelmTjqCQb+ODBbnzgQLf29o3aM5c7avIaAHGEWpRxPMAgcqwFAAhBjUNkNQ7RQInLgG6R4UROGE5pPNEsUgeQDWl82ttH1t1sa3Co+NbMRHBq/6GuDy78O2NIZOLuxX0NwCw9dzvVkNMFcyLqwumbVX0/+EDvyiRhsExd6T2AMV9pbLrqSOdDACC5tZqGR66BmlyWE5abrzCGcTBkQBAlS/RahKCEl80SXk5DLkpDnfSeoLo4JHpVSfCobRzPfLCreerW/GAAgKGbiFYxKHEyi/MDIGJNbAxAg7M0+YDg0JOmKnuxpCJoYNJLBiKuK71ZPtTi01453BB5UBLoss8np/Pm0r85ZMO/2pAud1S9OTYZ+MBpaXgYBA31HtNilOO9/tSJeCSwqHHZTMI5gyWpLA303DDRy0SQG2VMTMogDQBC5LafhwN2Om+LJNyreMl8MuF1PPO7AP4Rm9jw5W6AY6RbwsYaDW022hxYlpvMRLyFRgdk2Cv+RwH8b3qhtRezhAHACW5/V24jY6IXWgXY9clrgplc1gy7+owpd8wMu3makhvByAaJIpGYJt6nDxZBHwwOr1Y5sF6YhnCDWnwBdfNEMnLy3qmuGkx11QBgYYc/01PSNEZLW0cbFY9aBgBmRl7RW4MxYGraV0i4FADglM2OhUSxEAg8cz68Z/j+6bjz2mjYHYunFVfO4FsEnhKBX6pHYIY/FF/0WRICeWfLmFsUrFM9QyXVsaSzDiDgOGrWVUSuP/xAZ21GlftudlbFjKT/w0vPTwggC6ymxG3UlLhtPqRbZDyR4wdTmkBzJqnB7aZpq6K+bmrdVR2nT+2Y9DExoO3pj8vK7QnK0KRugCxKK1DKt0wNlZ2sbh0pBoADrZN79rdM4kJP2ZXVzhGZDqwiNiTkrRN7y5ofvg4trUybuqA6A+nafAQBAKgm9GeGi48CABGsacGVGxDdOY3wlANAOdEKgDAnCGoEtwZ13PmglZOAaS8DYR1iXbxPcanV0Mk2soaFPOGQkLzZNO8wqJmVeE42c3JtbJ4Q7PxPr1698r8/ssNXHsnGR+2gy1Tcee3WcNFhgHCVADJxCSeuOS/vrJshZcHFj6PRsGdRnsKl6KMz46WthLBOUTKmGSOcZfEOUTLSPG8hm5OsC9frDquwogZYZw0Ru3nbetzvcKml6YR7xDTF+fsznHAsS6caYLpGWJ0Ghl5oFg8kBZBxCSReySQwoFQE+ehnuS//4bP08WUk5v2E9yxJAICk+vQ3vI5nXgLwH97tsawGH5MmsIHa5c2Gg2MzDm55ueipP/xYS82BnpQad8V9VdH1TJRNsN3NfgmASi+0vg6bMHyH2981UOhB6JB3H3Eat+DXWohI8+a+aVacTL3ePMMMbgdANsW7fzHIEDaxQVgy7E+tvVXecYTUuDs09HYrht5uBeGsAV95ZKiudZjCLr9qBxYTvbGxom7GuILvr/JgekM23oQApYHsrtKA/RCnFFYmJ75GCD4MMEN2aB0A4PJkYoQst05WZHP3nvZR7GkfBaWYTKSc/T6PWsNxbC8AuJ1asNirnRwvsFBY4llFscusKHbZz2jdIpPJHN+f1ASqmaSKgdQt34vB61ELJlRz+MTHLjSKoiUvJAgAkAx7867+A6WxRSkIQoAWfw6JGd/VwZsNHp63EmUN40ZRRfgAIYBp8jnTEFZNpxz6wTdVABh6u6VzsrPmWFHd5In2Ry4e4Xi27DlupBzzFvXM5EuMhKvESMwXh3RiNirlrJl5SXRrj1rB9Bk96jkCEALFqrR+eFTNus3dMEiWn5Ruyuf8IjG4lfxoXHJRZh8ACE4dcBiniHibOPrqZx48+LmvvyrOyEcv/+vRvnTY3zg87Ykt7Zuim/ze64PFV8qCQ6t9DCj2qaMAqWKMtOmaPP+Zaar9s+gcCZw2wbivSeNDBDj2Mb/05sRwhUU4dk1WcpO8aJUtJAkpVVr2zBEWOoAS8BYQsMACGljNOPQTNUw+Djv68JVZD4VDz9LHT6w68Pco3tMkYRabVmJ2t1BGnXdksLJZ2O5EB8mj3yCE5b70479MBUVP/sSX/2ijh3cA+NjsC/RCaxft9/0TVPE8gJPCk2dWjjKkpY+ztHQY0y4wwrrg1idJIBeA29hOCHgz5uhNn2h0g90NcjCPNV3e1oPwWGhTyl0tky+3NHECIHO242kAHbArKCoBNL59qXFdJLS6OLUpvxmOA+9xGgoACII5GSqNLBOemibXe727crK9ccKryOauBfuWBXzL7aqjYV9BBjn5IPGsLOQyy0KzpMGwyKspjfckNV5TDa6cgTSXlcb7CMG6q4x8gXTehFxsqihP+oAlRclYtPplDBjuqJOoZVfPmJTDaFctxnurOtsfuFGSzij9LQc7rZ63Wg8wxhFfSXQwHfUOWia/AyADAKSyD3QmIRpn9ZykA0BksOz4mb/56PDRn/l2NeEWiwyNlGO1qoQogBOcrPt52XwIwA25KO3So24LIDz3yaHLxG3aJE9kTqta25OtnuogKhfmhxw9wpBicjGxklDSACCJJaF6Urn8/pIVTWREVvZ+6rT3hT/8gVfCCcey7+CBPX2veHlumb3+3sbpmqmYU6eMkwCgxJ9dVTvkc2luf1H8hJU1qywKa0dJ2gOQIkZJUS7r9GKBlbVFYViULPusJJAVq1KCTFhIAD8G20eh87Pcl28BeORZ+viXVhvfew3vB5Lw2wD+GAWW6rzjYAj7mHQPOEIy1Mr5hUiMcSVggKHKsqXzBi9Z6+lQmf9sDE1QhV+H3YlNNZ8/8gaA7wD4lvDkmaVRho/c3pG0IiW3spQMgCWZSDuMaU+W8FY7M+/a7Wohv4HShsAYJtSUc8PlrhYlqUTcdSWZchYzxrUdbh9aGM50w67smcNkXe0MGR0NnU0kndsAsmrqjRA24lLMTUurwO7OB7cvPYA8JYzDk4GxCzfqjk+Gvdc/8uDNzGxToLxgDMjl5E37HYs8Kws6zR1Bp/3xmRQJxWGGJ0eKR4MlsaAomW2ErNXme2UwhmQm4VpGztyB1BXZqS0l44xay6M91OLbbp7eFW5/5GL6gYM9H7j/o2/dohZnyE5tZzbpdE32l79148RuXzLsLdINyeOpjjc0PH5+IPa/PjHa8AMXR5hFzIjq6fTXT4sisQRmcTyLKE5m8nvyDNnA7a6fIuXpy4RgN2wDrAwAAsKA1sRSEV8WQAtzUN5sy4TMtgwAZInKhblRuVscdJpcVCiDRZoIz24QmS4jiywpewCAcCj+D7/y4nbh7x8a67lWKxDGIZ5y1nzogY4T9VXh47omnYvOBLdRi59fWA3PeG5Rxs1/nj6Xtmr5ankwu6c8mMVRBpqjyMocBNhkZhi2KVoagAIA8bTSl08QbILl7YHjZNwpN/iFtucibLM6AHgKwLOf5b4cepY+Hl5tjO8lvOdJQlJ9+jtexzN/Absd6KY05NlMOCF0kVXKx94pFAvo4FfoojkHXjTDvGQVXD++KtLSDYDM1uHDAdso6TEAf2I+f6QbNmH4LuxV8Qr1/MQLgz8oB7KQA1mMnWs6RXWRKh7VLzm1bXfygF+CW9hY++HzsMnFIr1ANuXswzrLXRmDmckoV+MJV84whPsAMv8gCpVGK1bZtWzP7oGynduH+r75T4/wSVW6GVeFcFrnA5RhG5Y0TvI6tVUNmdaJCGbJuSDkT8uqOYnwHNWPH+g2ViMIAJBJO/phC8Q2AzksWeEKHHxm1nXw7Ct2mwiOt7TSypmrVQ3j8eLyiFdS9LalNfqrwdDErqV6BACwTJ7LJFzdDk+2gePsVIChidPASs2wSMhXEfUDgKQY82F6ly/radzb90Dj3r4ApYQZTvUqALirYvUP/sE/WlZO9IoufdG9RwAYhJxE/mbyNwG7yySlxOx4dV/o0E++kiIEHovyJwHyUfi1cVgkA26Rb0K+3+g4c9Amq1kNWc0qYCHtuiomiSboZkJ5hVOMYiJZzYTAxRgo0uI8QeJ5VvGxT77VO1ERrdENvmNkInCivip8lBDwkqzfx+jie3Yi6p5/fhLCTJGnBUXNeALOxc+T6lcBfAC2DfN8miOekSeRRxDMbF+OZRBW+PsstnOEPnJs56j1+qEvfuWhc59bRYj63sF7niQAQFJ9+ve9jmd4APdcv/BS6rgnPuOdLqzJbEWHXrAL3VpgUSW+ytsts69fhl23v/bxGKZycfeDAIGeVQCwDMfTK7IrpypetZYXzdo7MCxaT+dMBnsCAuzfD4Nt7HUQs6uT6HioYPLCGBCLu08nEq56gCwjS053dpIX6Kqr6/Gh0hNvvX7fg4xxnFui2912NBqB0vDFQPUUvX6jVh0bD5abptBcEcysZqqzTrAeELglWe8XJDNvBGN708SxbY0TGgogYTPTwTHYVuKbgU4s7r2xDNTi5Ynhst0Tw/Z8SDhqllSEr9c0jUaLK8J+STbaCcGK6Y9c2hEWRGPINITahZYCasp1tOdiGwCW40XzhtufTqRjHj9A8lY2EI6aYn7fhh7Mfm4cx4ic5ed1LhzPeKLkl5bwflXzPNT9JlUlI9dVXG5Fnc0AOYNZzY2zOnxq/HK9x1sZ1gAQgL1OQhmBm9TGaEyuxJ+29Uk/1vWmXkR3gocH9sp7KcawIHorhblrHI/DcJr7EJZOJl/ftgeEUT6Y7ZQq44Mi5T7CGCgh4CydvzbxduMxAJBEq72xJrxw8eLkOBq2LE4BbPKcVsX58H7AneslZEN+Gcfta13cIbOyKE37J3yXdFNYpNWygLyst4SJq7pDhnxqmUsxHwMQe/3QF00Atx4697nVRRb3OO6JCWyT8C3caySBwQhRx7ueauDA9CJh7Ye0K5TKbNpJM+Jqq9+FKGhq11OO3sUPWeKiFr9fTbqgJl0A2KggGwOKR5VkV24bx7P19IBYjyveTdzuTzA3qR8HMA3gImM4GJsKFvydM4ZcMukMrVTOV9sy0ovl7YDnkUk5zl86tWsvYxy39D2vL0t37xrcv3vXIAAgnZYjHWd3GLDr1etwhxGFUFnYkBVdZgythGDZ+edASGF6j2jYv5nPo3W7hjLKCVOjJTunZhX4HG9pZdXTlxvbB8PBklg54VgbWdB7wtKExtLK6VrGMGEaQr+acSCbcdZbpjB77xPFMsQ9iZnVg3O+8kgXIch3z6xeLRRVbjCH6SduY2F1CeMkegCS5uN9GsSyFBjFG2bMUfGtvz1yKySYad9E0BiKO/t2fPxKOUBFgDwglKcdDmHsZOZMQ6VcHxmVJ/jjloPdtDxsO4B8F7DoOxVnuPkoEe9XywAGMMJZEVcbhRVGUQa5qPv09PXaVksX2lb62RMCzu1Ldyai/pLZ/xce3DE2YphcTyYn6jxvpQFW4Q6krlQ2jVarGUd0rLtaskxhrefbFSxO0wEAFMmqenjvcNPwtPd817hfjxigUWKVZEDzdlLNgE4p4OpWOkmJP+sC8Pjs63MAml4/9MVvA5h56Nzn3unePZuC9xNJuAngOjYWNr4rEMDdEMDdcfvYO8WsN8KarbQjfaW7hs41X6091LN7rW1XAzO4KTCybgX5akiNB1echGyQKlOTqtKaBFOiHZWfvGyYU+6YPhgsNsOu9lU8FOLYHD1CCYASIplndv/Sd+n46Tbn9KW63cziV/2NcRwUtzs3nUo5866OqhvHVo1KON3q/YyRvJOJJBuLlpqE8jGRXyRcHWUM/cmkU3S5cy0CT9fh9w9Isl4N2A/29eyXD4wBqipv5j1zx+3YqcXL44Ple42sfKJ128AOQmhWcmrXZI+aEmXDb2riLsDumChKZrkopeANpMAYRg1DHMplFKhZR7VpCDWrceGS5vGVonyLagFZVminKjdMFFpDCMDiMjDtbGcE/VDMcVKVaiASTWFxmqWPcNgpFqmh7rRj8i9e3rHtJj/T9et7o1C/uper/Z1vpwDECcGEUJzZ6/1Ix9tENIMMLGm5WTsADbZmR8NiYlALm8QoxEScWPNuqRQcmwLH6kHt0klONi3GEE2NB2QzJ63oEzEH0xQW3fOyaO2VRQtuh4GKptGzxdVTnjkBtsOj1gfLohjvqzht5CRLlA0uMh4KWKaw1Cwr3+8wB6CRI+DqSpMHK0Kpzn/sKHJkcitHCzRCtdVin8VedSHx/gLsL/4nAPz564e++K8PnfvcBque3j28b0hCUn2aeR3P/DmAP3u3xzKHIirfEzdEm6PQ75l4z/zJR1n1vj6LE+iGw9IsqvQAWPNhUPDxGFh6PFCwjS7hGDjJ2iVVJyBVJ8AYojQjdRujPlMfCjTQrLwwytEBbE4rZwAQy1Kmq2HmuK9hBm2fOZXMjAWuj7yxXZ6+VL/7tWn+6kPBRW1o17gOajrd6qpRCUIgVNROdg73Vi87riTrizpQRsdD41gs8K0C4IvGvJ5ozAtCWLcs65Nut+p0ObV2jmMragh4wZwk5E7aTNuYmPGeLC1KtiYS7uF8+f0NwsQmWTgDQKjE1gswxjm1jOM+LbO6bIEQVEmSUSVJxhxpmDINoU/NOqhmCtV62rEoPRGomlnpcyb2eWFqM9439bhzF3oqalyHB27QrJgWdG4bASFgaIAqNrDeQD+qkwPEYyz8rTTCvsdDv/qb3zoSzrhe2RXjaz/1yatDf/xvB9w/QhCCnSZrIARRGjSmLIWVEA1XQfAg7LTNXgBnAaRgE4Yg7F4lFwEcEeJcF7HFf1cBNICgDez2BarjweMAIEvmQblu8nuw0wUqY0RllKjRkWINjDAAFrWIJkqG5PEn3mCMk6nFiZRyIrU4mVLO4Q3FakmelqEVjePzvRiKq6fC0cmiNyf6KncAZI4stsMWLy4kj/1Y0ORM4lnbT20PTz17qcyiLH+vCxNsFYrAwg7ZXKhFmhtnM2zi9iEAX3v90BcJADx07nN33e5+M/C+IQmz+ApsAeO7LhQEgFLq3LS6+41iJW+ElaAlnXv+8Ud+bZBwzHz4N/81V7l3cP32tQl5swSFAABmcZ2WLm54tU8IgrxbP8S3zUBpm4FajJeMnOJgl4pcrNtnQF8XH1o1PSLXR+cfEoTA666KHWn/zGm0/fiZ6d/+kSf0q8PCKz9VYxX5Ie4FCNF1Ycg08p+/rHr6BiGr59XjEe/pmfFQXiGXKJmLHkKJsC/fZKRhts01Y6Qll5NacjkJYcAihN1UFD3sduc8TofWznFsfnZ0uNR+rJIGKQSGyXV+68TOBxTJSPgklgkom9aItAvIG77fAJiRTLgTbnd2WlaMkrW3Xw5CUCpKZqkoJdHw6JVJMEypCVd/eKDcmuqpqJJcubzfH6N4IDNc/CbV+W3AbcOwzJt1TQAUT/NkYtHdyEgDTC6fmyQDAM5jnf+tl//xEfkN70uCRcX/cWh4gEosQyWWEdKcB4Cs1pohJmM/bB3Cddgt0VNgcMEmAH2w9TeXhRixTIVdECNEAyAB9uremPCeAOOW+WQQAoCgCrP3DSEMjDDKi+awpYt1AMDxDC53FrArMRY0IQMANiM79DVTg6Jshkprp0KWyZ+eHiprA0ABUjJ7PQuJ6DItEkdQWufVrvcnlHVHpGXRGiIk79wTAPBZAC++fuiLLQCegN0n59p6z/Fu4H1FEpLq02Gv45lzAD7+bo+FMIw4IdS92+NYyRthNVDTHnfnt+47Ubl3cF3nYwwWjNU7B8Y7y4cg0G5/01RBLotqxDONdaQEgg/0z6y2vRUwgsRh7CcNKTAGDRnhMuv2JdmlonKMuVrANqqAZNN8MLvsgZ8eC5zp+Op+7ReKJCIbstXa2kNqf/DySbkkVX7i9z8RV8dDeb+f+tahNfPqlskzNevIO3k53dl5kkApMUxdzPe9mISwHGNQvq6mT6WIkdzPe0iLIH6EA9muqgpUVQHADI5jVx0OLe5xq6Gi0qyV51grgjGEKSUZnmfzodyJGd8UpVxbNieHyp3qZvpUrBS+H0OB/Qhug3SNjZQdy6nyCVnRu/yBpN/tyVZKkrnu1vSiKzdMCGpAAGcgU1oT6EXVjoFz6YFiRfTkbogelXKyWUcIqhgDtIjnHNWFPLlxooCjSUKWOs0yCo+eznOd26jAvpdtMR8EAfQHk06hQ6AuBk2ttFyWh93v7CZDfI5UizdcL/Oi+UquVcsyGTtgix33gGLc1S30ZFpMDTxec3YK1Br0al//q6Pqpz/30sO8SAUAsFLSqJWUVvoBZYHFHhWEgAtURGgu7TiZmPZV8tz8++NYQvQ4weonpHD9UEXj+NHy+gkrFfN09F9rMsGWpRzzruQ/2RDne2LKCadI8caopzas2gQGAHSwFVcUfre2mvBbBPDDs6+/hZ0ef0/gfUUSZvHvsENIm6WS3hDcTBwgm2jzuzEw1MgoVEC4DMPnmw+99D8eP/nIb//LUY5nheWdU4tKH/PCXTczzShXcM44OVq0rrJMpTyxWvlhzNUtVGRbzRGqoJoQyHCbLnJfZC/ui4BRhBGRe9i1IGVXi5qRlJZOwCuGCHlfrocQLJuwZy7XIdNb8dDhwx2XKn/8go/jqY9w9orj+H/9xqCeffnKyFuNiTN//LFFq69QWbRqrWv1+FN+e0iLn391rUNvlVaF54+XjPhuAmTP0v0JQVld7RQoRf+u/kDg7binFE6EOXGpzoCIlJLdmYwDmYwDk1OBHkky3g4WJbKl5eEKtyfblE/1wRj0F769P5JVpSIG+D+wv/tEQ3W4KRxz95+80Dw7CTBIPNvM3+vSiMkkgCEAB29m8N0WB2pFrmDSGQGASDhwnOetW/GYJyZJRrS0POItLokVpDcSFa3DyElNnorYMJa4eupJh8VMoUyPucv0mBsAYDH2ipZRJEkyOV60JgnJE7GhxBvrLf2ulnI4ireP7OYlyw+OdRGBfRB2md4J2L0SWhjYdLbF3AFiO3UyEblMg0WUKy5B7GTDPPgMpFwRs1iHFOP98JsSY0RjjCXZW0UT3MHIEHiQTLPpJwZM6FA5A+UdL94//epbDfLjYEk67LnO0qI/1VVRiZU9a3qwKDJgg3BoUDxqQ/fFtoHymgk2m04YAxZ3lJEcurZ03+6e8mvf+NYBPPrIZWPH9uFl1UGEY7y3KLmj/dCNyc5z29OMcZO4HQHLq8GRebZtR8iuXqz3arQvIZ/41147ZeIAt2K4q9iXLah8VtWEFt3kfh22CdM9j/cdSUiqT/+11/HMKdi5tHcNpdS5ak34O4GQgE5hY+VCsyByasK/rhUei61a+ggAEBSz4NwzY0hlw971ho5XCwV0EpAHnF3CeKbVHGUKqmBgWuxxjdJy1bC8tJqFtEPcwxMED0+AGaQXo65xdqXIyW75tsEQViQJYk0sL5GqeeR6cfnh7gvIiElepItSP7xo1Tl8WTR/6Dq79pXDY6mJQCUAOD2ZcV6gBRgyEbb0cuvbB8/vPnRz0b7R8aJVoxKUctEGQd7XYAdL16pDHwRIs65LmJwoxuREMQAWdjhzPUWhuFVSGq1xOHM1hACEQHrs2PWhL39nfzkAvH6+7fjpS0ZSN8T5FbLMsyFCVmkxtj6wPONPAXBnLIzcVPGhWyr4TwZxTeYKak0+/zu2LH5bNuNENgMk4p6R6angie07+47DVs4nZ8+9iOg5A6nTrkDmKGPQ3FWRZfelmVYWRTaoRa4lxkNtliGQDDAJsDJetIZltzosu3IQJKseQCVjBNHesjaA1GWmfSOKP3stcN9Qxm1H0LjZcTAGdi5Xa2WZiIcWnCYEN8vk9qcV6UywSJiQdxPBcQWtUUKK1aOUo1nILA2Vu0Bfq6ok90fGiIAqGEgSFZaQJCzVaPFvvrpbtji9Uu8JnRUp28dMngFEBFgREcxJMCIwiwstuD/jS6+fWkSLjIcuczzlGeP2J6K+k/6iBIc8mibZlaeRmFNTcjmx+cbNmpM8b11ubR7bSTjwS3ULskMvaz14y+y+0NZLLWEuGrHm74sQcE1+7fjOUPbC9bBzfxETVrxPl4gWl0F2qmcjMXfu5LXqY5JgNZ7mXtAB8iJsQmTidnnm6P/IfS4DgPNJ/e96X4j3HUmYRTeAPwHwX96VszPkgkx510sfd7owjTsUcBGOsYKjCMB6Sh8LgqlKt8DIupoRgYBjFhnUx7ydck0iCODAgneZvQmpcHUJY5k2c0w7Uz7j4NNcroo0mX/dIvE/0z0BHhVggJCDLvIqE5rHJNSPE2bwljnlOaEPBYrNsKsNbM5/nlGpOp53dcrLZgsvm2CBlUvaCAE58tR3+r7733+0EiCoaxnpB9aOAhm6kJuLJNS3Dp3ftq/LKcnGss8rHfesmk9njKwnfTCMZR0CSUjNOkKjww6MDpeDEDbmdmc6A0UJpbgkWlccTPXORD1NAKAb4sIoUsyrmCPAppGEbiwxUcLsw9fJQX/Uj7deipPDEZPFKtY2gM5HOAAAe/fdYopDkwBcgu3HwMEW9w0AmALYIUEy+1yBjN1kiTCZk8xFK2zGEKH64pQktbiMZQi7YAsEx0DQZxl8QzbmqeF4elKQ1EpqoTsbd83wpRnDCjsFWFx1LuaupiJ7e/HgmamVUcv0s4cAnIQtQHTBzsUzSJjJhQw58uLOVN2BrsOz55y2FPIWS3Im4cCpbmPAPyZSq8Yg0jlfllOFRqst0atlhTHtYzd8/LeanDSkutLnazi5OHEdHN3prp++Rji2kxBwjGHaykqd6rSviRm8vJTQZhLuvrGemvnKK8vkddgCv6VfhSY69GXfRVVltOXDH7py7uVX9xw3085zufHSDMexaVEyoo17eqoYwFIR7yjhGHG41cD2I9cOTA5WnJ4ZLvFiHZVnH61NtB4qS5+90lciqBlHnnt1mWhx6ftqy77Ond99Ze8lxgivGUI5bHO5mwB+BcAM7JRE45Ov/5+nYTsJb0/oDZ/xSf3hhN5AfFL/uyJ0JGw1seZ7HF7HM/8RwF8BKxui3A2IjLu4zyxZwUXwnQEHZv5wEZJL28SuH1S775HLr1Xd3+/17hxrFr25FasWmM5Nsp7gHYnZliLWX3Iq2l2Zt2YZACRn7qrDm02kI956yxCqAaDxl1/rN4b8o2bE5XYdGKmCrd6em5hmsMAXgYFFM6OBU/qNEsYcFr77Vx+WP/HNv5BdmilxKmkiICteD8vxZ7SLlaKRUnIMhHg/0rXiOAuFpfMDseHQGLtSxos8LajqIjrtP6VrEi2tmj6ysI5/DoYuxG6e3uVfpQwUlKLPMIQJXRetcMS3THS2BBeQp+Y8Dy4BtmiWMTaSM8lQcdMI6equakwkXHOf6+fbi7O7CMETBRyvEJwEVtbgUIaxnhz6mxU0cGRNfUIPlpjvAIAk6TP7Dt2Icxxrhi2uW6j1MCVX7qSnKBkgPG0ixBaFcpIx5K4LL5pcGEVfqre8ccnfesODZU0AILpyI9VHOggAMni6/UawNL6TEFTkUsrbqRn/vpI/eyXJuQ2vOekczJ2rGPPvGDFl3WrlDFIGQGNgN7VKmjaK6P2w3S6nYE9KOgCNpAnFC7W48W+HdvECi7Uc7JCkR4aGOFGzKOMcEAjPqcTQD6RVYRoiU0gpFUiddSXURZqTAU7RQC97LI9PbUt8c5sBkx8F0MYreqezOlJFCNyAXZ1BCITsmP9NM6McppQzJ/srzjIAM8OluxdUIIAXzDfLqqYOYraJ2dthueNvuzwzlV49/Uc/0P3Y7PGsxGTgjeHu2nJeNDNOT0adHg+5ZZEW8Mxl096iRIfsUJMOT658tLvGDQaOMSIyRtwAXIbJGQB4UWDupXuHk8rJ850Vy+4vRTIvPLxneMXfhKRo57cdvnHQsjjzj/7kE7dyOWkXbC+PcSzST7FoqHXmVO2xfvNjf/LvnwLwG7jd1O0XfVL/phneFYr3ayQBAJBUn/4Hr+OZ/4DZpkPvFIqosnmmRBtEne2NcGDtLdcCJ199fc+x8Rs1t9yeTGd5zXTCWROd8O0ZCTpro9sJdzvKwGLKqsY/G0FqrGjVFSbHU11y6scCjjCjFnchE/HwAAtkkq50+HpdorY8EdHTDtPdNvUIIRjE7XzpiwDuJyCVrvKElzCS+Yff/mGnGPf63GGUcQK3tsAtLVLJnz0g+We7InYFpuHXeohPI5Ct5vWIrObAS1Z9UcNUTeJ6caZAnykES+KrkpP4VKALIKv6ZHAcGmXZbJRlE9GYJ04p5wcAWdY7TYMvtig/F+pdT5+LeXJOCKnevru/Z9fBjoOPPHzNZVlkeCbsG/nOd/f+OEzn5liB21g1PcYRVLY6ChYvTiAPSTBN3g271wGwmCDAGUidc/qyFYRjbQDQcbnpjVTcwx385JsmlkRLCIdGwa1eMtOO+xb8rUnxZs9TSkqrDneK4BiJjoX6z7xysPYTT37bgsUjl3Zk+JLMOc5tHAIAoSxb6f7B3ioTEEwLt1ydQpQzyTABccpjXIhZ9IJZAgICBbZFugYAZEbgh8425wJlkT64TM1VFSnLZmQHeTDhgs6luJgoilcdaVBQsxgmTfApoV+5wl4vJ4o3OcxLnE/wq0cAgHMYUzTFUwDgJDOFWfdRwC7TBTDhqIgfABDOTPsuTg+XPZrvA7dMvkHXxH5JNpoBIKxy2QthpUJ2auMAYOr8cDrsG9SzUj21uEZqyUjkZMhioZUxpCQZ8ZcUV2unguXRfcFyu8CBUmISMM4wePV//eEPOQnBeKk/c7G2NFmcyYnpyqL0Do5jimlyeVfVXqcWgx2hWb4oI2ysdvtAAAB4ngoH9nenTp7aAdhpqiWEnATDXSU/cP/PnT8H+wHwe7NvdMAm5m8UeKGbhvc1SZjFp2A3NXnHTI1KqONdL31s38QuFpbFuycnig9gIoTS8kg401/ckukvBsASYiB7y9M2Sb07xpv4zS59pBg1MvKqn+VcIIwQEF6g+72lCaRPNfRExktck9fqrIq2QX3qrZZWR3XsouDWNcySBMbwMjN4pxVX+vTBoM+cce/50SdOTPke6amcW/2tOb64sjgHafIlCDtLWNgJgIE5zF7i18bh0Z0QaPtavQvmDzPtubWZ7bCjk0XrymtyHM1Qyvk5zoqVl0UbAAiWxV1IJFxWOqMwSvlCIhwXwFkVozHHRd3iAryg911/uzz5O6fLz/7Vz7/pK/Lk9peVxmuO7Bi9FZ8JmJmM43QmKxPDtjheU7C5Cjaz0Vve+5lS3jEyVDZWWz9Rt/Q9NeHaLYjmJdmttV3qLbn2hydr4sddovc+kLyzmFKctNLpxT9WVzAp+VomCfFrvapCG8/+yVG298Nvx7wN0z51PHBdcuaymZlFOp0xzKZ/pAgX4UxSxsB2M7BMrpFalgsHQfEaeDAAFDoUSFCsWoNVfOay/uZvfiIpKLSl6cFbGW5HRCMRicqXPLz6UCTDyjg4OjmBRV2mOeaJqtM+3vHIgEvblmOEQXffJCkABghjAKKcbPQppYn7CMHSKoAhQlAOIKTnJAUAbqbR2ZtF7AdKFvqUkDItJ3eLkjFGCEqdApv8QLnKP1aVjc8MlHSCkXqA1BAOaNrb9R1C4E7FPGRyoPIo1gFPMLGImM712BifDA4BZBtjqJ2MuWsnZ8WkNwZDTODZLUU0DeSJVpUFMgHY8+mbsJuwNWJWz1JePz7k8mXmu1uqWdkCWHTlZmzM3P0TF5eWnrcDy0XR7wTe9yQhqT6tex3PfAvvEEkgDKPvdumjTFjUuQ5vhELBcTSHRakb4jNirgeiZxsRPdsI2ZtJemvCJ1wlCT8nWtvzhb7XAy3pHFhrwjBUicXGgqcBsHjEnzV00QkAoqTH3aEYS0wEuboPXu8hhOqMooRpwkV9zJfVB4qepml50bE5yUoXTBAYJqDxdStvQQBVbGKq2IQJAISZzGVcJ/5cFG4jCI5ty/Mgta97MLieXhJrjRNq2rmu1tCKbEy4i5J9kmT4CbHFfYJA9xcVpWBR7kRmDTOh2TNnH/y/X0qQQNbxb4/9zECIM6yzpoZpVUj/zguH2B//4qtTTBNc2aSnTZIsTpLSNYGAHUk1DH4yk1X6sxnZ0nSxch1Nn/qxuVVNKx7L6cwtIV62LoTjrSgDgWVyb+9tnN73c4/czCrTZZq/NN4Ae3LxYMGziAh0m+TPnNPjrvlIj6M4lSUc20u8WtTl06s+8bv/OMPiShJZIaaUxevNjCwIRepbsMscm2DbaxsARCFBSgH0Wx6W0wIM3IjDi/asCX6+qiIlTHIZJca1GW5yM3ylwrf7Q9cTkd6yCbRGBTbkzpEol8sqFBTEb5XnsiZPOGfS4OFPU7dsyFZbihIeLgD70rtMJ7MwQb8hlwPgHWXxoRVacc+3d47N+DOplOPCP47pao9mVXwwiIRXmC3nJEx3e9MtcxUdx8pz7cfKcwBQBsbNRyldRcmTDm92PyEIOb3ZEU8wdWp6uNSdmPHvWS2tNvtdMZcvk/e77e0rz+czAYAQ0yLb09Z8R99h2BUzDwJAyKvWwE5pzpGBBOxSXF5y5Baluz/8yJVjXT0Vk8mka0++M5XtHj+n+LSjzCITbNh1k6vKVEJg1TDIlXc2cW7jfU8SZnH+nTqRm4n95M5WQneMbU7cWK83QiGoqZu4QGwXtrzQkq7mmRuu5hkAnGClXWWxW96qiCZ7sw2EW299OpAcLVqzsoJaAkctHAWAVNR7lVJ+NwBMzXhOXR/zpxp7S9VP//TrZS4+4kq8uK0WjF+WZ9wQcvz6Oj0yIiAt7WTp2V85R9PMq3cSn5ZlDqOccGiee7SZ065Nc6vUVHkUbH33Y3FxYkVXSFWVVm1FvQCHnKJ2lTsVjJYJlnDsV7+lhZ99VPzxL30L2oAz4T3cPRF+cUcWjBxeuqMoWmV+X6YMvNl7+VaFR+AQ9shmn1e2NIdISwnQvIIV9Dg2jySMwnakzAuHK7fo2ekri50kHCO6KhE9K7tlV66dEODBbVOHuL2jAxzHqnG7V0YvbKX/PkLgkkMpvx53AiAQPdkTclH6OATrAjy6bfvs0/cSnw5GkbMulX2Pk0yv9NCEBLuccAw5Mg6FETC0c1nSzBjcQopLMI30mZSkZ69jCkAWUSEjjwgB4rYESDTj+aVrAT4B4uSEqsnXqgeCNI3v/fnHrLKmcebsmY7UBcZ8vKrQaMytnj/ZIrbdN6wXPxrdyxmW09kj3LJcLKwXMRc1SSWRDJ2TTAH25Fk7e40mbHO722F4jrnCEd/+n5NZ6p9Z/Ho6qiRK9/ZO6mHfAZ6wGUKw1M9jYdkiAAaHN9syZ1xEOFbt8mWq63f2g1Iy2HOx1VJTrpUrFwjrW6lp2tBQyQoEg4EjSFI2r5+ogR3BGQSYW5Gspb/Z+d/JWHeN4AslNI5n88+zbFZeMcXmKslYzCRjVz/xGa+VkQ4ffPIlnZOtfpGwXjy54lXdNXy/kITNUk6viXuh9LFeuTthqXjME6iqmSpoW2ry7tRo6EBqNASAQXLnBr3V4WFXadzFy+Z2Qm7nLPOBMRjpiUAhFSLzkwUvWFGRN0443VkPlbLaj/3nl/ny5slyd1nifmPMd23TCAIAlpDvrJsi5dyIK/tYXIGeVoZHTrdFRH+2x9UQzhFVbBSkzXEfjE0UDWKVyW49oBQapVxBegSOp+P+a3KL4k34H/vFb5x8+av3q1V+PfDA3r4hsZ78HWHkVasq8718+2oGH73WH7o+nXC2AaTYpEBMFUMx1Y7+c4SlPLLV5ZPNjEOkxRxBy2zU6o57SCzAEFb53CTJWPQ7T0wGjwmS0e8rj2Y5ni0KFYtudQzAwmjO3AR1BXZkgYNgneB4xitl8e0I5E6Sssxhwi1+PhMOilicdQiK2Zp+rcKiY+4TZHtEd3YLIlNIhjmpAXemgY64z7H2pEsrsSTm0nOwy8EbmM7ljH9s1a2YVMO59J8m//Um+DT5TXmKD8a+tmPkzJeOO44/+Yr2wM+84RB1UPPoNEn2OeKiwRfx90X0w1KnV/SqKnfef9k6GvEzip38NV/GUZFscf7gjaTZG+ghOj+nNZiBTRKaAfRhgbZDU2UCAIQQz5NBh/vx3/q/1YSgCEBxLu68NXZumQP7EBaRBALL4AcEyVqmf+I4Vteyr9OYHCg/MzVUdgiMW/g77QKgOdzqfIvzpYhEPUscExkag7lzIs8CBGgYTUqXszrfQBkkBqIDGJFFy7GC0yIAwDTEPTfP7LraevBmkZGTBka6aise2TMsqrowcbGnrDSlinW39UeMhdqnudS1sm4rI/MGRekTn/uBwQ87XJP/Zfzn12Vgtln4fiEJve/IWRgyRWz9dp6biaDAeu7MG2FllJRFVwjFrQUCPe2oC3dU14U7qkE4qrlK4xe9VZGM7M9UcXmMdCxduMEsvpAUUQ1szUldScXMB+f+6HSpaHqwKzpX3aEPBTa3A1tC2bRmRMnR4BBAHjTirqL4pbm5h40IsjEou3Oi4sq1cALdUJVKfMa/aa2hVVXuyGfItBS8YPY//ORLg4pLqweAQFms5fGff2WANcX9fIw7itnSzqkLjYvILGNIEgLvdNzRN51wrVhhQRnxJHLCvsTsYp6AqT7FetklWbJLsq7xBG2rtXguECuWfQmCmTR0IRuL+E6UlEV2zt1jpi42pCPetz3FiQFCbpMCwZNb6TvYAyBn5YQ3jKzs777YVrf7P79y3V2REQDcohIrJgYChJHbIsCKlEUy0g2HyDLZ6+UuIUECfOMMYWMenYG5NcFxQesPmIKDT8lnJN7052TrkajFnBCJRAXux/sUFpUf8u+4PNT5nQ//cOXOfhU5ZHIGT7IpVzmvk4hDzpratM9Mf6uRFH28Sxw6uy0VSEUdzqMjMWlaVGhlhloiuGyzOexKqpTwZIgQNAt1MRlp+QRLyMXIiK0AmRPuLmoO5fJm5j/bQHk0Mutp0AoAkjuXT/i8rHQ4HfF6fWUxOhdRyqaUoVxOVoPFiTZCIJY3TBwprp6+3vVWe8DQ5AEAu2CXsxK3L30y35dBKTHua5yeHJ72qqbJ0Zmk4/6motwFiWfzqaAqr74LADUoudwbcfgAVGsGP2mYXFLgqSdfXwkAsExh960zu/pn28E7OQ5wKWbNsZ2joBTDr1yuC9aXJq62PDCYLqmJOOPfacx6/Qlfb1LsPpEjbBi5vnennv/7hyScA/Aq8tbfbh5k8Dc4rLOmf5Ox05lfkb0ZCBXHNkWQySgnpyeC96cn7HlPcGgT3qpIn7ssJghOfRsh8GamfIU2xwph5V4d8w8jM+JaOzVACqsHZhQ9MLlN+4wzU/48ExqpNjWp2tQkZCJegLBuUdYnFY/qlFxaG8ctL89aNk4GaFmlAEOmwlC9u39av1F/Jj4ZOAiQvM8OQdI7fuTz/1wjKYYLdg6+BEArXEaYl6y5KMQxxpBM9JXOk1nGoE1NB4YYAz8adayLjDIQRzwnNMdzQjMAEDDdKdHrXtmMuiXqFDjWstzCeE2sGEUwTcF7+e3th5vbBk4sLTE2cmJFasY3AYaopyTRTAjzcqK1mphSUad9Ldmke5TqQt/4izvam586aRCOVWcbzLNMRjlMXBUyJC6P8g0ccJT5tIx2pSoci7qmVV2YSMdd4qlv7lQa074bh45deyUV9XxAHi32EcZMpTai+3ckBK3Y6mQCAiKvMWvStw07MNT564/9e/Rw78f3/tzrIbE4a4QqIpOSLydYPG9pCkh2Z9R99f/sT+6oj0iBimmSerOYJQdDxPXfL9VLI+KUHKP1xG1kCSNTANIQ2UMkoHEkoIExpGFynUhKWTbt1EFvW7V7i+PzgtDaHYMOLOjcGx8oCWO5z4V/6YdmGXwAjPQwsIaut7ZdmJ70s0TK5Tn8yNtdRaWxVgAQRGtnsDxyemqwYlGK1BtK5H1eZBOu3pA394GQNwfT5CaHR4rHCVncdMzWETG+pnra6I3UtAOcByBFL1+qZU0V8TOtVbFVBJRkTpeyKBVMCKo+tHewj+fQhhGfOD2y00sI7S6rmiwzDelKhVNu/qvj0/++8nHvLr4vSEJSfTrhdTzzSdhdyu7KBAoAxVQx1t7q7oGAWSXisnzepkHTxGmHQ1+XCK4QmKpcHu2pKI/2VACEUUdR6oaRkXWAXQOwAyB3FEamqhCGxa3lIgjCscKqALLiphExRkFNVVo7hM9Ii5GTW4ycDMwwixB2S3ToM4pH9UkObRvhlq+c1bSjfx2ivzXR9uD1ih0furwjFfVM9LzV2jd0o45Phv3zynReMPse+8/fcMwSBBduu+adQCiz8DvktZiri1Fu/gFMCOSS4picVZWJxKhvvZqMGBZ8HwxEyuj8zsxs465BkrshiXSmSsJEhQTBzaOOs5X2KyGKZWZRS8FQUhpdFumiplChpYUKAPAgEWa89RqADzKLJAnPllmRU5OLMJNPuf3J4u1f/eeoEMx5tBnuqhTmJsUol9bLKSBgt+ljYLAuOQeFcQD7I0OhWzNTXtfZqy61NFauvdzrtn7Ax3PPPveJFz9838AnHM2TfE3ZFPoHS1F1QufZsE9jFon4a6ZYdqC04s1f/33e68s9rcWlJKeS4pA7HTzyyIXtRlnuJD4+yctJkVZnTH5qZq/4xq0qo96fMdyGwJsZWW372viI7NAcxuHIDb3c2ufqEDqJgcOZbeYVR78ww+eIQggUiLQWRbkyFlXOQr/99SsubT4yEiy3O2zOwcgoS1WxDHlSA4o7N0A4dgQA3IGU+b23mo1IRmws7q68PkcSACAyXuxccjjL5Us3AsCtjqpLZ8+3Zn/siZO7HIrhTUR88z0/ojFPFyFkUTSL561MXePoxdKySB2lpBRX6haInAnpHffvKg9mBrzOVZ+RBLZlNg+7nFgmBEW87dkxN9aTjHGHshnnpX2hzINf/9BkhhBcX+WYdxXfFyQBAJLq06rX8cyvw+7tcFdQQp0FtzO+G6iVcYmQgkxuNgAGWdbviiDT0IVhJZCK8AQBPe2oU8PeHQB2BGumpwhhaS3t6MilHIapC43rEgvOQh8OdKOAzqBCaWoSBQjfWFzeNN2JGnN3AmSdxI7wjJFtelaBnlUQTThPXR8o9ezeNZiorZkukySrFQAiY8Wj2CQhHy8aGV4y2wHAE0yV3/eRt8vv+8jbmOgtvzV8qy7csKevLFQ93bKCrvw4Bv2MivQ8KU874DTrop0V6tKNOA4tkqSXWHSl0rAV0QOs7AnCQLioSZqiJpqu2ZYWcHFswuFQrzWIvLOSFytEQhZGXPqQr959AQjHdNgVBSvCUKVbTOCtVG/ZIBiChGNXiWilOdE0HeXx46AkmR4oiYKRPbzAwBnkDCFIGyXUMEooBfABAKcwq6DnJ+Uw1dh2xJST3R01WTdnph+tyhgx9xRh/VKqtiHq9CQcfzww7SGOsJeTDzBLtHil/4V9WbEiSjyaKJz6+kFz357+djUn/pinZOpgafW0ltScWt+4R2oGxBf/7Ljx6U98SYLT4KbfrolNioQznTn6uZeqah4rpuO7XBwLnWni+UdvGayISiAoAcNgpt2MMgF+rczinYPCbtiT+3kAPaDIwl5BCwDM7vOtSQB7AGaEqqfnP3fGkM6GvUvJfC/yEHJqcfOkuKpl5NhHpNyNdMYx1tY6epxaZF4kaJm8f/H3Rvs4nrUAwOk321XGCMeoXZ6ajnrn715ZMqSFVTyCaMT3HbwxIQj0GACEY+5TWEZeiPfSmPf00cZwlUlJTrO4jGaR3PlJ95BqcjhQli4udxmczLO1TNeOAbhFLc40dLFfko06YIskvFN4FfaHvem6AY6RDhn8htsZbwa2OXFXhC2uYOzmmyP+yN6sg3e51XVXKayFRNQ3ULx9WM9O+W+vtAjrnVMgO3zZgw5fFowBpi705ZLOMT0ru6jFbQPIavV4BAD0UX9BaQSxIrmmWpAxGEjKmxatSY0WzdzpMXqHi/mu7qo9Xd02h3O51PC29pFexRSzboENcdydC3eL6qc6CMGyqofypolt5U0TBRyBEBj8QTbsA8C0yROteY2mZhLOvtm87Xqwot01AHgYH84ssSlIU5Se1NL36TotBgAXIfEdgtyzW1SypRyfDXC8RQhZUc/Rvr3vLCFLjXAWIzEZPBaonjkHZkdzGCV+pnGgmjBpppUr6qRPBiPzzwz6Yk0p97NdxYTMR2BOw665PwHgODfsdGBGroxnxO5vXanIHS7NOd3VvcbOR9/k/ukJIiRuVIZunG327G3s9VMKM+0ypqo+2F1UaliPXvrK0ZdyFqH79/YVO4JJRdeFXZouJYYv1tJXv7Hbmoz63I8d7jnLMu6TZlT5gBDICQrJ0fEYb33m2ZcDZ574xETNZH17kmT7J8JBWi5RRrpcEidr32RJsdT83R1ZWCQkHRrqRnkKsD0mDjGGMEx+EUHvebv1LQBvzwoI51PA2WnfRWrySz/TKeQhCbmUY59l8t8WRNMtOnR/TU24gRDmsEwucu3kXqGkdvKCtyhRzChZdO8rTm0as1bbjzx8NVBSnChzOnU/AOSyyrwegnCMh23xPQagaefu7jFBoPPahKHxYN7o5sW4ePD8pbKkwCFqUtTZvSzsqNRAUobI0Y5f2jNlCFxeD445B0YNniQMRAABAABJREFUwM5UwsunEp4s4djrH3zzv696j99NfF+RhKT6dHbWgfE6Nvna/UxaVzvjzYZEWMJ1F7wRAODDP/VKtK2/NHH1G0eOhUqi50tKo7rHm9lJyPJc4UYQKoscj/csblMgKvo4ljB1QgBRNhvF4mQjYE/Yela+kks6E3pOLAPjlkZyCGMATcoFpQZ4t+ZfcyOL3AJbvcvlepANe9c+5xroHSpZFC3IZByhC2+3zD6YGdyKMVxdnBoqD6YlRbLaCcGysPdaKG0Zzd7pOOfAGNGSE0V5xbUZTdjIw3DFHhcMzJomxjJyQ0A4EVxKh00SMoz5zxu5/ecN+/QKSGq3KHceccqZeokvM3WhZWHaizEC0+Bv8IIVytulcfbsvGDlu/eK1Ql/aJmuY9zVhKxwBS5zz+xf5vLbrwIwch+K7gTDlHLLrT35o2cF7lDE+pvfepD7g8dPs0hfWe6Jf22cqEv5rWMzvkjAmSMdfRVc8Yv79Z/7h786W/0bJ62LTz+iq9RtHPjg9YqSy23Rpr1DyuD1imydk8kBZnia2ofuc7tVM/xP22Nlv3LRIXpz5Kef+a5LSVre//cn35L+7xeqbv2Hhy8KxfuHZGtHWqAuKrmuKZxeZcZRkw6TtgQzmpMSH2WykOHs79fkBgCyiCQIomkCOKymnfi/v/lTmuTQrwdKo9PN9aPHAVtAyHFsbhJdgeCTC4YqP2aoMtTkvNh3FECZw6lemx4qPzo9tDzo6Akm549XXzc9T/Ytk8syejs1l83KKmWsdUBK3NwtSUOyou9ZeJzBsaJliyUGNmkSlAEEJs3fYdKgXPufXi29Vu4yYo/UJJr8slU5G30bhe3iOTeHnAQgAURmlIzn/wzeGXxfkQQASKpPd3odz/w8gL/ZzOOWUde74oY1h3Ynrq3mYXAneOmvPtZm6oJsmoJ3crzk4OR4CUrLZ94qr5gx3B7blnWzoXjUNYV5hECUXdoe2WV3kLVMLqylHd25tMIsXWgCoFhxpQ8gBYn32LJ+inmQluKFHKsQUJNTqcHfUVTCsshAOquskgMlSOekmo6Ropru0eDl1uKsy6HoN9xuNeJwaAGOY+2E5HcWXAhvaWzT7Lb1jNINRvJ6MTSUJe7rGQtMYpWeGUsQxiouixTopiQ/ea+3vGM3hGjedEwOzHPeyO2vbO679Js/falNz4mZsa7qrsHr9ampgTJfx43G/QBx7jt0/YKi6HnHKshGDyH5tCsrRyjYlWAKh6eNJd+JAKATBDthYJqVqMVV+wcntO5S/bDHUT7cXTb1jb85SrjxYjYERl5IwtrpVYTHD/TTuuaRSPdfHQ2cmnbmYr3lqC9LTLn7S1LNR6871ZjHCyrqOoPL61bVL52teeNwa7jJmzKumq+Eckkq8oH7k25ckMwb39qXCJVHyTfPtdIPxPyq8SroeMRt1v+3txw1LYPHubr+6/RUaZQFzQ+oQRBYVgefI2GpV47yVcnL0Pksm3GWg5GGTMId5HlrRpaN0WxW2aur8s6pwbJsU+24YFlc+tzpPTlCWNbh0Ea9vnS2qDh2w+tLN/E8m9MyDCJv4zrbD8TQxRWfxd5QIm8EKx339AJkviuoZfE8R4iv0QF3ff0oEwQ6/zxiDPFY0lm39Bgm2AgKsKXPWdyugaSMv7xRrIkc6360NhHdXpQzgEXP74Xixr9Y65h3E993JGEWfw/g57FKHnNdYJj2MvFdTTU0yGvn3DeKbNK17Ic1NVF8YGqiGKJoRAXByoRKYsO19eObRBiYJjtz6+6iyQs05PRnQk5/BowBL/36T7xsaCJXUz0ZraibbBclcw3iQdZMS7C4vO5+DCshM+27tYHQ+iJMRz0jWFyDvyIoQxAgvJqTd6g5uyKNEJp1OrVrHreakWWjjOPYMoGn6MhFeNFaU/hZKGJjoRV7m0zGXLcEnjlMq2CS0ItV9CYcUCwxMqoTtkxP42biYYXxYzlirZhC+4H7xlMAICmGq353/331u/sBANQi1vRgWQeyQkaLes6bqtS4dMWcTTnHkje9SX9pLO32p4o5nrau5LI5B/ZK1YOkLXEORdrCXhsOzLZZ5l4ufYv2+6oC940pX/qXg8bOD10ae/OfD2HgVp1psqSrmXmNiJgkzlSg4oEv/ll29PVm3ZwMTJ17q55vclLuTG8J9+offMJ6eG9vKhMNYHCwbKfgSffUFGWMv+otE6Rwce+PPHHakekKkt/4fx+lH/pAZ7ypIYxXLtZYP7RjUswKmlb3a2ecgmC6dk7LfO6BWADAGSLRB/mHJwB7BSyCxwOWi4H5jUsE1l4AIMUqqElSpi5UlZdFLymKcSyXE08mU04XtTiZUTT1dNVdBMhxxgiyWUdNNuvA5ERI27ajryMYSrhhpx/SsO/5vBEx0xRWEL4y3enNzBNKxgDGiMlxTEiGffGFW1oW5wAAkvYe9/r7Fq0dsqrUCywnuSrYOqNtRDYoafnmgD/eFph08yvLs3vWd9zNxfcrSZCxRiOY9cABoZeAvGuRBB/PBkTu3Ul1GIYYNAwxODkectbWb05UjPD0JtmE1MlkT8VOxrjy8FApLp3eZZXVTL/0wIfeVgAU4XZntdtgWFWTwBiSyIib5kGRGgvecSOw7sHSgrt0SDwbxRJjMcY4ZybjuH9OpMVxNO5y5rrLKmbiPM/KNVVuL2ke7wYW+uvfGaa7K1ec1PvG/ZppceshTmtVFCk6WF53OwLC7zCLZt4Wp1ckCUeaI3n35XjGlzVOtGM2PMwYoKeVwcxkYDQz7SN6Wq7JJN2ubNp1f2yqaHYfK+sJJrv9xbGEy592ibLRnLc0MyMYKNIWDZWonCmeCr5s9XsDnEU0OuTD/Z6sUOmNUsc+y4oanPDq1SJ3EDQqK5zwq1/98yTGXen6w53+/q6Quj2UI15CzM4sR6oEroYwvv+rVnS3G94+X46zqkyB+432dOq+j9zkLveHuEFOc34vZ6jel/e7HeU5NZjzOC7cUPhDD19h2J6sNyW23aQZk9NwlvLwLbCxOgaAwjZwahNSi6N4nMA8TYe6vm1O+B8CAEUxjilKAgAQni56NTLjX3afcRwb9gVSrbDJ0twkPwWbIDZggYkWY1gxCkUI6xntqglLii6Ex0KKqYu1AGTJod20zPkpWgPwlsuVS6dSzm5J0t2ELE5njU/7M7PXaMB+hggAkCYWQ+EdUufhk6wunsNqpfPX1nO8zcb3K0lQAfwE7JTDHUcTQnRV88C7jp0uDKPA1eTdgmGIRbeuN15t39FXunKOtjDITu2O26Gm4u4hxrgFEyLh1bTiBjAX7UjCfpBpsO1y67CKgQ4AwOA6sIk+GLmYe92VGgvBGKyB0VDBpEWzSI1hkWmRZysSWko5fyrtPNAeil+RJHMnY8hJzJLTk74TjqJ0kBOs9jvpycEYMokV9AiUwspo4nq9OFb9DE2gG2RlwklWaSMv8tQMuIyCKpYIAWRPrk72TNQFm20h55m//fAiAy9q8c7ETGBPYmaOd7DvOTzZCn9JbKq0YRywuHqAVEEVFpLVywCqHF8vYYSRSjE029+iKnup7DfPJab+dnsiPVDOTU/5nS3MWR/iBOGxpmTCec1nya1jgqbKGUiU/OjDN7jRvgpnezUh933svJaZCAh1o6FbHYbpHoo7NIeYUDlNwL/+wUfot3SVxIlBDE71/wsL72tPBM9czzLzuJsaJ0+08w1vX55xHJ4AnyHdzn7hQQAnKM/ilpv16w4uavC8j/PqAGG9vb//IUkKZm446yLU0zJVBk+u/M2zjY4Kt369oii9K5UVRwSeSW6HUdPfV+UGyLLKIUq55vNndnUePnZl4X0Th+2nkINdjRIFQLJpRwZgy/UeABjjItGJ0DKxqa4qLtjWz2dgzwcPFgVTKAqmQClTLYu7yvN0Xoc0PBHIwK46aYF9/51mYDVJZu0AgQPL24evABb9j+2RaJFiLkhzkCTHMe+CKqHRh859Lpx393cI35ckIak+zQDc8Dqe+RCAswDWHdqeBwMroY53rfSRgLEycZn5yDwYW7vdyWYhGvHvfvvcjsmauokTJWWRHbNWq+uG4lFXFKIViuHeqmEsWzUv+iC8WEwQJ9InG/7/7P1nlCRpdh0I3s+0uZbhHlqLlBGpRaWormrd0IJNgsAQIHAoBgvODMGZAbi73OEOFsQu2TPDGWLIQxIEuFgSaEKLRndXV3VXpKrUOiMytI7wCNfStH37wyIiQ3jIzKwqgnnPyXMy3M3Nzc3N7bvvvfvuKwgt2Y+ExlwXI5mbj70g6Jse2ydMlUtR++UMmXSDHdQNbuO0uG1AGqdz4o22kFqz3TVBGFvneafdkRBIetF1fPGhU7onrKW4ooXH3rpMUQqWwwxn9eyFNOgV8Tm1q2cKGAZsPFgeW8h4dttqm7FBm0uwH8tgavkXDn8AgArs0QQxFuBM5hPhqOTXpahZkAgobGycBUGBd9pztwBsO2J7OwguvaDoW3KQDEAuKEU3Z5pcpPNzD0MAYBvMojkos1aoctX2mTIVaC9TYdOEromOvdojtrZUEJrsmo4j49p0QeDfaXIjqruG2+tna9y83ZoerB3y1yV16zNZu/y7vN3auBSqe/upRjxmka0pl9zu8fqxX/iprrqA8kiw+ANioDBXUaTsXYXScb7IaMRmJULsH5F8twomp/bIDFspi/TiO4Ns8f36iq878U22zPBwFkOGsUiAZEmw8IediczDllMAQGQ9CZuJ6ilvk57yIne3BZSi+MWzg775ZKD0cKT22VLG12VTuFmGDrUGNAjcWp5OEyxrZyPR7FIm7d9YWlgxW5Ow5v7t9ipweRSDUvLcNLhUNhU0TIOPwZnKWO06nYFjnFW1441hiFzMeVV/KE8JAaEU1tR8+DNwshoruABg3mRWy15RbOii4wktuBmkACBvoYUChbcbikO1bmM1c2IYbGJ2LuKOx7IDsqyvkIwH1Y7r48R/kSRhBQXll4o++df+r3Au9F/dzz5YkEEBLyc+exnUC3jIEGeqHKVAUWMflHS2yDGU0SxilTQ2Tgg0jqGVuEf3eER7D4vK3qFpYnxkqCU+PtZQiMXTH9XE0/bSQph1exStJp4+trOyni5yovHS437np+KbxHiUbkuXau2SVKs+rYX6tBZg7SEuUk4ITVkvHy8cJByVaE56ZT4RpYXQCHbh3bAd5pYCe7bJ1izmvGmTJM/SLbUVwWBhkBBU7eCgFiuXE8Hj5YQTDRPWqrhr8o88ddmSHCxFCWv3bDF8CQCQnY1umyVK5uVd20/nYQ7PEqOGLk+qJBRTEsiMBMZmQYgO26gQ+ytrDx9OW1sKzrlfoE6r3qbMymErdPWv1C/R4mh8knBWilqMWwyVCmLoxcjfneAKlHQlt6UMZgDABcJYRmPf6DMsi9YY3o4JFRKj70VoYTT+jIiWQmsqc4ZuDVHJYPi6osnUVI6zFXLA84x/qJfFeLAm31d/6rt3jhfcmu4z5jPDdYstX3rkRn2ZgLXV3r92g9JFVzNTV35KAcGSbDr22xdSHn+ppJqkmDE85V95LLX9o5CvwroUlegwwcDkKeMeLHJM0GOUacUtlACu7tAU29i1UE+meA9h6ZzN0pwesQviU89VuuS6iLIwDwCUYpFWhDlCMAC8aBUlBF6XbJzoaEqikg5eCTK8mwJjmskkSzqjBVjrEbN87cXi6aHOnqnLAA7YNllXVlLKUlkQ9STL2ZuuY0LAE0J7eMGgLGc9MQ3+YC7nvpbLu8M8b10LBkqGy6XVwgkiBgF8fuW1msHeJ4RylbKUs0wWskuVNMMnSO7KVUkyLpkWM2pabDcAaLAKFWLlglRoMhzR4kpwE2VBnzSIuOdmoM7rqH03AMouT8ekFBolNO1lOd2ymAzL2iFKgceTkSeczbrrGHttkPRwl5fba8N/0SQBAArKL/2JT/61b8GpufbCST3vOvYOUvGl+9z3Co6x1e6g+rQ3Uqn4CMtlUn5bNZnpiYwECrLJBY5SQLcIpvOiRYBRt2DN+UTLnVF4lVIwfslEQDZ7OGZ7A5m9wDI53/xs7Nz8rKMh8ocK1xva5u8aivjOdq9jBXN0TZ/4vkApUC64NgntqL2HnIrFdJuL3m5z0QuA6oxLv+ppSNtw0ps92MM1Ug3F+eBLe1oMT8S2nCS3NWjBoqhs185QE0/ndr03i3WttdhmOLPoqskPeeK5shQshxjO6iLkhf5nOz0CpaCmxTRv9fxGZIilU/LCLIoSNCugzYpjF3IXL1oIV0DgpIhXro0uBgQnzOgDhZjFUTZ/AIAdsqUJD+VPH2pOPQAl7dTg2gFAS/lga1y/XJvf1h9hBe5wgU1Pbll5O2aa5L1Yd8Lw1mXWWzsb7Pif/odLyc+ee94HlRPptO9ExYb96MPjScLQqL9zYaD357+TogWhDL9m+Y4MgI4HRW80r5BYmUaPT38eAIpxeh0WKZpdZcpLxgj1mWfotG/WGKpJixmmy3dieJCtSPaN7CG1j5LhpKAwFTUSrSdQZmkJRWJ9ZoDLDP98LU+XBEYPhfJ2+nGbFW3IJiULQcKitdJtjlEeXzEihRFuVv8OnaxRKejkwkxdKFafAMvSQ3BKe5uCg3JZDhECQoAOmbc7ZOeiXI2cczmvNDMVvxGOZhtdLq1x7WuVioxMMqTUNc/b25DSq4bOHwQAv798wTDZK6oqiMWSbLlcWg8cTcGqIyOlWPzgYVMvpYR18dZAc0BrWMx4H09kpdPkYTP9qR+4+ThbcOWwbBv9bX7xkZdy9mfMmqYK7HWtu14WwhkvTgDA4Q0FFEIgEpD2clluL5elqZaWBffwXOhWIut6G4B5VDDXZinub/HZPjb8F08SAKCg/JIG4Ad88q91AvhDALuOtuO266VT4zuDIu4yRg9HlPnugOp28/bhF8Y2BjIF6dvjS8FzANkhSicsBTpKOtdR0l989UtlAUtlnrp4+0pTQDvH7KIlbg/Hnj169tlQ+8GptwDAUPmR4pI/bZlc1TSu5FFeepJfLu2r2va4QyZhGxABJsMAZKVFKQ9Hz6ADqMceXQ0dkZv8Uk6IlKI0uxjcRwaL+CoGOyhx5paLsT9Q3Af5cGCbnLc0Hz5ZmneqNYSxJ+VwseCty2SlUMmbT4S21FDkSuIIQHbdRaHA3k4sXMYuiZwA9phAWRw3owkAYQISAyhq/JVNOh+jJDeLZiHHcDSw+iChOijZVFfwRPPejY+tgFIYs3M1b8/MxoTYgZlrlEJPL/nvf+PrF+WBh6299XUZk5x/vkqulLI8CZA2ahOYFVEmBG+ToAY2K15Xn8TGJY/uhjNRkgMAqrKT4tfjDUVBWMJfG+NNj5a33fSZ8ECO6Qv+Q6HG9BxlTa798Cz5waVQy1+8V+P+nqEu/a8/MaQwBOKSbnP/5oOD75mZ4KVyhgg+hjKC7hq5fr9NfDbQLB26MJLu+V++kaYcWmFhiNHIklQ0z4qtS0u2r5L4R39xpOgeDOd/7q3hgYhH31TOtW1iWSZbLWN4DMvzDTRVPDM1UY9c1nvlSN/IOpJgmSwBSJNpcrM8b26V4WvneHNJt9gIIUA0UrgEAJZF8svPr7vPLeVczyl1rJgrBntwOCVnKFaytIT8p2+daKwJFzMr23+fUfsWWRZNloi9zv45b2G32prY0csPE30MPbawELw18qRNJwRrA6mHu9zPa8MbkrAGBeWXRnzyr30RzhezYyqYUEx7KP9aZkG4OCtzJKI8PxKukJBkdRGCDmzRD95Sm/v8aML3UDe53UxN3AKEVAz20vOkPBd1G5MRl3l+q4lmu4Uoq/ff/v7r9S6PukoIeMnoDNSn47mF0HVL5ze0TFIqedWX7h6YGaufh1OD3LD3/c+A4L3K2nPhB9apkRfhiKfo8vtuK9w0StIUKNl1xFwNZUV4bttMVa+BnZAo8n1Fje1vDmibImKGsVSOs15Zpwy1mblK0v9WJemI+GsbEqptMw80RSxUSq6Ipoo9K74B8xlPAi+i/G1hgRbszYOA1oKB442/6ymYZFkV75H0/JdPjj9hmE2ZCICSltJ4bM7TkagwIXWOCJaGiNJFB8LRjeofT7hQJSNGVY4377m9ZbamPiGKwXJSZg3XzHDdt65+92jDwMO2XgA4cnhy3blgGUuJxJJXsulgh8tXnszeb5zzHVjwUptNGouBVikyR2EyDXTRXUMz0gJkY5STTHH898/6u//6WL0VpAUYANeRTfm6MxVlzp9fuN1RbOsZFo63fS/xv0WPafa8jxGKnqih85ZXrkjIhVoYwKTAJEAuhYIlNsLaCstQl3wsOWV56SU+yVwRZ9nThEE3pbBAURMJldt//R//waO/80s/0eWXDQ3YrE0qO3NFNt07WdYa8QaLxB/K35wabj4LULOze2rTNWFZrAgA2VRwoqY2uZEkUABXCMFll6cyyhD7karIq+UzlqVrO0p0LGc6FJ1b9/1ZlKzL8KiaEJyeD9fDCRL8zBpzrTLsdSSGOlqJHREI5++yLL0AAHV12QsCnboyO7R6a8jDGZP9ieINSdiAgvJLcz751/4+gN/EDjeYbiuYf8l1dA0omrz6YF+0stTm16KCY3Kzq/onISAXD881fvCwKYkN4q29g9Qny0J9usI/jXt01StaPSyD1cKqYZHkQlEYaPBr55gtx/FSre3A1K2jZ59dqha7Myz1BmozhzIz0SfUZlbFPYTQIYa1X5okJKZjVTMh+88kALxP2c6QKQasK5FMwBFESXBKE+syPIW50CZR5V4xvRB6GQdETubsqtd2MFwYJMsal62gGkxlNOUd9UmGuylY2dOUSUIgsax9zOVR4PIooBRl22aGNEUsutOSyqY82soCsB0KsIaw/ZySi3CyPYsAjsOJsrcBTcZ4PLsYU8yv/PC1PknWt5vmV29K9COpvnQOAKjGfoQqLdCiR4kTxnrmDxYqhFCGMJQXRa2FYekyOaaFUDQPhqFLTd3zP/ATXfPK5EhdspDzRFuak+uuYWozqijrl2L1iwYtcgOp73Wf5wvcoCtabPa0LWnErxsIKUVQskSfhw/DYIsAuo7/8PWC/Qc1I2adZqmqNK8USJdUl6vli6w3Hl8aKl1voUJT1n/mwkChMh3RcsPxykfvn7SVsuv0DwahAwAheJsQ+7ssa59hGFoPIB8MZpaYLHtNuO8hNC3naHMhhSlfI+DM3chNxa0vBal3fKh5tKN7upZl7XWtupl0YBFAZ1lnnieKQrrGY3i8onnkK3/9vWaWszsphdZ+cOq6pgqKxBl+reRaR7xtm3EBgKEJF3MZ/5VAKL9qPkQprgK4TAjg9lQus4zVrypVO4VNOCUpAcBMQ6TIcYzdv5R3eVMFuc3YMPdhGZ3YMMmRgqYM0I2BQVizkROZ7V1pm7tn1n3PgZpsYA1JePjOzV/elaX868QbklAFBeWXftsn/9o0gPewVYsURTZAhf13RQCQOSt7JKwMHg4riMhmN3Gc4fYVxUmCFTnesfTg/mhN5FX0M9iUHJ4vikCRWs0BbdAt2Ac0k8yOZSQA5HJBZe8GZGtTJMuy5vDFr3zEBpdTe1uBYWlA9lWeVtYIu3hZX0RVJ7Xdg1KgXHRVze4QQneczVAVhI4zvL2X8kArXrSk2nDEUUtwyMLB6qOh94ahidg+2yep2ujXRryiXXURrKnJFKo9vhYFlc9842l98XLnorYLktCy3ZOEwM2y9nGXR8FbJ8Zw/viYrhvck4WkLzM+E/VML4S6dIPbtMDniLVpQFQV9Cz/u4kqXQoENBHhMdwjIxjncYQQvA1Vnv3m73xWkN3qneMXHrtq6lNVf+PGTNCWOpd1o4Ry8Kt3UeEbYLAv/P8JUN86x1CL3ZLMEEKPGUW5bJTEsds3e+YKOc8lAAgESuuiY8tk1eV98oRAYEXjphwq+SjFAUIwAELzhEE7pRQWoR8wNnnXyLlumoqgWRpXdlcyZ3mgd+L9o6rorzxpODccoZ0Zzc7KYX0s2mBX+Elv+2K999zIUX/Xwtwf/dOvmoS8CA4oJRcVVbjhdmmXAfjDutLDfjPaBkAByBCmfJEVggAAwdpMhGXtTHIpfDm5FEo2NCXuNbUsnGOcmQjIZb0UAGbyImxK3tIpfd8bKN0EnDo+IRD9oeJbtkXGcsngRKngvun2ls+u3NnomiFg5YL7LQBXAqH8pXTONfln3+sNft/bj8cjwXIbAHC8uRXpHMWL+00jx9LGhmgJDcvaWptiTtG42WxJUtMF2Z0qSPWqztUuB2JTWCb6JjAFsjHzTNiHZfrojHf7+R6ErG+54njraF377NX5sYaL+BR0NgBvSMJ2uAonGqx6E5TBDRKQXSudHVDUuo2RY9HKfGdADYksPbzbbMFuUBsqH4sFK/2LWfeuhFW7A2ENm1QKKvtwtiA0rLjKZRRODcjrtXc1dckrZz939xzL2rvSNLiCpQuGxvcbinAJwLzsqwhwBpyMwqkXNsARku4a+bSvynhkSr2B0kcXvnhrX66BrKS/zDRFBnhB/ihFxVSElyJCNkUymfHuo8xFM2GX+cwr2lvad/sCxR3FqzVereF//NzAbjo9ZrDH748QCKJgHmmpz6ClPgNKYRsmO7CQ9Cev3293lSpSNwCfgs2q9i0wglWCQClP8KxWQLpdQizCoaeKp8c4QC4pZfnUjfdOJz7zg1cn/aFiy8adWnmpm1JQQkCIYJ9CfYkiLV+ji+51++NEI29UtkpIEh+1SY4QJqBRZm5wrDZ+7p1Htx7d7O5iWbruvAkutevZ3Z7+xYVIQJI180DfsF0ci18AqMUHyovGeFROT8We2AbrFzyq2HBmuKQmfQ3LVsUflacik562pT5OMh7XHJluoSzV9S+lPs/lyTWpNL/ELTFtpW/1TMt12Q88vkrMG8rPFjOBNSU1whcKrojbpYHlzQrDWR3LZSIeAAeQdeQyEMs1iW71nlJwtwAkOjtdG52biU22dcwsxutSZ8plOWpTGJRClnnramtdVnz7+2+cWnvvKOXcV0cfdJ0HZdoBoFTw3KqpWzpCCHXBaTNcOTa2XPBckmVl8FtXDxdOH50gkWB5NUPJclYbQG1sHjm/rf8AQ1Dvlsx6t1RCQ6Q0CSBMKXK6yWglRVh8OF4jqDpXq8Cu2rFj7yIH8OTmoWBD63yZF6xVeWNN8+JFf0321sxg8+2d9/D68YYkbIGC8ku2T/61XwTwB6hSdojZ8q7q2wJjlw+E1Ge90YoecxkdjOPl/lp0DABwvGPx4vsPmh8ZJvvKhhAtFIQgAHut7axqMl0rHgyE2IlTn3mQqG9JbJs92AhCAF80f5gCSYa16wlBGo5Jysp+9mzhODNeP4dNCzohfeefhiSXtq+WQ96rvDK3LDXrngbIvkkC71anlxaCs/spK7EEMzGPsSVBsCmUW49bMhdOjO338DZiCnskCRtBCBiBtw421WZov9mVh1M2GA6BSxaolddBOzZHcWtBSx4GN5slWG0SumVmR1FyYPWVlMSvffPMwy//xPtVcnMk8u3fOfWd717rEr/w/Q/d73zx2QFwto1I5QYoIUQydZqSWzmXrhmVrS8ftSgPugKVc+G2JekX/tF/6qIUpbam5BUAX167nWWz7NxM7CJAmFKRA7GYj/KJQH9e5cVYbdYliuZlarCPYDNNekEO5YZrJ1iWriyUDdRiG0uT0Zu1X3iS50FFvbeogUGHGaQRW7BVZsSjcB6NYUXrXSXhv/PuD14/Zujck1wqkJ2fjomJ6ViLUpZ6bBvj4YZkgRD0rTm8YwByWkW8J0jaiQ9vd9xrr8v6lYKrXRD1u4RQ2zJZ0TS53rGR5pbJ8foHtsUctilKzUFNc/H2xULGZ2iqkHW51Rrn3KMw9rDrNCizeu+1TO5MKhG56g0UAgBZJQG+YP6qx1c6YFtM5Uc/f8+WRGuja2PQ4yt+WCr4TmJ96W8vqfxZAC2EQBB5u0/kVbTUZr41OBW1i8Sq+gXzu6hFWxbbeeUb56+/+8NX1+mzRFk/03F8ZGAPx/fa8IYkbIPl9sivA/hxrFXCUlhRW95CWU4Rc5njfdHybFdA88mcfYiQVzQjYhdgCJhLh2frP3jYtFStTrof0E2ROQCQmrLBDMQjhfyl77txUJSMvv3sm+HstaKmoxuersPymNzd7i8xXVP1mp4ablyMxDP7WJypzb8CMeUKinPhfZdUCGNrjRcGo8Gce+5dwbp+7/qB9lzat2t3S4rtu1Z8/sLg7dHa08OTseHPvzVg1Nbke1iGssWymPC4tDiw50LWrkWDO8E0mSFVX7XE7opTvisOHhQUGqWTBVhzeWIRjdA2gbVyzQFlus5wN0R59DLb+DZUwRG88FGAYfBhy2JVjnuxEFAb9vWbPd/59avtnwmAe1z/dz/0EAIXAtrldafHr5X40cKAklrfdESIXZJ8yhPBpRrUZooARkyFrwi8BULg6eiYd5ul9TV0apPVSPixXervVNl2oSI1fPM7fWM//dPvawDgj2el3HwoB5CAVnQVXYEyAJri6vLjYnM2x9WUTqst5iOFZRTxShBmh1IAYNsyrWckY0g+NxWExvWbU6FWwlAS7FooiC79aE19KkAuPZwSY4WbuiqAYWg1O/FAKeMJ/Pmv/2SiYEL9NjXSP3Huqex26ycBwLaJeePKMQ0gomVxIgCeYxDkGDt49rN3+sOxbFAQjSKWPStMnRtZMd2aKgiDD5OupZBksmfj5VOGHpGwMj6bsbJubzlKCCIsZ0dYDpMbD4wQ8LJbbSkVvAxAbsJxWb2oqryp6fxNQijDMJQXBSPO81a1Mt4Cqghql2zKPmPUBCgOwBG7F+CsqTUAWos2cLeIK8c8OM9uYzhWyHqP3v7esf7Tn3mw9j5nwClTfuJ4QxJ2xn8F4NcB/B9wBFDgQJ5wYPpWNljJFhyNVvS4y2hjnN7tl2pxexlIghU52bX4+O5wLLzdxLmXAy2FazKjn/3h6z/wmh0dj8HpFa7FDha8lAKlorvqebfMfTY2EDpEWPrK1P6VpG/PY5pX4I7lnhKCE95g+dznf+gWPv9Dt2Do7PjMRHz23vUe+em9jh5VEbcU6NkUNds5cNbVJwv+hO9hviSf/YsrRwhAyy5Jf65oQr3Am48YhpqWRYT2pmT2SOd86/OJ2GRttOCfSQRzyYyHZPPuGCHUYhhqtDcmc2eOTtTw/P5kIBuxkPJXJVcEBBJIiwSmpYbyYImt/80fuu4lBD2WRUYGn7YP5LL+vRiIETiCxwsAQG1S983feXfonR+8FnD7KnUAkEmE73k1+cApib2t6Wxrc0u66ndKCDxyUzZaeNYAwthFyVt5IvsqPMPZvYSsn4WhJgKN6hJ9yopGxlL5TdbEsluLCKL+CBTcYoFkHsyEDWQCGVHWJEEwewCAl4xuEAyDIsAskxoiG+OeMzOXAUdgZwZoL4jFaG/lvgknqja5BDdPXMYpQhChgj7naU4HQYlOCN7ytC6OqEv+Uc6tlXmXfllw6QAwXe3z+nzl3Je++kG6UpYKtz440eJ266szMQihHBzyFQewxgSMIt64dIQQhOB0o9wCcEYpulZT+HG30ZiY4vNLCk/DsjlwMKQeB+gFwtAJfyg/zTB07eLaAqdUfBEAJufCj+4PNBZPdy7FCYgHy6UnSnF/IRG6AJA1xJnSUKh40+etnF3zGynB0RVtuvf0RpTojQVPt2kzt7E5kNGTBqlJgqYYguu1AsIRDi38GhH4CxDv3ETt8dLxoTmPv7Jyzp6FfvX3X5nD68vgDUnYAQXllywAN3zyr/1HLJOEsC3mYy7jE8sW7AaxQOVofaTUP5fyvkJ9ggOOtZ6+dXDe75GNH1icrL0eb114LeOil+GDc941ONHDOWwhJi3l3dOgpGp/sk33RxI4l76EfYpJq0BrujjAqXn3lVIiIFWW/B2Wzu/awMrXkN4UwfGC1dbWPdfW1j2HH/uZD2xVEZ5NDNWn7t/o8Q0+bj1oGtyqaIshSGKb1t5gqBj+0qVn4bvPmq6MTsUuA8RdUcUTAKDp/GrGYnCsDoNjdQDQ+Hio+r6ej8fHW8NKMBjJ3nN5lJeadAkAI1M1O44OBwDLZoRiWXrg86hnWJZ2Hjo6apdLrmuaKliTE3WtLGvrpaJ7J0fPNeljwpoGf/DJ7QP9Zz97rw4AQrXpU+G6NI5cftiQF+0PZK/6uZWtbYvQuaHasZqWVB0vGvKf/ZvPZL54fuAOw9q92+uPiASbHLYUEQDyhDMXeI82RSkINVnZNlj/2R++llhIRJQf4RQ78bDZKiiC/2d/5v3KejOh9QPPuUg5D+d3AzCgbJnELQ8t0qD5JQBAji0L/ZEjNqU2czhzR4/aRW4Ql8lyFogw6JTjeWe42QssAC98ACgF1EV/v1FwXZZcGkRZm/viV78rlzOeG0refQogPCFAc+v8+FIiPKEoIlk5xmA0N0zIapTOwmktvl0pucyV/Yss9fztI8l1wlPCWs9PfbU/PPHhYWs64xr93/u7rR84Mpu71JE8A8cUr2CYjPDdm92yabG9qpa9I4uruwSlRN5MlwnJZHxns1nPcwDl2tp0QRSsOlRptaUU5r96Eu02bUaGQxDGsT4wFAD0AASjKjCqAp22MC2CyfAsnZc5W/MIltsl2A08Q+OEEO93//hS4gf+xrdWXv+pEC0Cb0jCrhFgqeekH3c+F4La4VI6WKLs2UTn40Zva/JyOi/fVQ1uX/30m0Gt2lD5Wl/70sWVFG5iova4L5wfdfkqL22lvANEOD/Ga9jspAcAmJ2omwG2MDHZZyMR71P2HflvhFYRnk4/aynVd894oodmDpLDM7JtkXG9KM+VFwNsaTHQbFaE+q08gMRAedv6PiFgZJd+6OCxCRw8NgFKoVdK0qPRwcbcvesHQuXZaJlskUZgGEvhOKvHx1u8R9bHed58Zhjcvrt3Ah5tFiBt2VTohGXlb3h8pfMvkXEy2huX8tFg6aplEzI2Ha3P5D1bDjQbnqwxTx52gl1CwHi8lQsebwXhaA4AUC5JNx7cPXhum+JJL5zZAKtZmaW5aJ2ucQVBNFeH7zAMZYImDq5kZ77zb9++/p1/+3Y3paQNAGFYe47a5N0vX3yqkA3twpZFrJnZyJAkGVI8lltzH6HT3s4ESwjqsSF69SDVHF5Mvx970PrZ3sNzoIfmCoGORJ4Tzee2wWaoxZg1blUtLQYyhLE9ACDUFWqWPw+ITeAa46DWWTeNqJPh0f733jDjKz1kzycE45DRDiDMEdig60tFhMBHKahRlO9xspZnljNE1CKF0nRkgBrcSjAySYjT0eIJl867QyXFMti7Wkm2G5F4q7E5gVJR/s7URN3jXNZ36MjpwUUsp/IrBdcYy5tuUdZPx5oTlXLO+7SY8VXLAmnuYGlR8imHms89TyW/e1jTLfbQ04VA/4X2JGUIfKWK0P8f//x074o511JeVppriqs70A0uBaf8toDlrMPqN0CZLAAzkQj5auMZWRCqmqOOMQQ6XsxnKMDRLVQX9FIkRbBNAGBYpMmwGBS0leWXZliCKbesZ67f6Em2tizWRCKFT9xpcQVvSMIu8de0ZitulCsdcvocS+hLt7B9HCAEuHh4tuP9B83zFOSlnCEJodOnuhLFqF/ZkJkg8si9bu7wxUdllrM3pUlfA05ieQztxicWpmJbUoHG9rl9LPZU49zqK5vLMf2spXTjDy5dBgCGtYy6ztnHHSdGsrUd8+FQ1/yBcPc8S23MGRVpqrzot0uJQJ1elNsAAs6lzTEs3dOkT0IguL1qb+/pEfSeHkFhrGZSKbjuLc5FSzNj9dF0ItRNl4VhwVBhaEWMdvro5AWWta/fe7Z/K4e6cGk1Gi9k/ecrJdeNaG3yGMPQXY+2XoPBlvrsO0AWANDTlnj43rVDw4tpX9VulcGx2p4Th6bpVmZgbo96viaWubu0GN6KPPNwRv6uRv6WxXZ+83c/O37us3dn1rVFUlJfnAk+0wkJvP/vLrdQyqxmamyLbQCASkUcdru1Dd4ThM7MRLLHj42vy1IxgjlDCNZl5myLjNkmm2cF81huuK57hUQSAh+12ABhjCZ2OUr2unR467OwDeauUZCvspFS38YPJ0zwQZphrlGv7f+t375UOHxySHwnXHxbLHED2uWMV/mBpcfyt8JhonItes51S0t7eCmW50CJpSYCJxnBmHQ3p2xqsAulqagBStZG+bNY0/ZKCGROsM4x/srDSs5NAaJ6vMq7h46OMZRiMRjNHgacbMTwvR4JgBlvmb8eb0281Xpk1PX89sE5XZFccGi+H06Q0OaLZTkAcEWLhztak1d+/uLzgQ9H4zAtogscFV2S3o01ItSnk5GLTdHiEiGO5kFVBALHnC4Kpz15rX7LC8Bl22zb/EL4eXPTkkE2uNASguTP9y6e+rdPo7cLOncaQB+c0sT15eNcR24Yx69jC7ExCVkUoUJFxIdXjuDDK0cA4MnX/ln1rT9uvCEJu4Rpsb86m/Ip8xmPt6cho7fECmdfIjL62CDwduBUd2Lu9lC8BlXGp+4GHkm/cf7g3FGeo1WjdEqZlpF7PTd6zgy8VDtnpSiPp2ZrZpoOTG1XIpHgOLiZ2HD9FnPeLdryqBpvXNrzYk9Ye5Aw61TcL4XRe12rhku2xfKzz5uPzj53FmJe1EvNhycH246PKNHGZDzYvtgVbF8EpciaKj9CbcaGYwG9IyiFTSmyDLPe6Y5hKCu7tRMtXbNo6ZoFpShrivh8aT5SsjV2Xf3zxKHpt8ZmIjcoJSRflE+ur93ufAS1wfK6FK1p8OcXpmvVWP3iArdGHEYpEvmcd1jTeBqLZ7b63rNr/5BFs+/SqeGPfu9b1dd4RRPCusE+FQVrSy1Ca8eMUMi751RVqnpO3Z5yoVxaz3lti227/u3TxZOXH95rbJ8/AQCmyej/8mf/VqVQcG+ZdZmZjRR6uufWPcayNnfxwuCmMh3nUVeJLqUwchOxW5nhugusYGQ4WR81ytJqZxRh7DGWN6uWqxjePilGShOMYG/WqEz6S6LJXgCAkr9w/Z//yam33v373xvkVDbNPhNnGFjdaMs1lL7b8aFVkN8GAGU+OI/lQMPWuRazLHyozId6ALLx/B2mFJZpsOratj6Gtfv88Wx/PhEKYlmgTAilsBkbjA1CAG+wMFrKeRtiLYnzzmtom22xN+CUIWrhTFa8DACh5qXcyr7jx8dPdudcN7pjJT+csqQIslE0S8h4wj/UXpuvAYCKIq4s2H44k4DXkoQg4GgHKGV6FhKhq7XxzMUN93ubZyD+7SPJk/+/wfC1hYpwYfk1K9/pquESQ1GpodzM8nv5UWWORRU82sU2HwvekIRd4mv2V20AX/tF5utHBqYjf2N4Lvj0RMciE/G/ukjzdSHqVw41RotXZpK+PbUoArTQ05B52lab3zFVrJbl83Oj9VfrO+a2bLHb8l0oMD9WfzU5HTsFkDa3v3QnXJfezk0vCsdLYbXEoVbElG0zLdU2Zll7huXsPbedsi49v/NWuwOlyCxNxbbsajA0wTN6r+vU6D0nOJa95VT78dHhtr5R6o/lOhiG7nro1f2Peq7+4b9/59yv/Kv/M08I/Mvvn7UNdt0NnRC4JZd2oqljDvlE4KG+oVXvx79w/zyloJm8e+Lb1w5lyoq4q7IVy9Ahga/mnEmkxbmY2+0tX/EFCn3pVODB0GDrZYDEew6N9W+zy02zJCTBlBnGNmybqUpeJuci6e7WxS13yPPW0RNnnlmJhciVseGmS2tLPPHa5O3W9pnLH107VsCmeSjEe+9qb7SmYVH5zvvHbj8daGqytjFLAoDnQ/WejSRhLUyTGVmYis9PPG+KNRyeNPo+55SjK0nfjcxw3WUAsHQ+9EK/Qgux3slRT23u+HbvC8GaxwtTrxefoLHoo5P+cVDS9ld+8J4tedR7xCIq8emXWQoABBSAXpJYhqLgTG5dm4kkUObDl0yDfczx1sbumoCh8w/+4j9+NhqOZ+6d+9zd0yudIbysN7OCPmjpwnIXE4mnp2tAiD3oi+Wstt7RS5bJFtZmgGyLCcCZ0guslhIp9cUzq/412bH4PdjsZ5f/VAFcBQWVRGNe1YTVVvDpJV971K9cd0t6XNP4tf43B7FCLhyRYh3WXBByoNjYcfHJd6fvdfYairRCvEOA0032UwfSF/5i0v/tp2k5DhADTtZj9bvxgBkOg3tnef934GRDt7ujTnzN/mpum+c/VrwhCXvHHwH4G6bFHr41VAe3pH90qivR5JbMXUV5nxSOtKQuJfOuO6rObXtDWwHPWo/fOjQXcUvmrrMDyenYBcml7rTAr0Mx6x2YeNwO22JXycXM8+Z2b6iwKEjGdgvjutTdwkzNOLYQ5flChQT24U0h+CqbPOf3i0LSPwRKNvZvbwml6I487e+NPO3vBUARqkuPd5wcnm06NOly+SoHCEHV0o6hs6N/8FvvnLdMjlfK0nOXRz0KALbBPgeqvz+lMHVF2CTOcjwwQCLBctuxg9OpG/fbZ2zK7Oh7EPSoS9iyzZP4ykX3MX8o74nUZI8NDTpr2PBgy+lQ+NEUw9B1NQ5KUSYEm4i4LBl975x93v/+jYNVsw9PhuuatiMJy5+Pra1LXbIt5urEWONFABBFLdHeNd1ICGS3W7lfLrs2RfumyYT/+f/xAxOGyW2X8VrF2Fht54rx0pp9jCxMxedHnrQ15jP+Ve8U3qPZ848z3xR4M1CYDVcVQ0cPTw95anPrfmOUgsJZIHU4kbc6f7+thGnto5oT452CT4msEn2Wiiuag572pYs97UvAhF+nB9JlQuCmFKBDofvDz1ppJekbPnHpcTVyyHznDy5rDENvyR5FAwVqm5b4hrb5jkLWm6OUOZZaiDRc/9bpq6c/c/+A7NYihKAFlFnYuCMKsLyktxMCwvGWn9pQQcDqirhkW+ym755h7XFOsFYXeW99uiU3sXqrkABcZBjgr3/fbfO96wf7ZxIhZ2iTxtddfdpQxxB7sCeqrl37/HBGdysAIEia0Xxkgu373H2G5SxekLU2QtASqEvfePLnZ7v0ihTGGvJFCPCV1vwXXJx99fai+8JGrYuPsvnlfT8EwAM0CWDTrI81+NRkEYA3JGE/0Nb+UVaFcx8+btRq/JX+vvbkcZ6rkt77FIAQ4OKh2a73HzTP0c0pwjWgZkOkdP1oa/LS3gc8ETLzvPmQIOkD3lBx2wwLpaCj97uvlfOeKpkHEhr86PD0wbeeZHihajr1KTbU/JLzkS1tek+9/WA/7oQl1qW/stbH8UftL9ELSJCZj7Td/tNI2+0/PQ/C2FZd5+yTjpPDmaZDUwcYhq6mSjneirMsNQkxqexWVxd+sySZ1fcNUEqGQZltv6+D7YnTQV/l+XeuH3zIspZRVsTWteZaa1EfKW2rTWFYewhONGU1tSxcc3sqlsutigDt0lThuSDq3bl04IqqSG6Wtco1dcmqi3Eq492yBJLJu1sti0yx7HrSUQ11DUudiiLeKhVdgSN9I2RFbV/XsCSMDLWs2zZZ5q4ny/xJgOw6g6hqgs802VFCYM9PxheGH7e3FHPeqqZqibG6g0G5kgxH8ucoRcHQuaccZ1UYlnrhLDBibjIayY7H50Dx3DbYU7bNyLAJDxAZwIrmw6MqopS+Gjk39kenQBh71te6NN739759EZwdxIYSDigRkJJv04hyBjbRtIqgtR4dDzz69gnTtsg0w64vNd6/euR9tSJdBghfKTkDENOLYTy9cwCE0AMANQHCZZZCF7/19c8ChC663MrksRODDZuWRsp0paZio5JHmQNAdEXQXIGyr5D2p1GlxCb7ywmsccItLwW2cPYkpdM9C/Dw9rXns+FGSlEHEF7kaDWnxdXv0x0oT57/0WstGzdw1+Zox4/cGhz+g7OsqYibgqfPNBYvBiXz9rU5L1cxmSa6/PuYI8a5AjGvHJGIxHNW+VR3omtoJnRtPBHYKuv6cIvHPxG8IQl7R5UUNBGX8u7L7913pdri+YfdjZm39mjg8rFA4G3/mZ6F+ZvPa2uq1ZgZYo+d6VkwQ97N0wF3D+Iae9hZ2316YEr2qFveoEfudV+rFKoRBAeUEpnlrK1qd8WND2STgaquZyxnDrm9ynbTAquCcPYg2X6A0J4wdrfrlXV/UJth54aajswNNeEnf+XfTYJ5odckBJ6/9//4nZvFvEsn5MUQGqPg2vK7MFV+W3vaFdRGCz3/1Q/dBAAUy2Lio4dt/bOJYK1psWsEhNSKB8vbmkVJslrRVPGZJGuHmloWVjtVVEV8ml6MHAZoCXBG9kqyumUZ4kj3rOvhc2dt6KzPXHOLpvVwPHrZyeQSzC8FJhtrszuSBEIQ7+iaicOpea8uPpGa7JGRoWZlefEFpUBB5QhAdhxAtQKG0Kc+0Ur/xe+802KbQjN2MeVyaKDtbN/JgY8Sc1EUi+7wkd7hMFY7qQiM0upU4llsU98WJa3VyXwTUJtpyI/FG+79s69cO/6D10WAnAIA2yIqw1IJAOiS6wJNySnNRR9kS3732Pf6BFMVFrOpQDkcy64jCR5/2QUnYBqB43vAwNENtFFKNht8URLTNV4kZIuBR5R0qEVXB5wDnipnvDmG0s95A4VrAKApYo2uiV0AEKhPrWs38MSzLeWE/5pREV22yQYB1BLGfpaej1QMXbzcVltAfbicnkl57wzNBo83hMpF0M1LX31j4oZSkdwth8YT2DhzxKP3k6bC5ZqOHLSydHPsj85sej0A9EWV031RJ14p6kxyuihMEwDtAe2syNKVNt7romAtHwClLbHCVZ9Ll59OhkO2Y0H9sOrOPyG8IQl7x6Z02QuQyHgicHFy0Td+pDWVrQ+XTnzaxI1hn3qgJVa4MrnoX6dPCHuVK6e6E2dZ5lV0bpDg8N0D6SMXH6maIiYqBVcyVJs+tXIuCmnfk+0IAgB4Q4UBhqGXbZuYWkWcVUquNMtanDdcOMgw633tKQWUslR1MYg3LCUsi2lb8YS3TEbPpX2T4Vhu25s151F3M0BoVzANdqyc9+xpWuJu4PKVkyxnt2x8PBrPnY3Gc6A2lso577Ag6Iatc5/Zaj8sb8qiR7mnl8UeSplddah43Vo8Fi6O54pyKldwr55LjrUHOZZua15UKbkvVUpu8IJ+zeWp8LJLaVcVaUIpy8vnnKx6IrjclS1dQyXB7An6SlOGLpCO2lwDw6ClLlwqWDZZYAiVGYurakZj28QafNp2TVVF/tjJwTMrQ4fg+GE8w3LLIMvakijptzRVPAPQvMmYtzTL9fntzww1OIY+9kuWEpLNbn75XNjm7n9Wts2I928fPhcM5R73Hh/a7jrd1i6cEDTxgjFi6MJqxqI0E7kw+K2T95p6x+4uPW7mqU341ncfd6s5z3BhOrJUSgQOAuRzdT9+98qJf/DnruwH3bbHrWz6rXYeGT/P8eb1Z3d7KqbBf27NUwU4mT4KwE0pGglxBLSBYHEca2r1W2AcQDu1nRjL6y+eB1Dx+ousbTMPTIMrQmPL1IZNGCcQ4116Xd3pUaEyF3pua/xxANBVvml2vGm1JCkKVri9NncuFihPS7x1Ol/wXDFNhiqK5HQbMbbW0jZ3hhCwvKi9KDMSOoWQOs3Ey6uBU2GiZlcTWL2CHT0UVqNTBWFAt4gqOmTsDoCe5pqCh4BeL6mCdag5fSmZl5+xDK3YDv15uJv9f1x4QxL2jh3b0GzKtD0ar8HzmdC9k50Jf8Cjv24PgT3hYFP60lLOdbOi8WcBunSsfWm2Llzeo6hxe1Cb6Ri+133d5S1bmYXopZnnzQvh+uRIuDZdP/6oY8e5A+W8p+bR945NU0oaANKCVWZPK7xoLLQeHZ5OL4XzDGvXSrLqopSpejOdm6y7PDdVuxSrXxosF91CKe8+3NI9k9iJJAg+5aVaRtdiaTI2hy0Ghb0Mmo9MjGHLtipALckTf/7Pf9gOhwp8IFwYjzYtzoSqdBCwvH3KV5MHpbAoJc9Kae9cKeONc5zVs7G/fy3aGpNtt5+0rGs7DXvV9Fbbb4ShCxfyGQH5TACoqiehBV7Ut8wCEQLpQt/kkK3Kn1/bFsix1ImuTb6BUuSqRK+2KBmEZW2jWHAP+wOllbIShw3fU0fXlNs0uPvhaO6oZZNLI394fjWzsAZFOAsjANR1htXjey/VbUY+5+umFKtte1Ww4/3b6y/OZ5LhdWWN5Gj9ieJC4GEokjtKCKXj7/XpAFk3gVaZDHORS6NNdkN6ysrKqz6d5aKcGHrUMTw7Xhe3TG5Fr3EVwGk4wjwflltHKcXcWEZUATLjFa1RKUgUTeeeCLzZSciWBGcWa76DZaMoDwCwrH2MZXVA57Bwr/1K3amxSwBgKvxQZTYsWAZ7imFsOvig67qhc+VM1uP2epT2FatlQkC8LqMZACLhwiXbhqYo4sNSWc5zgu43beLPVoTFibRcmvyX7yaPHpyf+/LP3LwAk+khFBYhYCmFUh9bcoVOK9eGbh+wAOKFU+rY8nf44aw389WuTCOASThmcGAZitZ44czy50XUrxwK+5T+fEXsVzS+qqPlJ4U3JGHv2LW7oGZwJ64P1Nt+t3btZOdijyRY+xow9KpBCHDh0NzBW0Px753qSvSJvL0Tu98X1JLrLbUsLw9pIrXpuSifnosGdtOKaVvsFnoA4jI04ZRWcT1oaJvvvv2949Pzk/EGOAxdhTO+dX2rJiU1i7Ox5Rst1Q6eeL5xRsQG0DQjGq+M2I3e7Xot/hHNhye2tW0dHahP/n9yWfu/C9AOseRqMzR+a4k9HCEfIfRQvuBJ3b3ed5RhLDUUzt+P1aaLPn8pxjB299rFz+vW4s116f7JuchqS2NDpOivvve9wzTZ4ZvX+nynzz+u4zi7muPitXgs21wqGFcLWX81bQtjGtwAL6wX3zIM5Tu6prcixffxYsAYgqHialaEYSjndWu3imXpDJze+iEALjgtfcuiUIKSzjz2ivYO19jOkGV1Hqhm47uKnbpvioKoV82IqWV3nx0s3GU5+ySqZCQKA3VdgePT97xvj11M3mrpf/b7ZzEzVh81Df4gsGly5kU4GodhvDAX+sim8OgWUwuQKYvRi/eH6n7g/lA9GGJb0VBpuKk2s3SwY94SBasWDjFgsb3qfxVK2ntSy8tjgletrcyE2wDC3+k/1p+YjnVTirMAqQDw5vPufCSSv+v1qJvElwwD0e3W+njenM4rwugv/tGJPqwJAr9UW6Bjv/72HMNaM/5js9zYt4/yNfXJY6G6TJfkVtYOvKNw7j9BrOm4+r1FXP9CGIfyOudVLWZU4qwNXhng4FhctwDAic6lywD637n5y/u0fns9eEMS9o5N7VjbgzD5snThg4dNpbpQuf9Ia/I0x+7LUOaVguds34VD83GscZV7LaCkDqAZAGQrodt+YOq8RgjCp9++b73/h5czpYJnrX5gGs4EQgnAITg3cgCAL1i8K0rGtkSP4a3hjd76+wWl0KcHmvftXLgdwvXpqsOdLJMZXRxqnCk8azVqvAb35yM101/oTs32Ni1+ttr2GzE10hABANtmpVQydDyVdLSjHGcWwtHcULQmo3h95TjL2l0ca4PnzEHDZFsAMNGA8srEnooqlG2bPXnreu94c+v840g02ybJehwAKKUlgHQQgrjHV6phOas/mwxtypKUi24mEN5VJ+tVOIvUtvbqhzrmtJuP2leEs1Uj/HSFz3lFrdpTe8Kh3pEcIdtmLteKYafhGPacWv7/BIAYw9pdHG9Mmwa/yeNEqcgVj6/arCagkvbY1/7Rj2ZMhv2LmaG6LwAMW3XDFwjC0XSsgLAMjtT7lT+viRS9Ia9a98FDpyJoU4ZdTPu6FtO+LgpcP35wpguAppSl90yTlSRZvcbxVgshW7gXOrt3zd7sFts+/9AFQgcXZ6NWYrqmfY0egjr/SCCV8jeoqtDvkjW/2631bdyTIFhNlmFsit5Vg9XSczE1FM3yQc4Sy2UpSQ02RQja1LJ0Ay+yXwTOec/BsVM+BgD/aRF117PM/VqTnBNZ+6Mdzt8KHu5yu48Nb0jC3rGlSnx7EM98xnN5PuNOdNZn73fW5c6/ipTkS+IA9jhlcT/wh3Ij/2pcMv6qX+4jZNvIaNfQFEEHAMKg5vL3X3+aSoTHZkbr/fNTtX1wMgkrN0UDzg+3AKC+/eDEjqZAnFep6sO6r+MsSwOWwfe9qv2tQJC0Asub1bs2KCaH77cpmZQv+pMSXyjEKxVdY3myCzEtpaikFsJVhYemyfkWFyKnFheceyPHG7mmSE4+0fMk+UffPVoCwLEM3Rgt7RvlshxyjolpmxxvaJscb8BbX7j5XjiWDak25A8fNKROR/U4IQgKou4CqLFRkFspuQ77Q/lNjnlVEMcu2mS7WpZ6bz5q82wX8FYMpn27QVpVMAtgDC9Mg1QAOsdZgR1e54fz+62HE8E2wiE7vXB+008JAefxlyZyqeAmklDI+fpMk/uI40xDEHUGlHpKRU92fKxeVlXpLEAPtrYs1jc3LY0lFkOmpm1uk92AIJzfGofl7qOmaCkUCpXPAwDL2B9aNsPC6SQIE9Dk0e7ZFXIvKhW5Rim7ThSW+y4YxpoQZW1OklVLkHQXy1qtZO1YcEoaKkv+R5CM7M3vnGqhlFlLKsrL5wcAiZdKrni5LA263UuwLJJjGOoHsOpSXhsqlX/5c4s3/vRJg+dZInDUOV7K0uiS/kNXoqGfHL+0WJ8LCI0ds8MA2kyDrXaPCAA48qCIb/y7OQQNir6cIkzXgsglnW2TuapLRxccsrfy2/xUtT8Cb0jCnvCLzNcJgB96ub2Q+MhcKD6+EBg+2pos1YXLryXVvwdcgsNe+17XG+QygYYf9pLSqyIIAKCr4mpKThDNw3XNi6hrXsT8VKz/1gcn15IeHsvMHgAGH3QmlIp8raF1nvX4yweqqa0Fv/LKSg2zQ42vzJBpLRoOTg8TgqrmRqzLqL/wc+8PzS9Fyrlr7eFi1qt3HB7flXJOU8TnlDK7uiZNgw8kFqKnEwtRdIQ0sKz5Uaks3fe41Ze+pimFoaqbF6VIPNPDsLTpymDkm//6dqN0/EtjcxyD+uUpg5uIAKWMx7aYeyxnbzdkKocdRIArkETTL4nGA1UTtiFDpL5iMM/dQjVDqU0YhFPP3kTU52Zi11jWnonG0gGet46CUM0VKN1hecuydI5TCq4KtZm1osEFOFMOV87DYQCQXYqZq5IApTbjKxc8NXBIShxAB6XIapogrXwOTecmRMFsr41nEtMz0axts1tlUnUAmeXP8SGWyw6qxocIsdIsZxd8HoXNFtwXATwEaKilPj3AsS8mOOqasK7d0bbZVqXs8ihl13K9n4LlrIQoadOSrJZY3gwkHrZwE5N1OdtmAssv6zco1YsWFQIsQxmy2kKZo5QUpmci1y2LPQNnYbYIoaM8b2YOHhmt93jVw3/rrVH7/aH4tSN1OSYg61wAcEUl23tjNFpMEZWK0x7t+2OFb4ndSbHBZ13JPK/jKg6psEBQAoUZ5eEpK7LcQoW7p4JGVuZyVwRficKUWqqctwTWl2/ekIT/nPE1+6v0F5mv/20AvwWn9r1vWDbT9WAshoFp85MWNxI4F2kWey6l7PYNSL30igdWF9L+DtsiGsPSde1osfpkw/GLj/of3zx00jS4TVoAteKKP3/QFX/+oAsglMbqkk/aDk5mIvFMjOWsbsLQCYa3XtngrpE7L6yYXyVaj45VzROT5vwVuIxagcEPtXRkUWa07xoL/rfheN7viMW5yKb20t3CsrhzmYz3rkvWlH3OaFiFbZMhSsm6LolgJDfKsLQDAOo41viJGGjZxgMfoeH0Yti7VXRfKbsqXn+p6nN4EcG37PbY+npm8rcet1FKt84VpCv8olvQtiUJlMKiwBJDqk8ZnZqovwAAbk/lis9fQklWr0WD5XdXnjc0/oqhvLj8F3LiUm1A2yS4ZRjaw3HGrGnyK5H2MF5Mcmxf/gdKoSiK+JhSZnXhXkwEs42NyUZCEK+vS99ZXAoEdV2odq+6hRdEpwWOeZCpaWKPIGl/Ea7JXnrHXSJ/8N4JADB4zvrws+cHVztuKMW0ZbLVbN/J2v9aJhcvF7ng0PPmpGlyDQAFIZQHcG/5M/3ktGH0f7dAcwdYefLtqO5hWPuurglvAeSMZa1b8lhKyUFd523ZpTmZSQLmcz2JlbbcRQDev9ts3/2jHP+Wm/If9PltP/HqavT82OUoxtYd6NRvnXumpTxHP7jTmuo2g70tVLgWLVr+s0emKseOj3FDdw7OgpKNJZS10zVNOKZOnyq8IQl7x20Au2qB2Q1WxY0u/dqJrkS3LFg7Kv9fA+JwPtenatz1tqCkfmGs/kp91+w6ARrL2e3NnbPt8YalB49uHqILU7EjjnVvlfs5JWRxrubI4pxTWhZEPVfXMbt4gLfnwg2pxmrthXs6RIpsciq2Z4+G3aCmZbG6vkNnQTzGfwvg9ykFbyx5zsIZPLMr85+Z0YaXuv5sm7iLJfmR31c5u/PWW0PT+DQAMIxlHDkz+JEg6rSuJbFaDmj1q3V1p0a6Ii1LRjnnuW5MCe9uta9y0d25BUmYhJOq31UWAU7Uv3S0e77dMLnr9541V51GCgBlnWneqeSwWOL7BZZyIdf2FczHD7ouPrbUb8Ya0tG/W/si0GQYezWbZlPgw6H6+S90pPOhcH5TViIQyU1Qm4wVcj7D0IXPorpfQ34pGVinxbFs9vhCInSlrjZziePsU/V1GRSK8pV0erWF+gmcDMLaoKkFwBwcbcS5mZl4T7gm65IlY8XT4VRn89KVtedGKcsTwKYx71uVQtmaaG5yfiHSwDD2pD9QWnK5FcGymKZgOP/4N67HiiG47b76dPbMqXGaz3n5p4+6tvyOCUMnWdZeDQyWiuLck/nAyLvdiySV9EvWXB2CLJ3MWqTVJxne4vM4qy76Pqr/sXvtrGwESiM1T4rP6sp62n3askniRGOmJKjCe4VEzRcVlcX37nSUO4+O3zpy8WFg5nlzf24peGiD6HEFA+/c/GV1q+P8pPCGJOwRX7O/WvlF5utDQHX2vz8QJl8RL3z3YVOlNlTuP/rJiBvbAXwPwJb99J82JGdrDsbb5ktsFeW7KOvHTn/mAQyde8pyVnB6pGH0wfWj22ovdE0ITD5rOzf5zLlfuIPFRFvf6Fhr7zjx1+Ta9zI/AQCKad8QpcxLLZbVwAmGwotGVfJBU3Inguq/JgSyXRRvwGJ3batNKUqpROilSA3D0JLHo7y0UNMfyZVEf/lOx+Fxwx8sbepE8NXkT1IKgxD4BFnbZChkmkwqX3APcqzFsawt8JIyLsl6PcdZLp43AwxDeThRXMsOhzIAIAXn97HaJnj84HTo/kDTltkECtJCRPXupc/fUyRZEzjeErNJf/7elb5eXRMCAK7HPMZZ0yaDW74zobSnd+RGoHFR//5g8QDL0paSyhc8kuGjFFSviOsInaS67FJRKYeqCDVFSb8IALZdupdNVZ0JBUIQd7vUu6WyvK6MpWnCpYnpyIcNdZnjPGf7fF7lkm2T97JZ72GAHKm6MydD4wZws1xySbdvHB2qrV9MAvQiQEhtNO8BgLECN3VjSZrvyTYoIa86LEnGWvJSNf+oqsKNxGLoEgC4PYp+6Ojo6u9aszH0E8cNz7moXqnoogZQyR8o9tU1LH40PxurKkaW3cq86C+bpayr/htPG+9/dzh25K+emHIDODU3Ex+SwHZ/NoDMU8UeSCbC8/G69GUj445O/uuLBRDkQMlqKYtjacvBeL5OMvmP+lPhim3T3I//6Edpt1vtYznb03J44rJtTeqzQ039mUT4LYCchKOfcMPRc3zq8IYk7A//EC+tTagG4lrIeC4vZNyLnXW5e5312bc+BnHjKJy62Ck49cxJ7CH1+gnDS8g6Jr4JvGAeBoDmrplIQ9v80O3vHdMWZ2O7ak8rZ73xJ987Fn/yvWMAKIK1mYn24yOzTQenBE+o2L2le9wyJh+37VPkuj3qOudWxzpvgslEAaQphVW521i1+2ErqBXxOaXMroY4bQXLYk+lU75+EEpZUYv73XoTx1LXzq9cj67jI+e2sORexYoYkReNvlBt6k5mIbLa4ZJM+QdUVbz04u/AhlfTIiE0IIjG7VNnn67NoFE4ngdZOGLAqhmYUkUciARLwWTGu2WZMKNymXBNbtV8KdaQ1A2DA5zSz1uEgBzoHdVzaf+AUpL95aJ7pX7+BECOEGo3tM8LXn/5MwBgWsScWvLNHWpK+wDYlJIXUxYJUNuQII3NqW1NymS30ptN0SqDqxyEQkW2VJY25UAMgzt8ZTz0/rtdqSgAPuCvBCklw/m820cpU01rdGj5cwgA+io6p/+Dp65kjC1cOWL5L/u9SgAA/t+Pg1PjGfnA/+RzBalXHVm3B0INhtibdBCKInAAQAhVa6K5UdNg3XKokPTXZRegs6dP2w1jDT3TdirjFT28NVheDB5r65g919CUeHD7xtG+jZ+ttnPOajg33EUplPrLA4Efz7of+kyOLS35b1QU8bjzXggccZHh0eHmn+F4qz8cyR0jhPhANzteEgKhvTF1ua0hlfdECtMu//qsGsNSoeng1OVI49L4zGDLrFJyrZCXJxv39WnAG5KwPzwH8G8B/Nzr2T2JjcwHY2MJ/0hva7L4GsSNFMBdOAKnPqzp7YUjQNKBrU10PkXIzTxveu6vyfn94dzRFQe2aiAEIsdb3afefjjwzd95V7Usbrcp5pU9ILsQbr37jXDr3W+cBSE2jTQtDbUfG11sODDtcvkq3YSsbycdvde1o/HWftBydHxLMSSJlW8QgkvmkuubVl760l72uzQfrd4Pt0eUK1K3btPHoxORHkLo5KXDs6xHNnYcDLUCQuxJXjBbdr89+FA8rWSWuy4si8mqqrAD2SFeSolXU8WmYtE14vVWKnB8B3rwotd/S3jd2qkfevdhIV+UP/pP3zpZNUJN5dzrtC2EQHj7+65/eOM7pw5rikQAwOVRjCOnnx+kFJWp4cYr0bpk5M6Hx3PZZOAMtRnhwz97a/b4W4/761sTlzmWcoea0geW98V6a/Lp4lJgNc1/tj3VjB1amgkBx/HmU9Pgq2aYWNY+Fo3k+9Np33GbMl442oLbPItDR7xGGI4nAgAgGChDlvSrC4lwNWISBvA2gH5KKf7HwlJRoCT6juULAMB71w+mfuL77rT933oz7c/uHxpgCA1IorE6sZEQuxytTQocb5qVkqtf1wSXaXBeTRXaikXXEYCiJpq7ZVvclxfnYvapSwNEdKtWaTwaOvpzV2xz1sfzNvsFSvERF89cAQH0imDLLmVJqbjWZwQNtjT9oP1G/ZHxDq9bP+J1O/YjgZYk/vqpUTs/Gf2glAxwoEg+vdP9cPh5y1nbZvjGlrmrxy89DloGl80nAodtiwmSNfyDEPhti9kyUHB5lbaO40PKkyvHVoSmnzrRIvCGJOwLywLG176I2jbTuSxuvH+ic9Eb9Gj7GFS0DkU4Ka0WYMu5BF1YMwv90w0Syy6GY9nFMGrbZ2/Emhd3TK3zgnnw8z/+4b1v/u5nt1O77whKGZKcincnp+Ld+GOAMLYda10Y6DgxkqzrnPPyou4tpn0v+31VRbx9fut59KIFqrLXmaT8JdalPbEq0o4L3gpmxupekY8FiafLwjAAUEpacmXx7gpJKCn8rEc2tul/BySPMos9ZrPcgdLZhu6pK1MDLX2JxeAzgGypF9iImcna2YNHxnZdZtN1Ls3zZpgQ+Pxe5fTn33rW/971Q5tKWabJdiwlfZM10ULLymOBSOHtL/3VD4rXvnVmILUQOaiU5eWoGK6W7plLAPD2999AMeee6f/z8zlDF44U855JamORMFi3uPGSvrGGfxhOaSQCAJWKOCOJeoyC2Klk4Gkh5y3LLpW3TC7LbdMM6/Gol3WD7c/nvQyABizrAijdnNRkWZtjGLtg20y1a7IMoJcQgguC/OyLoqd3MuWUEGrCRQUA4iKRg8dGXfl0sHdlffUF81c9vlInIY5xndtbuez2OjIwSpFkBX1wbKT5kiRrjQAg+Ut3ZX/lNLVIVmzM9QvhyvcRg+3Xx6NjhKBecOkcgDpB1vGFr34vvTAV67/z4bHzts3yAKBnvWcmb/WESnOhD5t6x2oIa2typGgS1vaCgOEY2uoLlNoA4Pzn7x2jFEZyIXzNHyr4GZYeZlgD4ebk0sid7v5gZL0ehGHtbddYajPGmj/fkIS/ZPgncH48L9XlsBtoBnf8xkAd9br066c6Ex2yaO1VMT8Np4xwHLtb/C/BUQu/1EL6cSI1F+VizduPBl6BKGu9feefXHn00aELlDKvZBAXtRkmMVZ/MDG2kjGmBl6IujxwItSXdl5kWMuU3OqWqnla4jlk5JMAgRgqZyuV3SVMtvNH2A9KGrsqu1/IuMscaz+KByu9NwbqMvWR0vih5vSW16E/kt9TiY1SIJUIjT26eRDFvIffC0EAgGzG37lbXwPbIvrtG0d5Quh4Z89ktiaWPWFTAp9HmTvSOTdWF8txLENZ02Ks8ZkIXZwLldeSBAAgBN4LX7zVBWdB76v2Pt5AufHLf+392vvXjva7PAoIg3lgPUmwTTaHzXbW4wAilAIDjzvmdF1QbZvULde+l9+f6s1Ni8WNmS/AOZeZrPdqoeDyw+l+WC35mNZmQyWet841NS4VCgXXlUzWewEga39PBM71b/6I7Fv5vrMAMD4TOTxaH73f0ZQ8zvOmr5z3LloWFwMAt7ccJmSTq+PKuYvW1qeiS4vhYUURFyJ1af7EX7kSB/CYsPSoGCk+pRSgoDace14dnPNcA4AjBOG6lsXLP/A3vpXLLAXuXf2Lc+A4+12AIDUbeyc1G0PL4bEHgZJ8xrah5lP+u6YmcKJLG/OFC59bPga+pm799auW5ZFKyX3RMrkrHG/C7S238IIRljzKtoJh0+BWRPDz79z85eR2235SeEMS9omv2V99/ovM1/8NgF/5eN6RkGJFfOu7j5oqsUClv7dt6QTP0Z18Bx7DMWY5hY1WxTujGWuikk87DFU4oZSkbSdProAQcC3d0weHHnaklIq85QChlwPhsT5tbcFRxyfhqOm7gO01DdUQa00MEYKthYFp12o2hRHMXZOSvfgj7ARKAcMmqwK0pZzrZDLvyrCM/cS02KNTi74Fj6zfboiU+lYGipVVbv76QH2yqz6rtB1/vqvvxDRYZfhx+73RZ60Ry+TWzR7Y2/GShlzW+ywYKu4ouCyX5SlJ1tKWyQqBYLENAFrr02da69McIevHGof80wCxh6rthxBwWP5tTQw13m7unD3JMHQdYWVYyp28/GglMs3AUfsH4dhAwzaZau2qOgDYNtE0TWygm1vuQCkRymXxqqYLgqryhGGoHY3mD6mqMJ1K+dyUMivlg0dYQxJ0na+aASIEPr+/csnl0m7PzkXX6jtccKZXXsVymYIAWQoEec5abK1PHV05F+F4enxpLhZz/qZbZ8qWEa3JzNiG0Bltn59mGPoWnPubTou8t/DHh5MAWckMDS+/dwFrJmYSgoChCxzD0E2/kcmnbYclt/INtSwfB3BGkPT7B849/dzG7VZAKVJjDzu6AcJoqnRJUwFdE67FmxbmGJZu69yqlGRjeR+fyiwC8IYkvCz2LMh6eRDXYs59+b37LenGSPFeS7zQ7JX1ljVRkAbHRzyO5ZvJPhGBk034z4IkAISdGWyZ6Tr1fFeZnXzGN6RUpF3P4XgFYLFGHb+MMQDzAGU5zmo2TbZ+J+v61t6xXY11BgDC2n1g7CJsZkfr7aX5/fsjbIRqknGArKnHEzelcJsW2wgAFKT26WS0NpFxXznVlXhrZD740dh8oJWC9D6bClsz//Hy/WCgfOXc2eeheCxX73ZrQa0sPmZ5M8DyVtPSXOTp84ed2cxSsHevWYOtMDtdmw6Gdj4FXl+l88SpZ+0AsKKB2WZYEUCZblNnZzjB2lKToauCqSlCIjkdH2k6MLVVB04ITobvysoDps5v1JDkAByybaIqFWmeUtKGLZBMBb1Y0/I8MxOdAchGg6heOKOgV7IY3ZZNiixDq15PHGd1rYym3oBV4aFPtGbyGtdmmGxDMusZj0eKXQBAsEKQqIEt7K7XIhTOx9OLkXrZX55Y87BAGNLM+5QrRmHFgAn1psHe6f/GuYjLrU4ShtrH3npMeMGM3rvS25fQmDvPp3xXLtSVjrh4e/k4yUO17PoSQBM19UsJjjMa1IJ8SytLmj+eO0U2eIAkxuuGTF1Ydy8xdOHC4nz0z0ZGmvsNnSOmzjGmybGWyXKWyQqWxYi2zcjUJgfhtNQ/3Okzf1J4QxJeDrvLb78WkPBMynd5JuUDAZ3zu7XxE52LmiRYxwC8khsnnHLDfyb6BKBSdB3dbdo4EC681XZg6ub4YMsrb1HcA9oBtAuCOV5fl66nFIumyU5UFFEvl6WYpvGdG9K3qOua3XV2gBCwrGAuWKqwI0mYGa0P77TNblHQuDk4EeS2SBXkc9991PRIM7g11ythCwX3qULBjanpqNHWvHj1828999gmc4wwdCiX9X7nyYOeLaO6/SKf83Tt9trZTiBbDUrePe6NFrYkCd29Y+cLae/TzELkcqxlISEuz6fYgAk40fAqibBtZnl2AwUvGYOZpeBYIeM/xLLWSCbvVrD9d9AHR6O0fG2QrY6vE6t21fR9htDt5n94sN5ieBlUWyEOEm+LeQ0AiO9Pv9tr/JUv3ZsNeJUG23YEfoShCpxsyLbBiSTrh2L1i4NGThZLCX+/FKi4WdFoJAQxOZ6/ZKn8dVvn3wLgppTIhYyvtZBxXJoT0zW2IOkZXRP80yq8Yzn3maGsdO8X+pbs5fd2A2BC0cwiz5vHAIJS2l8PAJW867o7WF4lBLZFxhan4lWDjVLOG16Yj+62BfnxLrf72PGGJLwcsp/0AQAABanPlcUIz9mAM671VeIcnDT5K/SFeB2g+frOmUFCsOtFX3Yrxs5bvX643cocgDZCEON5K+bnK/D7KqAUJctmhlVVKJbLkl9VuXaXr7LtiOuNoBazY7aLUpSSC+FX9v2WNHaX/pqE1wxumxIH4T2SKdsmexoAqE0O+3zleLQmc69SkYLlkmu3zpgqnIUrsfz/Fmzq3iHxbMb3JBQu7FrouVtoJSnqjRZKcDQqxDKJN58KqosTtYauCrHOE0N2PhnMAMDwnQNLodr0SF3H7IUN7c9FOJH9Cmxe1EtSrXKFk4wuSolr8nnL9wEEpsnVZtO+tcOfqkGA0+G0ax8NhkHNDiRKJQQCpVRgGDoWDOdnGxoTcYAWVFV6YOicFiwLarAgX1E0gaoax5TKYingVRYIoSVC7GFqEzm5EElJsjbgCxa3DU443kpqGe+lxcwLDswKRloKlaZcoaJp6mw/y1kNA/e7CmuzG5QyjKZIEQBY1J0SkVewFbwIrixCrI8kl7p6L5EjhSfB1sUE1VkPIbhjG6xb04TJgQ/7PrNV9o/nrTpCqE4p2Y3I/eEutvlE8IYkvBy+75M+gBUInD1QbbgOpVAAMITsmzzwcJj1iuHHpxK17XPPoo3JXd/wgD0N4HmtcLu0qsOHCIGHY+3jHrcKj1sFpdR+8udnn0fb51PBhqQkepUOQl7UjKvB1ZBOlCZi23YTvAp/hLXQrE3OeftGa31qXQsZIYh0HZiwshnf8/GRRlEQjWIh710ruDTwIhrMwamrN8NJebvgRKhZbBABAsDsdDwbChc2PvwSoAWWN5/K/srKXIlzAKCWXe9ND7Su+ieMPex85AkWKQBYJnc0ORNDai4yfODsM78gGSvHeRTE/hCU+AFSAnBA8qpfXtlHPunvB0gz4JQ/PB71VrHoPrPDAe447GwtbJs5ms54r4ZDxapeDAxjz3UfmJz1eIutkmys2D0vAIh7fYofcFSEVQ9EMFHXvLDyZyMcLYEN575TgdOKqS3/M+BYGKuWySwBICxnRwHA0vlwOREMlxMvrBVS8+EinEAnDaceEgbQRin0ku1MmjwWLa/eH3WDneU5WlfMe2/4AkWnw6Imn5XDpdUMFrWx9Pi3PxuzbXZL0ztC0OKS1QflirzT0DMVjl/NpxJvSMI+8YvM148B+Cuf9HGsIOqvrN7dKIViWcyAqgqlQtEVi8cyAiE7p3+3QRMcA5hXVcZ4pRAk7dZu2h83wuWt7BRtfQyg4DhrV62ShBAmNxc5mJt74ejqiRTGo+3zc6HmRVYOlJsZhq4Tz1FKdjR0WpyteSX+CABgWGQJ2CyW2y/CgXLtxscIQSwULsRC4WegFMr1/uM6QDQ47b19eFHjrTo4CcB9OGRhXcajkPf0UAr6kgZmWULsx55IwS161F5CNkfqsqfSvvZvy+R688nAOlMwarNdAzeOaKysT/X84O2noaOzIXBWW/FbPQFgs4FPZiG8uioynPm0qWMu9ezBjkmnXqwrOeyMQsF9kYBeDYVKFwFAt8jgeF6YP39wplWWjG4AGx07y3AW/JPYfZChw8n6dC0fW9Xjs23yYWoxMmoa/HmGte7FGxLHN353lMKyLdaNKhM+F3TcXdG0PE651PZw8TtByRInJpsORyP5O7JblSgFWMEs8LK27vuZe9I6ZCjSOrKkadw4ALtUkucNk6MuWZUqirjuu94CT3828TOvxXjtVeANSdg/tiLFnwjqI6XVGh4hkAmhUlnjFsHr4wyDL2/32l3iAoCbwO7T+R8XGrqn95zhoBT06e0De0rdvw4Igjm+fwJHUEr520opf9vELadaIPnK89G2hclI24LtDhcbWME6y/sqd42Ca8tMwfRo/SsbQlXU2HHsQni2G4iCkec4e6fBZ+LRY0PXlhJhLpPx9+ia4ItEM6OpZGg7UeoFOK2CG847qcmk/Y/CkXxvtRdtgxScSNUD4Cil5CInmtNbjadmWNouuSuTatnVsua9Vxc3MVSYr780NBY7Oe4SfMpRQpw268J7XTerzDcApbArBZfmC2e/Wd812yvK+mFK0TL4uF23LXarVPconPkKIpyovAe7FCknc27/hGF/eHfJ3ZnTnK4SOVC4fb4tVe067gDQCiejsNvf6WPsQgeVXIjyK6ZQtsWeyKaC/aFodh0p1FThXqnorjqTZsHAapAwXxYuhmpyc1GPVm9azLdAGdEXyLcRArhjuSFXtLhuv+mp2Oq5ohRIpX39pZJrZZsOAFCc4VsZOPdNDdUJK/Ap9UdYwRuSsA8sj4zek5vd6wUthr3Kuh53lqWHYuHSIUrxKhnqQTg3lvqdNvw4YRqcto+XWZF4em561LUpUv044XaruxL57RZqwV0387Cjbuahs7YKLjXljeSMYDh/RfZVYgxjdxLyQlhGKQrppZeb17AWBY3VX9W+muvSw9ja9AuAM7XP5y9f8vnLKBZc13JZ33A4kmMURRrfQbNQVU80Ox0vhCO7mu6dgBMh++F0Ea2JKgkKi4H5UGOqZasX1zQvTk0PtC4/T+FpyIw3fGZgJto7FWcEs54QCNiwqHo/O3LSSHgfKo9rDaoIpyhFxtT4gWLS3xSrT57iRH1UlPU6wClVNbbP3Zkablp7/oaWj7sZzkK2loBROJqJLBzNxjoyYtgYmNGRel5Be8lmjmJZBAgAAms9j/tUP7bGCPZW2mDgEJct1ydd48dNk1tHbrN517HJIv/+4fpsGy8YjYSA13V+y3vDkrF+/5ZNLACorV/6DAgEQkDc8ex9W+d4SmEut63Ctsh0IRFaDTByeff1NQQBADW83sr9Usl1jFISghNY6XDKX4Eqh/KGJPwlxP8FwM9/0gexAlkwBxmm+gTHlQv7FcEHYApALTYpmD85zDxv9rmDhbJpsprbrW1bo18BIeDq2xbK06O7dgt+LXC71Nf6G9QrUiQ9HY+kpx3BPMOZRX8kNxSMZStufymqqUIRlLyy6Z+qybwy34n2xtSepq1Kku5paEo0A/DJsvq4XNpWs1l18SgW3Adtm1gMQ6uJLyfhXP9ROIR5y9kYlsGd1xVhQJD1qmY6/mg2Fj7MPa6/NJgLdCZaGM5uw3qyeAaOCdpKhqFEGPpUqCsc4GsLo8rT+Dfm3z94ntrsagnQ1PgOy2TusJx9CgA6D48bU8NNgwCW4ETz1coBKyBwfD3ScDIiY5RidlwFhhR0lJxWvU1gCJ37x195XOcWrO28DTLYg0ASjunbY2zTwp1eDBdASd/ax35/JPyIEIoawrcRQiuCpH9ULklbltLKlpMNJqCpv3FmfDruU505DYyj3xL95eFY7+ThZcK2isTzxglQ1ACUZVg7dejocN29O4dmVUVqAIBYTfaGy6VfFnjzSjqzOi1zBs51Uw2f2s4G4A1J2C8+VUr/2lB5P5H0fnEEW49wfW2YSXruTi/5lPpISagLlw4InO0DAE7Q7zV0zTB37nU86r9y5DzPm0ONjclE75FJX1fn/CGOs7dUFheznk+Y6FBwnLVTOv2VwjY5bzYROZlNONnSZ2npgw8yuBvnUW4SEY3y6GZI9el7O8GyUbbp2hHE1AJgLxtL7RmxcGFPHh28YPYBgKbx91LJ4AmvrzRULHi2WhS3uPeRcCoZuFcTy664jY7DiYTb4UTeLbs9nsKiX4u0JFHOu0ZcvkrnWqEsy9o9h3/uuxOEpdt5mTTCGeHeCIecnAFACMEJ+XCizN1pzRhZT9CwyERZ59IB2Thpm6zKOl1O8PjLfSxnkmWjqd3iKZzfdjshaJ/X8bhkky0zh43B8rhbsLYdKgWHIPTDyQrt1ltmS70QpYBtM5uCgYUK32RTMjmUlR70hNRjuipeMgz+SrV9GBQVG2j2UOZpMxWi00NN5c5QJSEKpkfgLQ8AhDoWFghZP1KbUujqvP9gbdOCAIdQhQCE2tpnrw487WgAAJ63RAAollxrtTnt2Lqd/A1J+EuIpU/6ANaiMVps+Zjf8iJ2YPqvGpOLfq1QES/myhKeTYVtjqUDYV8lcfb8gOwNF049+5PTtwHAMLju8fHa7vHxWgBU8fsqt7u65tSjR6YaYjW5trU36uaumeDggy7FMrmPeyw3AIDnrWlC9uyE+UrxJOWqVWxycEIDJjQAoGWJ4HlMQKlJRKiGRw+7RW19I0o6OwSQ5ZZGqoq89exM90LkwVjNbFERzgCEa6vNXTVMhqYLsuCVda2s8XxJETZFmRxrqTxv7asMwjA2d/rc43ssZwVuXuszKWWq3edYVO3pB2anays1sWw/nMW5DS8mQu4J1GaPLc4Hbyw8bz1S1zH7UU3T0hr3PQIj4Z0W6gvbDQEjAJophQaKmmV/hl8B8A+yt1seWBWxdzzlvvrr92rREE+rv3Ak+wEnGm+vvpjA1do9/dHos7ZtXf82YB2hOOBCbiFP4WUxU7Q2eynInLVb8a8PgEuIFK+bZdFtK0JftY0ohb082XXL8gUhgMtdmaiU3U1rXocGwg7FfVolIpuGTfEhQ/C2ujwxciNKOhnrsMWCCHKGgHBLGV/sd75xGoTYs++cHRqpCRVrlSdN9uF3nq17nanyD6nFnV6+j6x6iwTD+R5B0JO6LkTTOU/DuXfu9Td0Txo3v3c8YluMIIhGQalIbJV2yamfTfzMp6KVfiu8IQn7Q98nfQArIIROe2Tj415oGDjitBz2YS28V1AKFCvCmpspYUyLHFzMeg7+yTdO40++caoCJ0W5AUTOF9yn79ztwp27XSDEXoyEi2MHDszQI4em2gOByuFYQ/LB/GTtTi1KrwUuWZ3F3u2yXxkoRWWmJGxYiIlbpTgxpQFTDmnQBILHER7ZJhHeWh7dPFNdgFbQ2FXLQpahU+/2TR8nBOTSkbnmosJP3x6KL3bVZ86yzAvS8WwqfKWkbE721MdyQ4RgrwJCAADPW70AUCrK10RRlzRNiFO6ae7ACQDXAawROFLV7ancbe+ciQHrIshe7ONafzDYcOPOk5aOd/qm1IXx+kioNp3neGt18dOGo/VC/fYtl5RCplO+DFSunsrm93TJ/poK5oT36FxL4NSk/ve/9HfyB6lbpCkhFGoYZcmGLFD7oQlu9NmuJS/j2LA4R3gc/PEwkgDkm0V6f8FAd5uIx0EOOgXQG9V25TUiN6aX3G2pj1zNmXot6UkvfrO6HcXYTPTq3GLAFfKXSvFI0YoESx3VWpVNc/2IKkKAHr8upguuQFhKqYQ4rY2iZBiocoo1nctJYDZlQChlGj74qKcWAFjW1n51A0nQ8i6l2nETglgwnL9dUaTKuz98JUCIk2n9yl//jr2sAZJuf+/Y7NzEJr37p1qPALwhCfvFp6YeH3BrU/hkFpo4gFtwUqCvFSWVn6ZVVN0vQHaVwqSUiSVT/ljyqh9Xrh4Gy1oTImeXQqL9wCNaPQzBx5pRcLvVT7QFs2Iyz21KqpCrtSCiTnF0XgfmdQCgFkcwGOKQbBAgNojolBjHq6Gss6uRFcfapZLCT3tko5kQwCsbTW8fmc1bJnuH4a2GlQzKYtZV1b+jrTGZe9nP5/YoZ3sOj9+aGq9LZzOBalmvdgALhNharDY11dw638fzVrU2Xw7AM6wjFNvDMBmlr2f2/L2nzdM2r48TkMDgzUMTB84+a+N4p35v5eQOapFxwtLtVnEWNuFhES9Kwmcmrjf/hytXjhJL48b/10wxfCEf8nfVmHVvvf10XJSMTU6UkkvtY1gzZ1tcYBeHPQOgTaV2+V+Vc/ffFV2+I7zUSwgeAug750Nk2XflxEqdvrUhOQ4sE/mCe9jnL1ftGPIdWgj7Di2cBAA14as6yIhSGA8HGwOZvHvNyGiaEHkz0dmyWKwJFz2SYIpPhuqWvFQMsqx9q6E+dWpFiNvXlrxkWkwJa1omZUmrr2ZoVDGYbdpcCQsAlsU2F7LulC9YjgDA47sdDyqTET7q2VzdpRSJVDJw8Pzn795f6UZxjt85tnJRvpWYqal2DT7Z+jg+HXhDEvaI5RHROw6C+bjQGC3ubszf68EZrBne8rowm/K+FiJkWWxrxWJbKxpAQEfbw6pHYOmWYrRXDUEwP1HV5ERB3Me8BsKaFAeWDBxYMoD7ZYABHQ9xZCpOX+hUNIM7ceVpIxhij33p1GQ7ADAMPbi05B+ORPLzPG81AcCp7kTt4HS4P5l3rdO41NXkdxpetiMoJTOaIprZjL/KzZnaoqhPt7TPsJFo/gQhO2oNdv07oxT4D392epTjbI1lKe8mbKdtk3rYLJ7fOnjv0FtPlvv5CYyEd0aoL2wiCZQiTwj8hMCDlrxFh0MlahLUuLTGiaQwfn1R0A3WvtbdPcOfP7QYD8azm8hWpSQnrn3zzJRtcQa28TgxLJKaKwgDEbeheAQbJoV+jpfYlM5MLlAwtQJWrxOyhkjzvPHA7VGOAcDocNMV22RMr6/ctTbyn0i7hykl9H/4uz/p+tn/5oNbX/mR+2f0pHc1GrcsMpFI+2Zk0eDGZyI0lXdpBHSOwNFBUEriqs7Hnwy/KO/XuPW0x232miaDyalYmuOsUZ+3Yns8yiGOtb3TMzVPfd5KzuutdJVK8hIh9jil7OexBprF7Ervks14M75gOfKn//HSlevv9178kc89mKimeSUE8a/8tffvsLxdtXXT5VEOMAw1LGwKRB7s5jg+SbwhCXuHH06tcksYsBUAYEAEAjDMa/P2o2ZtqLTtKNKPAadQtef81SGRce9L+LYXUJCOsbQ02hNVrI0p29cBlrUW10YcnwQG0tKuOkF2gg3SljOQjW+YMwEAkmAl4UTsIASsJGvpUllCMOCIzr2y0dTdkDGS+RfJIEIodUn6rgymtgMhtFZRxFm3pzJWLrmXTW1oOhjOP23rmOmUZX0vXR292DBJcCtMzYce6QbfqxtAyKVfLZcl2+3W6gHA1IUTC2P1V+s65i4CgDZc07BScqA2pgiDZlrhrtIJ/1s0pN4kHl2jc97DsAhncfR7i1M1LlMu8W1CyF5Q+cI3cor7HZNE5by7zEs5CwBLbaj3r/XemhmrPwyQM3DMo7bETF54rprspZkcM9UVUUoehgmeEV3nIeIpgMNwfCA26TeaW+fMSlmadHvUFo6zSFkVyNo73a++d+hGoiCv6k1+/Z99Af/ff3Px0f/8T38vyhB7TtX4xd/+k3N9lJLVUiIBTQxBRQekRxxI1XJTSWcjIZc5zhC0ASRsmlw4k/Uhk/VqhNBBSpnDubwHOYdnrnTbzGD5vm1TmJRiNyZH9//lP/mxoCCYA5oqXAKAybnwvGkxZiRYauFYui47YZmcwvLVO4AJgS/euHhvZqzhxIan3pQb/rLha/ZXk7/IfP0PAPzVjc9RUHyXS/aPM+XzABQWZCFA+fkfNuoubDRxs0CtBaI+b6DyvrMSPGc/41i6r7rtK4QE5wai4dXPjQClQEXjPhbTI46hGUL2LlDbDzxuZQxVrIE/LlAKZaoovrIuHQ/YUpV3MTvqcquEi1JohYLrYFPj+mwzz9luhtBJm5IWAKivyV4nZPfunpTCTpfFO8mSqD2ZD4jvdC0i5NbaGIJoQ9PiRVlW+4eftyiNzYlCXcPSKYah++nM4eCkhncsOTTVZg7Jkv7vFJX/SZ4BTSYDfpbLjEii0QkAmYWwp65jDgBg5aR2a9b7HaIzTVC4biqbo1B4JzOXkc/SjAxTZa8YPFG1gsS5ZI07KrgZwyM3BcshVz/Ne3RZvaNXhEBmOnrftphGgMRdog6ArJR/+rA89p0u+waqJjNq2KRk2dBV02mjpEDUpqTIwhlBz7JWPOAvXymW5AZd52cAHAMAQmyLUoadGGuQCUHB5y/19xwau7yWICQK0nSiIG0KYIp5V+/v/fa593/orZEZySSNlELDmug6D2vIJCDDUPsOUGmaVCkzVgz2QF5l+4OytdEMS6S0aqvmFThdKQMANM1kvADZze/cQ22mVVNfcIH7A80X7g80A6Aay9pDHlnLREIlvTaal5ttKE1ti5RhaNWg8MSlR72E0LvTo40rxmZlOAHWpxpvSML+8PuoQhKSRB8dZ8srNyDeAvWlid72jC3cPGz51zkVPmBzH0lguAZr/2XwGn8lt+8Xv1p04DWVHbIlcQggr8zsZzuIHK0qSnod+KT1CIrJDNkb+sxfBn7KbspKRP3K9cYNTnWNDakEIVh3I3eJZvwzvdOpXFl8tJBx5y72Tu1J80MImLBbO3BtPPrs6UKg9XM9iSmGvOhJD4bz9NzFR4f3+pmqYDeDesAw+Ju/Uvmp3/5F5uv/vqCyvxB2mbMLC6Ev+33lq8Fg6Xy4LrmGUBGYs36B96rONa7wGxYvqpDairb0ByfaONFIcz619FO/8a9P2gYzWb5XHzcXAhoBTmVmoqptsqulskg0e3bGXZmsOK6OTFlnbqfKvKdsMC3O5yAdDgVfu7QT10xeuN8a1GI8b8411KdYQnDJ662oxaI8nF42UDp0dPT63ExNYEXnYRjcVWbNN5YqifO/+t7h6uVBCnu+/3D9v+8/2v1f/8PfG8WaVkcKmi4Q2zfG5fM/5pZv6jnpna3OsWWvy1oV4EzJBJwWyw68aCPY2HY4XtKZOezcraIB281FITOWxXbnSy7kSy6MTdfg2r1OAFSFp/JhwV3ytsVzZndDxtXTkKmrCVTqGQLuyJkBqa4l0f/sbk+LKOl3f/DhP/gUWMNvjzckYX/4JjYMTp9gyg+vcqmqDPIjNnOwzXKnXeDCNiidJ8qzB2zu5Ntm9Fm17XeLxmjxE4tEq+AinJ7uV2bMAwCzKe8itjaAeaWw6Ut59u8JHGepcNzvPjYNxFpMFYVd2QruDlQLMNQWeeOmJJi6LJjUJRlcQ7i0LgNECERCaNXymCRYkbhQicSDFUgu9cZej4AQ+C61L3FfPjRXEFi6zqWRYSBjw+91n+gFUIJjNrQVfrL7d//FfwCAr9lfvQJnkcJvxH/Tly+45/IFt+mrTa0jG1rG08h71XU7oRQVEFpkOrOUF+zPhXrmhmlJPOZ5d3iOECywgn3SFagMlRL+IKXEkryV0UrWu3otEQLR5VKzyyQBLEM9ZYPdUC/fXAZVTfZCxWAGO2rzKUIc0k8IJK9XOZPO+CyAsIoiorNnyrp9IwAAKBbcR6/3HxsXRCPp85fUWarb2Nq2ni4RM+mnbOlf/pMfa6aAe+UgNNChOOW4Lxs14UaiCuPbeCWInK0AeL78Pj6sn5A5IElaSRQMU9N5hmHoA1UV2m2b8QFo84sWQ6lxTaO2VdH4uGWTjhWx4jKycAZKbWe974NjErWOSFDAGiuzXWrF1zad9OHDJytciZZkwZyOBSq51nieHOoZTbfX5pqTv/Tjl6O/9nv927zPJ443JGF/UOAMPFqNnG+zGVUjdtW5Bqet4FMZ7HkKiv8kzN4uEvMMAFCsJP/2A5oJedWenbf7WNGJV7zwLeVcH8vkSa9g9tf79V2r118GLGstsiz97PKf03jhrNeCj8nyejAjB17VviSWDn3x2PQrK3txvLkvrUbIrW9l4dwJx9vkZUm1AOAelqc5bsBtAH+v+3f/xa0tXusHYBJCC+Wia13h2ta4NmpjjDBOnVxLu69pWU8fbAjeloLGCja8b03m9Xnf+5xP92O55KFXpMJylk3lRcMmjF2gzkIIAOBFPV1Q2f5UhW9UTeYSnPkJO9qQzxWESqvNWGsfIwRBt1u9Vy7LJ8ZHGs8tJcITa571U0r8miq2JVURT0qoamDkbAq2DPtSGbahgf0oAp7hl2dGSGCW9QuUTmalEZ6hjxmGhjWTtG7kd/MF8XRbSB3hWVpNI3KQUjLk8SgZD1TCsjZhGDszNV3DUsq4BY62NEeLhVhN7iAh4EyLKNmiNLaUd2XSBYlRdc42LOb8DpyyBtXNkQo6aBXXUeJRdP7g5JIfk0t+fO/xKnn4H779a3hDEv6y4Wv2V+kvMl8fxRqSEKOSVkC1siy0Hst7mIBgnCk/WCEIAJAjhrr5BbuDWzKGCKl6s/okEYQjxInh5aM2WDYMzWBfu7slQ+hgg1+//LrkpRuxQY/QhPWdG/Nw7H9tAA3Yg8PfbkEp9Im8+MoIJsdQfanCTURls/VlzyHLmQlCXjlRCsFx/IuiSvsyw1qLwYZUgtpEt0xO4UQjkp6s6UEVIWa118NxZPxvtyEI+OG/+Y1fN3SWZzk7zjB0Q4RKYJakWd6ntlOKvJb2Xlj5+SiP6/o956bijMs4JXWkLTiDpEBtMk0NlgecSF9w6ZcEWbuvlOTGxZRv8P5AU2huMfDZDT/DEeyCJJg2cyJVFN5zu9ar+P2+slIuy6CU4YsFz5Y6oQC7C20SAV+mNlsLEgFgApTzidb9osYe8QjWs5jX0ASWnqEUmmGT29kKZ6UV7tzK57EoCeRUVgu7zEmmSmeKpgndc/PVXZADgeK1gL/cumJZz7FUjgaUw9HAi2qjZcPIl8WhVN6VSBclUlQEv2EyLQBZ6yOxqWuGgNQeoFJKofRBihjeArH7djgRX/gC91ut3zZ/emL77T45vCEJ+8c/A/AzAKDBKo4wpY2q1RUQldjFCqzch1xy9QKTKXv/uBXYd2q+Llz6tNayerEL22bTIuqD0ZpbdZGSJxaoHOBYusnrIFVwDQLktbk6MoQ+sSkamvwa+bgIAgC43dp2310d1qc5UwDG4NRIo3BMfl6q+0K1yHOTvrrzWjLYk785EAVA0z7BGjsWKxUvNedqdVXoAsie7jGSS53Aqy/BbHM9UjvUlAQh6AVLwfI6KEVqC4IAONd3GY7Y7h4AFcBPdf/uv5ja4Rge84L1/Vs9qWU8LZxbe6plPQWArHYEmCl3hBpMifC2BwBLKQLlm833zYT3+MpESEqRGn7S/NHdR62NU/ORCKVkqwmKq6smAVXDLvNe2GV4ATAjabnVpmQ1azc0GzrbVFPME/LCXEkQzMMANXf6ToPcdrX8F/A54zEGAEpaAmraJdALlEIj5IVZHSEQBZaejnkNhFzmvazCqSWdrY26jRkXb9UwBFPYJZFeKPJXKgbLNlNa63apU4JgbUlGWQZ8yKvVhrzaaqlzWUQ9ny7Ic6mCXMmXBK6i84cAElj7WgKE6iRbOu61AjYwuqhjbtGAuGSgtmCRjVkyFsAvfIH7rf/+2+ZPW/gU4g1J2D+G4KSJm25z2QcgW4w2JRCeMYWxCbbstghdJRJnzBDlwOxKCFUN9eHSdnaunzQuAKvtU1WxlHMNLOXdl5fybgDU4Fn7ccSvZOvDpWDEXznAMuBnk57XalfKElrpCKsMx+BjLdsIgrEXf4QI1o/wrcC59gpwTGO6sfsRvACA2aLwms4rCRd0LjynMlei8dRBSqHYNvNM14ScWpbciiJ3rU2HV4PsUnbl4LfXA4NTHpTgKPTXkCzCGKowJMj62lKEl2GtpG2x1UJRCY4m6TSc9t/7uyAIAPCP4WTa/usqz5VtnZ8rjsW92DgIyWQPqSORa/LBpQsAQAga+Fhx3FjwpTJLwYGRp22+helY70JB8GcVvm+HYzgQlI0PatxGjCHoIuRFp0Z7SL03npFaLUpCAKVuwRq2KY6ya2WNBIGVksN2b5I1kdnqOZYiEwT3PEhZhgfRAGQAckHgaG75PbbMQvAsPVHjMVADA3jRct0GJ7tSNeNoWEglSsIz1WBchs1cAoB4LHOFZe1dEBmaZjkLsebEo9R8pE1XpHq3ZNa5pWJdU41jHXFnKNa/lHevElCG0EKtV3/gl6zLgJN2qhfRUS+unBt67aMCWjfMw/jvAMS/wP3W3/q2+dNV0tGfLN6QhP2DAcAasNUhprhtz+0ztnAa5IUZC0Mx02y79i3GI4ROuiWzZb+v/xjAwrkhbtlXPpvyrjHyIbxhsUcXMh4sZDwAqCpw1hPDYl+b9wJAFzvC6smPwxNhLV6BP4ILy61oy7Dh3CSTcFpQ27GeVGzC86z80kZF26G3PicAjvEOy9q9skuF7FIRRA7Uxrhh8PNqRSJKRW40Da5pbUqcF4zXIcZtg1O6mYYjPHSiY2IrwbrMfVYw1xFuQiCGm5NRU+Ou5xaCB6nNBjfsL4QXvv27iv5Cv/r7RuYf/tgvwPlN/BIcsvcIzn2kFw45eFjttdpwTTtM9op0OHEwNRIffPQ7b3ln77eFKGVWAxOeoZxHsPpLOrttBs8rWDzLbCbvPEtPNAa0qwQgAmuHWQYn5+aidxobkifWjhb3+8tqubx9R9bTyhrDIArIICNhys17wUYY4CBZkykBAI6x77Bk+5Hg20AEkDFtZLIKNyhx1JuucLmIywhxLPUuFIUpxVh/TgyLISxLd+x2CdRkky2HJ1oBXPRHczcHbx6q31hFjYfKbifQcdASVJ9I3IsWW0oBg6IoMI4LZJDDhS8GMfetLJ0t2WTtAKjvB/D/AvDf7OMcvFa8IQn7xNfsrxp/lf2tb3+LT7xDd3JrI+vd2r5sxEsCmH277QXc6jReQ636FaMewE04s9Q3IVOUtlkMiKSb3A52wS8LEhtOyQ86I8oBhuzeTe9l4Xar43i1/ggMnChqbSQ1BWB2+f/1WHOtUAo6lhdfq+/E0brsluSOMGgTRKNNEA34gkVQipRlsmOqImlqRfQS8lp8PxoA5OEY6qzqP0KNqacsZ28Sq9oUYAjAieZb4eaklp2NTFsGt/o6hrXqbYupLNuB//FuDyL0q79vZ/7hj/0/4WR/vojNAsg+AHcBrPTRg1KkssnA2MgHx70L07EQtdmqbcaEgFQMZsfrKl3hwx6x+tBYF2+v27dlsaeWlgL9sVhuddETePMwQI2tJntaFIZhI+YHey9E2YoLTDsB6YQjHq0Kj2BXXqbcV9KZwnROrABk9buczjMUQBrYXHoJeLQdgw9KAW+osCpSEF3a2SOXHj6beNxOSjnfaodOuiCvjqLmGTshsrRv7X6mNdwrWFAOu3Bh5TMyBPVfCsKwQJ+XLKTvFFGTtUgngJ/7Avdbv/pt86cX93QCXjPekISXwAf8kmjvbOe6HhTFABVequbaGC19IlML94GzcNK864xxdIPJWfbH432wHSyKwzZF5eMkCR63+nHUHZuX/60gBce0RVUtAs1aH8m9SjDEng26jIadt3RACCIcb0U8fBkeX3kczkL+Opwo/XDaIJ33Zawsw9pVydLN53X3z/XMHyfEySr4Ytm5Utqb5UWzJPkqIsPaR4op3425yRit6NxHu7mQB/7mz52Ix3PPQr/6+0rmH/7YzwP4DQBf2rDZMJzSURnAI11nyV/8h8+dpJTd1liKUqBiMLpNHQJEQQ0AHKnS4lg2mEOUIkHI7nQfFUW8lM+7PvL7K+cAgBD4vR7ldrHkWqenWonkswrHHqTkCAHZtiSxfOSF9pBa4Bj6UgPWTJtEAbLhmiMEW2TUDJNJirxdVY+gadzoUjKQMk32TM+5p+t0UixnH2o/NpJKzUavpOcjTWrZ1eJ16QLSzvMB2RghBKfhZJfyAEKPyoBKcf65gukfDaORWXbVIwQ8B8gBDtZnA2i3QcdVG6myhSYAb0jCXxZoxN5zqjpChUcytv7RLxJ1uEKsio9y/gAVmtj1/bv4lFgx7wUn4QjvVksy8xnP0LJd7CcNcywjj3WFlWNkoyXma4Ig7H4BfYVY1TUQk7vyI2EYCZ0+ndaQTxrwaRSdAHklJYgajzYNJ3LfD1YivFWxpm2TKCG069WUhegJbyT/Z7xL9zGsfYKQzX4HSkUY/Fff6G34wxsdN756ach9qnOxlxOsc4Ha3CyAI1jubhA9qruU9TPPRuM/Oh3/zWsFRv3+gGh+sTlW+jvv3PxlGwB+I/6b5GcTP0MB4A/++Jwdj2X/0Y/ix/4YwB0A38ZmkmDByQjpAM4LggVBMu5rCrtjVo1SgpVBZ9e49M2TZtArg+3bvCVBxWCG3YK9y0CFkEzWe8TtVhMc57zG5yubhaILqknGMhV+tqizYZvi0Eokv/sfEvGVdXbQL5uxsoXEsILhcRV1ACyGQGcAkxDYDGCzBCZHYPEENk9AOQIIBIQnYHiTlHd8q1VQcNXbJmFZTGYhESaUkpXs5ybxKiGIRBuTl4LxzOOnV/vAMfbqNgHJkgEU4WiFggDGe1zIPyyDHnVjjrzIYl2BQ1qPAmgmBGCBNjeLNjeLVjjXx6cGb0jCy2FjrXJHnLCCW85JB4DbXCaRYLRLAEAolmqoOPR5I3ZUAusHAJ61BzmWVp+z+umEBCeCW7Vtnk97qhucf0xgCH3eFNAYibObk2W+jFfQrrkbsKyV2sUgodeKclkSOAK5QURfw7KYyqaw0yZ9PqNhaUGHWLbRDqc1bc84WJt/FZmSVVJz99bhJdNktVAoPxSO5gp+f9HLC2ZXtQV+J3jCxUeST13pMNDhTHZMw7kP1gFosRQx9U9/+EH4f/7mkdb5tGcCnatBXQPW9MULkn60rmn+iiJXumNu5VYooHSlClL/r7x36N/eaPmaS6r45SDLRH4j/u9+FyB/+H/P/OyD/577ndit250/d+b0yFOsH0UNwptXqcGtpPtXBc2tPdPK8wcvNmUYKwMQzrYZH8eZs5KkL7CMbYRCefPGcHyaUtKUI0aEgla2Og+pCh9yC9VLDtVBPKWS/NjvL4ctm3lSrkjqeEZ8oFnsMWBXMxCqgoKaV8tWSKsQHk5HyypxsejqRjsfHbXGI8S4FqJcjw476QJzYGvOT1CoCHPBDSWHUkm6l0z56wHieFVQu/LReDD9zuFE1b3k0j5xKen77lJeZgGAY+wkx6AO67MXbV0y2lwM+usFeAjBd+DoWbbqPgGA/+034r858LOJn3m68yf/ePCGJLwcfhfAF3a9NUW23pa3VNJboMYi0VZry5SgZpFoNXe57JULZuQSAET8ypbK4U8x1tk258viJzb9kCH2XKNfs1w87QGAmMd4rRMs18LtUkexg6jwdaNckTbd1BkCJsqjJ8o7XR6UAgWLTs5omJ3XQQoW6m04cxV2Qm999pXpLWwbc7om1ANAKhk6lvr/s/ffYZKlZ3kwfr8nn8pVHao6p+nJeXZy6N1ZaYOEJBAIASZoWROMAQPyZ+/6MxhsXWhtI3/+YZtkltVng/gERoDSaqVN3ZN6cp7pnEN1V44nn/f3x+nu6VAdZnbCCvq+rrlmpk4+deq8z/s893PfsRlCOqG211vorqhMTfkDOVZ2qdUMQ1ft9rFMdn7nxBI3V0qR4SXdI3uZu5891JdRWHvxjHPOCpsQSP7KrHyoabo/M172l0pROnX38pb3P7Mh2f83I8JUXIxrxwuV0SqRPOPn8W9ej7zxcdt2vXP+4saPspz9u0/t7X8RhI6DkhoiGnHfi10bYZGev/t3n+4/sGX4kNutBQGgZevgzoHbjV26zm/2+YrnQsHcdstiuhiGuhmGbsW8rE3bjtHxt2/U3m4zyvMy2GWzjQWd2U4pJglZXTNhFumMW01n3DqlzF4AaA5pqYGUNKCZzAOTiykwJoNNa2vjfi6/H4LmGMzmGDEBoDxA2fZaKuyAw83Jwpm1755dfzrtYoLz7J7jcV9HLi8fm9/2+g01f+nOd5uEN5Lh87tPXJ/LepoGW+y91Xy1+/qGOmqTp22TebPKq3UEJOvIrO7CYtSKc+23fVi9E6kKzpiyHiT8A0E7HB+HH1nT2gTBLibbuc1e6ONAQXGXyZ2/wCU9lCy1oa6ypblZU2157qE49z0BHAdwvqhxDbNGPk8C1V6jzy3Q/XB63PNwXvxb8AG1B9YCt1t9FO19awalGLEsdlXLbUIAP4dGP4fG7TOvNMWi8TEdfWM69KSBSgvYuFRLgOYbQoUP7N44C1UVR1BKgZISJpf1bMplPXN0AI4zs4Fgtq+sIp3z+fMeQTBaCEFg/maiW10xgCEEftGt7RHdGo5XZmbTxlE4XREKnPRzAjPBQsWO4TLerV+LfOn/+7PuH/vlI4eOXpdu9dSmn69VRR/LmX/XzdbrikA8HIqGRX4NwO/ncq5X3vrunvbwztGuLc/e2WBMet+XNsfqCEHL3/3XF7vPvr3rxditpreeOtgVaNo8fFAQTW9Ty8SkVpSaZ8XTOM4u2Qmg6lyWBynzgV/FNI6gYDC9HsFeLUgw4HRc6JQyBPMGOEIQbAiowz3xBWX7XjhiYEexhrGFAWkMUz6ZJg+XpqPAZiiol4DMch2KcK5jN+D4PpgmGY/FA/2yrHO5vLx38bN8x9Akytnj34iybLQvcO2FDendABCPhvq7rrbuBIgXAMpl6iKwjs3vAFkBG+DwTla7751rvdbHgfUg4QMgq7wy6JNfewnAs1hD6YGlpK/Z9mzqZnIXz3IJLwV4HiSnww7YBCVr9DJlr7TYnpmaJC2U+5RHrkD4CLFpNOa9iCfkVwAALsEKwmkjnE+sysPRgS/AkVvdhNLKeh8IgmCspAX/yKHr3AJ2//1AZlHeKqO8dYYya1IoUzrtGdGQnjbg1Sg2ekWzhyFYQ/0ca7LjTid9aw6qTJPzxWOhvXPZBlC4PcpAWXl6IlSWJi63Wp2eCMll9bEEw9llK+7MIZ3NkoMXpMFnMA5glHHko3/S/PLRPwP29PKC/Z8OHb27leOMTYomxmq2D0x0X2sVmpui8cH+6vrxidAboOSWwJuMK+prILJZKW+bnjBVPpIbCnad/7un6oIe5RZvc8/fOL91OFI/Ne1yq5WV9VPuse6GFTVVLItJxpPeYRAQ0NV/X4kCX+YpXXLQ4bRmGnB0TvbD4Uos0YLgGOwudxln40X+CJxafAROF8NiT4PZGvxs50oWM+JgBuh98AnWBo3Q44PQO5qpOJvWd8EJEIYBGENTftZUpF6vaD+tqqVkGShedvlzXyqk/K+PM8cz/kD7CxvSAIBIXWyHx184l894DgMA4wSi5+C0Irdi9XfbJBaVmkrgKIAzq17oY8J6kPABkVVeyfvk134awNexSm2bgrLf4ifvpohxCDORp7VK0U2FVWfC1jgwoshbXQyDNbCGP7QIBNxaeKn73OMBAVVYUlJ0xYN5bWdwXnjdcGYgs0HDBzpfhrGSDPPg9duHgWLx4TVxcARyjYhdNfN4DbUbR1cthf1GZ9mNH2wopE5UqavaNSfigQ9QmiEo5F3NhbyreWSoGh5//txHPt1+eI1PnR8z1srLLK8BUKOnPR1yZfYEgN9oeeHq73GfO/MfzS8f/a3ieHC3S+fKGn50KPdM9qKojQSZQ/H+4wBgWYS707l5tJhxqwN/0Fa24dfe2VccLOu69afPTKbdSeZEvUqj0fLtDKG6ofEUbhWhqsSese76zCJJYJgmE88X5J5Mxu2ybbJJAHmh1qdfH80I9gqKkQDmuhwmCUEVpdAJwTUAJhwy3eJMxV1gaYYTAAKyycWLPAB4AXoBTldDHM6gOQUnA/MUnLHmNJzgoQHOb2yCB1mQfhcJTXhYxDkCM28hKDAopsw12TovgIKSxMwCgDClZINlk5J+CZKk3QlXpllvXvA8n6Vanct6/5WPDs2VbiyT0ZSCNH9CuAcO0XD2nt2Bo1nShKUBeRyLxbJK49DrkTfkl6MvPTZX2pWwHiQ8BGSVV77pk1/7VwD+80rr2QRNKWLcl1IiJajoZwoXm2z3lupgPvuBTvRDgHCwuDMSLLRHU55VB4mHDa9o3SVrmOnCSTPPDxqycIIGBY607X1nGtxurRconS16XCgUpAfKIqwFDAFTW5FddVB/ZVdK9/J01Vo4pdCymeX9Ae4X257qct1HWEqBhaWKUjCyclCuzALATwH4hPnlo1cBbJerU+W2znYwCZGDxh6yde4GQC2AsAxDI70xX1yerLBaW8aux88085nLDdtNzpi+USDa7X4i/aSL6rDYVlDH/phhKC97izeVnHuuK8o0mfjEZNmgZbELBh2PYO1iCLpsupqKKCmMZYUrqsGUGza2bypXZZbBcoRoN5YJkgWWHthcURzULTKZKPCFjMaxuDdTXvw8zO/qagagscB5DjTTIuPORgmsxICSeVlVSqENarTjWgH7WEDTKAkBAAG1ARC6cLJB4Tg43i2jnIGlM/aZwZ6qvqoYRM11UVOkeQERtSTRSBkGS8t92rGXZfY8xyF89+xO1u3PXy0UhWjv7eZjAFl8b/fAmfkfvXcMAE6HzhicicYWOETZ1d57FpxswwP7+jxsEPpBjAjXMQef/BqBY8zz8F/EFDoA9bVWGt3sXjVV9aGHTWG/faXhtmGxj7VLo86vtntF+2EEJ/edaYhEEh2yZKzEan6koBTRoeFw5FE1chBiW0dOXNUIwRIPjgeBYbA3z5/Z/RCfDxr/yKfbFW+gsFbSbB+cGvJK+7Q9zdOpZcoXRQBpOLNwTkvLhmVz1aYixOK3a3dNx31x3WTUhvrYYKg+JtkmK/zdrUrt17/XsFMA0v/PLmX0xbY7zzAs5QAgE/NfH7y5YZdpMlOT0dCgabIHl8vGxQrcmVhBKOVoqgC4AWcw3YV7JRV4BfN6XUBfTsjqLFaYARd1pnsyJyQ0ixzEkpbt1eGXzPdrfPrT8z46B0djZe76KIUJwDQo+pMmEmEeWwF4xzScO5cnEpzgvQFOBkMAhUGAgS1UqmdA5q4zFMqd++xnTtWHgoUaQ2cL3/qL53OUkiVZB4E3BnWDa6qvn77FMo464/BI5XXbZpa7RyacMtVypawonJLm08ssn0UPgD0vR19atjvlceOh113/sSKrvEIB/Okj2TmBQAi1Nrq+/wMEwJl1Hts2Xg7QR+rNsBgu3i5tC3f/mM00nACwGU7QcBFO7bULJRq3RMF4YjwMADAMdvBRdnr6/IWehxUgAEAu615TFw+lsGxnACkJlrWikqTdIYTKo/01w8W8NEUp1mLSHlv96ITR0667JRZYcJQTDTjPSJvYlBI8xwe3+j/afSh0eOBW7eYJUhNOb3EHCy3U5EIExNuVFFxewRyijL3hEsnwswECAPjKM7smo8H20bGKStPkDq1Urqtwm0cr3frZmf9qcJ7Ns3AG0INwBuAFgmw5nduVVdnlLItX5IawDJUtCvFBAgQAKOrMYnG4w3C6oeZACDhCIAkMtkUEnCAE5YRA9HCoh3M9LXAy4w53g4CnBJt6iHo7B+smzxtdn/3MqZv/7Oe/czgULNQAAC9Y7hd/7G2xcdNwB0AXpPZ1g28CCNJpTw4Acjn54goBAmaOvRIDMwInQOiA8z3MgdIF22lwgrkPDdbLDQ8XX4CT4noOzgzzoSEioJshpSWOvx/hksyqnU2xSzcGK59afe0PDobQHEMemZGTDwvruAvKE4TYYUKWl6V9HCgq4rID6cNAeWUyhmVMdh4EuaybAaGUZS3VMrl7gwih1OUpTjIMtbJpV2AoJV9XTaZ+W0NimCqu47xgZC2LkWyLFQBAlrXRivLsfkoxMjFYZcBkusJV8aFgbWIHIUudR+dhTaqmZk5mUb7Ek+ccFvfCZ4Qgqh0Fx+DBwZ3eLZNd029vKWaGynfwktYRqE6d+FfHxmO/cmgyJrI0SghVMI8XQQjgL8tAHa9cS6R3J68zcQDncY98uAAEVANAKcgMUYXCI1rLKV2ueEyRo/URr5Ecy6zuEL0UNFfl1Uu1BR4HMEkp9RDidBKUwrSBKdwT4VoCk+Ap1Z9p//zPvXOcZZdOikVZD+45eutEy9ah4VNvHkrqqrhA/VGWNR0ADINbLf2fRwkOS4+CzhCHYN5CrlGam1hcBrDBpJiM6pi6nMeOpzy4WS2glRD8+KwA14cF60HCQ0RWeYX65Nd+Fo6A0C8B+O2Zf39gHPKvHM1/P6KuIv/UeMLTkci6Hnka3idaXeTBTWTu+3CY92KmlORGxyquyLKWd7vVMlnSNxGCktr3jwqFgvxIOyuCwexDlbbe+/T17fuYqzYhkJWCdMkyWVOQdBcvGNsAhAiBFI0GTk/93eEmLeOqeu5TZ5ICb3WxnNWYz7gvv/21pw8DgK7zQQCQ3crgzobxI4SBaJvsMCjRACdIMDRugBPM+kV97mvg/1DwDUkNPq2TpsW9hJkTQVo62JJ7s0dC4OP96oGaH76K7I3qb1q9ZScBgGVQ4RFsEQ75bS+cjNSVmX9j857eqqnxZeces+2HLQC2iiyNF43SHBgdVnRrmTbCM2jRLHKFI1RiGVStIPS1asBU0Jn75EvRfEg2rwRks17iSpotEY3aPb+Vjbv/L0/IKGe5xT4XAICEgVWF2aaycttXz2x655+cvL2NmlzJjJ4vmG/4+E+83fB3X37hPLVZC85kr1yW9AYA4AVjtXswgoV8BABAjwKmaKMuyOFK471fyL4BBe9eKuDkLL/hTA5tLob2Fm0y/vJqF/SYsR4kPGRklVcMOOm5/+iTX7sC4Dt4CGWdp4N4YgJEjxL7N0YPf+9K413LZh5pa6dfMp9gCo94LYvdl8+7kM+7AFCVYegdSdLTHrfik2V9M8PQR+bHQSnSus7dV2dF1sSISaGH+NXq8gBAIUr6w+vcIHSAYe252aHsVhdnm1gAKC/P1vzcy99VGMaeYBi6mcx4cKQT/rmBg2Fsg1JkfYFsPWFmbYhJQ3Kk/G5Z43SQ2iSbngjpsk855w7ljsxrzVyxNEUEM+1p6+9iPfrTVoG/kLvUoLAuvVOuzOiMYH1kyQY+LUNNkgBLQ/MlwAWX5lcZjMMpWeXhzNrnC3yxAG4C2BGsSG9kWKvfttjZex2FU8OuhFP2mstWGTZDAToKkAXvDQqKfqJPCxr0rS6USRxdC9t+1fq4i7fdKYXqAFmxVXMWAktvRrwrcnQuCSAtn5I9A0GGXfbZGtNLd10sOBahmeJQrelqaK8kwA0t5tONrPwUQGBo3IBlcBnTJMbty1sUajMHAUgHnrn8nmWxPjXtKRSz7ogs6au9f+1SH7bKsK4XwOzzLNRGiBoILk7QFG3SCicgfHe1a3qcWA8SHiGyyivf88mv/TGAX8QHKAgzoNF66UPv+vhAYBnwx7aNu9tv1uZmBUoeBWR+rVr1jwNEsm2yq1iUZtoSqUkIvcO7lNGQrxiSBGsTIaUtth8EpsX0AOTA6mveQ9pC3M2sTfrY5VaGHqbcNCeYE1ghhTy3HmeX7BQK106XEWJblDJsMJCPE4It8anyHn8ok3a5lT0AQCmzxVD4G8WMJw1KDioZl8jy5hXZp+yHI7yzbCmCr0ued+0bbyQz5T9z0qcCxG8VxRP5oQqdEY3TciRTz4rmPRJzSj5BU1IWDO1CS9pPBLsaABjBsgHUwQkGSmWXQnCyBCAEqKqfHBwfrJ3APe3/ks91fUBrixW4s9MFvm6+RPEEMdotgra8hUvLXV8JrDrw+0Rr37gzm14DcZvqAclcJqVOBwGkAfIUIQRHBNeyPiCUAmSegvNisKDqXg/ON4rY7fNrfpalDICdciQDqTLTqyW8sdStemsg4Wq+PCV3e0fCByXilF9kt8qFKtP7ACA+VnF+6E5j4woXZGEZgaQaAY1BDoMhbmEpLmGWEAlz8FvPc19+/y3zcyWDjieBdeLio8f/D8tEmWtFnYT+x68q8PjgkY36rfWJ249q/yyhKYZ8mEmfhLNtZtONofLj715v2P/ti03es3equ4emfB1FlTtP6azP3INBUcT7bqeqF7G3jF/bPSuvSI+tvtbaIbjUD/S0C6K5/fkfffdasCLVIwhGFQBQSgTQhYTSTDS0zVDEvQARJV9xdCZAAJyZ+ZKSARHMtPfZnrOufeMg5J7dN+PR6fy1bE04VhgurzUYfAucNT1vmQ82s4X2Bk07x98AAFvjGDglyWXLT6oitF7v3Pqtb33lI9fGB2uehZNp2LnafVANYkSJ0QEANqgxDeNMilhtADCiYXdNy9jp1fYxg+UGtDkQApA1Eu68onWu3G0uyWCIot7d2DDF1dXG1syfYZyWx4XnAmrtcNHTP1SGdJOENkLgzyV9G77x+z94upB2TwMAYdAqVeSOsKJB+8fK7vyn276Wr2bV9pSJAQC4cX7rXPDl8ucrwo2TdznOLEVSBRyRppJdDR4WVZX8wgCBUqQVe1nOWhuAC89zX/7C89yXPxTj83om4dFDgpOue+BZ8rHAQzuXDy2aItlDk0nPd1J56YWHvW+fZPbgCWsUrAabotumZKamSUgqL21K5aVNTuRE4XPpA1WhwkRloEg8kl7HMGtvtS0UpEfqFzE2Et6Xz7mul1cm04FAzsMLZusHyYSIbm3VQWk1yG5tnyEpbwJss23TsVBlvMwXKrCmOn9STHQ4AfxNNevabRTFTlY0NXcwz3CCueCesQGl3/N0n48QHIGT4p8DV5nb6NAH5sU2nF0UNsfbYJFe2l02jAXkQVJPh/xaLu67Y6VdJb1DNEVI9d1uuj3cU+fSVGEXQD5+v/dAlE1PyoTPR62uIaILlGCuLdIG4XYfuRnJpXyD2aRvNe0WFUsucCncghXN69yqztlMib2wrDVdFUm6CUEty9q19XXTVxVFyKbSnk3mMjwCQoBtLiRvzBVDKFoldO50I8KShfb0oKQ8OVF+rLtzy+m9L1yqBIDMcPkZLeuKiNNV9jPEuFXQxQOTOnqDHJo37+mdwEz5xuVVml3esWbJW7x7+s1DBkAWB3QpOC2QaxpPpwxcBsizK6yyb+bPZQB/u5Z9PkqsBwmPGFnlles++bWvA/gnD7qPE8Enq9T3GDAG4OUjWyeGv3WhuQXAH+PB7YaXwC9ZT9R1ci1QDGaFljuCbFFszhbF5u4xR+1W5M14OFAciIQKasCtlnMs3VjKYIZSKKoqPNIsim2zcjIR2JVMBGaPCren2F9emRqvrZtiZ0oRaxz4aYrlzcYPek6KwmcvXG55ziXpd7duHrvVumOEr2mNPpvorj6bHq7YTwBKbSIBZAiOD4Vomdwhy+Rg6dy5UF38XjTBWXnPiX6WkDmeQmD+sQiDKiJYGapzfgCQdkycEpuSNQAidMTndvaPDjiBqjizTaurIptRKc4bGfdcAKtrXO6dr7X1qIq4GyDLWsqvBf0azdvArkGis1hki3h829glnrN3nfzUKeVbf/Fc1tD5lYK6jXB8albUGKlwG3V5ndUAsmybA0PobZmz5yh8LGvFKyrSdyTR2EGIM7smBIRl7T1ut2onEr6CU46DSSlZQo71sHCBAgFCLj8dpILArtwBFhuptAGA2sSMd9XUWhaTPHj0utSS9LLFrC9xob+SZ1hbC9fElmRqysKpTXAIrYHZzygFrheQIATnBALWxUAMcqjwsqhfnP2lFNnrBdzuUcmzmOcougx2v2V+7vpK1/K4sB4kPB6cxQMGCRyhw2GhBFv6+xPLzUb+48nOV78LACeB7s8zX/0snDLNQ2mPlDj7A89MHzUyGreaO9wCaAZXPhLzlY/EnHc7y9hqmU+5UxUqpMp8ilvirVZC4Ldt0gOQlfq7HwEICnl3i1KUjLr6qdm203E4aVkLQBiOUNGSdCrD2v2EfPDvXRBM964dQ6cDwQJ34sWLgiecOwkAZZsmjpRtmgAAWDp7fejdnUvuja8yPc8IisJ7ov8qYen8Gf845rU4U4U7zUcyRB8pOwoArF+lhKXNNCF1QOFnB4IT739771ut20ZbaxpizYBjKAW6UFugEPddkSStTFWkFTUHDIvECaF+jlm+TJG2wAOEW/iLo+AYqn9if78602HD1zZNnB/sbnjgTBvLWgUA8LBUqfOTc6rJcAWdCakm02JTZl7AQKc3VygLiIY1NfEoy9DlBstRlrWZ+po4GRsvz5gmt+B3bFNYYwV2cjMVCEfJPlBlfDVv6dotIzwA5CaD10CZp1iW1sj+/HmLEG96qmLqmX392tanugkh97x4FINoL/1d6/nRjCj9C9Z28ZQNzC2zMdWjkueWHokWGCDGEWR4AtWhT0At2mT2Wo/AEV/yL90WAPBnz3Nf/uW3zM+dW/GCHgPWg4THg68D+Ek4IiH3hQ0yRlCqperDi3cBvA3gXwD4Fpxaax+AVwD8NJx2qpdxb1bSCeB/zN/Bl+zPnv0889VPA/gZAB/B6lKmy4Jj7DhDVifBPUlQCprT2A8027dsRppOu3dOp2djDQqfS+/3y0aSp+SsyNIGQtY6m384cHuUGDCnTVGDhdmEPJy0fQ6AH6CbACLzsv5QDH9YlrI/8PFLbQCQi5a96Qnn5pZRCsSGw92VjVO7GtpuvZ0aCHuzoxVzg2Qx4x71VWaaAUDaOnWa9WvNcHwHZmf2c+1+NCu003HvflEwRtndY98WGtLbCUNPUIpRGnXPpfdzGdeVt7525KNv/h+in3j+yqmP/eiZ4wBSRl6a+951he8yNf7Elu39/Zm0t723u3GbofNzZY+0yl6ezAluUAgUpHmAqNdrZTu73Y39HFnaai0SmLl5/39hy8SZ57dMtDAELpm5x5Paeeh21WB3PZxxrGQc348Ss15R1KINzRPdFZXJnYQgOD5ambD66572ijYq3IDfl+/wBwq1isZNpfKinshKEqVsJZmbYlOQlXlxdTU1cYMQiLU1cd402c5E0uf2eQvJeEYu9owHy3028+K9+8MNlrkMH8ssX9oduLrB2nb8FiyN0y8Ml13qHCor/vTBgfrahtjUhY7dh8JNk2cJcYiaU3k+/sNf3TwwlhX2As7gfkZST7UJLoMQp+QgMwgDNAuQRZkY4rYBt06BeYyV+RwQDkDKgD3JgrQwIDwFRY4YXTosxUP5iATuh+BobjxRrMsyPyb45Nd8AP4A95lR+Ge19MLzZbgvZvoTRBrAjpOdry4gsr176Is1APInO1/NzPyfADgJ4DUAnz7Z+erocjv8PPPVXwfwXx70hEKycS7iNe47OHucsCm6u2KuVWu5HxQ8Y0e9ojXokyxT4uwqArQQ8uhkGBuaxs/WNURXbbEjrJX0h1N3AFJJGHucEywBQDUck5wPjNhYRYenNkErW8cPEQJxrKvu5jtffm7LD/zK314rq0nuoxSx6JXmaDHmd1LMxO6uaJrexJYVujzHB5pnBo0knFkfC+AqgD2UoXdBIdP+QC8t8FxuoPKw7+NdXQxn77Yn3Z1IyocAQNe4nt/+5V9otix2blIWKMte+NmX3zJlxpE7phQ0PlTZDcrMCX7ZNhm6cmGboKpidVplL09khXqAVAAABbW6iZo2CcpCHD3dJEJokRe+J4oWpr6ZgvSju0cvyIIh7K1LHZ+1NBbdSrsvnJkLvg2dvasU5JRlsvapNw/t8/gLk5mEf35wfQPAToAiVJa50dA0kXO5lYPzS1zXLm/uy+fccy2zoqh3VFclFwQXlGIilfYM5XPyJssmocaG6SFC1v495xV+5HJveCyv8hQgJaSn6Ui1V58KyFZJTRRKqUL23j515XYt1zcROFHj0W/+/IGhQjHnLctmXbFDz58PCJKxEwB+4zuN7V+7W75kglLJsCP/2lPmYggpT5kY+F6arHUScg6LJoo32cSpAjFqqmz3RJJROZVY98olFL+ZVV/5whr3/ciwHiQ8Rvjk10Q4bUJrVGOk9p9vQ97DPbx2uEeM/+dk56u/sdaV3z30Rf5k56srikR9nvkqD+CvAPwAHiDz1RRUT8u8/YFqu48aBZ3pGE5Lj93XgSE06xGsXr9k5WXeCrAEm0iJGemDYs/+28Nut7piFoyX9C5/VXLj7OC1CFkAvZoixBnGruAEcyMha2vLZGXtLu9RY0ZedF/+1uG9IKBNB7rP1O3pP/5Xv/vjl5Wse1/1xtELH/3Ztw4AQDHmbZ+8vMEZEAjtq9w4Uen72J0MYRbok7QDeMrm6S3TZ2tajQ3Y2AoLaenNctO0+GHjk5PHPdekW/m3Wk1KGZ4wtvGtt/biyvnNS4IlQuypH/n0uejG1old+YT3lJJxLyExWhbTc+r9PWU9CZmfP1uloOYkMS4niXUQACI8bT/hdzJuhNgGpQwPALqN3O6dfVdC5ZkFgx0h9u2yhukN97Qj7oFSUEJALJMZsG2Su3BmV7o6lKSyS69mGLt6ue+gv7euc3K8ch4ngGZrqhNJQVjKMaEUum2TqwxDN85P7S8HRWOnrvSFe9IF8TBAOAAXgNKTJ49gtdcHtJLZR5Oi52sJshEAeMZWfn3PlDp7fEqptfvkFZMQiO8P+m6+/PetZRYlJUXI/rWn7GyE5Y68ncbppLkm7sgtOOqXAAAN1lQfm+5WieXSib1SeW1vVnnl6hr2/8iwXm54jMgqr2g++bUfAPA+VujDnoWLwR0Ph1JqZB82qAD+I4DfuZ+NVgsQAOBL9meNmdLDTwL4WTj65xfhOK8NYBVvdomzG+/nnJ4EMir7UNUK1wqbEl9W4/ZlNec1QEBNmbfv1JfnR2TeDmqq0Ao4rnv3D5pyuVYOEACA5c3sMgECAPgoRX33ha1bbIt1gVDb5S30+CvSU76yLCO61BqGoY0lr03nKvhA0c8HioHjP//tKVPno1pB0lLRwE0l62oGgPK62NzzJwaKM9dpU09ZNiU9198Dhh6igEXueRK0UdCLxWazhkozxFoWvWCRMg+kM/wdF+X/KpzNRYNJavAvAAAFi6cPdd28cn6pIjilTPiv/+ZIZUtz9L2n9/TtYEuwEBjG3sB6c98hSXkvpfcmCwQwqijfkIQFEIB3FX2bd4y9J4umYJqsdfPaphMAIDDUGyzLLMlSUUq22jYTZ5mlfiaEgICx77gi2bQQKO55cfuYnOiuOp0eDG9YyYI6GMpok+OVkCTttqoKmwHiG58oS9XVxmIct/A4hEBgWboJzqRp2SBBN5jMtYHKa7GMfBAg84OoEm2S1K5wG+cq3Oay5UkWqCGglIKQQ5HCZTKvC4IQwlKbDP+rdxpG//p22bH5XhQ8Za4ajv10AATy64V0wz+Xy7qTJrPWCcgCL5IbXHzKJMtyMebjkz75tbtZ5ZUn5gq5HiQ8ZmSVVy765Nd+C8DvrbbuLu8H649/jPjnJztf/bNHtfMv2Z+lAP7355mv/iUc6dMuOC2lbjgExx8stZ3A2uOEPLwuiUeFnMY9UV+HWVAQrmiwW1s2jsRqwumDlAKFvKs/Nh2cSMYDnKKIzQAJr74nQBCMVQmIkq943lOWXbZzh1Lo4711/bbFOrNTSphi1rOxmPVsnOx31mE5M1NWM91Z3TIpwekcqAVQSy22vDBUMepuiE0TBo28ZEzzkvERStG757lLN65+d39bNuafU7lkOGtrsCV6JrhhcoP6VC6hb8x/TAfAZcg1y0Mb3He4FKGkmYDsd3dzRa3Wftsos1sw0yZnllk3Rt7cN54bLLu67efe8+jXqy5QRThAKaw//OMXV2hXJaR/oOqZ4ZHKrs88f9n0urXIwqVgjh/o+di+HYPxv35r33VN53cxxB5tqosPfGfMWw0gQkDxG892lbMM3QMAto1xfyB7x+1RMn5/XjdN1uJ5KwJC+0DhAUgEgGXp3BTL6UuCBN5f6JAqsyfms/ODLVP70oPhJEr4E8x+V71djTsBwOctxgXBvJDNug8DpGF0rGKkRKCQhcNXKjkJYjhTmcgLZy7faDgAkFKD/pIUuEewT60UIACADcRAUR+QzMnDVfklkdvH/2zPyN08uRcgUBRbLN/tIJU28WB8CszRFKONeSgfHGbIIBwH2LVgwfmyYBRzRT8oAMDtrPLKfU28HgXWg4QngKzyypd88msTAP4Qy7Nb8ZEQHnAW91jxOoDvPY4Dfcn+rAmnNgoACQCJzzNf/Qk4xEYK54X9KQCjAF7yS+Yg1tx692RgU/RZlKxB+vjxobIsGwGcPnSPt9ji8RZbmlrGQSmgKOJofDo0kogHUCxIdZQyJQdAXyC/KgFR9hUtQkoPOgAQGwmfj49VltQRmIVlcn5dkSQsJLemAAzYBpdXpgKGqyrdCIcvA0LQuu3ETS4QTrfXbB6dq4UTAjbUOnnUimi37Y35j8wyNcwA3Q0AxSZz3D3AnwbAERCRzcMyytAEJ2CdgkC5W5ZMdr143Zarsk+JgWIh+53NNiGEfelz73T9wR99rGT3gOxSbypFscE0mU13ByJnDuwYLqkJ4JKN8p/6ZGfoelfd2a0bJreLgtmWulb37rnB8rof2jl2YX6HAMOgZsfu3nnPPZ0KVCfP8ZJx2DLYCVPnrgmyvokwSz0TOK/SLlVm2xa372XHyq4DZNn2Qsti+gyD38px5oTLpR11ubSJfE7O2ZTxAqR+bLy8p642JrAsnX3fXUUJQjJhbKvxQPfZ2p39m//sv34yvJQQeO+QC/9Lp2v9Wil77HtrUFjJIjfWSrnYobqoRhZrKQAAJcxMOWPmhOBKMlqx0nL5AEAGVyfbXB0AdKu0GWtoDS0FPxW0abKq9tTP3+9+HwXWg4QnhKzyyl/65NcuAvhRAL8KYNEMjeZ3eZcahnzI8NcAfv5k56tPTEL0S/ZnFTj+GADw1ueZr/41gH8DoNMnWh8KxbKVoBrMBLAWf4THA1EwshxrlyzhEAK4XFpdfeNkXX3jJABAU/npeDw4EJ8OGoW8K2LbTCsAlJWnV+XRMIy9bJmFUhjx8Yo13RdfeWZxjTwIR4wGZk6CynnPimU5OpsoZzm7qX7b8BKyHBWtlPp0MoQSehOswuQoaC0BSQPYweTJVdg4AwbumeNln913m/M3xkIAehjZ3CVunn5H66loCAYKBwXeKOoGP1NipJrbo979iX/2prBhy9gOQ2f1m5c2XMlNBtMrXSfDgNmzdXSW20B/eNfo/h/eNapjmdr8PZCwqXPdvGSA5a1qlrdK1tkZUW93VaVLDnjucLox0bV8Uo5l7Q0sa+YYhqoAGEUVhrze4lgm65khZjIbJ6Ohs9VViX2Mw4NYrCKAmp0D55oOdtUwM+2mw33VK5VkF/y2WYJJsgLXi1Lkx7NCd1bjjgkArvRUZXc0xi/WV+YWEBw/EoJ8d1F4W25Ly3RLEAFOgLCWQGGBtDVD18QX/rc++bUfziqvPFHr6PUg4Qkiq7zSB+B3ffJrfwHgNwF8AjMPejmPu9zjcy18EJwC8G+eZIBQCl+yPzsFp/0S/zP8xo/CMepZLp2v4iG5dD4ochr7WN0gV0NjTaL7ftwyRcmorKmdrqypddSHDYPNJuOBrmAos6phV2oiFAnVxadnRXQWIaerYkk9/MXwhrIrsOMJ9JTniJ52qXI408551f2ELOUDUVAoL8b7wZQukRiV9mFbtC8yaU4Vp9kzhFDAgjoTJKSgk91eRiU0K3QSr/EcAMhbpn3S5ukNys3I904+c8Po7at27z1ytxhumm6ta5rePbtvXrCEvUe69xkKf3GkfXsBjgvkrMohD0cVcvEseZCsubWXWrykr/iut03mSi4ZrHTXJ0wAxcWKmVx1tpuR9GlbFZaTgy7YNuOzLKZ5ajpwQ1GWEnENgz8ST/jbKysybZjLoFJUto5fbj1x08vy1hzz3zSZEU0VVlIVXZBlpQC7nHQ9pYgOpcSsYrL77n1KfDeHyvf1jAcv1Vfk1Naa1FFCQEQGS5ghXiqs5qC6D877cBdQkmSuAAtFnpKMuionDcCLAP7AJ7/261nllfQa1n8k+NDPtP4xIKu8MpxVXvmncGxefxNA/rB/bTroTwjfBvCRk52v9j3pE1kJPzf10l8BeAbAPwfw53DcOaMzi38UwL96Qqc2h6zGNj7pc5iPlvrYB3rueN7yhasSIY6zV5Uht02uOhfzl2x/NXVuaE0HJHScF8zVy3KUkZRosC0/WJm3VO40XeTjYFfqd6GTaTiDc4mThWH5sNcwuKyZkQgIscCjBY4gThMbY9vtCm3QBGtR0OtwbJv3EwLwFYXKfXsHPvZjP3q6rbY6UW/xRskBm3WkoN1wJJyPw9EmOAxHVGyxb8B9ENkIm54o20ZpKbKfowSYjgZDatq7ZfJyS9fwe9tNSnHPc0I0z7DVhTZWMpe4QVIKY3Q4cvrqxa1ZShkWABRFWhJIcLyRF2U1HixPz5YJNglupf3wz3z3+uZnr+1jeWtB9iqd8E7YoPY00fqcr2oJBWGBiipLaMnn1rJxqy8hQTFL6ZAQRjO4p3ongse+c6mx/1JPuF20uCVk6n42011q3/PggfN9LdeF0A8slrRa89j7OaxQkn4cWM8kfIiQVV7JA/iCT36t/ZmQMxv+kOL9k52vfuiljgHg5ehL4wD+4PXIG38M4L+9HH3pwuuRN/bC4Tb8/cxq78HJKDxWPQVKMW7YzIeKMxEOZdfYnrsiolhDCcWymORId4NWlo+djjROHiHMvRdnIelNMIyVsW12xRekIGmjuA/eCTXZysJIRSUr6T1ydarAcPYeAGBiwiYq0BE4aWELJu4ApBUclYiGcXc3ZxRbrEHJNGym3MrYg14X6yv2Ww0qAwpDP1Un613+iHxgZLzQampckekTJxk/sYmb8alhALAMdvzatYa+//belo987Q/e0ETBWtB+SBhaB0Jt0CUdBOcNg3nqm+/vPJ3Jy4HPPH857HbpgbVeM0BpoDp5iRA8vXQJ1Ew0OGLp/HYAUOK+7QAwcmrreM2hnotcJKeQSOEEAHjLMpYh6Fdsi9GpTSxKCaU2sUemffl00jPEEDrEEMfrqaIyZblko1L2KIXapoliZU18DyEopxRtNkPfnB6vLI9sGmV42SipBsrxFqdKyuC36ZT1h63smVzWZd0ZKZ+fncjMX98l2AsCGEpRSCrc5ak8f9wplK0MmzIbptLuDTTtSlTz0TsTRN06O6wrxFzLrB8AdgCIYanNeG7xiiFbzE+xa47HnwHw5bWu/LCxHiR8CJFVXjn17qEvfvpJn8cy+MrJzlf/85M+ifvFy9GXLDi91Xg5+tIVAHg98sY/g6MA2QMn07ATTmr3kVlWz4dmkWE8RmKlDnsySay+Mspt5mdEeeaD58w8x5XmI9wn1lRwzST9N02Db5saqkZsNNzVvKuXeAKFTZTCMBThaGX19J2p8fBmSplltRE8gbz2ICdoqcLG/EAleJ9yUaxMl2kfi0/AZT8Pp75MuEGXKIyxHSSsBImBBrXBmrLd9Bm11n5TPi+bWs5VMH05i5igrEpu9p9uJZt+5PxlIVh4zrLId40yO2BfDnWwBS44MxMOgrGqUgkPbbbF8VTcJ0WqUwvS6YSA42Vt3ChK858JDcBOVeezzXVx5tKtelkSjQAc++gV0+CE2LdFj5pwBfObWc5+Fk5K/DgAUIq4ZXA9xZQ7byjiktKAWRRrorcatLrd5yOEAGZaGmApOcp6liYw7g5XjNuLgt3Wp6IdB57qW8KpIgQsx9jhTU/f2LvSufuD+X3VbnPqP/nku+Wu4jPlrixsSk51jZYdx4xexfz1rXlFT9PG1aGUVKlbzH1rj7AE4k/zZemCqLz991q2ccQyN+jE3m/ASvFgV9NzCMGR7O7BvNbskGikPYLdIbCUiowNgaVES4hF2HZHkViiCstrgpZRoAKl24GfaMZ2PUj48OIUgOeADxV5MQngXz7pk3hYeDn60sjsv2cyDV+BIxH9WIKEvMY+Vj5HjJi9KWKdiMNMSSCnvWDho2y1BNJMQNBQnewmBPvmb0MpqKoKFyRJbyAEJZn3JbCmwEd2KwGlII8BpNa22M19VzeN+8vT7S5PsciAPsdy9lO+YLYjkwws+7L3V6RXFeJZHgRG1rXffHHybSZk7pj5sFZ6OzTOxPgdzMZULzHILr3M6jD9TvcAmwdLfJrbmqa6MeQritsSgglmDyOpZ/LXa4j18p3vUQ9OCFHmOk2JPqUrfAhANwCRZYFkMuh3gYmIwtLUPQAIPmVqUZAgAsh73Vpk1+axyObmyRzLUh7Ou3sci+41IfZd0aNOy4FCM8dbGubJKVOKZi0vvaPkXDWmym8GSDkAmAb7nmWxNaKkLwgQtWlfc3GorN3dlGhjvVodQOOz26wGr0ddVqWPWuxePeXqFILFZbslCAE52Harzx6qLps5d7U5ktkzFveeySvCEpJgQWerKEU+UeSuThf442uMU+cgcVZfjU+PCyzd4zh98vg1LmTfMfX2Py2kjwEAAbVFluYlzs5JrK3IHFUlztZkzjYk1rZkzrYkllJCqDlREKIyZ7Muzua2lylPCyyV5x/PmGpK7UNwwbNrg9oFmNEsMZMZYuTSxNCyxKCVVFRn/GzGvmR/9sx9XdhDwHqQ8CHFyc5Xv/buoS9uA/Dvn/S5zMOdk52vTj7pk3gUeDn6UsfrkTcCANZElnsYyGjcaoSoh4Yi7J4MLKeUQBBUQY+pMBEjJkCR5oC+Yp6NV0z4r2+qzG7mGBofGqgdScQDjZoqHjxw5PpVQTDXEiSkATSu5Zxkl7qLENpLZ5nelNRkYsEaWKTT4yuwAOD2FrZnkn6UfulT2xPIfaDOEPLJ4XZmS+YjcGZrYQAtZqOS4Fx6O8vRF4tN5k1boubs4S0PpMxAJMbZFiNuTlZalHjs/75trKUmysnhjNvudIuo1kZ0gVP13orZTMEUZvrpf/Hf/JXHBhGN0rQESP5ioRBdMHZQOPyGMgAQBWs2gN0IoAugOggdlDxqVA4UNnC8tQXAFgDnscgevZD0jigZ9xKLYpaznhmdKD8bqcgoskudS/+Xn+g55WpMHAcAu8hHAVK3eNu5M1wEt1tdcZRWY/5DFOgUZwIFQ+Oz3e/svpqeKKuSfMX4jo9d2NBUF8+NjIaf4kUjdqMv0jsU9e+3KbbCmaws4KGUebS+muqYPNpdxd5vgAAAPtGaFLkFJl4ghDDbeLHt9/yV/TVVSdUwuD5CKLFMVvf5Cnttm8mwrB0uVczYVrYqZWTJXWNAGC/4iJfykZqFMUX7zN8fua+LekhYDxI+3HgdjtfDI9f1XyOuvXvoi+6Tna8+FBOeDyFsOC/00i/DhwhKkdTMNWu+f2AME61oE+wuuZAgYAJPDaRdGDjbCpbQ3s+GzbBpcnMcjULelRNC2ZKbL8IYnBLOsnbBs9A1foBSZknnCccbNqUYMA1uJiAtpdEPEMbuYz9AeYTZO32K7E3MzkqbAcQBlNMKVeRUttpm0El0UOqZuRYbg/IQswMfGz1VPFureL9VGS3WwjV0eRO/7dlLXLEgZ1xV+driBrNAefMZribzDgxmxJz0HQEIGMEcYXj7KZaA8Iv4CLMQvMriwYMAKJFxooOiW51yBfIiJ1qbsPQdsWCUsm1yW83KO1ACQ2mp9/du+AsBlKv/4QduQ6rMDVb/0NU479XmBk2tt2IA9/G7cMnaqmOLFvNt5dzaKCtYdd3v7rqaHAm3AUAx6ds4cnZze3ltrM596M7VTFbOD53e2GZTwsMhLM7vSMoBuB7yF32CYNWZNhMtdazVQFeILFiCSDQamgbInINlLue6YFpMpK42Ns2yduXqrIcluJ8sogvAwJfszz4Rs6f17oYPMU52vjoBp3Xmd+Aw858U7sIhzvzaP+AAAS9HX8oC+KPHcSzDJn0PMuN5oGOBJiyHb7Em+FlMmSa3oJVrbCRcTulCstgy2A7HBOkuQDtEt3KKFYzBxRMn2yaF5HSolKJoLDFVcWRipLpmeiJ8dHoivKxAjuRWp5dbthqIaMQ99dFtxMS1mY8YOK2HoCI1CptMo7DRLEjj7F5QaADOEB03CpusXtuLbfLeaDlyAqdfL1OrtvQVR27UeyRery/42AnKY7+rlzvr2Tf2Uc+hkWddB4avyFXJ77rrY/pqhlq8SysVPMwMKHRMkLV3A9WJ2+VNU02+cKaNE63l2j8XtNYyDN1W1jidcZdlv8vy5sj8ZZIhGDvUcuOXDoxp7obYN+p+7ALLe7UFbbD6WGDZrFepAVaSjFWDRID4imNlU0raNZ4cDjulB0Lt5l297eHGqTaWs7dLbvVwV1+1y6bMfJ2BWTLrZThuosfyKi9TCsuyiFjuK3Zsqk2era/IdkSC+fanNkZvHN061tdSnXxvOStpSlca5okbIAvus2mxBwBSPzpWWVkoSFdWv9YluN9S4+jnma8+nhfGIqxnEj7kONn5ahrAb7976IvDcGx3/y88rtHFwRCA7R82PYRHCAnL+OU+TBR05rFpsSeIeQcEKyoXzkeNsHTykEn7tl65sG2U481oeUVqujKc9HK8uQnO/bJAMU2YOSIdByfl3eoO5WMsb1VRiiy1mH5D47PZpC8fHwnvsCxusR6DDkfLvwKUrDrIeIO5B3an8xwZ6mYIOerq5lDYao7Bke8+AeC0LcEHGzF5gK2yPLQfLE4CmKASNsFRcywnBrlqZiVGmnaFCGtmKp/qrx+9uuHu8J9teHHX73zz79lA6lMAQE1yg7PggVc7CIfQtiJY0Shb+AlN8pI27QoUb/Oyvm1GZnwCTlvdspLWwNJ+f0JQ4/IXTdlbLGSmgrcNRdwGAJGK7GahItH/nWEvdks1xme2jpbZ5copJly898zQ+xsreH5tHQHUYvx33947CKAmVBW/WLtx1M+w9gLOwY0bjfNLSmEAZ+FMmubWa45kFIYB+9y+4eWCYRrw6MrG6vRopiAOuSXDp5tssf1m7SHn8h78985x1lq7H+ZAAPs+Hl4DwC/PyNM/dqwHCd8nONn56hsA8O6hL16GQ2b8d4/p0K/8IwoQAOB/w7H0/g8A/umjOkhG5dZEAHsYSMFck3PiLGpFlDRmUhSpDgqQy3o2DfbXgmXtAsPYKQCglHhq6qZO+/x5W5I0lyAadYTAZjir3LZhMQx8hLP3iJyGCncMFXUxUIqorgrDhbRHzSR93kLKZ5g6X1K+uBT8Fek1+UgsBhfO3WQD6lEAYEwScvVy6WKrWYQjsnQUwNsAapQWywtnQP4WHNtqHxzdpY7o3TBIXxX5+3e3JNr2DtreyYqr492NP1R9/O75gD/1EQCnqUEo7Q0dBp2T+W0F0IF5ZMLFIAytAew8y1s3XIGCKHrU3YTg+UWrVc/sZ0mQEI+GuoZ76qZyabe1ZW/vjXBtbP6gqQAYJQyOBapSyE77L2t5aRcFyXz8QO/Wnz1V8eX/+W7lzqd/zTxVOSk/b9ukg1QUd1CLYWCvzbNj7h5z1qrPXC4nxcenvIl4zNNw8NiNy7xoLhHxMk1mIJtzLS7LHQFwFqAWQ+g4QInPpa/oEwLgDIBjDAMEvVodAAi8jY/tH0wDMKIxf0YtyivuoDSoxjD0QTZc1bhhHngA732e+eqWL9mfjT/AsT4Q1oOE7zOc7Hz1r9499EUejkHKlx7x4TrwmHwZPix4OfpSLwC8Hnmj/1Edg1IUiwbzWHgmGuxxy+nfXhMI6KSXXUt3AoFlsW7LYt2znwwP1szTwrdpVVXiu7msixi6iPotw62yV1mQsiUEEVHWI6KcRNXOwU4hUDxgm2xPPuGbSgyF2cRgpF7JuGuXIS0WZU+hZDBDKexlnSUJtd0HhhdkKViFNPNx5h2jwn4WzsE+CgZRAFeJBpmKYABMApiGEySYNU2TbOyQTAb+9Fl8sq3bYzOMsPOfv9UR2jx5YuYcdtNRX3RegAAAPKVgCXFkfCnFNGwybZts1tY5w1J53tJ4X3njVAthyBJr6cWXWerDQHmm8dS3D9W4PMqdYEUqDMcxdcfM+ceBe34F3vKMz7LIrVPntmTPjHt+P6BrP/OJjdOavjVfbmnuM2xSPkGz4uVCNJBfxmQJAFBXF+8bGalcUI5gGMejgVJkARQIWUgIphTT0cnglVf+bovgaRntPSaaJ0vte2y8bAwoqSx5pCKU6/jBZ6+fIARIxoLtSsG13Dlm4WS2loAQBADAJRnBBwsSiBiP+0aqqpJ1K7iZPgyoAEp2xDxqrAcJ34c42fmq8e6hL34dwC9gFavkD4g/Odn5anL11f5h4fXIG3VwukpehFPeKfkCe1BYFN0A2fMw91kKJmiyl2iVpbwIloOHxSAeQodHKJg/LYnW89lk0B7LCd+boGbvMwf7lpVP5j1aIyFgWN7a6I+kNvojKTQf6gKlSOlFcSAbDeUTQ2FPcrSixVTFAMPYvZloKBuoTh4jBEQriDe0gpQyFMFlW0w1w9pR2V/UZF/hKUMT+ljeLGM5OyxtnzxDFrHYAYBViN9YOMuPgOIFuY/tNyrphCXYhu1HBkAlGMAedenGxQrRH05Sjje9VnMqZ8raBtvERSPq04nB5Ng8f5yw98Zy22KGLnzlaf+uT3YGGZN0aAnvUYAsEa9a3ox5AUqKTHGcJW3f33WVMDYRRDMMJz0/6y2wMEhj0PqNd3ZfHB2rOOEDpDbQLZ9o7f9uIMuOmc1KiEnygM7stJIrZ9M/8bGLG//HH3383n4JpZloYNDS+RrbYsoB4iOMnSWMXQAlPKVEoDYpC3D0hSlf8s1nGLc81lPXUVE31SpIetXUUORMNh6gIJRe7K5aduROpj2V8VigPVSWqQmWp44pBXkSIKWe3WtYIXsDAJKoU1nWbpaFsoVE0ufWda6KUrAsa2eDwfyQwJvS1HQgbBh84+JtdcPJCk4mXdemUu5MXUWuMuDRmlmGin1p8cZwVkw/XZs9yhAwOYUfTubk6BSjZ7OUDhqgHgCopjz1gyv1TsgD6ARwE853/tgDhfUg4fsUJztf7Xv30Bd/G05v/6PC77976IsXTna+2vsIj/FhRAscl8l2AH/xsHde0Nk1tQl8UEwR4xbIyi/HxagW7isNuixyebnO7y8CIEralzUu9lb6nzm4nCYMHWd4q2T2ghAERbe2r6JlEhUtTrODbZGhzHjZZPJO3Qv5uK/DW5E9oRWklJaX52aStsXWFJJeFJIeCpCtAE37G6fO+VsS2wEU4MgfO+sKdFyttXwA9sIhwzlaETYKjE2yYpTsKWywTwEQYCGNSY7+z/+zm/5Y65Txw5+9SPquNsbe+uOPHXjpS3/RV9mQbOMY+zu5aw0vzF4b4a0xVjIKd09vb9ILru2GKvT4wukTjGBdUSYDrQB5EF2OHXAGkCVp/dYdA4ctk5mviLqsP8mP/egpbzLpPd1+aluur7/aO6m43EeLaqXaagV1v/Yud9MXZKNa0spLS1onZyGKxoIoQpbVlKGI4fmaCtRmfNRmlvgafLlt+mDvWFl3fKzycHysEgA1Z7tZKIU5Ol62rLKrX7Smu++0tAVDmZvbdvaxLk+xp5h3Lw4SzgOr83FE0eQi4dQOAIiEU3PZKErhI8Qpv9XWJKBp3OmrUy4StOR9AiESAFBA+e6VhjumxUQAsns84QVANb9bO3VVNY2izbBGPHDOMtl9AGkE0DhNlCmQe6Z+ozDiEmVGRTCLO0jSAD72JfuzT4y4vt7d8P2N/wNnMHtYmJj52wLwrwF8Hc6L6B8b2gHsfjn6koJH8BvJqmzgYe+z5HFg3bfme7eCXd9I4uLFHNozJr3pvLTvH4bBNdg2eZfhzNQvvPzetld/tp0Cjl3v4nUZ3hpZuoflwbC0MVAXP8mKelzNyUeVrHzB1LllJjyzrHUS8B0bsmaMizg4nQwjAM4XW8wJMNgI57tuhSMrfYfLkFOwIekx71V72t0MGzY3IOnSEGd94gd62ffe3WoPfGcfV12R3fYLr/7Npc1He45RijGkxJNCIH+OMHYGIDXU4A6ysubhRHMUAHIxfwoAeK+6112fmALoxNLzBqhN5u6VabBZXeVjI3ca2vuubOyYHKg+13Vhy1XbJiW/H5azBdOYC0YPALhUaj1BsDZXVqY3yuUFHQCqf7BPMsM07+rlKuQoc5IrzzcRz8rmUKmUZ0GXSSicSdafuEMJY69asguJdmhnVW5eIHCvPGNaTJdlM8umMcpcxlYASCV924cGqr+Tz0umbZP5Hhdn4QR8ayElLvidz5YOCFlIABVF89iQkMUr2enCl3KJ099W86ezJtymxWwDyDwdESJmCtLxSkvmZDDVpsnuBIgMAAZofH6A4KyO8l6iRVTYQ7Mf6bCjcGTCf/hJdTYA65mE73dQAD8EhzewhpajFXEKTkryBwG8DyB9svPVJ8KmfdJ4OfoSBTD1euSN/ww4NcuHBUph5HX2sfARBJCsskzL1/IgPsXG/kENaKzInWuoLJiKIt4qFKSMogo+22Y2AWTl/DOh2PH0tbP1W0fsYCRZZDl7Y1lVerIwXAHLYCdTY+UmIbTIClbSHcqFPIHCfc+SCIFQsXXsdvRqc1s+7nsKqwSzrobELSmcm63Hs7inZFonD7GXbBGXLRctmAFbpzxYAEfNEC3P+8y7TDJb4M6UZ/Q3a2zK60Yu6yI2ITQxXM6HiR06c6Vl5DP7ez2pd1rf9lWmmhkWglSZOyxW5CaVycAAgKwYUNo2PX09momGLle2js+RM1nJ2OBpmo7lhyq6QJnNAEApkE34b0z01hYiTROCtyzbOnSr+Vo+5d00yw3Ip53kQ8/FLWc3HbhzZHEDn2Uyxdtndsa3HLp1Q5CMMqC0uyUAMAwqP/kL7YFdv9Vxu2xTbKtRZo8L04wA6tTsGZe+4gAVDmca5/+/IpLO8C59Q8PTtzIjHVtv2ia3IifGtpiS+0/lxUk4M+ndKJExmcwJt+v8+nFCCDM2UrUJQBNAUV2VaBdF0w+H4LhWlJwMZHVm2svf00GY0hH9XhIiBcrGbPPYmGbifbV456fhpkypVlAw3mYqRsi8bFEB1jCApcRlAr6PaI08JRerKS+NEd2SwWQaqPDnBORv4Uh0P3asBwnfxzjZ+aoJ4NS7h774RwB+FQ/exjMJ4MdmgoK/fVjn9/2M1yNvEDizEB2LvOA/CGyKbgqy/WHtbznEYJxVQD8Q76E1oNYzDCS3W9vtdjvvJ0phqapwp1CQYooqyKbJbgDIAvW7j/7smx3VrRMLyhyGyvstC6MMa/sBcJQyLlNjkJkMRv0bJh7ouXVVZnYS1ipSi3WhtEWvA2LbVZ+6ttx3eJ1VmP2sAnBpGjOCtgjABwvvgIUbHDxSEmmtwBHB0u2RK83o76pjBsaC9mAsIA8Z/B6fR7vd/f626qBPrc+6IyM1B3uusYK1mxBUyZF0P4jTqif5lIjkG48AQC7m75d8xQpeNHwMb1d4mmJsfqByhFJS33tp8+lizn0MAIbvNAOgcZe3yABLOwzUgnwkNlp5rrJ+ek74ilIgOlR1mdrkqTtnd9T4K9KdNa1jQUHSl1fMTIhC80/c2QYA1EIc894lQk2mUh9YvhnHtBgdoOxs1iZcnVAAgBUsf8Mzt1pHTm29aKnCsvbjhs6X/G7G4t4yOCWgKTiiSQNwzJM2AphQDJa3KQq6yYxInN1MCM4CZNvEZNkRWda+53ZpGVHUeZ63Di7OCMwHpVBtSqcFwZq0TG5BueL8pKcLQNdHG7InAOB/jqM7YSwkcZqgFSaoIoC4I+WZrpOHunOFoqCOTQXJ5dsNx7AIOWKvGNAahO4fgd5HCTbkYad0Sj8tgjwxQ731csM/AJzsfPXX4JQHLAD/9j42jcIh9RyYEW5axwxejr5EX46+dBJAJYBfwXIqLPeJUcuItnOx9jGi3LYeMJW/FhiEmiBLZ19rBQGNBsSlPAFCwMqyvrW8PNtWVxs/0NgwFaquig/4fflTHGecf+7nvvX+4gChGPPeGj+3aWtyJKxlp4LXFmUiQpxsLlFdXNM5EgTLNk6UTKPPR2jLeDsBcpRiklKkFi2ezw9R+CS5Jg+w7Z5b3DamAA2AbTOgdkqyYrca2emxCi5dFJmf/KmOj/3fv/H3FRxHP/nxthuucCSdZnlzxChI9UPvb2uxbSgABgmDnfNn+ZQC0a7ai1e/dsx37o3npPR42V0AYDg75G6I24nJ0KlizrVIQIqUF3OuZWfFE/01tbblDCKWyRTunN1xKTYSOe6kt4mYiQXb7pzdzueS3tvL3qSEuMs+FT5LTWKAxdb8NrPXdNuL7alLgmMtcb7TYnl4zg4aDEulhhO39wre4rKeA6bBLXlOKYU2nXbNZnvCcAh7rXAyRqcBRCyKrd1xOTuYkrYNJKWrNsVTAPwca10KBAqt8YRv9/hExZGx8fJYIuHtNAy2k1KMUor0/GPpBnc5l5erAmXpJdyrgsnQGq/uAQDNhnY5u7RLggIeFVaWIbb18babssel7Q+X547v2TKyAQA2NUUvBn2FIYCCIbZVhL2qrgIlcy6q/6qX0b7xpDQSgPUg4R8MZpwZWwAMw2FprwQK4C/h9FsfONn56tgjPr3vW7wcfSnzcvSl/w5g9GHs75xR9Pew+bY3hei2PxOGjP8tDF99m5tqH2DyV3XYD0XNkoLSPGx+9TWXR0C0BtciNUsIIIpmcyiUP15Xmzg48P6uA/1nt5xTsvJ56tgfIjtelgAIASUbDFVYEEAwnNVDGPrAGU1fXbwFhC5LtmQFIxGoi++jPaF99E5ZFe0K8dReIGpkwfm9fIuA9EuT3DEux7QRkIirnzsCE7xab/tTQxE3BMNLKEefeeaW7Gmavi5V5M7+8s9+t71x24jbH0nvDNbE1VBd7HxZXXw41Rc5PXP9CzIcekGa7nl/105QUgEQof/s1iSlQHEy0F4cCyV5wfShpLrqCj0PlKkr5twDycmySzc7dhcNTShRWiBlA9c3eHRVWEa2mLjoO9UHMRNsgMVWpcWq18LWaUjmssRHAGAYyhJiz+03VJFdMGsnDNjaI91HXRWZ9qVbA5a51A5cM9hbls3MP24LHM+GLXDaOBmABABSAwCaRRos2wn4GJZakmi0loWyXQBgmlwkm3MfGhuvODQ0HKkbHavQLIvcBABKoQi82UIpwwqCvsQs7Adb0m1bQ+peABAZiNUilvIsCOSvimNVe45dO82ydK4tlxC4w2WZ7hNP9W78zAtXGusiqZufefHyRZPYJVt3l8H4W+bnnmjZd73c8A8IJztfHQYw/O6hLxbg1CCXi1i/AOAPZsoLT1Lu+fsJvwfg11C6Z3vNuGVq91pWCWQV9p5BtohBtghQ2BxIVzkVp5tsl9BouZs94Ja0yK0ECmp1EfWmRXB49bWXR7Nfe6DnwjI41/iNlsPjN1pQ2Tp2edPJaxtsnVs23BB9ynIttncAVFLOlqnLTjBZrr7USoRBTaBx6mx6MFJipk1Rta//DplTmySATTx03FtAdf4uTDJGRLsGDj9hGEADnBr4LQDHCCW8q4urob1BxbW3/wSzKXWlPn2TsyY8Fgj2ZAbCxWLac0FXhIlAdbKRMNjIMhbA2J3e2njNLCt+7mwo0Htq+935mgPFlKdZTXrO2SovUovd6y/PonrD+LmJvtr7+v7Ge2qnlLxrlzNwlgalTENspLKjZuNYybID2R8/gwLXAlF3z9wutx6xj9GKQq84Mv2+2lUZNDUup+VljhVMTS9IIU40sq5Aofw3/sXfuy5d2XBWy7hsfzC/pG2REKBq30Bb7E5tR3ak/Pj8zAPIUoniaMpdKmCugVP+K9UWWU1BpgAKSdIpAPh8ysFM1n3eNLkFAl2WxYYnoyGlpjoxOh0LxCsr0psJ6Dhh6G5e0Acskw1xvBnVNXHz4oP8ywZEfn0Z3cwcrMCia/Z+4uSNECFOu+oLx2/XpIq8Tku0vS4DCkcA64liPUj4h4nvwCk//DqA4MyfWVAAF052vvpARij/WPFy9KX/9nrkjb+A48T23+CUIe4LBqX9RUqXl9IlYEzQzVGibo4yKs5xSTAUI0EqjNbbLtpsu2uDlG9cSf6fgLDVVKCj0O1lvOnXhE1B5b6vbzGme2v35WKB0bLy5LJZDVdlZt4yCoBocPrC2yih6eJnpiz+pmdUuOUtGSQAQHBDtDI9GMZ8Sg7DmdFAdXKUaou6Htz6HVJZ0GhX2R64dJCm7Gz6uAaOOVUtHC5KBwCWtQixywvZws3qdi5m1or+QpHhaLVZEPpkr7JNzbgNUxPqkiMVl4N1sWYxULwhhTMnZjwaegHE4JgvpRKDkXxyOLKgni26NNVIeBcEBJX1U4cn+moMgPAAwAv6QLA8NamporWcbbaSd7fBKZ2cgUPYK/GQUMTGKzZUtYxrlJI0ANZQhGHLZFW9KFr+zwz6+ZBeAwBWRkia496ouDWxFQxa+y+13hHj0mZQsmv+HnVFRDHtVAu2NUwrhspvlF1ad6lzBICKrWMnxoYjb8f7qjyBYE5ze4seSpdOZkamS37fXgC3cU8j4hIAFaBHanz6dZ5xODgcZ809U26XqmeyS6tuhsE3jo5VTFsWu2d8vHxUcqkuQsBWVMVEUCQpiBkdjRQoZdzztzNLdOc4oHSz31gSfDEEFbP/JgSh7mn/RWDNlutFoETm4jFjPUj4B4iTna8qAP77u4e++D/g9Eh/FsB/hmOv+rsnO1/95pM8v+9XvBx9KQngr16PvCHDMby6L4xZxgRW1ttfApugPkH0+gSj4yrSAEXcC66/1pa1JstdHqHSRhYLVP1AQW18IO8Jmq/xGA9FpEsU9YFAVXJbZjJ0wTbZA4uXuyqydeCsKVJV6IJbr6TdZS2gzkzbqtaGQbDLbFCJcMsLytoqKOGIvfB6GZZu8FSlLucnQ/sASt2h3GnZX9xDCPbraQ9Yt97OuzVncDZYPzgK0pI6C9HaAwDUYGLI8YPwGDki2LVwZqpzgzER7JHieLA329naHGiNDpS1RDeainCaEGwP1sT740Nh07YZr2UxN8VQvnWeiZMLjh7DXQBWcrRiETudonH7wBQWiRwBQFX95G1D57OEUIYXjN2EoJnjTcPlKd6KjkXqS2kOwCltHOV5/R2Xt8ixrAWGtQkvGJW2xWQoiM1xZlViuDIHgANIGe6x7M3Un+5u56rz7VZC5u2kGCCCreETwxev/5dndFeWhFtaS2cgZmFqwkZCKBjWJnDaiP3AUudRd1lOuNG++9DEeBiE2CNHTlyttyx2WleFUVWR8oWChJwiLKeeOF+WWACwGSDMZE6YjhVoZ5nLoHUudY7j4vMXt2aybg1Y6gViWWwlABgmV8dbzBDg+FuAAAQUVfWTsVzae62Yd22wLC4MAFM6ltM40VlClyVHzuL2pH+tYkg5AJvfMj/3xLli60HCP2DMlBMUAF9+99AX3waQwRNqo/kHhu8s/YjmAbISUXDosqF88F5ngvIczPK7bA532RxAoYhgbv+EXpfgwPgBtHrANgOGhQf8fbs4u5cheMDOCDpNGJqhlMjeYHagcdtgGwCE6mLl2alAu16U5r38aUpojfWTgP4UmekAQEXxFJ12HwcAfVeuAADcuGhR3s4pL8bvsFOCKp4PLBlAyjaPM2rC2++vSioMu1BRURkPnmCbp68wnL0XOltDu8pqSF22nUiWTClA+wOTsJgDAKVUsM6Spsw2ws3JClt00J8v3zr2rJL0nkv3Vr2Q6Q/3BaoTGidYIAzqfZHUtwRZ/xgh2Jgfqhj1bogahAEPJzsRBsAlhytvpsfLcyB0on7zUG8wkjyoFqQrgqTXlrqLDGPXiJK+m1KgkHf1p1PeG8mE39y6vX93pDY6DmCsmHPFcxnvLttm/TO8Wiq7lavB8tT+GT2IefubPwEu+RhydkKu0xPyXHBIVWD0f+19K3M38nyOsTWOs89U1Uy7BMFc9tlgBTNBCDbC6UA4e++QtA8Aa1uMfO17e2cJifD6CkOEoJ7jrErOo1S6PAoguIZWiHHnd0LshFMaytuUPK9bBJM5EWSovL0yUPTUlOf3caxdxvPWWcPgVmmHXHo8QlDhC+YqvIHcWHQ0krZtNtBVWK7Vloi/ez3Y+evbMxvqPeaysuaTGWkt798ogF/7MAQIwHqQ8I8G6+TEh4oYnIBrjnD19E++3ff+n3909zLrnwfw1B1DX5EA9kAgkHlKwhyY2TQwZYG+ZirezsEiWVg1Gmjj/eQVaj36AytCcrw5vu3ozZ2EoSyc1L1zmgTwR9JtxbT7bCHp2QcQkXXpg0xQXzjgB1U/pp0ML/WZDQBgbCk8ZWwp5GGhTLjobzSaipeYLOdnE0IrAFBQnW7MZQOxSYsUBR6O5kcN5vgjhCjjQZerPjFKCOoAgI5627AlMQCgFgEtY0+7hrPxwLjLV/Tx3axCg9oNS+NtPelSXIK+GYRa1Qd6yobf29FDbWYjITSZT7vbXb5CpejSPwWnRHIIIHX5gfB1T8vU9pm2Ow4AvOFUvTeUOdOyo0/gRZMFIMkedaWBSwMA02Sz1y5vDgOkBQDu3m5u37G7tw0APP4C3L5C1rbIJcJAJIQGAGwiBO7ld7siFvJDCJ0cv9xQD0C1bcYeHa46OjpchY1bBt4vK0832pToHGs3EXLPlpp37LtnnSz3A/Q03IZNGrInCAHspNxx7LmLwqWOXclULLjR58/PlsR64WiSVHhcWjlAS87+4dzP23B4JGdn7tMz81eYSHraJpIe9E0EzjZXZfhQIOeaji3hJC6AvSg7teA2ENT6gtmOdCJ4wlyBQnh6Sm7z8Xb7b+5JVXHMwlJfwSAFmaPuRFGqg/N8HsXyjQPffMv83FdXPOHHiPUgYR3ruE+8HH3Jfj3yxn/DTLspYezJYCRZscImlkXpdIraH9gToRQaLfcQ7tU5CQFpdYG0uiiDMHhQ0LRJ0ZeHVcgSy1+AvdEmy5JasSmoPnDrpGnwewpZd5cnkF9C+gIAV6BwhBONO5nJYLkUyeaWrMDSLeCtSW17oQ/MDOGQQAYgg0NF8dNTt8HTveyEeIo5G4iAof3Ks8mA7TVbyfeaqwGgWJCmCSiR3do4QMcZwYqIFblpQlANp87rAmfHCYNmy2TG3vjCD8tSyjttZL1HAOCf/Ps3+sgUK3IsFQpTnuLA3fpG3q2MmIpYD5CEbZOrPZc3B7SifCAYTrQ3bBvaAmfmHAdQTli7uLgvnxMNX8P2QbdZlMpRQhgITtA5AiewcYNABwU41vJgTq+fxssq0gsCTULgY52sxwO1kS7CwiCWkqqd2/oH+3vr/iCT9rkBvAYAPXebn+6KyXdsSrbu2jx69sCOocOzJRbBq84PMHkq2C6jVsuIBtMOwT7EerUKjz+/5elPnIVSEC+lJkMUwBU4egjtANoE3vLwnHXXMLlSpkyzegsGnIEWcFoil+gRKDoXApATRWPV3x1dIUgAAJenuE/XhAsbfLyKxPK6dd8ec7cdj6hXP1KjLMi2/LsroUu5gqSJlDw389EpOKXHMJbaer+52vk+Tqy3QK5jHQ+Gt+CkBSG4lO/94ettlwxKB2aWnYdTU7wGhwC3K2FbQ4/qRBpt14pCKwQkwIM8FQTX1kDF3VuoJG21pR4XZb4D5wXbC9xjmDf5tQcecCS3crZEgLBAD0KQ9a2hupjt2zKxhARGCHgSKfSYLcWyxcucjek2EDBWjbat+PHYteKnp3fTgNkIjZmkFOi+3nK2/RtHRtIJ3zhhrRgI3eluiFWzsn4UwAbLYK4aebFdz8tXKUXWMthi//WGnbcHKpooxQgA3Hx/9xQr2LsJS3V/feJFzqV1GgW53raYfCHn6ouORrZoRSctn5oKHS1kXL1w+D63AcA22L22wSyQKoZNcs4wShU4AcEsJuHIofvh+DEIAPIAdb5TAmb7rt4RjjNvAOAKeXl+utqAM9gsLyQFgFJMLfp/FgAsm4xSuoA9v6T04XKrR3bs7v0My1o3Fy0iAHC9q+7I372zu//bHdtu/cU3DkwPJ7wLau5WuZHTOIHJvLml1spIl8DZ0xCMCdavnJbd2lOSR1Fse063oAwzbP5IeXa1luP5ZNiSXICgV52qLsvVsawdBuiKXADbZlZsGSYEbk8oHXknRVbsbqpxmeePhNUlAXJCY5h8zj1/vN0Ah/w8uGjVd/AhE7RbDxLWsY4HwMvRl04DOEwI/W5loBCqYAnzh9ns3WFDfxvOrMgDh7R1AoD7m+rStrCHhXIq3lcnAgFhGJCNrOP2eAzOLFQFcIMh9N3bCflORmOj9D67swmxhzbs7d4Op43wLJwZYsfM3+0z/74MACxvR8zuiqNWVjy7eD/UJLb1x1vSM/8tzGx/DsB5ULTDxvsABLjs43NlFNnOJ3cWv33n8qaNalF+6srpXay3ZXq3uyF+nhAwszN7lrePapqkqyNlH1Wu1HTzkhH58d/+m3OmzZTHCvzcwKQZ5CyAA4QANQd7NlKgc3Kkyp1OBNsond+/T7j+qxvtGZ+FmcGKiMXxUN/8+0dY6nPVpNpcdYlh1qXOBnWTcMSB5tss8wA8hDityYQAgWBu68Gj13du39Uz2dQytg8ACGPf8FSkLwN0G4CwwyCgsVLfSzoRGFGKUnsyFrysKmLH5EgVd/N24/fe+NqRyDfe2zlsWRic+W6Wk1UcsG0Sxrx2aZvey4bEkt4NY9HQ9oIiVlY3xBaoJ1phXSA7kwdBSXPu3Q17lWvVxH14qMfT1n9AbI2d+tqb++oudO5otizmFpwAaRRA+/GneltZ1lrOEWw+8ljkWspxZp+/PP3dmyplf+9yJPJnd8qH7hRxaVpHh2Hh8uKgCQAMg+Vu9lR35IpCOyH2nVJBBUcQGM5zy2YlJNbu/urJ6B4XR5f81t84ETu+x8POz9RUwcnkTwLoBvBdOB4bH33SugiLsV5uWMc6HhAvR18aevfQF3+WUnTUSyT5Ux5PXZBll0jnmpQO3TS13Y/iHHhKChzIfc/8KaiWh71t3kcuADttSvDOqB/vjDqqi37RGmr0afqmoFpe69FbOQYlZlx0kmHsXFXLRC/HWz44KfO5erttkTTD0sC8DTooxTFiM2LundYj8p7xdqEhNds2CDOiBQifUODI8FbDCboAEzddg2yQUQhT2GwWqTA3g9YJQWPFp7sirv+SThZHg9BVoV4rimdEl9YGZ7YdBLAdAMhP93rwp1su6yOh/UbUl7EzHhWgRlLhdpS7jaE/f6+5UD4qS6++fBqUIllMeW5PjkYOUsqUlA+2bXbT8J2G9xq3Dz0D4CqAPbbOH7ENdoIVrLkAwMhJV22DLVhFcQcAQyuKdwBsFl2luGwL1TidYCG/BcTukX3KlDuUO04IIEhGFMAEpbBti81mpwLj1GZ2z99WdivFxFR5GwAoBafK1DNaXmPbDB+N+zd/5ZsHEp88eUPwe9VSl0cBjFLKTMERnuJngp/qUiuXh9MLym52pV5BCCSyN36B9vib9OHQCX002ON7rjslb48ej8V9Y2Vlem1fd/35jVuGWgmBCGCjx6XjZ37wnP7t9h0d0bjvEECWk9W+BdC9brd6eeuW0cJTe/tbQqH8BtMijf/2fx87n5j2I67wjXGgEQA22uKUACbMEppy8daIS7AzLt5meBbuc9daTuRN9tInf+DiVkoBy2BHDEWc1BVBNXVetk2m+r8fjg3+7KlwSVfJj9cVp0TOrucl4yYnmHmWswyGtahlskwh6T0+kZFL3bMjM/f1C2+Zn7u4zDU+UawHCetYxwfAyc5Xx1+PvPFZAG8G2dIzsWHLGMHMS+pho9Z29RCQ++5EICCiADKkgTYutw4FiaQ1LnItxuFazA03b1365V3TIhyCmxtOHd636cBdTfYos4z2JchOBW/wks4IbrWKE8wWvSj6s1OBEdlfHCWEkvjX9lZ4tkQvh1+4vYkQeM0aM8tsmWqbOw2KaSHK9AnTzJHZ7kL3Xc4yyuwOy0VdlocWqYAThACbfvFc79X/+2NVdZ+63ad8YpoKHd4cMZjjcMo/HQBOMApr0F+6u83+vR1DVOcaG2Xl+V/8ubdG/+Arx7q7i1BPq2JVJL7ZfyGintImKjZSiw1iBf8OQdS6XbJaryt8lyAbdQCGAFBLEWKsoFQDgKVyA8pkoApAACDXAAxlEz59vLeO23b0ZpwXjQXPDiGY4chRShh6W3SrSdlXtDjRfGb+fWZ5a15bogXRrbaruYV0E1HSthDGfo/aDAtAsCwYPpfGyKJ+oSqS1OMpjzue8mg+j2oS4owJNkVsOuHrSudkq2cgsj2TEdtEjp4XOVsUOdsnCmZG07nD87sCWM40eMGca+ekDO21A0YzADCfGD0AjDqljpSQLPR7uk//wYHgzqaYoWf9tbHpsoMeb/FUde30HjIjJ86xVPjkyRsnvvHejo7J2BJ9iEkF1tWf+NSFSOuGSZvnrX3zF3Is5UJe1RyeXijmqIHGBCBsURLM6Vwwt6hQl8m43TP3H5xg1XNCsV6e17V4op4EvmThu9enZflqQsRgjg/mTdIAEO+5mMiVN06LhGCBoRWlKCTTrk7TZg5hKVg4QcLics6HButBwjrW8QHxcvSlS69H3vh3AP7H/M8ppemvq/m7Vwx16zKbfmA0264H6kSgoKoOuiTrsRJqPfqskue93QDdqYlQN61MTYpurWnhoAXoinDHUPlDhioIxbQHrGCcsXRuF0A8SsbdOLte7k4V+OOTb3kbYjJsFOCQt2xYaJKGmCkuz1ACch0OD+R5AsIKCfYEEoBSZ142QxSgiG95+hqqL3RflsL5bYRA0o6ZHWJH6CAozRGbobbLumaH9eOEgDD/tDtu//HmIkBcPl9RrZJN7WZcqjhepmd+oDm6Vx2NzOrnZ+EILaXhWLMTACcAClHWbpZVJhoIgS8TDfWXN0z5CAMTQFad8jdaGt8hlmdPKFOBCYDMkusOAUBFXQyTAzV3+65uTG85dHs2SLABnBJcGiUMPSX7C5tYzt4+cw6rqmBKXqVqcZBACCoFQY9rqrQVAFgWd7fWJ7dEaicvsZy9hPAHAOmsq/fr7+5a2Eo6k9sQRL34gz/93sW9h7tSus5NKwUpm0271Vg0kAPw0bnjBtUooSjQeVoJhMCHkH6IIt+5O5ja+hWif6++cvrtpqoUsqnAkfGhal4QjV6XpzgpuVSZZa3WnZvGA5Mxf54htN+mTBpAXRGWWbl5sGLL5rFtlJKorvIxQTIWyFF//ocuHf/Z//pCn26ys98jVNhZ7/JeT9B0bkU3XUsT+k9UGM+dqLj3VdgUGC1wYzfyyKBECZ8QuAcL4nKchz+CQ4Be7CnyocF6kLCOdTwcvAOHsDgnAZukdvf7evEDySOvhogteVdfaykIiBQE25mEVWp2UxKbguriYxEAm7SCNFVI+g4XkgBAx1jeHBHdmi261Wol44oBZC5IsnR+kXkRQBhbLf+nV9rZo9PPz0u8TwOQwEJQW+yvAvZ/8F7nAYDYIv1douHnCEiIEtpnBuk+Nkc6pBF2O7Gx17Up30VnrNOtiH6o+Jlogr/rHuJveSKkyGyCM+D6SJXSDMG+G5sIJE+9vVff15T82JZq7oLAWRpDUMC9LoRWUVK/QwFTV8WjAGHd3uz3/KHcpgWzRkpaMlPBjkBV6gSACEBgpN2VZk56k1rsi4uvm1Ig3DiZKmbcsyPOGJzBos1bsST288FRUzQBHMcyfDJT59KlPveHMunpibmSuMN3YGjJLhbbRjSdk5cNSH7kpXcu7jrgtGGKohkUxTwCoTzKqlMdv/XLPz947Nkbsaq6uLnjWBcj93FlWo3VYXnoPgBZEEwDUOA2C7JLsz/7uQ5f5NBwcOCP2gyl4LplmfwBQxdaM0mhNZMEAFvTqZn76U912tNJb/o7p7a3AchZwFma9bquv7dPBNBQVh2bqts8suA8WYYy+zdOjp+5U3svSCDUWMmqzbaYFXl6ak5OL/6MIUCDx6jeu206vZzfyYWeqlL6CjkAv/+W+bnESsd80lgnLq5jHQ8BL0df6gbwcQB/MvuZQu1H5vIIAISCymAfWBkxQvkqgZLxta1NaZOvdNeDIOnz3iOk1jL4I8W051hqvLxZL4o7AVyEQ1xczOQG71LHG0/eHBHqsotbSCvhpO3PAXjNLwxQZn83ZfZ324XNppjfbsrFJvOm0mhFiYab0gCzlTFJiNjE47nLP+W+zaWJjosgEMCgymgtbgfI7BTSSTKb6Os4t7Xrbuf26ubK/DOEYMotmSbP0RcAuiD9K7nUYEUksaeqfsJye3LXCjnvNkrJkvKSoQjH1bz0Jpysgw7gNLXYnaXuGyFAuGHqSNPOgTY4AcA0HAnnO6XWh9Py1wYsMKhagGzSlxa8xdNSIH+KsFY3QA0A4HhzmyDOySXvBHDL0PmSRMf3zm+auNFVu5ig1w2Hb9FeXRcrORV3V2fwH975YmtccZl/8Ycfa/tff/SCm9XIoGuA4zw3uaj7BlsOinIAh4hoP0uOT92qaI3JnEff526OuSqrYzuAuQ6h2Zs02toUOyqJpi/gVWajnDtesM9XM9wciSITC5R8Nn/8RNeCNkod9oqqiDZdaqQ1Q0p1lttMyTDAW5E5xbB0gQW8qrNznSPdY8ElJlZwSg0PzYb+UWE9k7COdTwkvBx9KfF65I1fvKwrgRqWr/1yMdP4KI9XQcVeAvLAQQID0lBPhbN9RFtWIW4WHEN7JY6WPJZlshbE5SaeJIiZ3nbbJu+YBkd5wWgkBIynKnm5cudwCyGosSq16UUb5gCE2UG5RzoXOIDP4TQAZPTmCIBfk4fY71FQqM32ZhDUqS32XaLZ14QE28wqpJmxSNDVyzUVNpt3wGIrbGJS3s4yFgvxTGDEimh3hIv+o2GCmOLSm0SX8o5WlI8C9KjHlz8ruxU1NlmB2Zp7NuXzur2FmnQ88L5SdG3nBeOuofNhUdIX3RNCctP+bYKsXWNY6kKJ/v2F946JE4Z2Mww9Cqf97xIcvYIzcGrVIThdB/PLOEvMj2KjFZ3To2HG0Litx3/+zdrZGS2loNRiCrbJ6rUWMY1K/Y6tczBVXucYSyIWcz7fHdaU0dCJmfULAm+ZiYx7vjmVASf7sh8Avvblk+///CtfmyZkkX+JZLIMS8lP/e5ftX313xvfneyvbMEM6ZRQAlvCJVcXyzAmSQFg7JxhMPWZSOpS/XvqZGAfw1C5oipmFvOu9w2DZ3RNqPYHM5Nw2gXhkvQQZnglADCd8jRtrHa6SU2DC8bHKjrKamLbCJkTc4JNiU0InaKUhJ0LoSvaNFN7aZDQc3nTudZ93YcIoZxtMtJMGysBiAQADGdFRY+6QHLcson96v97/Pprn+vYwzKUyylCqd+OC8CzAK6vdE5PGutBwjrW8RDxcvQl+uvya4ME+NFH3cfUZLunsAxZcK0QQXayFCmLLDABW4Kwy1z2WKoiybK7JDN+DpTCnBoLb7JtplJ2FU9t/sg16qlKtRECAkKHXCPsuFZpjlpB1MHJItwCUEMDxj4AL8LRcwCAXQDetEEJqzCc3E/ySouVsTxUgQc1ZsgMsgXSIY6xEUYhDZ5bXDC/zbwOyd6lfDzGur5e+T43Ij/NjThdapK3WCyPxC/KniLtv7YxGSxP9YuSftyymOtwBmnWOX9SoyrSjUB55gCfNa95A/l59XoKV6BwhuEsS83JVaYmtGaiweFgTXKJV8V8KFn5Qj7uay5rmJ4tWciY1xUCp7TAwRkY5wcJGwFMAQgbGh/nBKMsm/Brhiq2AUA2GuzyV6U2A46yFuFsN8PZbg40JDckooRgQVDo2zZuTH59V3txuKyB2EzjsX19zUPjZXnFYmfr8124J2KEWDTkKQxVaO7GWGL+gKzHvJoY0PIA3J/9rb+tS0V9w5jxKjHddpfSYu0AoMJEJ0xQ111mByGokWvSvK1yJgAIorGxWLAmysOZp4gj+FWOGZEll78wBtBnZgO3Sn9xCjMBBEAw1lN/YqK/Jl+/ZajdX5E+TAiEb1xo6aGUzJEerXtqkCVBF+kzUwqq5uWaW6d29Ww6eCtgm+xeyauc8ZRnT6Qnyk6bGn/MW5HpIeSe1welwFfe33Jme0Ocirwtdo0FuwBSUlwMTnvvhxrr5YZ1rOPh4/TjaHSus+VVDWVWAwHxbKTSBKErW9K2+NVlj6UpYiOlS+1+FyEvu5Ux2a3c2v6DnS5vderpOSMkwRpnVdIkTnO/BSelPQRnljVo+c0wBT5l/MXh2ozeLMHCZmGUsTmFqWYtYnIFJiUNsgOgaASwBQRBqrO1uFbO07tlhN4un3b9dThS/F+b3pn+1yfOG1lB0gvCO5TCUqe9p+o3jOwqq07sN1UhGKmdqhYl/TgAMIy9KVSZPH3v9Ik/OR2qIYQK3kBu3kBOrWBN4ow7lD8q+5QTgepkc6Amftoy2O1aXig5ANgWySXHys7k4/4DANyEocvJKHMArNHuOrPr/JYz8z73AhhJTJR13j6zg7nZsbs7l/TPyVuPXW9ZogMwcw0snXQvkWdnOMrXfPpaW/MvdAQCTw2dYngrHgzk5xf4Z9NEnQC+oCiC3za4uvxAJTGLfAcAUIpx9XbVycw3trL591v6qUmywUj2acxIPRshewpADCrai4pgQoJgRtRBSpGVqrIb+IAyV0KxGBRTSd9sO2AAQJsrmPuOP5x5JuAvTM6u53drS8p5tsV6hm61tHVf2DoxHfO1v3u9boF5FgWq6QqkBMtkF0ycCQEJVKZGqE1kS+fVdMJ/SVcFG0CMsJYOANQiC3Z4Z7Ts7rcvNR30uzUWANTx8NQLAXrOxdBSzrvLupt+WLAeJKxjHQ8fu1Zf5YPDR/m6h7EfFmRbPRUurLTOxoBa0ogIAFjOSgHLGd84IASBQFnmUFXLWFH2KfsXLHQbCoCfKm4yT8Ehf96FUzcX7DdaR2ATHga5DYpRsVs6JsTYCmIjZwo2BSDyOYZz9bEJUFAmyQ1I3ykPEJs0Ob31pAo247r8J8cIYGqjHVuy+YKbyfWHu/S0ez8hpNw2mataXtq36HwlUdKqJZdybd7HZa5g/p3yxuku2V84R4hdKKuPXedE8+i87VheNI+VNcRyglv3YqG6IrSCeCMxXJm9R+AkcnwwnM/FfO2l7lt6OnA6MV55Ui24jvZc2nRq3qL9iYlyAyAh22IXzFJToxXLl48y0o5ZZcnFYF1GoKKt93jLr767+VO//bcyQGcJdbNBwiEA/9Y0HS0QarGh4njoAKUYpyY7DoDCYmUr7dqg9ZcbABhLYy9pE763uDSruG+wFYTFwa53G5n+v2lK2WGzoNTZl4pN5k0mpOYBgJe0O5v39n2MkQzGspguAJC8xYuuQOE5AAiH03MB0EDUv6yImFqQGydubTjxKz/eeWvBAgLeBF1c2pqDaS1VXqzfOtS2/fj1CpdPaSYs9NHe+meuvbtP/MrfHGnOF4VLhsYv2GZbfWLL//zV76oHNkZrACA2FhZ8HA5/PIjwp8toV5int2dW/QYcrseHGuvlhnWs4+Hj6wB+Ho9wluCibIwFeShBAgC4wISWX0rjIcla9loMna/GmtxFaabmYO/BxZ8S0ToLRxq6BcA2AAQW3pn+m7rMD//2YddXfvXCcNOPDPUz/VKRSoaXqkzBTkkm69f8INgHYAw2KYCA8Lc9owQLpXMNjbv23Of/zmuwrAEKhfdo9YzLCJgp+W7xQsOefMKLUi6ADENbfYHsWU0VtYra6cvhhqiP5a3nAcAdykXdwdwUYWaEnhZfE5n77s8BOExt6JmpYKehCMcdyuKCtf2cYJZsvfNXpA9uPnj77Fh3fTAYiRfgBGMeAJDcCi1mFzYoUAozEfdNpaIBPhhJNyzZoU1c9G5ZBbYkRuad4xySgxVDN//m0PDwudaIwNIe3SJbMK9jZ+Z8fdm8dNfnUbeAMlJ+oNJFKdkM0G7X4SGDsJRjdOTsEe91bcIvpO7UCn70plx18dtUQGrPiSFYf1VfOfEne5N1v3jB1DZYjd5ful4u/U3j94KtE6HC6Wa7dcfg8XQ08K5RFIjg0kg+4TutFwXRL+lzGS9V5+bLUStwslAzWR6KTU9f66jcOP6MvyzX/u57W8Seocpqw2IEq8jFeZspGWDYFrNkTCQEYDnbAwCtJ240jA/WfPv893Z6Ujn3ia9882Djppbxzh/+kbMqIfe8LzyS4fNIhg8ACllX/cx+CAds3u3GmbfS+BMAv/yW+blV21qfNNaDhHWs4yEjq7xy0ye/9r8B/N+P6hgNtmsAwEqmUvcFFmSHmzJ3CsReoungE+xBQpaV7AWljBcz5karHMagAF04QlINAfVZAP9cmGTOWy7KMzoIBtzZcsbkXv/xoaHkWBVtzoyqwk2vx47oxXgViP1eU8H/ids5MSV83bYZhvg1AaZJ9Y2FSjbGjxCXMUI8ugXZDIgsPQCTuaqeb5DNmPs5/pm+QcZlRKz+sl5KoemqIM0oHFq8ZJw3VH5O4Y/jzdrWPV0XXH7l+AKZZYLIGp01Dxez0luFhH8D5tXGF6OYcbOmzrV7yrKHQSDMhhGEQJLc6pENe3sAYAtmgg5T59KpqbIFGQNKkY/F/T2Fgnz0zukd7Ud/5NTSIAEAKJGtce+oHdCDgkfzAkAx6Y6/9Zs/1pMeqTiCGeEvibPbdYvpgxOUbMK8SMryKTYr6h2WIp6YEZuC2BK/LkQcroY94Geh8Bt5ybip5aWcvT+1Sx0X+mBYNVLKHNd0FuENkx4CagMocJVKj/iTXQI/wO1j/eppKyMf85ZnN3Rd2NRtWWwzw9AdhEB0+4tzNu1Fjau2YfVzHK2xLfYqKJkbcJsP3z1VuXH8BCEgB3aOtrWI5un0YKQeADqvNw3e6C6dGDNMdkX5dPfe8ZGalvSGoT8/NhMQUHzko9eY+QHCfBTzcpRSZiEHhMXWWoGeeL340molug8F1oOEdazj0eALcOyKP/codt5oudfiS39fCFM+MUCW7rbOq61ojgMApsFNzFfbKw1Snh0t7ww0xu5pMzC0lzCwASjCJCdYvUF3NuUZd1cnBNIfYlvDU8y7p7blpzubmN4LkdzWzZOS9aY/L1fmZKsnpE3djkDPeGjl9lG3NFScYhoyhnE8bogTrJs44kUsAOhRL2/GPIcyMX/7xF8f1Gf4sRoAAI1USURBVOu3Dg+bMU8LIbhb3hDbTSkASgwlK/OGys/xLwhBvV6Qhlx+BVevNV8427kZL7/0vc3yzCxxOegan+292Xxj4G5D0DLZbQeO3PDy/BI/qznYJrtfzbmg5mTbFcyfcQcLS/QkZnBYK4odXee31lHKtMx+SClik9FQStOEvQAweK2l5tAPnjFZzi75jrfjrv1f+aV/lhZ9St+OH+5UL335mQZqM0dm9gWbIp/V2J3AHKF1EvM8EmLRoNJw7O4JIy/eUCaCAQA+bEnqlFCNUCISvzYGrz7NuQyrwnuTox4jgABRmBQ3RGAXvW0D5QCIkeUoYGQBtFkeSilDb7kPDbdm39qU6bvdNNLT1fxRdAGcS+v++O/85XTibm0Ip5xOQ5dbzR57+btgWSsz0LkF4zdaeACQA/nRmh0Dm+c4LwBMVZi7+SFfYVlDNNsm87k3BWCx7TaFZjJQdb4KANpO3D4dCBSX7WCZHA4PYSHpFITgu0d8K6k1fLiwHiSsYx2PAFnlFRXASz75tWYAy84gHxQVVFihPPBgkEG2gIKCLJwjbwyoS3q8CbGL5ZH4ZVWRiFqUqi2LtXisLAvBu9Rz2dFyGmic154vWUk4yo0RvdrsTr1TOeWpTrFGxmMNXWm2JibK7fM3arnGSJqp++Sw8DO/0jb9837Pv3WFk/9pcCzgqffp/MZtg1b/+9vjNVtHglR1a3Jo1ADoIQC3KEWTMhq8lr0TYTND4XcmB2qeJaw9FBCLBZdXjWOGl0UIAEJlNS8ZcJQdbTjGV35DFY5nov7vxeI+PpNxP33nTv35fXv7l5RNqA06PlR19e7VjVo+4947T2ERVy9tvbj/0M39y4ntzLuzjJqTedmrJBnODk0OVJ8uZNx2qCrhVguSouYlJZsI1AHknvwxxdDYeDlvmtxc94llchvefuP5jud/7s2Szx7HW8LBj3Ve6vzWkY9d/LNnFyyzKFITWeEGgPlCYAIct8pqAJgcLS8CAO/RduLIyHsqJ2jmvvyOvIkLnrucbjUUObZIyohNtnhbEhdzvD1m1agSTCJQlRhwWlwNkhD80v/nTyd2Wh3ubbETRoAmeVvf6mnr70l+d79JGHu85sWuvmO//k1bVuxnIjrfE65ODP/Mr34jGqrI7iHE6Z6QvEo3HBdNXUl76qZ6a9sjm8bmyJy+ung4P+n8ZAL+4rIkXHpPJ2EaTlfHgvuX72jhMlOhQZE304rOba8Kp5aP/ACMD0VKBSTKy9GX1oOEdaxjHcALerg9T0wMsIXyKFE32uSD/+YYCkMA84FaH0shAesuCBaZ11Cr0bdYC8A5DRAwvkDuqC+QAxxTp2XBSfrF+hN3l6hPEq/OATii1JvfMXx0kxVz99swzFhPrTk1GpF7klJa5wzmv//lIYRv1apf+Nhg0Cq6/8t7t6qY8ZiHPvfptzmxyHNcQdJHxsLZSp8J+XqZZLjMK1pPBQvBikfPtzztDuXOBsIp3luWPaMVxbTsUT9uUyQ1lR+WZD3NufQxJe2asnS+FQu5JBpANL0oP7unZXIyHfddvnCxle7d0z/HLMgkvUN3Lm8anhqr2EQpU5KjoGvC/u67Te2btw62lVo+H7bJHei+sOVdTRW3AdgOkEA+tSBxkcBMece2yZ3RsfKIbbNLgsZof82JM//nePvRHzm15JiUIl4VTn4sUjfVHh2tPA7c0wcwLCaR19m5kgvL0CsEoKZ9r40vMR2YG+SIYDPM05MvALhGLBRtAT6lxZIZFaY4yr7P5piMdEMkao3hFjuDSeI1uYmCWymGNMY9GrJ6//apqZbb/f7MD4jt/kNjbVyOu6TeDRcOP3cxZH5k/Jx0cPqEdIeTAAJlPJj41d/+y2qOsxcEaYWET4aT9egA7FrbdESPbJMpTN+qO2+qIg9gMwD4PKorGMiPF4uiX9M5z70qCgXPWwqlSBOCPjjB66zKZYvzNyGFWEA6uXv0QF7hx/I5SaEUc54Xi5FOlCRX/tDrkTd+5+XoS0OltvmwYT1IWMc6HiHqqGsnKE5ssX2goKoG+/YUo6WHmIJrlFGaFGKtVsdfgioq9RCQbauvOYfTFW49Gy/wJ+mMAEwpcFjaxsgztFdg6ZIeb0oZKTkVrA+Wpy8ThvopJbYg6k2ElHKJBKoP9JYmWbqNHIBxYhFD7OOuqwFFMC1WH+yPWOfuVjP9oXHebQbMtJCxxNv1tPHX34OmuPlDedn6k2Gv9fW/PqIcaJ02tx27xYTL85Ln0OhGAPW5juYOKyOfAAB/JNXBy/pOQhAAAFHWTvde3nTqL69XZSt0t/Srv/MXpsun7BcrsnpmvKx6EWtCnPkDAlJzcPvI9W++s7sylfQMTQ7WjA12NQQMnd+OmTo+IbbpcqmjhYJrSeklPh08MV2WvlwZTu1bvGw+bItkNVV8ZinBcQ5+ANcMkxkYH6/YSeny32nfpU1t0f7q8z/0L7+6lxCMgqAclET1tGsKIMcPf/RS29RY+a1L7Xs0Q+dClDJNMm/Hm4IaTSrchG4RUuXRawkBn1a4CwWD4TWTqU3FvXNjh12hz37nuymPM1q1lQDgtUXACFgp0wsfczZIbU9B16I+y+Wf8tz4/WfMN6/UWTvL9Jw/lK5jzm/JSGMR1ndgLG7yNEUMYroODuXMjSmvxeAqCDYnByoTnX/w4lMbf/f1zOLrlLzF2TrZCYBY6pS/eyy78ZSek2upzWzGPNdKSTDKf+kX36wBAEphwSE92gC8hECGo1dRyhL6DICjxaz7PAB4ZL22yqv644NhgKDfNNkuTRc9vlBGDFUlw7rGBi2zpCKqH8D11yNvNL8cfelDLckMrAcJ61jHo8bcYEFAJAnsrgbbhQbbEX6zQEczxBgdYxRrkCmEY0RrpWRlSlyT5V7zi8UCLV5mU1mNquIJErEW0wbnwwc2sthDKOwypzEzA1uyb4uvi09VJDAjaBMsT15OJwIix5sZSVapy1NsYjkrTAi9aypCipMMnTB03iydpiCZzwOANM7W0ALXLriLsb/5/U9W9EyGwv200G9Ey3VLztNtLoap88Wt//iFF+Uf+813rL0vXNl2p71s6leeGk5s2jhJtIwL/s2TEmbEcjzHBo5mvr5tBJSpF1z6XMpYiqQvUdaSix179u0u0zo2tIxkOd4KAwAnmntrD3efGTu7+WipbgcA7S6XeqyxIl9s//sTAYA0Ll7B5VaGd+3tkm5e29STy3qWqDH23G1q9voKE7Ksl7RaBoBs2ncNWJ7kCIBjWCOViockQuxhStlNK6yLfMp78P3/9/nv7Tl08wTL2Syl2EDIPaXOcG18+4s/9vatqf5IZefpPVGAtMq8narh9QUZiLDXqAMczoJCyJx0tFWp3xMoYiFaXvpM9Mu7ToWODwnSEITYncpi8kqjvDl/q/LauztHJ/6ozhY4y18fTidDnOj9991ycRp65X9pmY42/tY+H18bp3JFzg9JL3D/f/b+Oryy87z3xr/Pws2oLeaRRqNhZjDEHkM4Dp7CpG6Stslp0+b0rZOeNm1/fZMU8vb01E0prt004CQOOeQxS8OoYY2YpS1pMy98fn8sSSPYgiFnZrw+16XL1oJnwdbsda/nvu/vd5yzpgtV35XTq9vOfOmRGoDw2ZRlULQqdpbXpooMXcXRqQCX49VmXeJ25CTOE4+4m7Npq6ukMjj9fpdQCkoICCFgcc2jYz5yAMYohZwKu5pzEqcZ0y4E2bT1rM2R3QeKZem4YzSVcO6MjXpVXlTaHN5k+RP/63uHNRAhOuKTxvqKhIHWioJUxF1FKcliQkPiTscMEkxMbi+xhVayIBU+KlT4NAFrNTcoaCIDrWOEyaW6mLRnhMnWKWSm2E6pbl2y3jsLYosRxZ5WKEspM59oDwAgB33SO2CKOk9uPvc6wCjsmnqry2UtKUqZTYosQJEFJOMusJzycnH52ObhU/VrOKs0XLG7tZMw1EsI/GBpF3Tip8bvHk3haKK70D+ua9EzlvHxNi1XxmvcsJRT9UM5Vtsoprk6hk997ekG65tXijqfcrgIl7GznKA6RXc6oVI2J18uatGiNs2+s8cuLh9PSm2FFdPfyAmhLCsYD5dqT27n9vcdiRLmWpeI4MytJAyVqE5mtyQ2AdhHCLBufXvr6RNripEHry8RZBjsWrO+bajl9Mr+bMY6q82QeM+dXjmybdf5AMPQvPdW15gF8tU07i2Idtoc2YdKKsbQ1V7RPDJcuGCQAAAD7RUPDbSX6yyrd2194EyuuGJ8hp0xw9LVNlf2/PpNrc7zZ1cMU8rMW4xHCGBXhFUAQEF13atUT6zSAGxmsjhcVjrAXfyDR3WbPyV4PQl9eDDgKB4uiLuKYvp4V0lYAsSINc5sKdSJfbCQphVS+4ff3iC9Ry4O9a86X/QXH2hNruN1v7w5NwLAefpLjy7TZW4ZABz7xsNJntNk3ipftXmTSXdxhLH7EwwMEa4hizUnypIwFhnz9ek6uxegs1IChA/1FPWynBbkLIrEW2QbLyoBVlBLCcnrpXAZwCYANlXiIkMDgcRxJvb6o2W5QKk9O3WfclnLRMsE4YI9pZE6bxvP6pDthYm9roIEqtb0orqxv+nKy5uW6Tr52UOnnror6hLMIMHE5PayoFb8bAiIyw5uU53uQJ3uAAXVVdD2CJFH+5kM20PS5Q6wC+b/Z/OwWrQvA3XR2QcBzJwCxeWe3EJaD50wDIcAANm0bU7uW1M5l6YyOsvpULNiac8r60FYLVO558oZqnDp7ItlmwEKImjRTJp15CLu5bvW9TV/5t2n93/zH977xsC4w/7jbDwtqZT+/p4R8hff2ahf7OZ1nVBeKoyphWUhO1Run5bkkL00GQPRHAgslsYxsA75dK6tUNFTwiZLYaKFs0trQSghjK4Rokuj56sHczE7cVeFrmYjDretIBEFoZswkWKYoGn6dXK86jLep+emA3z+uA4ADIOy9ZtaO86fXdGbSduqZ9wTjV158dzyQ+s2tu2ZvT8A6Hp+jTtC9PZA6biV59WpdAU1WjeXCGE0ja0fGwo0F1fM9XYSrHLc4cyuE0QlJOXmd0xmbfLVmg+3BKmoDIAwDNgJ+3AKlu23NLO6GuM8KbZu6xUkRr2aUBLRlotqRs7xfH+EZQM1w/jnQzX4UcapHmwTE/cxPkeUxs7sVQoa/vjPvtd+ekyw/Psry2L/sG4sxIxCVEVwFSv6IsNtFcWKJNhlidd5TncrWXFDPCsiPlwAGEWG1QCqHa7UGcLQ0mv3kVhH+ks6XN5E2OFKT3TWkGpN5aq1FAcpZUxIWD2pJocvZYFRzDqp/ZHEZLqJUJvoSfWt2XJl5G8OBTYmI8rQn1VNynbjTU1ltwAAyytp37Jhai2NtAhOaTWM6TkjICQUhIBhWfq1pX9uv1rMIMHE5DbxOea7LhjKgTcMAWF4kOVF1LK8SLOgjjj6/1Po4+xgTxfrlnS1bneV6pZlFrALtuTZwC2oWQ8ALLCMUKh04o2LgAY9oraQ+dM6AOOYX6/hLEC2R8Z9hwIloakHItVYW9+bqzd6K8dPc5xuHEnmvImIW0qlxdahgSLLm3/9xKvFVLT5GZbdpHvbsxpb+ne/1JXlsBBGJdIoIzG97YX1wv7zc5Qe+crYCUKMhzpXHtskxoVfMgozzIrqVLFb2Zqeo0MXatamR70bACDSXrobALKhObfxFQAPTV/AcXpjWcXo0aGB4il5Zo5XYvUNfeedrvTUdbIsrff6EkczaVs1CNUxVTlPYXdkFErRRQhqMFf5dk7wwfHK0UDJ+AaGodP7+JXaukG+sCh6SJZ4va+3tGbuzMVcxoYL8n7vU2oEPStWdvsutDT0U8rkHcu/cXB0/V++fB+5wo0xCim0X+CC2rA7DY2MM3GeTSVtmlosq0nWwnd3FQc2/T+nQsJpn2ZP0YoT39o9uKUw6wuqWrpQF9I1jJWkrcnUfbqgVVUNnykUVfKwl9Mf/YNzNvpYeJlGEOGv2sL3feyNvf2XK5vCOTvT+PhZT/9L65uCx+unB6XX0mIEHCh0gEx9mJSSOkJolFLohORXGhYssh9GfcKkIijFzM8i4ls+kh7pLbdus7HHm4Ycjr761LEqhwpdRyNrk9rW7D+dcQTi6wkBBbBhYr+jMESegiC6BELHRfvcjqE7FTNIMDG5fazFLbaC7WHSAyDYlYbm62LT6GINQ0CGotdD+aFy3aZX6rZAIRWXsSALpQryIXIgYwpoKQC4Ra2XEOSdVp8gTx/5FBlM9NjLkrAplxEviFZp7fSJf4Zcc5U8dLms6VRrmRrqLC+zg+Hq/Klkp65yLXIysUUvWDYGpIajDmeQpNN74bWxLk378AOH0xaLFIWRM54UtwnZNgzuBgCqkhi6PCd4Rn8M4syazJrtrevS484zWLg9dXIG4TiAlQCmHjrVtUOrwyHPUC5rKSsqCZ2qW97XOBmYTKekbJzzF8QOOZzpmisX6+O6TpiVazp1jtMfmNhEBtANo2NBA2BXFK7s2vOJanZn+ojbF9+bp4xRYhhwLnd6BwAUFMaQzYrHL7Q01HGcmslkRA2UsQLQqHEcmVIo8aQ1K8tsThC0vAWPTlembte+FrRdqW4aH/PPuabA9j4OAHQrPcMoZAWhpIxNsVTPcbZkT4ATiiPjqYseWrKv1eb53fN1isRGRk8tExJ9zkgyaZGbc9z4o36FGYjbhGxO0HeXpuy8M5EtqYiQdMhZwbB6OjbmjfowKBCKOqHF5QCAylX9+6qWRXshatUDaeEQRxElhjFZDNOMsVSFSwii7BREqVmWxInPl5BY2Ls1FvYM2xyZTocrVckLavX06+JEpRLAuWmLZtxxwuqj2bg9WvfrzdaK8x+yaoqudsT4pF8X7fGoO9XfF6js6y4Z9hRFzwZqRuKNO69csNpzpQwDDwyDst2BmrGrgU/9PEB1FAB/le/233GYQYKJye3jKozipFumadDHZPLmMXWC6ghRqiNMHBcQBygkEcy5XUpRzE05iwtMtQBmoQc+CAhXRvnOXmIU1VW7pHlFZyboxFyfihCM670KTEoWE1t4rGAty6knQEEoCGu1Z0KB2uD+yZ1+2LSiQEs6ohV2pUtOWx4PpcWwneXk9/IW/oKWVR6oTfncHUX+DNWVRx9pGdv56FkvrzGh3Jj7IQBBAO0AVvPl8cuEwT4qM0Ha6c2AEk/eayWwBypH9XRw3o9meoqhHkZeWoBRmV5DCFyNq7ovDvQVj9Ut76vLSlzSZlHnFMCJorzJYpFZAFi9riM7MdZ0BMwqDC2pGIWuk8sj/SVZQZTGPf74o/Oco2Ni30sAVgOA1Spt37bzAgDgOz/fMpJMW/J95qsbu0paVjYObpi+kOXUGZGU3ZHlx2e4HFDKCFpv4e6eAACoYEVWpxHCoIY65dPIcoxYHdYiLdVW4shkoLIy65MC6e82eH7+w7XR9aUp69qacaV9yG95JQx2N0tczTlVD7X64kHi1N/XVutt5anN7Y/1M0W51Q998mIJyTAh6paaCad7kWPFvvYS23/+0a9fLHXk2BUV0UmhpzEYRlDGdbAaTwhIQVFo23B/aQ4zOnpIaSZlL82k7CBE7yAMTQLQOU5NBWqD9wHzu6HKWTGRC7nFyz/dAX2sgN0ClTSoUlE8QlYChOd4vV3KWtYmw+5QbNTLv/jCzigRFPHAH7wYqlwWtICSLsLoYBhgeh3MnY4ZJJiY3D7iAH4bwAu4RWZqUaLMWxU/AwJRp9DSRN+dJQoXBACKIA/S5wQjOSnrs4OpY2a1RHIgU98J9Z7cYsFNLM+yDhgPLgGzAiRN5aam+1WVPwwAmYwQazq06oI/4d1EgKJltizXr2iXYjnhYQBIAl1O0OJYmD1V7FBSW+4/F1ux/arF7szt1mR2YGK44omfrJ7mqRYRz5ERRwCGh0NyvpPPjM9rEDSjBgFGx8QOAGB5pdXiyjZlIo6tdkd214pVPeAE5fKRs7XDG1cO8DarMqOldaJ6fpIxzA0S8iJlxRBA9smSBVJWaBWtcuM8m3phqErOSfvUVY51t7RW5mvlQ/OhVbmVjTMNIQkzs/WFkJm/M4LW9/7OLxfB+IxfjXUVabYBZtReEn0pc6XIqipsSlN4aybs2l/gygxlm2vL5Gb2DZ3RktvL49xLl0rCanUf1tZE6PrCce6pl6rH3ml1iL+QklIDsbBdMUGKQO/dpnsC93/o9Q4AdZZX/F1MjqnQqhJl//HF37jY01K1ASBMeXXo2PTbNf08FYWXOF4DYSA6XKlDqYQzb+0HpUw9nZBC0ln9zMTi/FLWAHhRqfe9+2L41YGCnlNyqT0BKbLl118rjvUUn0qHXWygbjDX11WqbHj4bLioJrhXVZjI2IjvwqHX1mrPfPW9rtVlsc6kxLO6K/V6Mm77xlMH5jvSnYUZJJiY3Ca+qn9Y+Rzz3VcADOIWmD1loUU1QpdWtEiRXaX6CplpD30QFCugxRFoiBANoNAI0G4BGXVSlnGCLRaBHT7KnohAXVfplBerms9X3cbBeHB5AZwEsDXPyYVs9rTSeq768A9f2rwCIHsJAIbQHkKwutIt6V0RS6+sMf0w+t4hsNhusWmWQHH8hC+Q3KRpRJdj9h4A0/UXrMhxRRh2FoLAD1BA1CyQuP7UiKcjdLXcY/GkU7bCuJ23Zd1UY+f4VABoBuamDabdQ4vVldknWKUT8aC3RrDK/YJNyr37fce9maijJ5e0LaR7kVedj1JEAWQoJUmGMTQp0in7teBKYxeTxXbBmCYP6DqGVJUbFwR1/aZV/dUtrfn/7DSNmQpaMynryYsnG3mPJ5HtuFIbIqBYs6Gty74umPC70aRkBK788VaUPNBRACOtsw7Aafs7BjbrRwqPqoSOZ61EUTfF3Z3f3pDgYpamgKD5obB+nmo7e87W/vR8Z4kUZyjSQwFx67/8QJWdejZ+sJyckWVZAMEJEqMEQk4mxCUTTSqVsquRYPtoVChpeXN9TyJq71jnixatvj9+OJqwgQc73dp8Rt9uZMy/z+OPNtmdmX1We7YslXAucvsAiz2bBjAKoGjejSh8jEUtL93YJ39OZjJngm72QltxdMfGPicmZs0a33EOAAXsyhWOEqZseafuSdqUlsPL6r0x+0hIQVgdKfv8P+gfWdA19U6CUHpXdGGYmNy1fI757i8BPHKz47QxyVPNfGjL4lsaFOiWpnrNs6jC3wwoUizQ6WLR9+FVYyVOQatl8ps7HYHxdj17huQUrhV+AYbt8zgADqAri6qCl4prhnfGg742JSc0pjN86PVjK0+HYw63z6omfTZ1LwCLaJGOss643tpdsry+erQTKbsnnrJZLPbcJbtFLZWyfMED7z4adLgnq9UBvjTWYq0KJxC27oGoDZDqeIRwdEPk5eWvRy5X7KI6MxXU2LzJwzZPulyTuSEpI1I5bSlSZT4IzFacvAZh9KTNm2qxuTN7AYBS5AiBZbIYTs4IzfGgb6Eah05MaEpM3W4Kmoi6jqeSjk2gRPAFwk0WW64uFCxIyZI4EaTRBMPq7cXlwQ2zZiYwa6yL3R0VKVVlhYaVvZsA4KVDKy/0j/jzFM/S2CP7z7bVVI/amn60b83sWklCdHXdoy2v1/7jwWpG1PIJAvXC6CY4gywkS4+QziyX+f96+FPFoy2l9E+//X+vyhYGtCSn5U4XkX7eQmr2duHY1zegoa4Ppe8e0n/2N1v1M1/fQ16Mydo4J23ZqLn7NmrebQ/tan1z230XbWCon0rMmhe/8Uhs45bWDotVngo4OU/6aOHjl8sTvYHR5IA/Fu8otmfDDp8uc1UAsQqidLWgOOTXNYYEB0sWFSwrrBo5XLpseB2MtNKM+pNJFIk7nIzZrb6tfcr4Gyu2A3Ssdv+5AkKQhJHuKodNiZPKhEpYunriM8HgS40Hv/fiek9naxXTL2GtDvIbB9UD31vsnO4UzJkEE5Pbz1XcgiBhkMmEYOSf65H/LX4GaaJcb+EiQODQgPVRHfF/vVi4FaDwiNpQvSfXX+eWlCK7UiAwdDkhqMbcAEGDYWo1nUbjh4bqN10ds7szuymFpOT4egCw25SCdz14/pHertKXBweKNwIkByDk9cVzdcsHHti4agAACjv7A0fPH28o4+JicX1BbjMAvPKDfdVb7m9pKq8Z2ScsCx23rhnZRAh4vTDdo3hxRYjjcYBAF6lleoAAABZn1kIIqjlRreZEFYTQQ2qEnzdAAADC6CmrKzNVPzDp/DdZLc9b5WpWUHp0hS2ilJnR+irYc8dFmyTJWSFm86RkVeIVVeHYYHcpm0o4p+SqI+P+fSC0G3T6zBNx6Rq7OR51H/L44vOeo64T68hw4RqAIpOxdsOW6uM4fZ4qeuJ56eCmbQAN1flzQYGlM2oX9n3/G0fB6pQRtRSANhj1D5P30A9jpugIAC+jIw2r5v/JJz6oDBytXlG3cuCXvW80amVburjImTqt6MF2S+O6tI0kmeSDv3mKp5WKHRJCez7QRv7P3+/jk4yCUs12tYqIGmG0RPuoS7EeWZlat/OSmlbEBKXMNlVlkhDlX5w/tsbZ8L4Wyb92WIRX7i0p7txbsr1z6rypDmRDrsF4dyCFXnd/LmrnWF4t0RS2EiDz6oQ4PCk3ACeA7TCC2tPARGsngEzc1pwOO/cCBONvTP4JkPFIW+lX/SuGH6EUWyIdxWe8+9tcHEs3AABVmFTy9frLzhy3/4ld7S3/u7uwRJdsnwTw/fnO407EDBJMTG4/fwRgOYDHbmaQQSZXDqNATQVwBUaRIA8jlTGnVVGk3M141U88EAhiEld2atRRdmrUqMvjGT1b7pDHV/hynVVO2eoUtBqGIABjyns+yWGf1ZF1AgDVSQdAVk9f6XBIYmXFODsS9F1RFG5noDA6Yzr29KUqhoJUaVQv5kXpLEcgZHPi6lNvbNznKHvlJ1VrRh6fFMtRC/WgXKY/LuvoFc9bRtS0MCNA4C1yK8PqMwouGU5f1LZXV9kiGKmjvBCCSndR7CRh9ESkP1B/LVCgcAXiXsLQBovTmCXnJkwJc5ctc1X3KBlDHi+MdMK+0uVJJBmG5p0/lyRhHEAdQJBO2Wpp0mYbjYkLqvrxrC4UFMRO59K2elnmp1wlmz78G7sfO/6PLQA2UqNLdfIaAaNNMAYgQCR0CmHWwSeY5NrlXdHqd+BHUtbC/fhgI9lwrsZ6vM8ryf+5LVfnlbMPfeJ14n9Pr0JlaJCguWJE+MAjLalnXllhr9NcTHVZRBGt8uX+0QJCgVypxEsshY1Shi9aNuq02rP3pVPWAdsjvf3OXb17dIqQPMicECLsVK0LYQBbYaLc6kqr1OndDABVe1tBKZBL2IYTo96R+IgvHRkIIJuw+xhCawmBzerMTPeODkxccRMALpuwKOmw6748t+8n2ajjndSiFbW3LHvV70hi6IUNZaxTavE/dskhv7o8RWWjDsdtl1f8j/taf+0fX9z044Pqgbtq+t4MEkxMbjNf1T9MP8d89w3cRJCggSoy9MkaAQ7GlOh05rjW8TdXKzlvDYWiM9aehGV9T8KoeSzg9OaH/Jpis0my3SY1C4LiYRhaP6GDDwBgOe0yw9I1AKDK/JwHlywJXpbVveVloZ3JpOWkw5meCno0jSiJlGUNAGyqHzsecGd3UYqe/oFAatuDZ894BPk98oD7TbYxWiSVa1FdnLg3DKpDPRVD6dbSqcDFUxo+xFuUOW/joj23Nc1pQV1l53QDcKLcafOkh9JRR9VC9wUAWF7bSilyDKuHNdUIEizO7CnC0LxpIpbTMqrCTy8QbYHxNpsH4o+Oe5v8RZEZKSRNIx2ppH2kv7dkdtFkcblbiraF8ut5FXnS5zfWjxYyBI9QVzZNKWkdGva7VJUroxpDOv9zy7gUsTcNv7LcHdjeF7eVJph1f/7KHhjeBh4AbiqgkM2QFIDyNQ+2nhqKi/2HXlpPY70FavOQlj2nZUigOKufulTAXPnmGu3Pdw8TPqrJtNWLy8fKFSnrEWtVBysTmR4esfKfrKfxco9Unkq4Q0d+cB8vUEYCcHmUERINm9sOPm570e7ul13MSNFFaBCUlelccm1qEDqSREeGaJAoASM2OTPshJ/GxL2A1Z0ptbozpUXLhxDsLm49+O/vbDRMnZQ+R3tFvzcQg9OTcguishZAAYB9rFVqtrvTPg1Mk5yy2LQcXw2QyQLRL6TCzpM91exQV1uSfaOjAJ/85PHcj/55x8AnHvr1dz/zru6XH6yLAkAPgLWP/fCfUjf1lvArwgwSTEzeGjYvvsn8DJPsVRCsWWCTQmBmvtpB+XmNfxYhhGmGOIuR0ohD07jSZJIrTSanVA81i6i02uy5catF4iycOvX2JGfEGWkQSgGqk6m3WKczt3VsqOiyvzjcKwjKrv4R32WArLeJylDAnd1FCDjC6LUPP9F01ObM7gOQzp6t8DP3x3LEcq1fHgCsxYlUGsah3SWRZk6cGyAAACGwuouiZ6NDBVNBAico3c7CWJDlte2EoE6wSaH5hHhmwbGCGtJUQ+/A4U/mffPXddIjZcXqaYsmLZTnJZe1bMtmxOO5nCCHxnzWcMizXFW5eszTNaFTkp69jCFU2VwfPFrgzu4lEz4hhMBOCG0sLQmfGxouuKppLNf2L7unUmRDv1gJsSB1dt2fvzK5qBXAdhB40vXqKcsg28kGqdtlkd593/0X33SIjdK/XvFpQZ1oq3VR/I3d/Tp+96KVlMtZqd2lMmnWmrhUqVdl+aDq1ktGElYloquOguJ+54WLy6LhuM1aqbObdQauhnUdRxq3t1ojOcuyg8892LViWXCwqrGfL1g5sFlpSJeCwAkWoCwwKXSd2xuL2n5YmCDTBJWm8/LXH5tIwxAoilB18eTKKgBwepL973h/MwCAteaO2MqjewkBHEUJAICmsMlcxHE+F7Mlc3G7Fuosk1+9//cKxiWypZNIbcqbGzprS2JWhiLxwmvrah+se5MCeJ/vSy/cNYWKszGDBBOTtwAK+hqAHQTkhrocutn0Usxg/DCEf7YCYFTo1yHZO4MeIG+xYl44gjx6CoTNSUJjThKmWveGhgtCRWXjPWWloxXMtDo5qpMEZn2Z6zpbNT5cWGG1Z85c6SyJAkB1UbyLEJQxrJZp3H65lb/2wD8DSvbq/75ilPnDS0lCYKQ1UlwLeaV0v6Mg0WxxZisnagjmdbjiRHUXJ8rtqiQs5wSl21MWLiDk2rQ/yV/AmQ/C8mpaVZmOQ2eXxd5fOe7nrtkxDMDI5ztCg4FBgEx3izyBayp98w1tGRkoVodHChYqkJwimuVmfDYum9S1o3FY41iat6CVZel6i0U+m05b51heWwrS2UxT+SnrjuG1RNDVSWFqLkE4ytFmQrHFsbOr26az9k2Eeu6PrY/WbOjm+0826EOOmP6ulaNy4unVGG0vtl5tC2hfv+wm1aq7UJNFutGfYd60jrJ/c7oosSFrq9d1YtvzrsPnAwWJKoalu3JXii4PjAW6OvoCqziVP9bZV0DXRPxHa35t+DEGc2bvL8Kiv6yVSZu4Ict9+a7zY3/5X/5v/dkBCZjp0eEtDo9YyyI9rEX2EGZSg3La/eE1p70ovs5eFIeuY/xn37u/3SlqiYQkOlZSqyeeUHu+GLFkV2iuwQJ7hgPwsu9LL5xf7HO6kzGDBBOTt4BOIv3CS9n3FoC/oSBhmMku1QNiOyb6/GVoeVvulsBiLXczSOuoUXSkeGZhN71cxlLQ11FOykvH/JRCi4U9R9y++BbD9XCO8qEDQGs2bdu0sjx+IZ22Hir2pq0cr0Qad1waZTl9MoXQholZGhoXfNqg4whXkbqPJrkz+j+tWgFKYHVlJ8fuW+xa3EWxVLg/ABCqETK3wn0pUEpG2zvLIi+9uWYHpYT88uCGU+96/IwbRtFpHQz9jBabM5NxeJKtqZijcSJ2scAonlsQQVB3CoLSKct83WLbeqwqH8pwAAgKPekLm+tHGwhZuOjV503SdNo6Z7nc7yKJZ9asSX53xdWir72qf3rF75/auL8j9fu/83I9FfAnul1+D/xKPwUD267B9HtDrp2RiOdCwi7L+hhVTv/tdqZWYza5JKG6N8X/ROeoYpMFuqYiSqSMTf7LHcHo6RYb7c8NXmn0anz5mr4CtVA7T696JajsquWeiPOj7zzZf6S9mFlVFlcK//DkaobXJ59hKoA/BPDfbqE7Hpdri3M7YkcOvu8jr4SHvOJAT1HA5UnHSypCUuWyoOC0SZRSbJgdBJSv6s/xdmkygLoybZUOow4jASAFIEUIXJ/42/9oSMQcvaeaVx351ovr1FfSGVrL+LfkKFP27q0XXgPwJ4t9Rnc6ZpBgYvIWkCX6owpymQLqHp+W01wSFBQpaIs+EKbh4Clztlp37Vx807ws3lg+DQpS1JKmTVudC+gLTMALSreUE0ei415Z19m9mZRtdLot8izbpDiAYYHT1+5aNQxK6bnGHZd6WE4PwBDQOYEJdz4ASI65jyp/v3VVweePHaL/tmI35rwHzishPQXD6Rtt3tTrVldmUa+L2VCKeF97xflzx1ZVXwk6HqQTV3LhYs2WB++/eN5mm2G9XObwplDnbQelGOm7XNMZG/Mt6fMiBExRYTQ+MDifFtS0bQGGADkKKm5cNmZdLEAAgNEx7zTVQRp1uVMXVZVljftJLFpScP7d/7x/IJjVoh96/wmr5qLdUrH+KoBWEKzl+slZfvcwL14pOlEyJHHxsENQEoWOwaaSdFHj4CWrPTe81qFbXpEtWo8QF99ZmI39V4tnkD/W0DheMKaud9pozJIlalqIWNb2WLi6oYR0qeRQ9mphfUFZJPWgRX0YjB6314UnOzdyAB52C92Hpl3G+Hc/+uv/X/trjU9PLgiNCgiNenHxtJGZqfNnhwV2hhcG/GWh6c/ElTACAhVGDYYP08TBCDF+PL5UwTvec0JXCkKvJb63bUcuw5QxRE8XuLP/cLfPIgC3SAXOxMRkYWSqVW918nbVF+0dIPIZDbR1qftGiNwDMr9cbB7KeDDZGUJK0+hgY039TPKwBn2+GYPrCUgA0PRq25xCyryEEtb0kdP1IV1nJyWbp8RrKAUu9RY0x9NC98QiHsaXtLElIeszCXsaRvGgCmP2YerBb/ellutRMZ3+yibMChAoDP2G0BKuZcBXP0IZTp9P4XDuHhRScDDQ9PNvPUTOHFpbOBi1jlOQaQ8fgh/8aKcwPuy7Eg25umbvTwhKyuoHrCynLjlvzXH6Joc9e3qx7SSVpClgWV4WPcKydFG1R0pBAwXxoNFMCNjs2StrN7Tv3bilddfGrVd26jrpuXB2hV7z4g7PZ0vUcjFs5dkUw9q62ZNcggyCoE0rVAW4FNm3u1f0VgdRtaZP9/ljWS3lEnIKpxNCa9asGnJ+YPOwpcGvOhPj3tKtK4fLEiCsOFpISgtTtt/47MsWpihLk99ZyamjrkHdJ0cTo95OiytjI0T/MigZUjo9PROn/WezAgQA4Dp+2bBgF0FvVByavczhS87+d+bANMnn+SAEzGO7ux66b9NAN4Cv6JRZv+mZ//z5YvvdDZhiSiYmbwHPrvzqR8ud8t98r92X6k+KjaDQiyh3pADcuvmKqyY5xUYOn+PiuxfaZgqKsQrd0VGi27awYOaYS42SzMluLrF1YttUqW5vqdQde8i1VH0vplWFL4VinjbtdS8+iwAAQ3HhTFziNpX4Uk0blo3tGxv3NLGsTjhW44Ixa7wv5Nis6gSPbO51eUtDbyaCPgtAptk0K9FVuy9wk3UHs0lH7M2cqKiCTWYnzH88MGYaCmAEFiEgn2kVDQmezBUxkNhJCDg5YW3KBRcWoqIUejziOnbitU21mZStBABiWbZpOCnm3W9tzfgvH3nfob28oOWd0aAUcVXhujtOryiSc+KihaO6ju6+/qIqgCwksJTqTwgD+9b2FxCyuF+ALHNHR4K+Vbo+aRtOk5u2XUparfK182E0ue4P3ogBlANDRV3EOT0lhHKbMg+A4CwM9cEr7CjG2AEnkVakSnWdR/hcabRkVFYEaA4CuInCpKhOyLmXt7DVG6+GL/1io+oqGbcv++vDOm+XoLggj37q4XoobB0IHfOuHPo2l+U+5/vSC3pcri2E4a54GsCn3EJ3fPa1fI757pcBPDX/1dJQYyBbcC2cpPiNLz+TnO9vawmEFIXZbP3EoUXTWncTZrrBxOQt4ONXPvedjzq+Hojm2D8HABAwo0TdE6FasIYKrQKYbfPt289k5y22y4MWZDKWKJFOWMHpAGCnnFik2zYRgOtlE9fa7Qgcw2x6zziTPbtO9dfwYL0AhnFdQQKVNzqQT5EvLymFrQaAkYhj33jcemWZV/KyjGGnLQKo9+cGUiousgx9bPVjpyqGL1aP9p665n+kKrw3NuZt8hZF8z6I7b50EYD55KQTmPOdR9OcI3faWhTfRFh6rX3Umd0ihRxBqnJ5TbEyKevJ469tKoiH3bumL1d1Mu/s7NV+/9q1fcWXq+qH8khVA4TADQpKKeEFa25IzloWsukGw6DW600dikbzexMAgKcoMl5RqYwRQhsXqNmcgufVNQyjx68FCcSZy1r6HK6MOiFIBEtxsocwdOIeE7ASdrG8Au4CN5at1lyam54H4NGKUKsVJjnnJU5XXdoYu6d3nfUi59Zs9FCmSnPav1+yhjAUG3/r1VPUrhWstynB155+POPo6XAU7+2zEWDU+79OjUe/ss3H2uTegkdaNRizQnAL3WNYYMbrc8x3WQBPLnK5CgVkMuHUavekgou4ni7GAM/rA4tvdndhBgkmJm8RkRz3TzCKq6by3Qqhxe1EKrZS0l5OhYgIZk6PfIzI5bOXzQtBiQK9RCE6UhOS9uMAeplkkAWJ6GRuWkAh+sbT3HjcR8WmhziXvM7GnIlpSB1KYN9iD5YCDscd7IJ2y1NQiiFNJ1MPPlVjV7aFrChyKId8VnU9IXASQsvLihPd5Rs6DrGcuhwEc7o6Bq5WbfYURsOEIF/dwEJdIB0AJoIxqrJW5ai1JLqS4fQ5AQchsNnKI+fTvYUzHhqKzLaeenODMjpYlPdBbxN0ID05Ozvz3skaU6Zxypx0w3Q4Qa1dufMiEx/3XOm9tGzBIAEA3K50YyxmT1HKTBWNMoyWKy0fO1O9sjex7L7Lj1IK78Wfb7sUGwysXmgsANAA20nEmxt0r8vOMC4AGAn6x+o3tS/PJWzNuk6Ygt1tOowH9IwZDAJSaO1l/VKZ3qT49QEYOhnrc6JwTjhjK7TEU2cI1fZzaZJ1XGZCuYeCF1WRCOIQq/AaFV3b+pdXXbjapfygNq4WR6KJiEtDSowV/ucvrbaT9gyAzwH4MQzb5cV4EFh45sTG6x1k2qxSoHJsGHlnmZbMBgBfA/A7NzHGHYcZJJiYvHUQGBXu1bNXZAld3kEkWCjpLqdCUATZTkCYDNSQTuZ3pruOIxdroPN/ARK4I0Ta57eqrVZWaLSywINuHDqapMuzOpnH9Ibmtjtn2hwvhKKTQcxRhiQYTQl7khJ7tdorrSCEjj78yZ+vtLszAQAYbS+TZo+ja6w9PBQ4U1A+ni842QFMTXnPZgtAjzO8Rq2lkTJW1BYMblhB28HZc+fVtGUd1TE00FFx/syRNY8tFDgJrO5412OnXl+5cqD2jaY1IydPLd8xff1PfrptzWf/50/nndImxMh/hwYDi6YGKMVoLObopJTsBACnK9Ves2xw1OlKryMEu1iK05Njrtp/anzkSvWh7mMr5511OJXAhS/3wK9DfdyC0KW/dgUaWUJYjz/BEQLB6s7sJaLaIpQnNBg+FAyMmzH1Rk9AWMsQ+4A4xITTjapGBQhqfaaI2rQhS5BugqFj0EgIapVf1rykK5xNyoDy+7oVjdN71n/gmEIHXI19/7CxteficlawSasL1/Yc421yDsAfMFvaFg0QJmYR/vdi22UUdm/ruHWkMZAtJARsoGrsVmgZrFaf2yVwB44sZrN+12AWLpqYvEUcVA/oAD4E4EfzbZMjtLaTkXZ2EmkgC/1IAmobDGfCKzBy6vPCU2YJhXkLIlez19rq/Dz2vNMLyz4XbbYxNDh74wCPkzYWi5fYT5CRmVz+NVQvtCvjAEApU/zClz8WOP3zrYdUibuUjTnytowOdZZvoxST5yTDcJw8BiCI/AECCKuds5WHPI6a8R2sqC2pFdVSFGfCw/6mC80bCiJDhY+5Xemj821LKdK9UUthSVECHKdXP3j/hepH959pZhh9qhU1k7F4W6+Wn13omJRiTJGFefPilCKVSlma+voLXcmUdX1VzfCR7btbrqzb2Lbc5U7vmWzdJAydOi7L6/Vla7s3eivGLlpc6SGWV6aLLLUCuNCaxrgOY6YnB7p6RFf7AECRuanCNdvGQZYQbIaR0qmHkZ6aAwHxMxIZA2ADQSO16hZ0eYeoxL4KQ3L6KIlzFRhw7NHC9lVUI3tzdbqWdTIa4fUB4s2yo7acdedffO+idYQTtAw7yGxp+78L3bdp/P+wgFHXLAKYeA4GKsauJ603H7sA/NUtGOeOwQwSTEzeWkRg8Ry+RGhVFyPtusKmFRhV/CthBAnnYeggnMG0yn9QYINaMF6ruppv/MRIJ0/IDDVEQuAuErD3cS/smx20mQGdCFSotM2x9FkEAEjJ7HwFmoyV12fUEQS7S8j5lzYH53trpzojjnSXXoURGORgCEjtQL7pYqK3Wkui55zLxjZyNnVJ50wpsqGeoqbj33hH1cDV6n2TBlEeTyoginJ7vn0IgX2ZL+eTsyILAAxDSzZu6N773nefOGyk0o1n7S9+uXkTpfOnRTSVHZEyloo8q1QAh8JhxzmdUNfajW1ntu8+h4qq4G6O0+ekkdSsUE91Mj2wtKx65JR768feKEsGwk0AoOpQACg5DfUvjmPZ9P1fVKK9m/aeO7Nh18WpFBjhtdkqnpMBxFkYf5fGQoo2kmJGQJEGAOGkuwQMlSijLxv52erXdYWpcezsFYhVHiWiSlQbLlARLroh4QSvW10rRvXA2lElm3RzuXJVzPjZosgXnpg3sHt9+5fZzzHf9X6O+e6XAHx+vu3mQjhFJ0EAcPoT16OLkYWhuzEMzBET+231uV1zhSbuUsx0g4nJW0sFgFVL3TjM5Kbn3S0AphsTURhvgWNuKkgMyC6V6OEbPbEilp13X0LgrLVgb5WI7h+HaYmXwynbEmsRJknL7BzToonRoeqkn2fp1KxEeCiwOxb0nakoH9fms0ce7y/aVVI7HJ5f9Ij2i4HEsODJbJ+jmDAPlEJLjnmOXnl50wo5bZ1Tq8AwqPd4Us2jo768gR4hsNrs2Rnn27hicN//+sMft377+b3c8Ii/XpJ5R8u52uaNG7rn3D9KoctZMZ8081EYfzt7CotiQ8Xlo25CFhavAohv7FJlU9Havn0T58YSllbmJK7tn3+6dke9R25qi1pW3V+WjP9bOHtOpXQHAOwrzp5vCYs1KV51Viwb2jT93qWOVmvux1tVwtDJZ8c+AIdguH32TG2YEILiFddDAiioTQsyGW4ZqY9YKUiKZDn7wHPbg96dPZprf1udmhKGE7KjX0C0BgykzAfGGkQCbjvtK9TGxXOqbqGkNr39588/+J/xf/uvf+6JWl78qv5h7fXtX3bBCJwESvGGRVA9OZmrvvZZUJVj9IwoaNGAKzPscUjyue7C3ZRe6waxifJgoT/RnUzaONEq5asBUWAYlx0CcBFGXUsHgHHuwBEKAOpzuxgANTAC1b0A1sOwS7/hgP1OwgwSTEzeQg6qBy7u5577BZZg9kRB1QSRF+ptJ5iwYo4zMo6TUZ0F8QJ4DUZAUYFFDImms4yd0zE5B5agliMY2eZcekcDAFCKfo3OL0lNCOVnL9M0dlMyaTvhcmXydn5QyvCjfcWdxdXBWbMHNCR4061iQXIHIUu//mzcduLywc1FmYhrwalqTWVhxGeT1ohULysfPZ7LijZVZ5yegvicokZRVBsffOBC839/6/56AHjltXXb1q/rCTLMzDqRgatVZyIj/untrqcBuIFrnhS6xpXFo+5jHl98Rr1DPlhenREe9Q56jz73g60JnbLJtqh1H0CBlJN+we7Y3qrKF0jRyOAn14YfkzX0gtUVTea6OVG9FtypbGP6WFWzY1fv9ABn8n5ZAbRQlbA0ZvEDAAEByXDFAI2D10sZAhTs6npJD9oL+15avXnkcP1rYkNQ5DwoFdZE14NAgiGUZeEi5MrgP2/kSY9nB4AszylMX0z4K47VvvjK1q90sgw+MHHcLCGwPrCuP6hqzCWO1YsnPiDvRADpAoy6HkkJN3eOeFYVurPdFkGVA+6M7nVKe1lRPkoY7IThnXEYxgO+GcBZ7sCRedJkBtyBIzqAromf7yz2mdxtmEGCiclbz7sA/DaAf8JE+1U+dNAOnWDJoj4gYDTQJIzK7klCALphTMn7YaQ65jyQAaCRF5fkTbDHhRY7e32OlrJGhrBAwBLPcQm/bW7JRTjiLHQ6M5SQ/HmHYE/JjsLK0V6GodUAzXI26aS1JLaRsHRJOenB3kDXT75533BZ9Rh1pG1+gRotmgvhdGb3OhzZ3uFhPwpLQsOV1SNlDEN3AgAFnSzom0NFeWi9KCgpSeYdqsqJR4+v6Ny9s3VGkGBzpbORkQIAuAAjCslrDJZOOHbYneljPK8uGCjwNkkEAEohj4Ud5z735fesz0m8DcDVQo6+uduFSo7BXo7TEu9pCMYKiyIrAEBgUQ0w1dEhf9BXERpmeW1KJ0Edc+6VBtxnxYr47NoPO6VYSXvdw5C4mhlrWNpHiNHqyoqKjRHVjQWNg80jVyrpuW/si25//LjN+nzxJeqXI8qKtEv3K+VKlzt08jt7iK80fCo+5vWVNPaqG1R2qCiQ2EzIjBk1K2C4XvKcvmB3QnVRYmt1UYIQgklXzj4A/82K6qsAfhfAZe7AkRuVM78nMYMEE5O3ng0A/gcWCBAAIEXUceA6ggSD1ZjxmosCzDRrkmB0WIRhqMnVw3jTUqYXLS6Ej8djmPCHWOpJZRRmwWpvSudrGSA1oZC7KRCI5z8WZdjhrtKRqrXd/daSWN52RjXLjwVbatr9K4b9ckpMZMOuHMNqsFWNrfqnv/pICUCW9XeXQODUloc29i/haqjOC+rwhi2XS3lBmyGlTEDqUmFns2iXAoJVnvHZEQLXJz9x8PTLr6znurqLG0+11Kza/cD5HuSEqQeqpzDqHe4oP63r7KKuofGoSy8oDI9OV62cTTbizLmrQuge8J/6/b96/y4YWhHNAFaMqWRFc5I2/c62ngF/IFbCsnqe9BEp1lTmqhEkUMqI6hHohGNjfBH1c4dgVXdNOmNSiihkthUSO1deWtCuiR2JGsDqEXdVaK+jNNxb2th/0VUdepCq5OfMuLiBHZ8Sklq950NNV17/xsMPAkDPqUawrBZkBeWKrnDbALL41NfsqyFow7WZgqYHjn9+9NraBXSX3saYQYKJyVvPeWBx86AoyeV941+EDLBgrlqEEUhMQgF02ECG7I4MISpfIkl8LeaRdJ7GShi52UWlfgEgJbGehdZbeV2fb10mKy4oSR0aKNpR/+D5iwyn550JGbtUdVVK2PcOn5x5qq0ttS0w2lFtACCr3IaRiL2lxJeex4mRRjlOveorjJTwgjqvz0IuYd8rJa2t3vLwjDdwAHA5s5ufeOJImnFnmq11IZ7xSNbkKw01VMfQcFdZ//hA0Y6liB4BANUZzlkYH0yOeeYNEmwFCRsABEc8KRgPxu2YZqYVUsi+NC+1F7L6vOmjVMgdcRZGT7lqxkXeIU2lQmiPpwZWpQOViQxYWk87vBIUVsk7iPWaVThxKHtREztOO73bcyP+AcjcJqojRBhwMBQymwGsAFBY3thfyvsjb8oh33ZCYKmsHzzrLY7YYyO+U5ok7sp7rJmEAPwMwEsA3njg+OfHlrCPyTTMIMHE5C3moHpA3c89dw5GgdO8RBjpRvQRCgH0Y+m1CARAfRZ6fWlBMkYIPJRCppRcVRQ2lM2JNJsV/JIk1FE6480tMHGcJZFW2GX5lnOMPl5oV67YeH1ei2Qy1VEx/xZXDm7m1r3nGAAgNeJpGbtUaXNXhsZ0jaHZiD3vvSgvim+oKQs39QwVTM0+nO8OWIq9aeQrdCyuCBKW1RetAwAASpnGyEBBqKBmVCVk8nuWamJB8qjgTTcSgoeIS2oiDnmb6sv+4vIPd76D6syi4kkzj0EYiyO3KRuXD6mSkDe9Eo7b1K//+/6jwSH/CjC5vH9P0YyQqvGn860CAGgKVxcPei8Lvgzh7NLMe5Pl62mbDyDIgpJiGNpdc8jFbIqt5NoxiKhvVwT6opYT3s270ydAUA1gPwwr7b0wAtBRQpB55ydfcv3TFz96pFgg/MXLFTQ7IqZWWzmnzTLvnwUF8EMA/w7g9QeOf/5GLdNNYAYJJiZvOfu551YD+MhC21DQUQkz30Kvgz5cR8EiAFRZ0EMIagCAEAiE0BWiqEIUVXjcaVAKlVJyVVa48WxWINmsWChJvHspb706Ra9OSXW+dUUO5Yrboi2YtvB4U4t+ycdH/I2ZmO14JuhR4r2FKwHij/UUzWirpBTQNHaU47SpN+9lFeN8z5A/AxAbAGg609g35jpRXZSYUSwpWnNNLDs3lTEfhOhX3KURAYAXoODdmZOWQKKQMNP6922KCwD8ezsfXVucPnTx3x7cpivcoi6Nkzh98SwAeEqim0O9hf3A3MLQX3xnr310xL8WoGOlOt+cJro9A71UAS2Z/Oj++2RNwZrSmMyzdM70vZTjT3V1VCRWrul+cOxiFUDoGWdpdNPMrQhAMdnyN6e1U0pYe4aONmwhL+vtJe+6INlrQ2uoxB5lcky5GIg3i97M9DRHN4yC2woYLp/k9JCdbD/wWmLwpa2CPep6xBYrispMst0qqhoAdlZA1wfgfQ8c/3zLEm+jySKYBk8mJm8x+7nndgI4stA2OagnWvjQvH4OeUjBqMzuBrAMRqX1OiycepjiQ0X0yMeKsZTp2ykoRU6npFORuUg2K7LZrFAoyXztbMMhSSVHuyLWOdPzPKMP1/lzhdfetOc9Uk9ZaYgVhIUFkHhB6gqUhGomc+SzzlUPj/oPSzlxOctpfSyryRyv0kzKtqV90HemY9g7NY3OEL17/+beamZiHI5TjhWWjW2bNW4WwIK98BZXutlVGhIdFVErYenaGSsJVUljWCHk2hiawnZfePphOd5dtAQtBxpZtfsCeEH1AYCcFa7ER7wrgJneEd/48fZYTuY9s/fWQC+1MrmJtBPV1pTEDn9iV9dUEEQpckMDRad6u8v2AAhu333OyXG6nTB6e+3D5xfqbLkEIAZAA2ClFBXdL69TQJlKY1yqFt/f+rLDknuMUpKvIHUQRg3NRRhthEhkudQLlwqPvPfxDv/PP/npAl1l+cqKMedYjhu7MG7rf0dlfB/LgIWhV7DhgeOfv7L4/TNZKqaYkonJW88xGH3v8xIn8hw54mn0wCgcbJ72AxhCQrtgdC+swyIPselsduG63xYIgYVl6GqLRdnr9aZ2lZZG6qurRpXKytFLJcXhZrc7dUQQlO60zOTNU7tErW/xAAEASE0o7O5dbKtczlIxOOJtyrcum7a2SDnLXqMQj9smS+KeTMq+FyDW8kCyjiH6pD01dMrUdg55jwI06/QkjhSWje2YFiBEYNxvimniQbMRHNnewvW9oqMqos4JEADArrRODxAAgOW12nW//9KSHAg5Qe2dDBAAQLDKKwW7NMMumVKo29b1nG+oCZ4SeCUx41ggq22UaTN+I6zTcq1VUtOYq+fONA73dpfvmZgpcuk6IwEAZ5UX0+FohFFXsA+GbgB/LUAAQsHAUQuj3gcQzNOxUg6j7XOy+wAuq+r4rS3D+31pnmx98rVBi0Xuikhs5L9b/Z5zIdsD/6elqKsj6DxyrjvwrBkg3HrMdIOJyVvMQfUA3c8914Rpve+ziTC5fMV6OowZiBos3FmQz/hoQSotS5dXXghCYGEJXc1aFFgsCuBNYePWkZiqMRfCcXtsMOjl+od9pdGErZqZJhu8GLLMz5t6oRS0a8RzrH3IWwmK1QfedzTD87ptYp0+3Fc6CJBN8+1vE9XiMn+qaSDkmtID6Bz21G3f1D5ktcqTsysJAC0wWhInp8f3wQgUGmHUgoAV5VDx+p5W0ZPZSYw8+8W898kl5VVcZFhaZvEnR3JhZ8l85ztx0QylmPEm7iqM7Q73FrZSyjQCACHgGmpG9zXUjCKdFS4NBn0zDJ4sILHMxP83FMbtsaT1WE9PSTQd8czwpxBE5bIgqFsAQJM5G9WhE2bOCyYFcBWGuNGaqeskKABollJiDQULmmVJ3Hvux7uObPpg866lClxN0I80zy2v7+WDazuCf/eL1cspSAEoAJ3JXO0vWMGAfO+6RjRZEmaQYGLyq+FfAfwx8vwbpKBKnMj57I4ZGG9nS85bLwUrQ9MiQd7CwpuHKiyrr+U4nSsrjKOsMI5ta3tBKRI5ibdlUramSNhtjUbc1bIkzBuoUAo3pdCnT/lTCgyGHKcu9RZ4dcpMBVzNZ+qbHtzeto9SQNeYCCa6FxZC1Wc+8yhI8enLlc17NndO5sYLkD8w2wfgLGE1MbBq4JyjJLqZkBm+AflNg5zyvN0t1kAinD9IoHD54xeKqkfiNmfWP/tNnBCwntKIJTrkzwFkhnzyWNg5J1WTgRbgGf3qukBmfKSrXD015r0/YJcPB+wzS0BUhZsKWHWFXTtwuPFE8abuCsE+R6HQi1my2LoO6Dr08JgRIABAJuLaFRvyX/GWh+dISU+wDoaux+Q1HAawCiCVRGWkNdvbjzx+oebMYFzUw5KSLNGdF5Myvv5V/cM3611ikgczSDAx+dWQhSH5OuffoAbaQfNYOsNw3luSlsH1sMaBzlniNLcMQZS7CJnr8UAIXFaLstFqicNfYLTQU4qglBP64jFnLhJ2u2NR5zJN4yam30lA05hRjtOLAGAsZr14rqsQisZumT12V39g05ZVfR1y2j5ipBgWp6E8UjcSmV6+QbVkRlQARGHMHBwC0AbD2Gg2Gyt2XT3G2+R9lGKMUnCETAVyFkzvNrGoXeC1BFg6r/4Fw2tTMyyE6IqvJNziDsRyTm+ijjCYnroYBTCj/ZET1RqrK9OcTdinrlvXEZQVftrDm44VuLOtm8tDZUV2dQWAFVmJPTU45gFD5qadXO7kMKb+7ghRMpbt45crmks3d3oJg1MglEVhOo4sXwCFiUNlnLCqo2CoquVYZaS/TALI/RPHBkBg86YWmrlyAjgOw9b7EAxFx2YA+wAiugPxstXlkUxtnfqdsYjtBx8/+3vdC4xlcpOYQYKJya+GBzFPzUCaKPO9EQ3hNgQJW1yI3eoxJ7HYcuPA0oygCEGxxSoXW6xhFJUYqW9dJ73ZrDjc21XuDIXdEasjnTrdURTJyfyc4GDaSI6XmtcEd60annhQ0hiARL7q/0lsolrGMnq7ppMCjzN79aFdV8q9rux05co9MO5/BEYKoQKGmyEAYOj48gbOKrcraUup4Mz2lG3rmLzmDTDSRFfBay5SG6sAcJ4QzHjTn075fVfi6V5/V2nt8KDTl1hOCObIPE/Qj1lBAgDY/cm9UtpyRtfYTQCQk/lhgPrsonq2rizKl/pTGxiSb1aEgGPoHL2KWNS15eypxiMbNrdOpQhyEefe3jdWn6158NI+ymlH2YLc48bLvwEFLVUK9OMSRLLu/rNrpKx4XFNZ1ebIVF8+usYy3lnWUb6ue16Fz0zCkrS5codxLbWzHUYtj0+V+K9QlX/h8Vf//FZYO5ssghkkmJj8avg+gL+DUag1gyiR8okonYGhTXDLWeW4temL6VisubzmTEuFYWi13Z6rFqzZV890FQo5xVMLkHlSIzRpt0pXPa5sttCXJN5A+IJokcsYRvcDcIwMlFyiOrM6356EAA9u7AkWlYwzVqsyX63I5PT6PhhT4FNBgibzPk3mfQCQizpW6BrpYFg6qd7EAEhBYZchzb0i50RFLEgDRheAZ+rsKUJyn/cyN2QvW7GldQwgC9WdpJAnQJi8Fk9ZuCLSH4jqGjMUCXniD23sywmcvj3f9gCQk7kcADgEzTdr1RWAaJm0zdF6qfbNxtXd97mKYk2EUCRizlgi6HX0na3PVL7n7BXv8uBKANBZGsvUq1epiO2IaiOEoMBik6YCgg3vO3ze6s3M0ZtIx23jLS9vvjLWW2R91+//qBwz1UaPAfgKgFfsn3lNf+AzC9wZk1uKGSSYmPwKOKge0PZzz30GwF9iprMjIoyU743XD0Md8JZTKCCfLfEtgePVBbX0l0pLV9HynMIu2AJJCNIfe+epLfMUxHFFZaMVoWDBUVXhZwQBDKO1+AIRi2iVr8vVcpFzmR141VOKo9FTleg4tpxs+eyrp1ibekrN8K/rOf5z0VPVjJBhKsi1wKAWRpV/PnnmHAxvh3kLX1lOL4yEPc3ZpGMvASBw8wpaAgDcDqkeABgyNVOl49pUPwMATlfmKMPoadGeqyMEZdHW6kNDbVXLAVoc/af94ra//P4o25Dslkr0ehAYAYlVdcJIi1zFRE0Hx2kzUhqqwsqvPbv/eLC7ZAcI9r33j144xotqI4w2yu8C+Cp34MjZBS/A5LZhBgkmJr8iDqoHfrKfe64FhgAMAICCjknQZheEDeA2BQhujkY4gutS+lsqhOgZGJ0YNwWlSMQStkXFoSglxWMRZ3uRP5m3j59ldXdR2djOdNJ2Mpu25hSZd3n8MVjtuXnVHhfAvtDKWG+gz1s7Nv2c3QB2tL6y4dR3z1SMijZ1qHx9/4b4+fLfoCrrAABnINZkceYmuzgYTJ+/v0YYhvT2vAHCJNmUfR5r7rkoMtdR5Mi1eX1xTzzuyFKddWNGoSZVa9d2Od2+JCETfy9Vq3qWZZO2lt5LtZUAnLG+kuP2++L7CJmYHcmRUQI9C9A0QKZSa0rKGuccRodvLmUJ//DvPhxSZX4vAOz76GtN7kB8B4CvAfg77sCR3qVeg8ntwQwSTEx+hexYMSQSghNX+v3LYmlLgQStB2ROO2IYuD1v+2sc6AUwe4r5liBY5M5J57+bISfxXQBZ0oP8SlfJyHxBwiR2Z2ar3ZkBDOGfvOmHJbBg8MNZlKlXd0pB1axwKjnkV34W0UODKY63KczG2JmqGcGZnBVsFueMuICHIRA0XQmxH1i8yFTO8eOgZE4qaz5Gx7wr/DYttmH/6Y6KtT2bRnuL25q+/WBRLmWd+NsgXDZtpR5/cqpTRLTKpSyneVfvPq90nG3otfba3kNb/E1YFQ3Qo4ExbpzLYPuYUwNWaRqTpTrJcrxmVZOWulSm8BznzCWO/Gi3osr8gwDQuOvS8ao1PWkA9dyBI0uW/Da5vZhBgonJr5C/uPSnHZEvPPFxSnFcp2RoNCGqhwYdTS93ebzHB53L0jJrB3BTef2F2OxC8naNbbVlY7dinPGoI7H4Vgb9w74FzaBm0QigFdfvtLlglwlhdNlRHF1OddKbHPL1R7uL6tWsuJVS6K/1OzobKNfRO+Qbr60IzwgSpJR1Aw0khgnB5GxCDnOdQpNYgghePOTpxhJrWChFTteJlRCarFjbs5UwKCiuDRZ96E+/leg5t6wpl7GQC6+vXzM+XBBzeZNBmyNbnEnaho++sqUvFbdvA6C8+zd/GQAA+mLVvvR3lo8ce2VL6OGPvuoa+tkazhFIDLz+w/ts5cuGOjfuvrgPIGVUY8vCPUV9Qz0lU3UV4/2BnxKCv+UOHDG9Fu4gzCDBxORXjO9LL7RGvvDELpbQvyj15D7wYU8OH14dAqWgOZV0dkWtsUN9rkOvdbsD54P2OkVnbtm/29X2hafNbwbRIl23lW8+BoLeJRdWSjK3KidxCYuoLuqyCSP4upHe+mktgXOhOsmEWiv6EgP+bcA1zwpKQR5O1Eiyxj3+6rHG1DvFi62lhfFGALC608fsvmTDtAABQN400JKUMeNj3oUUO2cwGLO+QSnzqGEZhSnFR0Lgqt1gSDU37rwcOXtwM/fS9+4PnM3SVzmArLMyD07Uf7hOvLa5acdDp/ZdPVd/pP18XZmuM3tf+ubDP08l7IUcp8maxq5LRJ3BafcCh1/aFgZIFYA3AfzJe049ddK0a77zMIMEE5M7AN+XXrgU+cITHwHwmzDysQIhIFae1q0uzNStLszgd7cEQSmkpMxevTxmi7zR4+aael2l7WFrNV2ivfB0CCj8/K1vqZyE5Rb2WlgqgyPeJU+bA4Tt6Cu8smb58LyV/LO4kY4RB4xCvHlaO8mVxEDBnJqBSx2lx2WFm6jqJ46fvbmm5H3vONcV8CcrRHtOJWRO2scFI4iZ7AyIwNAOWJRM0rYkBc2Uwpz9Wa/b/YgX0Bl9dCzsSOs6oXarbI8mrLFsjpcaasfrCYEvWxC2/jiKXlVn3wEAPpaerRSxEQDGhgtqXvruA6dzGeuU/0cy5twAoGc0ziteqyZFx717Lp1acWjV5qt7Try2qUnKWtwwnB9feTL4cdNE6A7FNHgyMbnDiHzhiZOYpl2/GJQiEc5wXS1Be+KNHo+1qddVOZQUihdzaKy20J7/03DzhYX5YFl1rLhi9KalnilF9D++v9u7FLfJSXzu9JEn9p9dqlnVVRiGWPnaThdCg9EGWQhgDIZnxuRL1wnkeZgPBj2XftG8ejlwzXKbgI799pMvjxUGEvPVRhyZGHuS6UFD/hNTmfTF5vWW2UZb+cip5NI/nitqYIEeQqCplMxJvXz0XWcPn7tSSlu7imfYUT/kod1eDvMWR4YUHIqpoFadzTAasZe41D0ATbi8yZOJqOubAP77yeDHF267MPmVY84kmJjceTyL6wgSCIGrwK5ueGhZHA8tM9QLNR3joymh9/SwPfNGj8dxqN9VHcrwMzwdNrowglvQfZAP0Sr1AjfvB5HN8T0AuZ46A0TitgZKkSEEQzCKPiUYehQhGHn+agBVMB70HIxiwOuVpWYB7Ibxdl8Hw7BrL4x6hTX5digvjq1euWyk+UpX6VSrJQUpfObZh+Tf+51fjLpd2Xy6B1YYfgjcxDEW7Wro6i5uu9xXIK+qCi86myKydIVT0MNJmV0+XyLjOz/duDvf8mMJjD3qRe18HgwyhX42jX0EGiUgnbtlXCriSToRdR14MvjxocXOzeTOwAwSTEzuPNpudgCWQaDUJQfe7ZLx7hVRAICqY3g0JQydGbZnXuvxOLbBlbtdXwEWWy57K8YZizhvoLCSFIyGXW3FBYkGAPXTVkwGAmEYxkyTD/owDLGqehhT/EuFxbV0RSWMmQUGC9Qr7NzQtftKV8kMbwVdZ8r/5d8e7f79T/8sZrPJnmvLyWU5I8aktHhOk/lCqzuVtLrydUXO5PzF6mTvqHsfpThR6k/JksLp/WNOf5E3EykvSAZYhgYA6FcHfJ0rKiLL95Ulrv6sx5tXmGkhUjrZfjRJm3a58puN9eXgNJJaIBSwNyfIV/e66A//NPLx8es9lsmvDjNIMDG5w/B96YXXI1944kkAz9zKcTkGpWUuubRsInDouVjbEh+/rpf0JSOI8pIsjxdjYMS3ZLtrjzs1EovbSwCCq91FwYkgIR9+zDRr8sNoAyUALgOIYwlv7LOonvjpx7U3/znICpfCnO4EmgAl+MEPd576yAcP23WVVXJJq1fOiGsBsgqGb0FlOux055K2Nt4qh3lRUQSrVEIYLFdy/GFOUAoJg+UAMDBY4AGAvjH3tr4x99RRQgkbLvcZmQoCqlOQQgocC8p59RiWxJBM9oUV2uHnZwRjyOloGVGwHIYo098D+KuD6oH0jR7H5FeHGSSYmNyZnL/dB0jHHFW3Z2QKhtFviavkQNA7b/Ejy2qKprE8ADgdmfFPfeKgNDBY0NzUvLrYH4hf74NvctJ81cR/p7sQXg8BGGmNvEqToqC6LKJyLifxazyuzIk9mzrVkkBiL4wZjNrY0KxyA0I77L4kkw67QCnrViXWrUoCpqZpiJ4FyJaC6lEFADSNUbNZYb7gaAoKwgBAT9Czo4tI7SA3XhowICHIEfAAiJtDAYCftGfxhkpJLYCTB9UDP77hwU1+5ZhBgonJnYlyOwenFEF1hjPgrYPj1X5CcNOdDTrFWCojzjlHQnT6qd8+eHw85JZ/8KMd+7Zu6Th2396LAY7T62qqx6prql8HpagN9xamKGUcs/dXVUYaHne3V5ZE89YOAEjg+tIOxvnqJBMNeU5arBIjWqQ+ltNWEoIZMyqEAI/uuUwEXg26nbmdMMyi5oVh9LjNndmdiTrOU52ZK6JEGSvLq/2awg2ynObv7QtkAbL+es7bS9lglugLClAtRHuO7GnP4bCTpf/6qBc/fTL48SXrWpjc+ZhBgonJncklGDbFtyUfoEj8IOZ5271ZRIs0Atx8kJDOiH2rV/UPrFrZn+rsKrF0dxeXb97U2bVmdZ/HapV3+HwpfeX6zlceevD8Q7P3JQSc6MhdziVtM7oMFJXIP3plw3FNI1zlO0/Pd+ggDBGjJc0kUAqk4o6jiZirFiD35TKGKKHNkT6p6wzv8ccsLKtPdQ0EfKl1MHwRymAUOR4DMMfwCAAkSdB++Z0HzhYWhVOV1aN5j68pXGV0sKASAC6fqzm0lHOejgds8fCNxaQ9AL4F4BsH1QMdNzKAyZ2PGSSYmNyB+L70gh75whMPAvgrAA9jrvLeTZFN2m5bfli0Sjec457OaNiVeedjp2pZlm6qWzalwzMlMHSpr+BwQcW4QCl0QuaqEFqcGSV3TUUYAMCxVOhT9fSozBR9dP5DLwfQDSNYWAtDFyEvuax4JTLmA6XMnBqGTMq+NZG0nuzvK3Ss33w1TcgM4arp8/sOGOmlOK5ZI0+cr7Zp45Yro4RgGYwCyxkdKrMZGvUuqsY4mwS0ERjXvBTiMAKDbwE4dlA9YPbQ3+OYQYKJyR2K70svtES+8MQHYPTy39JWxWTUddvsoQVRdi++1eK4vMkUy9J5PSvWVIcWdG3kRLXOECi81qNHCPDZx849dKrf/zKMgkAbjFbN2TMftRM/TcDc6n1VZUfDo/6u2Y6Sk+g6yYwEfWdlmd8NAFcv156rqhnOWG25BkLg13WwzLXH+fS0x6Tz4uT5cuSaAdd5GNbSefUPJJlNRuK26zKrGodyKETUxbwgKIA3YLTm/vCgeiBzPccwubsxgwQTkzub9bgNDpCpqPO2pBoAqjGMvmjh3FJoXNW/VNXEvBCCYk5UOlVJmNGSyLOU31kTcmHawxhGbcDk270Mw2lRgFGIeAmGHsJeXSe5WNhzIpu2bgFI3gAhl+OvjgR9FoBM6QuEQ9714ZAXPK9EKqpGmnu6yiq37byY4Hhtdu3DJhj1KPnEndbBCGzyBkctrZXnALIn37rZUNDBFPSuUaLuW0CnqhPANwH810H1QO9SxjW59zAVF01M7nAiX3jiHJbg/Hc9SFnhRDZpy6XjdiGTsHuzKVulrrG2xfdcGEGUWgMloes1TMoDHQrUjt60hXU2YW1Khdz5+vhHYQQAS5mezwG4kk7ZBmIhz2aA5D0vSkFDYVdzKmXdBZBFX8A83njzqrWde/OIEc1bozDBnECBUuDZH+5sVzV23rSBDtqWgBYME7UsC1qXJziQYAg2vQzgFwAumukEE3MmwcTkzudxAIO3ckDRKm8TrTI8hbGpZVTHkKrwwVzGks7E7Uw6bndlkvYyVeb8S5VFttqz47h+V8U5MJw2iPwGR9fDKG+VsgBAKfRk3HlMU1mN6oR6CmKbGIbKWFpxoiWbsbCxkGf/dBGk6agqExwaLgjqOpNXWCgfsah7by4nDlmt0uzr3AwjzWHBXHlnCsAJ4BUAUwWbI+PuK6rGrpx9DApKFdCTw0Sxp4i+GsD0WZ4kgJMAXocReJw6qB5YsjGUydsDM0gwMbnz+fRbcRDCoIwXlTJeVOD0XhM6pBRJXWdGVJmLShmLlI7b2VTU6c8kbdVUZ2c8NC3W3C0psOQtyo0WPwZhVN0rALaznP6O8aD/qKZwnKZxUx4I8pBwyl8UZnlB3bjYgNGI82I67qwmJH+AkEhYT4QjrobrbT0EgEvn6wetVqlrxaqujRynTxZI8jDqIHQY3g0bAbQDiAEoBTBZd9ALw5FSPXaudsYbPwWVsqAnh4hcKRE6GWjEARwE8CqMGoPug+oB0zvBZEHMIMHE5M6n51d5cELgZFndyVpliFYZLn8CwAgoBaWU9KsyP5pNWTOZhI2jMsNRCpWQm/tuEW2569YpAHAWxgN1qt6CEMBVkGBCg4Vbp2+oadyWseEigNAuAioBIC5vIu5wpWfUQUQjzkutl5ap5WUhwrIzZ94pBcbGPE2ZrGXJswezkXLiNiknYmig6HBVzchsjwQGhrlTCPnTTdUAqmWFTYVjdgIAFDSSgH5xhMirVII9AIYAPA3gJwCaD6oH5Bs9V5O3J2aQYGJy5/PfAP4Z1+9UeFshBIQQWilY5ErBIsPlj4//+NnHAgxDJZc71eb1xcMeb4Kz2qQShqHX1Z3BW+UbUWxcB6ALs8yafCUhNTQ4j9cUJcsmbbbjEXfUYs0NcbxWBgCxqKP18oX6KoA4+wcKw1WVYxmGoVN1G4NDBcdUlbvhAGESmz1zpLJ6ToAwnQVdH8+1VpyjwPIw1DOjRNlCCXYA+CGAfwFw2JwtMLkZzCDBxOTOpwZ3WICQj2za0guQgK4TMRZ1rYpFr00GsJya8HiS3T5/PO7ypERRlMoZBuX5R6L9DEtvRIyJhWHbPCNIsDqyy2e3QuaHeMNj/taisrGyRNzefun88lKATCgmEn8k4mwuKEjszeb41kjEFVZVdtFUxeJQdfXajob5nBSXwmu9XjlIcm5KsAHA3wH42kH1QH7lJROT68QMEkxM7nyiAL4N4GO/6hNZiMi4d97+eU3lXJNtgJNwvBJj3fGj1Jmy1/pTFq9NKmcZlLG8NoQbU2zUZ+8XD7kvDLRWSQBpheHLsOBbOdUJn0raui60NPgBMkPvIZmyruQ47Wg05lgPkFvQwQEwjN4tiOoNSyL3hO3tIzJTAYLvAfj8QfWAacFscksxgwQTkzsc35deCEa+8MQ3AKyAkXO/IxkbDFxXC2VO5scv9xV427KBqXY/h6hEGkqj/LaE2NRQFrH4XbkKlqGlSxwyDKAIANIJW2fvxWVRRRK2TFsvwygE9MO4l7OglDJ67NyZFTUAyaNsSAqiMee8QQal0IYTwjmVEpEhVGUIFJZQjSHQGUJ1hkAnhFKGAAwBCCjxeTNpLF3tcA4dY87XAPJZs9bA5HZh6iSYmNwlRL7whAW4ZgB4p/HL5x8M5TKWBd/Up/NaDO1hlSzygKT4/I7+brcvGXUVxFN2d8rGi0o1IQjMs8OgInO2y4fXcrJOLIdjuEAA7HBjlYXFdNvpSxZHOi6lrdsoZTgA4ASprbu71EopUyRa5Fg2bS1a6rVMHTwunE1I3JIDufc91HI44E0tVI+wEN8F8LGG5582aw5MbhvmTIKJyd2DBOA/AfwabrGXw82i62Qkl7GULHl7isGkhkUfwl5RG9IVoTY66kd0dPLlnkKwyEGnL9HnKojnbM60kxPUZYRgGEBjKuo8C5CNTVGc+udBbN7tQfN93ukBAtVq1nRproL4LgBjsTFva2TE769d11mwYltrjOU0MjoUuHri1U0+XWevqxak1CW75SjpzKls3WLbcqyWK/Ck1l/P+NM4C+CzZoBgcrsxZxJMTO4yIl944nkAH/5Vn8d00knriZe//8Bs4Z95iak48nKM7Fpsuw2B9ImHqxJLGJdCsEr9Ln98mOOVRLCnbEVCJa7facXQH1ZB2+LC2sktq1Z1n/UWRRd9229vr3zj8uE194NQgC69spBShKI57kooza9UdTLvzMq6FQNHt63tzSvtPA8XALwJQw3x1Ybnn9auY18TkxvCnEkwMbn7+HvcYUFCeNR3XeJHzNLkkFHrXqqjJIGctVSGBi2ThYvnXRxyX6mH+s0RxLZMNVrQpKcwuqgJEmOTT2z87EvVtR84fdjmT9lOP3ufTnWGdDet2rTomRAUuATVEUpzWKijYs3yoaWoPR4H8DyAHzY8//TAErY3MbmlmEGCicldhu9LL5yOfOGJSeGgO4LRwcC8dsr5EJkZ9QHzUmxXnDd2Rob4UKUF+FwVWiYXcrzaSQgWDBJYX/qQY2/3bkJACupGawBg7x/9HKrEdcUG/N2R7uLa+fbVdFwcSgh6Sl64LsFhywWtojLfeeQAfAPA0w3PP31xoXFMTG431+09bmJickfwWQBPwpDrnYJSQM4Il97qkxkbLpj3wZkPkcF6F0v7Ft6Kws7p1TdxWgBoUmSuuWja3anEvFtSYJRTXrPtNgKE2es5UV32+N9+U3MUxoKz1+kUbUMJ4XRbyLomJXOLmnFtXt3XlucYaQB/C6C64fmnP2UGCCZ3AuZMgonJXYjvSy8cAnAo8oUnHgfgpjphpIw4kBxzlwNwB2rfOi2dWEpsk3PidVtP11nQdzaNqvnWe0RtmBAstf1xDoXLB06mQ65UOuJ+wFhC9UDF2DzSi8COr689HUwJD7Jf3RZs+/HzlgKP5Jm9DSeq9Zt/641X3/jy+wRJJRdTMsskZbYwqzANSzXBAiiWVYxPv+4cDEXNv2l4/unxJV+giclbgBkkmJjc3fwGgPJQb5EC0A6AMFZ3+hClKCYMZUGoAp0Rb+UBQwnL6LHW0q7jbaV6/5irwgISfcwHDsasxmpgaamEZRasuZyhEYkSX771lU55ALj+IMHiSg+tfdfxIYszu7X53x6f8dAdGygac3hTeYWQPBZVSspM6+H/+rE7X4AwScXWjuXtYZHVdHYfgFMT5zgAoGIp51daGL/CsnTSsfG/APzvhuefvqUunyYmtwqzu8HE5B6g7SOf4WEUND4AUIfoTXeXf+yUh4hqXeKXK2Qq8UvWL5iOphN9MOTsu9BTMHKirVTvG3eVqxpTPX2bapE2bXVi0sPgKIAlV+y/EUfruJJfvXCnW3t1TSBtdbvTO/NN/8+GsJq8fN+FY4X1Q1sJMQKV+Ij3avexlePJMc92gPAMo7Wv3nO+mmGpQCmQSFpH3a7sVCsm6081O/f27AWA5rPFVy53+RK/+a629TaLNqPI8L+/8KGWC6+tnqwpaAawl2P0g1UeyRVSYY8nLGvzzSwQQumvvfv4eauo6gB+t+H5p08u9V6ZmPwqMGcSTEzuARqef1oB8Addv/Gp/6Hr5LOuhqBELMpGALBtGWhOH67du9D+ySwff+1c1eWBkFPJySzG4jY+nLB6sjJXC5AaGP4ReSkRMN2x0QpAxczvlrMAMjBcC5MwLI/dlKIqrGBeIycfw3iiMefmeMJ2rjAQ5y0WeRUARGOOw253eh3LUOfkO45glaLbfv3VBMPSGYZL7pLoijXvPF453lnW0nuqgVOywpqx/uKTxTUju/oHClq//fw+11N//ANMeifwJckpXYT3f26/W9dJ4WO7+2NVJakZ6ZTHfu8V+cJrqyd/3QgALIHOs/qa3336uTFP9djIP3/iE6mBy+X10/drrB1psorqDwF8zWxhNLkbMIMEE5N7iFd6/C/1jbme+Pxjl9RMFQ4JlyyRzKmKeRX9VI1oPz9Ve+L55hUrAXI9PftTFPIzAogNMOSRR2AIPo0D2ARgdrufpgFtOrASeaGwMka9gq6z64OjPhBCO4sKo3I87tgdj9uDMDwt3ACia/ZcvMKw9LF8I3GCZitZ2b+1ZGU/TnzrgePBnpIVgYrRTGlJtOZTn3gpbAQIVAHBiFAWnyrA7Prpt10uu+LMZ77kK4tudhfGx+Jj7kIADgCQNGZ5V1w40nWlxHn16/dZBy6Xz+5eeLWzP/BbT3b8odnKaHLXYAYJJib3ED84Vl/xyLp+JdxSlqmwZjOkJL1az3FuXWVHwglL+NKgL5vKCUoiI2iX+gqY/nFXPaU3FhwAAAEdEhmUzVrsn/iRML8vARtREJqv2M/KYJSQmYqMlJK64Ki3f+LI09/s3a7i6CKdEgBA+6vfdZZt+/YuX2zUe9BfFn7E502XAQARtEuux1o3TA8I3I5r7ZeZiP3M8X97iHng8z/eAACEgH3s06+0fueLT0wvhFymyLzl+3/9fgnA9G4PHcAXAXzpy9KvmQqJJncVZpBgYnIPcZENPXzhgk0d6t03+jfvurwxdqymPdxT7AJIyYkef/u3z9TsW3yUpWNlMATMCRImWbBg0sKAI6CUYu67ehGPfmB2kAAtp7IyS/RBgZtpM11YNbqU4szCorLRHvHJ194MvdHQSOm1QIQKWhpAChOzAlPH1MgVEGrJRh1ZTeLdE+cxCsC7+r7WYqNxkpDCmrH+sZ7CChie1NMDhHEAH/2q/uHXlnB+JiZ3HGaQYGJyD5FhNLudMjmNk6q6/vk+HyFYAwCUIvZmZ9G87YY3ip+7ccMpF4edWx1oOpmiuykIO31dsYAZttOyjmRvDhfDaTbnoaJ3eUHWyzKwT663e1Olug4QYvwEx50jHb0FwcKClHN59XgdIQClhNcitjI3l6tB3cjBvjSv/OAXG0o+/fg599ef32ZdE3Oe3bi1hw8UJv0AhSCq/t4frRsqrg6K3q1Byzu++P1KAEi+XpfR4hbx0GtbqNuinVnznlPqO5580/YXD38+h5kzJ2cAvO+r+ofN9ILJXYsZJJiY3EMksk990WX5yrsYlv6apHBXpLS1gxFl+vThZaUjCVvDrT5eiXDtQX0jVFmwjyNoOpLEPgZU1kEEAPBzM9soZYpkjwTazsQzj8JnnR4gZHWE3v2p3woA0ADCAlQFSAmAEgDgWK1vz5bu3gc398gXfrBv2/v/5LvnPbVjVU6JpWq4U/rU0w/bV1aG5Gf/6YGV/6pwU8cloLh/Uzf3ySeDSd6my3yKOUcp1rKeXFgKOTMkI4Z277uS3veHv7wfFDJAh6elT34E4Ne+qn94RrBjYnK3YQYJJib3EB9i/9v2OIqfqCJZW3nD4BZdY/hEyH31kzt6277WXP/KeJZ/FJi/o+B6KeRRufhWC1MmYt99hDYHeKwaUeilk0lssDIz9REcLEoDPDoridXmpJo6fd2AhE6AbL+2hMz4XlM1tuqN4/VV3z1V8kqQjfU8qgFOYBWApuqCFP/FRy7a/uqlNamcyuTIlMYD1X/ncwdPvvO9LbsYDgJShjitOm7/pZ7iC3hBjW5/z5Gk/12XHiUELAis3pJYe3TECwBPA/jsV/UPm90LJnc9piyzick9RJxoG22UL2sfcY8Tgo0sp3/FWxztWbGu+8Q3k5/4nwfVA3UAPDC6EH50c0ejIRuLeRUMr4dCAXsJgb9UwL6HPbjIkJk1BwBgZ8E32Mh9RQ5lz/TlAxKUxcbnGT3zv1akxT9bpic6X9maIjWxtjHJGm85uyxpE7RjX9x/ccwKvUWg6ssZSC9ufbTl1Xc/0bKd4WZZcmtMqRaxbwKn+QveeflBhqVTaZJAZSgM4N8A/L4ZIJjcK5gzCSYm9xB9TJIu0/0nrWD/3felF3oB/PXsbQ6qB+IAzu3nnnsCwO8A+DIwQ+tgSVgY9AG4IZGmhbCx+RUbG6zYkW95VJ23cBIAYGH1xKfWjPVbOLq3BIC3MtJEbGpD6fbekq99/n9wclb8NAC+Dvj3HmR/0U+i0qqtnXlVILlAerllxdgxvjxWSTjdCgDSuOOIFHIouQGvBiNAMBXqTO4ZzJkEE5N7iGE2/evfFPv/v6/qH+5dbNuD6gH9oHrgazBEjv4SwM8ALPkN2MsidaPnuQjJ+VfNfP5mNIzpIPOaSzl4LfR7a0dHLRydUj7yNQ7ZAIAQuB751GunCdGftQrKn7iduV94weUe8zN4/56BvBbVhNOtlsaxXaxTnpJgjp2pFEZ/sab+kd2tf/lV/cPykq/SxOQuwJxJMDG5R3BZv/IwgE8B+AWAF5e630H1QBTAXwDAfu45PwDbtB83gPcD+CRmtTQWCwu3ON4E84xLsXNvS7+mspFUypqIRV3ciQFPGrA+lG/rcod89aMNYS9DMEP10Lt8ZKqOYs9Hj+/d89HjUCVOP/X3j4kqk5OGLi5jE0FX0rcstKST9W7tHUhcLnt3w/NPz3GHNDG52zG9G0xM7gFc1q8UAngKhkTwhUT2qd+/lePv555rAPAfAKbqAR7x0AEXtzRTo+skr1mSaJFGtmy/VDJ9mU5Bv9a8/FD7uHMNQLzT132sIdRc4VTmyFETVusXPemR+g+dsPlXDq2ZXE4pIld/sPmw0uWXR4M+uvvTL3usdeH7APCzx5jGJQD3MVvawtd5jSYmdwVmusHE5N7gHwD8JoB9AO671YMfVA+0TYz7OwCyAE042dsSICQwj5uix5uYozfAEJDP7Gvf+4F1A23Tl6/2Z07nCxAAgGpsZS7s2ibFbDNSCoTAt+yBi7q/dIw5N+LEX/3JB7Uf//PeH2hp/gSlyPc21QNgvxkgmNzLmDMJJib3CC7rVz4I4HsAxv/BXVQE4CEA7ieDH//+rTzOfu65j4uE/v57/Fh/K8ed4DyAdflWrFjZ1VRQGMurGDkSt/R9+ZVVrnIBvcssNL6lbnQlyyzcebHtiy8MWwtSM1otKUNfk2O2hBbjdT4mPPRfX3/H63WVY8tWrx9g/A90uAgzFcCEAOxgtrR13sA1mpjcNZgzCSYm9w7vZIErD4n2dkrpOQAHAfyfZ4qfFRbZ73p5brUNx27xmJPE51vhcGVs860rceeqPrBuoG1tQXaoxEJ9DIF3vm0NaNzinxkgAABlsYL3ZgLW8oRfjjou7ayMFCaGCoc/9dfvEjJR26Q/hAzg3WaAYPJ2wCxcNDG5R0hkn/rNZ4qftcLwC5hUJCwF8OVnip/9LoDzTwY/Lt3scQ6qB+gzxc/mbRG8Bcz7nSSKct40xOGuguMdUWuysjRU8GDt2ENU5QekrHhKyomqIvNOXWeqADLjfDm71EvI3BkLRiFlAMoocE7XOGdJ7choSe3INvfqvhM2X2ayQPJJZkvb7QqSTEzuKMwgwcTk3qIIRlfCdP5o4kd+pvjZMwCOATgx8d/BJ4Mfv+6c40tRWH0cmiQ+y1RyXHE5y9WyZKb/wg3iAHAWRhskB0AFUCMIcowQrJ29MaUAHS6jyNHsf45y3Ony6Ik/WRfbK4hKnXOiQ5NSQFPZoCwJQ1JOTMuSwHlqRpOUYi0h+W0oCYP1jvs7k5nT5TFlyOPc8+snSwgBA+BvmS1t37wF12licldg1iSYmNxDPFP8rAVAFIBlibuMADgC4PjET8uTwY8v6Dewn3vOM3EM5KCeaOFC20AgiyBd2wTr+Hstzr3DMs4X8ljGMzNdFRchC6P9cXYaNBUoCp9vaOzdlW+nSNh94crFugTHqc7tu8/nrWfIBxHUtLUk3mOrDketFREi+NKFhNNrCZn58qTnuIOMRd0P4DCA+5ktbWr+EU1M7j3MIMHE5B7jmeJnU8ANGy/pAC4AODkk4c0jSXIBwNWD6oEpkaX93HMPAJiyPg6RbFM3m9igEeriKHr2o7A3ojL3+zh6aKcTDdch3XwJwOp8K+obepuKSsJ5ixYVmYtdOLf8uNOZsS5v7L05K2xGV8VAstdaER0nrK4rUatNjtlc1rL4xcIH2j7HbGnrvqnxTUzuMswgwcTkHuKZ4mcfh6GceNO0Z9F8Lk32wpj6Pw1jpuEkgG0wNBmmoKDhMMldclPRy4OZSgtscdDTNRZsXuIhD2GaDsN0Nm69dMxmk/LKMgOApjE5htEtJG/y4KaRAGxueP7pS7dldBOTOxizJsHE5N5i/60aaETGZLGfE8D9Ez95ISD+Amqd8xZfzKP6Og457yPeYpFL5lsHACyrLzW9ciP8sRkgmLxdMVsgTUzuEZ4pftYN4F23YixKoYcU1N3MGAR02MpelwFU3rQEy2pphqHVN3MuN8HLMKyfTUzelpgzCSYm9w4Z4LoKBedFA7o1kJsKEtJE7fx6Kjm6ShCTdawg+Bi2giVkPsdGBUBeoyaXO9UNYE2+dbeZJIDfbnj+aTMna/K2xQwSTEzuHY7hFlk3JzWMAjc3k2Cj/N4hWTj0PS05JY/sJEy4gRN6VnFiuprj7S7C1DCE+AF0AViRbxyPNxG9mfO4Cf644fmn50hBm5i8nTCDBBOTe4dbJnA0pkC/FeNwlJkxTpLq/tNKzn9auWab4GfYoXqWj6zlLU0VLO+2E1JLCHFNrnd7kr+KtOhxGIZWJiZva8wgwcTkHuCZ4mcZAB0AigFYb3a8ERn+mz4pADFGci+2TVjXysK6VnZ8InAgQLCY4cKNnDDSyIvqMmjUTpEjZMnaD7eCzzQ8//QtCZRMTO5mzBZIE5N7iGeKn/0WgI/dzBiUQvthGKoGIt7s+ZziRhMqoa7Ft5xBJ4BhAFNpCo5Q7YM1qZN/tCY+bxukpDLtAjtXDOkG+EbD80//5k2OYWJyT2DOJJiY3Fv8OYB34yYKGDWgSwNZfrMnQkGzBbr1SpDNbL+O3WIwvCd2T1+oUsK6BF2jFInRpOUSQyjjs8nFqk4yPKt7/uNoXbg16F5tFbTEUw9d7rALqlfRmIxOiWYX1EZCsNSARwHwxes4XxOTexozSDAxubf4n7jJDoekhjEANx0kEBBriW4jQXZBlefZeADknS0YD3lS/zpW3Nc66t5pLKGYJq1QCgAZmXP/+c/XzRBvsnBaamft+Jk9y8ZYn01ev0jA8FzD80/3Xs8Jm5jcy5hBgonJvcULMNINgRsdYFTGLctBJsm0CsWbZGAkUD4CZpps89LkFXMq63i9vXjn6+3FEDktva0q1FLsyskip1GR1xFw5PgCu1TEMbSCEPztrTpfE5N7AbMmwcTkHuOZ4mcdAP4pQ/XNAoiVBXyEEO9S9389hqshleRtR7xeOtjY6RCTW6os8/xQ6NvVIomA3HRR5nxwjH5B1Zn1B9UD5peiickEpuKiick9xpPBj6diutb/p4nx1X8SH1e+lo72UkqXVKlPKeSIenP6CNMp0xxyueY4fLPj2MD13s4AAQBUnXnGDBBMTGZiBgkmJvcgb0oZOwBsEsTUfosjCUBbZBcAgErRqYPcsjSkDdzOUt120+2UHl0M3orzWQAJwH/f5mOYmNx1mEGCick9yE/jv/+/OODhD1ldhXWcsJcQwi9lv4SG8K0+lzRRx292DDcV1FtxLgvww4PqgV+VsqOJyR2LGSSYmNyjRLJPvcIRIlzPPmPKEqsBr4MokW56ZsJOOeetOJcF+OFtHt/E5K7EDBJMTO5txq5n4xH51ng/TCfK5MpvagAKcGBqbtHp5CML4Be3cXwTk7sWM0gwMbm3+e5SN6QUSkTFslt5cAoay0KrvJkxRLBDBMRzi04pH00H1QPXJeZgYvJ2wQwSTEzubZZUiwAAKtClY2m1C0tFgd51swkMjy4M3pqzmZdf3ubxTUzuWswgwcTk3mbJNQkJFaFbffAkUVI3O4abirdMkGkejt7m8U1M7lpMxUUTk3ubvwTwTgBrF9qIUpr7r1wkMMRpV22UG3dTgVTCGiA6W0dB2Bs9eITJ3ZRENAA4KG+72TEWQAFw4TaOb2JyV2MGCSYm9zBPBj8uP1P87KIiRK9JmdMDurobBEgRZUUKCjyc0vSkzVupUNoV0xAZlcENyyiNa6heqiRyjMi1N3sNApiKmx1jAc4dVA/It3F8E5O7GjNIMDG59ylcaGVIU4//XErtnr28ihV4QmAVCFYXMkAhD6yxA5QiKVPaHVURD8rggwrKEhoqZwcOOuiQSvSymzlxnjJhAlJ8M2MswsBtHNvE5K7HDBJMTO59kgDc+VZolI78QzrSmGdVbo9oXZdvH0LgFAnWFQtA8UTFA6VISJR2x1QkRhXwIzJKRjVtBMBNBQkuKvQBuGnFxgUYvo1jm5jc9ZhBgonJvc8rAD4+fQGlVB/TtRPfyMSLM5SWzN7Bz7DnLITZvtQDEAKXhWD9ZOCwzg5QyvifoIHzQ7oSv6rIXKsqlY7qWvX1mCN4dCF5HZvfCP23eXwTk7saM0gwMbn3OYqJIIFSmu7SlDPfzSaWhXRtx3w7PCTab7hYcRJCiNNOyLrljIjlnDj0bjjdlNJkhtLuycDhqiqXBnV13sDBSYXrUoy8AW5aMtrE5F7GDBJMTO59RJ3SUIuSu/zDXHJ9htK9C21MgPBm3rLxFh6fAggBKCOEYFrggHcDuBY4qPE2ReJapwUOItjbWY8AAJHbPL6JyV2NGSSYmNzj/Ec6SjpU2akA+5ayfS3LX2YJWTCQuE4OAZh3vGszDgKWcwLeBSNwiGj61Vdj2HwLzyMft9zQysTkXsIMEkxM7nGuqPI7AIhL3f4xi6PoFh6+G8C2692JEOJUdNZKQG654dQsbre7pInJXY2puGhicu/zCQBfX8qGFpALNSyftxPiBtAAyLiOAGU6owreCuvm66mjNDF522EGCSYm9ziJ7FPjAD4J4NwCm0mPivYjf+0KRAghBQCOABgC0AzgFG5MT+AwgBU3sB+A22NbnQdzNtXEZAHMIMHE5G1AIvsUBfCtPKuCAFDGcKcftjh2sYTUwXhw7oKhcbAXwBYAFTCChaElHvICgD03c85J7bbqI0xyQ7McJiZvF8wgwcTk7cPKWb8PAHAQoP/37N41E8ssC+zvwtLevKMwVB5v+PtFp9BUemttq+fB+RYcw8TkrsUMEkxM3ga4rF+5D8BvzlrcDcARYNghG8O4JpZl5xniNIzZhCIAoHTBVH4ngJtqXYyq6AHI7dZIAAD7W3AME5O7FjMfZ2Ly9qAHQA6ADUaKQQQQAIByhp9ucBSHEQxM5zCAnQCYmK6N/0s62jmma2vthHSWs3z8CYtzWQHLTcovN2OBdselYmXgcjL0jaRO1uL2yjLfbh0GE5O7GjNIMDF5G5DIPtXnsn7lNwE4YNQV/DkAFgA8WYc4Du5IwK7uQn5xIT2t6xd+KaUSMqV0TNf2AUCa0nVtqoz/NxXOruXEpsctdrGQ5XfeivO1sSjkGbRDhwfGLIYCw+76Vr/537RLpYnJvYwZJJiYvE1IZJ96AQBc1q8QAGkAIVD8tES3+jSdBic2y9dRsPdvU+EzCarPN0NgvaBKWy+kpGAjJ7TuFx3xSpZbQwi54VbKnI5oRMVaGIHMpKASBdALYARGa6UdRnHlHO+J68CcSTAxWQAzSDAxeZsx0elwFABc1q/8ow2c1yXm/kdCRf/rCTBl9uyZLaJ10+T2GqVaiuqLPYhbAOxsVWW0qhFwgLJbsP18NS86K1i+VDC6JpYEpUB7FpcAMrs7ggConviZcUkwgof4xO9uAJUAPEs43K0UjjIxuecwgwQTk7cxiexTX3um+FkWgL0nh35ZJw+1pvhxHyOry3hhGwCcVnJndaMNcj6OwqhZmEIF+DflTO2bcqYRAKpYvv1x0TFcxwmrCDFqIWYzpqmDBQxbPqqQi1ezcwIEALgKwIu5D3YXjFTEnCEBDMKwyrYCKAVQPmub0gWuy8TkbQ9ZpErZxMTkbUax5e+rc9Be2ydag++0OJZ/KRXuCuvafNLKQzAe0tNbCc/AeOufYRIlUvbEJrVgS4WIljU2aDYGGwkxXlQ0SrU/ToxFCSDtVAsHNcrkO94QjPTCyzCKLjMwUhCbsXDr5nSSMGYdYhO/OwBsPqge0Je4v4nJ2wpzJsHExGQGGaL2A3jxDTlT1a/K2bCubZ9nUwrDIKls1nIFwJx9yjQ7oSBMv4RN/RKQYrJHy22qvkOw2lsVSaXAFpGy8wUIiWnHEQFsmLYuiKXXFjgBrJm1rBxA/xL3NzF5W2HqJJiYmMwgkX1KB/BHAFq7dPVBGG/wXXk2bcbcaf4kZj7ADShiAWqdsTwEJfbLXHrDnydDG76TSyoAoEJ3jJJMkwbaOWuE6cefXh8Rwc0XH84OckxMTCYwgwQTE5M5TBQ3fh/AjwEsh6Gd0IRrhkitMKSbZ3MeE1LHBQx7PMCwxwBIDspfZED4adtlq3XXvtWab4hQXMZEakIl1NPNJfad5EfrLrLhjhiRmmRoR2HMFqRhqEQunzbO5VtwuW+FR4SJyV2JWZNgYmIyLy7rV34XwFdhFP4BwEUY+X8BQFWeXV6BIdi0/rN233AVx9dnqJ7ol/QjfVluRVwjNRPbHYEhE+2NIHeuj0vmctA2eyg/HCdKBSXXHtweXbzQqHknZyz6Ycg+J2EELJMCURqAVbgx4aUPHlQPvHAD+5mY3POYNQkmJibzksg+9S8u61cCAP4ShtxyOYy3992T2xAKnRIwHCU9KqEijIf12eezceYzVn/cyjIlDSJjW2FBTThH3rySYJk4qzAZSpwATvtgkX2qZXUayqE1up2zgOGGSLZjmMnxPIieMwoUJ4OEyomffJzAjQUJZrrBxGQezHSDiYnJYjwL4D0AdiayT/lgvLkPAcBa1X30t+Tq8D6loOn9ctmwlbKrQNFSrjpi0Nn4wThZ80KYlK368Cvs8jWdL+UURikknPNxH3bUWujLMESRNgBos4O/v5uRi0JQ28updfU2zbdzo+bdvV5zq0U6d8hOmVZCkVvgPNUbvL7ZMtQmJiYTmDMJJiYmC5LIPjUAoxZgko8A2GjV2U+s0FwUwJXlulMEsLFCt740BqW0gjosewsTdHtxUA5muePLyyKC7s6ysidGf3SxoLsz7YplEt4CQskaSqADkCbGDoaIujVAr301aaDED64iQEk1BaWUolcBDWahS2miW9PQy1RQtwUkniE3lD69GcVGE5N7GjNIMDExuS4S2ac6AHTs5547PMAonwVVLhZS7l1WkFQfTW/9sFaellh1cEtZHERns3UlYb33UkVw5ZZu157q8RX64CM9/vph8o2fl6z3g6EKpReJL5YO57iXohJH7JRtJyDrAYCCav1EfgcFeDuYK1VUGGdA1ogg1SIYeCZiAgqqp6FX9xJ5/hMHzgLgq0U6ktBgTWrwKhQ1ADFVF01M5sEMEkxMTG6Ig+qBof3cc38KAmWMqE8C2LuXKzhVGIiMjRJZLlk24I26Uq0//cm21PrhAibWX5rNqfzpnq6yklRbWSoKesIN2G2sXry/LrYVAP71QuCENccnJ49BgR5KUAcAaegr01S/5ATrAnAKRrHiBgAiAWE4ENs8pzoMQ0BpJwA0WJF0c8b/UwpkdWrOJJiYzIMZJJiYmNwwB9UDMgDs5557H4AXd5SkuA2BzKP66raX/+/rK3WvP5Fqt40pP+l3un93vFzMKhz/chw4x4YcAc4q+XWrssIp92OiGDGlsEV+MFOpDRl0DMCU78MYUaJOynK4JhOdhOESadWNWgnAUFO8CqPeYQWM+okp2egxBap74puPEMDGov7W3xkTk3sDs3DRxMTkpjmoHojUeXKfXunLnmZY/fB9uzse2LC+h/z7JWf95UFP5n9a/frPlFj4ZE7qs7MIrRZ5MsSmSlr48W3dSYYOjDte6UjjFYnSIJn2vSSAZEt1vplQyACQBV1JQbVph3bC0GvYmIQ6CEPg6SIME6i9MGYbZvhODMvwzTp9/pniZ4VbflNMTO4BTJ0EExOTW8br279MALy/g8k++fKlUjaZER9eYdde3Fuc9bucaYFhaeClXvcVLWNVjulx/Yqe2/dBuaztgxuG7b9+RbfUKt5gKxtbu0xzXazRHQXFlM9awWykoKE+Io/sqo5EHTqbSKctReGEtSIr88UAQEFTV0iOo2RxDwcWNPd+P3hCwE5bXPpk8OMjt+u+mJjcrZgzCSYmJreMB45/ngJoef18pTWVsWwnIB2DWdbzk4QUb4rTuEwh6zoRfKJa0U+lUlCcyEJznhixjtcrvhGOshQEHjvly9JEb+RhiC8RkIIqKjSu9mcDUWtKiHtDzP3rBqLv2ND7Lw+u7ztt8yUOLSVAAAANxKLNlZm+EX0FE5N7HjNIMDExuaU8cPzz3WmF+RSAzwFIpXSsfDGquv9pkLg/ehHVD64ekNrtY+MpSreBwHrWNRgLFId5kXJahigZUChWcFWgCFJAG4NyhIBmnKJ6lQGW/WgUzOUxy/CPe53nv9PhC7SELXJQpUv2byCgNKfT8VmLze9CE5M8mP8wTExMbjk/k3+rHcBBAOUaaIcFXDmALV5RH15WmNj/2c3BigkbCG+DV3LE2RzXxkZ5P7U+UKHZXxtlMkdkopwGcEUF1byuTMf+nVfDOYU9+x6LUxmVGbl+xWBxLMvtvDTo1QeS4so8p9GFa14TEFk9+fGV40f+eFNQKXLmblR4ycTkbYXZ3WBiYnJbOKgeGNjPPbeNBdmjQf/SH6yK/ey9VekaQsA5eLry67vHm/6z3en5sw0xBoBzrWckxdHx/phKSyMa1Z1Ze/bwmeWuYnCNFVWDb/oDsf3+QAw5vuhn/bkCy7evuHI7NGsxA1JaDD16FdJx3Ug5rIFhMlUIo4hxGQD7AxWJs4U2dR8ACKLKpzMzTpeHiYnJHMwgwcTE5LZxUD3QA6DHZf3KuQ/Xpgp5BvsAfA/Ao+v88r5/3BGe2tbpk3BNeBH4QUvBsR5GXg9Kg09UjLsBIBl3HPUx3Ds/WB9Dby9pGiSZy5XUviYNOqQT7JvYVYUxi7AcwNr3LYu8UWxXnDZOn/J8EAXFPetUF5J7NjF522IGCSYmJredRPapCxP/+yoAtH3kM9+G4QnxIxg20O+EYRrlA2AHgLZxVykAWHjd4bfLjcE01yVHXOvYCX/IT1eg8BOJ0YGP5KpXjxM1Mu1wHKbZSZc6FLeD1zdOPx9BUGcLKEVvyYWamNxjmEGCiYnJW07D8093AtjT9pHPkIbnn6ZtH/nM92DUD6wCYMvKrBBMWJ4FkNMptIxKuA+8XgRNR0xk0OvkkPp0GZxfLPDQK2HpYCY3UwthEgKq2zm9bvZyltV9hOhtlDINE4sis7cxMTExdRJMTEzuUPZzz1kBeP/ysQt//OKQWP30Ffd7J9cRgH7G7m2u5YR9OqucOhnjmRIeqT4J/ohGawap1NrNxgvtDDP4zbXaznzjj455mjIZyz4A2SeDH59P0tnE5G2NOZNgYmJyR3JQPZAFkG37yGf+/oUe32YYaYgAgIY/cfjPFLHcPgBoqB/MeEYKfIm4c1+lSFO2ovGTL0ao3hXGZobRIhoFbUniUqEAVyGPQpGBlRDA5UoXZjIWAEj86q7SxOTOxgwSTExM7mgann96qN3wX/iJy/oVngMqiljuYwBW6hR2f0GssCAQY44fXqcCjOOr3Tzbocn3A4BMYflUK06HFDKVjigS6Ik/q0FpEa/UAHQcIGaQYGIyD2a6wcTE5K7jmeJniwD8uD1k4XVRDolF45YDD11WBjoqHB889v9v7/5B4yzjAI7/3jeXu+TaJrHGUlsEBx0ioihCEimN1EFcpHOnKyU4uTjoVFAQBCvi4CD4L5DRQRBEEgp1qJOLHYwKFeqfpn9Ma6I1fy5v3tfhOmh9KLqkvOHzWY97eW773v3ueZ7Bu7uR3f7SpioWp8p98dxdUeRZtnni8vF//W8BcJgSUEMnLh+/8tta35tFmT14cT0f/uqn0an+9vq+RycWJp4fv3T1Pzxiz548vj29nP388VJsvDT80a1bIoEwbgBq6tIfrTNVVJ/diHJyf7t7dv33weXVtcaFFycXn5o/P3Lh+2vt+//xhiqWhqvmQiPy6kC56+C1Kn/65iufvrHSWdn2DwA1YNwA1NaxvtknlmPr9fHR1a0rI1f/nH74+sg3KwPV42O/7J54/5HxrIqLI1Xr/N6y1R6tBh/LI0t9MXpnrui8sO2LhxoQCUCtPds3c8/Bqv/d1t6l8sfY7H9yKGJ+sV2URXNsIPoeyiLrRkQzIrrRO1lx6JZHnJwrOq9t+8KhBowbgFr7fKvz63Q+u/DJ6toD/UWz/GFjc1ezqL7eH43RiPgiIhYi4nBEnIuIt6J3p8N3EXEkIu6N3v0OQIJIAGpvKJpvr5fZ5I28ezTfaMV90R6LiKmIeDkiTkXEwM1zF/7uw21fKNSMcQOwYxxqvDfcjsaXWWRj0du99epc0XnlDi8LassWSGDHOFtMr2SRnYqID6J3adPsHV4S1JpfEoAd55nGTB4RrcSIAfgfRAIAkGTcAAAkiQQAIEkkAABJIgEASBIJAECSSAAAkkQCAJAkEgCAJJEAACSJBAAgSSQAAEkiAQBIEgkAQJJIAACSRAIAkCQSAIAkkQAAJIkEACBJJAAASSIBAEgSCQBAkkgAAJJEAgCQJBIAgCSRAAAkiQQAIEkkAABJIgEASBIJAECSSAAAkv4Ci8tgXwshjxwAAAAASUVORK5CYII=\n", - "text/plain": [ - "
      " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# map another variable\n", - "map_variable(tracts, 'med_household_income')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "It looks like these two variables might be negatively correlated?" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "r=-0.0802, p=0.0000\n" - ] - } - ], - "source": [ - "# calculate the correlation coefficient and its p-value\n", - "x = tracts.dropna(subset=['pop_density', 'med_household_income'])['pop_density']\n", - "y = tracts.dropna(subset=['pop_density', 'med_household_income'])['med_household_income']\n", - "r, p = stats.pearsonr(x=x, y=y)\n", - "print('r={:.4f}, p={:.4f}'.format(r, p))" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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      " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# scatter plot them with matplotlib\n", - "fig, ax = plt.subplots()\n", - "ax.scatter(x=x, y=y, s=1)\n", - "ax.set_xlim(left=0)\n", - "ax.set_ylim(bottom=0)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "m=-1.9798, b=66681.6564, r^2=0.0064, p=0.0000\n" - ] - } - ], - "source": [ - "# estimate a simple linear regression model with scipy\n", - "m, b, r, p, se = stats.linregress(x=x, y=y)\n", - "print('m={:.4f}, b={:.4f}, r^2={:.4f}, p={:.4f}'.format(m, b, r ** 2, p))" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "m=0.0013, b=10.9475, r^2=0.0000, p=0.6713\n" - ] - } - ], - "source": [ - "# estimate a log-log simple linear regression model with scipy\n", - "m, b, r, p, se = stats.linregress(x=np.log(x), y=np.log(y))\n", - "print('m={:.4f}, b={:.4f}, r^2={:.4f}, p={:.4f}'.format(m, b, r ** 2, p))" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/sarahluw/.pyenv/versions/3.6.2/envs/my-virtual-env-3.6.2/lib/python3.6/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n", - " FutureWarning\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
      " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# plot the log-log regression line with 95% CI using seaborn\n", - "fig, ax = plt.subplots(figsize=(6, 6))\n", - "ax = sns.regplot(np.log(x), np.log(y), marker='.', scatter_kws={'s':2}, ax=ax)\n", - "ax.set_xlim((3, 11))\n", - "ax.set_ylim((9.5, 12.5))\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2. Spatial weights matrix\n", - "\n", - "Spatial weights define the spatial connections among our units of analysis (tracts in this case).\n", - "\n", - "### 2.1. Contiguity-based weights: rook contiguity\n", - "\n", - "Using rook contiguity, two spatial units must share an edge of their boundaries to be considered neighbors. This isn't terribly common in practice (since queen is usually more useful, but it's worth understanding)." - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [], - "source": [ - "# get the tract labels (GEOIDs) and pick one to work with later\n", - "# tracts_subset = tracts[tracts.index.isin(df_merged.index.values)]\n", - "labels = tracts.index.tolist()\n", - "label = labels[10]" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/sarahluw/.pyenv/versions/3.6.2/envs/my-virtual-env-3.6.2/lib/python3.6/site-packages/libpysal/weights/_contW_lists.py:31: ShapelyDeprecationWarning: Iteration over multi-part geometries is deprecated and will be removed in Shapely 2.0. Use the `geoms` property to access the constituent parts of a multi-part geometry.\n", - " return list(it.chain(*(_get_boundary_points(part.boundary) for part in shape)))\n", - "/Users/sarahluw/.pyenv/versions/3.6.2/envs/my-virtual-env-3.6.2/lib/python3.6/site-packages/libpysal/weights/_contW_lists.py:29: ShapelyDeprecationWarning: Iteration over multi-part geometries is deprecated and will be removed in Shapely 2.0. Use the `geoms` property to access the constituent parts of a multi-part geometry.\n", - " return list(it.chain(*(list(zip(*shape.coords.xy)) for shape in shape)))\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 19 s, sys: 952 ms, total: 19.9 s\n", - "Wall time: 20.4 s\n" - ] - } - ], - "source": [ - "%%time\n", - "\n", - "from libpysal.weights.contiguity import Queen, Rook\n", - "from libpysal.weights import KNN, min_threshold_distance\n", - "from libpysal.weights import DistanceBand, lag_spatial\n", - "# calculate rook spatial weights\n", - "w_rook = Rook.from_dataframe(tracts, ids=labels, id_order=labels)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2.2. Contiguity-based weights: queen contiguity\n", - "\n", - "Using queen contiguity, two spatial units need only share a vertex (a single point) of their boundaries to be considered neighbors." - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 16 s, sys: 641 ms, total: 16.7 s\n", - "Wall time: 16.9 s\n" - ] - } - ], - "source": [ - "%%time\n", - "# calculate queen spatial weights\n", - "w_queen = Queen.from_dataframe(tracts, ids=labels, id_order=labels)" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['48213950902',\n", - " '48213951400',\n", - " '48001950402',\n", - " '48213950400',\n", - " '48001950100',\n", - " '48213950300',\n", - " '48001950902',\n", - " '48001950401',\n", - " '48001950901',\n", - " '48161000100',\n", - " '48213951100']" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# find the neighbors of some tract\n", - "w_queen.neighbors[label]" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]" - ] - }, - "execution_count": 35, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# this is a raw contiguity matrix, so weights are binary 1s and 0s meaning neighbor/not\n", - "w_queen.weights[label]" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "11" - ] - }, - "execution_count": 36, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# how many neighbors does this tract have?\n", - "w_queen.cardinalities[label]" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "count 5253.000000\n", - "mean 6.291643\n", - "std 2.106332\n", - "min 1.000000\n", - "25% 5.000000\n", - "50% 6.000000\n", - "75% 7.000000\n", - "max 23.000000\n", - "dtype: float64" - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# convert cardinalities to series and describe data -- looks right-skewed\n", - "cardinalites_queen = pd.Series(w_queen.cardinalities)\n", - "cardinalites_queen.describe()" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
      " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# look at the distribution of number of neighbors\n", - "ax = cardinalites_queen.hist(bins=20)" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "5253" - ] - }, - "execution_count": 39, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# number of observations\n", - "w_queen.n" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "6.291642870740529" - ] - }, - "execution_count": 40, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# average number of neighbors\n", - "w_queen.mean_neighbors" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "1" - ] - }, - "execution_count": 41, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# min number of neighbors\n", - "w_queen.min_neighbors" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "23" - ] - }, - "execution_count": 42, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# max number of neighbors\n", - "w_queen.max_neighbors" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0" - ] - }, - "execution_count": 43, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# islands are observations with no neighbors, disconnected \n", - "# in space (can cause modeling problems)\n", - "len(w_queen.islands)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### Plot a census tract of interest, along with its neighbors:" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
      " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(figsize=(6, 6))\n", - "tracts.plot(ax=ax, facecolor='#999999', edgecolor='w', linewidth=0.5)\n", - "\n", - "# plot some tract of interest in red\n", - "tract = tracts.loc[[label]]\n", - "tract.plot(ax=ax, facecolor='r', edgecolor='w', linewidth=1)\n", - "\n", - "# plot the neighbors in blue\n", - "neighbors = tracts.loc[w_queen[label]]\n", - "neighbors.plot(ax=ax, facecolor='b', edgecolor='w', linewidth=1)\n", - "\n", - "# zoom to area of interest\n", - "xmin, ymin, xmax, ymax = neighbors.unary_union.bounds\n", - "ax.axis('equal')\n", - "ax.set_xlim(xmin, xmax)\n", - "ax.set_ylim(ymin, ymax)\n", - "\n", - "ax.set_title('Neighbors of tract {}'.format(label))\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 3.3. Distance-based weights: *k*-nn\n", - "\n", - "Find the *k*-nearest neighbors of each tract" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/sarahluw/.pyenv/versions/3.6.2/envs/my-virtual-env-3.6.2/lib/python3.6/site-packages/libpysal/weights/util.py:1073: ShapelyDeprecationWarning: The array interface is deprecated and will no longer work in Shapely 2.0. Convert the '.coords' to a numpy array instead.\n", - " data = np.vstack([np.array(shape.centroid) for shape in first_choice])\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 1.14 s, sys: 42.3 ms, total: 1.19 s\n", - "Wall time: 1.2 s\n" - ] - } - ], - "source": [ - "%%time\n", - "# k-nearest neighbors finds the closest k tract centroids to each tract centroid\n", - "w_knn = KNN.from_dataframe(tracts, k=6)" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['48001950402',\n", - " '48001950401',\n", - " '48001950500',\n", - " '48001950901',\n", - " '48001950600',\n", - " '48001950700']" - ] - }, - "execution_count": 46, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# they all have exactly k neighbors\n", - "w_knn.neighbors[label]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 3.4. Distance-based weights: distance band\n", - "\n", - "Here tracts are considered neighbors of some tract if they are within a given threshold distance of it. Distance band weights can be specified to take on continuous values rather than binary (1s and 0s), with these values being the inverse distance between each pair of \"neighboring\" units. The default (linear) distance-decay exponent is thus -1. If we set it to -2, we get gravity model weights." - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "91602.1289312889" - ] - }, - "execution_count": 47, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# first determine the minimum nearest neighbor distance so each unit is assured of at least one neighbor\n", - "x = tracts.centroid.x\n", - "y = tracts.centroid.y\n", - "coords = np.array([x, y]).T\n", - "threshold = min_threshold_distance(coords)\n", - "threshold" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/sarahluw/.pyenv/versions/3.6.2/envs/my-virtual-env-3.6.2/lib/python3.6/site-packages/libpysal/weights/util.py:1073: ShapelyDeprecationWarning: The array interface is deprecated and will no longer work in Shapely 2.0. Convert the '.coords' to a numpy array instead.\n", - " data = np.vstack([np.array(shape.centroid) for shape in first_choice])\n", - "/Users/sarahluw/.pyenv/versions/3.6.2/envs/my-virtual-env-3.6.2/lib/python3.6/site-packages/scipy/sparse/data.py:117: RuntimeWarning: divide by zero encountered in reciprocal\n", - " return self._with_data(data ** n)\n", - "/Users/sarahluw/.pyenv/versions/3.6.2/envs/my-virtual-env-3.6.2/lib/python3.6/site-packages/libpysal/weights/weights.py:172: UserWarning: The weights matrix is not fully connected: \n", - " There are 2 disconnected components.\n", - " There is 1 island with id: 48443950100.\n", - " warnings.warn(message)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 7.66 s, sys: 914 ms, total: 8.58 s\n", - "Wall time: 8.69 s\n" - ] - } - ], - "source": [ - "%%time\n", - "# default alpha=-1 and gravity weights alpha=-2\n", - "w_dist = DistanceBand.from_dataframe(tracts, threshold=threshold, binary=False, alpha=-1)" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "127" - ] - }, - "execution_count": 49, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# how many distance-band neighbors does our tract have?\n", - "len(w_dist.neighbors[label])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### Map the neighbors, colored by weight from nearest to furthest:" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
      " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(figsize=(6, 6))\n", - "tracts.plot(ax=ax, facecolor='k', edgecolor='gray', linewidth=1)\n", - "\n", - "# get the tract of interest and its neighbors/weights\n", - "tract = tracts.loc[[label]]\n", - "weights = pd.Series(w_dist[label])\n", - "neighbors = tracts.loc[weights.index, ['geometry']]\n", - "\n", - "# plot the tract's neighbors in blues by weight\n", - "neighbors['weights_scaled'] = weights\n", - "neighbors.plot(ax=ax, column='weights_scaled', cmap='Blues_r', edgecolor='gray', linewidth=1, scheme='Fisher_Jenks')\n", - "\n", - "# plot the tract of interest in white\n", - "tract.plot(ax=ax, facecolor='w', edgecolor='r', linewidth=2)\n", - "\n", - "# zoom to area of interest\n", - "xmin, ymin, xmax, ymax = neighbors.unary_union.bounds\n", - "ax.set_xlim(xmin, xmax)\n", - "ax.set_ylim(ymin, ymax)\n", - "ax.axis('off')\n", - "\n", - "ax.set_title('Neighbors of tract {}'.format(label))\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 3.5. Standardizing weights\n", - "\n", - "A spatial weights matrix with raw values (e.g., binary 1s and 0s for neighbor/not) is not always the best for analysis. Some sort of standardization is useful." - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'48213950902': 1.0,\n", - " '48213951400': 1.0,\n", - " '48001950402': 1.0,\n", - " '48213950400': 1.0,\n", - " '48001950100': 1.0,\n", - " '48213950300': 1.0,\n", - " '48001950902': 1.0,\n", - " '48001950401': 1.0,\n", - " '48001950901': 1.0,\n", - " '48161000100': 1.0,\n", - " '48213951100': 1.0}" - ] - }, - "execution_count": 51, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# inspect the neighbors and weights of our tract\n", - "w_queen[label]" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'O'" - ] - }, - "execution_count": 52, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# check the current transformation of the weights matrix (O = original)\n", - "w_queen.get_transform()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Typically, we want to apply a row-based transformation, so every row of the matrix sums up to 1." - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'48213950902': 0.09090909090909091,\n", - " '48213951400': 0.09090909090909091,\n", - " '48001950402': 0.09090909090909091,\n", - " '48213950400': 0.09090909090909091,\n", - " '48001950100': 0.09090909090909091,\n", - " '48213950300': 0.09090909090909091,\n", - " '48001950902': 0.09090909090909091,\n", - " '48001950401': 0.09090909090909091,\n", - " '48001950901': 0.09090909090909091,\n", - " '48161000100': 0.09090909090909091,\n", - " '48213951100': 0.09090909090909091}" - ] - }, - "execution_count": 53, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# transform the queen weights\n", - "w_queen.set_transform('R')\n", - "w_queen[label]" - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "('WARNING: ', '48443950100', ' is an island (no neighbors)')\n" - ] - }, - { - "data": { - "text/plain": [ - "{'48001950100': 0.014979349650672217,\n", - " '48001950401': 0.020309161978431498,\n", - " '48001950402': 0.027131414696176744,\n", - " '48001950500': 0.017319400840410566,\n", - " '48001950600': 0.016637060192047873,\n", - " '48001950700': 0.015244318419098868,\n", - " '48001950800': 0.01408277142653615,\n", - " '48001950901': 0.016907375123011943,\n", - " '48001950902': 0.012200189058361914,\n", - " '48001951000': 0.009177966871647854,\n", - " '48073950100': 0.005665470063253171,\n", - " '48073950200': 0.007562144118312603,\n", - " '48073950300': 0.008237583597924463,\n", - " '48073950400': 0.007526067312122535,\n", - " '48073950500': 0.00794901702501413,\n", - " '48073950600': 0.007274746294178255,\n", - " '48073950700': 0.00780389323825469,\n", - " '48073950801': 0.006242441029268617,\n", - 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" '48423002004': 0.0062903757929929525,\n", - " '48423002006': 0.006446009311873876,\n", - " '48423002007': 0.006164926496797508,\n", - " '48423002008': 0.006705674003381338,\n", - " '48423002009': 0.006060951844700154,\n", - " '48423002101': 0.00530503989256266,\n", - " '48423002102': 0.005359990467584851,\n", - " '48423002200': 0.006474205519002173,\n", - " '48423980000': 0.006586479545901954,\n", - " '48467950100': 0.005051437574980212,\n", - " '48467950200': 0.004943369352290823,\n", - " '48467950300': 0.004783563865037059,\n", - " '48467950600': 0.005942567063798955,\n", - " '48467950700': 0.005935711750343549,\n", - " '48467950800': 0.005996467435603836,\n", - " '48467950900': 0.0073887097114497015,\n", - " '48467951000': 0.00716038582911822}" - ] - }, - "execution_count": 54, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# transform the linear-decay distance-based weights\n", - "w_dist.set_transform('R')\n", - "w_dist[label]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "PySAL supports the following transformations:\n", - "\n", - " - O: original, returning the object to the initial state\n", - " - B: binary, with every neighbor having assigned a weight of 1\n", - " - R: row-based, with all the neighbors of a given observation adding up to 1\n", - " - V: variance stabilizing, with the sum of all the weights being constrained to the number of observations\n", - "\n", - "**It can take a long time to calculate a weights matrix for a large data set.**\n", - "\n", - "Once you've created yours, you might want to save it to disk to re-use in subsequent analyses." - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "metadata": {}, - "outputs": [], - "source": [ - "# save your matrix to disk\n", - "import libpysal\n", - "f = libpysal.io.open('tracts_queen.gal', 'w')\n", - "f.write(w_queen)\n", - "f.close()" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'48213950902': 1.0,\n", - " '48213951400': 1.0,\n", - " '48001950402': 1.0,\n", - " '48213950400': 1.0,\n", - " '48001950100': 1.0,\n", - " '48213950300': 1.0,\n", - " '48001950902': 1.0,\n", - " '48001950401': 1.0,\n", - " '48001950901': 1.0,\n", - " '48161000100': 1.0,\n", - " '48213951100': 1.0}" - ] - }, - "execution_count": 56, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# read a matrix from disk (notice its transformation)\n", - "w_queen = libpysal.io.open('tracts_queen.gal', 'r').read()\n", - "w_queen[label]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 4. Spatial lag\n", - "\n", - "Spatial lag tells us about values near some spatial unit. Calculate spatial lag of the `med_household_income` variable. If the spatial weights matrix is row-standardized, then the spatial lag is the average value of an observation's neighbors." - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "metadata": {}, - "outputs": [], - "source": [ - "# pick a variable to investigate\n", - "col = 'med_household_income'" - ] - }, - { - "cell_type": "code", - "execution_count": 62, - "metadata": {}, - "outputs": [], - "source": [ - "# first drop null rows\n", - "tracts_not_null = tracts[[col, 'geometry']].dropna()\n", - "y = tracts_not_null[col]" - ] - }, - { - "cell_type": "code", - "execution_count": 63, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/sarahluw/.pyenv/versions/3.6.2/envs/my-virtual-env-3.6.2/lib/python3.6/site-packages/libpysal/weights/_contW_lists.py:31: ShapelyDeprecationWarning: Iteration over multi-part geometries is deprecated and will be removed in Shapely 2.0. Use the `geoms` property to access the constituent parts of a multi-part geometry.\n", - " return list(it.chain(*(_get_boundary_points(part.boundary) for part in shape)))\n", - "/Users/sarahluw/.pyenv/versions/3.6.2/envs/my-virtual-env-3.6.2/lib/python3.6/site-packages/libpysal/weights/_contW_lists.py:29: ShapelyDeprecationWarning: Iteration over multi-part geometries is deprecated and will be removed in Shapely 2.0. Use the `geoms` property to access the constituent parts of a multi-part geometry.\n", - " return list(it.chain(*(list(zip(*shape.coords.xy)) for shape in shape)))\n" - ] - } - ], - "source": [ - "# recompute spatial weights for these observations then row-standardize\n", - "w_queen = Queen.from_dataframe(tracts_not_null)\n", - "w_queen.set_transform('R')" - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "metadata": {}, - "outputs": [], - "source": [ - "# compute spatial lag\n", - "y_lag = lag_spatial(w_queen, y)" - ] - }, - { - "cell_type": "code", - "execution_count": 65, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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      " - ], - "text/plain": [ - " med_household_income med_household_income_lag\n", - "index \n", - "48029181003 37832 45585\n", - "48201251000 95956 103259\n", - "48201312000 66250 39739\n", - "48453001850 60253 73079\n", - "48085030523 109949 120137\n", - "48491021100 60122 54787\n", - "48013960201 61125 52209\n", - "48029190504 42353 43969\n", - "48085031405 174462 93669\n", - "48485010100 19963 28442" - ] - }, - "execution_count": 65, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "col_lag = '{}_lag'.format(col)\n", - "data_lag = pd.DataFrame(data={col:y, col_lag:y_lag}).astype(int)\n", - "data_lag.sample(10)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 6. Spatial autocorrelation\n", - "\n", - "Statistical models typically assume that the observations are independent of each other. This assumption is violated when a variable's value at one location is correlated with its value at nearby locations. This is called spatial autocorrelation, and is common in the real world due to proximity-based spillover effects. Substantive spatial autocorrelation can be explained by social or economic theory that describes a spatial relationship. Nuisance spatial autocorrelation stems from data problems.\n", - "\n", - "*Positive spatial autocorrelation*: nearby values tend to be more similar (e.g. income, home values, temperature, rainfall)\n", - "\n", - "*Negative spatial autocorrelation*: nearby values tend to be more dissimilar (e.g. fire stations, grocery stores)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 6.1. Moran's I\n", - "\n", - "Moran's I measures *global* spatial autocorrelation: do values tend to be near other (dis)similar values. Values > 1 indicate positive spatial autocorrelation, and values < 1 indicate negative spatial autocorrelation." - ] - }, - { - "cell_type": "code", - "execution_count": 66, - "metadata": {}, - "outputs": [], - "source": [ - "# calculate the statistic\n", - "from esda.moran import Moran\n", - "mi = Moran(data_lag[col], w_queen)" - ] - }, - { - "cell_type": "code", - "execution_count": 67, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.6191256645132304" - ] - }, - "execution_count": 67, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# show the I value\n", - "mi.I" - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.001" - ] - }, - "execution_count": 68, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# statistical inference: show the p value\n", - "mi.p_sim" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If we generated a large number of maps with the same values but randomly allocated over space, and calculated Moran's I for each of these maps, only 0.1% of them would display a larger absolute value than the one we computed from the real-world data set. Thus there is a 0.1% chance of getting the observed value of Moran's I if the spatial distribution of our variable is random. We can conclude that the variable's distribution is statistically-significantly postively spatially autocorrelated. We'll talk more about inference in the other notebook" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 6.2. Moran plots\n", - "\n", - "A Moran plot scatter plots the spatially-lagged values (y-axis) vs the original variable's values (x-axis). Moran's I equals the slope of the line in a Moran plot, which makes this all a bit easier to conceptualize." - ] - }, - { - "cell_type": "code", - "execution_count": 69, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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1RqbreILRmB7vnmLcTNfNciYZQtXRf200mfr8Y597/WOfnW4v/3bgLK8dtwMQZzHx5bULuHNpbsCwTCevxsh0HU8wGqHD2RQzjOm6WU6WIZwMQ3YtbLbXOgNurTBAH/tc0ejgqTdqsHcNALAkP5mvby4hP9UWeE5qvJW0eMu08WqMTNfxBKMx/d5JxZiYrpvlZBnC6erRKaYnoTpoLo+Pn79/nl99VI9PgsUs+OOb5/MHK2cHpGmmq1dzpZhIg/O7CTyXQhFgsgzhdPXoFNMPl8dHa88ALo/m1Zxp6+U7u2s429YHwMLMRL6xpYT5GQmB50xnr+ZKMRbxzmzgSSBPSnmHEKIMuElK+VMAKeWfTdIaFYpJYbp6dFPFTMqNXUkcTjcdfZoOmtcn+dVH9fzs4Hk8PolJwGdXz+FzN83FYtakaZRXE5mxeDg/A/4T+Gv/93XAr4CfTvCaFArFFHA5IcVrwViF6qA1dPbz1O5aqpq6AZidZuORzSWU5SUHnqO8mpExjf6QABlSyl8DPgAppQeYfC0EhUIxKawuTOeO8hxWF6aP+bm6sdpX1TLh63I43bx4pAGHXxpmsp4zEv0uDw2d/fS7PEgpefVYI1/6xZGAsbl7eR4/fmBlwNhYzCbyUm1jLnee6HVPd8bi4fQJIWbhr0YTQtwIKC01xZRwLdxRTxTRvleHznawu7KZVfPSKVgZH/Fx4ZjM/Nd4PK+JKgCRUtLe66JnQDMAbT2DPL2nlsMXOgHITIzl65uLWTk3DdDkvVJslnF7Ndda4cpYDM5XgdeABUKI3wGZaEPYIiKE+A/gTqBVSrnYf+xvgS8Cbf6HfVNKucv/s28AX0DznP5CSrnHf3wz8EPADPy7lPIp//H5wC+BWcAR4AEppUsIEQv8AliJNkbh01LK82N4rYppxrX2h3k5RPteXY7RmMz813jWNREG0Dj2WUrJ/ppW/mn/6UCvzYbSLP5i3SIS47Rt0xpjIiPx8nI111rhitB1f6J6sBAxQDFaCXStlHJEP1AIcQvQC/wixOD0SimfCXlsGfA/wGogD9gHFPl/XAdsBBqAj4DPSimrhBC/Bl6SUv7SP5H0uJTyOSHEg8BSKeX/J4T4DPApKeWnR3t9q1atkocPH47qvVBExniHDUyIZ6I8nOiZbu/VTFiPUQfN0e/m+/vqePdUO6AZ14c3LuKWRZnA5Xs1042J+HyEEEeklKtGe9xYqtTMwBZgnv95m/xq0d+L9Bwp5btCiHlRXuJu4JdSykHgnBDiNJrxATgtpTzrX8cvgbuFENXAOuAP/Y/5OfC3wHP+c/2t//j/Av8ihBByLNZVMW6Md9jAhHgm13pF2ViYbu/VdPNOjeu5a3kebQYdtPfPXOKZN2vp7NfupW9eMIuHNxaRnmAFNK8mMymW2ClQVp4qpvLzGUtIbQcwAJzEXzhwGfyZEOJzwGHgr6SUnUA+8IHhMQ3+YwD1IcdvQAujdfmLF0Ifn68/R0rpEUI4/I9vD12IEOJLwJcA5syZc5kvSwHhwwTXSshAMZzpFjbS13Hjglk0djrxSUnfoIfn3jnDropmAOKtZr5y20I2l2cjhEAIQarNQupV4tUYmcrPZywGZ7aUciL0D54DnkArPngC+EfgTybgvONCSvlj4MeghdSu1DquJkLvsCf6rmm6hWgmm5n+eqPxuMbzGsf7viTFxvDJRRmB3Mzx+i6eeqOGlu5BAJYXpPD1zSXkJMcBk+PVTKfPdCo94rEYnN1CiE1Syjcv54JSykAdpRDiJ8Dr/m8bgQLDQ2f7jxHh+CUgVQgR4/dyjI/Xz9Xgzzul+B+vuAqY6BDAdPrjD0e41+twutlx3A5Sm2cyHdc9FqaqMs3p0goDPD4fLo+Pn/72HP97pAGJZli++Mn5fGpFPqZJ9mqmW5hxqhiLwfkAeFkIYQLcaIUDUkqZPPLTghFC5Eopm/zffgqo8P//NeC/hRDfQysaWAQc8l9nkb8irRH4DPCHUkophHgbrVLul8DngVcN5/o88L7/52+p/M3Vw0SHAKb7H3+417uvqoUnd1UDYLOap3zdE22kJ7syTUpJR58r0O9S19LDd3bXcOFSPwDF2Uk8ckcxc2dp0jSTnauZbmHGqSLqKjUhxDm0ZPzJaDdvIcT/ALcCGUAL8C3/98vRQmrngf+rGyAhxF+jhdc8wENSyt3+41uAH6CVRf+HlPLv/ccL0YxNOnAUuF9KOSiEiAOeB1YAHcBn9KKDkVBVatcm093DCYfRw1lbksmhsx1Tuv4XjzTwxM4qHt1adsWN9Gifn1EHzeP18d+HLvL8Bxfx+iRmk+CBG+fwh6vnEGM2IYQgLd5Ciu3qy9VEYlpWqaEl4SvG4ilIKT8b5nBEKRy/Ifn7MMd3AbvCHD/LUCWb8fgA8AfRrlNxbTPdqrqiIcVm4f4b5wJDmz9MnYc2ne7QR/JQjTpoFy/18503aqht7gFgbno839hSQlF2EgCxFjMZidarqgItGqZrldpZ4B0hxG5gUD84Ulm0QnG1M9neUTTnH8/mf7nrnk5GOtzr93h9tPe66Hd58EnJy0cb+cl753B5fAjg3pX5fOHm+cRazNekV2NkulapnfP/s/r/KRTXPJN9dxjN+cez+U+3vNXlGMDQ19836KG9dxCvT9LcPcB336jlWH0XANnJsTyyuYRlBamA5tVkJsZijRmLrOTVxbSsUpNSfhtACJHo/753shalUExnjJvjaHeHl+tJjHT+yzn3dAqJwcQYQJ9PcqlP00GTUvJGZQvPvn2aPpfW1LllcQ5fvnUBCbExAa8mNV7dO08lY1EaWIyWiE/3f98OfE5KWTlJa1MopiWhm+NIG+TlbqQj3X1ezrmvVEgskpG8XANo1EHr6HPxvb11HDyjdUKkxVvYvqmYmxbMApRXcyUZS0jtx8BXpZRvAwghbgV+AqyZ+GUpFJPDRORcxrI5TqYnMd28lGiIZCQvxwAaddDeO9XO9/bWBcqfbynK4OH1RaT4e2mmwquZiVWPU8VYDE6CbmwApJTvCCESRnqCQjHZjPWPeyJCN2PZHCfTk5hOiftomQgjqX/ma4szcXl8DLi99A54+Oe3T7PXP58nMTaGv1y/kHUlWQghptSrmW75senEmKrUhBCPooXVAO5Hq1xTKK4YY/3jnolewdXERBjJfVUtPP56JQ/2LmRjWTZHLnTy3TdqaevVimdXzk3j67cXk5kUe0VyNep3LDJjafxMA74NfAKtafM94Nt+4c2rAtX4OfNQ4YvRuZreI69Pcq69j31VLVw3N5X//vAirxyzA2AxC77wifn8wcrZU+7VzGSmZeOn37D8xbhWo1BMEpHumKd6k53Om/rVEuLRddBMAgrSbXz118dp6HQCkJ9qo6vfRarNislkIj3eSkr82D6H6fwZTibTsvFTCLEX+AMpZZf/+zS0+TW3T9LaFIpxM9Wb7OVebzLVkmd6iMeog9bR7+J7e+r44NwlfBJiTII/WjOPLUtzOXS2g1tLMslPtY3Lq7laDPNYma6Nnxm6sQHN4xFCZE38khSK8IxlU57qTfZyrzeZaskzsbhAx6iDdq69j2+8dJLWHi1XM3dWPOuKs9i2PI/kOAufXT1nzF6NkZlumMfLtGz8BHxCiDlSyosAQoi5aLkchWJK2HHMzpO7q3G6vNx/09wRHzvVm+zlXs+42U1nzyWatU3U6ARdB83j9fGbIw385+/O4fZKBPCp6/IpzEjg3949S36ajc/dNO+yczXRfobXauhtIhiLwflr4LdCiANoIwM+iX9SpkIxJYiQr6MwURtDfWc/z751mgfXLaQgLX7c5xkJ42YXrRjnlfBcovGqLnd0glEHzd7l5B/eqOFkYzcAuSlxfOOOEhbnp9A76CHFZuHWkix2HLdPmQG4VkNvE8FYigbeEEJcB9zoP/SQlHLYyGaFYrLYtiwPm8U86h29bmicbi9P76nl8PkOHtlSOu7N6Nm3TvPLj7Qp59+5d/Sht5dr6FYXpnNHeQ6rC9Mn5fyXc75IXlWo3I/T7QU5dskfXQfN4/Wx82QTz75zhgG3NtF+27Jc/r9bFmCzmomzmJmdFs/S2alTrpZ9rYbeJoKx+qCxaPNluoEyIcQtE78khSI8+h39aJti4A5UavpZOyua2FfVMuJzRuLBdQv5zPUFPLhuIaBtli8eaQh0s0e6/niveehsB7srmzl0tmNSzj/S+UZ7bZE+A+M5UmwW7ebAGlnmP/Q1+HyStp5BWroHaOke4BsvV/C9vacYcPuYlWjlqd9bwsMbioiPjWFWQix5hsKADWXZPLq1bMoMQLS/h9OV0T7jyWQsVWr/AHwaqAR8/sMSeHcS1qVQjBvjHei25Xmsmpd+WZtRQVp8kGcTKaSi37WvLkyPuAFOxLiBaO+wQ68VjY7ZeMNFoWsa7TzGxxt10N6uaeUH+0/RM+ABYF1JFn+xbiHJNgtxFjMZYfpqZmJRxJXMA13JkOBYcjj3AMVSysHRHqhQRGIq/tBCN6CJ/qOKtOHrf8gjTcGciHED0W6wodeKRsdsvOGi0DWNdh798V39LpocAzj6Xfxw/ynerm0DIDkuhoc2LOLWYk2aZjx9NdORQLjX5eWZvbXA1G/6oZ/NVBq/sQ5gs2AYvqZQjJWrIeEaacOPZrOeSC0xozdh3CwieVrRXHuivIXRzuP2+mjrGWTA7eXDc5d4Zk8dl/pcANxYmM5fbSxiVmIscRYzmUmxWMxXh1qA/vu/fVPxlIYBjYR+NtOy8RPoB44JIfYTPPFTqQ8oomYyE65Xulw1ms16pMdEu37jBgEM2yxCPS09Zr+hLHtaGPmeATeXel30DXr41wNn2HGiCQCbxcyDty5gy5IcTS0gwTpj8ySRMP7+T5fXNl0bP1/z/1Moxs1Y7qCnUgk63LUcTjc7jtlBaBVyY90gRlt/6M+N69fzKeGea6xiS46zBB6vM5Z8SqQ1Tobx9vkk7b2D9A56ONng4Kk3amhyDACwJD+FR+4oJjfFdtV5NUamY75pWjZ+Sil/PpkLUShCmSolaIfTzVO7qtlZ0RR0rX1VLTy5299PYhl7P8lo6w/9+UjJe6MB0KvYVs1LDzsALnQDGanMOtIaJzrMMuD20to9SL/Lw88OnudXH9UjGRLcvPe62VjMJtKuQq9GMcSoBkcI8Wsp5X1CiJOEURaQUo7emKBQjIOxGpDx3qntq2phV0UzWxfnDvMUnC4viPGFG8ZabWZcf6iRCPV+RjpvKEYDVbAyuHE10rkmKswipaSz301Xv4vTrb18Z3cN59r7AFiYlcg37ihhfkbCVe3VKIYYdTyBECJXStnkl7IZhpTywqSs7AqgxhNMf8YappqIc14J9GZGYx5mvGscy3Mn6r1wON28UdHEsoJUzELwP4cu8ov3L+DxSUwC/s8Nc7j/xrnExpiVV3MVMGHjCaSUTf6vIxoWIcT7Usqbol+i4mpmsjbx0fTUxhMKuhJx9dHen0jej7EAINr3dSyvb6JCaTuO2/mHN2r4vRX57K1qwe7P1QgB998wlz+6eR42q9ZXo7yaa4exFA2MRtwEnksxw5k0uX4BUsLRi51hhSFniuzIaO9PJCNxue+rrgv3wJq5VNt7RmwCHQ9ev2JAeX4yN86fxX99eBGPT4ui3Lk0l+LsJG4tyWJWYqzyaq5BJtLgKOVoRYDJkuvftiyPykYHOyuaWLMwY8SE+WSGyi733ON9fy73fdV14c6191HT0gNEbgKNRKTX3u/y0NYzSFOXk6f31HLkYhegNXF+Zd1CpA/WlWYxPyPhino10zGEeq0wkQZHoQgwkXL9oed9ZEtpVHI1k9nQdrnnDvf+RFuifDmvRdeDM3o4YyX0tUspudTnwtHvYm91K//81in6Br0A3F6ezVduW8j7Zy7x3IHTZCbFUpSdNOprnkyuhubjmcpEGpwoReMVipEZbROKdtOdrPBafWc/79S28skFGREVnSMx0msbT4nyWDdsoy5cWW7KsJzQWLXeBj1auXNbzwDf33eK905pAvKpNgtf3VjEJxZlBEYUZCTGRpQDCvfaJouZEna9GplIg/PABJ5LcQ0zUZvQZBUDPPvWaXacaMJqFtxakjWs1HgkRnpt4ylRvtz3Klq9tXB0O9109El+e6qN7+2to7NfUx++eeEsvrqxiFkJsaQnWkmOs0RUJr4Sm/90bL68VoimD6eHEfIzUspk/9eKCVyX4hpmMjahSEoC0RwL5cF1C3F5fZTkJEe1xtBZMRD+tUXaCEfaIC/3vYqksxbufPrr6B308I9v1vKFT8ynuqmHNyqbAUiwmvmzdQvZVJZNfGxMUAVaNMKhiqufaMqikwCEEE8ATcDzaOGz/wPkTurqFDOKiYrHT8YmFG7Di/aYUQzz0NkONpRl84/3LR/xesYpoYfOdgSdcyyvLZrw4kgyONGce3VhelQ5on1VLTz+eiV/cvN8ti7J5b8+vEhrjyaruGJOKl+/XZOm0b0aIyqMpYCxhdTuklIuM3z/nBDiOPDYBK9JMUMZabOezKTweGfMRHtMf113lOew2383P9rrM04JfWRLKU6XF6fbi8PpjrjGcOfSNvmqEaeWjjesNtLrCsXnkyyfk8qffqKQutYedhzXZICsMSa+9Mn53LMin4QQr8aI8mSmL9N1PEGfEOL/AL9EC7F9FuiblFUpZiQjbdYweUnh8cyYiXR3H25j1F/P6sL0QHXcaHNN9GqwB9ctJMVmwWY188TOqhE12YyvY3VheqBfRp9aqmunGddvDNOtLkznxSMNQZ7YSBtIuNcVeu4UmyUwIK2uuYffHGngYkc/ALkpcfzNnaWU56aE9WoUM4PpOp7gD4Ef+v9J4Hf+YwoFEP4udipCKSOJU0bCeHe/s6JpRA/C+Lr0AgFdeibSXJPQKaHR5EaM82ue2lUd5CGFloGHbhL3rpzNi0ca+PbrlZTlJFPV3B34WSTCvS7juaWUrC/Npq1ngBc+vMgLH1zAJ8FsEqwpnMXR+k6auwbYVJaj1AJmMFMZ7hyLWvR54O7JW4riamQqQikjiVNCcD6lIE37ufHuHmBXRXOQBzEaAWFPw7lGkpwZLTcSOik01EMKZ8j1MF19Zz+HznawujCdrYtz2Xmyia1Lcse9gWwoy8brkyyencLR+k6e2l1DXUsvAHNnxfONO0rIT4vneH0X25blXRFjo5o3J45pNZ5ACPHPjFylpgawKa4oxs03XI5Ez6e4PD7WLMwYlhyPtpEUgjc6PUwGUNnoYFfFyHmQkdZv/ArDPSTjtfWQGQKeebOWykYHuyubeXRrWdBrGe9GLASsmpfGb4408O/vncXtlQjg91fO5gufmE9KvIXMxFiW5KeM6/wTgWrenJlE4+Eo+eRpyHS7w7tclWYYPio5WkbLkejeQmFWYsQZM+MRt9TX7XR52VnRNGy8QbTvSbR3mKFJ/u0btXCenoMJzUeNFa9/QNqZ1l6+u6eGY/UOQCtJ/ezqOXzplsLLytVM5O/sWMJA0+1v5VommrLooMFrQohE//HeyVqUYnSm2x3eeNYz2qjkcETaPCJtQA6nm0NntfwMQHq8NcjAjXXNxusYFZxtVvOwNU30ZxQuya9fr2BlfCCvpF9vLBut0+WltXuA10/Y+dE7Z+j3hwtjTIL1JZn80c3zmJ1mI+YywmcTqZhwJRSwFZdP1DkcIcRitB6cdO1b0QZ8TkpZOVmLU0RmuvU1jGc9xud0D7ijSvzvq2rh269XcvBMOysK0gKK0dGqKxsfE1rdNZqGGYTf6CJde6I+o3CeWLhcVej1otlopZR09Lk4197HP75Zx/tnLwGQnmDlK7ctwOOV3Lksj/xU22W9hnDrMzKZRsF4XeXtXFnGUqX2Y+CrUsq3AYQQtwI/AdZM/LIUo3El+xqi3YhHw/icHcfsvHrcTnleStg5NzobyrI5fL6DV47Z2V3RHNDpGunxxq/hrh/qGeiEhs/GulFFq1yt/6w0L4nnD14IKm4IXUe41xopNDiawRv0aOXOe6ta+P7eOroHPACsLcrkoQ2LyE2xkZFoHZNXM9LrnEzFhJEwXjfSZ62YGsZicBJ0YwMgpXxHCJEwCWtSTHMm5W5UhHyNgK4WXZ6fAnJog4q00UVjCMNtdg6nG6fLy/ZNxawuTOepXdXsqmjG6fZiswyFz6LNQ430nuk/K8lO4sNzHZxr7+PfPrcqcI5I69OvFak5dKTX7nC6uXCpj3/af4p91a0AxFlMfOW2hdy1LI/0BCtJ48jVjPd3Y7JuoBxONzuO2UFooy2mW2TgWmMsBuesEOJRtLAawP3A2YlfkmK6Mxl/tNuW5QU28tFIsVm4/8ZgL2i8G10kQ7WvqoVn9tby6NYyDp3tCBQFIINzTdHmoUZ6z/RjpXlJPP5aFZVNDvZVtQTOEc5Tcrq9PPNmbeD5B8+088qxRsrzU4a9N0Y8Xh9tvYO8W9fG03tqae91ATA/I4HWngESrDHkp44/VzPdNvR9VS08ubsaIFBQojybK8dYDM6fAN8GXvJ//57/mOIaY7S70dHi5BMRkgs9x1g3utGUAjaUZdPR5+Lg6Xa+cMt8vnVneeDceoGA8Xrh5HGinWNj/Nm/fW5VkMcUim7g9Ao1/dwlOcnsOG4P9AaFe61rFs7C0e/muXfO8OpxOwCxMSZuLdKKAs629XHnsrzLCqFNBwmbUBUGp8sLYvoYwWuZsTR+dgKq50YxKqN5G9F6I2OZHRNpoxvJgxlJKSDFZuFsWy8vHW3EGmMK6onRK8D0QgPjdUeaYxO6lkiGd6R8kbHQ4dDZjsBxm9VMjNmEzWoe9tr7XB6eebOWu5bm8XZtG41dTgDK85L5xIIM/ueji6wvzeb/jOAZReJKVYCN5XdjpJygYmoZS5VaEbAdmGd8npRy3cQvSzGTGc3biNYbGSlpH3qO0QwLBG/6RhmZkcYQ6F9D1Qqi2WhHqxqLdA79uNPlHVZurb/2p3ZVs7OiKfDccCFJXd35j9bMozw3hf8+dBGfBItZ8Edr5vHZ1XOIs5gpykka993/lQqhjfT+T7ewnmKIsYTUfgP8K/DvwHC/XXHNcTmJ+mgwbhyjVYxFSpxH2vSNMjKRMHb7f+PFEwFts+/cuzSqTS30fYgUhgsty9aPO93eiAZpV0VzUKNpuPf8+vnp3HvdbF49Zuf8JU1wszAzgW/cUcLS2alkJMZiNolpOeRuNKKpPlRMP8ZicDxSyucmbSWKaU0k6fzLkcXXnzfW8QLhrquXSxtVlcOFsJxuL9s3Fo969xv6XKO3A9GXPBsJ3QgjlWUHNZSGKaQwvhfhruf2+mh2DPD8++f5xfsX8PgkJgE3zJ/F1zcXsyg7icTYiRz2O/UoozIziUZLTe/E2yGEeBB4GRjUfy6l7Aj7RMVVRaRN3vg1WkLv7CMl7iM1bUa6bnl+CuV5KSOGsJ55U6s8G62YIRDWcvudeUlYNWmH062Ft05qitP6wLWRjE+4godwWnD6z4xlvfrr0l/jT949S01zNw9tLKIgLZ6eATfH67v4zu4aKu2aYvTsNBu3FWXx6vFGTrf2smJOWlTrUigmmmhuc46giXfqHRJfM/xMAoUTvSjF9CPcJj/eu8zQO/tIiftIhiXcdcMZE+PzR/NudMOhC3AGwlou71BZrT+fYtyU9fBWWW4yOyuacHl87K5sxunyRkxWhwsPOj1evr+3DiCorFkv65VSEwgtz0sJGGeAp/fU4PJKLCbBwxuL+J+P6vnXd84w4PEBcPeyPL586wKSbRaKc5IY9PgiDoHbcczOk7urR1z7eFCGTKETjZba/KlYiGJ6MxkhDH2OzdrizKDOeuM1R6rYMjKaQXzxSMOI3s2+qhZ2VjSxsSQ74NXoYS0EgSbTUK8ptGrM7nDi8fqGPCM/xk3XOL9HP9/64iztgSG67Lr3c7S+k50VTZTnp7B9YzFOt5e1xZl87fYSKu0O1pZk8Wf/c5SPzncCkJFo5eu3F7O2OCuQqwkVOB1mCKJsvg1lNIOitMwUOmOpUosHvgrMkVJ+SQixCCiWUr4+aatTXNXoc2zK81OCuvdheINjaId/KNGWE0fybpwuL1+/vQTQJP/1TTm0yTTUazJer2BlPC98cCHQx2IsBNhx3M6Tu6oDr0OvMHtw3cKA4rM+OiH0dd1/01y2Lc9jzYKMIKMXF2Pi967LB+BrvzlB76AmTbOhNIuHNiwiPSGW355qj1jZF2oIxtJ8a2Q0gzLRVWPKY5q5jCVz+J9o4TVdO60RrXJNGRzFuDCGrUI3rNAGR+Nj9PHLD65bSHKcZVjexXgeHWMiXjcE+nX0HNL2jcUAbN9UHNagGM8D4XW59E3b6fYGVc0FPBc5VOAQOvQtnCBnuPU7XV4e3lDEnFnx/NWvj/NOXRsAyXExfHVjEVuW5DIrMZZXjjaOyRCM14vdUKZ5hU5X+HlEE+0dK49p5jIWg7NASvlpIcRnAaSU/UKIMTrfCsUQ4eT9dUIrsYyPMY5fXjUvPcgQjaY4HU6KZvumYrZvLOZofSd7q1v41p3lpNgsowo9jnTnvrY4k6MXOnn5WCPleSlsW54X1FNjHJQ2ljv2fVUtPP1mDZvKcvjh/lN09GnSNDcWpvP124spykkOVKCF82hCS8cnYsNOsVmwWfzhulHEVCcC1WczcxmLwXEJIWz479WEEAswVKspFOMlUolxuBJigAfWzOVcex8PrJlLfmp8oMLrQE3biKOmIbIUzb6qFvZVtwb1toy2sUUqXtD7fFbMTWN3ZTOI8CG/sSoYe32S3JQ4kuMs/OZIAwDxVjNfuXUBv79yNhlJcZhNQ/eA4fqAQkvHwzGekNVUGoGroST6Wg0LjsXgfAt4AygQQvwXcDPwR5OxKMW1hbH7/wdv1rGzoomOPhfpCdZhf5AOp5ufvnuO4w1dfHy+i7KbUgLJ8EjVbkZCN6twpdaRZGeiIXTjNeZEjKXWxnxUpAZQ42vedaIJkwme3FWjFTIAy2an8I0tJSzJTyUhir4ao2elX2t1YToHattAEpgtNJ6Q1eVq4U1XJmud12pYcCxaanuFEB8DN6LVsfyllLJ90lammHQm6o9pvOcxFgZ8d08NRZlJHGvoRCCoae5mb3XLMOUAvQzZ65OBaqrQSrHxELphRjIO4ZpJjX0ykQyacZ2hOSv9OS+8f2FYWbKUkt8crucf3qjB7dUSQWaT4PNr5vKlTxaSGeLVRPs6da/qjvKcgJCnHg6bSG8lWsmh6cpkrfNaDQuOpUrtZuCYlHKnEOJ+4JtCiB9KKS9M3vIUk8lE/TFdruLA9o3FbF2cyyvH7AghuGtZHg9tLAIJrxyzB0nu6wlq5FAjZKSO/ctB3wg6+lyB6rL7b5wbtpk0VP4ehm+0oXNzQnNWAAiQEo7Wd7JteR5xFhO/O93OL96/EDA2Oclx/MO9S1g1L52E2JioBrvphtj4GKORDp0tNJEhq0i/G1dqwx3rzdFkrXM6hQWn0tscS0jtOWCZEGIZWnn0T4FfAGsnY2GKyWei/pjGex7j89aWZOLy+ijJSea+6wtIsVlYMcefAwnpTYlUHj1a6XO4P6rQaZsPrJnLxxe6AiGmHce0u3+nyxsIQRnDdnqfjFH+PrSJNFSoM9L7tLY4k9eP23mzqpkl+SnUdzr52cHzeH0SIWDlnDRuLc5kyeyhENpIxl7/2R3lOdr7aHiMccMbaX7O5TKW5t2pYKw3R9PJMEwWU+ltjlVLTQoh7gZ+JKX8qRDiC5O1MMXkMxF/TNFI7kdzfT1pbzXMYjFWdo02v2YszYfG5L1+3Dht80SjA9C8EH0NupCmLvppLK/Ww1/6MafLy+sn7RRnJdPR58LhdAd5FLrSc2i47tDZDqqaullekMa/HjhLc/cAALMSrDjdXubNiufff3uO3BRbVPJC+rHSvKTAtaN9vyaK6bZhX6uhrJGYyvdkLAanRwjxDbRJn7cIIUzA9M32KaaE0I08VDZ/NIxhny2Lc4IqqIyVXbqh+cptC4eVPgf0zEKuGzqIS19jqE7a9o3FXDcvlZ++e46c1Fgyk2IpykkKDDO7d+VsDp2/xKx4K+mJWrl0oCHVMEJAP+9Xbl1IeW4KxxscVDQ5ONvWyyNbSgNhP13pOXR66A2F6dwwP523a9vw+DS37vq5afzV5iLqLzm5ccEsblqQEeRJjRS2MxrV0Oq9mZJDmWjGYwBnSoHDeJnKm4KxGJxPA38IfEFK2SyEmAM8PTnLUkw2E/VHVJqXREl2EqV5SUGy+aV5SXzjxRPDmjNDw1m6ofjWneVBvSk6gTzPpqEG0N2VzZTnadVpehPozpNNbF2SO6I3pP9RrS5MZ31xFr893caB2jb+emsZZbkprJibxuM7qjCbBFaziecqzwBw/01z+e7uWs609/Ht16rocrqHGlLdwQ2pd5TnAFDV3M2Wxdr/jQ2ew6rYrGbWlWZxsrGLb71ayccXuwCtifO2oizerG7mdHNfwIsylnuHM/Z6GA8YFsIzVsFNxl3t1boxX6vGeTIYS5VaM/A9w/cX0XI4ERFC/AdwJ9AqpVzsP5YO/AptkNt54D4pZae/ifSHwBagH/gjKeXH/ud8Hvgb/2n/Tkr5c//xlcDPABuwC61yTka6RrSv9Vpgov6Inj94gQ/PdfD4a1U8dlcZWxbn8OC6hTz71umwzZlGDS+n2xs01yXcnVakBlB9o7+jXPOKti7JDVSzGUVBdd0xYwe8Lqnj8UpABnJETpcXn/RRlp3CvIwE7aC/AGxtUSZHL3ayIDOR24qzuG5eKs8fvMCmxdkBg3ugpo1Xj9v58toFbF2cGzC2Rsma0Ne4ZUkOL3xwkR/sq6PP71FtLs/h65uL+e3pdvbWtATWMNJYbV0LzthHZHzfwhVVhL7Xl2swrtaNWYXhJo6xVKn1MJS+taKF03qllCkjPO1nwL8QbJgeAfZLKZ8SQjzi//7/AXcAi/z/bkArUrjBbzy+BazyX/+IEOI1vwF5Dvgi8CGawdkM7B7hGgo/xj+icBtNtJvPg+sWcq69j6qmbp4/eCEQujHOj0mOC66MMlanPXZnWZAxCSclE65hUp8Vs7owPeAVGSuw9Eo2BDy9p5bKRkfAIOmJfqfHiy1Gy9PoSAkVdgdbl+bxxN2LA2v+3Jp52Luc7DzZRFZSLEfrO3np40YOnevgbHsfzx+8oFV7Aecv9bG/pjWsRpyRM629/O2OSt47pXUXpMVb2L6pmE9dl0+8NYZZibEkWGOGvW8wfKx2qGHWHxPpMw/3GY9mMEb7nbhaN+bploeayYzFw0nS/+/3Ru5G68kZ6TnvCiHmhRy+G7jV//+fA++gGYO7gV9IKSXwgRAiVQiR63/sXn3ujhBiL7BZCPEOkCyl/MB//BfAPWgGJ9I1FH5G63SP9m41Oc7CncvyuHNpHmtLMoM2f31aJgQn6iP1zYReM1TA07gW4/pDVQVSbBaQ8OTuah7eUBQ2N2SU39eT/QBmkwmJHCbRojdNujw+XjnWyIO3LuQz1xfwqZX5vHykMWBgKxsdPLBmLmsWZITViANtQNqLRxr4hzdq6OzXmjg/sTCDv7mzlKykOF460hjo64m2lDiaTTFSr5G+vtEMxmi/E2pjVozGuMb++Y3CK0KIb6F5D2MhW0rZ5P9/M6D/ducD9YbHNfiPjXS8Iczxka4xDCHEl4AvAcyZM2eML+XqINxGE+3dqnEOje7JRHpcaDgn1NCFXtPoCW3fVExHn4sXPrgQaLCs7+zn2bdO88CauVTbe7Su+Zo2EAQKAmxWMw+uW4jL46Oj3xVWXNKYJ/ra5mJOXOwaFoYDbUPV5WrSEqx8bs089lW1BIXydA8vkkacvaufR1+tZH91KwAJVjMPbSjiD28oICFWO0e4vh79+qHe3njRRUB1oVL9/CMZjOnkwVyt+aKrnbGE1H7P8K0JLcQ1cDkX9+db5OiPnLxrSCl/DPwYYNWqVZO6lulKuI0m2rvVcNVfMHo4R/9/R7+Lg6fbWV2YTkFafMQ7+n1VLTy5qxopZSA8pueJzrX3UdXcTVlOMscaujAJwTe3lAZCYjuO2dlxws7rJ+zsr2rhsbvK+Ph8V8CL0ENwTpeXmqZu9tW0sr+2lfR467DSayQ8vKEIgB3H7Xx3Tw0Hz7SzoiCNtSWZQT06xvfQ65PsqWjib3dU0dqjSRDOnRXPP392BYvzUjCZRGBI3MMbisI3hhK5vDvcpjvSpryvqoVn9kaeDxSO6eTBXK35oqudsXg42wz/96Al4+8exzVbhBC5Usomf8is1X+8ESgwPG62/1gjQ+Ex/fg7/uOzwzx+pGsoQricO8WRktihRDJqZ1t7eeloI9YYU1AILvQ5et7laH1noOpL91zmZSSQn2bjzcpmFucmc/uS3IAXBIDQQmXZybGBAocTjY4g42WzmHlyVzU+6WNxbgq3L8kNVHXpTaGFmYn86J3TgUbK7RuL2ViazY7jTew82YTNuiTICIA2RbPP5aGqqZtX/U2kVrOJW4sz+dZdZeSnBledfXdPDVsX54YdZ218b439PBB+0zXO4Alt7pwqb2WyPJHp5G0pomcsOZw/nqBrvgZ8HnjK//VVw/E/E0L8Eq1owOE3GHuAJ4UQ+iD2TcA3pJQdQohuIcSNaEUDnwP+eZRrKEKI5k4xWj2s8Qg4FmYl8nsr8nlw3cJhUiyhwpL6ILIVc9JwuryBCrBvvXaSjIQ4bl6UwcHTl1jU2hu07rXFmTjdXo6c62BJfgoPrJnLrz6sx+XzBYzX6sJ07licg8vt493T7dwTY9bKrSuaKMtJ5sNzHayckxbo2QFYW5IJwK6TzWxZnDPMy5NS8sTrlbi8MlBtYxLwpVvm89WNxZhCNNA2lIWfk2PEWHFmrPALiwz5GuY8k81keSLTydtSRM9YQmqz0Tb0m/2H3kMrQ24Y4Tn/g+adZAghGtCqzZ4Cfu1XKbgA3Od/+C60kujTaGXRfwzgNyxPAB/5H/e4XkAAPMhQWfRu/z9GuIbCj3FzH0lhOdArc7JpmJDmeO4yjcZrX1ULP3r7NI9uLaMgLT5IUHJ3ZTPri7N47Xgj+Mcu3X/T3GGzV1YXphNvieF8Rz+9gx7WFmcGNmwgoA5wtrWXPVUtfOb6AuovOdlf28r2jcXcWpQVWMve6hY2lmbztU3FIAhs6A+smctjL1dSYXdwz4p8qu09gV4gp8fLlsU5PLSpiBSbJdCLs2R2Mj9+91zA2JhNgj+8YQ5FWYnctTw/EEIzGvLQOTkjEa4qLfT9NSo1XCmUJ6IwMtaJn/8N/IH/+/v9xzZGeoKU8rMRfrQ+zGMl8JUI5/kP4D/CHD8MLA5z/FK4ayiGMM5tGelOUe/vKMtNHjZHxViibJTVH0k0MpymmPGrnkvZvknrn3nthB2BABHeSOpVbEmxZtr7XFhNpkCptc6GsuyAMkFombbReOrexYo5aSDhz25byIDHw3NvnSEjORbRJPjw3CWWzk5l+6ZiAL6/tw6Aktxkapq76RvwsL+2hf01rYFcTUailR98ejlrFmQEeTXhQl6R3tNQjHf4RsVqJGGbXSeL0UJmyhMZG1d7McRYDE6mlPI/Dd//TAjx0ASvRzEBRPNLG+2dp7Fn5c6leVElsvXcwsbSbHZXNON0e9m2LC8gknlHeU5AmUA/3wvvXwhsmP/y9ulAA6ktxgxCE7Y0qhIYVQPuXJo3rDz58R2VlOQkc/OiWTy1q5oH1y0M5Ij0eTKh75XuXXT0ufj+vjruKM9hx4kmXF4fJuC6OWnsq9aMiV6Q4HR7qbF3c6Kxix3Hm/SXQGvPIAJYOTeNTWXZzM1I4OWjjUEVeLp0TriQ10ihqHD9M3pl2ze3lI46E2ikc40VlbyfWK7293MsBueSfyzB//i//yxwaeKXpLhcovmljfbOM8VmwWY1j1jRFFpNpoeiCjMT2V2hqT2HUy7WvwJBG+aWxTnsPKklw40lx+FyFofOdrDzZBPn2vt4+r5lFKTF81e/OsaLRxuxmO2srE7jw3NaBFY3OPuqWvj2jkp+c7ie9aXZ/Oid0wEhzdK8JB57pRKfz0dJbjIlecnsPG6nurmbjCQra5OysJgEqwvTSbFZSI+3sr+2lT+4bjZp8ZZAX02yLYbH7yrH7ZX8/a5qzrX3BV6v0+3lyV3VPLyxKKi5NNJ7asThdPP4jkp2nWwKeEb6TYFxJk+0XO4GN5Uhs5ly938567zaQ5BjMTh/gpbD+T7aPdlB/HkWxfRiIn5pIwlfhiNSxzsQmNqpoysDGBUCgKANc9uyPFweHy8fa6QwM5H0BCuleUkBrwfghQ8ugNQS90VZSXxwroMvP3+E4pwkclLjsJgFm8ty+PK6BTx/8ELgefoa0uOtfHCug8yk2IBG27dfryTdZuV8Rz+r5qRx3/Va0aQtxqxVx51swusDswkS42J4cN1C+gbc3FQ4i+c/vMCA2wfA8oJUzrb14PXBpvIcTEIzUIWZibxT14rb7cPnk9TYu3nsrnKAYeGzSDcE+6pa2HXSP4DO4BnpuZqxbHB6Gfb2jcXj/l2ZKDHMaDbpmXL3fznrvNpDkGOpUrsA3DWJa1FMENH+0o40IyYgoW9QQ4404GukjVL3evQcSnJceIWA+2+aq+UijttBQkleMrsqmtlT0UxdWw8bS7IDiXoEPL6jCpMJbNYl3L44h6P1nVQ1dVNh7+beFfl8685yEJCfGs937l0alBM5dLaDpm4nZiFYOjuVe1fOpr6zn1eONvDxxS4EWggP4PHXKtlV0cRXNxazoiANp0cLoe2qaKZ30DM0fRTNuD52ZxlrizN5q7qV1YXpQe9PTVM3O443EWOCVXPT2VfTypqFGQBRb1B6GE+v3IuklB0NxqbdkQzVRHsW4TbkaDbpmXL3P1PWeSUYS5VaJppu2Tzj86SUfzLxy1JMBtFqZxnDYojIm+G+qha+/XrlsOo147V0xeb1xVnsrGji4Ol2HrurPOzG9euP6nlqdzUWs+BvtpZzz/I8Xj9hpzwvhXmZBjFNqZUXL85NoWCWjYNn2nnI34x5/lIfD20s4tDZDq2Szd+tbxxJ4HR52VSWw9LZqWxekhOYYVPZ1IMwCcwC0hKs7Dhm57UTdk1gjaH5OLUt3bx7qo191a0BY3PLogxuWZTJbSVZEcUyS3KTsZyws7ksh69vKQkUVHQPuIeNXIhEis0S1FNjDDUa1aCjMQwjbYyh1YQT6VmMV9liptz9z5R1XgnGElJ7Fa0Ueh/gnZzlKCaTaLWzhh2X0NHv4oX3L7Bt+VCOILRvxOjN6CMDNpZmaxVnLi87TjSxs6IJa4yJR7aUBtYU8ACau/FKWJGXyrbleQFRzZ0VTawvy+aO8hw6+10gYXFeCnWtPXxvT53WIzM3jXuW5/O5NfNIsVlILgtfut3R5+LpPTWYhCAxNoYDNW08s7eW7ZuK+erGIk40dFGUrckGOj1eYkwmtizOwWY18/jrVeypbOKd2jZc3qF41r3X5XNj4Sz+flc1aQnWiOHF+64vCIQYU2yWoNk0ofNqjIzkYYTmzyZqmuVI1YSXy+UoWyhmNmMxOPFSSiWAOc2JdnOCyNpcoX/8Nqt5SN/LIGoZ2jdiLAzYVdFMWW4ye2taWLMwg23LtJ6Qoxc6A+XVMOQ9rS5Mp2/Aw/KCFL6yfgGPv1ZJSW4yD65byKp56YE5OG6vlv+IMcGKOen831sLaesZ5ER9J8cbujh6sTPgQYWWF28oy+ZvXjmJzyfJSYvj9ZNNuDw+tm8qDlTRvXe6ncTYGH76u3Ns31jM392zOKDRVpqTxJtVQ6IVZblJ3L0sn8/cMCeslxL6PkbaVEfb0EfyJEfKn0XzOxGJ0D6fiR5loLg2GYvBeV0IsUVKuWvSVqO4bIyho1Bp/NDejdAhZeGMj35czxuEbmZGo6X3x6wuTKc8PwWny8udfq0yPRS0bVle0HwY0IzNw788xuELnZhNgh+9dYbDFzqxHG8MTMvsHnBztL6Trn4X79a1U5qbTHVzD29WtNDe52JpQRonGhyBPhrjFM7HX6/i8PkOCrMSeaOiGa+EFscAK+akBQyiUZrHWNhw6GwHe0428w97anD7vRqzEHxpbSHbNxZh9o/E3nHMzqvHtfCfUYlaZ6QNeqS7e11kc2Np9ogKBKPppo01JDaaxzFTEviK6cWoBscwB0cA3xRCDAJu//dSSpk8uUtUjAVj1Vc4qf/Q/I0+SXNDWXZE7S3dWIwmcWNsIrVZzDzzZi3bNxYP66Y3rmd1YTo/2FvH0QvafDwTkrVFmcxNj6fP7eWVY/bAXJn9Na2sL8nCEmNi67I8kHCioYs/u20hm5fkcKC2jc4+F68ca6S2pYeDZ9opyUlmQ6mWP9pYor03ZgF3Ls3joU1FgTyK/jqNBQ0vHmngsVcrsJhNAWOTFm/hB59ZTnuPi16XlxSbZnD0IWn611Al63AjFmB0T0EX2dy+sZg1CzJG9ILGIpx6uajEuGI8jGpwjHNwRkIIUS6lrLz8JSkuB+OGbpQ1Cd2QdAmWtcWZFKT58wYjaG+FnsPoDY2UBDaOYNbXteO4nQ/PXGJvTTOLc1OosHdjMgu2lOVww4JZgV6Sn7x7lreqNS9s2zItn7O6MJ01CzJYXZjO1359nA/PdZAYG0NBWjz33ziXb7x4gsMXOslKtLLzRBM7jtv52u0lgQqzktxkbJbgoWvhaO0e4H+P1NPv8iLxEmMSZCXH4uh38dzbZ6hq7g56TTCkIu1wuocpWW8syQ4aBaA/TquCaw4rsGl8H0cLXY1kAMajcTdauEyF2RTjYVzzcCLwPHDdBJ5PcZk4XV52HLcHJPhhaEPSxywbE9WjaW/pDYYd/S4ef62SvdUtgbxCuCTwhjJtNIBxo91X1cLf76zC5fEhgZP2bpbkp3B7eQ73XV8QtFHZrGbMJkFNczfdA1po6UBtG2uLM3n2rdNU2Lu4YX46D6yZy4tHGgK9LqvmpFHT4qAkJ4lKuyNwvu/vreObd5QGQl7hBs+B5jX96c8PB6RpFmYlsnVxLv/5/jmW5KdS1dTN1iW5Qa/p6T21lOYkUd3cg81iDvT9PLBmLs8fvBAI9YV6mbsqmob11IS+j6Fh0NHCcldKPUCF2RSjMZEGR4z+EMVUYZQ70UuDR5seOdqdsK468ORubS7Nstmpw/TVdIz9Id+6c6gMekNZNgfPtPPaMTsSyaKsRI43dHF7ec6wJsDr5qaybHYqb1a1cKatl5MNDmJMgtePp1HV1M3tZbmU5Cbz0/fOsbe6ha2Lc9lV0cyG0izuWZGP0+OlrrUXm9WM0+3F45U4PUMFlqHvgdcn+eH+Uzz79mk8PokA/vCGOTy6tYxBr485s+KDFKyN5zl4pp3XjzdSmpdCR5+L5DitoGJfVctQ4UOIt6cbcH1N4YbDhX6m0WzoO47ZeXJ3NU6XN2w+aTTGGy5TYTbFaEykwbkmh5dNV/TNDDExYZag8/oLCK6bl8rzBy9QMMvGN148wYPrFgbCc3ovz8YS7fH1nf2BfMlj28oDApkfnr9Ehb2bGn+ISn+uXu1W1dxNeW4yhy904JVQkGKj0u6gPC+Fktxkvr+vDilh2Wxt7ABoZdRrFmSwbXke6fHWgKdlMkGNvTuwsRsLHhZkJfLoKxWcbNQ8orzUOO5amseXb1tInNVMHOag/JSuVq2PZVhRkMaO43aq7D3UtdSRnmAFCMptOZzuQCGH/hlsW54XMMy6d6kLcYbK1ES9oYfkk6aKiSptVqG5q5eJNDiKKSQald7x3N1Gc019I9SbDt8/c4nzHf1UN3ezdUkeNouZtSWZPHanJhnzzJu1VDY6AlpiG8qyAxvvdfNSaese5AufnB+4zurCdNYXZ5GXFsef37YIBNy8MIPz7X184Zb5PH/wAjtPNpGfZuPhDUUcudjB/qpWnnvnDF/fXDJMMmfHcTvXzUtlcW6K1pEvCDSHNnb284P9pwDw93By36rZLJudytNv1rIoOymqJLyeE3J6vNhigsOS4YoSdIxNtrrxG2nEdDQb+rZleUGGbaxc6dDYlb6+YvKYSIPjmsBzKUZAD1ftqtA28PH8UY5msML9PFzj6MHT7bx6vBEBVDQ6qLJ3E2M2Be7+6zv7qbQ7eGDN3GH9Ojo1LT1U23soy00B4EBNGztO2DEJwfKCVKqbe3jszjL+fP0iQBsxcK69j71VLayYk0Zz1wBun2TniSasJhMPbSoKjC3Qwn+aB1Rh78Ltlew62YTVbOL1k3aQImBokuJi+JutpRw624Hd4eSTCzPo6HMNC3WFbvx6RZrRw9OJRqpG/6p7XE6XV1NEcHtHDbOF43I9jSsdGrvS11dMHtGURY9YCCCl/Nj/9caJWpRiZPQZNSNOexyBaPS3wt1lhmscLclL5rXjdhbnJ7GpLIc4izmguvxXvzpG14DWN1OYmcgXbykMPN/e5eR/Dl3k63cUD5fTF9rMtRiT4GRDJ3ctn01pXhJ//l8f45Y+LGYTFfYuFuel4nR5qW3pRWhPY5dfyWB3ZTNfuHk+ZTlJZCTH8rtTWol0ToqN6+ak4vR4cbp8ePzWJjcljp98fiX/9f5FXjzaiFkIzCbYX9NKukE9IBx6RRowbEy2UR/OqNKgE6459P6b5gYKGnQvJ5ow00SFoiIZrKkKdSnVgauXaDycf/R/jQNWAcfR/raXAoeBmyZnaYpIlOYlUZaTzANr5o67Cmk0/a1Q42Lsm9Hn2jx/8ALpCVZ8UnKqtZei7D5WFKQF8hIvHm0MzIepaRrK0aTYLPzudDuHL3Ty5OvVlOYm++fj9LChLJu1xZn80z4Lrb0uMhKsFGYm8tP3zrHDP7LALGDJ7BSONzhYX5bNX28tDeSrbDFaOK88L4VXjjVy5GIXK+eksqk8h10nm6hp7uFkYxeNXQOB9dxUmE5lUzf/9f5FHlgzF5fHx7zMBOJizFFNzNQr0oyK1Mb3+sldQyoNkZprQwl9/6MJM0UqW58o46BCXYrLJZo+nNsAhBAvAddJKU/6v18M/O2krk4RlucPXuDDcx08f/DCsDvqaDBuZpE2kdAyW20GSzMbyrLYX91KUXYix+odLC9IwRpjojwvhV0nm9hd0YzNauaBNXOpaHRoigdWM3cuz+WvfnWMktxk7ru+gMfuKuPx16pITbDwy4/q2V/dwoDHF7h+v8tDUqyZbqeLf3yzhpKcZHJT4mhyDCCBWQmxeH3dIAn0r+hGEaDS7qCmyUFmopWalm7uWTGbuBgz//txQ8DYLMxK5N7l+dy5Io9n3zrNrgpNjVpXQoh2oy5Ii4/4OYSqNOjKBwdPt7NibhprizMDVW9rSzKDJqSOVlU42ucaSQ4nEqN5MCrUpbhcxpLDKdaNDYCUskIIUToJa1KMwkh31KMRuqnoG6LTNZQvCH3MjmN2XjvWiATqL/UjpSQnOQ6ruZv1Jdk8cOO8oHLh0rwkHn+tipqWHtxeiVnAj/af5vDFLqwn7IDm8awvy2ZZQQofn++ktdfFgowEVhemkxxn4eDpdnZVNLOuNIeapm6ONzi4c0kuTQ4nFXYHl/oGMZu0XJH+uvS81pbFmpbb4vxUKu0OSnNS+O2Zdt7w57ysMSb+Yt1CcpLj2Ogvx9bfy07nIE/uPktHvytQ4Xa54Sldpw2GBE9fOdbI7spmKhsdvHpce08q7UOFFWMVtwz3uRqFVaPxSEbzYFSoS3G5jMXgnBBC/Dvwgv/7/wOcmPglKUYjOc7CqnnaxjxWQjeVFJslqMx3Q1n2sImSCIgxmynPTaa2tYfi7CTcPh/rirOIM8zK0T2Nb7x4gg/OdbAkP4WuPhctPU4GvT42l2Wzcl46Nc3dvHi0EeuJJrYtzcUx4CYjwUprzwCHznZw78rZzMtIwOXxApKOfhcr56RSlJvI0oJU5s5K4M2qZsrzknG6vIGkvT7KQC9QWF2YzmOvVPBOXZs+YYCVc9P4waeXc+icNr5ACMG9K2dzoLaNV4/bWV+SBWjl0/trWwOhukg5GCOjSf+AZnDK81MozEzEZjWztjiT8vyUgIdjrLAbidFGTejCqrqmXTTFB6N5MKpcWXG5jMXg/DHwZeAv/d+/Czw34StSjMpId6KjbQq6pI1R1djo5ew4bg+aKKmLR24ozWLOLBsAxxo68fi0XMq7p9tJjw9OquveQl6qjWffOU1Oio2Tjd3My0jAZjEzb1YC60szudTjIi3BgtcHvYNuPrVCk9x54f0L7K5owiu10NrWJbmkJ1r4wd7TWMyCv9pUzIWOfo43dFFhd7C/uoWqpm6Ks5M41tDFx+e7+P1Vs3lqdw3v1LYhAYtZcHt5Dk/cs5i0eCvJ/vdGz2E5XVrzZVF2EomxMTywZi5rFmYExkHD6DmYSJ9LaKhLH3qmP8YoaRNuPEGk3wFjyCxSI29oz9BIKMFOxWQzlomfA0KIfwV2SSlrJ3FNilEY6U50tE0hVNLG4XSz45idoxc72VvTwtc3lfDXW0sDd/T7qlr4/r46PF4fIJDSx5qFGZxv62NTWTbzMhODCg+6B9z84M06SnKT/dVmgrKcZJodA7jdPh5/vRKBtrm19rq41OtCCMmtxdkUZiby3TdqeONkEz5/1+L5S/0cq3eQbIvBKyXFmYnUNHVT4W/QLMlO5OOLHeSlxLN8TionGrs5097LHT98j3PtfQDkpMSxODeJd0+1sfO41mCpG1zjPJwn7l6M0+Xlp787FwhD6TppxhzMWEUyoxkhMBYcTjcdfS6KspLYeXJI6WE8ow/GgvKApoar+X0cy8TPu4CnASswXwixHHhcSqnGTk8xI92JhqsuM3auG/s89F9svVdly+IcENp45QM1bVrD5NxU7ijPYV5mApV2B/urWunud1Pf6eRYfRfzMhM5UNPG02/WsreqmcMXOmnvdWE9YeexO8t54u7FgXEFvzp8EY9XEmc109rrIt5qptHhxGwStPe4+N7eWjS7JthYkk1zt5Nqeze5qTYaOvoB6Ox18fpJO6U5yWxdmgcCTjZ2c76jn48vdOGTkp8fPI9PgtkkuLUok4Nn2vntmUssn62VQ+sTSHdXNvPwhqJAWbaev9INkm5EjUrZpXlJEadzhpvBE+2GMZZNRr8JkFJyz/L8EY3JROZdlAc0NVzN7+NYQmrfAlYD7wBIKY8JIeaP+AzFlBKpWVM3KJWNDh7ZUorNag70eBglcICAKoCeyL57WR77a1t5dGEZaTYr79S2kZMSR4zJREVTF1W7evjmllI2lGbx0sdaYUFGgpXPr5kXJKvz4pEGGjq16rB+f/hq0O3FJ2F2chx1rT1sKNUeu3R2KvddX8COY3a+ba+koaOf0rwUquwO2voGkVIT/UTAk7+3hEfuKOXQ+UvUNvcE+mpmp9nYWJpNe88gf/KJ+Xx45hJVTd3cuTSPR7eW0dHn0hL0IfIvesL9qV3V7DzZFAhZ6ZuAbqgizb3R3/ORxnKHK10eyyZj/MxC5W+uJKqKbWK4mt/HsRgct5TSIUTQX6jST5tGhJtns6Esm44+F3sqm9l5sonyvBQQsH1j8VCy/6ahsmKbRbu71x+3tjg4kV1p14acfe32Yu6R+YHHfHj2EgLISY6lLD+ZupYe3j3VjtPtZX9VC41d/QhgQVYCCzMSWZCdyNs1rdQ09bAoJ5EbCzM4cq6D/bWtFGUnseO4nc5+F+W5ydQ093DXsjw+fX0BTpeXnSftHKt3cKzewdd+fZzcVBtv17ZpeSc/q+am8cIH53H7ICPBwoDby+L8VNaWZJIcZ+HXH9WzvjSLExe7ePd0OzC00euNtWW5yez09/48uG6hZqj6hwxVJK9kpA3D6GHuOGYPGn6n59GMunPhqgYnQ7ZoIlBVbBPD1fw+jsXgVAoh/hAwCyEWAX8BHJycZSnGRZh5Nik2C+kJVupae9i6JBcEgaQ1EBT6CTdLJ8VmGcr1HLdTmJnI1zYVB8JuAG+cbOaNymZMJoi1mNlf3UaMgFXz0jlxsYsPznUwOyWOxu4B6jv6+KOb5vPh+UvUNPUgTILfnb6Eo9/D0YudeCXsr2mhsrEbn5RYY8x8akV+0OiCmxfN4v6ffEhHv5vKph4qm3oCrzfWLHB7JQsyE9m8OJedJ5to73OTlWil0u7g2bdOU56Xwvf21uH2+ogxwfKCNErzkoJGUcNQebcxT6IbZaRm4MMNVRtpw9AVt0OH3xkT/KHl0ZG8n6s51q+4OhmLwflz4K+BQeC/gT3AE5OxKMX4MM6zMW5G4e649Yq0p/fUBjUHDg0Fa6Kjz0V6gpXVhek8+9ZpXjlmRwh44u7FHDrbwd/vqsbrkxTnaDP6FuelcKatl8X5yXT3u6m0O/iLDUUkxsXQ2jtAg2OARdnJ7K1q5sApzasoy0rEGmMKGJs56TaW5CVzvN6B2aR5T72DHn5x8DxxVm2jP17fSfeAO6BiAJASF0N+mo2qph7MAuIsZm6YP4uiHC3HVN3kICHWwusnmyjPT2HL4hx2VthZnJtCVXM3P33vHLsNg9D0sFro7BvdMHz79UptqNrG4qhCH5E+j2GjvP3jCrbnFQ/73EJVIa7mWL/i6mQsBqfM/y/G/+9u4C40iRvFNMB4Zx06XMy4Iel30ts3Fvs3Xi3UZrOa6eh38dpxOz4pOdHQxXun27mjXHvMliU5rChICzR5bijJ4o3KJioaHcSYBTcvzMDjkzR0OOlyar01Ny+cxRc/WchP3jvLe3WXmD8rgR0nmgJrsVpM1DR1I6WWTmntHuTAqXYkWr9Rv8vDu3XtmEUTZpMJj9eHj+H0DXpYkJHIqdZeNpdpxQ9P7q7mm1tK+f5nV/BHPz3EmfY+bpyfzrZleWxblseahdrk0ENnO7B3OXF7ffy2TvPakNqYg61Lcod16uuipbsqmlkxNy2q3hyny8vTb9ZGHFinf342q5ln9moeqH5e/XMN/Uyv5li/4upkLAbnv4DtQAWE/Zu/ZpmOoY1Id8X6z/Sksz4crKPfxZO7q7llUQY+KTGZBEsLUrm1OIvVhemBPE6KzcILH1zgyV3VPLyxCKvFxM6TTZTkJPG70+1U2Ic009r7XHzzxZP8/As3cN+qAtLjNW+prrmH2tZespKsVNu7yU6OY1G29vzF+SmBkueOfjfHL3QBsCAzkQG3h4udQxpoRVmJZKfEcvD0JTaUZfP1LSXcWpLFhjJtpoyUkqMXOkFCa+8AN85P5+n7lpFis1Df2c/h8x2sLtRCZS98cAGBYH9tK++ebuebW0r51p3lYfMoOhLt/CMl7vW82sMbiwLG3dj5H04hwPj5hftMjd6W8mwUM4mxGJw2KeWOSVvJDGYqQxvRGjd9M9KNg7GQwJhH0NWIf/Lu2UCvjTXGxJYludy3qiCw2QI0dvXz+Gvn6HN5kVJiizHz2LZyrGYTrxxrBASzU+JocAwZhWMNDv7f/x5n5bx0bDFmkuMs/P6qAr77Rg0ujw+XV3Kx00mKzcLWpbl84ZPzee7tMwEvqNflwSSgrXeQzn5tHSYBdy7J5e26VuKtZswmEyvnpVOQFk9ymRZqKspNJC/FxpvVLayYm8bfblscVDF38Ew7L33cCMAjW0pxur2sK8nE7fURb41hbXEmBWla7uqFDy5w9EIn+2pacbq9VDY6eLO6meWzU9lX0xrQXjMapMD//TE/W4yZR7aUDlMSCKcQMFL+53J/v6bjzZHi2mFMZdF+aZv9aHkcAKSUL034qmYYUxnaGLNx8294TpeXF96/ECilNSoOOJxuapq7MZsEK+em8YlFGYEcj64D9sTOKhZmJHL4YicWs+CuZXlapdVxe0BhuSQvmZsXzuKpXTWcaOjCaha09rp5s6qFN6taMAFH6zv5wifnk3LAQnufC1uMYMArqbB3U9nUTU5KLNYYExYTIARz0uJp7HIGjI3FJHD7JBaziTuX5PHy0UZAM37G96ckOykQQjP2FSHhmb21fOXWhXzmehMPrluo9bXs1ZtbIcZs4tbiLApWxgfUnvWeFyTsqmjmziV5PLhuYaCazPi5dPS5eHpPDR19Jdx3fUFQAUa0zaKThcr7KK4kY5W2KQEsDIXUJHDNG5ypDG3o0zDtDicvvH9hVH0vvZDA6fIGTZIEAooDAPuqW7lneT6bl+Tw7FuneflYAz6fVgK/bZk2zfLQuXYOX+zk+nlprChIC/T3FOckcrLeQf6FDgZcXt4/047bB5sXZ4MP3j93CYfTgw/YdbIZJHT1uxCAyyMRaL9QUsJLRxrpd/vYUJbDh2cvccavFhBjEmQnx9LYNUBGgpVP31BAXVMvhVmJ2m+hv0xZ37gLZtn47q5abl6YwYGatsBr/+aW0mGNnh19LtaXZlGUlaTN8zGMJNDLlXXlBWBYBZ/+OP3r469V4vLKoJEMkRitWXSiPRKV91FcScZicK6XUhZP2kpmOFMVqjhQq03DBEGMWQzTyArXsxEk0eJvxuwecAc8nOQ4S6AH5Adv1rG3uoXFuSlUNvXgdHkD5/tn/yjmuuZe/uZOTYHA5fWx+2QTXuBih5P9NS3cUpzF2zWtLMhMpKPXhcPpCQxIW7MgnTPtvZgElOalkJ8SBybBnopmrVAg3sKChFj2V7fg8mrumc1i5sb56bx3Skvodzld/Me753irtpU/X7cIe5eTXRXNQY2szx+8wNGLnRyt72RjWTZfXruA85f6uG5uKtX2nsB79dSual4+1ohJCG6YPytwDmPC3qh1BqMrKT+0qQhrzJD3FI1HoT/O6fIGGbTRnj/W3zuV91FcScZicA4KIcqklFWjP/TaY8pCFX7Jlg2l2dwwf9awO9Udx+w8ubsap8vL/TfNjThxcl9VS8DDuXflbGwWsz90pI1j/urtRfzqw3pONHTxTm0rvzlcz9c2F/GtVyvpGfTw03fPsb+2lS98Yh5HL8bR4J8xkxxn4VLPID4JF9v7WTonlc1l2TQ6nJxq6eFSn4tj9Q4yEqycaunhTFsvD28oAimxO5xYTCZ+e+ZS4PXExgh8Ph+/PdMOAtLjLfQPemnuGcDllRyobaOurScw/VRXVnB7fXj979f+mlbS4q3sr2nFajYFelycLi8vH2ukJDuRrcu0cFm0n+FIG71xPk5ymWXY+Idw6J+j0+0dUyXadA+RqZyRwshYDM6NwDEhxDm0HI4ApJRSlUUzdaEKPazjdHuHybIAQ8f8X8NNnPz1R/WcqO/iK7ctDKpmW1+SRX1HP1VN3fzqw3p2nLBjEoL8VBsfnutgfkYCD20s5u93VuPy+fjKrQvZU9FMQ9cAQoAJeP/sJTxeyZw0G0W5ifzLW6cpzUniwqV+ti7Jo8/twdzgoGfQTWluMjnJcZxo1Mqvk2ItNHcPBL2c20qy+MSCTJxuL3sqmjl8sZMFGQl8c2spv/qwnnmZ8dyzIj9gTPXcVJ/bw9vVLZTmpbC+JJs4i5nt/oZVPXG/47gdnw9qWnr5fX8z5/ZN0fXVRLvRR6vYHNR0axkK6Y3mkUzU791kGYbpbhAVU8tYDM7mSVvFVcBUhSr0CrO/21mFy+Pjw7OX+LtPLQlsEnq+RR8tsKEsG7ujnwM1bRTMsvHUrmr+9+MG3F5JYlxMII/x7Fun2VetDQm7Z3k+hZmJ7KpoYsuSXD69uoDv7anjUyvz+fBsB5lJVt6sbOZ8ex81TT3kpsQxPzOe7n4Pbq+P2pZeLnY62VPZQn5qHIcvdHDHklxWzE3ju3tq2LI4lybHACcbOzlR78BkEnilpG/QG/RaU20x/Mkn5lN/ycnakkztoIC6lh7qLzlZMTeNJ3dpvTZAoJpsb3ULG0uzeXTb4kDRw+OvV7FlcU5AwHRfVQtrizN5jDKcLm+gCu2xO4f6X8JtwsZR23ouaDTGYhTG+ns0Ub93k2UYVM5IYWQs4wkuTOZCFNGzujCdnOQ4znf0s6uimRsKZwUpGoNWiaXfUZ9v6+fwxS6efL2a2pZe1pdkYTGZKMxKxOF084v3z/Orj+rJSYnjs6vn8Lk18+gecHO2rTdQiVXT0sPLRxp58eNGXP5qrpONWlK8yTFAS/cAPgkJVnPAyapoHEqaN3U6KbopkaLMRDr6BjlysZPMJCttPa6A4CZAZpKVSz0ufEC30xPI1awrzuLd0+08vKGIe5bn09Hn4kRDF1Jqc3v2VbXw9zur8fh8LMtPYW9NCyvmpAWMg7EHBghIy+gyNXtrWgJhOZ1wm7B+zDjPJpRIebTpyHgM6FiYzq9dMfWMxcNRTBMOne3gUv8g89Ljae5xBkqf9c1w+8bioM2jJC8Z64kmvFICkk8s0ryFv99ZzZHznRw6fwkfYHcM8OKRBtaXZfH8wQvDhCtL85Lo7HdzoK4Vp1szOqlxMXQNeACwmKDP5WVOuo1EawxVzT0IIC3ewonGLrb/6lhQ42Zrj2vYaytIjaPNcPxYQxcur+TcJa1azWY1Y7OY+btdVfh8sG1pLmtLMnnjZDPFOYnUNPVw+5JcPhtvDeRDHt1aFhgKV5qXxMfnu9i+qTiQs9m+qTioyVPHeHc+lo15JoWRojGgRlRORnE5KIMzAwjXja5rbtlizIG8TiSNrvtWFXC2tZedJ5u4Z3k+25blseOYHY/Pxxv+BLrFL3p5vqOfb754krPtfaTHW3nx43reP3OJb91dxuOvVXGsvhOPVwaqznpc3oCmmd/xYVNpNucu9VPV3EOyLYYepxuPhMauAQSQGm8J9NXExpiwCEGvWwunnW13Ato00VuLs3i7tlX73iS4e1kea4s1teeDp9vZWaEZxAM1bYH5MJ9akR9oWK3v7Key0RGQr9ld2YzL42NnRROLc5P5+pYS7ijPCTR56ugjqx9ct3CYVFA0G/NMCiONda0zyZgqph/K4MwAwnWj65pb2zcWBxmjSJIpxvn2v3j/PHXNPdxSpPWo5KbasHc6A9dLiDWTmRhLfWcfLi+c7+jn//7iCC6vJC8ljvREK2uLMrnY3s/e6mYyk2209Q4wPzORuuYeznX0805tm38dHuItZnz+2TdmkwgYm9mpNlp7nCTGxwYMTlluIicau+kd9BJvNfPwxiIO1LSxvCCFnx08z0m7g7uX5/OFW+bT2OXkzapmXD4ftxRlgA+tL8fPgZo2XvUrXAOsL84iJzUOl8fH4YtdfHdXLZVNDnoHPdwwf1ag8ODZt07zy4/qAQLVZsaxAqHVZjMphHa5zCRjqph+KIMzAwj3R65XYzk9waKQ+kZonI2jJ86R8P19dQy6fXilZF56PJYYE59bM48TDV3sOtGMlJLqph46+t0UpNmwdznxSgI9MXbHAHbHANmJsaQmWLm1KIt3TrWydUke7b2DVMseTjX3+MN3Gv1uLxazwOuVgXzN3Fk27r9xHv+wu4bm7kHSEyw4+t30u3x8ZlUB/3nwPEXZSdyzIh97p5NzHf14pJYXqm7qYcXsVOpaeyjPS2FfVStenw+flOytbuVEfRdfv6OEo/WdSCmpae5md4Xmya0vzsIsBGW5SWQkxeJu8LGropm91S2BnJceftO/Qng5IKMwpz7XZioNzUSEt8bqsVzNxlQx+SiDMwMI90euh4i25xkUn/NTAl33Tv9UTT2hrucqvnlHKU3d/ew83kxb3yDLZqeyeXEOmxfnYDWZqGvt4WRjN4lWM7cWZfDLww1kxlto9udVYkzg8cHxRgftvS5ykmPx+QQlecns9E8JDS1tBnD7DZYJTVWg/pKTrn4Xi/OTcXl8nG3rIy3ByrEGBx19Lkz+yoOHf3mMwxc6WZKfjAmIs5hweXxU2Lv41IrZfGplPk/urGZWgpXW7kFO2rt5o7KZxNiYgHrCg+sWsqIgTXtf3F7217ZSnJPE3qoWZqfG09ztDCoYMPbR6Dicbpxub9A4AuP7OtEJ92iMyUSEt5THophKlMG5glzOHapxo9i2PI9V89ID8jVSSjaUZbO+OItO5yBOdwxfuW0hnX0uzrf3UZKbTEe/i5ykOKqaujlQ26YJUlY1kxKnraPX5eXQ+U58PsmcWfEBg+Px52l6nNr3zd2arF5Ll5MUWwwJFhNen4/k2Bi6Bz3D1u0DrGaBzyd5u6aVqqYeSnOTcHt99DjdLMhIoNHhBCHYX91Chd2BSUB+qg2zEBxrcLC8IIWtS/JAwHd31XKs3oHVLPja7SUUZSdRkpvM5iU5QQrXxqmm6fFWSvOSaOx0Uml38KkVs4eNIAhlX1VLYHBdkOq2X/bmSvSvTISxUB6LYipRBucKcjl3qKEbhVG+5sPzl9hT0YJEsqdKS+lvW5rHjhN2XF7JZpeX9Hgr9R19bFueT2efi98cqScpzhKk9OxwuvFKOHqxC4DsRCtSaNVlg3rfqdA00N6sbuFih5YHKstNor3XFTA4FpMg2RbDpT438RaTNgpawJk2TWKmrUe75m2lWfzJJ+bz5OvVeKWkqsmBRAt/vVPXxsKsRCwmwdaleaTHW3n01Qq8Ph+r5qRx++KcoKmgQEDnLNSw67Nlqpq7uXNp3jBjY6xI08U5w1WsbSjLDjR1QrDG2miMdrMRjTFRxkIx01AG5woy0V3iqwvTsVnNLM1PZV9VKxtKssEEeyubcXl9fPGWQj480wFSq0YzCyjJSWZPZTMeH3T5k/mgGZMUm4Xm7kH8FdDkptk4Vu8I/Hx2qo3bSjKpsndTmpfE8x/UYzVBlXHkc4wgPd5KW88gJgFeKbmtNJsDtUOl1e292nXbu1386sN6jjU4sJpNLM1Po6LJQbfTjdcnOd3ay++vnM19qwoAgkQ19QbWFz64EFbG59uvVwbluYwKC6Ebv34jcEd5TkAGJ1J/TiRJmtEY7WZDGRPF1YgyOFeQ8W4qoXfgTreXZ96sDWyQ2zcV83f3DM1/SbTGsOOEnbgYM90DLuZnJpKTZCUl3sqRix3UNneTkWChvW/I4EjgbFsvKbYYHE7NU6nwG5sYAZYYE/VdTl4/0YTbp82QAXCFjOYb9Eia/GE3s4BNZTkAAWOjY7MITtq7SIm3sHx2CuvLslhfms2f/9dRzrT3sWpOWpCEDRAQ1dSVlp1ub5CMj/7eri5MpywnmZ0nh4afRZqiqT/+jvIcHlgzNxCWC2dkjB5TqCTNaKjcycSjeoSmP8rgzED0zW99cRa7K5t5eEMR2zcWa0ntTcVBEygdTjd5aTaQ2gROgNrmXgCae1ycbu2lIC2er20u4l/ePkP9pX56XV7MQpAUF0OHwespyUviVGsvgx6Jx28wOvrdxFvMvHe6fcQ1mwCzCZbOTuVEfVfguF6E4HRLTALermkl1mLm91cV8PzBC7T2DrJqTiq3L8lhbUkmO47ZAzN9dK/mqV3V7DzZxMbSbB7eWIQtJnjjP3S2g6rmbrYuyR02ATVcubNekKEbJ4fTjdPlDeishbtRmAxJGrWBjg3VIzT9UQZnGuJwutlxzI7TrUnVh44w1pPVH569hJTa3TwSvre3li2LcwN6avpmrGunZSRYKcpJ5IOzHdqkTCFwDHg439HPcwfOUtfcg7+YjFgz9A64g9Z12m9sQFMVcPu00Fq/O1gDLRw+IDnOyoDHy97qFmanxNHYPYDHB/EWE/1uHwLwop3zzcpmjtZ3cueSPAozE/n+3jpq7N3sqmjG65McvdjJQxuLePat07x+0k5xVhK7KppZMSctUCBgfL/0r5F6mozlzsZw24tHGgJlz8aCgbEyHuMRzQaqjNIQymuc/iiDMw3RJfY9Xh8xZlNgIzRS2ejgnbpW7lmepykHHLfj8UleO6GVJn/6hgK+/usT2Lv6Kc5JJjsljvgYM0U5SXxwtoNev1CmxQxuL5xuGTI2AE6P1KZuGhgIMTYQUNWJyNz0OBJirVQ1ddM76OFATRten4/m7gFyk+OwOwYY9GjGJtv/fb/by7un2ilItfHAmrl8fKELgJLcZABeO2Fn18kmrGYTO082UZ6bws2LZlHV3IPTEzzZtHvAHVANMOZujJtS6LHQcNtElD1HMh4jGYxoNlB1Vz+EyntNf5TBmYYYwzw2q5nVhelByfB9VS2BMcd6EnzbsjyOXujkteN2Xj3eyIG6Ntr7tKmap9t6SYyN4a1zrfxejImy3GQq7Jqwpu6c6MbESGg+Rscd4TiASWjTOfVG0T63lyZHL5vLs0EI9lU1U5KTTG1LD229g5hNguykWFq6B2jrHQw6V32Xk5++e44v3DKfpfmaUdm8RMsB6eOsT7X0crS+k/Wl2Txx9+Jhk00Pn+8IUg0YSzgsklTQeIhkPEYyGNFsoOquXjGTUAZnmmKzmgMJ8hePNPiHo0kq7Q4eWDOXDSVZ5KXZ2HHMztqSTA6d7eDTNxRw5GInjV1OegZczEmLY15mIvExZhZkJ5IQa6autZdkWwwmAUmxMUgp6R4cHhIbzXPRMQuCPKMYoakS6PpqvQMeXF5Jv8tLhd2BxwcpcTHkp9jIT43lg3OdtDgGkEBhRgKnWnuZn5HA2bY+JJoY6GMvV3D4YhePvVxBbqqNfdUtlOQm89w7ZzhysRMJHLnYyV9vLeVATRsPbygKGOqOPhe/tyI/SDUgWsZyxzxaaCsaozYeJvquXoXoFJOJMjjTDD3vsssvxaLnFJxubWaLfnx3ZTNurxaK2nYxj/21rZRkJ3H+Uj9z0m0sy09l6ZxUvr+3Do/Xxzun2ki0xgQKBwAcAx5SbRbyks00dbuiNjI6WUlW2kMUn3XnRwKxZoHX369zrL6T7gHNsH10oROXV9I76MESY2JhViK1zT30uTz4JCTFxRBrMVGSncRbNa2YTLAgI4GTjV0cqe/CLKCmudvfa6TxVnUraTYLuyubAwKbLx5p4EfvnObRrWUkx1mCigUmmvGGtqZbGEiF6BSTiTI404x9VS3srNAqroxjie+/cS7bluWxYk4aTreXL99ayJ7KFmqbepiXGc/2ucV09rvweCUVTQ72VDVTlJPE+pIsup0u6pp7ae4ZHHa9LqebeRkpLC1I4926NvpHipdBwHOB4PECMQI8ckiJAGDQK4mzCNw+6BvwYhKaeKfuAXX2uVgxN421RZnUNffQ1j3AjfPTeeyuMj4+38WH5y5R09LNrYuyArI0dscAWclx9A16+eIt8zjT0gcmKM9LJi4mJkh6ZqRigYkmUmPoTPMSVIhOMZkogzPNCDQS+iuj9H6SgFCkx8v399WxviSL2qZuEII0WyxIeO6dM3x57QIQcLKhiwN1bVTauxn0aErNoSRYzaTHWynOSeRXHzVGtb6CtLigmTY6egrIhCZdM+CPsw24ta9ZSbHEWsyc7+gH/OMMgKMXO0mJt1CWl8zNCzKIs5r56bvnKMlN5t1T7WxdnMeZ9l6O1TsCIxGaHQPYHc18Nr6Af/3cKgB+8u5Znt5Tw5+vWxRWPVvvrVldmB7V6xwrxmuF6+2ZKUw3j0txdaEMzhQSzZ1vUCOhdaifxNh745OSY/VdmM0mbi3K5Gh9J/MyEpBS8mZVMzVNPSzKTuToxU5K85Lp6HVhdww3En0uL/0uJy8ejs7YAFzsHCAnOTagoWZE934GvMOtW25qHCcbHeQmx9LaM0iyTZuJ45NaOEwCPU4PFzv6cfskZc3d3H9jATuPN2N3aEYqxiS4aWE6Z1t6Kc5LDsrL1DR34/JKXvy4gY5+zfPSPZsNZdlBvTW65M1kMRO9hJnslSlmDqbRH6KYKHSjoY+BHgv6HfoXbpnP4txk6judLM5NJsEaw0sfN3Kgpo0tS3Kpae7B7ZN092s6aBWN3dwwPw2rWYQ9r2TIO4kW3diElk1LIhcbnGzs1vp2hMBiNnHd3FSW5KdQkpuENUYwLz2e1p4BbFbtpFVNPbz6sZ3zHf3kpcbz11tK+fodJVxsd9LgGKRvwBvIyzicbh7aWMSN89O51DsYUH42vt/hPBxdoUDXoJso9JuGmbRxX87vpkIRLcrDmULGcucbmnM4UKsNEyvPT2FtSSYf13dR1dTNnPR4CtLiOGnv4vbFOczLiGd/VQtWsykgxPlmVWugTHki8Yyc7gnC7Y/pNTkGWFaQwju17ZiQxJhNbCrNod/jJbE7hgG3l+4BbZy0D1g1J43HP1VOWW4KLx5poL1vgAUZCTx2V9mw9+jfPrcq6C7daGTCeTjGmUG6TM54uBq8g5nolSlmHsrgTCFjiY8P2wAkSCn58Mwl3D6JT0K/28dLx+xYzOCTgpc/bgAB1U09QZ5Gn2t0JYDRCC1/9i8pKhZmJtA76KG5e5DkuBhcHh9en2TJ7BR+f1UBRy92suNkU+DxOUmxxJgFDqeb22+ez/MHL/DguoXD3pOPL3QFigR0dQYMjpzRSOvqC0Ebqgz5Ok6uhsoulbtRTAXK4ExTQjeAtSWZvH4ilb01Lbi9ErOA2BgTy+emYjObeau2jarmnhHOeHlYY7SJneGaQXU9NCN5ybEgBHbHAC6vj+WzU3mjqgXHgIf+ll5unJ/O0/ctIznOgtPlZX2pmyMXOujq99DWO8iWxbncsGAWRy908tJRLcf0yJZSgIB6wK6KZh67swyAp3ZV8/KxRkxCDCkzGAxKuA112/K8oDzZeFHegUIRHcrgTBEjhV3C/cx4x762OJNn3zpNpd1BTlIcTQ4nS2ancvuSXH709mk+uSiDGLMgMzGWZscAZvOQgsDlYCyBdrrDuwFJcWakV9LrC7Y4LT2DFOUkYQJaewbZtiyBxY5kfD7JmgWzaHEM8srRBs639bOnqomMhDj6Bz2kx1voHnDzRlUzNyyYxUObikBAXqqNx3dU8mZVM+W5KVQ2ObhzSV4gV7Oropmti3NZMTctoIG2tiQzrEExvt8TcVc/Hu/gagjDKRRjRRmcKWKksEu4n+l6alJKXj+eSmWTg/K8FCrtDmanxVPb2sM9MbPZvqmYD89cwmwSZCVZSY23BM2jMaOVHxv1z6IlmkhTz0B4y+ZDC+3NSbcB8MoxO02dA8RazMRbYjh8sRNThdaXk5MUx/mOfgTQ5/Kw2P86nS4vBWnxrFmQwaOvViAlLJudQlVTd5CsT6gEzU/eO8vTb9Tytc3FfPGThcPWNtJn4XC62XHcPmymzkRzNYThFIqxcsUMjhDiPNCDth96pJSrhBDpwK+AecB54D4pZacQQgA/BLYA/cAfSSk/9p/n88Df+E/7d1LKn/uPrwR+BtiAXcBfSiknPnMeJSOFXUIrqHQ5/Ic3FHGioYu91S0szk3mq7cX8d1dtVTYHSzOSwlI2hw43UZ+io1jDd2k2sxB5/ai9dtMRB5nLMTGCAbcksYOJwXp8YH+m4VZCawtyeREo4OFWYmcau2hKCeJlp5BPlmUQWqchXmZ8dS19moq2Bi05TzawLU7l+UFKWiHehg19m5cXh81fr24UEb6LPZVtYSdqTPRqDCc4lrkSpdF3yalXC6lXOX//hFgv5RyEbDf/z3AHcAi/78vAc8B+A3Ut4AbgNXAt4QQaf7nPAd80fC8zZP/ciIzUqmsXkF16GwHoG16z+ytJT3Byg2Fs/D5oKKpm18dqud4Qydur49jDZ384M06SvOS2FiSzeYl2SyfnRLW4yjMsE3Ka4ozlFrHxmj/t5o0yZsBt0QIMJkF8bFmkuO0e5v8FJs2SG1ZLnPSbcSYTMRbzTy2rYzbirLYW9PC+bZ+Ht5QBDCktHDTXGwxZr6/rw6ny8u+qpaI5cwPbSris9cXaOG4EKIZ7fzNLaV8847SSTUGM7F0WqG4XKZbSO1u4Fb//38OvAP8P//xX/g9lA+EEKlCiFz/Y/dKKTsAhBB7gc1CiHeAZCnlB/7jvwDuAXZP1QsZDePGZ/Rw6jv7OXimna/cqlVldQ+42XYxl5IcTZp/l7mJ4pwkKhocvHbCzoVLfVQ29SClJCspdlglGcBJe2/g/yahhdfC6HVGTawJBn3BDZ45yXFc6HAigUF/7C4z0UpeqjaW2uxXkV4531+iXNGMz+cjOc7CO7Wt3FqcxYaybCrtDnZVNGONMbG7sjl4NIPfvtU0d7O/phUIH44qSIvnO/cuDbv2aEY7X06J9HiJZAhVrkdxNXElDY4E3hRCSODfpJQ/BrKllHp9bDOg32LmA/WG5zb4j410vCHM8WEIIb6E5jUxZ86cy3k9Y8K48QGBHpHD5zt46eNGPnO95nw++9Zp9lW3ahpqLi9bluRy57JcHn25goauAQ5f7CI51kxuajy1Lb2RLhcgwWKiJ6TUTKAZopFadTISrXT0uvABSfEWBnuDvYsLHU4AkuIsgSmhl3pd/MHKAjr6XMybFc8ni7K4b1UBoOmi/e/heo41OJiXHs/qwnRSbBYe2VLKqnnprC5MD4x31llbnEllo6aWvWZBxrg8kGhCWVdik49kCFWuR3E1cSUNzieklI1CiCxgrxCixvhDKaX0G6NJxW/ofgywatWqKcvxGDe+7gE360uysHc5yUuLC8jp7zhu5+WjjSzOS8Hp8vK9vbV4fJL3T7djN0jLdA968XVqG35+ahwtjoGI6gGhxgYgPoocT3uvK9Di0m4wNiaGFKJXzUljeUEKP/3deSwC0pNjqWnp4WKHk2bHAHcvHwoh3X/jXDr7XRxvcNDU7eTZt04Hyp4BkuOGV34dOtsRUMvWCwbGSjQVZVdik49kCFWuR3E1ccUMjpSy0f+1VQjxMloOpkUIkSulbPKHzFr9D28ECgxPn+0/1shQCE4//o7/+Owwj582GDe+fVUt7K5oDkz4fHhjEd/dVcP5jj48Ph+VTQ7uuS6fLYtz2XGiiaYwOma9foPR2DVcM200oi0o0G2YrgwthFaQ0DPoJScllu9/djkHatswmQQun6TZMUiKrZ85aTYWZSdSmpcUmMa5tjiTuqYezCYozUlmZ0VToArv9ZN2Dp/vGGZUNpRlc/h8Bzsrmlg1Lz1shdlEeCbj3eQv5/qRDKFqyFRcTVwRgyOESABMUsoe//83AY8DrwGfB57yf33V/5TXgD8TQvwSrUDA4TdKe4AnDYUCm4BvSCk7hBDdQogbgQ+BzwH/PFWvb6zo826cLm3C529PtfFGpaZptXx2CgsyE1lbnKmNJ5ibxts1Leyvabti6022aWGzlDgLXf7EfVv3IH/7WiXxMWY+sWAWB061IwCLyURtZy+NXU4c/W4qmhz4fIKyw0lUNnVjEia2LsvDFmPmaH0ne6taKM9NYVdF8zCjYgy5hRsDMFGeyXg3eRX+UihG5kp5ONnAy1q1MzHAf0sp3xBCfAT8WgjxBeACcJ//8bvQSqJPo5VF/zGA37A8AXzkf9zjegEB8CBDZdG7uYIFAyMlhPXmTmOZ74dnLwGQnxZHQXo8b1Y109jl5On7lrG2OJPfnmojLyUurAL0VKDnaLqdbjISrbT3uvBK2F+tOaRluUmYTYKy3GQe3VbKd3fVcryhi4qmbhZmJVHZ2M2xBgcxJrhrWR73rSpgX1ULe6tb2LoklwfXLeRATRtO99A8IJ2RxgCMxTOZjDyNCn8pFCNzRQyOlPIssCzM8UvA+jDHJfCVCOf6D+A/whw/DCy+7MVOACMlhP9+VxVenwQJ9980l/rOftweH8sLUlhfmsVP3jtHVlIcH57r4Gu/Pk5+qi3g/cxKsHCpL7LSsd70qZMcZw5M3YxESXYidS29RNMjWp6fzKmWXlJslkCJsgDOtvfi9Ulqmrqpv+Tk+59dznffqAGfJuJZ0diNCbh7eT6PbSsnxWahNC+JspxkHlgzl4K0eGxWM0/srAquUgshdIM3jnaINN0zMFfIP28IJs4bUeEvhWJkpltZ9FVJpDvf1YXplOemcLKxi6P1nawtyeRrvz7OB+c6sJggJ9nGnPR40uOtxFpMHD7fwdyV+STHmfF4ZVhjY5SjCTUtWUlxASVmHasZjCmcaIzNkvxk5s1KoCg7iSr7KQY82jpSbTE4nJ7A0LXNi3MC0jP7qlpxe73kJMWxviSLGwtncd/1BQGD8PzBC3x4roPnD17gO/cujcpbiLTBR6PqsH1TMY9uLVPeiEIxhSiDMwWEbozGu+xTbb2smJPGvupWrGYTFY1dZCRYKcyIZ09lc8B45KfF4ZGw62QzPSM00YxUZlff0TfsWGi9QKixSbSa6HP5kGil00vzU6hp6aHK3sOcWfFsWZrD6ZZe6lp6GHD7KM1Noqqph9LcJL5+R0lAeubgmXZe+riRBscAs9Pj+eItwZIz+jA1/avxPdPfL33MwGhhsJGMlX5MP5dCoZg6lMG5AuhzWB7eWMRjd5axujCdA7VtOF1eSnJTOHKhE9CMh0UAQhBn0vpyRjI2ozGWp+qeUq/LhwDWF2eSmmDl06sL+PqvT3C+o5/Xjtpp6x1kUXYiAthYnk1RVhLVTT3UNffwyseN5KXa2FCWzWPbypmXkcD+qhZuXpQRlJtxON0cOju8Kk1H90ruKM9hd6VWFj1S6Gqk0Jb+M2P+Z0NZdthcmkKhmFiUwbkShMxhSY6zYLOYeebNWr5y60IWZSXyiUWz+PZrVfQOuijJSeHj+q4pX6LFDGk2K629Lg6evYTX6+NMWy9Njn7mzYqnqcuJxyepsmtioUVZSZy/pHlRWhFBS0BD7d6Vs/nzdYvIS7HxxM4q8lJsgXzLU7uqI5ZCQ7BXEtoMOl6MXpAulAqMmDNSKBSXhzI4VwB9DovT5eXx16s4fL6DB9ct5NGtZYFQT2efhwGPj2Wz0znR6Lgi63R7obXXBYDTL1dTae8GBAsyEkiNtzArwcq7de24fbC7oolz7f0szk+htrmHVfPTiLOYKc1LCpwzNNyljxaIVAoNwR6LPq3zcjGeUxcHRagKM4ViMlEG5wqgb3b1nf28fsLOzpNDjYwvfHCBv3u9koVZiWQnx/H+udHzDOGmcUZLrFkLta0sSKayuYcBt6Qg1calvkH6DfMMYoTm9awvySLBGsNrJ+y4vZIl+SnkpsRxsdNJTXMPMSbBXcvySF9j5eDp9qBCgHCE5lRGml0zWaEuXRxUoVBMLsrgTCL6bBW9oTM0P3DobAdVzd1sXZIbaGQ8eqGTQY+kwh5+eqfVP1zNaF+8Usv1eOTIRQO6QoCROGsM3gEPJ+3duLyaAYqPNVPf5cNqFrj8liw7JY7Ofjfl+Smcb+/jloUZvFPXRrXdgSXGzKo5qawtySTNFhuYI7O6MB1rjIkH1szlxSMNrC5M59m3TrOzQpPL09WSw3kv0ZQvK2FLhWJmoQzOJKLPVtEla0LzA6sL09m6ODdQmfXEjkpePd4YZDRMAnyGA94IiX99IKd/SgDCJHCHuD0eqWmt5afGceh8FwAOp8d/Yu3LoBcc/VoYTTc2OcmxLMhKJN4Sw5uVzZxs7Gbbslwe2lDEnqpmZqfZ+OutZRSkBYe7dNXmF96/wJO7q7mjPId9Na1sXTxkYHWDob9foaoBI5Uvq85+hWJmoQzOJBIqWRO6aepzcFbNS6fK7uDFj4eMjS6KaTQ2MSZBkjWGzoHwzZ7aCACIjx1SbAatQbSjz41E01pzjibU2ecO6ucxmwTv1rVjEiClZgTxQV1LDxWN3dQ197KxtCNifsXp8eLxSuZlJvDYwiHj8dSu6oAYJxDIZz2ypTQo1xPJe1Gd/QrFzEIZnAkkNMQz2myVDWXZuL0+jtZ38sIHFwFtc1+cl8zxhuBCgQWZCXT0uejsj6wsMCvRitcruRTymGSbhezkOKqaehBAz4A7EC6zmAWz02zUd/QTb9WUCNITLEigrUfzdLw+icUkKM5J4nRrL4vzUjhwqo3CzARiBKwryaKjz8UL718IO5bZFmMmxixIs1mDZGl2VjQFvB1gmDDnaF6L6uxXKGYWyuBMIGMN8Vy81MdP3jvLmTatlDjVZuHRbSW8U92OT0qq7N2BYoCzbX0j5mcAWv0GIpTz7f1Bz02INdPl9AaMzvlL/eSlxAWUptt6XUgZmHdGc/cgN85P5+n7lnHobMdQLuZkE3+wqoDy/JSIY5kdTjdOj5c7ynNYW5IZOB7OgzEKcyoUiqsPZXAmkNFCPLoHdGtJJr84eIF/efs0Xp/ELAQ3L5zFR+c7+J8P6jl8sYv8lDjmpMfT0NGPW0JcjAmnJxqFsyH0sJiJIZkbCXQ5vZiE5iHoxsU41qAoKxGzSeDx+jjd2kdeahwr56bxRkUzthgzyXGWYcoARy90Mi8zAafLS31nf6DibF9VC9/fWwfAmoUZgbBbOO9kojwWVUygUExPlMGZQEbaMOs7+/nar49zoqGTtIQ4Gru0gWkZiVb+7p7F7DzRhNcHZXnJ1Lb00BiiBD3g8WExBxcCJMfF0D3gwWqCMHPVQvtLh9Hu77HRPRmTgKLsJOpaekAIpE9zc1q7B/nXA2cwm0zEmAU2qxkYmlLqdHnZWdHE4txk6tp6qbQ7AooAeh4LOXW5FlVMoFBMT5TBmSJ+tP8UH5zrQAD9fmOzpnAWFXYH//rOGU40ODCbBFX2bnoGvVhMYGiDQUKQsbGYCCT/jcZmYVY859r6scSIgIim/uNYv2H65KIMUmwWXj/RFDh3WW4Sn1oxmxP1XVQ39yD8ZkpIyE6Jpb3XxcbybG6YNytgOPSCiE6nC7dHctLuYEVBGg+smRsIjY2Wx5oMVDGBQjE9UQZnCmjo7KeqWeurkUB2cizP/MEyls5O5ald1fz6cD0+YGl+Ml9Zv5Bvv1qFV/qo74w870YzRsN9l4YOJz5JwNgYKciI59ZFWRxrcPBn6xcyZ1Y8//nbc/S7fZxq7eVsWy9fXrcAhG7cJPtrWmnvdfGpFfnDZGdsFm2EwPriLKwxJspzk6lq7qba3nNFPQtVTKBQTE+UwZlEpJT88qN6nni9in6/N3L3sjye+NTigIbYp1bmU93cTUWjg1kJsXz71aqA/lg4Qj0fHV1tYMDQ2WksbQY43drPpd5GOvvdfON/T/LHn5gf8H4yE2N56WgD5Xkp3FqcpfXAbCzmEwszg0QtjfkRo0rAmoUZEdUCxkq0ORiVq1EoZhbK4EwSbT0DfO1/T/BOrTYKWgAP3DiXx+/RZsJ9Z2c1v/yonnPtfZxt62NuegIH6lrx+LS5Ml3+hkwTkOrvowH4/Jq5tDhc7K1pYcBgecJJ2+iHls9OobV7AHv3ILMSrJiF4GJHH06Xl42l2ew62URrzwAg6HQOgohl+8bisCXOofmRUJWAidA6izYHo3I1CsXMQhmcSeD143b+5tUKuvz9MJ9clMG6kix+7zptU6xqclDd3M3t5dn8xYZFPH/wAq+ftHNHeS6Y4ExrL11OLQQ3PyOe/HQbRy920TPg5eCZDp65bxln2nupatIeExsjGAzVrPGzel4aBWnx/OXGRfzbO2dZOS+NH797FtBKmG8onMW+6laKsxOpaenhfFs/P609z6Nby8J6DZOVHwnnOY12DZWrUShmFkKb3qwAWLVqlTx8+PC4n+9wuvjrlysCyfjEWDN/vbWMz1xfgBAi8LjP/Nv7fHCug1Vz0rhnRT6dThd1TT0snZPKgMvL9/fWRZy6aRJwXUEqhy92kZUUi8froyQniYMRholl+MdQ/96KfFbMTeO3p9vYX9XK5sU5fP2OEg7UtoGE6+al8vzBCzywZi7V9p4pD1Pp82ke3VqmvBWFYoYhhDgipVw12uOUhzNBvFPbytf/9wStPYMAXD8vjX/8g2XMmZUw7LGP3VXG469VcfPCDJ7cXY3Hn6DfV9NKZqIVHxAbA4Oe4dcpzEwgJd6K2SRo6xkkxixIjrdyy6IMPjrfERgjAJAeb6Gr302qzUJ1cw87K5pweyQmE9wwfxaHznbwzJu1PLq1jGp7T6DM+UqIZCpvRaG4+lEG5zJxurz8wxs1/OzgeQBiY0w8vKGIL91SiMkkwj4nPzWeO5fl4XR5+fKtC6hs7NZEyoRgb1UzAs3YJMdpUjNGTrf2caa1jziLCadP4vZK3qpqJifVhtPtI95qxu3xkp8az3VzUnnpmJ1Op5tOp5uCVButvQPcuSSPbcvz6B5wc0d5DqV5SXx8vouv3LYQp8sbNI0TpiZXoirLFIqrH2VwLpOqpm5+/v55AMpyk/nep5dRkpM84nP2VbXw9zur8fp8bFuax4G6NlweLxvLcjAJgc8f5lycn8yJegf9Lh8+tMKD7ORYmrsHgzyZZJuVhg6tt8fp8rJyTip1rb3sqRwSxjSbBPetnk1eSjyrC9M16X+31rB5qlVr1tyyJJf9Na3D5GmU96FQKCYCZXAuk5Vz03jw1gUg4aENi7DEmIc9JjQktaEsm4Nn2tl1somSvGQuXOrn8MVOQHLX8jzSbBaONTjo7PPQ6/KRYDGxal4a75/tpDg7kebuwcC556TZ6OgbpCw/ma5+F+29Lm5fkktufRevn2hCAJ8smsWmstxAabOeL9m+sZiti3N55Zgdr09SkpPMmgUZwwyL8j4UCsVEoAzOBPC120tG/LkxJKXriz20sYgVBWkg4PFPlfP8wQsUZibyz2+foiwnmbrWHtYsmEVNcw99bh9n2/qRSGzWmEB/jRDwwJp5nG3t5eVjjXi9msG6b1UBNfZuJLBqThr//NmVgRCZw+nG6fKyfVMx25ZpobXy/BSQBJVBqx4XhUIx0SiDMwUYQ1K68Xl0axk2qznw/+/cuxSH001NU3dAtv+hTUWsnJvOzuONVNp78Pokbo+P6+elcLKxh+sKUti8OIfkOAsuj4+dFU3Mm5XAU7uq+fQNBVhjTDy4buGwfMwze2uDyp7DSc/sOGbnyd3VOF1eNX5ZoVBMCMrgTAHGkFS4fMjqwnRePNLAhrJsVsxNY3dlMyvmav0zX/xkIQAn7TVIYL+/kRTgg3OdPPvWaR5ct5AVc9NYMSeNo/WdvPRxIwDfuXfpsLVEnY8RIV8VCoXiMlEGZwKJJgylGx/jY3/9UT1P76nB7lhEms3KwxuLQGrnA6ixdxMjwGeQtZmdGsfi2SnsqmjG5fWxu6KZb24p5QufnE9jp5MH1oT3SqLNx2xblofNMnxKqUKhUIwXZXAmkGjKh3VD09Hv4vt763C6vZxo6MLllfzmo4t0DXjYWJIdkPe3Wc3srWlhdlo8Z9q1QW0mAZ9fM5/7ri9gx3E7H565pFW2Sai291DT0kO1vYey3JRxvxZVKKBQKCYaZXAmkGjCVXpuZH1xlnZAwtLZqew80YTdMcAd5bm4fD6kf+Smfq7SvCR++t45+gY9HKhrx2Y1k2KzYLOYefd0O1sX54LQwnOPbi1TnolCoZh2KIMzgYzkFeiejdOjNXIunZPKrSVZrC5M55WjDeSn2mjrG8RqMbGvupUtS3ID6pt6CG5FQRpOt5dPLMpkbXEmLx5pYHVhOo/dWYbT7eWZN2uxWczKM1EoFNMSZXAmmUAIrc/F9/fV8fCGIp64e3HAA3lqVzUvftyIy+vjxvnpPLSxiDULMnC6vDyztzbQhLmvqoUnd1cD8MTdizl0tiNIe8zhdKuci0KhmNYogzPJ6Hmd9SVaCM3Yxf/ikQZ2VjRxe3k2VrOJhzYVUZAWT8HKeM2AWM1BYTqnyxsUZtOPg8q5KBSK6Y9SizZwuWrRRnTPZnVhOgdq23C6vNisZtYWZwYNKVPNlQqFYqYTrVq0aSoWcy2iezaHznZgs5j50TunsVnMgVDYvqqWIK/kxSMNOJxuHE534P8KhUJxNaFCapNEuIq1SP83llMDI5ZWK8kZhUIxU1EGZ5IIzalE+j//f3t3HitXWcZx/PuzpYUgYguEXGUriyKylgYhIKLBymIwQg2LkUUjCSrBGIgsJmKUP8BIsGAA0QoiIqIsxgQqtGgJ0rJIe9tSLhQpEcQWXCCIgUIf/3ifoae3M3fp5Z6Ze+f3SSZz5j3b+8x7ep477zl9D4Mnpyo/VtnMxionnDbo/ytloOTUnx8VYGZjla/hjILBrsM0fqXc+/jqYW+7kZzcnWZmY41/4YyCwbq9/CvFzLqRE84oGCyh+P/MmFk3cpfaKBhut5dvhTazbuCEU7NmyWUk13TMzMYKd6nVrNn1HV/TMbNu4IRTs2bJxdd0zKwbOOHUzMnFzLqVr+GYmVktnHDMzKwWTjhmZlYLJxwzM6uFE46ZmdXCCcfMzGrhhGNmZrVwwjEzs1o44ZiZWS2ccMzMrBZOOGZmVgsnHDMzq4Uiot116BiSXgSerRRtC7zUpuq0m2PvTo69O4009p0jYrvBFnLCGYCkRyJiRrvr0Q6O3bF3G8c++rG7S83MzGrhhGNmZrVwwhnYj9tdgTZy7N3JsXenWmL3NRwzM6uFf+GYmVktnHCakHSUpD5JKyWd3+76jISkVZKWSlos6ZEsmyrpHklP5fuULJek2Rl3r6Tple2clss/Jem0SvmBuf2Vua7qj/LtusyRtEbSskrZqMfaah91ahH7xZKez7ZfLOmYyrwLMo4+SZ+qlDc99iVNk7Qoy2+RNCnLJ+fnlTl/l5pCfpukHSXdJ+lxScslnZPl477tB4i9M9s+IvyqvIAJwNPArsAkYAmwV7vrNYJ4VgHb9iu7DDg/p88HLs3pY4C7AAEHA4uyfCrw13yfktNTct5Duaxy3aPbGOvhwHRgWZ2xttpHB8R+MXBuk2X3yuN6MjAtj/cJAx37wK+Bk3L6GuCsnP4KcE1OnwTc0obYe4DpOb0V8GTGOO7bfoDYO7Lt23Ji6OQXcAgwt/L5AuCCdtdrBPGsYuOE0wf05HQP0JfT1wIn918OOBm4tlJ+bZb1AE9UyjdYrk3x7sKGJ91Rj7XVPjog9lYnnQ2OaWBuHvdNj/08yb4ETMzyt5drrJvTE3M5tfkYuBP4ZDe1fZPYO7Lt3aW2sfcDf6t8fi7LxqoA/iDpUUlnZtn2EfFCTv8D2D6nW8U+UPlzTco7SR2xttpHJ/hadhvNqXT3DDf2bYD/RMSb/co32FbOfzmXb4vs1jkAWESXtX2/2KED294JZ/w7LCKmA0cDX5V0eHVmlD9PuuJWxTpi7bDv82pgN2B/4AXgB22tzSiT9G7gt8DXI+KV6rzx3vZNYu/ItnfC2djzwI6Vzztk2ZgUEc/n+xrgduAgYLWkHoB8X5OLt4p9oPIdmpR3kjpibbWPtoqI1RHxVkSsA66jtD0MP/Z/Au+VNLFf+Qbbyvlb5/K1krQZ5YR7U0TclsVd0fbNYu/UtnfC2djDwB55Z8YkysWw37W5TptE0paStmpMAzOBZZR4GnfgnEbp9yXLT827eA4GXs7ugrnATElT8qf5TEo/7gvAK5IOzrt2Tq1sq1PUEWurfbRV40SYPktpeyj1PSnvMpoG7EG5KN702M+/3O8DZuX6/b/HRuyzgPm5fG2yPX4KrIiIyyuzxn3bt4q9Y9u+nRe4OvVFuYvlScpdGxe1uz4jiGNXyt0mS4DljVgo/azzgKeAe4GpWS7gRxn3UmBGZVtfBFbm64xK+Yw8mJ8GrqKNF4yBmyndB2spfc1fqiPWVvvogNhvzNh68+TQU1n+ooyjj8qdha2O/TyWHsrv5FZgcpZvnp9X5vxd2xD7YZSurF5gcb6O6Ya2HyD2jmx7jzRgZma1cJeamZnVwgnHzMxq4YRjZma1cMIxM7NaOOGYmVktnHDMzKwWTjhmgKQ/SpoxwPxX66xPZb+rJG07jOVPl3RVi3ktY5D0Pkm/2ZQ6mg3VxMEXMbPxLiL+zvr/TW42KvwLx8YsSbtIekLS9ZKelHSTpCMlPaDyQKyDcnifOZIekvSYpM/kultI+pWkFZJuB7YYwv4ukbRE0kJJ21fqMD9H5Z0naacsv17SrMq6r+Z7j6QFKg/FWibpo1k+U9KDkv4i6dYcjLHh7CxfKmnPXH6qpDtyvwsl7dukvtNym0slfW8I3+WynD5d0m2S7s7v8bLKckdlXZZImjdQXVQeAnaDpPslPSvpeEmXZX3uVhkDrPFwsz+pjGg+t9+wLDaOOOHYWLc7ZSTcPfN1CmW4j3OBCynDeMyPiIOAjwPfVxlX7izgtYj4EPBt4MBB9rMlsDAi9gMWAF/O8iuBGyJiX+AmYPYg2zmFMj7X/sB+wOLsMvsWcGSUkb0fAb5RWeelLL864wL4DvBY7vdC4OdN9vVD4OqI2Icy7M1w7A+cCOwDnKjyZMntKANBnpDfw+eGUJfdgE8AxwG/AO7L+vwPODaTzpXArIg4EJgDXDLMutoY4S41G+ueiYilAJKWA/MiIiQtpTyQbAfgOEmNE/XmwE6UJ2TOBoiIXkm9g+znDeD3Of0o5SFXUB5IdXxO30h5AuRAHgbm5In2johYLOljlCcxPlDGYmQS8GBlncbox49W9nUYcELWf76kbSS9p9++Dm0sk3W7dJC6Vc2LiJcBJD0O7Ex5CuaCiHgm9/uvIdTlrohYm+0xAbg7yxvt80Fgb+CejH0Cw0+ONkY44dhY93plel3l8zrK8f0W5S/yvupKeXIbjrWxfuDBtxj8386bZA+CpHdRkggRsUDlmUTHAtdLuhz4N3BPRJzcYluNmIay3/42dbDE6ve6KfvdYDsRsU5S9TtstI+A5RFxyCZu38YQd6nZeDeXcg1EAJIOyPIFlO4tJO0NbHQNZIj+TBnKHeDzwP05vYr13XTHAY3rFTsDqyPiOuAnwHRgIXCopN1zmS0lfWCQ/d6f+0PSEZRut1f6LfNAv7qN1ELgcJVh7ZE0dRh1aaUP2E7SIbn+ZpI+/A7U1TqQf+HYePdd4AqgN39pPAN8mnI95GeSVgArKN1Vm+Ls3M55wIvAGVl+HXCnpCWUbqT/ZvkRwHmS1gKvAqdGxIuSTgduljQ5l/sWZaj4Vi6mdM31Aq+x/rkkVecAv5T0Td6B57RkPc8Ebsvvcg2la3EodWm1zTfy5orZkramnJOuoDxOw8YZP57AzMxq4S41MzOrhbvUzCokLQIm9yv+QuNOuLFO0j6UO9aqXo+Ij7SjPtZd3KVmZma1cJeamZnVwgnHzMxq4YRjZma1cMIxM7NaOOGYmVkt/g/oeJkKl5FEqgAAAABJRU5ErkJggg==\n", - "text/plain": [ - "
      " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(figsize=(6, 6))\n", - "sns.regplot(x=col, y=col_lag, data=data_lag, scatter_kws={'s':1})\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 70, - "metadata": {}, - "outputs": [], - "source": [ - "# standardize the vector (i.e., calculate z-scores)\n", - "y_std = (y - y.mean()) / y.std()" - ] - }, - { - "cell_type": "code", - "execution_count": 71, - "metadata": {}, - "outputs": [], - "source": [ - "# compute the spatial lag of the standardized vector and save it as a series with same index\n", - "y_std_lag = pd.Series(lag_spatial(w_queen, y_std), index=y_std.index, name=col_lag)" - ] - }, - { - "cell_type": "code", - "execution_count": 72, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
      " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# standardized moran's plot, ignoring outliers beyond 3 std devs\n", - "fig, ax = plt.subplots(figsize=(6, 6))\n", - "ax.set_xlim(-3, 3)\n", - "ax.set_ylim(-3, 3)\n", - "plt.axvline(0, c='k', alpha=0.5)\n", - "plt.axhline(0, c='k', alpha=0.5)\n", - "sns.regplot(ax=ax, x=y_std, y=y_std_lag, scatter_kws={'s':1})\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Notice the 95% confidence interval shading and the positive slope. Given the value of Moran's I that we calculated earlier (and its p-value), we can conclude that the slope of the line is statistically-significantly different from zero. This plot makes it visually apparent." - ] - }, - { - "cell_type": "code", - "execution_count": 73, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "m=0.6191, b=0.0201, r^2=0.6310, p=0.0000\n" - ] - } - ], - "source": [ - "# estimate a simple linear regression model (of the line above)\n", - "m, b, r, p, se = stats.linregress(x=y_std, y=y_std_lag)\n", - "print('m={:.4f}, b={:.4f}, r^2={:.4f}, p={:.4f}'.format(m, b, r ** 2, p))" - ] - }, - { - "cell_type": "code", - "execution_count": 74, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.6191256645132304" - ] - }, - "execution_count": 74, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# the slope is the same as moran's I, calculated earlier\n", - "mi.I" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 6.3. LISAs\n", - "\n", - "Local Indicators of Spatial Autocorrelation: are there specific areas with high concentrations of (dis)similar values?\n", - "\n", - "Moran's I tells us about spatial clustering across the data set as a whole. However, it does not tell us where these clusters occur. For that, we need a local measure. Essentially, we will classify the data set's observations into four groups based on the four quadrants of the Moran plot:\n", - "\n", - " 1. **HH**: high value near other high values (*hot spots*)\n", - " 1. **LL**: low value near other low values (*cold spots*)\n", - " 1. **HL**: high value near low values (*spatial outliers*)\n", - " 1. **LH**: low value near high values (*spatial outliers*)" - ] - }, - { - "cell_type": "code", - "execution_count": 75, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
      " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# standardized moran's plot again, from subsection above\n", - "fig, ax = plt.subplots(figsize=(6, 6))\n", - "ax.set_xlim(-3, 3)\n", - "ax.set_ylim(-3, 3)\n", - "ax.axvline(0, c='k', ls=':', alpha=0.5)\n", - "ax.axhline(0, c='k', ls=':', alpha=0.5)\n", - "ax.text(1.25, 1.25, 'HH', fontsize=20)\n", - "ax.text(1.25, -1.75, 'HL', fontsize=20)\n", - "ax.text(-1.75, 1.25, 'LH', fontsize=20)\n", - "ax.text(-1.75, -1.75, 'LL', fontsize=20)\n", - "ax.set_title('Moran Plot')\n", - "sns.regplot(ax=ax, x=y_std, y=y_std_lag, scatter_kws={'s':1})\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 76, - "metadata": {}, - "outputs": [], - "source": [ - "# calculate LISA values using the queen spatial weights\n", - "from esda.moran import Moran_Local\n", - "lisa = Moran_Local(data_lag[col], w_queen)" - ] - }, - { - "cell_type": "code", - "execution_count": 77, - "metadata": {}, - "outputs": [], - "source": [ - "# set the statistical-significance threshold (alpha)\n", - "alpha = 0.05" - ] - }, - { - "cell_type": "code", - "execution_count": 78, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "False 3183\n", - "True 2019\n", - "Name: significant, dtype: int64" - ] - }, - "execution_count": 78, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# identify whether each observation is significant or not\n", - "# p-value interpretation same as earlier with moran's I\n", - "data_lag['significant'] = lisa.p_sim < alpha\n", - "data_lag['significant'].value_counts()" - ] - }, - { - "cell_type": "code", - "execution_count": 79, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "LL 2732\n", - "HH 1550\n", - "LH 512\n", - "HL 408\n", - "Name: quadrant, dtype: int64" - ] - }, - "execution_count": 79, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# identify the quadrant each observation belongs to\n", - "data_lag['quadrant'] = lisa.q\n", - "data_lag['quadrant'] = data_lag['quadrant'].replace({1:'HH', 2:'LH', 3:'LL', 4:'HL'})\n", - "data_lag['quadrant'].sort_values().value_counts()" - ] - }, - { - "cell_type": "code", - "execution_count": 80, - "metadata": {}, - "outputs": [], - "source": [ - "data_lag_subset = data_lag[data_lag.index.isin(df_merged.index.values)]" - ] - }, - { - "cell_type": "code", - "execution_count": 81, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(5202,)" - ] - }, - "execution_count": 81, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "lisa.q.shape" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### Now map the tracts, colored according to their LISA quadrants, to identify clusters:" - ] - }, - { - "cell_type": "code", - "execution_count": 82, - "metadata": {}, - "outputs": [], - "source": [ - "# merge the original tracts and LISA quadrants data together\n", - "tracts_lisa = gpd.GeoDataFrame(pd.merge(tracts, data_lag, how='left', left_index=True, right_index=True))" - ] - }, - { - "cell_type": "code", - "execution_count": 83, - "metadata": {}, - "outputs": [], - "source": [ - "tracts_subset_lisa = tracts_lisa[tracts_lisa.index.isin(df_merged.index.values)]" - ] - }, - { - "cell_type": "code", - "execution_count": 84, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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      " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# create figure and axis then draw the basemap of tracts\n", - "fig, ax = plt.subplots(figsize=(9, 9))\n", - "tracts_lisa.plot(ax=ax, facecolor='#999999', edgecolor='k', linewidth=0.1)\n", - "\n", - "# plot each quandrant's tracts (if significant LISA statistic) in a different color\n", - "quadrant_colors = {'HH':'r', 'LL':'b', 'LH':'skyblue', 'HL':'pink'}\n", - "for q, c in quadrant_colors.items():\n", - " mask = tracts_lisa['significant'] & (tracts_lisa['quadrant']==q)\n", - " rows = tracts_lisa.loc[mask]\n", - " rows.plot(ax=ax, color=c, edgecolor='k', linewidth=0.1)\n", - "\n", - "ax.axis('off')\n", - "fig.savefig('clusters.png', bbox_inches='tight', dpi=600)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Gray tracts have statistically-insignificant LISA values. In red we see clusters of tracts with high values surrounded by other high values. In blue we see clusters of tracts with low values surrounded by other low values. In pink, we see the first type of spatial outliers: tracts with high values but surrounded by low values. Finally, in light blue we see the other type of spatial outlier: tracts with low values surrounded by other tracts with high values." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - }, - { - "cell_type": "code", - "execution_count": 85, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/sarahluw/.pyenv/versions/3.6.2/envs/my-virtual-env-3.6.2/lib/python3.6/site-packages/geopandas/plotting.py:681: UserWarning: The GeoDataFrame you are attempting to plot is empty. Nothing has been displayed.\n", - " UserWarning,\n", - "/Users/sarahluw/.pyenv/versions/3.6.2/envs/my-virtual-env-3.6.2/lib/python3.6/site-packages/geopandas/plotting.py:681: UserWarning: The GeoDataFrame you are attempting to plot is empty. Nothing has been displayed.\n", - " UserWarning,\n" - ] - }, - { - "data": { - "image/png": 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      " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# create figure and axis then draw the basemap of tracts\n", - "fig, ax = plt.subplots(figsize=(9, 9))\n", - "tracts_subset_lisa.plot(ax=ax, facecolor='#999999', edgecolor='k', linewidth=0.1)\n", - "\n", - "# plot each quandrant's tracts (if significant LISA statistic) in a different color\n", - "quadrant_colors = {'HH':'r', 'LL':'b', 'LH':'skyblue', 'HL':'pink'}\n", - "for q, c in quadrant_colors.items():\n", - " mask = tracts_subset_lisa['significant'] & (tracts_lisa['quadrant']==q)\n", - " rows = tracts_subset_lisa.loc[mask]\n", - " rows.plot(ax=ax, color=c, edgecolor='k', linewidth=0.1)\n", - "\n", - "ax.axis('off')\n", - "fig.savefig('clusters_subset.png', bbox_inches='tight', dpi=600)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.2" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/data/data-pipeline/data_pipeline/ipython/hud_eda_reference_12_27_2021.ipynb b/data/data-pipeline/data_pipeline/ipython/hud_eda_reference_12_27_2021.ipynb deleted file mode 100644 index 82da3edb..00000000 --- a/data/data-pipeline/data_pipeline/ipython/hud_eda_reference_12_27_2021.ipynb +++ /dev/null @@ -1,1063 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Methodology per Blair Russell\n", - "\n", - "We may want to rethink the denominator of our equation for housing cost burden.\n", - "\n", - "\"Right now it’s all housing units with a cost burden computed. \n", - "\n", - "Alternatively, you could use low-income households (with cost burden computed) as the denominator, which would be a measure of relative cost burden just for low-income households. \n", - "\n", - "Both approaches are appropriate, but they tell a different story. You can imagine an area with few low-income households but a vast majority of them being cost burdened. In your calculation, you’d get a small percentage. \n", - "\n", - "In the alternative approach, it’s a large percentage. Just something to think about. It depends on the story you want to tell.\"\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Packages" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/sarahluw/.pyenv/versions/3.6.2/envs/my-virtual-env-3.6.2/lib/python3.6/site-packages/geopandas/_compat.py:110: UserWarning: The Shapely GEOS version (3.9.1-CAPI-1.14.2) is incompatible with the GEOS version PyGEOS was compiled with (3.10.1-CAPI-1.16.0). Conversions between both will be slow.\n", - " shapely_geos_version, geos_capi_version_string\n", - "/Users/sarahluw/.pyenv/versions/3.6.2/envs/my-virtual-env-3.6.2/lib/python3.6/site-packages/fuzzywuzzy/fuzz.py:11: UserWarning: Using slow pure-python SequenceMatcher. Install python-Levenshtein to remove this warning\n", - " warnings.warn('Using slow pure-python SequenceMatcher. Install python-Levenshtein to remove this warning')\n" - ] - } - ], - "source": [ - "import math\n", - "import numpy as np\n", - "import os\n", - "import pandas as pd\n", - "import requests\n", - "import zipfile\n", - "from pathlib import Path\n", - "import shutil\n", - "from cenpy import products\n", - "\n", - "import seaborn as sns\n", - "import matplotlib.pyplot as plt\n", - "import pandas as pd\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Table of Contents" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Comparisons\n", - "* [Implementation by Lucas](#lucas)\n", - "* [Implementation by Saran](#saran)\n", - "* [Side-by-side Comparison](#comparison)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The relevant denominator variables - all with line type of subtotal - in table 8 of the CHAS dataset are the following (CHAS data dictionary available [here](https://www.huduser.gov/portal/datasets/cp/CHAS-data-dictionary-14-18.xlsx)):\n", - "\n", - "| Name | Label |\n", - "|---------|-----------------------------------------------------|\n", - "|T1_est3 | Owner occupied less or equal to 30% of HAMFI | | \n", - "|T8_est16 | Owner occupied greater than 30% of HAMFI cost burden less than 50% |\n", - "|T8_est29 |Owner occupied greater than 50% but less than or equal to 80% of HAMFI\tcost burden |\n", - "|T8_est69 |Renter occupied less than or equal to 30% of HAMFI|\n", - "|T8_est82 | Renter occupied less than or equal to 30% of HAMFI cost burden greater than 50% |\n", - "|T8_est95 |Renter occupied\tgreater than 50% but less than or equal to 80% of HAMFI|\n", - " " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# ETL - one needs to only run this cell block once to replicate the subsequent notebooks in this draft PR\n", - "\n", - "##### Once run, Table 8 will be in the notebook's current working directory" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# Copy and adapt certain sections of code from data_pipeline.utils \n", - "\n", - "def download_hud_dataset():\n", - " DOWNLOAD_FILENAME = \"HUD_ZIPPED.csv\"\n", - " HOUSING_FTP_URL = \"https://www.huduser.gov/portal/datasets/cp/2014thru2018-140-csv.zip\" \n", - " response = requests.get(HOUSING_FTP_URL, verify=True)\n", - " if response.status_code == 200:\n", - " file_contents = response.content\n", - " else:\n", - " sys.exit(\n", - " f\"HTTP response {response.status_code} from url {file_url}. Info: {response.content}\"\n", - " )\n", - "\n", - " # Write the contents to disk.\n", - " file = open(DOWNLOAD_FILENAME, \"wb\")\n", - " file.write(file_contents)\n", - " file.close()\n", - " \n", - "def extract_zipped_download(zip_file_path, unzipped_path):\n", - " with zipfile.ZipFile(zip_file_path, \"r\") as zip_ref:\n", - " zip_ref.extractall(unzipped_path)\n", - " # cleanup temporary file\n", - " os.remove(zip_file_path)\n", - " \n", - "def up_one_directory(path):\n", - " try:\n", - " # from Python 3.6\n", - " parent_dir = Path(path).parents[1]\n", - " # for Python 3.4/3.5, use str to convert the path to string\n", - " # parent_dir = str(Path(path).parents[1])\n", - " shutil.move(path, parent_dir)\n", - " except IndexError:\n", - " # no upper directory\n", - " pass\n", - "\n", - "CURRENT_DIRECTORY = os.getcwd()\n", - "download_hud_dataset()\n", - "extract_zipped_download(CURRENT_DIRECTORY + \"/HUD_ZIPPED.csv\", CURRENT_DIRECTORY) \n", - "up_one_directory(CURRENT_DIRECTORY + \"/140/Table8.csv\")\n", - "shutil.rmtree(\"./140/\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Implementation by Lucas " - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# Read in the data from https://www.huduser.gov/portal/datasets/cp.html\n", - "housing = pd.read_csv(\"Table8.csv\", \n", - " encoding=\"latin-1\", \n", - " dtype = {'Tract_ID': object, 'st': object, 'geoid': object})\n", - "\n", - "# Remove data for states that aren't included in the census (e.g. American Samoa, Guam, etc.):\n", - "housing.drop(housing.loc[housing['st'] == '72'].index, inplace = True)\n", - "\n", - "# Rename columns\n", - "housing = housing.rename(columns = {'geoid' :'FIPS_tract_id',\n", - " 'st' : 'state'\n", - " })\n", - "\n", - "# Owner occupied numerator fields\n", - "OWNER_OCCUPIED_NUMERATOR_FIELDS = [\n", - " # Column Name\n", - " # Line_Type\n", - " # Tenure\n", - " # Household income\n", - " # Cost burden\n", - " # Facilities\n", - " \"T8_est7\",\n", - " # Subtotal\n", - " # Owner occupied\n", - " # less than or equal to 30% of HAMFI\n", - " # greater than 30% but less than or equal to 50%\n", - " # All\n", - " \"T8_est10\",\n", - " # Subtotal\n", - " # Owner occupied\n", - " # less than or equal to 30% of HAMFI\n", - " # greater than 50%\n", - " # All\n", - " \"T8_est20\",\n", - " # Subtotal\n", - " # Owner occupied\n", - " # greater than 30% but less than or equal to 50% of HAMFI\n", - " # greater than 30% but less than or equal to 50%\n", - " # All\n", - " \"T8_est23\",\n", - " # Subtotal\n", - " # Owner occupied\n", - " # greater than 30% but less than or equal to 50% of HAMFI\n", - " # greater than 50%\n", - " # All\n", - " \"T8_est33\",\n", - " # Subtotal\n", - " # Owner occupied\n", - " # greater than 50% but less than or equal to 80% of HAMFI\n", - " # greater than 30% but less than or equal to 50%\n", - " # All\n", - " \"T8_est36\",\n", - " # Subtotal\n", - " # Owner occupied\n", - " # greater than 50% but less than or equal to 80% of HAMFI\n", - " # greater than 50%\n", - " # All\n", - "]\n", - "\n", - "# These rows have the values where HAMFI was not computed, b/c of no or negative income.\n", - "OWNER_OCCUPIED_NOT_COMPUTED_FIELDS = [\n", - " # Column Name\n", - " # Line_Type\n", - " # Tenure\n", - " # Household income\n", - " # Cost burden\n", - " # Facilities\n", - " \"T8_est13\",\n", - " # Subtotal\n", - " # Owner occupied\n", - " # less than or equal to 30% of HAMFI\n", - " # not computed (no/negative income)\n", - " # All\n", - " \"T8_est26\",\n", - " # Subtotal\n", - " # Owner occupied\n", - " # greater than 30% but less than or equal to 50% of HAMFI\n", - " # not computed (no/negative income)\n", - " # All\n", - " \"T8_est39\",\n", - " # Subtotal\n", - " # Owner occupied\n", - " # greater than 50% but less than or equal to 80% of HAMFI\n", - " # not computed (no/negative income)\n", - " # All\n", - " \"T8_est52\",\n", - " # Subtotal\n", - " # Owner occupied\n", - " # greater than 80% but less than or equal to 100% of HAMFI\n", - " # not computed (no/negative income)\n", - " # All\n", - " \"T8_est65\",\n", - " # Subtotal\n", - " # Owner occupied\n", - " # greater than 100% of HAMFI\n", - " # not computed (no/negative income)\n", - " # All\n", - "]\n", - "\n", - "OWNER_OCCUPIED_POPULATION_FIELD = \"T8_est2\"\n", - "# Subtotal\n", - "# Owner occupied\n", - "# All\n", - "# All\n", - "# All\n", - "\n", - "OWNER_OCCUPIED_POPULATION_HAMFI_FIELD = \"T8_est3\"\n", - "# Subtotal\n", - "# Owner occupied \n", - "# All\n", - "# All\n", - "# All\n", - "\n", - "# Renter occupied numerator fields\n", - "RENTER_OCCUPIED_NUMERATOR_FIELDS = [\n", - " # Column Name\n", - " # Line_Type\n", - " # Tenure\n", - " # Household income\n", - " # Cost burden\n", - " # Facilities\n", - " \"T8_est73\",\n", - " # Subtotal\n", - " # Renter occupied\n", - " # less than or equal to 30% of HAMFI\n", - " # greater than 30% but less than or equal to 50%\n", - " # All\n", - " \"T8_est76\",\n", - " # Subtotal\n", - " # Renter occupied\n", - " # less than or equal to 30% of HAMFI\n", - " # greater than 50%\n", - " # All\n", - " \"T8_est86\",\n", - " # Subtotal\n", - " # Renter occupied\n", - " # greater than 30% but less than or equal to 50% of HAMFI\n", - " # greater than 30% but less than or equal to 50%\n", - " # All\n", - " \"T8_est89\",\n", - " # Subtotal\n", - " # Renter occupied\n", - " # greater than 30% but less than or equal to 50% of HAMFI\n", - " # greater than 50%\n", - " # All\n", - " \"T8_est99\",\n", - " # Subtotal\n", - " # Renter occupied\tgreater than 50% but less than or equal to 80% of HAMFI\n", - " # greater than 30% but less than or equal to 50%\n", - " # All\n", - " \"T8_est102\",\n", - " # Subtotal\n", - " # Renter occupied\n", - " # greater than 50% but less than or equal to 80% of HAMFI\n", - " # greater than 50%\n", - " # All\n", - "]\n", - "\n", - "# These rows have the values where HAMFI was not computed, b/c of no or negative income.\n", - "RENTER_OCCUPIED_NOT_COMPUTED_FIELDS = [\n", - " # Column Name\n", - " # Line_Type\n", - " # Tenure\n", - " # Household income\n", - " # Cost burden\n", - " # Facilities\n", - " \"T8_est79\",\n", - " # Subtotal\n", - " # Renter occupied\tless than or equal to 30% of HAMFI\n", - " # not computed (no/negative income)\n", - " # All\n", - " \"T8_est92\",\n", - " # Subtotal\n", - " # Renter occupied\tgreater than 30% but less than or equal to 50% of HAMFI\n", - " # not computed (no/negative income)\n", - " # All\n", - " \"T8_est105\",\n", - " # Subtotal\n", - " # Renter occupied\n", - " # greater than 50% but less than or equal to 80% of HAMFI\n", - " # not computed (no/negative income)\n", - " # All\n", - " \"T8_est118\",\n", - " # Subtotal\n", - " # Renter occupied\tgreater than 80% but less than or equal to 100% of HAMFI\n", - " # not computed (no/negative income)\n", - " # All\n", - " \"T8_est131\",\n", - " # Subtotal\n", - " # Renter occupied\n", - " # greater than 100% of HAMFI\n", - " # not computed (no/negative income)\n", - " # All\n", - "]\n", - "\n", - "# T8_est68\tSubtotalRenter occupied\tAll\tAll\tAll\n", - "RENTER_OCCUPIED_POPULATION_FIELD = \"T8_est68\"" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# Original computation:\n", - "# (\n", - "# # of Owner Occupied Units Meeting Criteria\n", - "# + # of Renter Occupied Units Meeting Criteria\n", - "# )\n", - "# divided by\n", - "# (\n", - "# Total # of Owner Occupied Units\n", - "# + Total # of Renter Occupied Units\n", - "# - # of Owner Occupied Units with HAMFI Not Computed\n", - "# - # of Renter Occupied Units with HAMFI Not Computed\n", - "# )\n", - "\n", - "housing[\"numerator_pre\"] = housing[\n", - " OWNER_OCCUPIED_NUMERATOR_FIELDS\n", - "].sum(axis=1) + housing[RENTER_OCCUPIED_NUMERATOR_FIELDS].sum(axis=1)\n", - "\n", - "\n", - "\n", - "housing[\"denominator_pre\"] = (\n", - " housing[OWNER_OCCUPIED_POPULATION_FIELD]\n", - " + housing[RENTER_OCCUPIED_POPULATION_FIELD]\n", - " - housing[OWNER_OCCUPIED_NOT_COMPUTED_FIELDS].sum(axis=1)\n", - " - housing[RENTER_OCCUPIED_NOT_COMPUTED_FIELDS].sum(axis=1)\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Implementation by Saran " - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "OWNER_REVISED_DENOMINATOR_FIELDS = [\n", - " # Column Name\n", - " # Line_Type\n", - " # Tenure\n", - " # Household income\n", - " # Cost burden\n", - " # Facilities\n", - " \"T8_est3\",\n", - " # Subtotal\n", - " # Renter occupied\tless than or equal to 30% of HAMFI\n", - " # All\n", - " \"T8_est16\",\n", - " # Subtotal\n", - " # Renter occupied\tgreater than 30% but less than or equal to 50% of HAMFI\n", - " # All\n", - " \"T8_est29\",\n", - " # Subtotal\n", - " # Renter occupied\n", - " # greater than 50% but less than or equal to 80% of HAMFI\n", - " # All\n", - " ]\n", - "\n", - "RENTAL_REVISED_DENOMINATOR_FIELDS = [\n", - " # Column Name\n", - " # Line_Type\n", - " # Tenure\n", - " # Household income\n", - " # Cost burden\n", - " # Facilities\n", - " \"T8_est69\",\n", - " # Subtotal\n", - " # Renter occupied\tless than or equal to 30% of HAMFI\n", - " # All\n", - " \"T8_est82\",\n", - " # Subtotal\n", - " # Renter occupied\tgreater than 30% but less than or equal to 50% of HAMFI\n", - " # All\n", - " \"T8_est95\",\n", - " # Subtotal\n", - " # Renter occupied\n", - " # greater than 50% but less than or equal to 80% of HAMFI\n", - " # All\n", - " ]\n", - "\n", - "# New computation:\n", - "# (\n", - "# # of Owner Occupied Units Meeting Criteria\n", - "# + # of Renter Occupied Units Meeting Criteria\n", - "# )\n", - "# divided by\n", - "# (\n", - "# Total # of Owner Occupied Units that meet criterion for poverty\n", - "# + Total # of Renter Occupied Units that meet criterion for poverty\n", - "# - # of Owner Occupied Units with HAMFI Not Computed\n", - "# - # of Renter Occupied Units with HAMFI Not Computed\n", - "# )\n", - "\n", - "housing['denominator_post'] = housing[\n", - " RENTAL_REVISED_DENOMINATOR_FIELDS\n", - "].sum(axis = 1) + housing[OWNER_REVISED_DENOMINATOR_FIELDS].sum(axis = 1) - (\n", - " - housing[OWNER_OCCUPIED_NOT_COMPUTED_FIELDS].sum(axis=1)\n", - " - housing[RENTER_OCCUPIED_NOT_COMPUTED_FIELDS].sum(axis=1)\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
      \n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
      numerator_predenominator_predenominator_post
      0174765295
      1177720350
      22791291559
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      " - ], - "text/plain": [ - " numerator_pre denominator_pre denominator_post\n", - "0 174 765 295\n", - "1 177 720 350\n", - "2 279 1291 559\n", - "3 274 1635 525\n", - "4 885 4135 1090" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "housing.iloc[:, -3:].head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Concatenate GeoIDs with Derived Columns" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "housing_df = pd.concat([housing.iloc[:, 2: 7], housing.iloc[:, -3:]], axis = 1)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "housing_df[\"ratio_pre\"] = np.round(\n", - " housing_df['numerator_pre'] / housing_df['denominator_pre'], 2)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "housing_df[\"ratio_post\"] = np.round(\n", - " housing_df['numerator_pre'] / housing_df['denominator_post'], 2)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Comparison " - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
      \n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
      FIPS_tract_idnamestatecntytractnumerator_predenominator_predenominator_postratio_preratio_post
      014000US01001020100Census Tract 201, Autauga County, Alabama011201001747652950.230.59
      114000US01001020200Census Tract 202, Autauga County, Alabama011202001777203500.250.51
      214000US01001020300Census Tract 203, Autauga County, Alabama0112030027912915590.220.50
      \n", - "
      " - ], - "text/plain": [ - " FIPS_tract_id name state cnty \\\n", - "0 14000US01001020100 Census Tract 201, Autauga County, Alabama 01 1 \n", - "1 14000US01001020200 Census Tract 202, Autauga County, Alabama 01 1 \n", - "2 14000US01001020300 Census Tract 203, Autauga County, Alabama 01 1 \n", - "\n", - " tract numerator_pre denominator_pre denominator_post ratio_pre \\\n", - "0 20100 174 765 295 0.23 \n", - "1 20200 177 720 350 0.25 \n", - "2 20300 279 1291 559 0.22 \n", - "\n", - " ratio_post \n", - "0 0.59 \n", - "1 0.51 \n", - "2 0.50 " - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "housing_df.head(3)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "import seaborn as sns\n", - "import matplotlib.pyplot as plt\n", - "import pandas as pd\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
      " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(12, 8))\n", - "plt.title('Relative Housing Burden for Low-Income Housing Only')\n", - "# Set x-axis label\n", - "plt.xlabel('Ratio')\n", - "# Set y-axis label\n", - "plt.ylabel('Frequency')\n", - "\n", - "sns.histplot(housing_df[\"ratio_post\"])" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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      " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(12, 8))\n", - "plt.title('Housing Burden Relative to All Owned and Rented Housing (Currently used in Score L)')\n", - "# Set x-axis label\n", - "plt.xlabel('Ratio (prior to conversion to percentile)')\n", - "# Set y-axis label\n", - "plt.ylabel('Frequency')\n", - "\n", - "sns.histplot(housing_df[\"ratio_pre\"])" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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      nameFIPS_tract_idratio_preratio_post
      4203Census Tract 3923, Contra Costa County, Califo...14000US060133923000.201.01
      4617Census Tract 38.06, Kern County, California14000US060290038060.101.03
      5262Census Tract 2060.20, Los Angeles County, Cali...14000US060372060200.261.07
      5565Census Tract 2626.01, Los Angeles County, Cali...14000US060372626010.071.06
      12908Census Tract 303, Fairfield County, Connecticut14000US090010303000.091.04
      14086Census Tract 102, District of Columbia, Distri...14000US110010102000.151.02
      17668Census Tract 273.23, Pinellas County, Florida14000US121030273230.181.02
      18102Census Tract 208.10, Seminole County, Florida14000US121170208100.141.03
      19796Census Tract 101.02, Liberty County, Georgia14000US131790101020.331.06
      21166Census Tract 507, Cook County, Illinois14000US170310507000.161.03
      30658Census Tract 7053, Montgomery County, Maryland14000US240317053000.071.09
      41123Census Tract 6075.04, Camden County, New Jersey14000US340076075040.091.05
      42632Census Tract 363.02, Union County, New Jersey14000US340390363020.201.02
      45335Census Tract 3033.01, Nassau County, New York14000US360593033010.311.02
      51549Census Tract 69.10, Franklin County, Ohio14000US390490069100.141.10
      62114Census Tract 1917.01, Bexar County, Texas14000US480291917010.161.10
      66504Census Tract 22.12, Travis County, Texas14000US484530022120.181.02
      67740Census Tract 1016.03, Arlington County, Virginia14000US510131016030.161.01
      69094Census Tract 111, Hampton city, Virginia14000US516500111000.211.13
      69486Census Tract 432, Virginia Beach city, Virginia14000US518100432000.511.04
      \n", - "
      " - ], - "text/plain": [ - " name FIPS_tract_id \\\n", - "4203 Census Tract 3923, Contra Costa County, Califo... 14000US06013392300 \n", - "4617 Census Tract 38.06, Kern County, California 14000US06029003806 \n", - "5262 Census Tract 2060.20, Los Angeles County, Cali... 14000US06037206020 \n", - "5565 Census Tract 2626.01, Los Angeles County, Cali... 14000US06037262601 \n", - "12908 Census Tract 303, Fairfield County, Connecticut 14000US09001030300 \n", - "14086 Census Tract 102, District of Columbia, Distri... 14000US11001010200 \n", - "17668 Census Tract 273.23, Pinellas County, Florida 14000US12103027323 \n", - "18102 Census Tract 208.10, Seminole County, Florida 14000US12117020810 \n", - "19796 Census Tract 101.02, Liberty County, Georgia 14000US13179010102 \n", - "21166 Census Tract 507, Cook County, Illinois 14000US17031050700 \n", - "30658 Census Tract 7053, Montgomery County, Maryland 14000US24031705300 \n", - "41123 Census Tract 6075.04, Camden County, New Jersey 14000US34007607504 \n", - "42632 Census Tract 363.02, Union County, New Jersey 14000US34039036302 \n", - "45335 Census Tract 3033.01, Nassau County, New York 14000US36059303301 \n", - "51549 Census Tract 69.10, Franklin County, Ohio 14000US39049006910 \n", - "62114 Census Tract 1917.01, Bexar County, Texas 14000US48029191701 \n", - "66504 Census Tract 22.12, Travis County, Texas 14000US48453002212 \n", - "67740 Census Tract 1016.03, Arlington County, Virginia 14000US51013101603 \n", - "69094 Census Tract 111, Hampton city, Virginia 14000US51650011100 \n", - "69486 Census Tract 432, Virginia Beach city, Virginia 14000US51810043200 \n", - "\n", - " ratio_pre ratio_post \n", - "4203 0.20 1.01 \n", - "4617 0.10 1.03 \n", - "5262 0.26 1.07 \n", - "5565 0.07 1.06 \n", - "12908 0.09 1.04 \n", - "14086 0.15 1.02 \n", - "17668 0.18 1.02 \n", - "18102 0.14 1.03 \n", - "19796 0.33 1.06 \n", - "21166 0.16 1.03 \n", - "30658 0.07 1.09 \n", - "41123 0.09 1.05 \n", - "42632 0.20 1.02 \n", - "45335 0.31 1.02 \n", - "51549 0.14 1.10 \n", - "62114 0.16 1.10 \n", - "66504 0.18 1.02 \n", - "67740 0.16 1.01 \n", - "69094 0.21 1.13 \n", - "69486 0.51 1.04 " - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "housing_df[housing_df[\"ratio_post\"] > 1][['name', \"FIPS_tract_id\", \"ratio_pre\", 'ratio_post']]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Key Takeaways from the 20 tracts with greater than 100%\n", - "\n", - "1. Current college enrollment is not prevalent across all tracts\n", - "2. The results may make sense - given the median value (USD) of an owned propert any individual, relative to all other low-income housholds (regardless if owned and rented). These may be areas of gentrification, for example. Further analysis through time may be pertinent as a follow-up (not included in this notebook)." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.2" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/data/data-pipeline/data_pipeline/ipython/hud_eda_se_12_12_2011_relative_differences_between_methodologies-ranking-percentile.ipynb b/data/data-pipeline/data_pipeline/ipython/hud_eda_se_12_12_2011_relative_differences_between_methodologies-ranking-percentile.ipynb index deff9367..7d13e42c 100644 --- a/data/data-pipeline/data_pipeline/ipython/hud_eda_se_12_12_2011_relative_differences_between_methodologies-ranking-percentile.ipynb +++ b/data/data-pipeline/data_pipeline/ipython/hud_eda_se_12_12_2011_relative_differences_between_methodologies-ranking-percentile.ipynb @@ -1527,7 +1527,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -1541,7 +1541,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.2" + "version": "3.9.9" } }, "nbformat": 4, diff --git a/data/data-pipeline/data_pipeline/ipython/hud_eda_se_12_22_2021-revised-denominator_explore_austin_af_analysis.ipynb b/data/data-pipeline/data_pipeline/ipython/hud_eda_se_12_22_2021-revised-denominator_explore_austin_af_analysis.ipynb index 1eda2136..1fb5df81 100644 --- a/data/data-pipeline/data_pipeline/ipython/hud_eda_se_12_22_2021-revised-denominator_explore_austin_af_analysis.ipynb +++ b/data/data-pipeline/data_pipeline/ipython/hud_eda_se_12_22_2021-revised-denominator_explore_austin_af_analysis.ipynb @@ -737,7 +737,7 @@ ], "source": [ "plt.figure(figsize=(12, 8))\n", - "plt.title('Relative Housing Burden for Low-Income Housing Only')\n", + "plt.title('Relative Housing Burden for Low-Income Hosuing Only')\n", "# Set x-axis label\n", "plt.xlabel('Ratio')\n", "# Set y-axis label\n", @@ -785,6 +785,147 @@ "sns.histplot(housing_df[\"ratio_pre\"])" ] }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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      FIPS_tract_idnamestatecntytractnumerator_predenominator_predenominator_postratio_preratio_post
      2900814000US22071004402Census Tract 44.02, Orleans Parish, Louisiana2271440275751651.000.45
      3751414000US29001951000Census Tract 9510, Adair County, Missouri2919510005555751.000.73
      4393214000US36027640002Census Tract 6400.02, Dutchess County, New York36276400024850500.960.96
      7168114000US55025001102Census Tract 11.02, Dane County, Wisconsin552511026060891.000.67
      7168914000US55025001603Census Tract 16.03, Dane County, Wisconsin552516031460159919340.910.75
      \n", + "
      " + ], + "text/plain": [ + " FIPS_tract_id name \\\n", + "29008 14000US22071004402 Census Tract 44.02, Orleans Parish, Louisiana \n", + "37514 14000US29001951000 Census Tract 9510, Adair County, Missouri \n", + "43932 14000US36027640002 Census Tract 6400.02, Dutchess County, New York \n", + "71681 14000US55025001102 Census Tract 11.02, Dane County, Wisconsin \n", + "71689 14000US55025001603 Census Tract 16.03, Dane County, Wisconsin \n", + "\n", + " state cnty tract numerator_pre denominator_pre denominator_post \\\n", + "29008 22 71 4402 75 75 165 \n", + "37514 29 1 951000 55 55 75 \n", + "43932 36 27 640002 48 50 50 \n", + "71681 55 25 1102 60 60 89 \n", + "71689 55 25 1603 1460 1599 1934 \n", + "\n", + " ratio_pre ratio_post \n", + "29008 1.00 0.45 \n", + "37514 1.00 0.73 \n", + "43932 0.96 0.96 \n", + "71681 1.00 0.67 \n", + "71689 0.91 0.75 " + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# So only 4 that are > 90%\n", + "housing_df[housing_df[\"ratio_pre\"] > 0.90]" + ] + }, { "cell_type": "code", "execution_count": 15, @@ -2476,11 +2617,18 @@ "source": [ "seg_austin_2013.statistic" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -2494,7 +2642,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.2" + "version": "3.7.4" } }, "nbformat": 4, diff --git a/data/data-pipeline/data_pipeline/ipython/hud_eda_se_12_26_2011_relative_differences_between_methodologies-ranking-percentile-comparison-difference-presentation.ipynb b/data/data-pipeline/data_pipeline/ipython/hud_eda_se_12_26_2011_relative_differences_between_methodologies-ranking-percentile-comparison-difference-presentation.ipynb deleted file mode 100644 index d8af856a..00000000 --- a/data/data-pipeline/data_pipeline/ipython/hud_eda_se_12_26_2011_relative_differences_between_methodologies-ranking-percentile-comparison-difference-presentation.ipynb +++ /dev/null @@ -1,2992 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Extreme Housing Burden \n", - "\n", - "The Extreme Housing Burden indicator represents the proportion of low-income households that have to spend more than half their income on rent. These households experience higher levels of stress, report lower health, and may delay medical treatment because of its high cost.\n", - "\n", - "The Extreme Housing Burden indicator measures the percent of households in a census tract that are:\n", - "\n", - "1. Making less than 80% of the Area Median Family Income as determined by the Department of Housing and Urban Development (HUD), and\n", - "2. Paying greater than 50% of their income to housing costs. \n", - "\n", - "This data is sourced from the 2014-2018 Comprehensive Housing Affordability Strategy dataset from the Department of Housing and Urban Development (HUD) using the census tract geographic summary level, and contains cost burdens for households by percent HUD-adjusted median family income (HAMFI) category. This data can be found [here](https://www.huduser.gov/portal/datasets/cp.html). \n", - "\n", - "Because CHAS data is based on American Communities Survey (ACS) estimates, which come from a sample of the population, they may be unreliable if based on a small sample or population size.\n", - "\n", - "The standard error and relative standard error were used to evaluate the reliability of each estimate using CalEnviroScreen’s methodology. \n", - "\n", - "Census tract estimates that met either of the following criteria were considered reliable and included in the analysis [(CalEnviroScreen, 2017, page 129)](https://oehha.ca.gov/media/downloads/calenviroscreen/report/ces3report.pdf ):\n", - "\n", - "- Relative standard error less than 50 (meaning the standard error was less than half of the estimate), OR \n", - "- Standard error less than the mean standard error of all census tract estimates \n", - "\n", - "Formulas for calculating the standard error of sums, proportions, and ratio come from the [American Communities Survey Office](https://www2.census.gov/programs-surveys/acs/tech_docs/accuracy/MultiyearACSAccuracyofData2013.pdf).\n", - "\n", - "Note that this code creates a score and rank by state, for every state." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The relevant variables in table 8 of the CHAS dataset are the following (CHAS data dictionary available [here](https://www.huduser.gov/portal/datasets/cp/CHAS-data-dictionary-14-18.xlsx)):\n", - "\n", - "| Name | Label |\n", - "|---------|-----------------------------------------------------|\n", - "|T1_est1 | Total Occupied housing units | \n", - "|T8_est10 | Owner occupied less than or equal to 30% of HAMFI cost burden greater than 50% |\n", - "|T8_est23 |Owner occupied greater than 30% but less than or equal to 50% of HAMFI\tcost burden greater than 50%|\n", - "|T8_est36 |Owner occupied\tgreater than 50% but less than or equal to 80% of HAMFI\tcost burden greater than 50%|\n", - "|T8_est76 | Renter occupied less than or equal to 30% of HAMFI cost burden greater than 50% |\n", - "|T8_est89 |Renter occupied\tgreater than 30% but less than or equal to 50% of HAMFI\tcost burden greater than 50%|\n", - "|T8_est102|Renter occupied\tgreater than 50% but less than or equal to 80% of HAMFI\tcost burden greater than 50%|\n", - " " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Percentiles Comparison" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Current methodology for housing burden where tracts meet the environemental burden threshold (n = 7323 tracts)." - ] - }, - { - "cell_type": "code", - "execution_count": 85, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 85, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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      " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(12, 8))\n", - "plt.title(\"Distribution of Percentiles for Housing Burden (Score L)\")\n", - "# Set x-axis label\n", - "plt.xlabel('Percentile (although currently not represented as a percentage)')\n", - "# Set y-axis label\n", - "plt.ylabel('Relative Frequency in Support')\n", - "\n", - "sns.histplot(non_null_df[\"current_methodology_percentile_rank\"])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Below is the summarization of the tracts that are not common between Score L and the CalEnviroScreen's Ranked Percentile \n", - "\n", - "Here (n = 5013 tracts in the set of 67, 813 tracts with reliable population estimates - as per CalEnviroScreen's methodology). Of these 2, 741 tracts met Score L's threshold for environemental burden (I will be reviewing this with further summaries), but were not in the burdened threshold using CalEnviroScreen's Ranked Percentile methodology. Conversely, 2, 272 did _not_ meet Score L's threshold for environemental burden (again, I will be reviewing this with further summaries), but were not in the burdened threshold using CalEnviroScreen's Ranked Percentile methodology.\n", - "\n", - "It should be noted that 53 tracts, because of unreliable population estimates - as per CalEnviroScreen's methodology - were not included in the threshold computation. However, these same 53 tracts are considered burdened in Score L. This is equivalent to 3145 households.\n", - "\n", - "Below is a summary of the aggregate 5013 tracts. Each column presents a grouped statistic of the median percentage of the specified population characteristic across all tracts in that state. To present trends and contrasts between states, I prepared bar graphs relative to the midpoint value of the medians in that given column (really the median of medians). The attached Excel file represents the same data, but I highlight the cells where the given median for the state is greater than the median of all of the state samples for any given population characteristic." - ] - }, - { - "cell_type": "code", - "execution_count": 86, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - 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      Percent of individuals < 200% Federal Poverty Line (median across all tracts) Percent individuals age 25 or over with less than high school degree (median across all tracts) Percent enrollment in college or graduate school (median across all tracts) Percent Black or African American alone (median across all tracts) Percent American Indian and Alaska Native alone (median across all tracts) Percent Non-Hispanic White (median across all tracts) Percent Hispanic or Latino (median across all tracts) Total Number of Unique Tracts Total Owned and Rented Burdened Households (Current Aggregation Methodology)
      state_name
      AK33.07046910.5860986.5016045.09620012.63370352.24087210.264142125829
      AL59.38757516.9137957.56397874.4757010.00000016.8269372.3860825629636
      AR60.04119517.9231865.21064355.7103060.00000034.8001015.3937434321417
      AZ57.70065126.5266437.0756255.6077602.75893326.17866056.6612589961251
      CA51.26895231.9902329.1743124.0784490.4722289.84325065.462248897610306
      CO46.30058814.2493257.9066043.8809621.09258646.52824034.4803277247552
      CT50.17689818.9679858.59463123.3867030.00000027.06270836.6956305840980
      DC50.41085415.4830456.94641390.3525770.0000003.9939143.9702231711105
      DE42.44744711.2353687.16747548.9519210.47055836.9102358.757629147502
      FL51.10930217.0430947.58889422.7603000.00000022.10462426.459155234162836
      GA58.09138817.4952208.78173866.0021550.00000016.7491174.75131412178491
      HI34.6405239.9914248.0929962.0005260.00000011.6621984.947368178425
      IA45.76192710.9478677.41301111.2195120.02554969.9254357.9350775526664
      ID52.5401366.0718259.1437310.5292651.06339583.4355838.6047852518487
      IL55.34478318.7627467.25308638.0509940.0000009.25583112.976765203108186
      IN58.68210818.0456815.95164435.0849010.00000039.4594119.0637648246470
      KS56.97732518.2332798.18659411.9044760.45919356.78561816.8910105023873
      KY56.32823418.1950865.83430618.9645030.00000063.1455404.2362007136328
      LA61.15131620.0523317.09914376.7875130.00000017.4779322.4805105930785
      MA47.83000120.9422967.76858611.8248780.00000034.79487327.9524088457782
      MD39.75211215.3517317.62244149.9278960.00000022.1150727.0944708846574
      ME42.4021237.4773148.4269661.3470680.36040490.6806281.3089012715361
      MI60.18348616.2672816.96378869.9781660.00000017.6454671.91218114567070
      MN42.50854112.09616810.21069716.9693820.54782058.1542808.0309979148964
      MO55.19430615.2006196.15305936.8479800.00000035.8847104.6192215226962
      MS58.97967617.0385406.71198072.4340180.00000025.2270430.7057554320260
      MT45.0789855.35533213.3026610.1291611.84455488.8817273.6506382013069
      NC56.81125419.2268577.08257542.4155180.25968433.41225210.71495910970145
      ND43.2432436.99017916.4667397.1492562.12194877.9402937.502267138858
      NE55.4258249.50728713.6915898.2790700.53516864.62978310.8638482914442
      NH39.67807813.1355246.5123402.7739100.00000086.0097285.6445712415941
      NJ46.34573320.8433017.07307525.0784090.00000012.42603643.496410165110008
      NM53.60195416.7253529.3477502.7661803.11614729.44009657.2348172714087
      NV56.08303622.5641365.47547911.8682720.55708528.14339243.9473994830773
      NY47.53685724.0067428.26279025.8594920.00000011.30094728.649040603419587
      OH64.85730319.1609985.71620761.0523220.00000027.2796353.45515015767453
      OK57.61886716.7902917.02701213.9696743.42083848.52185013.8998857028634
      OR45.05710911.4072497.8693561.7578830.74264567.68737214.9577805139136
      PA54.89406815.2657607.53012031.8856820.00000027.3596186.15183216587243
      RI55.85143720.0353277.63214712.3701610.40053429.26877933.4685171911532
      SC53.74211015.1065456.65387146.2740380.00000039.1304354.8152876735985
      SD47.8048787.6506438.2104653.6046323.99484575.2877454.7727851911063
      TN55.41666718.7124467.28065853.9672130.00000035.9933964.4444447345269
      TX56.93950225.4102206.78726514.3435370.00000014.62720549.238149309204844
      UT45.8097409.5987419.4932431.4449290.38485264.48710320.4281663319743
      VA44.75079214.1815067.65054136.8306090.00000032.1986966.60759510265128
      VT43.1128236.8371867.7231700.9235940.05055693.7005651.605505158061
      WA39.37531712.5430817.4483175.8803020.90327159.32113613.89307010279613
      WI57.28758215.1812696.31025423.5638920.34694422.6303328.5417784322233
      WV52.37579511.7412986.8748136.8052340.00000082.4479241.6365712611981
      WY33.4140924.8757176.5539660.5780351.17455181.37824211.80827996319
      " - ], - "text/plain": [ - "" - ] - }, - "execution_count": 86, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bar_styled" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.2" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/data/data-pipeline/data_pipeline/ipython/hud_eda_se_12_26_2011_relative_differences_between_methodologies-ranking-percentile-ejscreen-burden.ipynb b/data/data-pipeline/data_pipeline/ipython/hud_eda_se_12_26_2011_relative_differences_between_methodologies-ranking-percentile-ejscreen-burden.ipynb deleted file mode 100644 index d99d8979..00000000 --- a/data/data-pipeline/data_pipeline/ipython/hud_eda_se_12_26_2011_relative_differences_between_methodologies-ranking-percentile-ejscreen-burden.ipynb +++ /dev/null @@ -1,1952 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "###### Summary statistics for census tracts that are considered burdened using the CalEnviroScreen ranked percentile, but are not included in Score L's housing burden (n = 2272)" - ] - }, - { - "cell_type": "code", - "execution_count": 128, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - 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      Percent of individuals < 200% Federal Poverty Line (median across all tracts) Percent individuals age 25 or over with less than high school degree (median across all tracts) Percent enrollment in college or graduate school (median across all tracts) Percent Black or African American alone (median across all tracts) Percent American Indian and Alaska Native alone (median across all tracts) Percent Non-Hispanic White (median across all tracts) Percent Hispanic or Latino (median across all tracts) Total Number of Unique Tracts Total Owned and Rented Burdened Households (Current Aggregation Methodology)
      state_name
      AK32.57785710.6818186.2790705.63882912.55868555.64245811.153285115004
      AL57.12393815.9896757.56397873.0238130.00000017.4479262.6160734222529
      AR57.35376017.0561915.11363652.3086650.00000034.9146115.3937433919130
      AZ54.30176223.3384187.0756255.5165242.98060729.27350454.6663266942252
      CA36.47130316.5462909.8486303.8562090.06978431.16028726.3238925734081
      CO45.53006314.1091667.8619193.0166341.06052352.63855226.1706955332424
      CT32.32465412.9340058.22306230.8233640.00000035.70255733.05184153597
      DC30.6557776.57853617.06884625.6285580.00000052.7733189.1400892716
      DE41.80097211.2518637.99319754.9051940.35814832.1242106.924979115159
      FL41.98744212.4903437.84136213.3955750.00000037.30916023.9107057345897
      GA57.69491416.0821539.55094664.4906280.09348720.4973104.1263496232938
      HI31.0869573.4203428.0929962.6030370.09480545.4446854.94736851827
      IA45.55075311.0216867.44970911.4546560.01277569.8716297.9979915426215
      ID51.9584195.7815158.8763120.5593731.06859084.3568487.9465512417542
      IL51.97947213.8489217.86427136.4231740.00000031.8832289.8849025526892
      IN58.24832917.3753875.68335634.1874340.00000038.7855398.8772855931457
      KS56.51626918.2332798.02623811.4993510.40810955.45402516.8910104822521
      KY55.03160718.0973715.82383319.1138350.00000066.4021754.1783716431935
      LA55.13853217.8025806.43234669.6881430.00000026.8866943.648757165523
      MA42.70767314.8974456.8201757.5939560.00000050.70831817.8142402314780
      MD37.93936714.2735047.84150744.0864020.00000022.6541958.0500895727737
      ME42.3752307.2452328.5389521.4356000.43946790.8797561.3420082614901
      MI56.88949915.8593116.44075267.0801350.00000022.3194022.6280058436969
      MN42.32558111.27971710.18301316.7792790.54782058.2260208.0039048947853
      MO52.46449515.2147977.43187429.8350820.00000051.9408504.8459964120105
      MS57.71878117.0385406.79012370.0152210.00000026.8817200.6628003516392
      MT45.0789855.35533213.3026610.1291611.84455488.8817273.6506382013069
      NC55.79595017.4128607.51599939.8244240.21400738.2738999.2800209057578
      ND43.2432436.99017916.4667397.1492562.12194877.9402937.502267138858
      NE55.2813289.48750013.8573178.3841390.47776665.9887749.7988842813947
      NH38.51253211.4067306.7050932.5656560.00000087.6694744.3754032013896
      NJ40.98778012.8032749.39597334.3605550.00000018.00051820.26089473691
      NM53.60195416.7253529.3477502.7661803.11614729.44009657.2348172714087
      NV49.95147220.6551015.81651112.2139930.51863925.97092636.024189188040
      NY35.28700915.8296259.12792736.1632650.0000006.91886513.014754156102
      OH62.16293717.5000005.58213754.6029520.00000036.4298723.0000008933549
      OK55.90928916.6415667.09677412.9116123.44462148.79032313.6877356527277
      OR43.54733410.8349518.0594461.6923590.79872268.77328713.5010944332728
      PA51.82364412.9706607.98263630.2978110.00000031.6046774.96915011460268
      RI33.4419826.86553034.4688767.1856290.10888865.0469589.97938173697
      SC52.83505214.5844226.07734842.0417830.00000042.8742694.4242135529033
      SD47.8048787.6506438.2104653.6046323.99484575.2877454.7727851911063
      TN52.86541417.2302007.61171849.7008150.00000037.8229904.1185315229145
      TX52.85451023.4685546.80347911.2162920.00000019.00769443.565114206131757
      UT44.7908829.4339709.3828001.5999280.38123568.92157120.1296443219148
      VA39.51673812.2543958.42661733.3449110.00000034.1666676.5101727545045
      VT43.1128236.8371867.7231700.9235940.05055693.7005651.605505158061
      WA39.04300912.0608907.5268825.8465290.90156459.80987112.9402139370445
      WI52.23724214.4072806.32466015.2783990.55155332.91595111.2344273015514
      WV52.37579511.7412986.8748136.8052340.00000082.4479241.6365712611981
      WY33.4140924.8757176.5539660.5780351.17455181.37824211.80827996319
      " - ], - "text/plain": [ - "" - ] - }, - "execution_count": 128, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bar_styled_ej_screen" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.2" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/data/data-pipeline/data_pipeline/ipython/hud_eda_se_12_26_2011_relative_differences_between_methodologies-ranking-percentile-score-l-burdened.ipynb b/data/data-pipeline/data_pipeline/ipython/hud_eda_se_12_26_2011_relative_differences_between_methodologies-ranking-percentile-score-l-burdened.ipynb deleted file mode 100644 index 4cbb98d5..00000000 --- a/data/data-pipeline/data_pipeline/ipython/hud_eda_se_12_26_2011_relative_differences_between_methodologies-ranking-percentile-score-l-burdened.ipynb +++ /dev/null @@ -1,1651 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As a follow-up to the above here are contrasts in the subset of tracts where Score L's approach - using housing burden relative to all owned and rental properties - is considered burdened. However, these tracts not considered burdened by the methodology employed in CalEnviroScreen (n = 2741 tracts)." - ] - }, - { - "cell_type": "code", - "execution_count": 115, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - 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      Percent of individuals < 200% Federal Poverty Line (median across all tracts) Percent individuals age 25 or over with less than high school degree (median across all tracts) Percent enrollment in college or graduate school (median across all tracts) Percent Black or African American alone (median across all tracts) Percent American Indian and Alaska Native alone (median across all tracts) Percent Non-Hispanic White (median across all tracts) Percent Hispanic or Latino (median across all tracts) Total Number of Unique Tracts Total Owned and Rented Burdened Households (Current Aggregation Methodology)
      state_name
      AK33.56308110.4072409.4856384.55357121.27976248.8392869.3750001825
      AL63.33585420.2736177.89540879.2360900.0000008.4367791.986055147107
      AR62.78581218.6580529.05639172.6277140.10245911.6507574.49709742287
      AZ67.89663235.3178426.8035856.8779112.06493020.32014363.4241583018999
      CA52.18890133.3458789.1456794.1038560.4856049.28976866.540085840576225
      CO50.79802714.9253737.9512898.3552821.21033228.21264936.3037751915128
      CT52.66257019.7992198.79765422.8709680.00000026.31832436.7860945337383
      DC50.88139715.8562376.47948291.5753210.0000002.9838713.0153121510389
      DE43.17124710.0204503.49417627.0983210.94004641.69626113.62909732343
      FL54.78608320.4034317.37691126.8486920.00000017.74120526.870748161116939
      GA58.31884118.8155468.75099466.0021550.00000010.8166006.2990895945553
      HI35.98409812.5218038.3583681.7069770.0000009.5053015.326514126598
      IA56.50446910.3132162.7076929.8311820.74478689.4240320.0000001449
      ID56.68056415.42500816.0661010.2475690.05305068.36427927.7630421945
      IL57.13485219.1528407.23355841.4607730.0000006.83374815.93533614881294
      IN60.36199119.8126806.40301341.5031470.00000040.1332849.3698372315013
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      " - ], - "text/plain": [ - "" - ] - }, - "execution_count": 115, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bar_styled_score_l" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.2" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/data/data-pipeline/data_pipeline/ipython/hud_eda_se_12_26_2011_relative_differences_between_methodologies-ranking-percentile.ipynb b/data/data-pipeline/data_pipeline/ipython/hud_eda_se_12_26_2011_relative_differences_between_methodologies-ranking-percentile.ipynb deleted file mode 100644 index e8e8391d..00000000 --- a/data/data-pipeline/data_pipeline/ipython/hud_eda_se_12_26_2011_relative_differences_between_methodologies-ranking-percentile.ipynb +++ /dev/null @@ -1,9297 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Methodology to address fundamental problem 1 itemized in Issue 1024 - follow-up compare tabulations and relative household burden. This time I extend the 12-11 notebook to look at how the percentile ranks affects the proportion of tracts considered as burdened versus the current methodology." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Indicator reviewed: \n", - "\n", - "Socioeconomic Factors Indicator reviewed\n", - "* [Extreme Housing Burden](#housingburden)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Packages" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import math\n", - "import numpy as np\n", - "import os\n", - "import pandas as pd\n", - "\n", - "import seaborn as sns\n", - "import matplotlib.pyplot as plt\n", - "import pandas as pd\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### ETL process for acquiring relevant tables" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### NOTE: If you ran the ETL Process to acquire Table 8 in the other notebook of this draft PR you do not need to run the ETL cell block again" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# Copy and adapt certain sections of code from data_pipeline.utils \n", - "\n", - "def download_hud_dataset():\n", - " DOWNLOAD_FILENAME = \"HUD_ZIPPED.csv\"\n", - " HOUSING_FTP_URL = \"https://www.huduser.gov/portal/datasets/cp/2014thru2018-140-csv.zip\" \n", - " response = requests.get(HOUSING_FTP_URL, verify=True)\n", - " if response.status_code == 200:\n", - " file_contents = response.content\n", - " else:\n", - " sys.exit(\n", - " f\"HTTP response {response.status_code} from url {file_url}. Info: {response.content}\"\n", - " )\n", - "\n", - " # Write the contents to disk.\n", - " file = open(DOWNLOAD_FILENAME, \"wb\")\n", - " file.write(file_contents)\n", - " file.close()\n", - " \n", - "def extract_zipped_download(zip_file_path, unzipped_path):\n", - " with zipfile.ZipFile(zip_file_path, \"r\") as zip_ref:\n", - " zip_ref.extractall(unzipped_path)\n", - " # cleanup temporary file\n", - " os.remove(zip_file_path)\n", - " \n", - "def up_one_directory(path):\n", - " try:\n", - " # from Python 3.6\n", - " parent_dir = Path(path).parents[1]\n", - " # for Python 3.4/3.5, use str to convert the path to string\n", - " # parent_dir = str(Path(path).parents[1])\n", - " shutil.move(path, parent_dir)\n", - " except IndexError:\n", - " # no upper directory\n", - " pass\n", - "\n", - "CURRENT_DIRECTORY = os.getcwd()\n", - "download_hud_dataset()\n", - "extract_zipped_download(CURRENT_DIRECTORY + \"/HUD_ZIPPED.csv\", CURRENT_DIRECTORY) \n", - "up_one_directory(CURRENT_DIRECTORY + \"/140/Table8.csv\")\n", - "shutil.rmtree(\"./140/\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Extreme Housing Burden \n", - "\n", - "The Extreme Housing Burden indicator represents the proportion of low-income households that have to spend more than half their income on rent. These households experience higher levels of stress, report lower health, and may delay medical treatment because of its high cost.\n", - "\n", - "The Extreme Housing Burden indicator measures the percent of households in a census tract that are:\n", - "\n", - "1. Making less than 80% of the Area Median Family Income as determined by the Department of Housing and Urban Development (HUD), and\n", - "2. Paying greater than 50% of their income to housing costs. \n", - "\n", - "This data is sourced from the 2014-2018 Comprehensive Housing Affordability Strategy dataset from the Department of Housing and Urban Development (HUD) using the census tract geographic summary level, and contains cost burdens for households by percent HUD-adjusted median family income (HAMFI) category. This data can be found [here](https://www.huduser.gov/portal/datasets/cp.html). \n", - "\n", - "Because CHAS data is based on American Communities Survey (ACS) estimates, which come from a sample of the population, they may be unreliable if based on a small sample or population size.\n", - "\n", - "The standard error and relative standard error were used to evaluate the reliability of each estimate using CalEnviroScreen’s methodology. \n", - "\n", - "Census tract estimates that met either of the following criteria were considered reliable and included in the analysis [(CalEnviroScreen, 2017, page 129)](https://oehha.ca.gov/media/downloads/calenviroscreen/report/ces3report.pdf ):\n", - "\n", - "- Relative standard error less than 50 (meaning the standard error was less than half of the estimate), OR \n", - "- Standard error less than the mean standard error of all census tract estimates \n", - "\n", - "Formulas for calculating the standard error of sums, proportions, and ratio come from the [American Communities Survey Office](https://www2.census.gov/programs-surveys/acs/tech_docs/accuracy/MultiyearACSAccuracyofData2013.pdf).\n", - "\n", - "Note that this code creates a score and rank by state, for every state." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The relevant variables in table 8 of the CHAS dataset are the following (CHAS data dictionary available [here](https://www.huduser.gov/portal/datasets/cp/CHAS-data-dictionary-14-18.xlsx)):\n", - "\n", - "| Name | Label |\n", - "|---------|-----------------------------------------------------|\n", - "|T1_est1 | Total Occupied housing units | \n", - "|T8_est10 | Owner occupied less than or equal to 30% of HAMFI cost burden greater than 50% |\n", - "|T8_est23 |Owner occupied greater than 30% but less than or equal to 50% of HAMFI\tcost burden greater than 50%|\n", - "|T8_est36 |Owner occupied\tgreater than 50% but less than or equal to 80% of HAMFI\tcost burden greater than 50%|\n", - "|T8_est76 | Renter occupied less than or equal to 30% of HAMFI cost burden greater than 50% |\n", - "|T8_est89 |Renter occupied\tgreater than 30% but less than or equal to 50% of HAMFI\tcost burden greater than 50%|\n", - "|T8_est102|Renter occupied\tgreater than 50% but less than or equal to 80% of HAMFI\tcost burden greater than 50%|\n", - " " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Below I also propose an alternate means for ranking census tracts\n", - "### These steps are outlined and commented below" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/sarahluw/.pyenv/versions/3.6.2/envs/my-virtual-env-3.6.2/lib/python3.6/site-packages/pandas/core/series.py:726: RuntimeWarning: invalid value encountered in sqrt\n", - " result = getattr(ufunc, method)(*inputs, **kwargs)\n", - "/Users/sarahluw/.pyenv/versions/3.6.2/envs/my-virtual-env-3.6.2/lib/python3.6/site-packages/pandas/core/indexing.py:670: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " iloc._setitem_with_indexer(indexer, value)\n" - ] - } - ], - "source": [ - "# Read in the data from https://www.huduser.gov/portal/datasets/cp.html\n", - "housing = pd.read_csv(\"Table8.csv\", \n", - " encoding = \"ISO-8859-1\", \n", - " dtype = {'Tract_ID': object, 'st': object, 'geoid': object})\n", - "\n", - "# Remove data for states that aren't included in the census (e.g. American Samoa, Guam, etc.):\n", - "housing.drop(housing.loc[housing['st'] == '72'].index, inplace = True)\n", - "\n", - "# Combine owner and renter occupied low-income households that make less than 80% of HAMFI into one variable\n", - "housing['summed'] = (housing['T8_est10'] + \n", - " housing['T8_est23'] + \n", - " housing['T8_est36'] + \n", - " housing['T8_est76'] + \n", - " housing['T8_est89'] + \n", - " housing['T8_est102'])\n", - "\n", - "# Create a variable for the standard error of the summed variables\n", - "housing['summed_se'] = np.sqrt((housing['T8_moe10'] / 1.645)**2 + \n", - " (housing['T8_moe23'] / 1.645)**2 + \n", - " (housing['T8_moe36'] / 1.645)**2 + \n", - " (housing['T8_moe76'] / 1.645)**2 + \n", - " (housing['T8_moe89'] / 1.645)**2 + \n", - " (housing['T8_moe102'] / 1.645)**2)\n", - "\n", - "# Remove the first 7 digits in the FIPS Census Tract ID \n", - "housing['geoid'] = housing['geoid'].str[-11:]\n", - "\n", - "# Find the estimate of the proportion of the population that is heavily rent burdened\n", - "housing['hbrd_score'] = housing['summed'] / housing['T8_est1']\n", - "\n", - "# Change rates where the population is 0 to nan\n", - "housing['hbrd_score'].replace(np.inf, np.nan, inplace = True)\n", - "\n", - "# Create function for calculating the standard error, using the proportions standard error formula\n", - "# if the value under the radical is negative, use the ratio standard error formula\n", - "def se_prop(x, y, se_x, moe_y): \n", - " se_y = moe_y / 1.645\n", - " test = se_x**2 - (((x**2)/(y**2))*((se_y)**2))\n", - " se = np.where(test < 0,\n", - " (1/y) * np.sqrt(se_x**2 + (((x**2)/(y**2))*(se_y**2))), \n", - " (1/y) * np.sqrt(se_x**2 - (((x**2)/(y**2))*(se_y**2))))\n", - " return se\n", - "\n", - "housing['se'] = se_prop(housing['summed'], housing['T8_est1'], housing['summed_se'], housing['T8_moe1'])\n", - "\n", - "# Calculate the relative standard error\n", - "housing['rse'] = housing['se'] / housing['hbrd_score']*100\n", - "\n", - "# Change infinite rse's where the housing burden is 0 to np.nan\n", - "housing['rse'].replace(np.inf, np.nan, inplace = True)\n", - "\n", - "# Calculate the mean standard error for each state\n", - "housing['mean_state_se'] = np.zeros(len(housing))\n", - "\n", - "for state in housing['st'].unique():\n", - " mean_se = np.mean(housing[housing['st'] == state]['se'])\n", - " housing['mean_state_se'].loc[housing['st'] == state] = mean_se\n", - " \n", - "# Find census tract estimates that meet both of the following criteria and are thus considered unreliable estimates: \n", - "# RSE less than 50 AND\n", - "# SE less than the mean state SE or housing burdened low income households\n", - "# Convert these scores to nan\n", - "housing.loc[(housing['rse'] >= 50) & (housing['rse'] >= housing['mean_state_se']), 'hbrd_score'] = np.nan\n", - "\n", - "# Rename columns\n", - "housing = housing.rename(columns = {'geoid' :'FIPS_tract_id',\n", - " 'st' : 'state'\n", - " })\n", - "\n", - "# Calculate percentile rank for census tracts with a score above 0, set percentile to 0 if score is 0, for each state\n", - "housing['hbrd_rank'] = housing[\n", - " housing['hbrd_score'] != 0][['hbrd_score',\n", - " 'state']].groupby('state').rank( \n", - " na_option = 'keep', \n", - " pct = True) * 100\n", - "\n", - "housing.loc[housing['hbrd_score'] == 0, 'hbrd_rank'] = 0\n", - "\n", - "# Create final housing burden df\n", - "housingburden = housing.copy()" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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      5 rows Ă— 280 columns

      \n", - "
      " - ], - "text/plain": [ - " source sumlevel FIPS_tract_id \\\n", - "0 2014thru2018 140 01001020100 \n", - "1 2014thru2018 140 01001020200 \n", - "2 2014thru2018 140 01001020300 \n", - "3 2014thru2018 140 01001020400 \n", - "4 2014thru2018 140 01001020500 \n", - "\n", - " name state cnty tract T8_est1 \\\n", - "0 Census Tract 201, Autauga County, Alabama 01 1 20100 765 \n", - "1 Census Tract 202, Autauga County, Alabama 01 1 20200 720 \n", - "2 Census Tract 203, Autauga County, Alabama 01 1 20300 1295 \n", - "3 Census Tract 204, Autauga County, Alabama 01 1 20400 1640 \n", - "4 Census Tract 205, Autauga County, Alabama 01 1 20500 4175 \n", - "\n", - " T8_est2 T8_est3 ... T8_moe131 T8_moe132 T8_moe133 summed summed_se \\\n", - "0 570 50 ... 12 12 12 80 31.721807 \n", - "1 465 65 ... 12 12 12 138 45.531874 \n", - "2 840 60 ... 12 12 12 170 53.722921 \n", - "3 1260 15 ... 12 12 12 145 46.288510 \n", - "4 2320 175 ... 17 17 17 595 147.221693 \n", - "\n", - " hbrd_score se rse mean_state_se hbrd_rank \n", - "0 0.104575 0.041032 39.237314 0.036604 46.298077 \n", - "1 0.191667 0.061614 32.146659 0.036604 83.269231 \n", - "2 0.131274 0.040927 31.176999 0.036604 63.653846 \n", - "3 0.088415 0.027822 31.467397 0.036604 34.615385 \n", - "4 0.142515 0.034760 24.390193 0.036604 68.221154 \n", - "\n", - "[5 rows x 280 columns]" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "housingburden.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(73056, 280)" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "housingburden.shape" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### As desired we see a uniform distribution for the percentile rank for burdened households" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Now we compute for a baseline comparison " - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# Owner occupied numerator fields\n", - "OWNER_OCCUPIED_NUMERATOR_FIELDS = [\n", - " # Column Name\n", - " # Line_Type\n", - " # Tenure\n", - " # Household income\n", - " # Cost burden\n", - " # Facilities\n", - " \"T8_est7\",\n", - " # Subtotal\n", - " # Owner occupied\n", - " # less than or equal to 30% of HAMFI\n", - " # greater than 30% but less than or equal to 50%\n", - " # All\n", - " \"T8_est10\",\n", - " # Subtotal\n", - " # Owner occupied\n", - " # less than or equal to 30% of HAMFI\n", - " # greater than 50%\n", - " # All\n", - " \"T8_est20\",\n", - " \n", - " # Subtotal\n", - " # Owner occupied\n", - " # greater than 30% but less than or equal to 50% of HAMFI\n", - " # greater than 30% but less than or equal to 50%\n", - " # All\n", - " \"T8_est23\",\n", - " # Subtotal\n", - " # Owner occupied\n", - " # greater than 30% but less than or equal to 50% of HAMFI\n", - " # greater than 50%\n", - " # All\n", - " \"T8_est33\",\n", - " # Subtotal\n", - " # Owner occupied\n", - " # greater than 50% but less than or equal to 80% of HAMFI\n", - " # greater than 30% but less than or equal to 50%\n", - " # All\n", - " \"T8_est36\",\n", - " # Subtotal\n", - " # Owner occupied\n", - " # greater than 50% but less than or equal to 80% of HAMFI\n", - " # greater than 50%\n", - " # All\n", - "]\n", - "\n", - "# These rows have the values where HAMFI was not computed, b/c of no or negative income.\n", - "OWNER_OCCUPIED_NOT_COMPUTED_FIELDS = [\n", - " # Column Name\n", - " # Line_Type\n", - " # Tenure\n", - " # Household income\n", - " # Cost burden\n", - " # Facilities\n", - " \"T8_est13\",\n", - " # Subtotal\n", - " # Owner occupied\n", - " # less than or equal to 30% of HAMFI\n", - " # not computed (no/negative income)\n", - " # All\n", - " \"T8_est26\",\n", - " # Subtotal\n", - " # Owner occupied\n", - " # greater than 30% but less than or equal to 50% of HAMFI\n", - " # not computed (no/negative income)\n", - " # All\n", - " \"T8_est39\",\n", - " # Subtotal\n", - " # Owner occupied\n", - " # greater than 50% but less than or equal to 80% of HAMFI\n", - " # not computed (no/negative income)\n", - " # All\n", - " \"T8_est52\",\n", - " # Subtotal\n", - " # Owner occupied\n", - " # greater than 80% but less than or equal to 100% of HAMFI\n", - " # not computed (no/negative income)\n", - " # All\n", - " \"T8_est65\",\n", - " # Subtotal\n", - " # Owner occupied\n", - " # greater than 100% of HAMFI\n", - " # not computed (no/negative income)\n", - " # All\n", - "]\n", - "\n", - "OWNER_OCCUPIED_POPULATION_FIELD = \"T8_est2\"\n", - "# Subtotal\n", - "# Owner occupied\n", - "# All\n", - "# All\n", - "# All\n", - "\n", - "OWNER_OCCUPIED_POPULATION_HAMFI_FIELD = \"T8_est3\"\n", - "# Subtotal\n", - "# Owner occupied \n", - "# All\n", - "# All\n", - "# All\n", - "\n", - "# Renter occupied numerator fields\n", - "RENTER_OCCUPIED_NUMERATOR_FIELDS = [\n", - " # Column Name\n", - " # Line_Type\n", - " # Tenure\n", - " # Household income\n", - " # Cost burden\n", - " # Facilities\n", - " \"T8_est73\",\n", - " # Subtotal\n", - " # Renter occupied\n", - " # less than or equal to 30% of HAMFI\n", - " # greater than 30% but less than or equal to 50%\n", - " # All\n", - " \"T8_est76\",\n", - " # Subtotal\n", - " # Renter occupied\n", - " # less than or equal to 30% of HAMFI\n", - " # greater than 50%\n", - " # All\n", - " \"T8_est86\",\n", - " # Subtotal\n", - " # Renter occupied\n", - " # greater than 30% but less than or equal to 50% of HAMFI\n", - " # greater than 30% but less than or equal to 50%\n", - " # All\n", - " \"T8_est89\",\n", - " # Subtotal\n", - " # Renter occupied\n", - " # greater than 30% but less than or equal to 50% of HAMFI\n", - " # greater than 50%\n", - " # All\n", - " \"T8_est99\",\n", - " # Subtotal\n", - " # Renter occupied\tgreater than 50% but less than or equal to 80% of HAMFI\n", - " # greater than 30% but less than or equal to 50%\n", - " # All\n", - " \"T8_est102\",\n", - " # Subtotal\n", - " # Renter occupied\n", - " # greater than 50% but less than or equal to 80% of HAMFI\n", - " # greater than 50%\n", - " # All\n", - "]\n", - "\n", - "# These rows have the values where HAMFI was not computed, b/c of no or negative income.\n", - "RENTER_OCCUPIED_NOT_COMPUTED_FIELDS = [\n", - " # Column Name\n", - " # Line_Type\n", - " # Tenure\n", - " # Household income\n", - " # Cost burden\n", - " # Facilities\n", - " \"T8_est79\",\n", - " # Subtotal\n", - " # Renter occupied\tless than or equal to 30% of HAMFI\n", - " # not computed (no/negative income)\n", - " # All\n", - " \"T8_est92\",\n", - " # Subtotal\n", - " # Renter occupied\tgreater than 30% but less than or equal to 50% of HAMFI\n", - " # not computed (no/negative income)\n", - " # All\n", - " \"T8_est105\",\n", - " # Subtotal\n", - " # Renter occupied\n", - " # greater than 50% but less than or equal to 80% of HAMFI\n", - " # not computed (no/negative income)\n", - " # All\n", - " \"T8_est118\",\n", - " # Subtotal\n", - " # Renter occupied\tgreater than 80% but less than or equal to 100% of HAMFI\n", - " # not computed (no/negative income)\n", - " # All\n", - " \"T8_est131\",\n", - " # Subtotal\n", - " # Renter occupied\n", - " # greater than 100% of HAMFI\n", - " # not computed (no/negative income)\n", - " # All\n", - "]\n", - "\n", - "# T8_est68\tSubtotalRenter occupied\tAll\tAll\tAll\n", - "RENTER_OCCUPIED_POPULATION_FIELD = \"T8_est68\"" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "housingburden[\"current_summed_methodology\"] = housingburden[\n", - " OWNER_OCCUPIED_NUMERATOR_FIELDS\n", - "].sum(axis=1) + housingburden[RENTER_OCCUPIED_NUMERATOR_FIELDS].sum(axis=1)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "housingburden[\"current_methodology_denominator\"] = (\n", - " housingburden[OWNER_OCCUPIED_POPULATION_FIELD]\n", - " + housingburden[RENTER_OCCUPIED_POPULATION_FIELD]\n", - " - housingburden[OWNER_OCCUPIED_NOT_COMPUTED_FIELDS].sum(axis=1)\n", - " - housingburden[RENTER_OCCUPIED_NOT_COMPUTED_FIELDS].sum(axis=1)\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "housingburden[\"current_methodology_denominator_sans_not_computed\"] = (\n", - " housingburden[OWNER_OCCUPIED_POPULATION_FIELD]\n", - " + housingburden[RENTER_OCCUPIED_POPULATION_FIELD]\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "housingburden[\"current_methodology_percent\"] = np.round(\n", - " (housingburden[\"current_summed_methodology\"] / housingburden[\"current_methodology_denominator\"] ), 2) * 100" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Now we construct the distribution of differences in the number of owned and rented burdened households\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Percentiles Comparison" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "final_df = housingburden[['FIPS_tract_id', 'state','hbrd_rank','hbrd_score', 'summed', \n", - " 'current_summed_methodology', 'T8_est1', \n", - " \"current_methodology_denominator_sans_not_computed\",\n", - " 'current_methodology_denominator', 'current_methodology_percent']]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### First notice here that **T8_est1** and **current_methodology_denominator** should represent same or similar aggregates. In general, we cen see that the current computation performed results in a differerntial that undercounts the total occupied and rental households." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/sarahluw/.pyenv/versions/3.6.2/envs/my-virtual-env-3.6.2/lib/python3.6/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " \n" - ] - } - ], - "source": [ - "final_df[\"differences_aggregate_denominator\"] = (\n", - " final_df[\"current_methodology_denominator\"] - final_df[\"T8_est1\"] \n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/sarahluw/.pyenv/versions/3.6.2/envs/my-virtual-env-3.6.2/lib/python3.6/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " \n" - ] - } - ], - "source": [ - "final_df[\"differences_aggregate_denominator_sans_not_computed\"] = (\n", - " final_df[\"current_methodology_denominator\"] - final_df[\"T8_est1\"] \n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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      " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(12, 8))\n", - "plt.title('Distribution of differences between aggregate totals that normalizes tabulation of poverty households')\n", - "# Set x-axis label\n", - "plt.xlabel('Aggregate differences in total owner and renter occupied low-income households < 80%')\n", - "# Set y-axis label\n", - "plt.ylabel('Relative Frequency in Support')\n", - "\n", - "sns.histplot(final_df[\"differences_aggregate_denominator\"])" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
      " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(12, 8))\n", - "plt.title('Distribution of differences between aggregate totals that normalizes tabulation of poverty households (with removal of not computed fields) ')\n", - "# Set x-axis label\n", - "plt.xlabel('Aggregate differences in total owner and renter occupied low-income households')\n", - "# Set y-axis label\n", - "plt.ylabel('Relative Frequency in Support')\n", - "\n", - "sns.histplot(final_df[\"differences_aggregate_denominator_sans_not_computed\"])" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/sarahluw/.pyenv/versions/3.6.2/envs/my-virtual-env-3.6.2/lib/python3.6/site-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " after removing the cwd from sys.path.\n" - ] - } - ], - "source": [ - "final_df[\"current_methodology_percentile_rank\"] = final_df[\"current_methodology_percent\"].rank(\n", - " pct=True,\n", - " # Set ascending to the parameter value.\n", - " ascending=True,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/sarahluw/.pyenv/versions/3.6.2/envs/my-virtual-env-3.6.2/lib/python3.6/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " \"\"\"Entry point for launching an IPython kernel.\n", - "/Users/sarahluw/.pyenv/versions/3.6.2/envs/my-virtual-env-3.6.2/lib/python3.6/site-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " after removing the cwd from sys.path.\n" - ] - } - ], - "source": [ - "final_df[\"new_threshold_exceeded\"] = (final_df['hbrd_rank'] >= 90)\n", - "\n", - "final_df[\"current_threshold_exceeded\"] = (final_df[\n", - " 'current_methodology_percentile_rank'] >= 0.90)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Only include non-NA tracts for comparison purposes" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "# first save NA tracts that were considered unreliable\n", - "ineligible_tracts = list(final_df[final_df[\"hbrd_rank\"].isna()][\"FIPS_tract_id\"].values)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### How many tracts are ineligible according to CalEnvironScreen but are considerd in Score L?\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "final_current_methodology = final_df[final_df[\"current_methodology_percentile_rank\"] >= 0.90]" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(7323, 15)" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "final_current_methodology.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(53, 15)" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# 53 tracts\n", - "final_current_methodology[\n", - " final_current_methodology.FIPS_tract_id.isin(ineligible_tracts)].shape" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "non_null_df = final_df[~final_df[\"hbrd_rank\"].isna()]" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(7270, 15)" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# number of tracks eligible\n", - "non_null_df[non_null_df[\"current_methodology_percentile_rank\"] >= 0.90].shape" - ] - }, - { - "cell_type": "code", - "execution_count": 85, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 85, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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      FIPS_tract_idstatehbrd_rankhbrd_scoresummedcurrent_summed_methodologyT8_est1current_methodology_denominator_sans_not_computedcurrent_methodology_denominatorcurrent_methodology_percentdifferences_aggregate_denominatordifferences_aggregate_denominator_sans_not_computedcurrent_methodology_percentile_ranknew_threshold_exceededcurrent_threshold_exceeded
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      \n", - "
      " - ], - "text/plain": [ - " FIPS_tract_id state hbrd_rank hbrd_score summed \\\n", - "0 01001020100 01 46.298077 0.104575 80 \n", - "1 01001020200 01 83.269231 0.191667 138 \n", - "2 01001020300 01 63.653846 0.131274 170 \n", - "3 01001020400 01 34.615385 0.088415 145 \n", - "4 01001020500 01 68.221154 0.142515 595 \n", - "\n", - " current_summed_methodology T8_est1 \\\n", - "0 174 765 \n", - "1 177 720 \n", - "2 279 1295 \n", - "3 274 1640 \n", - "4 885 4175 \n", - "\n", - " current_methodology_denominator_sans_not_computed \\\n", - "0 765 \n", - "1 720 \n", - "2 1295 \n", - "3 1635 \n", - "4 4175 \n", - "\n", - " current_methodology_denominator current_methodology_percent \\\n", - "0 765 23.0 \n", - "1 720 25.0 \n", - "2 1291 22.0 \n", - "3 1635 17.0 \n", - "4 4135 21.0 \n", - "\n", - " differences_aggregate_denominator \\\n", - "0 0 \n", - "1 0 \n", - "2 -4 \n", - "3 -5 \n", - "4 -40 \n", - "\n", - " differences_aggregate_denominator_sans_not_computed \\\n", - "0 0 \n", - "1 0 \n", - "2 -4 \n", - "3 -5 \n", - "4 -40 \n", - "\n", - " current_methodology_percentile_rank new_threshold_exceeded \\\n", - "0 0.512833 False \n", - "1 0.575315 False \n", - "2 0.479242 False \n", - "3 0.289696 False \n", - "4 0.444306 False \n", - "\n", - " current_threshold_exceeded \n", - "0 False \n", - "1 False \n", - "2 False \n", - "3 False \n", - "4 False " - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "non_null_df.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [], - "source": [ - "net_difference = non_null_df[(non_null_df[\n", - " \"current_threshold_exceeded\"] != non_null_df[\"new_threshold_exceeded\"])]" - ] - }, - { - "cell_type": "code", - "execution_count": 89, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(2741, 15)" - ] - }, - "execution_count": 89, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "predicate_1 = (net_difference['current_threshold_exceeded'] == True) & (net_difference['new_threshold_exceeded'] == False)\n", - "\n", - "net_difference[predicate_1].shape" - ] - }, - { - "cell_type": "code", - "execution_count": 102, - "metadata": {}, - "outputs": [], - "source": [ - "# Where Score L is considered burdened but not in CalEnviroScreen\n", - "score_l_considered_burdened = net_difference[predicate_1]" - ] - }, - { - "cell_type": "code", - "execution_count": 109, - "metadata": {}, - "outputs": [], - "source": [ - "# Inserted after the basic stats definition.\n", - "# Load demographic data\n", - "import pathlib\n", - "\n", - "DATA_DIR = pathlib.Path.cwd().parent / \"data\"\n", - "COMPARISON_OUTPUTS_DIR = DATA_DIR / \"comparison_outputs\"\n", - "\n", - "demographics_path = DATA_DIR / \"dataset\" / \"census_acs_2019\" / \"usa.csv\"\n", - "\n", - "demographics_df = pd.read_csv(\n", - " demographics_path,\n", - " dtype={\"GEOID10_TRACT\": \"string\"},\n", - ")\n", - "\n", - "# Set some field names\n", - "BLACK_FIELD_NAME = \"Black or African American alone\"\n", - "AMERICAN_INDIAN_FIELD_NAME = \"American Indian and Alaska Native alone\"\n", - "ASIAN_FIELD_NAME = \"Asian alone\"\n", - "HAWAIIAN_FIELD_NAME = \"Native Hawaiian and Other Pacific alone\"\n", - "TWO_OR_MORE_RACES_FIELD_NAME = \"Two or more races\"\n", - "NON_HISPANIC_WHITE_FIELD_NAME = \"Non-Hispanic White\"\n", - "HISPANIC_FIELD_NAME = \"Hispanic or Latino\"\n", - "PERCENT_PREFIX = \"Percent \"\n", - "\n", - "RE_OUTPUT_FIELDS = [\n", - " BLACK_FIELD_NAME,\n", - " AMERICAN_INDIAN_FIELD_NAME,\n", - " ASIAN_FIELD_NAME,\n", - " HAWAIIAN_FIELD_NAME,\n", - " TWO_OR_MORE_RACES_FIELD_NAME,\n", - " NON_HISPANIC_WHITE_FIELD_NAME,\n", - " HISPANIC_FIELD_NAME,\n", - "]\n", - "\n", - "RE_PERCENT_OUTPUT_FIELDS = [PERCENT_PREFIX + field for field in RE_OUTPUT_FIELDS]\n", - "\n", - "columns_to_keep = (\n", - " [\"GEOID10_TRACT\"]\n", - " + RE_OUTPUT_FIELDS\n", - " + RE_PERCENT_OUTPUT_FIELDS\n", - " + ['Percent of individuals < 200% Federal Poverty Line', \n", - " 'Median value ($) of owner-occupied housing units',\n", - " 'Percent individuals age 25 or over with less than high school degree',\n", - " 'Percent enrollment in college or graduate school',\n", - " 'Linguistic isolation (percent)']\n", - ")\n", - "\n", - "\n", - "def highlight_medians(s):\n", - " # highlight if the current median is greater than the median of medians for that series\n", - " is_greater_than_median = s > s.median()\n", - " return ['color: pink; background-color:#7272FE'\n", - " if cell else '' for cell in is_greater_than_median]\n", - "# Join the demographics in.\n", - "merged_df_score_l = score_l_considered_burdened.merge(\n", - " demographics_df[columns_to_keep],\n", - " left_on=\"FIPS_tract_id\",\n", - " right_on=\"GEOID10_TRACT\",\n", - " how=\"inner\"\n", - ")\n", - "\n", - "# these are not converted into percent 0 - 100 scale\n", - "percent_cols = [x for x in merged_df.columns if \n", - " 'Percent' in x or '(percent)' in x\n", - " ]\n", - "\n", - "merged_df_score_l[\n", - " percent_cols] = merged_df_score_l[\n", - " percent_cols].apply(lambda x: x * 100)\n", - "\n", - "for idx, row in merged_df_score_l.iterrows():\n", - " current_row = str(merged_df_score_l.loc[idx, 'state'])\n", - " state = mapping.get(current_row, None)\n", - " merged_df_score_l.loc[idx, 'state_name'] = state\n", - "\n", - "grouped_stats_score_l = merged_df_score_l.groupby([\"state_name\"]).agg({\n", - " 'GEOID10_TRACT': 'nunique',\n", - " 'Percent of individuals < 200% Federal Poverty Line': [np.median, np.std],\n", - " 'Median value ($) of owner-occupied housing units': [np.median, np.std],\n", - " 'Percent individuals age 25 or over with less than high school degree': [np.median, np.std],\n", - " 'Percent enrollment in college or graduate school': [np.median, np.std],\n", - " 'Percent Black or African American alone': [np.median, np.std],\n", - " 'Percent American Indian and Alaska Native alone': [np.median, np.std],\n", - " 'Percent Non-Hispanic White': [np.median, np.std], \n", - " 'Linguistic isolation (percent)': [np.median, np.std],\n", - " 'Percent Hispanic or Latino': [np.median, np.std],\n", - " 'hbrd_rank': [np.median, np.std],\n", - " 'current_methodology_percent': [np.median, np.std],\n", - " 'current_summed_methodology': [np.median, np.std, np.sum]\n", - "}).reset_index()\n", - "\n", - "\n", - "grouped_stats_score_l.columns = [' '.join(col).strip() for \n", - " col in grouped_stats_score_l.columns.values]\n", - "\n", - "grouped_stats_score_l = grouped_stats_score_l[[x for x in grouped_stats_score_l \n", - " if \"median\" in x and \n", - " \"Percent\" in x] + [\"GEOID10_TRACT nunique\", \n", - " 'current_summed_methodology sum', \"state_name\"]]\n", - "\n", - "grouped_stats_score_l.set_index(\"state_name\", inplace=True)\n", - "\n", - "grouped_stats_score_l = grouped_stats_score_l.rename(columns={\n", - "'Percent of individuals < 200% Federal Poverty Line median'\n", - " : 'Percent of individuals < 200% Federal Poverty Line (median across all tracts)', \n", - "'Percent individuals age 25 or over with less than high school degree median':\n", - "'Percent individuals age 25 or over with less than high school degree (median across all tracts)', \n", - "'Percent enrollment in college or graduate school median'\n", - " :'Percent enrollment in college or graduate school (median across all tracts)',\n", - "'Percent Black or African American alone median':\n", - " 'Percent Black or African American alone (median across all tracts)',\n", - "'Percent American Indian and Alaska Native alone median':\n", - " 'Percent American Indian and Alaska Native alone (median across all tracts)',\n", - "'Percent Non-Hispanic White median':\n", - " 'Percent Non-Hispanic White (median across all tracts)',\n", - "'Percent Hispanic or Latino median':\n", - " 'Percent Hispanic or Latino (median across all tracts)',\n", - "'GEOID10_TRACT nunique': \"Total Number of Unique Tracts\",\n", - "\"current_summed_methodology sum\": \"Total Owned and Rented Burdened Households (Current Aggregation Methodology)\" \n", - "})" - ] - }, - { - "cell_type": "code", - "execution_count": 114, - "metadata": {}, - "outputs": [], - "source": [ - "bar_styled_score_l = grouped_stats_score_l.style.bar(\n", - 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      Percent of individuals < 200% Federal Poverty Line (median across all tracts) Percent individuals age 25 or over with less than high school degree (median across all tracts) Percent enrollment in college or graduate school (median across all tracts) Percent Black or African American alone (median across all tracts) Percent American Indian and Alaska Native alone (median across all tracts) Percent Non-Hispanic White (median across all tracts) Percent Hispanic or Latino (median across all tracts) Total Number of Unique Tracts Total Owned and Rented Burdened Households (Current Aggregation Methodology)
      state_name
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      AR62.78581218.6580529.05639172.6277140.10245911.6507574.49709742287
      AZ67.89663235.3178426.8035856.8779112.06493020.32014363.4241583018999
      CA52.18890133.3458789.1456794.1038560.4856049.28976866.540085840576225
      CO50.79802714.9253737.9512898.3552821.21033228.21264936.3037751915128
      CT52.66257019.7992198.79765422.8709680.00000026.31832436.7860945337383
      DC50.88139715.8562376.47948291.5753210.0000002.9838713.0153121510389
      DE43.17124710.0204503.49417627.0983210.94004641.69626113.62909732343
      FL54.78608320.4034317.37691126.8486920.00000017.74120526.870748161116939
      GA58.31884118.8155468.75099466.0021550.00000010.8166006.2990895945553
      HI35.98409812.5218038.3583681.7069770.0000009.5053015.326514126598
      IA56.50446910.3132162.7076929.8311820.74478689.4240320.0000001449
      ID56.68056415.42500816.0661010.2475690.05305068.36427927.7630421945
      IL57.13485219.1528407.23355841.4607730.0000006.83374815.93533614881294
      IN60.36199119.8126806.40301341.5031470.00000040.1332849.3698372315013
      KS61.78077210.6703358.65369823.7452410.87474356.91916715.92658721352
      KY63.82604324.7813416.68640518.5387130.57803560.2690749.63391174393
      LA63.48164921.2974307.83115077.6525200.00000015.0530502.0414484325262
      MA51.64444422.6301127.82520316.0096540.00000031.34865132.9847146143002
      MD47.03989717.6980206.49960865.9347550.00000012.3831335.8508603118837
      ME54.94023916.3678434.6161320.5553350.00000084.9662830.4760021460
      MI63.34328416.9279007.13058473.7889850.00000014.2468241.2779556130101
      MN58.24654729.02049613.32462039.2776002.30616319.89362430.51320121111
      MO57.44125315.1291513.85446776.9371730.00000016.0209422.467344116857
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      " - ], - "text/plain": [ - "" - ] - }, - "execution_count": 115, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bar_styled_score_l" - ] - }, - { - "cell_type": "code", - "execution_count": 116, - "metadata": {}, - "outputs": [], - "source": [ - "styled_score_l = grouped_stats_score_l.style.apply(highlight_medians)" - ] - }, - { - "cell_type": "code", - "execution_count": 120, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - 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      Percent of individuals < 200% Federal Poverty Line (median across all tracts) Percent individuals age 25 or over with less than high school degree (median across all tracts) Percent enrollment in college or graduate school (median across all tracts) Percent Black or African American alone (median across all tracts) Percent American Indian and Alaska Native alone (median across all tracts) Percent Non-Hispanic White (median across all tracts) Percent Hispanic or Latino (median across all tracts) Total Number of Unique Tracts Total Owned and Rented Burdened Households (Current Aggregation Methodology)
      state_name
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      AR62.78581218.6580529.05639172.6277140.10245911.6507574.49709742287
      AZ67.89663235.3178426.8035856.8779112.06493020.32014363.4241583018999
      CA52.18890133.3458789.1456794.1038560.4856049.28976866.540085840576225
      CO50.79802714.9253737.9512898.3552821.21033228.21264936.3037751915128
      CT52.66257019.7992198.79765422.8709680.00000026.31832436.7860945337383
      DC50.88139715.8562376.47948291.5753210.0000002.9838713.0153121510389
      DE43.17124710.0204503.49417627.0983210.94004641.69626113.62909732343
      FL54.78608320.4034317.37691126.8486920.00000017.74120526.870748161116939
      GA58.31884118.8155468.75099466.0021550.00000010.8166006.2990895945553
      HI35.98409812.5218038.3583681.7069770.0000009.5053015.326514126598
      IA56.50446910.3132162.7076929.8311820.74478689.4240320.0000001449
      ID56.68056415.42500816.0661010.2475690.05305068.36427927.7630421945
      IL57.13485219.1528407.23355841.4607730.0000006.83374815.93533614881294
      IN60.36199119.8126806.40301341.5031470.00000040.1332849.3698372315013
      KS61.78077210.6703358.65369823.7452410.87474356.91916715.92658721352
      KY63.82604324.7813416.68640518.5387130.57803560.2690749.63391174393
      LA63.48164921.2974307.83115077.6525200.00000015.0530502.0414484325262
      MA51.64444422.6301127.82520316.0096540.00000031.34865132.9847146143002
      MD47.03989717.6980206.49960865.9347550.00000012.3831335.8508603118837
      ME54.94023916.3678434.6161320.5553350.00000084.9662830.4760021460
      MI63.34328416.9279007.13058473.7889850.00000014.2468241.2779556130101
      MN58.24654729.02049613.32462039.2776002.30616319.89362430.51320121111
      MO57.44125315.1291513.85446776.9371730.00000016.0209422.467344116857
      MS66.20366018.0787666.21738094.8784050.0000003.9307200.71454183868
      NC63.94933428.7051484.70401746.3207550.29252018.76923120.4315281912567
      NE62.76567147.1637694.5857990.8211921.11258328.31788167.8145701495
      NH49.84622518.8016185.0095024.6453460.00000073.45290213.49966742045
      NJ46.91510721.5162877.05805024.0194120.00000012.37740146.025136158106317
      NV57.43692523.2876545.03340711.7136650.59562428.48294844.7116753022733
      NY47.65609724.2640698.25713525.7262290.00000011.39643228.944737588413485
      OH67.32936920.1264705.74308270.9250590.00000020.0292753.6286546833904
      OK73.17715222.3287674.36324918.1390372.18978144.16058427.37226351357
      OR53.86003618.5121927.3918682.8698860.68837953.60466429.77829686408
      PA61.39221119.7604796.88259133.0663620.00000024.41661911.6991645126975
      RI61.53964725.7508446.40419516.7547030.45375222.64266349.720052127835
      SC56.54455916.8884439.84424061.0905330.00000030.1401225.667862126952
      TN61.55836322.1389936.12393780.8630390.00000011.1824776.2540722116124
      TX61.08414228.2543896.78503518.8251950.00000012.01619055.03788910373087
      UT57.19201718.71345011.7765140.5047032.59233841.47740353.6820371595
      VA52.26472116.4648916.13181653.9263980.00000021.9739919.3340062720083
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      " - ], - "text/plain": [ - "" - ] - }, - "execution_count": 120, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "styled_score_l" - ] - }, - { - "cell_type": "code", - "execution_count": 118, - "metadata": {}, - "outputs": [], - "source": [ - "styled_score_l.to_excel(\"score_l_burdened_not_calenviroscreen.xlsx\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# CalenviroScreen Burden" - ] - }, - { - "cell_type": "code", - "execution_count": 122, - "metadata": {}, - "outputs": [], - "source": [ - "predicate_2 = (net_difference['current_threshold_exceeded'] == False) & (net_difference['new_threshold_exceeded'] == True)\n", - "\n", - "cal_ej_screen_burdened = net_difference[predicate_2]" - ] - }, - { - "cell_type": "code", - "execution_count": 123, - "metadata": {}, - "outputs": [], - "source": [ - "# Join the demographics in.\n", - "merged_df_score_ejcreen = cal_ej_screen_burdened.merge(\n", - " demographics_df[columns_to_keep],\n", - " left_on=\"FIPS_tract_id\",\n", - " right_on=\"GEOID10_TRACT\",\n", - " how=\"inner\"\n", - ")\n", - "\n", - "# these are not converted into percent 0 - 100 scale\n", - "percent_cols = [x for x in merged_df_score_ejcreen.columns if \n", - " 'Percent' in x or '(percent)' in x\n", - " ]\n", - "\n", - "merged_df_score_ejcreen[\n", - " percent_cols] = merged_df_score_ejcreen[\n", - " percent_cols].apply(lambda x: x * 100)\n", - "\n", - "for idx, row in merged_df_score_ejcreen.iterrows():\n", - " current_row = str(merged_df_score_ejcreen.loc[idx, 'state'])\n", - " state = mapping.get(current_row, None)\n", - " merged_df_score_ejcreen.loc[idx, 'state_name'] = state\n", - "\n", - "grouped_stats_score_ej_screen = merged_df_score_ejcreen.groupby([\"state_name\"]).agg({\n", - " 'GEOID10_TRACT': 'nunique',\n", - " 'Percent of individuals < 200% Federal Poverty Line': [np.median, np.std],\n", - " 'Median value ($) of owner-occupied housing units': [np.median, np.std],\n", - " 'Percent individuals age 25 or over with less than high school degree': [np.median, np.std],\n", - " 'Percent enrollment in college or graduate school': [np.median, np.std],\n", - " 'Percent Black or African American alone': [np.median, np.std],\n", - " 'Percent American Indian and Alaska Native alone': [np.median, np.std],\n", - " 'Percent Non-Hispanic White': [np.median, np.std], \n", - " 'Linguistic isolation (percent)': [np.median, np.std],\n", - " 'Percent Hispanic or Latino': [np.median, np.std],\n", - " 'hbrd_rank': [np.median, np.std],\n", - " 'current_methodology_percent': [np.median, np.std],\n", - " 'current_summed_methodology': [np.median, np.std, np.sum]\n", - "}).reset_index()\n", - "\n", - "\n", - "grouped_stats_score_ej_screen.columns = [' '.join(col).strip() for \n", - " col in grouped_stats_score_ej_screen.columns.values]\n", - "\n", - "grouped_stats_score_ej_screen = grouped_stats_score_ej_screen[[x for x in grouped_stats_score_ej_screen \n", - " if \"median\" in x and \n", - " \"Percent\" in x] + [\"GEOID10_TRACT nunique\", \n", - " 'current_summed_methodology sum', \"state_name\"]]\n", - "\n", - "grouped_stats_score_ej_screen.set_index(\"state_name\", inplace=True)\n", - "\n", - "grouped_stats_score_ej_screen = grouped_stats_score_ej_screen.rename(columns={\n", - "'Percent of individuals < 200% Federal Poverty Line median'\n", - " : 'Percent of individuals < 200% Federal Poverty Line (median across all tracts)', \n", - "'Percent individuals age 25 or over with less than high school degree median':\n", - "'Percent individuals age 25 or over with less than high school degree (median across all tracts)', \n", - "'Percent enrollment in college or graduate school median'\n", - " :'Percent enrollment in college or graduate school (median across all tracts)',\n", - "'Percent Black or African American alone median':\n", - " 'Percent Black or African American alone (median across all tracts)',\n", - "'Percent American Indian and Alaska Native alone median':\n", - " 'Percent American Indian and Alaska Native alone (median across all tracts)',\n", - "'Percent Non-Hispanic White median':\n", - " 'Percent Non-Hispanic White (median across all tracts)',\n", - "'Percent Hispanic or Latino median':\n", - " 'Percent Hispanic or Latino (median across all tracts)',\n", - "'GEOID10_TRACT nunique': \"Total Number of Unique Tracts\",\n", - "\"current_summed_methodology sum\": \"Total Owned and Rented Burdened Households (Current Aggregation Methodology)\" \n", - "})" - ] - }, - { - "cell_type": "code", - "execution_count": 124, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(2272, 36)" - ] - }, - "execution_count": 124, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "merged_df_score_ejcreen.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 126, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "2272" - ] - }, - "execution_count": 126, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "grouped_stats_score_ej_screen['Total Number of Unique Tracts'].sum()" - ] - }, - { - "cell_type": "code", - "execution_count": 127, - "metadata": {}, - "outputs": [], - "source": [ - "bar_styled_ej_screen = grouped_stats_score_ej_screen.style.bar(\n", - " subset=grouped_stats_score_ej_screen.columns, align='mid', color=['#d65f5f', '#5fba7d'])" - ] - }, - { - "cell_type": "code", - "execution_count": 128, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - 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      Percent of individuals < 200% Federal Poverty Line (median across all tracts) Percent individuals age 25 or over with less than high school degree (median across all tracts) Percent enrollment in college or graduate school (median across all tracts) Percent Black or African American alone (median across all tracts) Percent American Indian and Alaska Native alone (median across all tracts) Percent Non-Hispanic White (median across all tracts) Percent Hispanic or Latino (median across all tracts) Total Number of Unique Tracts Total Owned and Rented Burdened Households (Current Aggregation Methodology)
      state_name
      AK32.57785710.6818186.2790705.63882912.55868555.64245811.153285115004
      AL57.12393815.9896757.56397873.0238130.00000017.4479262.6160734222529
      AR57.35376017.0561915.11363652.3086650.00000034.9146115.3937433919130
      AZ54.30176223.3384187.0756255.5165242.98060729.27350454.6663266942252
      CA36.47130316.5462909.8486303.8562090.06978431.16028726.3238925734081
      CO45.53006314.1091667.8619193.0166341.06052352.63855226.1706955332424
      CT32.32465412.9340058.22306230.8233640.00000035.70255733.05184153597
      DC30.6557776.57853617.06884625.6285580.00000052.7733189.1400892716
      DE41.80097211.2518637.99319754.9051940.35814832.1242106.924979115159
      FL41.98744212.4903437.84136213.3955750.00000037.30916023.9107057345897
      GA57.69491416.0821539.55094664.4906280.09348720.4973104.1263496232938
      HI31.0869573.4203428.0929962.6030370.09480545.4446854.94736851827
      IA45.55075311.0216867.44970911.4546560.01277569.8716297.9979915426215
      ID51.9584195.7815158.8763120.5593731.06859084.3568487.9465512417542
      IL51.97947213.8489217.86427136.4231740.00000031.8832289.8849025526892
      IN58.24832917.3753875.68335634.1874340.00000038.7855398.8772855931457
      KS56.51626918.2332798.02623811.4993510.40810955.45402516.8910104822521
      KY55.03160718.0973715.82383319.1138350.00000066.4021754.1783716431935
      LA55.13853217.8025806.43234669.6881430.00000026.8866943.648757165523
      MA42.70767314.8974456.8201757.5939560.00000050.70831817.8142402314780
      MD37.93936714.2735047.84150744.0864020.00000022.6541958.0500895727737
      ME42.3752307.2452328.5389521.4356000.43946790.8797561.3420082614901
      MI56.88949915.8593116.44075267.0801350.00000022.3194022.6280058436969
      MN42.32558111.27971710.18301316.7792790.54782058.2260208.0039048947853
      MO52.46449515.2147977.43187429.8350820.00000051.9408504.8459964120105
      MS57.71878117.0385406.79012370.0152210.00000026.8817200.6628003516392
      MT45.0789855.35533213.3026610.1291611.84455488.8817273.6506382013069
      NC55.79595017.4128607.51599939.8244240.21400738.2738999.2800209057578
      ND43.2432436.99017916.4667397.1492562.12194877.9402937.502267138858
      NE55.2813289.48750013.8573178.3841390.47776665.9887749.7988842813947
      NH38.51253211.4067306.7050932.5656560.00000087.6694744.3754032013896
      NJ40.98778012.8032749.39597334.3605550.00000018.00051820.26089473691
      NM53.60195416.7253529.3477502.7661803.11614729.44009657.2348172714087
      NV49.95147220.6551015.81651112.2139930.51863925.97092636.024189188040
      NY35.28700915.8296259.12792736.1632650.0000006.91886513.014754156102
      OH62.16293717.5000005.58213754.6029520.00000036.4298723.0000008933549
      OK55.90928916.6415667.09677412.9116123.44462148.79032313.6877356527277
      OR43.54733410.8349518.0594461.6923590.79872268.77328713.5010944332728
      PA51.82364412.9706607.98263630.2978110.00000031.6046774.96915011460268
      RI33.4419826.86553034.4688767.1856290.10888865.0469589.97938173697
      SC52.83505214.5844226.07734842.0417830.00000042.8742694.4242135529033
      SD47.8048787.6506438.2104653.6046323.99484575.2877454.7727851911063
      TN52.86541417.2302007.61171849.7008150.00000037.8229904.1185315229145
      TX52.85451023.4685546.80347911.2162920.00000019.00769443.565114206131757
      UT44.7908829.4339709.3828001.5999280.38123568.92157120.1296443219148
      VA39.51673812.2543958.42661733.3449110.00000034.1666676.5101727545045
      VT43.1128236.8371867.7231700.9235940.05055693.7005651.605505158061
      WA39.04300912.0608907.5268825.8465290.90156459.80987112.9402139370445
      WI52.23724214.4072806.32466015.2783990.55155332.91595111.2344273015514
      WV52.37579511.7412986.8748136.8052340.00000082.4479241.6365712611981
      WY33.4140924.8757176.5539660.5780351.17455181.37824211.80827996319
      " - ], - "text/plain": [ - "" - ] - }, - "execution_count": 128, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bar_styled_ej_screen" - ] - }, - { - "cell_type": "code", - "execution_count": 129, - "metadata": {}, - "outputs": [], - "source": [ - "styled_screen = grouped_stats_score_ej_screen.style.apply(highlight_medians)" - ] - }, - { - "cell_type": "code", - "execution_count": 131, - "metadata": {}, - "outputs": [], - "source": [ - "styled_screen.to_excel(\"calenviroscreen_summary.xlsx\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 50, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(5013, 15)" - ] - }, - "execution_count": 50, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "net_difference.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 87, - "metadata": {}, - "outputs": [], - "source": [ - "union_df = non_null_df[(non_null_df[\n", - " \"current_threshold_exceeded\"] == non_null_df[\"new_threshold_exceeded\"])]" - ] - }, - { - "cell_type": "code", - "execution_count": 88, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(62800, 15)" - ] - }, - "execution_count": 88, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "union_df.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [], - "source": [ - "# All together\n", - "\n", - "# Join the demographics in.\n", - "merged_df = net_difference.merge(\n", - " demographics_df[columns_to_keep],\n", - " left_on=\"FIPS_tract_id\",\n", - " right_on=\"GEOID10_TRACT\",\n", - " how=\"inner\"\n", - ")\n", - "\n", - "\n", - "# Group bys.\n", - "demographics_directory = COMPARISON_OUTPUTS_DIR / \"demographics_basic_stats\"\n", - "demographics_directory.mkdir(parents=True, exist_ok=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [], - "source": [ - "# these are not converted into percent 0 - 100 scale\n", - "percent_cols = [x for x in merged_df.columns if \n", - " 'Percent' in x or '(percent)' in x\n", - " ]\n", - "\n", - "merged_df[\n", - " percent_cols] = merged_df[\n", - " percent_cols].apply(lambda x: x * 100)" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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      FIPS_tract_idstatehbrd_rankhbrd_scoresummedcurrent_summed_methodologyT8_est1current_methodology_denominator_sans_not_computedcurrent_methodology_denominatorcurrent_methodology_percent...Percent Asian alonePercent Native Hawaiian and Other Pacific alonePercent Two or more racesPercent Non-Hispanic WhitePercent Hispanic or LatinoPercent of individuals < 200% Federal Poverty LineMedian value ($) of owner-occupied housing unitsPercent individuals age 25 or over with less than high school degreePercent enrollment in college or graduate schoolLinguistic isolation (percent)
      0010059507000192.0192310.23414614421861561560736.0...0.0000000.03.28731143.1952660.00000057.11094613750031.6770191.6191210.000000
      1010119521000196.1538460.26976717422264564564135.0...0.0000000.00.0000003.1032300.00000059.5946806690024.9512675.6472633.145695
      2010150002000191.3461540.22983928547512401245122539.0...1.0327020.00.38726341.1790026.19621349.9139417700013.2335334.2894061.862828
      3010150003000191.0576920.22950828048812201215117542.0...0.0000000.00.00000017.5010521.76693365.6289445130029.1840386.3500260.000000
      4010550010000198.4615380.30804613417743544043041.0...0.0000000.01.26671426.60098515.48205568.4882906540023.3372232.4108001.489362
      \n", - "

      5 rows Ă— 35 columns

      \n", - "
      " - ], - "text/plain": [ - " FIPS_tract_id state hbrd_rank hbrd_score summed \\\n", - "0 01005950700 01 92.019231 0.234146 144 \n", - "1 01011952100 01 96.153846 0.269767 174 \n", - "2 01015000200 01 91.346154 0.229839 285 \n", - "3 01015000300 01 91.057692 0.229508 280 \n", - "4 01055001000 01 98.461538 0.308046 134 \n", - "\n", - " current_summed_methodology T8_est1 \\\n", - "0 218 615 \n", - "1 222 645 \n", - "2 475 1240 \n", - "3 488 1220 \n", - "4 177 435 \n", - "\n", - " current_methodology_denominator_sans_not_computed \\\n", - "0 615 \n", - "1 645 \n", - "2 1245 \n", - "3 1215 \n", - "4 440 \n", - "\n", - " current_methodology_denominator current_methodology_percent ... \\\n", - "0 607 36.0 ... \n", - "1 641 35.0 ... \n", - "2 1225 39.0 ... \n", - "3 1175 42.0 ... \n", - "4 430 41.0 ... \n", - "\n", - " Percent Asian alone Percent Native Hawaiian and Other Pacific alone \\\n", - "0 0.000000 0.0 \n", - "1 0.000000 0.0 \n", - "2 1.032702 0.0 \n", - "3 0.000000 0.0 \n", - "4 0.000000 0.0 \n", - "\n", - " Percent Two or more races Percent Non-Hispanic White \\\n", - "0 3.287311 43.195266 \n", - "1 0.000000 3.103230 \n", - "2 0.387263 41.179002 \n", - "3 0.000000 17.501052 \n", - "4 1.266714 26.600985 \n", - "\n", - " Percent Hispanic or Latino \\\n", - "0 0.000000 \n", - "1 0.000000 \n", - "2 6.196213 \n", - "3 1.766933 \n", - "4 15.482055 \n", - "\n", - " Percent of individuals < 200% Federal Poverty Line \\\n", - "0 57.110946 \n", - "1 59.594680 \n", - "2 49.913941 \n", - "3 65.628944 \n", - "4 68.488290 \n", - "\n", - " Median value ($) of owner-occupied housing units \\\n", - "0 137500 \n", - "1 66900 \n", - "2 77000 \n", - "3 51300 \n", - "4 65400 \n", - "\n", - " Percent individuals age 25 or over with less than high school degree \\\n", - "0 31.677019 \n", - "1 24.951267 \n", - "2 13.233533 \n", - "3 29.184038 \n", - "4 23.337223 \n", - "\n", - " Percent enrollment in college or graduate school \\\n", - "0 1.619121 \n", - "1 5.647263 \n", - "2 4.289406 \n", - "3 6.350026 \n", - "4 2.410800 \n", - "\n", - " Linguistic isolation (percent) \n", - "0 0.000000 \n", - "1 3.145695 \n", - "2 1.862828 \n", - "3 0.000000 \n", - "4 1.489362 \n", - "\n", - "[5 rows x 35 columns]" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "merged_df.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [], - "source": [ - "import us\n", - "\n", - "mapping = us.states.mapping('fips', 'abbr')\n", - "\n", - "for idx, row in merged_df.iterrows():\n", - " current_row = str(merged_df.loc[idx, 'state'])\n", - " state = mapping.get(current_row, None)\n", - " merged_df.loc[idx, 'state_name'] = state" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(5013, 36)" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "merged_df.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [], - "source": [ - "grouped_stats = merged_df.groupby([\"state_name\"]).agg({\n", - " 'GEOID10_TRACT': 'nunique',\n", - " 'Percent of individuals < 200% Federal Poverty Line': [np.median, np.std],\n", - " 'Median value ($) of owner-occupied housing units': [np.median, np.std],\n", - " 'Percent individuals age 25 or over with less than high school degree': [np.median, np.std],\n", - " 'Percent enrollment in college or graduate school': [np.median, np.std],\n", - " 'Percent Black or African American alone': [np.median, np.std],\n", - " 'Percent American Indian and Alaska Native alone': [np.median, np.std],\n", - " 'Percent Non-Hispanic White': [np.median, np.std], \n", - " 'Linguistic isolation (percent)': [np.median, np.std],\n", - " 'Percent Hispanic or Latino': [np.median, np.std],\n", - " 'hbrd_rank': [np.median, np.std],\n", - " 'current_methodology_percent': [np.median, np.std],\n", - " 'current_summed_methodology': [np.median, np.std, np.sum]\n", - "}).reset_index()\n", - "\n", - "\n", - "grouped_stats.columns = [' '.join(col).strip() for col in grouped_stats.columns.values]" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['state_name', 'GEOID10_TRACT nunique',\n", - " 'Percent of individuals < 200% Federal Poverty Line median',\n", - " 'Percent of individuals < 200% Federal Poverty Line std',\n", - " 'Median value ($) of owner-occupied housing units median',\n", - " 'Median value ($) of owner-occupied housing units std',\n", - " 'Percent individuals age 25 or over with less than high school degree median',\n", - " 'Percent individuals age 25 or over with less than high school degree std',\n", - " 'Percent enrollment in college or graduate school median',\n", - " 'Percent enrollment in college or graduate school std',\n", - " 'Percent Black or African American alone median',\n", - " 'Percent Black or African American alone std',\n", - " 'Percent American Indian and Alaska Native alone median',\n", - " 'Percent American Indian and Alaska Native alone std',\n", - " 'Percent Non-Hispanic White median', 'Percent Non-Hispanic White std',\n", - " 'Linguistic isolation (percent) median',\n", - " 'Linguistic isolation (percent) std',\n", - " 'Percent Hispanic or Latino median', 'Percent Hispanic or Latino std',\n", - " 'hbrd_rank median', 'hbrd_rank std',\n", - " 'current_methodology_percent median', 'current_methodology_percent std',\n", - " 'current_summed_methodology median', 'current_summed_methodology std',\n", - " 'current_summed_methodology sum'],\n", - " dtype='object')" - ] - }, - "execution_count": 38, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "grouped_stats.columns" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": {}, - "outputs": [], - "source": [ - "grouped_stats_states = grouped_stats[[x for x in grouped_stats \n", - " if \"median\" in x and \n", - " \"Percent\" in x] + [\"GEOID10_TRACT nunique\", \n", - " 'current_summed_methodology sum', \"state_name\"]]\n", - "\n", - "grouped_stats_states.set_index(\"state_name\", inplace=True)\n", - "\n", - "grouped_stats_states = grouped_stats_states.rename(columns={\n", - "'Percent of individuals < 200% Federal Poverty Line median'\n", - " : 'Percent of individuals < 200% Federal Poverty Line (median across all tracts)', \n", - "'Percent individuals age 25 or over with less than high school degree median':\n", - "'Percent individuals age 25 or over with less than high school degree (median across all tracts)', \n", - "'Percent enrollment in college or graduate school median'\n", - " :'Percent enrollment in college or graduate school (median across all tracts)',\n", - "'Percent Black or African American alone median':\n", - " 'Percent Black or African American alone (median across all tracts)',\n", - "'Percent American Indian and Alaska Native alone median':\n", - " 'Percent American Indian and Alaska Native alone (median across all tracts)',\n", - "'Percent Non-Hispanic White median':\n", - " 'Percent Non-Hispanic White (median across all tracts)',\n", - "'Percent Hispanic or Latino median':\n", - " 'Percent Hispanic or Latino (median across all tracts)',\n", - "'GEOID10_TRACT nunique': \"Total Number of Unique Tracts\",\n", - "\"current_summed_methodology sum\": \"Total Owned and Rented Burdened Households (Current Aggregation Methodology)\" \n", - "})" - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "5013" - ] - }, - "execution_count": 68, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "grouped_stats_states[\"Total Number of Unique Tracts\"].sum()" - ] - }, - { - "cell_type": "code", - "execution_count": 80, - "metadata": {}, - "outputs": [], - "source": [ - "styled = grouped_stats_states.style.apply(highlight_medians)" - ] - }, - { - "cell_type": "code", - "execution_count": 82, - "metadata": {}, - "outputs": [], - "source": [ - "bar_styled = grouped_stats_states.style.bar(\n", - " subset=grouped_stats_states.columns, align='mid', color=['#d65f5f', '#5fba7d'])" - 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      Percent of individuals < 200% Federal Poverty Line (median across all tracts) Percent individuals age 25 or over with less than high school degree (median across all tracts) Percent enrollment in college or graduate school (median across all tracts) Percent Black or African American alone (median across all tracts) Percent American Indian and Alaska Native alone (median across all tracts) Percent Non-Hispanic White (median across all tracts) Percent Hispanic or Latino (median across all tracts) Total Number of Unique Tracts Total Owned and Rented Burdened Households (Current Aggregation Methodology)
      state_name
      AK33.07046910.5860986.5016045.09620012.63370352.24087210.264142125829
      AL59.38757516.9137957.56397874.4757010.00000016.8269372.3860825629636
      AR60.04119517.9231865.21064355.7103060.00000034.8001015.3937434321417
      AZ57.70065126.5266437.0756255.6077602.75893326.17866056.6612589961251
      CA51.26895231.9902329.1743124.0784490.4722289.84325065.462248897610306
      CO46.30058814.2493257.9066043.8809621.09258646.52824034.4803277247552
      CT50.17689818.9679858.59463123.3867030.00000027.06270836.6956305840980
      DC50.41085415.4830456.94641390.3525770.0000003.9939143.9702231711105
      DE42.44744711.2353687.16747548.9519210.47055836.9102358.757629147502
      FL51.10930217.0430947.58889422.7603000.00000022.10462426.459155234162836
      GA58.09138817.4952208.78173866.0021550.00000016.7491174.75131412178491
      HI34.6405239.9914248.0929962.0005260.00000011.6621984.947368178425
      IA45.76192710.9478677.41301111.2195120.02554969.9254357.9350775526664
      ID52.5401366.0718259.1437310.5292651.06339583.4355838.6047852518487
      IL55.34478318.7627467.25308638.0509940.0000009.25583112.976765203108186
      IN58.68210818.0456815.95164435.0849010.00000039.4594119.0637648246470
      KS56.97732518.2332798.18659411.9044760.45919356.78561816.8910105023873
      KY56.32823418.1950865.83430618.9645030.00000063.1455404.2362007136328
      LA61.15131620.0523317.09914376.7875130.00000017.4779322.4805105930785
      MA47.83000120.9422967.76858611.8248780.00000034.79487327.9524088457782
      MD39.75211215.3517317.62244149.9278960.00000022.1150727.0944708846574
      ME42.4021237.4773148.4269661.3470680.36040490.6806281.3089012715361
      MI60.18348616.2672816.96378869.9781660.00000017.6454671.91218114567070
      MN42.50854112.09616810.21069716.9693820.54782058.1542808.0309979148964
      MO55.19430615.2006196.15305936.8479800.00000035.8847104.6192215226962
      MS58.97967617.0385406.71198072.4340180.00000025.2270430.7057554320260
      MT45.0789855.35533213.3026610.1291611.84455488.8817273.6506382013069
      NC56.81125419.2268577.08257542.4155180.25968433.41225210.71495910970145
      ND43.2432436.99017916.4667397.1492562.12194877.9402937.502267138858
      NE55.4258249.50728713.6915898.2790700.53516864.62978310.8638482914442
      NH39.67807813.1355246.5123402.7739100.00000086.0097285.6445712415941
      NJ46.34573320.8433017.07307525.0784090.00000012.42603643.496410165110008
      NM53.60195416.7253529.3477502.7661803.11614729.44009657.2348172714087
      NV56.08303622.5641365.47547911.8682720.55708528.14339243.9473994830773
      NY47.53685724.0067428.26279025.8594920.00000011.30094728.649040603419587
      OH64.85730319.1609985.71620761.0523220.00000027.2796353.45515015767453
      OK57.61886716.7902917.02701213.9696743.42083848.52185013.8998857028634
      OR45.05710911.4072497.8693561.7578830.74264567.68737214.9577805139136
      PA54.89406815.2657607.53012031.8856820.00000027.3596186.15183216587243
      RI55.85143720.0353277.63214712.3701610.40053429.26877933.4685171911532
      SC53.74211015.1065456.65387146.2740380.00000039.1304354.8152876735985
      SD47.8048787.6506438.2104653.6046323.99484575.2877454.7727851911063
      TN55.41666718.7124467.28065853.9672130.00000035.9933964.4444447345269
      TX56.93950225.4102206.78726514.3435370.00000014.62720549.238149309204844
      UT45.8097409.5987419.4932431.4449290.38485264.48710320.4281663319743
      VA44.75079214.1815067.65054136.8306090.00000032.1986966.60759510265128
      VT43.1128236.8371867.7231700.9235940.05055693.7005651.605505158061
      WA39.37531712.5430817.4483175.8803020.90327159.32113613.89307010279613
      WI57.28758215.1812696.31025423.5638920.34694422.6303328.5417784322233
      WV52.37579511.7412986.8748136.8052340.00000082.4479241.6365712611981
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      " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# Compute the correlation matrix\n", - "import seaborn as sns\n", - "corr = merged_df[[\"hbrd_rank\", \n", - " \"current_methodology_percentile_rank\"] + percent_cols].corr()\n", - "\n", - "# Generate a mask for the upper triangle\n", - "mask = np.triu(np.ones_like(corr, dtype=bool))\n", - "\n", - "# Set up the matplotlib figure\n", - "f, ax = plt.subplots(figsize=(15, 12))\n", - "\n", - "# Generate a custom diverging colormap\n", - "cmap = sns.diverging_palette(230, 20, as_cmap=True)\n", - "\n", - "# Draw the heatmap with the mask and correct aspect ratio\n", - "sns.heatmap(corr, mask=mask, cmap=cmap, vmax=.3, center=0,\n", - " square=True, linewidths=.5, cbar_kws={\"shrink\": .5})" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.2" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/data/data-pipeline/data_pipeline/ipython/hud_eda_se_12_27-highlevel-summarization-differences-in-medians.ipynb b/data/data-pipeline/data_pipeline/ipython/hud_eda_se_12_27-highlevel-summarization-differences-in-medians.ipynb deleted file mode 100644 index a80ef075..00000000 --- a/data/data-pipeline/data_pipeline/ipython/hud_eda_se_12_27-highlevel-summarization-differences-in-medians.ipynb +++ /dev/null @@ -1,1458 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 249, - "metadata": {}, - "outputs": [], - "source": [ - "df.rename(columns={df.columns[idx]: cols[idx] for idx, _ in enumerate(cols)}, inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 250, - "metadata": {}, - "outputs": [], - "source": [ - "df = df.set_index([\n", - " 'Score L Relative Housing Burden for all households met burden threshold', \n", - " 'CalEnviroScreen Housing Burden Met Burden Threshold (Ranked Percentile)'], inplace=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 251, - "metadata": {}, - "outputs": [], - "source": [ - "df = df.rename(columns={\n", - "'Percent of individuals < 200% Federal Poverty Line (median across all tracts)'\n", - " : 'Percent of individuals < 200% Federal Poverty Line (mean of median across all states)', \n", - "'Percent individuals age 25 or over with less than high school degree median':\n", - "'Percent individuals age 25 or over with less than high school degree (mean of median across all states)', \n", - "'Percent enrollment in college or graduate school (median across all states)'\n", - " :'Percent enrollment in college or graduate school (mean of median across all states)',\n", - "'Percent Black or African American alone (median across all tracts)':\n", - " 'Percent Black or African American alone (mean of median across all states)',\n", - "'Percent American Indian and Alaska Native alone (median across all tracts)':\n", - " 'Percent American Indian and Alaska Native alone (mean of median across all states)',\n", - "'Percent Non-Hispanic White (median across all tracts)':\n", - " 'Percent Non-Hispanic White (mean of median across all states)',\n", - "'Percent Hispanic or Latino (median across all tracts)':\n", - " 'Percent Hispanic or Latino (mean of median across all states)',\n", - "\"Total Number of Unique Tracts\": \"Total Number of Tracts - Mean of median across all states\",\n", - "\"Total Owned and Rented Burdened Households (Current Aggregation Methodology)\": \"Total Owned and Rented Burdened Households (Current Aggregation Methodology)- Mean of median across all states\"\n", - "})" - ] - }, - { - "cell_type": "code", - "execution_count": 252, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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      " - ], - "text/plain": [ - " Percent of individuals < 200% Federal Poverty Line (mean of median across all states) \\\n", - "Score L Relative Housing Burden for all househo... CalEnviroScreen Housing Burden Met Burden Thres... \n", - "True False 57.052724 \n", - "False True 47.082927 \n", - "True True 62.331263 \n", - "False False 27.736502 \n", - "\n", - " Percent individuals age 25 or over with less than high school degree (median across all tracts) \\\n", - "Score L Relative Housing Burden for all househo... CalEnviroScreen Housing Burden Met Burden Thres... \n", - "True False 19.383813 \n", - "False True 13.224927 \n", - "True True 17.689811 \n", - "False False 8.785280 \n", - "\n", - " Percent enrollment in college or graduate school (median across all tracts) \\\n", - "Score L Relative Housing Burden for all househo... CalEnviroScreen Housing Burden Met Burden Thres... \n", - "True False 8.132533 \n", - "False True 8.685441 \n", - "True True 11.824205 \n", - "False False 5.847120 \n", - "\n", - " Percent Black or African American alone (mean of median across all states) \\\n", - "Score L Relative Housing Burden for all househo... CalEnviroScreen Housing Burden Met Burden Thres... \n", - "True False 33.692806 \n", - "False True 23.061683 \n", - "True True 33.455525 \n", - "False False 5.729181 \n", - "\n", - " Percent American Indian and Alaska Native alone (mean of median across all states) \\\n", - "Score L Relative Housing Burden for all househo... CalEnviroScreen Housing Burden Met Burden Thres... \n", - "True False 1.221395 \n", - "False True 0.772400 \n", - "True True 0.921755 \n", - "False False 0.537429 \n", - "\n", - " Percent Non-Hispanic White (mean of median across all states) \\\n", - "Score L Relative Housing Burden for all househo... CalEnviroScreen Housing Burden Met Burden Thres... \n", - "True False 31.321886 \n", - "False True 48.240042 \n", - "True True 34.495797 \n", - "False False 75.916536 \n", - "\n", - " Percent Hispanic or Latino (mean of median across all states) \\\n", - "Score L Relative Housing Burden for all househo... CalEnviroScreen Housing Burden Met Burden Thres... \n", - "True False 19.378348 \n", - "False True 12.553371 \n", - "True True 15.855933 \n", - "False False 7.463468 \n", - "\n", - " Total Number of Tracts - Mean of median across all states \\\n", - "Score L Relative Housing Burden for all househo... CalEnviroScreen Housing Burden Met Burden Thres... \n", - "True False 62.088889 \n", - "False True 44.549020 \n", - "True True 88.803922 \n", - "False False 1244.333333 \n", - "\n", - " Total Owned and Rented Burdened Households (Current Aggregation Methodology)- Mean of median across all states \n", - "Score L Relative Housing Burden for all househo... CalEnviroScreen Housing Burden Met Burden Thres... \n", - "True False 40949.200000 \n", - "False True 24719.098039 \n", - "True True 60757.901961 \n", - "False False 443680.568627 " - ] - }, - "execution_count": 252, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df" - ] - }, - { - "cell_type": "code", - "execution_count": 211, - "metadata": {}, - "outputs": [], - "source": [ - "def highlight_min(s, props=''):\n", - " return np.where(s == np.nanmin(s.values), props, '')\n", - "\n", - "def highlight_max(s, props=''):\n", - " return np.where(s == np.nanmax(s.values), props, '')\n", - "\n", - "df_styled = df.style.format(\n", - " na_rep='MISSING',\n", - " formatter={df.columns[idx]: \"{:.2f}\" for idx, _ in enumerate(df.columns) })" - ] - }, - { - "cell_type": "code", - "execution_count": 255, - "metadata": {}, - "outputs": [], - "source": [ - "df_styled_max_min = df_styled.apply(highlight_max, props='color:#FFFFFF;background-color:#00008b', axis=0)\\\n", - " .apply(highlight_min, props='color:#FFFFFF;background-color:#FF0000', axis=0)" - ] - }, - { - "cell_type": "code", - "execution_count": 256, - "metadata": {}, - "outputs": [], - "source": [ - "df_styled_max_min.to_excel(\"summary_all_states.xlsx\")" - ] - }, - { - "cell_type": "code", - "execution_count": 257, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
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      Percent of individuals < 200% Federal Poverty Line (median across all tracts)Percent individuals age 25 or over with less than high school degree (median across all tracts)Percent enrollment in college or graduate school (median across all tracts)Percent Black or African American alone (median across all tracts)Percent American Indian and Alaska Native alone (median across all tracts)Percent Non-Hispanic White (median across all tracts)Percent Hispanic or Latino (median across all tracts)Total Number of Unique TractsTotal Owned and Rented Burdened Households (Current Aggregation Methodology)
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      Percent of individuals < 200% Federal Poverty Line (median across all tracts) Percent individuals age 25 or over with less than high school degree (median across all tracts) Percent enrollment in college or graduate school (median across all tracts) Percent Black or African American alone (median across all tracts) Percent American Indian and Alaska Native alone (median across all tracts) Percent Non-Hispanic White (median across all tracts) Percent Hispanic or Latino (median across all tracts) Total Number of Unique Tracts Total Owned and Rented Burdened Households (Current Aggregation Methodology)
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      " - ], - "text/plain": [ - "" - ] - }, - "execution_count": 279, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "def style_negative(v, props=''):\n", - " return props if v < 0 else None\n", - "\n", - "diff_style = diff.style.applymap(style_negative, props='color:red;')\\\n", - " .applymap(lambda v: 'opacity: 20%;' if (v < 0.0) and (v > 0.0) else None)\n", - "diff_style" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.2" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/data/data-pipeline/data_pipeline/ipython/hud_eda_se_12_27-highlevel-summarization-variance.ipynb b/data/data-pipeline/data_pipeline/ipython/hud_eda_se_12_27-highlevel-summarization-variance.ipynb deleted file mode 100644 index caa4202d..00000000 --- a/data/data-pipeline/data_pipeline/ipython/hud_eda_se_12_27-highlevel-summarization-variance.ipynb +++ /dev/null @@ -1,8783 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Whereas we have presented statistics that describe the central tendencies of a data set, we are also interested in ones that describe the spread or variability of the data values. A statistic that could be used for this purpose would be one that measures the average value of the squares of the distances between the data values and the sample mean. This is accomplished by the sample variance, which for technical reasons divides the sum of the squares of the differences by $n-1$ rather than $n$, where $n$ is the size of the data set." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Indicator reviewed: \n", - "\n", - "Socioeconomic Factors Indicator reviewed\n", - "* [Extreme Housing Burden](#housingburden)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Packages" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import math\n", - "import numpy as np\n", - "import os\n", - "import pandas as pd\n", - "\n", - "import seaborn as sns\n", - "import matplotlib.pyplot as plt\n", - "import pandas as pd\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### ETL process for acquiring relevant tables" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### NOTE: If you ran the ETL Process to acquire Table 8 in the other notebook of this draft PR you do not need to run the ETL cell block again" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# Copy and adapt certain sections of code from data_pipeline.utils \n", - "\n", - "def download_hud_dataset():\n", - " DOWNLOAD_FILENAME = \"HUD_ZIPPED.csv\"\n", - " HOUSING_FTP_URL = \"https://www.huduser.gov/portal/datasets/cp/2014thru2018-140-csv.zip\" \n", - " response = requests.get(HOUSING_FTP_URL, verify=True)\n", - " if response.status_code == 200:\n", - " file_contents = response.content\n", - " else:\n", - " sys.exit(\n", - " f\"HTTP response {response.status_code} from url {file_url}. Info: {response.content}\"\n", - " )\n", - "\n", - " # Write the contents to disk.\n", - " file = open(DOWNLOAD_FILENAME, \"wb\")\n", - " file.write(file_contents)\n", - " file.close()\n", - " \n", - "def extract_zipped_download(zip_file_path, unzipped_path):\n", - " with zipfile.ZipFile(zip_file_path, \"r\") as zip_ref:\n", - " zip_ref.extractall(unzipped_path)\n", - " # cleanup temporary file\n", - " os.remove(zip_file_path)\n", - " \n", - "def up_one_directory(path):\n", - " try:\n", - " # from Python 3.6\n", - " parent_dir = Path(path).parents[1]\n", - " # for Python 3.4/3.5, use str to convert the path to string\n", - " # parent_dir = str(Path(path).parents[1])\n", - " shutil.move(path, parent_dir)\n", - " except IndexError:\n", - " # no upper directory\n", - " pass\n", - "\n", - "CURRENT_DIRECTORY = os.getcwd()\n", - "download_hud_dataset()\n", - "extract_zipped_download(CURRENT_DIRECTORY + \"/HUD_ZIPPED.csv\", CURRENT_DIRECTORY) \n", - "up_one_directory(CURRENT_DIRECTORY + \"/140/Table8.csv\")\n", - "shutil.rmtree(\"./140/\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Extreme Housing Burden \n", - "\n", - "The Extreme Housing Burden indicator represents the proportion of low-income households that have to spend more than half their income on rent. These households experience higher levels of stress, report lower health, and may delay medical treatment because of its high cost.\n", - "\n", - "The Extreme Housing Burden indicator measures the percent of households in a census tract that are:\n", - "\n", - "1. Making less than 80% of the Area Median Family Income as determined by the Department of Housing and Urban Development (HUD), and\n", - "2. Paying greater than 50% of their income to housing costs. \n", - "\n", - "This data is sourced from the 2014-2018 Comprehensive Housing Affordability Strategy dataset from the Department of Housing and Urban Development (HUD) using the census tract geographic summary level, and contains cost burdens for households by percent HUD-adjusted median family income (HAMFI) category. This data can be found [here](https://www.huduser.gov/portal/datasets/cp.html). \n", - "\n", - "Because CHAS data is based on American Communities Survey (ACS) estimates, which come from a sample of the population, they may be unreliable if based on a small sample or population size.\n", - "\n", - "The standard error and relative standard error were used to evaluate the reliability of each estimate using CalEnviroScreen’s methodology. \n", - "\n", - "Census tract estimates that met either of the following criteria were considered reliable and included in the analysis [(CalEnviroScreen, 2017, page 129)](https://oehha.ca.gov/media/downloads/calenviroscreen/report/ces3report.pdf ):\n", - "\n", - "- Relative standard error less than 50 (meaning the standard error was less than half of the estimate), OR \n", - "- Standard error less than the mean standard error of all census tract estimates \n", - "\n", - "Formulas for calculating the standard error of sums, proportions, and ratio come from the [American Communities Survey Office](https://www2.census.gov/programs-surveys/acs/tech_docs/accuracy/MultiyearACSAccuracyofData2013.pdf).\n", - "\n", - "Note that this code creates a score and rank by state, for every state." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The relevant variables in table 8 of the CHAS dataset are the following (CHAS data dictionary available [here](https://www.huduser.gov/portal/datasets/cp/CHAS-data-dictionary-14-18.xlsx)):\n", - "\n", - "| Name | Label |\n", - "|---------|-----------------------------------------------------|\n", - "|T1_est1 | Total Occupied housing units | \n", - "|T8_est10 | Owner occupied less than or equal to 30% of HAMFI cost burden greater than 50% |\n", - "|T8_est23 |Owner occupied greater than 30% but less than or equal to 50% of HAMFI\tcost burden greater than 50%|\n", - "|T8_est36 |Owner occupied\tgreater than 50% but less than or equal to 80% of HAMFI\tcost burden greater than 50%|\n", - "|T8_est76 | Renter occupied less than or equal to 30% of HAMFI cost burden greater than 50% |\n", - "|T8_est89 |Renter occupied\tgreater than 30% but less than or equal to 50% of HAMFI\tcost burden greater than 50%|\n", - "|T8_est102|Renter occupied\tgreater than 50% but less than or equal to 80% of HAMFI\tcost burden greater than 50%|\n", - " " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Below I also propose an alternate means for ranking census tracts\n", - "### These steps are outlined and commented below" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/sarahluw/.pyenv/versions/3.6.2/envs/my-virtual-env-3.6.2/lib/python3.6/site-packages/pandas/core/series.py:726: RuntimeWarning: invalid value encountered in sqrt\n", - " result = getattr(ufunc, method)(*inputs, **kwargs)\n", - "/Users/sarahluw/.pyenv/versions/3.6.2/envs/my-virtual-env-3.6.2/lib/python3.6/site-packages/pandas/core/indexing.py:670: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " iloc._setitem_with_indexer(indexer, value)\n" - ] - } - ], - "source": [ - "# Read in the data from https://www.huduser.gov/portal/datasets/cp.html\n", - "housing = pd.read_csv(\"Table8.csv\", \n", - " encoding = \"ISO-8859-1\", \n", - " dtype = {'Tract_ID': object, 'st': object, 'geoid': object})\n", - "\n", - "# Remove data for states that aren't included in the census (e.g. American Samoa, Guam, etc.):\n", - "housing.drop(housing.loc[housing['st'] == '72'].index, inplace = True)\n", - "\n", - "# Combine owner and renter occupied low-income households that make less than 80% of HAMFI into one variable\n", - "housing['summed'] = (housing['T8_est10'] + \n", - " housing['T8_est23'] + \n", - " housing['T8_est36'] + \n", - " housing['T8_est76'] + \n", - " housing['T8_est89'] + \n", - " housing['T8_est102'])\n", - "\n", - "# Create a variable for the standard error of the summed variables\n", - "housing['summed_se'] = np.sqrt((housing['T8_moe10'] / 1.645)**2 + \n", - " (housing['T8_moe23'] / 1.645)**2 + \n", - " (housing['T8_moe36'] / 1.645)**2 + \n", - " (housing['T8_moe76'] / 1.645)**2 + \n", - " (housing['T8_moe89'] / 1.645)**2 + \n", - " (housing['T8_moe102'] / 1.645)**2)\n", - "\n", - "# Remove the first 7 digits in the FIPS Census Tract ID \n", - "housing['geoid'] = housing['geoid'].str[-11:]\n", - "\n", - "# Find the estimate of the proportion of the population that is heavily rent burdened\n", - "housing['hbrd_score'] = housing['summed'] / housing['T8_est1']\n", - "\n", - "# Change rates where the population is 0 to nan\n", - "housing['hbrd_score'].replace(np.inf, np.nan, inplace = True)\n", - "\n", - "# Create function for calculating the standard error, using the proportions standard error formula\n", - "# if the value under the radical is negative, use the ratio standard error formula\n", - "def se_prop(x, y, se_x, moe_y): \n", - " se_y = moe_y / 1.645\n", - " test = se_x**2 - (((x**2)/(y**2))*((se_y)**2))\n", - " se = np.where(test < 0,\n", - " (1/y) * np.sqrt(se_x**2 + (((x**2)/(y**2))*(se_y**2))), \n", - " (1/y) * np.sqrt(se_x**2 - (((x**2)/(y**2))*(se_y**2))))\n", - " return se\n", - "\n", - "housing['se'] = se_prop(housing['summed'], housing['T8_est1'], housing['summed_se'], housing['T8_moe1'])\n", - "\n", - "# Calculate the relative standard error\n", - "housing['rse'] = housing['se'] / housing['hbrd_score']*100\n", - "\n", - "# Change infinite rse's where the housing burden is 0 to np.nan\n", - "housing['rse'].replace(np.inf, np.nan, inplace = True)\n", - "\n", - "# Calculate the mean standard error for each state\n", - "housing['mean_state_se'] = np.zeros(len(housing))\n", - "\n", - "for state in housing['st'].unique():\n", - " mean_se = np.mean(housing[housing['st'] == state]['se'])\n", - " housing['mean_state_se'].loc[housing['st'] == state] = mean_se\n", - " \n", - "# Find census tract estimates that meet both of the following criteria and are thus considered unreliable estimates: \n", - "# RSE less than 50 AND\n", - "# SE less than the mean state SE or housing burdened low income households\n", - "# Convert these scores to nan\n", - "housing.loc[(housing['rse'] >= 50) & (housing['rse'] >= housing['mean_state_se']), 'hbrd_score'] = np.nan\n", - "\n", - "# Rename columns\n", - "housing = housing.rename(columns = {'geoid' :'FIPS_tract_id',\n", - " 'st' : 'state'\n", - " })\n", - "\n", - "# Calculate percentile rank for census tracts with a score above 0, set percentile to 0 if score is 0, for each state\n", - "housing['hbrd_rank'] = housing[\n", - " housing['hbrd_score'] != 0][['hbrd_score',\n", - " 'state']].groupby('state').rank( \n", - " na_option = 'keep', \n", - " pct = True) * 100\n", - "\n", - "housing.loc[housing['hbrd_score'] == 0, 'hbrd_rank'] = 0\n", - "\n", - "# Create final housing burden df\n", - "housingburden = housing.copy()" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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      5 rows Ă— 280 columns

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      " - ], - "text/plain": [ - " source sumlevel FIPS_tract_id \\\n", - "0 2014thru2018 140 01001020100 \n", - "1 2014thru2018 140 01001020200 \n", - "2 2014thru2018 140 01001020300 \n", - "3 2014thru2018 140 01001020400 \n", - "4 2014thru2018 140 01001020500 \n", - "\n", - " name state cnty tract T8_est1 \\\n", - "0 Census Tract 201, Autauga County, Alabama 01 1 20100 765 \n", - "1 Census Tract 202, Autauga County, Alabama 01 1 20200 720 \n", - "2 Census Tract 203, Autauga County, Alabama 01 1 20300 1295 \n", - "3 Census Tract 204, Autauga County, Alabama 01 1 20400 1640 \n", - "4 Census Tract 205, Autauga County, Alabama 01 1 20500 4175 \n", - "\n", - " T8_est2 T8_est3 ... T8_moe131 T8_moe132 T8_moe133 summed summed_se \\\n", - "0 570 50 ... 12 12 12 80 31.721807 \n", - "1 465 65 ... 12 12 12 138 45.531874 \n", - "2 840 60 ... 12 12 12 170 53.722921 \n", - "3 1260 15 ... 12 12 12 145 46.288510 \n", - "4 2320 175 ... 17 17 17 595 147.221693 \n", - "\n", - " hbrd_score se rse mean_state_se hbrd_rank \n", - "0 0.104575 0.041032 39.237314 0.036604 46.298077 \n", - "1 0.191667 0.061614 32.146659 0.036604 83.269231 \n", - "2 0.131274 0.040927 31.176999 0.036604 63.653846 \n", - "3 0.088415 0.027822 31.467397 0.036604 34.615385 \n", - "4 0.142515 0.034760 24.390193 0.036604 68.221154 \n", - "\n", - "[5 rows x 280 columns]" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "housingburden.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(73056, 280)" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "housingburden.shape" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### As desired we see a uniform distribution for the percentile rank for burdened households" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Now we compute for a baseline comparison " - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# Owner occupied numerator fields\n", - "OWNER_OCCUPIED_NUMERATOR_FIELDS = [\n", - " # Column Name\n", - " # Line_Type\n", - " # Tenure\n", - " # Household income\n", - " # Cost burden\n", - " # Facilities\n", - " \"T8_est7\",\n", - " # Subtotal\n", - " # Owner occupied\n", - " # less than or equal to 30% of HAMFI\n", - " # greater than 30% but less than or equal to 50%\n", - " # All\n", - " \"T8_est10\",\n", - " # Subtotal\n", - " # Owner occupied\n", - " # less than or equal to 30% of HAMFI\n", - " # greater than 50%\n", - " # All\n", - " \"T8_est20\",\n", - " \n", - " # Subtotal\n", - " # Owner occupied\n", - " # greater than 30% but less than or equal to 50% of HAMFI\n", - " # greater than 30% but less than or equal to 50%\n", - " # All\n", - " \"T8_est23\",\n", - " # Subtotal\n", - " # Owner occupied\n", - " # greater than 30% but less than or equal to 50% of HAMFI\n", - " # greater than 50%\n", - " # All\n", - " \"T8_est33\",\n", - " # Subtotal\n", - " # Owner occupied\n", - " # greater than 50% but less than or equal to 80% of HAMFI\n", - " # greater than 30% but less than or equal to 50%\n", - " # All\n", - " \"T8_est36\",\n", - " # Subtotal\n", - " # Owner occupied\n", - " # greater than 50% but less than or equal to 80% of HAMFI\n", - " # greater than 50%\n", - " # All\n", - "]\n", - "\n", - "# These rows have the values where HAMFI was not computed, b/c of no or negative income.\n", - "OWNER_OCCUPIED_NOT_COMPUTED_FIELDS = [\n", - " # Column Name\n", - " # Line_Type\n", - " # Tenure\n", - " # Household income\n", - " # Cost burden\n", - " # Facilities\n", - " \"T8_est13\",\n", - " # Subtotal\n", - " # Owner occupied\n", - " # less than or equal to 30% of HAMFI\n", - " # not computed (no/negative income)\n", - " # All\n", - " \"T8_est26\",\n", - " # Subtotal\n", - " # Owner occupied\n", - " # greater than 30% but less than or equal to 50% of HAMFI\n", - " # not computed (no/negative income)\n", - " # All\n", - " \"T8_est39\",\n", - " # Subtotal\n", - " # Owner occupied\n", - " # greater than 50% but less than or equal to 80% of HAMFI\n", - " # not computed (no/negative income)\n", - " # All\n", - " \"T8_est52\",\n", - " # Subtotal\n", - " # Owner occupied\n", - " # greater than 80% but less than or equal to 100% of HAMFI\n", - " # not computed (no/negative income)\n", - " # All\n", - " \"T8_est65\",\n", - " # Subtotal\n", - " # Owner occupied\n", - " # greater than 100% of HAMFI\n", - " # not computed (no/negative income)\n", - " # All\n", - "]\n", - "\n", - "OWNER_OCCUPIED_POPULATION_FIELD = \"T8_est2\"\n", - "# Subtotal\n", - "# Owner occupied\n", - "# All\n", - "# All\n", - "# All\n", - "\n", - "OWNER_OCCUPIED_POPULATION_HAMFI_FIELD = \"T8_est3\"\n", - "# Subtotal\n", - "# Owner occupied \n", - "# All\n", - "# All\n", - "# All\n", - "\n", - "# Renter occupied numerator fields\n", - "RENTER_OCCUPIED_NUMERATOR_FIELDS = [\n", - " # Column Name\n", - " # Line_Type\n", - " # Tenure\n", - " # Household income\n", - " # Cost burden\n", - " # Facilities\n", - " \"T8_est73\",\n", - " # Subtotal\n", - " # Renter occupied\n", - " # less than or equal to 30% of HAMFI\n", - " # greater than 30% but less than or equal to 50%\n", - " # All\n", - " \"T8_est76\",\n", - " # Subtotal\n", - " # Renter occupied\n", - " # less than or equal to 30% of HAMFI\n", - " # greater than 50%\n", - " # All\n", - " \"T8_est86\",\n", - " # Subtotal\n", - " # Renter occupied\n", - " # greater than 30% but less than or equal to 50% of HAMFI\n", - " # greater than 30% but less than or equal to 50%\n", - " # All\n", - " \"T8_est89\",\n", - " # Subtotal\n", - " # Renter occupied\n", - " # greater than 30% but less than or equal to 50% of HAMFI\n", - " # greater than 50%\n", - " # All\n", - " \"T8_est99\",\n", - " # Subtotal\n", - " # Renter occupied\tgreater than 50% but less than or equal to 80% of HAMFI\n", - " # greater than 30% but less than or equal to 50%\n", - " # All\n", - " \"T8_est102\",\n", - " # Subtotal\n", - " # Renter occupied\n", - " # greater than 50% but less than or equal to 80% of HAMFI\n", - " # greater than 50%\n", - " # All\n", - "]\n", - "\n", - "# These rows have the values where HAMFI was not computed, b/c of no or negative income.\n", - "RENTER_OCCUPIED_NOT_COMPUTED_FIELDS = [\n", - " # Column Name\n", - " # Line_Type\n", - " # Tenure\n", - " # Household income\n", - " # Cost burden\n", - " # Facilities\n", - " \"T8_est79\",\n", - " # Subtotal\n", - " # Renter occupied\tless than or equal to 30% of HAMFI\n", - " # not computed (no/negative income)\n", - " # All\n", - " \"T8_est92\",\n", - " # Subtotal\n", - " # Renter occupied\tgreater than 30% but less than or equal to 50% of HAMFI\n", - " # not computed (no/negative income)\n", - " # All\n", - " \"T8_est105\",\n", - " # Subtotal\n", - " # Renter occupied\n", - " # greater than 50% but less than or equal to 80% of HAMFI\n", - " # not computed (no/negative income)\n", - " # All\n", - " \"T8_est118\",\n", - " # Subtotal\n", - " # Renter occupied\tgreater than 80% but less than or equal to 100% of HAMFI\n", - " # not computed (no/negative income)\n", - " # All\n", - " \"T8_est131\",\n", - " # Subtotal\n", - " # Renter occupied\n", - " # greater than 100% of HAMFI\n", - " # not computed (no/negative income)\n", - " # All\n", - "]\n", - "\n", - "# T8_est68\tSubtotalRenter occupied\tAll\tAll\tAll\n", - "RENTER_OCCUPIED_POPULATION_FIELD = \"T8_est68\"" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "housingburden[\"current_summed_methodology\"] = housingburden[\n", - " OWNER_OCCUPIED_NUMERATOR_FIELDS\n", - "].sum(axis=1) + housingburden[RENTER_OCCUPIED_NUMERATOR_FIELDS].sum(axis=1)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "housingburden[\"current_methodology_denominator\"] = (\n", - " housingburden[OWNER_OCCUPIED_POPULATION_FIELD]\n", - " + housingburden[RENTER_OCCUPIED_POPULATION_FIELD]\n", - " - housingburden[OWNER_OCCUPIED_NOT_COMPUTED_FIELDS].sum(axis=1)\n", - " - housingburden[RENTER_OCCUPIED_NOT_COMPUTED_FIELDS].sum(axis=1)\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "housingburden[\"current_methodology_denominator_sans_not_computed\"] = (\n", - " housingburden[OWNER_OCCUPIED_POPULATION_FIELD]\n", - " + housingburden[RENTER_OCCUPIED_POPULATION_FIELD]\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "housingburden[\"current_methodology_percent\"] = np.round(\n", - " (housingburden[\"current_summed_methodology\"] / housingburden[\"current_methodology_denominator\"] ), 2) * 100" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Now we construct the distribution of differences in the number of owned and rented burdened households\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Percentiles Comparison" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "final_df = housingburden[['FIPS_tract_id', 'state','hbrd_rank','hbrd_score', 'summed', \n", - " 'current_summed_methodology', 'T8_est1', \n", - " \"current_methodology_denominator_sans_not_computed\",\n", - " 'current_methodology_denominator', 'current_methodology_percent']]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### First notice here that **T8_est1** and **current_methodology_denominator** should represent same or similar aggregates. In general, we cen see that the current computation performed results in a differerntial that undercounts the total occupied and rental households." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/sarahluw/.pyenv/versions/3.6.2/envs/my-virtual-env-3.6.2/lib/python3.6/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " \n" - ] - } - ], - "source": [ - "final_df[\"differences_aggregate_denominator\"] = (\n", - " final_df[\"current_methodology_denominator\"] - final_df[\"T8_est1\"] \n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/sarahluw/.pyenv/versions/3.6.2/envs/my-virtual-env-3.6.2/lib/python3.6/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " \n" - ] - } - ], - "source": [ - "final_df[\"differences_aggregate_denominator_sans_not_computed\"] = (\n", - " final_df[\"current_methodology_denominator\"] - final_df[\"T8_est1\"] \n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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      " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(12, 8))\n", - "plt.title('Distribution of differences between aggregate totals that normalizes tabulation of poverty households')\n", - "# Set x-axis label\n", - "plt.xlabel('Aggregate differences in total owner and renter occupied low-income households < 80%')\n", - "# Set y-axis label\n", - "plt.ylabel('Relative Frequency in Support')\n", - "\n", - "sns.histplot(final_df[\"differences_aggregate_denominator\"])" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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yy+XzWPC5dybwbBb+3OtXv9srLPr5M965Seexiczsdrwa71j4O2Bm/XzdmJJs/RQgImZGxLci4u/1GH06sGaM8bbIhiWxD05k2+237rHOxfrZBvv+jKh6HUcmarxzkpF6vtic1+Lo5zx31KOB67JmVV3mO16OsxrlQkPfFisRqw+Drk+5B3UhmXlHZr4vMx9LOWC/NyJ2HB3co8pe5aM2bHRvRPmG6xZKhjuzEdc0Fm7Y8er9P8qG0Kx7PgtvhP24pcbUWdd1fU5/PYsu4+K6hvKt0zqZuWb9Wz0zm6/UbLbLLZQdbcvG+Gtk5lgH1+a091Kei+kWx1GNOtfMzFUy80CAzDwxM19I2eH+RLlc38vDbVMf9t6Asu6uoXyj0ZzHapn54i7LOaozETuNRROx8eq9BnhRx/BHZGa/67vpB5TL5Rtm5hqUS95Rh11fl3V02VemfKtJt+EsvA2NarbBWOvkemCteqLVrb7/R7nN7AWUE96NR8PqMp/ReY3VhmPFCWX9btjxcH9zn1po/Ih4DGUbejuwdmauSbm1J+giMw/KzKdTrrA/jnKPfD9xjec/6jRPzszVKSeVzRgmWl9TP8eZxa5/om3YMe3jKSfTr8rMzi8H3tyxHaycmWfC4q+HiHgu5RabV1Fu21yT8lxEM9Zexw0oJ0DNbb3rw9V9tMmEPmMiYhXKPrw4x4pun1f/12PcXjo/Z0anH6/uoylXL3YFLq+JAYxznK8626hbm30P2DUinkK5RfX4CSxT01if6WOeL2TmXzLzNZTE4HPAj+r66mcZ+4onIoKyDrqt/zHPAzLzB5n5nFpf1hi7uZiyLzWdRrkC/TTg3Nq/M+W20NN71DOZY1W3Ovo5N+nXmMfCLG9+PI6yzb4G+EVm3lHHex/lSukz6zF6u1o+7nGOJbMPLk7d462Lsc7FJrIN9qvXcWShfYxFj6vdzhPGOieZVffB5rwWx0TOc68H1q/ttMh8x8lxoBy/LppIcBNKxCJi9fot0TGUy4OXdBnnJRGxWV2I2yhXCka/1b6Rcu/2RP1bRGxRTxIPAH5Ud7Q/A4+IiH+J8urKj1PuJR11I7Bx9H5T09HAeyJik4hYlXISdWzHt9Hjauz0n42I1eqH93spHy79OA54XWMZ95vI/DtiuZ5yy91/1fW1QkRsGhHP6zH+Q5QTjS9HxCMBImL9iNi5j3k9RLmt8ksR8eh61eRZEbESZdlfGuX3TKZFxCMiYvuI2KBeNdi17mD3UZ6DemiMWT09Il4R5a1L767TnEW5rH9HRHwoIlau83lS/aIAuq//MykH4W2AczLzMspB6pks+EAar95vUtb1Y2p7zY6IXcdrrx5Wo1yJujcitqEkPKN+RGnDf46IFSnfhjYPDscBH4mIWRGxPuVkcSw910lm/p3yren+EbFiRDwLeGlHnPdRvs2fSdlXmjr37fHasFPn9GdTTpY/GBEzImL7Gs8xPcZfhXKQvxkgIl5PuXKxiIh4Rv2mdPTB9Xvpvf1N9Ji1GmV7vq2uk87EYnGPgUviODOevtuwKcpVrJ9RblXv/HLum5RtdMs67hoR8craPZn1sBrlBPtmYHpEfBLovJrW67gB5fbC/1e3y10oX8R0M16b3AhsUPfPbo4GXh8RT63Hxf+gPJ/ztx7jj+Vo4OP1eLMO5dblia77T0S5IrAl5VmcY/us+xjKMzr7suBqGIxxTBkjhkX2gcy8lpIgHAX8uHFFdaLG+kwf83whIv4tImbXz7V5tfihxVzGUccB/xIRO9Z5vo+yHZ7ZY9zR4/kGlFvCRmN7fETsULehe1nw8oZufsWi2/NplOdCL8/M+6m331G+LLu5Rz2LfazqZqLnJuPU1c+x8AeUO29ey8Lb7GqU9psXEWsxsfOtJbEPLk7dNwJrR8Qa3SYc51xsIttgv3odRy4EXhwRa0V5c+C7O6br3Kb6OSf5VD0neQ4Ln5P0bYLnuX+gfLa8s557vIJyvkidrmeOExGPoDyPfdJE4us3Eft5RNxByV4/Rnl47vU9xt2c8paVO+sCfSMzf1eH/SdlQ5sXEe+fQJxHUe49vYFy+fWdAJl5G+V+7O9Qsvu7KLefjPph/X9rRFzQpd7Dat2nUx7kvpfGwW+C3lHnfxXlSuEPav3jysxfU54NOoVyyfOUxYxh1J6UBw4vp9ym8CMWvo2i04fqfM+Kcqn+fyjJSj/eT3nb2bmU244+R3ne4xrKt6cfpZzEXEM5KV2h/r2X8i3KXMqHxr5jzONnlAPqCPDvwCsy84F6MH4J5baDqynfenyHcsUGuqz/LJe5LwAuqx9IULbTv2f9eYE+6v0q5SrWb+t+cRYlkVscbwUOqPV8knLQpMZxGWW7OobyLc2dlOcE7qujHEDZ3q+mrLMfNYYtYpx1AuUD61kseOPesY36jqRcnr+Osl11vhr5UGCLum8f30cbduqc/n7KQfdFddpvUO7x/1OP8S+nvDHqD5SD/ZMpzwV0szrloDzCgjdSfqHHuF8Fdo+IkYg4qMc4TZ+ivJjhNuCXlJcFNC3uMXDUYh9nxjPBNmzainK8+HKUH9C8MyLurHX+lHJMOKYeWy6lrFOY3Ho4kXJryZ/rtPey6C1wXY8bddi7KNvXPMp2f3y3mfbRJqdQ3kZ2Q0Tc0mX6/6E8o/Fjyj68KbBHj2Ucz2coJyYXU465F9SyiTiNcqw/GfhiZv62n7rrSfQfKC8AOLZRPt4xpZte+9QRlPYd77bEsfT8TO/jfGEX4LK67X6V8uzOPYu5jNR5Xkm5Kv41ynHspZSfA7q/y+ifomzLV1MSlmY7rER5mcctlPOgR1Keue02zwsoXwQ1P5POpDwrNvpl4+WUtul1NQwmfuzrx0TPTcYy5rEwM8+uwx9NeVZ11FcobXEL5XPsNxOY55LYBydcd/3cOxq4qn52dLsVsNe52ES2wX71Oo4cRbka9DfKNnxsx3QLff71sW/9P8q51VxKwnzkJGLu6zy3tssrKM8szqV8hjQ/x8fKcV5KeU5xQldJR99mKGmKq9/wzgM2z8yruwzfl3LyMOFvGHvM71jgT5m52FdoJQ1elOdkrqa86GFCd3y0JSK2o3xD/pj0xGRSImInyktLdht0LFp2DMNxZJAi4mzKy6kunch0k3lZh6SlLCJeWm8BWIXyavlLqC/KiIj1IuLZ9RaPx1NuOfjpJOb1jHqryApRbtfalcV/VkOS+lJvmXoX5fevTMImKTN/axImtSsznznRJAxMxKSpblcW/MDj5pQrXqMnKitS3jp1B+UWqZ9RbuFbXI+iPDtwJ+W3MPbNzD9Ooj5JGlNEPJFypX89ym1jkrTcWC5vTYyIwyjPr9yUmU9qlL+D8sN/DwK/zMwP1vKPUF5t+yDwzsw8sZbvQrmPehrlm7wDa/kmlOd61qb8ps6/T/J+XEmSJEnLkOX1itjhlAdzHxYRz6dcfXhKfZ3qF2v5FpSHq7es03yjvt1lGuX3b15EefXya+q4UB6S/HJmbkZ5IHXvpb5EkiRJkobGcpmIZebpLPpj1PsCB2bmfXWcm2r5rsAxmXlffUHCHMqrLLcB5mTmVfVq1zGU30EJyu91/KhOfwSw29JcHkmSJEnDZfqgA5hCHgc8NyI+S3mt6/sz81zKD1c3X9V9LQt+kfyajvJnUm5HnNd4o0xz/EVExD7APgCrrLLK05/whCcsgUWRJEmSujv//PNvyczZ44+ppclEbIHpwFrAtsAzgOMiYon9mGEvmXkIcAjA1ltvneedd97SnqUkSZKWYxHx90HHIBOxpmuBn9Q30p0TEQ8B61B++HHDxngb1DJ6lN8KrBkR0+tVseb4kiRJkrR8PiPWw/HA8wEi4nGUV4PfApwA7BERK9W3IW4OnEP59fLNI2KTiFiR8kKPE2oi9ztg91rvXpTXikuSJEkSsJxeEYuIo4HtgXUi4lpgP+Aw4LCIuBS4H9irJlWXRcRxwOXAfOBtmflgreftwImU19cflpmX1Vl8CDgmIj4D/BE4tLWFkyRJkjTlLZe/IzZV+YyYJEmSlraIOD8ztx50HMs7b02UJEmSpJaZiEmSJElSy0zEJEmSJKllJmKSJEmS1DITMUmSJElqmYmYJEmSJLXMREySJEmSWmYiJkmSJEktMxGTJEmSpJaZiEmSJElSy0zEJEmSJKllJmKSJEmS1DITMUmSJElqmYmYJEmSJLVs+qADkCRJkpZVmcnIyAgAs2bNIiIGHJGmCq+ISZIkSUvJyMgIex58MnsefPLDCZkEXhGTJEmSlqoZM1cfdAiagrwiJkmSJEktMxGTJEmSpJaZiEmSJElSy0zEJEmSJKllJmKSJEmS1DITMUmSJElqmYmYJEmSJLXMREySJEmSWmYiJkmSJEktMxGTJEmSpJaZiEmSJElSy0zEJEmSJKllJmKSJEmS1DITMUmSJElqmYmYJEmSJLXMREySJEmSWmYiJkmSJEktMxGTJEmSpJaZiEmSJElSy0zEJEmSJKllJmKSJEmS1DITMUmSJElqmYmYJEmSJLXMREySJEmSWmYiJkmSJEktMxGTJEmSpJaZiEmSJElSy0zEJEmSJKlly2UiFhGHRcRNEXFpl2Hvi4iMiHVqf0TEQRExJyIujoitGuPuFRF/qX97NcqfHhGX1GkOiohoZ8kkSZIkDYPlMhEDDgd26SyMiA2BnYB/NIpfBGxe//YBDq7jrgXsBzwT2AbYLyJm1WkOBt7UmG6ReUmSJElafi2XiVhmng7M7TLoy8AHgWyU7QocmcVZwJoRsR6wM3BSZs7NzBHgJGCXOmz1zDwrMxM4EthtKS6OJEmSpCGzXCZi3UTErsB1mXlRx6D1gWsa/dfWsrHKr+1SLkmSJEkATB90AFNBRMwEPkq5LbHtee9DueWRjTbaqO3ZS5IkSRoAr4gVmwKbABdFxN+ADYALIuJRwHXAho1xN6hlY5Vv0KW8q8w8JDO3zsytZ8+evQQWRZIkSdJUZyIGZOYlmfnIzNw4Mzem3E64VWbeAJwA7FnfnrgtcFtmXg+cCOwUEbPqSzp2Ak6sw26PiG3r2xL3BH42kAWTJEmSNCUtl4lYRBwN/AF4fERcGxF7jzH6r4CrgDnAt4G3AmTmXODTwLn174BaRh3nO3WavwK/XhrLIUmSJGk4LZfPiGXma8YZvnGjO4G39RjvMOCwLuXnAU+aXJSSJEmSllXL5RUxSZIkSRokEzFJkiRJapmJmCRJkiS1zERMkiRJklpmIiZJkiRJLTMRkyRJkqSWmYhJkiRJUstMxCRJkiSpZSZikiRJktQyEzFJkiRJapmJmCRJkiS1zERMkiRJklpmIiZJkiRJLTMRkyRJkqSWmYhJkiRJUstMxCRJkiSpZSZikiRJktQyEzFJkiRJapmJmCRJkiS1zERMkiRJklpmIiZJkiRJLTMRkyRJkqSWmYhJkiRJUstMxCRJkiSpZSZikiRJktQyEzFJkiRJapmJmCRJkiS1zERMkiRJklpmIiZJkiRJLTMRkyRJkqSWmYhJkiRJUstMxCRJkiSpZSZikiRJktQyEzFJkiRJapmJmCRJkiS1zERMkiRJklpmIiZJkiRJLTMRkyRJkqSWmYhJkiRJUstMxCRJkiSpZSZikiRJktQyEzFJkiRJapmJmCRJkiS1zERMkiRJklpmIiZJkiRJLVsuE7GIOCwiboqISxtlX4iIP0XExRHx04hYszHsIxExJyKujIidG+W71LI5EfHhRvkmEXF2LT82IlZsbeEkSZIkTXnLZSIGHA7s0lF2EvCkzPwn4M/ARwAiYgtgD2DLOs03ImJaREwD/ht4EbAF8Jo6LsDngC9n5mbACLD30l0cSZIkScNkuUzEMvN0YG5H2W8zc37tPQvYoHbvChyTmfdl5tXAHGCb+jcnM6/KzPuBY4BdIyKAHYAf1emPAHZbmssjSZIkabgsl4lYH94A/Lp2rw9c0xh2bS3rVb42MK+R1I2WS5IkSRJgIraIiPgYMB/4fkvz2ycizouI826++eY2ZilJkiRpwEzEGiLidcBLgNdmZtbi64ANG6NtUMt6ld8KrBkR0zvKu8rMQzJz68zcevbs2UtkOSRJkiRNbSZiVUTsAnwQeFlm3t0YdAKwR0SsFBGbAJsD5wDnApvXNySuSHmhxwk1gfsdsHudfi/gZ20thyRJkqSpb7lMxCLiaOAPwOMj4tqI2Bv4OrAacFJEXBgR3wTIzMuA44DLgd8Ab8vMB+szYG8HTgSuAI6r4wJ8CHhvRMyhPDN2aIuLJ0mSJGmKmz7+KMuezHxNl+KeyVJmfhb4bJfyXwG/6lJ+FeWtipIkSZK0iOXyipgkSZIkDZKJmCRJkiS1zERMkiRJklpmIiZJkiRJLTMRkyRJkqSWmYhJkiRJUstMxCRJkiSpZSZikiRJktQyEzFJkiRJapmJmCRJkiS1zERMkiRJklpmIiZJkiRJLTMRkyRJkqSWmYhJkiRJUstMxCRJkiSpZSZikiRJktQyEzFJkiRJapmJmCRJkiS1zERMkiRJklpmIiZJkiRJLTMRkyRJkqSWmYhJkiRJUstMxCRJkiSpZSZikiRJktQyEzFJkiRJapmJmCRJkiS1zERMkiRJklpmIiZJkiRJLTMRkyRJkqSWDXUiFhGv7KdMkiRJkqaSoU7EgI/0WSZJkiRJU8b0QQewOCLiRcCLgfUj4qDGoNWB+YOJSpIkSZL6M5SJGPB/wHnAy4DzG+V3AO8ZSESSJEmS1KehTMQy86KIuBTYOTOPGHQ8kiRJkjQRQ/uMWGY+CGwYESsOOhZJkiRJmoihvCLWcDXw+4g4AbhrtDAzvzS4kCRJkiRpbMOeiP21/q0ArDbgWCRJkiSpL0OdiGXmpwAiYtXaf+dgI5IkSZKk8Q3tM2IAEfGkiPgjcBlwWUScHxFbDjouSZIkSRrLUCdiwCHAezPzMZn5GOB9wLcHHJMkSZIkjWnYE7FVMvN3oz2ZeSqwyuDCkSRJkqTxDfUzYsBVEfEJ4Kja/2/AVQOMR5IkSZLGNexXxN4AzAZ+Uv9m1zJJkiRJmrKG+opYZo4A74yINYCHMvOOQcckSZIkSeMZ6itiEfGMiLgEuAi4JCIuioinDzouSZIkSRrLUCdiwKHAWzNz48zcGHgb8N3xJoqIwyLipoi4tFG2VkScFBF/qf9n1fKIiIMiYk5EXBwRWzWm2auO/5eI2KtR/vSIuKROc1BExJJcaEmSJEnDbdgTsQcz84zRnsz8X2B+H9MdDuzSUfZh4OTM3Bw4ufYDvAjYvP7tAxwMJXED9gOeCWwD7DeavNVx3tSYrnNekiRJkpZjw56InRYR34qI7SPieRHxDeDUiNiqeeWqU2aeDsztKN4VOKJ2HwHs1ig/MouzgDUjYj1gZ+CkzJxbn1U7CdilDls9M8/KzASObNQlSZIkScP9sg7gKfX/fh3lTwMS2GECda2bmdfX7huAdWv3+sA1jfGurWVjlV/bpVySJEmSgCFPxDLz+Uup3oyIXBp1d4qIfSi3PLLRRhu1MUtJkiRJAzbUtyZGxNr1ZRgXRMT5EfHViFh7Mau7sd5WSP1/Uy2/DtiwMd4GtWys8g26lHeVmYdk5taZufXs2bMXM3RJkiRJw2SoEzHgGOBm4F+B3Wv3sYtZ1wnA6JsP9wJ+1ijfs749cVvgtnoL44nAThExq76kYyfgxDrs9ojYtr4tcc9GXZIkSZI03LcmAutl5qcb/Z+JiFePN1FEHA1sD6wTEddSnjE7EDguIvYG/g68qo7+K+DFwBzgbuD1AJk5NyI+DZxbxzsgM0dfAPJWypsZVwZ+Xf8kSZIkCRj+ROy3EbEHcFzt351ypWpMmfmaHoN27DJuUn6frFs9hwGHdSk/D3jSeHFIkiRJWj4N+62JbwJ+ANxf/44B3hwRd0TE7QONTJIkSZJ6GOorYpm52qBjkCRJkqSJGupELCK261Zef7BZkiRJkqakoU7EgA80uh8BbAOcz8R+yFmSJEmSWjXUiVhmvrTZHxEbAl8ZTDSSJEmS1J9hf1lHp2uBJw46CEmSJEkay1BfEYuIrwFZe1cAngpcMLCAJEmSJKkPQ52IAec1uucDR2fm7wcVjCRJkiT1Y6gTscw8AiAiZlB+QPm6wUYkSZIkSeMbymfEIuKbEbFl7V4DuAg4EvhjRLxmoMFJkiRJ0jiGMhEDnpuZl9Xu1wN/zswnA08HPji4sCRJkiRpfMOaiN3f6H4hcDxAZt4wkGgkSZIkaQKGNRGbFxEviYinAc8GfgMQEdOBlQcamSRJkiSNY1hf1vFm4CDgUcC7G1fCdgR+ObCoJEmSJKkPQ5mIZeafgV26lJ8InNh+RJIkSZLUv2G9NVGSJEmShpaJmCRJkiS1zERMkiRJklo2lM+IjYqIlYB/BTamsSyZecCgYpIkSZKk8Qx1Igb8DLgNOB+4b8CxSJIkSVJfhj0R2yAzF3l7oiRJkiRNZcP+jNiZEfHkQQchSZIkSRMx7FfEngO8LiKuptyaGEBm5j8NNixJkiRJ6m3YE7EXDToASZIkSZqooUzEImL1zLwduGPQsUiSJEnSRA1lIgb8AHgJ5W2JSbklcVQCjx1EUJIkSZLUj6FMxDLzJfX/JoOORZIkSZImatjfmihJkiRJQ8dETJIkSZJaZiImSZIkSS0b6kQsIv4rIrYcdBySJEmSNBFDnYgBVwCHRMTZEfGWiFhj0AFJkiRJ0niGOhHLzO9k5rOBPYGNgYsj4gcR8fzBRiZJkiRJvQ11IgYQEdOAJ9S/W4CLgPdGxDEDDUySJEmSehjK3xEbFRFfpvyw8ynAf2TmOXXQ5yLiysFFJkmSJEm9DXUiBlwMfDwz7+oybJu2g5EkSZKkfgz7rYnzaCSTEbFmROwGkJm3DSgmSZIkSRrTsCdi+zUTrsycB+w3uHAkSZIkaXzDnoh1i3/Yb7eUJEmStIwb9kTsvIj4UkRsWv++BJw/6KAkSZIkaSzDnoi9A7gfOLb+3Qe8baARSZIkSdI4hvo2vvq2xA8POg5JkiRJmoihTsQi4nHA+4GNaSxLZu4wqJgkSZIkaTxDnYgBPwS+CXwHeHDAsUiSJElSX4Y9EZufmQcPOghJkiRJmohhf1nHzyPirRGxXkSsNfo36KAkSZIkaSzDnojtBXwAOJPy2vrzgfMmU2FEvCciLouISyPi6Ih4RERsEhFnR8SciDg2Ilas465U++fU4Rs36vlILb8yInaeTEySJEmSli1DnYhl5iZd/h67uPVFxPrAO4GtM/NJwDRgD+BzwJczczNgBNi7TrI3MFLLv1zHIyK2qNNtCewCfCMipi1uXJIkSZKWLUOdiEXEzIj4eEQcUvs3j4iXTLLa6cDKETEdmAlcD+wA/KgOPwLYrXbvWvupw3eMiKjlx2TmfZl5NTAH2GaScUmSJElaRgx1IgZ8l/KDzv9c+68DPrO4lWXmdcAXgX9QErDbKLc7zsvM+XW0a4H1a/f6wDV12vl1/LWb5V2mkSRJkrScG/ZEbNPM/DzwAEBm3g3E4lYWEbMoV7M2AR4NrEK5tXCpiYh9IuK8iDjv5ptvXpqzkiRJkjRFDHsidn9ErAwkQERsCtw3ifpeAFydmTdn5gPAT4BnA2vWWxUBNqBceaP+37DOezqwBnBrs7zLNAvJzEMyc+vM3Hr27NmTCF2SJEnSsBj2RGw/4DfAhhHxfeBk4IOTqO8fwLb12bMAdgQuB34H7F7H2Qv4We0+ofZTh5+SmVnL96hvVdwE2Bw4ZxJxSZIkSVqGDPUPOmfmSRFxAbAt5ZbEd2XmLZOo7+yI+BFwATAf+CNwCPBL4JiI+EwtO7ROcihwVETMAeZS3pRIZl4WEcdRkrj5wNsy88HFjUuSJEnSsmWoE7GI2K523lH/bxERZObpi1tnZu5HudLWdBVd3nqYmfcCr+xRz2eBzy5uHJIkSZKWXUOdiFF+zHnUIyjJ0vmU181LkiRJ0pQ01IlYZr602R8RGwJfGUw0kiRJktSfYX9ZR6drgScOOghJkiRJGstQXxGLiK9RX11PSSqfSnnRhiRJkiRNWUOdiAHnNbrnA0dn5u8HFYwkSZIk9WOoE7HMPGLQMUiSJEnSRA11IhYRl7Dg1sSFBgGZmf/UckiSJEmSNK6hTsSAX9f/R9X/r63/Dx5ALJIkSVrOZSYjIyPMmjWLiBh0OJrChv2tiS/MzA9m5iX178PATpn598z8+6CDkyRJ0vJlZGSEPb54PCMjI4MORVPcsCdiERHPbvT8M8O/TJIkSRpiM2auNugQNASG/dbEvYHDImKN2j8PeMPgwpEkSZKk8Q11IpaZ5wNPGU3EMvO2AYckSZIkSeMa6tv4ImLdiDgUOCYzb4uILSJi70HHJUmSJEljGepEDDgcOBF4dO3/M/DuQQUjSZIkSf0Y9kRsncw8DngIIDPnAw8ONiRJkiRJGtuwJ2J3RcTa1B91johtAZ8TkyRJkjSlDfXLOoD3AicAm0bE74HZwO6DDUmSJEmSxja0iVhETAOeV/8eDwRwZWY+MNDAJEmSJGkcQ3trYmY+CLwmM+dn5mWZealJmCRJkqRhMLRXxKrfR8TXgWOBu0YLM/OCwYUkSZIkSWMb9kTsqfX/AY2yBHZoPxRJkiRJ6s9QJmIR8fbM/HpmPj8itszMywYdkyRJkiT1a1ifEXtDo/uogUUhSZIkSYthWBOxphh0AJIkSZI0EUN5ayKwZkS8nJJIrh4Rr2gOzMyfDCYsSZIkSRrfsCZipwEvq92nAy9tDEvAREySJEnSlDWUiVhmvn7QMUiSJEnS4hrKREySJEkapMxkZGQEgFmzZhHhaws0McvCyzokSZKkVo2MjLDnwSez58EnP5yQSRPhFTFJkiRpMcyYufqgQ9AQG+orYhExMyI+ERHfrv2bR8RLBh2XJEmSJI1lqBMx4LvAfcCzav91wGcGF44kSZIkjW/YE7FNM/PzwAMAmXk3/sCzJEmSpClu2BOx+yNiZcpvhxERm1KukEmSJEnSlDXsL+vYH/gNsGFEfB94NvC6QQYkSZIkSeMZ6kQsM38bEecD21JuSXxXZt4y4LAkSZIkaUxDnYhFxM+BHwAnZOZdg45HkiRJkvox7M+IfRF4LnB5RPwoInaPiEcMOihJkiRJGstQXxHLzNOA0yJiGrAD8CbgMMBf15MkSZI0ZQ11IgZQ35r4UuDVwFbAEYONSJIkSZLGNtSJWEQcB2xDeXPi14HTMvOhwUYlSZIkSWMb6kQMOBR4TWY+OOhAJEmSJKlfQ5mIRcQOmXkKsAqwa0QsNDwzfzKQwCRJkiSpD0OZiAHPA06hPBvWKQETMUmSJElT1lAmYpm5X+08IDOvbg6LiE0GEJIkSZIk9W3Yf0fsx13KfjSZCiNizfqbZH+KiCsi4lkRsVZEnBQRf6n/Z9VxIyIOiog5EXFxRGzVqGevOv5fImKvycQkSZIkadkylFfEIuIJwJbAGhHxisag1YHJ/qDzV4HfZObuEbEiMBP4KHByZh4YER8GPgx8CHgRsHn9eyZwMPDMiFgL2A/YmnKr5PkRcUJmjkwyNkmSJEnLgKFMxIDHAy8B1mTh58TuoPyo82KJiDWA7YDXAWTm/cD9EbErsH0d7QjgVEoititwZGYmcFa9mrZeHfekzJxb6z0J2AU4enFjkyRJkrTsGMpELDN/BvwsIp6VmX9YglVvAtwMfDcingKcD7wLWDczr6/j3ACsW7vXB65pTH9tLetVLkmSJEnDmYg1/DEi3ka5TfHhWxIz8w2LWd90YCvgHZl5dkR8lXIb4sMyMyMiFzfgThGxD7APwEYbbbSkqpUkSZI0hQ37yzqOAh4F7AycBmxAuT1xcV0LXJuZZ9f+H1ESsxvrLYfU/zfV4dcBGzam36CW9SpfRGYekplbZ+bWs2fPnkTokiRJkobFsCdim2XmJ4C7MvMI4F8oL81YLJl5A3BNRDy+Fu0IXA6cAIy++XAv4Ge1+wRgz/r2xG2B2+otjCcCO0XErPqGxZ1qmSRJkiQN/a2JD9T/8yLiSZTntx45yTrfAXy/vjHxKuD1lIT1uIjYG/g78Ko67q+AFwNzgLvruGTm3Ij4NHBuHe+A0Rd3SJIkSdKwJ2KH1CtOn6BcnVoV+ORkKszMCymvne+0Y5dxE3hbj3oOAw6bTCySJEmSlk1DnYhl5ndq52nAYwcZiyRJkiT1aygTsYh471jDM/NLbcUiSZIkSRM1lIkYsNqgA5AkSZKkxTWUiVhmfmrQMUiSJEnS4hrq19dHxOMi4uSIuLT2/1NEfHzQcUmSJEnSWIY6EQO+DXyE+hr7zLwY2GOgEUmSJGmZk5nMnTuXuXPnUl6cLU3OUN6a2DAzM8+JiGbZ/EEFI0mSpGXTyMgIex58MgBH7rvIrxpJEzbsidgtEbEpkAARsTtw/WBDkiRJ0rJoxszVBx2CliHDnoi9DTgEeEJEXAdcDbx2sCFJkiRJ0tiG+hmxzLwqM18AzAaeADwPeM5go5IkSdKwG30mzOfBtLQMZSIWEatHxEci4usR8ULgbmAvYA7wqsFGJ0mSpGE3MjLCHl88npGRkUGHomXUsN6aeBQwAvwBeBPwMSCAl2fmhQOMS5IkScuIGTNXG3QIWoYNayL22Mx8MkBEfIfygo6NMvPewYYlSZIkSeMbylsTqb8bBpCZDwLXmoRJkiRJGhbDekXsKRFxe+0OYOXaH0Bmpu8WlSRJkjRlDWUilpnTBh2DJEmSJC2uYb01UZIkSZKGlomYJEmSJLXMREySJEmSWmYiJkmSJEktMxGTJEmSpJaZiEmSJElSy0zEJEmSJKllJmKSJEmS1DITMUmSJElqmYmYJEmSJLXMREySJEmSWmYiJkmSJEktMxGTJEmSpJaZiEmSJElSy0zEJEmSJKllJmKSJEmS1DITMUmSJElqmYmYJEmSJLXMREySJEmSWmYiJkmSJEktMxGTJEmSpJaZiEmSJElSy0zEJEmSJKllJmKSJEmS1DITMUmSJElqmYmYJEmSJLXMREySJEmSWmYiJkmSJEktMxGTJEmSpJaZiHUREdMi4o8R8Yvav0lEnB0RcyLi2IhYsZavVPvn1OEbN+r4SC2/MiJ2HtCiSJIkSZqCTMS6exdwRaP/c8CXM3MzYATYu5bvDYzU8i/X8YiILYA9gC2BXYBvRMS0lmKXJEmSNMWZiHWIiA2AfwG+U/sD2AH4UR3lCGC32r1r7acO37GOvytwTGbel5lXA3OAbVpZAEmSJElTnonYor4CfBB4qPavDczLzPm1/1pg/dq9PnANQB1+Wx3/4fIu00iSJElazpmINUTES4CbMvP8Fue5T0ScFxHn3XzzzW3NVpIkSdIAmYgt7NnAyyLib8AxlFsSvwqsGRHT6zgbANfV7uuADQHq8DWAW5vlXaZZSGYekplbZ+bWs2fPXrJLI0mSJGlKMhFryMyPZOYGmbkx5WUbp2Tma4HfAbvX0fYCfla7T6j91OGnZGbW8j3qWxU3ATYHzmlpMSRJkiRNcdPHH0XAh4BjIuIzwB+BQ2v5ocBRETEHmEtJ3sjMyyLiOOByYD7wtsx8sP2wJUmSJE1FJmI9ZOapwKm1+yq6vPUwM+8FXtlj+s8Cn116EUqSJEkaVt6aKEmSJEktMxGTJEmSpJaZiEmSJElSy0zEJEmSJKllJmKSJEmS1DITMUmSJElqmYmYJEmSJLXMREySJEmSWuYPOkuSJGm5lZmMjIwAMGvWLCJiwBFpeeEVMUmSJC23RkZG2PPgk9nz4JMfTsikNnhFTJIkScu1GTNXH3QIWg55RUySJEmSWmYiJkmSJEktMxGTJEmSpJaZiEmSJElSy3xZhyRJkpYbna+rlwbFREySJEnLjdHX1QMcue+OA45GyzMTMUmSJC1XfF29pgKfEZMkSZKklpmISZIkSVLLTMQkSZIkqWUmYpIkSZLUMhMxSZIkLbMyk7lz55KZgw5FWoiJmCRJkpZZIyMj7PHF4x/+7TBpqjARkyRJ0jJtxszVBh2CtAgTMUmSJElqmYmYJEmSJLXMREySJEmSWmYiJkmSJEktMxGTJEmSpJaZiEmSJElSy0zEJEmSJKllJmKSJEmS1DITMUmSJElqmYmYJEmSJLXMREySJEmSWmYiJkmSJEktMxGTJEmSpJaZiEmSJElSy0zEJEmSJKllJmKSJEmS1DITMUmSJElqmYmYJEmSJLVs+qADkCRJkiYrMxkZGQFg1qxZRMSAI+puWOLU0ucVMUmSJA29kZER9jz4ZPY8+OSHE52paFji1NLnFTFJkiQtE2bMXH3QIfRlWOLU0uUVsYaI2DAifhcRl0fEZRHxrlq+VkScFBF/qf9n1fKIiIMiYk5EXBwRWzXq2quO/5eI2GtQyyRJkiRp6jERW9h84H2ZuQWwLfC2iNgC+DBwcmZuDpxc+wFeBGxe//YBDoaSuAH7Ac8EtgH2G03eJEmSJMlErCEzr8/MC2r3HcAVwPrArsARdbQjgN1q967AkVmcBawZEesBOwMnZebczBwBTgJ2aW9JJEmSJE1lJmI9RMTGwNOAs4F1M/P6OugGYN3avT5wTWOya2tZr/Ju89knIs6LiPNuvvnmJbcAkiRJy7DMZO7cucydO5fMHHQ40oSZiHUREasCPwbenZm3N4dl2dOX2N6emYdk5taZufXs2bOXVLWSJEnLNN8+qGFnItYhImZQkrDvZ+ZPavGN9ZZD6v+bavl1wIaNyTeoZb3KJUmStITMmLm6byDU0DIRa4jyi3qHAldk5pcag04ARt98uBfws0b5nvXtidsCt9VbGE8EdoqIWfUlHTvVMkmSJEnyd8Q6PBv4d+CSiLiwln0UOBA4LiL2Bv4OvKoO+xXwYmAOcDfweoDMnBsRnwbOreMdkJlzW1kCSZIkSVOeiVhDZv4vED0G79hl/ATe1qOuw4DDllx0kiRJkpYV3pooSZIkSS0zEZMkSZKklpmISZIkSVLLTMQkSZIkqWUmYpIkSZLUMhMxSZIkSWqZiZgkSZIktcxETJIkSVNaZjJ37lzKT7hKywYTMUmSJE1pIyMj7PHF4xkZGRl0KNISM33QAUiSJEmjMvPhhGvWrFlEBAAzZq42yLCkJc4rYpIkSZoyRkZG2PPgk9nz4JO9AqZlmomYJEmSppQZM1dnxszVBx1GV6NX7HxeTZNlIiZJkiT16YG772Sfb53CvHnzBh2KhpyJmCRJkjQBM2auOugQtAwwEZMkSZKklpmISZIkSVLLfH29JEmSBqbzdfXS8sJETJIkSQMz+rp6gCP33XHA0UjtMRGTJEnSQE3VV9VLS5PPiEmSJElSy0zEJEmSJKll3pooSZIkTUJmMnfu3Ie7pX6YiEmSJKkVo29InDVrFhEx6HCWmHnz5vGuYy4A4Kt7bDXgaDQsvDVRkiRJrRgZGWGPLx7/8OvqlwWZybx585gxc3VfOqIJMRGTJElSa2bMXG3QISxRD9x9J+85/AwefGD+oEPRkDERkyRJkiZh+sxVBh2ChpCJmCRJkiS1zJd1SJIkaYkbfTEHsMy9nENaErwiJkmSpCVuZGSEPQ8+mT0PPnmZejnH4hpNTH29vUaZiEmSJGmp8E2CCzxw953s861TmDdv3qBD0RThrYmSJEmatM5bEbWoGTNXHXQImkJMxCRJkjRpo7ciAhy5744Djkaa+rw1UZIkSUvEMN+K6DNcapuJmCRJkiYkM5k7d+4ylbSMPsPli0XUFhMxSZIkTcjIyAh7fPH4ZS5p8RkutclnxCRJktRTr98DmzFztUGGJQ09r4hJkiSpJ38PTFo6TMQkSZIE9H72a5hfwtGLL+fQoJmISZIkCVh2n/3qxh9Y1qCZiEmSJOlhy9OzX76cQ4NkIiZJkrSM6XaL4WhZs3xZfA29NCxMxCRJkpYx3W4x7PbSjeXpVkRpqjERkyRJGnLdrmx1u8Ww20s3lrVbEX0Jh4aFiZgkSdIU1e8thl7ZWmD0JRy2haY6EzFJkqSW9Xo2q7O831sMYdm7sjUZg34Jx+hVOZ+/01hMxJaiiNglIq6MiDkR8eFBxyNJktrRmVD1k2D1Ku/3FsNl3TDdcvjAPXfxtqPOZc+DT/b1+OrJRGwpiYhpwH8DLwK2AF4TEVsMNipJkgTjJ0r9jtdrus6Eqt8Ea6zy5U1n4vXAPXcN1S2HK85cjekrr2Yipp5MxJaebYA5mXlVZt4PHAPsOuCYJEnLqH5fTd453pKcrtu0k52uVz2Tna6fRKmf8cZ6NqszoRqWBKvf2+r6vULVOV6/03V71mvQtxyOJzMXSrweuPtO3nP4GTz4wPzBBaUpa/qgA1iGrQ9c0+i/FnjmgGIZ09y5cwcdgiRpkkZGRtj30NMAOHjv5zFr1ixGRkZ449d/xXfe/mJmzZrVdTxgiU03Ol5z2slMN1Y9S2K6sf4323Ui/5vTPXD3HQsNH6u/3+kWlN2+yPyX1HT3zL2Bfb51CtOmT3t4nXWb7p65N/D6r/6c777rpQ+3ded4D9x9O/fffSev/+qf+O67XgrQ93QP3HPnQmXz77mDh+bP74i9+3QPPtA5XmfbLBhv3rx5k55u/t13cf/Kd/DQA/fw9m+dz4yVVmHaiivz0AP3AHD/3WVYczophuE+22EUEbsDu2TmG2v/vwPPzMy3d4y3D7BP7X08cGWrgU5t6wC3DDqI5ZDtPji2/WDY7oNhuw+ObT8YU6ndH5OZswcdxPLOK2JLz3XAho3+DWrZQjLzEOCQtoIaJhFxXmZuPeg4lje2++DY9oNhuw+G7T44tv1g2O7q5DNiS8+5wOYRsUlErAjsAZww4JgkSZIkTQFeEVtKMnN+RLwdOBGYBhyWmZcNOCxJkiRJU4CJ2FKUmb8CfjXoOIaYt2wOhu0+OLb9YNjug2G7D45tPxi2uxbiyzokSZIkqWU+IyZJkiRJLTMR05QQEe+IiD9FxGUR8flG+UciYk5EXBkROzfKd6llcyLiw4OJevhFxP4RcV1EXFj/XtwYZtsvZRHxvojIiFin9kdEHFTb9uKI2Kox7l4R8Zf6t9fgoh5uEfHp2rYXRsRvI+LRtdy2X4oi4gv1GH9xRPw0ItZsDPNYs5RExCvr5+pDEbF1xzDbvUW2q7oa/ZV7//wb1B/wfOB/gJVq/yPr/y2Ai4CVgE2Av1JefDKtdj8WWLGOs8Wgl2MY/4D9gfd3Kbftl37bb0h5mc/fgXVq2YuBXwMBbAucXcvXAq6q/2fV7lmDXoZh/ANWb3S/E/imbd9Ku+8ETK/dnwM+V7s91izddn8i5TdKTwW2bpTb7u2uB9vVv65/XhHTVLAvcGBm3geQmTfV8l2BYzLzvsy8GpgDbFP/5mTmVZl5P3BMHVdLjm2/9H0Z+CDQfFB3V+DILM4C1oyI9YCdgZMyc25mjgAnAbu0HvEyIDNvb/SuwoL2t+2Xosz8bWbOr71nUX5bEzzWLFWZeUVmXtllkO3eLttVXZmIaSp4HPDciDg7Ik6LiGfU8vWBaxrjXVvLepVr8by93i50WETMqmW2/VIUEbsC12XmRR2DbPcWRMRnI+Ia4LXAJ2uxbd+eN1CuPoLtPii2e7tsV3Xl6+vVioj4H+BRXQZ9jLIdrkW5HegZwHER8dgWw1umjdP2BwOfplwV+DTwX5STJE3SOO3+UcqtWloKxmr7zPxZZn4M+FhEfAR4O7BfqwEuo8Zr9zrOx4D5wPfbjG1Z1k+7S5qaTMTUisx8Qa9hEbEv8JPMTOCciHgIWAe4jvIczagNahljlKvDWG3fFBHfBn5Re237SerV7hHxZMozGRdFBJQ2vCAitqF3u18HbN9RfuoSD3oZ0e82T0kGfkVJxGz7SRqv3SPidcBLgB3r8R481kzaBLb3Jtu9XWO1t5Zj3pqoqeB4ygs7iIjHUR5kvQU4AdgjIlaKiE2AzYFzgHOBzSNik4hYEdijjqsJqs/AjHo5cGnttu2Xksy8JDMfmZkbZ+bGlFtUtsrMGyhtuWd9g9+2wG2ZeT3lpR47RcSsevvoTrVMExQRmzd6dwX+VLtt+6UoInahPBP5ssy8uzHIY81g2O7tsl3VlVfENBUcBhwWEZcC9wN71W9LL4uI44DLKbeyvC0zHwSIiLdTToamAYdl5mWDCX3ofT4inkq5NfFvwJsBMtO2H4xfUd7eNwe4G3g9QGbOjYhPUz7MAQ7IzLmDCXHoHRgRjwceoryx8i213LZfur5OeUPfSfVK8FmZ+RaPNUtXRLwc+BowG/hlRFyYmTvb7u3KzPm2q7qJBXcHSJIkSZLa4K2JkiRJktQyEzFJkiRJapmJmCRJkiS1zERMkiRJklpmIiZJkiRJLTMRk6awiNgtIjIinjDoWHqJiI9OcvrtI+IXtftlEfHh2j07Is6OiD9GxHMj4pURcUVE/G5JxN2WiPhORGwxgfG3j4h/7mO83fqpNyL2j4j39zv/YRMRh0fE7kuh3qdGxIuXdL1taO5HE5jmbxGxTr/lk4ht64g4aEnVt6RFxKkRsfUA5juh7TgiNq4/+dJt2ECWQdLEmYhJU9trgP+t/5eIiFjSvx84qUSsKTNPyMwDa++OwCWZ+bTMPAPYG3hTZj6/n7qWwnIulsx8Y2ZePoFJtgfGTcSA3YC+E7ypIiKmDWCei7MtPJXyu2JLez5LXMd+NKVk5nmZ+c5BxyFJU4GJmDRFRcSqwHMoCcgejfIVIuIbEfGniDgpIn41+k1qRLy4lp8fEQc1rjTtHxFHRcTvgaPq1aYfR8S59e/ZdbzZtc7L6pWcv49+Gx4Rx9d6L4uIfWrZgcDKEXFhRHy/lv1bRJxTy77V7cQ7InapcV4AvKJR/rqI+Hr9kenPA7vWevarbXFoRHwhIqbV/+dGxMUR8eY6/fYRcUZEnABcPs54p0bEj2oc34/6K7MR8YyIODMiLqrLsdoY9awXEafXGC+NiOd2WdaHv52OiDsj4rO17rMiYt2OcTem/MDwe2qdz63ffJ9S53tyRGwU5YrZy4Av1PE2jYg31fguqut25jjbV7d6p0XE1VGsGREPRsR2dfzTI2Lzui0dVpfrqoh4Z6POruu+Lvd/RcRFwLM64ugad5QrBAfVdXFVLNjGo24jV0bE/wCP7LF8p0bEVyLiPOBdEfH0iDgtyjZ8YkSs1xjvczXuP9c2XxE4AHh1XZZXR8QqdbnPiXKVdtfGNntCRJwCnNwljvfWbePSiHh3o3zP2vYXRcRRtWzdiPhpLbsoIv45Oq58RMT7I2L/RuxfbWx/2zRi+nrt7rWvrx0Rv426rwMx1vbSa1ki4gOj20BEfLm2AxGxQ9RjQkcdzSvgY21L3dpnkW22lh8eEQdH2aeuqvM4LMoV9MMbde4UEX+IiAsi4odRjrHdvLK5PdRpHxER342IS+r6f35nW9f+X9T5T6txXVqneU8dvmlE/KZuh2fEwnc7bBfdt/cvNOp5dZc2XTkijqnL+1Ng5VreNQZJU0hm+ueff1PwD3gtcGjtPhN4eu3eHfgV5YuURwEjtewRwDXAJnW8o4Ff1O79gfOBlWv/D4Dn1O6NgCtq99eBj9TuXYAE1qn9a9X/KwOXAmvX/jsbMT8R+Dkwo/Z/A9izY7lG49yccvJ3XCPO1wFf7+yu/acCW9fufYCP1+6VgPOATShXk+5qtMFY490GbFDb8Q+URG9F4CrgGXWa1YHpY9TzPuBjtXwasFqX9diMO4GX1u7Pj9bZMf7+wPsb/T8H9qrdbwCOr92HA7s3xlu70f0Z4B3d6uuj3t8AWwIvAc4FPlaX+epGfWfWsnWAW4EZY637utyv6rGd94r7cOCHdf1sAcyp5a8ATqrt/WhgXrMdOtr9G7V7Ro15du1/NXBYY7z/qt0vBv6nx/b3H8C/1e41gT8Dq9TxrqXuHx0xPB24pI63KnAZ8LTavn9m0X3rWODdje1pDWBj4NJGne8H9m/E/u3avd3oeCy8H/Xa1w8CPlm7/4XGvt6xDH+r67nXsmwL/LCOewZwTm3v/YA3d6lvexY+LnXblnq1z1j7wjGU48muwO3AkynbzvmUq5vrAKcDq9RpPjS6/F22m27bw/tYsM08AfgH5Vj2cFvXYb+oy/h04KRG+Zr1/8nA5rX7mcAp42zv/8qC7X3dOt/1aGwXwHsbsf0TMB/YulcM/vnn39T5mxK3UUjq6jXAV2v3MbX/fErC8MPMfAi4IRY8M/UE4KrMvLr2H01JIEadkJn31O4XAFtEPPwl+Oqx4ArcywEy8zcRMdKY/p0R8fLavSElkbq1I+YdKR/+59a6VwZu6hjnCZST+r8ARMT3OuLsx07AP8WCZyrWqPHcD5zTaIPxxru2xnAh5cTmNuD6zDy3tsHtdXives4FDouIGZSTwgvHift+yokalHX5wj6W9VksuGp4FCWB6+ZJEfEZSpKwKnDiYtZ7BuWkfhPgP4E3AadRlnXULzPzPuC+iLiJcoI41rp/EPjxYsR9fN3OL48FVw+3A47OzAeB/xu9AtPDsfX/44EnASfV2KYB1zfG+0n9fz5lO+hmJ+BlseB5u0dQEhsoJ7tzu0zzHOCnmXkXQET8BHguJen5YWbeAtCYdgdgz1r2IHBbRMwaY/mg7Odk5ukRsXpErNkxvNe+vh11/WfmLzv29W56LcvBwNMjYnXgPuACShLwXKCfWxC7bUs70L19xtoXfp6ZGRGXADdm5iU1zsso63QDSoLz+9oWK1K+gOmm2/bwHOBrNZ4/RcTfgceNsVxXAY+NiK8BvwR+W9v9n4EfNtbHSo1pum3vz2HB9n5jRJwGPAO4uDHddpTEmsy8OCJGhy0SwxjxShoAEzFpCoqItSgnI0+OiKScOGZEfGAS1d7V6F4B2DYz7+2Yb694tqec0D0rM++OiFMpJ6KLjAockZkfmUSc/QjKlZOFko0a5119jndfo+hBxj4edq2n1rUd5YrC4RHxpcw8cox6HsjM7HOeE3U4sFtmXhQRr6N8K784Tgf2pVxt+iTwgVrXGY1xurXdWOv+3noiOdG4m/MZ99a5Lka3hQAuy8xn9RhvdD5jrZMA/jUzr1yoMOKZLLzNLWnzWfgxgs79Lsfpn9C+PlGZ+UBEXE25MnQmJUF4PrAZcEX98ma/Ovobu1Qxkf1wLKP1PNRR50O1zgcpCXM/z9v2sz2M6rp+MnMkIp4C7Ey53fhVwLuBeZn51HHmC4u3vS+kRwxvmGy9kpYcnxGTpqbdgaMy8zGZuXFmbghcTfmW+ffAv0Z5VmxdFpy4Xkn59nPj2r/IswQNvwXeMdoT5Zksat2vqmU7AaPfxq8BjNQk7AmU25FGPVCvCEG57Wb3iHhkrWOtiHhMx7z/BGwcEZvW/sV5EcmJwL6j842Ix0XEKpMYb9SVwHoR8Yw6/mpRXsDQtZ66bDdm5reB7wBbLcaydLoDWK3RfyYLnhF8LQsSos7xVgOurzG+to/59Kr3HMq39g/Vk/cLgTdTErSx9LPuu5lo3KdTnt2aFuU5r35e3nIlMDsinlVjmxERW44zTWf7ngi8I+LhZwmf1sd8zwB2i4iZdbt7eS07hfIc0tq1rrXq+CdTkuDR53vWAG4EHhnlma6VKLeMNr26jv8c4LbMvK1jeK99/XTg/9WyF7FgX5/osowOe3+t8wzKSf8fs/hpZj61/p03zjxG9WqfXttsP84Cnh0Rm9U6V4mIsa5odTqjzpM63UaU7epvwFPr8XhDYPQ5vXWAFTLzx8DHga3qFfarI+KVdZyoidJ48x3d3mdTrn6d0zFOc10+iXJ7YtcYJrC8klpgIiZNTa8BftpR9uNa/mPKMymXA9+j3Ap0W73t8K3AbyLifMqJZOdJ2ah3AltHeej9csqJE8CngJ2ivBzglcANtZ7fANMj4grgQMpJzahDgIsj4vtZ3g74ccptOBdTnm1YrznjenK/D/DLKC/r6Lx1sR/fqct/QY31W3T/5rrf8UZju59yYvu1KC+WOInyDXeverYHLoqIP9bpvtqt3gn6OfDyqC/roJxEv762578D76rjHQN8IMqLAzYFPgGcTUmm/9THfLrWW28Tu4YF6/gMSkJyyViV9bPue5ho3D8F/kJZH0fS+/ayZmz3U77c+Fxdrxcy/pspf0e5pe/CKC9I+DTl+aWL6+1un+5jvhdQrvidQ1nG72TmHzPzMuCzwGk1ni/VSd4FPD/K7XXnA1tk5gOUF4ecQ2nTzja6t25/36S82KfTWPv6dnVZXkF59mjCy1IHn0FZ13/IzBuBe5lYktQ5r17t02tf6KfOmylX7Y6u0/+Bcpt0v74BrFDXzbHA6+q+8nvKl2SXU24PvKCOvz5wapTbnr8HjF4pfi2wd12uyyjPtI3lp5SrjBdREtQPZuYNHeMcDKxaj88HULadsWKQNEXEgrtkJA2LiFg1M++s3xifAzw7M29olAfw38BfMvPLE6h3JeDBzJxfrx4cPMZtNJIGKMotwu+fwJUmSdIU4jNi0nD6RZSH8lcEPt34hvRNEbFXLf8j5crNRGwEHBcRK1BeLPGmJRSvJEmSGrwiJkmSJEkt8xkxSZIkSWqZiZgkSZIktcxETJIkSZJaZiImSZIkSS0zEZMkSZKklpmISZIkSVLL/j9ahgORVsy87AAAAABJRU5ErkJggg==\n", - "text/plain": [ - "
      " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(12, 8))\n", - "plt.title('Distribution of differences between aggregate totals that normalizes tabulation of poverty households (with removal of not computed fields) ')\n", - "# Set x-axis label\n", - "plt.xlabel('Aggregate differences in total owner and renter occupied low-income households')\n", - "# Set y-axis label\n", - "plt.ylabel('Relative Frequency in Support')\n", - "\n", - "sns.histplot(final_df[\"differences_aggregate_denominator_sans_not_computed\"])" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/sarahluw/.pyenv/versions/3.6.2/envs/my-virtual-env-3.6.2/lib/python3.6/site-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " after removing the cwd from sys.path.\n" - ] - } - ], - "source": [ - "final_df[\"current_methodology_percentile_rank\"] = final_df[\"current_methodology_percent\"].rank(\n", - " pct=True,\n", - " # Set ascending to the parameter value.\n", - " ascending=True,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/sarahluw/.pyenv/versions/3.6.2/envs/my-virtual-env-3.6.2/lib/python3.6/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " \"\"\"Entry point for launching an IPython kernel.\n", - "/Users/sarahluw/.pyenv/versions/3.6.2/envs/my-virtual-env-3.6.2/lib/python3.6/site-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " after removing the cwd from sys.path.\n" - ] - } - ], - "source": [ - "final_df[\"new_threshold_exceeded\"] = (final_df['hbrd_rank'] >= 90)\n", - "\n", - "final_df[\"current_threshold_exceeded\"] = (final_df[\n", - " 'current_methodology_percentile_rank'] >= 0.90)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Only include non-NA tracts for comparison purposes" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "# first save NA tracts that were considered unreliable\n", - "ineligible_tracts = list(final_df[final_df[\"hbrd_rank\"].isna()][\"FIPS_tract_id\"].values)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "5243" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "len(ineligible_tracts)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### How many tracts are ineligible according to CalEnvironScreen but are considerd in Score L?\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "final_current_methodology = final_df[final_df[\"current_methodology_percentile_rank\"] >= 0.90]" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(7323, 15)" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "final_current_methodology.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(53, 15)" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# 53 tracts\n", - "final_current_methodology[\n", - " final_current_methodology.FIPS_tract_id.isin(ineligible_tracts)].shape" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "non_null_df = final_df.copy()" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(7323, 15)" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# number of tracks eligible\n", - "non_null_df[non_null_df[\"current_methodology_percentile_rank\"] >= 0.90].shape" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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rNF5aVq5cWatWrVroMCRJkrTEJTmrqlZONM0nQ0qSJEk9MNGWJEmSemCiLUmSJPXARFuSJEnqgYm2JEmS1AMTbUmSJKkHJtrSBmjZ8hUkmfFr2fIVCx26JEkjo88H1khapK5Yu4YDjjpjxsudePjePUQjSdLSZI22JEmS1AMTbUmSJKkHJtqSJElSD0y0JUmSpB6YaEuSJEk9MNGW1Du7E5QkbYjs3k9S7+xOUJK0IbJGW5IkSeqBiba0wGbbrMKmFZIkLW42HZEW2GybVYBNKyRJWsys0ZYkSZJ6YKItSZIk9cBEW5IkSeqBibYkSZLUAxNtSZIkqQcm2pIkSVIPTLQlSZKkHphoS5IkST0w0ZYkSZJ6YKItafHaaBMfTS9JGlk+gl3S4nXLulk9nt5H00tza9nyFVyxds2Ml9txp+VcvuZHPUQkjQYTbUmSNKUr1q7xn15pFmw6IkkDli1fYXMVSdKcsEZbkgZYcydJmivWaEuSJEk9MNGWJEmSemCiLUmSJPXARFuSJEnqgYm2JEmS1AMTbUmSJKkHJtqSJElSD0y0pTky2wedSJKkpckH1khzxAedSJKkQdZoS5IkST0w0ZYkSZJ6YKItSZIk9cBEW5IkSeqBibYkSZLUAxNtSZIkqQcm2lqyZtuv9bLlKxY6dEmStATYj7aWLPu1liRJC8kabUmSJKkHJtqSJElSD0y0JUmSpB6YaEuSJEk9MNGWpAVk7ziStHTZ64gkLSB7x5GkpcsabUmSJKkHJtqSJElSD0y0JUmSpB6YaEuSJEk9MNGWJEmSemCiLUmSJPXARFuSJEnqQW+JdpLlSb6c5MIkq5P8fSs/MsnlSc5pr8cNLPOyJBcnuSjJYwfK921lFyc5oq+YJUmSpLnS5wNr1gEvrqqzk2wBnJXk1DbtbVX1r4MzJ9kdOBC4D7Aj8D9J7tEm/wfwaGAt8O0kJ1fVhT3GLkmSJK2X3hLtqvox8OM2fEOS7wLLplhkf+CEqroJuDTJxcCebdrFVXUJQJIT2rwm2pIkSVq05qWNdpJdgAcA32pFz09yXpJjkmzTypYBawYWW9vKJiuXJEmSFq3eE+0kmwMfB15YVdcD7wZ2Bfagq/F+yxxt57Akq5Ksuuqqq+ZilZIkSdKs9ZpoJ9mULsn+cFV9AqCqflpVN1fVLcB7+V3zkMuB5QOL79TKJiu/jao6uqpWVtXK7bfffu53RpIkSZqBPnsdCfB+4LtV9daB8h0GZnsycEEbPhk4MMntk9wV2A04E/g2sFuSuya5Hd0Nkyf3FbckSZI0F/rsdeQhwDOB85Oc08peDhyUZA+ggMuAwwGqanWSk+huclwHPK+qbgZI8nzgFGBj4JiqWt1j3JIkSdJ667PXka8DmWDS56ZY5g3AGyYo/9xUy0mSJEmLjU+GlCRJknpgoi1JkiT1wERbkkbRRpuQZMavZctXLHTkkrTB6PNmSElSX25ZxwFHnTHjxU48fO8egpEkTcQabUmSJKkHJtqSJElSD0y0JUmSpB6YaEuSJEk9MNGWJEmSemCiLUma1rLlK+xOUJJmyO79JEnTumLtGrsTlKQZskZbkiRJ6oGJtiRJktQDE23NG9t4SpKkDYlttDVvbOMpSZI2JNZoS5IkST0w0ZYkSZpnNqfcMNh0RJIkaZ7ZnHLDYI22JEmS1AMTbUmSJKkHJtqSJElSD0y0JUmSpB6YaEuSJEk9MNGWJEmSemCiLUmSJPXARFuSJEnqgYm2JEmS1AMTbUmSJKkHJtqSJElSD0y0JUn92WgTkszqtWz5ioWOXpLWyyYLHYAkaQm7ZR0HHHXGrBY98fC95zgYSZpf1mhLkiRJPTDRliRJknpgoi1JkiT1wERbkiRJ6oGJtiRJktQDE21JkiSpBybakiRJUg9MtCVJkqQemGhLkiRJPTDRliRJknpgoi1JkiT1wERbkiQtCcuWryDJjF/Llq9Y6NC1RG2y0AFIkiTNhSvWruGAo86Y8XInHr53D9FI1mhLkiRJvTDRliRJknpgoi1JkiT1wERbkiRJ6oGJtiRJktQDE21JkiSpBybakiRJUg9MtCVJi9NGm/jwEUkjzQfWSJIWp1vW+fARSSPNGm1JkiSpBybakiRJUg9MtCVJkqQemGhLkiRJPZg20U7y1GHKJEmSJP3OMDXaLxuyTBuIZctXzKrLLUmSpA3JpN37JdkPeBywLMk7BiZtCazrOzAtXlesXWOXW5IkSdOYqh/tK4BVwBOBswbKbwD+oc+gJEmSpFE3aaJdVecmuQB4bFUdN48xSZIkSSNvyjbaVXUzsDzJ7Wa64iTLk3w5yYVJVif5+1a+bZJTk/ygvW/TypPkHUkuTnJekgcOrOvgNv8Pkhw801gkSZKk+TbMI9gvBU5PcjJw41hhVb11muXWAS+uqrOTbAGcleRU4BDgi1X1xiRHAEcA/wTsB+zWXg8G3g08OMm2wKuBlUC19ZxcVT+fwX5KkiRJ82qYXkf+F/hsm3eLgdeUqurHVXV2G74B+C6wDNgfGGuKchzwpDa8P/DB6nwT2DrJDsBjgVOr6pqWXJ8K7Dvc7o2O2fbksWz5ioUOXZIkSROYtka7ql4DkGTzNv6LmW4kyS7AA4BvAXepqh+3ST8B7tKGlwFrBhZb28omKx+/jcOAwwBWrBi95NOePCRJkpaWYR5Yc98k3wFWA6uTnJXkPsNuoCXoHwdeWFXXD06rqqJrDrLequroqlpZVSu33377uVilJEmSNGvDNB05GnhRVe1cVTsDLwbeO8zKk2xKl2R/uKo+0Yp/2pqE0N6vbOWXA8sHFt+plU1WLkmSJC1awyTad6qqL4+NVNVXgDtNt1C6RwG+H/juuBsnTwbGeg45GPj0QPmzWu8jewHXtSYmpwCPSbJN66HkMa1MkiRJWrSG6XXkkiSvAj7Uxp8BXDLEcg8Bngmcn+ScVvZy4I3ASUkOBX4IPK1N+xzdkygvBn4JPBugqq5J8jrg222+11bVNUNsX5IkSVowwyTazwFeA4w1/fhaK5tSVX0dyCSTHznB/AU8b5J1HQMcM0SskiRJ0qIwTK8jPwf+LslWwC2tqz5JkiRJUxim15E/TnI+cC5dM5Bzkzyo/9AkSZKk0TXMzZDvB/62qnapql3omnd8oNeoJEmarY028QFgkhaFYdpo31xVXxsbqaqvJ1nXY0ySJM3eLet8AJikRWGYRPurSY4Cjqd7uMwBwFeSPBBg7DHrkiRJI6n9CjIbO+60nMvX/GiOA9JSMUyiff/2/upx5Q+gS7wfMacRSZIkzadZ/goC/hKiqQ3T68jD5yMQSZIkaSkZpteROyd5R5Kzk5yV5N+S3Hk+gpMkSZJG1TC9jpwAXAX8JfCUNnxin0FJkjQqli1fYS8nkiY0TBvtHarqdQPjr09yQF8BSZI0Sq5Yu8ZeTiRNaJga7S8kOTDJRu31NOCUvgOTJEmSRtkwifbfAB8BftNeJwCHJ7khyfV9BidJkiSNqmF6HdliPgKRJEmSlpJpE+0kD52ovKpOm/twJEmSpKVhmJsh/3FgeDNgT+AsfFCNJEmSNKlhmo48YXA8yXLg7X0FJEmSJC0Fw9wMOd5a4N5zHYgkSZK0lAzTRvvfgWqjGwF7AGf3GJMkSZI08oZpo71qYHgdcHxVnd5TPJIkSdKSMEwb7eMAkmwK3Be4vO+gJEmSNHeWLV/BFWvXzHi5HXdazuVrftRDRBuGSRPtJO8B/r2qVifZCvgGcDOwbZKXVNXx8xWkJEmSZu+KtWs44KgzZrzciYfv3UM0G46pbob8s6pa3YafDXy/qv4IeBDw0t4jkyRJkkbYVIn2bwaGHw18CqCqftJnQJIkSdJSMFWifW2Sxyd5APAQ4PMASTYB7jAfwUmSJEmjaqqbIQ8H3gH8IfDCgZrsRwL/1XdgkiRJ0iibNNGuqu8D+05QfgpwSp9BSZIkSaNuNk+GlCRJ62ujTUgy49ey5SsWOnKpN8uWr5jV52KxfjaGeWCNJEmaa7ess7s1aZzZdkMIi/OzYY22JEmS1INpa7ST3B74S2CXwfmr6rX9hSVJkiSNtmGajnwauA44C7ip33AkSZKkpWGYRHunqvq93kckSZIkTW6YNtpnJPmj3iORJEmSlpBharT/FDgkyaV0TUcCVFXdr9fIJEmSpBE2TKK9X+9RSJIkSUvMpIl2ki2r6nrghnmMR5IkSVoSpqrR/gjweLreRoquyciYAu7WY1ySJEnSSJs00a6qx7f3u85fOJIkSdLS4JMhN1DLlq8gyaxekiRJmt4wN0NqCbpi7RoOOOqMWS174uF7z3E0kiRJS4812pIkSVIPpk20k7wlyX3mIxhJkiRpqRimRvu7wNFJvpXkuUm26jsoSZIkadRNm2hX1fuq6iHAs4BdgPOSfCTJw/sOTpIkSRpVQ7XRTrIxcK/2uho4F3hRkhN6jG0kzbY3D0mSJC0t0/Y6kuRtdA+u+RLwf6vqzDbpTUku6jO4UTTb3jzsyUOSJGlpGaZ7v/OAV1bVjRNM23OO45EkSVPZaJNZ/xK6407LuXzNj+Y4IEmTGSbRvnZwviRbA/tU1aeq6rqe4pIkSRO5ZZ3PQZBGxDBttF89mFBX1bXAq3uLSJIkSVoChkm0J5rHJ0pKkiRJUxgm0V6V5K1Jdm2vtwJn9R2YJEmSNMqGSbRfAPwGOLG9bgKe12dQkiRJ0qibtglI623kiHmIRZIkSVoyhulH+x7AS+ieCnnr/FX1iP7CkiRJkkbbMDc1fhR4D/A+4OZ+w5EkSZKWhmES7XVV9e7eI5EkSdKSsGz5Cq5Yu2ahw1hwwyTan0nyt8An6W6EBKCqruktKkmSJI2sK9aumdWDlZbaQ5WGSbQPbu//OFBWwN3mPhxJkiRpaRim15G7zkcgkiRJ0lIybT/aSe6Y5JVJjm7juyV5fP+hSZIkSaNrmAfWfIDugTVjjWYuB17fW0SSJKkfG21Ckhm/JM3OMG20d62qA5IcBFBVv4yfOkmSRs8t67xBTZpHw9Ro/ybJHehugCTJrgz0PjKZJMckuTLJBQNlRya5PMk57fW4gWkvS3JxkouSPHagfN9WdnESn1ApSZKkkTBMov1q4PPA8iQfBr4IvHSI5Y4F9p2g/G1VtUd7fQ4gye7AgcB92jLvSrJxko2B/wD2A3YHDmrzSpIkSYvaML2OnJrkbGAvIMDfV9XVQyx3WpJdhoxjf+CEqroJuDTJxcCebdrFVXUJQJIT2rwXDrleSZIkaUEM0+vIQ+lqmm8Argd2b2Wz9fwk57WmJdu0smXA4OOD1rayyconivOwJKuSrLrqqqvWIzxJkiRp/Q1zM+Tgg2o2o6tpPgt4xCy2927gdXTtvV8HvAV4zizW83uq6mjgaICVK1fWXKxTkiRJmq1hmo48YXA8yXLg7bPZWFX9dGA97wU+20YvB5YPzLpTK2OKcsGtXTVJkiRpcRmmRnu8tcC9Z7OxJDtU1Y/b6JOBsR5JTgY+kuStwI7AbsCZdG3Cd0tyV7oE+0Dgr2az7SXLrpokSZIWpWkT7ST/Tuvaj65N9x7A2UMsdzywD7BdkrV0vZfsk2SPtr7LgMMBqmp1kpPobnJcBzyvqm5u63k+cAqwMXBMVa0eeu8kSZKkBTJMjfaqgeF1wPFVdfp0C1XVQRMUv3+K+d8AvGGC8s8BnxsiTkmSJGnRGKaN9nHzEYgkSZIWGe8FWy/DNB05n981HbnNJKCq6n5zHpUkSZIWnveCrZdhmo78d3v/UHt/ent/99yHI0mSpElZwzxShkm0H11VDxgYPyLJ2VV1RF9BSZIkaQLWMI+UaZ8MCSTJQwZG9h5yOUmSJGmDNUyN9qHAMUm2auPXMkdPc5QkSZKWqmF6HTkLuP9Yol1V1/UelSRJkjTipm0CkuQuSd4PnFBV1yXZPcmh8xCbJEmSNLKGaWt9LN2TGXds498HXthTPJIkSdKSMEyivV1VnQTcAlBV64Cbe41KkiRJGnHDJNo3Jrkz7aE1SfYCbKctSZIkTWGYXkdeBJwM7JrkdGB74Cm9RiVJkiSNuCkT7SQbAw9rr3vSPXb9oqr67TzEJkmSJI2sKZuOVNXNwEFVta6qVlfVBSbZkiRJ0vSGaTpyepJ3AicCN44VVtXZvUUlSZIkjbhhEu092vtrB8oKeMScRyNJkiQtEZMm2kmeX1XvrKqHJ7lPVa2ez8AkSZKkUTZVG+3nDAx/qO9AJEmSpKVkmH60oettRJIkSdKQpmqjvXWSJ9Ml41sm+YvBiVX1iV4jkyRJkkbYVIn2V4EntuHTgCcMTCvARFuSJEmaxKSJdlU9ez4DkSRJkpaSYdtoS5IkzcxGm5Bkxq9ly1csdOTSnBimH21JkqSZu2UdBxx1xowXO/HwvXsIRpp/1mhLkiRJPZg20U5yxySvSvLeNr5bksf3H5okSZI0uoap0f4AcBPwJ238cuD1vUUkSZIkLQHDJNq7VtWbgd8CVNUv8QE2kiRJ0pSGSbR/k+QOdH1nk2RXuhpuSZIkSZMYpteRI4HPA8uTfBh4CHBIjzFJkiRJI2/aRLuqvpDkLGAvuiYjf19VV/cemSRJ2jC1/relUTdtop3kM8BHgJOr6sb+Q5IkSRs0+9/WEjFMG+1/Bf4MuDDJx5I8JclmPcclSZIkjbRhmo58Ffhqko2BRwB/AxwDbNlzbJIkSdLIGuoR7K3XkScABwAPBI7rMyhJkiRp1A3TRvskYE+6nkfeCXy1qm7pOzBJkiRplA1To/1+4KCqurnvYCRJkqSlYtJEO8kjqupLwJ2A/cd3s1NVn+g5NkmSJGlkTVWj/TDgS3Rts8crwERbkiRJmsSkiXZVvboNvraqLh2cluSuvUYlSZIkjbhh+tH++ARlH5vrQCRJkqSlZKo22vcC7gNsleQvBiZtCfjAGkmSJGkKU7XRvifweGBrbttO+wa6h9ZIkiRJmsRUbbQ/DXw6yZ9U1TfmMSZJkiRp5A3Tj/Z3kjyPrhnJrU1Gquo5vUUlSZIkjbhhbob8EPCHwGOBrwI70TUfkSRJkjSJYRLtu1fVq4Abq+o44M+BB/cbliRJkjTahkm0f9ver01yX2Ar4A/6C0mSJEkafcO00T46yTbAq4CTgc2Bf+41KkmSJGnETZtoV9X72uBXgbv1G44kSZK0NEz1wJoXTbVgVb117sORJEmSloaparS3mLcoJEmSpCVmqgfWvGY+A5EkSZKWkml7HUlyjyRfTHJBG79fklf2H5okSZI0uobp3u+9wMto3fxV1XnAgX0GJUmSJI26YRLtO1bVmePK1vURjCRJkrRUDJNoX51kV6AAkjwF+HGvUUmSJEkjbpgH1jwPOBq4V5LLgUuBp/calSRJkjTihnlgzSXAo5Lcia4G/Jd0bbR/2HNskiRJ0siatOlIki2TvCzJO5M8mi7BPhi4GHjafAUoSZIkjaKparQ/BPwc+AbwN8ArgABPrqpz+g9NkiRJGl1T3Qx5t6o6pKqOAg4CdgceO2ySneSYJFeO9b/dyrZNcmqSH7T3bVp5krwjycVJzkvywIFlDm7z/yDJwbPaS0mSJGmeTZVo/3ZsoKpuBtZW1a9nsO5jgX3HlR0BfLGqdgO+2MYB9gN2a6/DgHdDl5gDrwYeDOwJvHosOZckSZIWs6kS7fsnub69bgDuNzac5PrpVlxVpwHXjCveHziuDR8HPGmg/IPV+SawdZIdgMcCp1bVNVX1c+BUfj95lyRJkhadSdtoV9XGPWzvLlU11gf3T4C7tOFlwJqB+da2ssnKJUmSpEVtmAfW9KKqivYQnLmQ5LAkq5Ksuuqqq+ZqtZIkSdKszHei/dPWJIT2fmUrvxxYPjDfTq1ssvLfU1VHV9XKqlq5/fbbz3ngkiRJ0kzMd6J9Ml1f3LT3Tw+UP6v1PrIXcF1rYnIK8Jgk27SbIB/TyiRJkqRFbZhHsM9KkuOBfYDtkqyl6z3kjcBJSQ6le7Lk2INvPgc8ju5hOL8Eng1QVdckeR3w7Tbfa6tq/A2WkiRJ0qLTW6JdVQdNMumRE8xbwPMmWc8xwDFzGJokSZLUuwW7GVKSJElayky0JUmSpB6YaEuSJEk9MNGWJEmSemCiLUmSJPWgt15HJEmSlryNNiHJQkehRcpEW5IkabZuWccBR50x48VOPHzvHoLRYmPTEUmSJKkHJtqSJElSD0y0JUmSpB6YaEuSJEk9MNGWJEmSemCiLUmSJPXARFuSJEnqgYm2JEmS1AMTbUmSJKkHJtqSJElSD0y0JUmSpB6YaEuSJEk9MNGWJEmSemCiLUmSJPXARFuSJEnqgYm2JEmS1AMTbUmSJKkHJtqSJElSD0y0JUmSpB6YaEuSJEk9MNGWJEmSemCiLUmSJPXARFuSJEnqgYm2JEmS1AMTbUmSJKkHJtqSJElSD0y0JUmSpB6YaEuSJEk9MNGWJEmSemCiLUmSJPXARFuSJEnqgYm2JEmS1AMTbUmSJKkHJtqSJElSD0y0JUmSpB6YaEuSJEk9MNGWJEmSemCiLUmSJPXARFuSJEnqgYm2JEmS1AMTbUmSJKkHJtqSJElSD0y0JUmSpB6YaEuSJEk9MNGWJEmSemCiLUmSJPXARFuSJEnqgYm2JEmS1AMTbUmSJKkHJtqSJElSD0y0JUmSpB6YaEuSJEk9MNGWJEmSemCiLUmSJPXARFuSJEnqwYIk2kkuS3J+knOSrGpl2yY5NckP2vs2rTxJ3pHk4iTnJXngQsQsSZIkzcRC1mg/vKr2qKqVbfwI4ItVtRvwxTYOsB+wW3sdBrx73iOVJEmSZmgxNR3ZHziuDR8HPGmg/IPV+SawdZIdFiA+SZIkaWgLlWgX8IUkZyU5rJXdpap+3IZ/AtylDS8D1gwsu7aVSZIkSYvWJgu03T+tqsuT/AFwapLvDU6sqkpSM1lhS9gPA1ixYsXcRSpJkiTNwoLUaFfV5e39SuCTwJ7AT8eahLT3K9vslwPLBxbfqZWNX+fRVbWyqlZuv/32fYYvSZIkTWveE+0kd0qyxdgw8BjgAuBk4OA228HAp9vwycCzWu8jewHXDTQxkSRJkhalhWg6chfgk0nGtv+Rqvp8km8DJyU5FPgh8LQ2/+eAxwEXA78Enj3/IUuSJEkzM++JdlVdAtx/gvKfAY+coLyA581DaJIkSdKcWUzd+0mSJElLhom2JEmS1AMTbUmSJKkHJtqSJElSD0y0JUmSpB6YaEuSJEk9MNGWJEmSemCiLUmSJPXARFuSJEnqgYm2JEmS1AMTbUmSJKkHJtqSJElSD0y0JUmSpB6YaEuSJEk9MNGWJEmSemCiLUmSJPXARFuSJEnqgYm2JEmS1AMTbUmSJKkHJtqSJElSD0y0JUmSpB6YaEuSJEk9MNGWJEmSemCiLUmSJPXARFuSJEnqgYm2JEmS1AMTbUmSJKkHJtqSJElSD0y0JUmSpB6YaEuSJEk9MNGWJEmSemCiLUmSJPXARFuSJEnqgYm2JEmS1AMTbUmSJKkHJtqSJElSD0y0JUmSpB6YaEuSJEk9MNGWJEmSemCiLUmSJPXARFuSJEnqgYm2JEmS1AMTbUmSJKkHJtqSJElSD0y0JUmSpB6YaEuSJEk9MNGWJEmSemCiLUmSJPXARFuSJEnqgYm2JEmS1AMTbUmSJKkHJtqSJElSD0y0JUmSpB6YaEuSJEk9MNGWJEmSemCiLUmSJPXARFuSJEnqgYm2JEmS1AMTbUmSJKkHJtqSJElSD0y0JUmSpB6YaEuSJEk9GJlEO8m+SS5KcnGSIxY6HkmSJGkqI5FoJ9kY+A9gP2B34KAkuy9sVJIkSdLkRiLRBvYELq6qS6rqN8AJwP4LHJMkSZI0qVFJtJcBawbG17YySZIkaVFKVS10DNNK8hRg36r66zb+TODBVfX8gXkOAw5ro/cELpr3QDvbAVcv0LY1PzzHGwbP84bB87xh8DxvGBbqPO9cVdtPNGGT+Y5kli4Hlg+M79TKblVVRwNHz2dQE0myqqpWLnQc6o/neMPged4weJ43DJ7nDcNiPM+j0nTk28BuSe6a5HbAgcDJCxyTJEmSNKmRqNGuqnVJng+cAmwMHFNVqxc4LEmSJGlSI5FoA1TV54DPLXQcQ1jw5ivqned4w+B53jB4njcMnucNw6I7zyNxM6QkSZI0akaljbYkSZI0Uky0Z2G6x8EnuX2SE9v0byXZZQHC1Hoa4jy/KMmFSc5L8sUkOy9EnFo/053ngfn+MkklWVR3tGs4w5znJE9rn+nVST4y3zFq/Q3xd3tFki8n+U772/24hYhTs5fkmCRXJrlgkulJ8o52DZyX5IHzHeMgE+0ZGvJx8IcCP6+quwNvA940v1FqfQ15nr8DrKyq+wEfA948v1FqfQ15nkmyBfD3wLfmN0LNhWHOc5LdgJcBD6mq+wAvnO84tX6G/Dy/Ejipqh5A14PZu+Y3Ss2BY4F9p5i+H7Bbex0GvHseYpqUifbMDfM4+P2B49rwx4BHJsk8xqj1N+15rqovV9Uv2+g36fp312gZ5vMM8Dq6f5h/PZ/Bac4Mc57/BviPqvo5QFVdOc8xav0Nc54L2LINbwVcMY/xaQ5U1WnANVPMsj/wwep8E9g6yQ7zE93vM9GeuWEeB3/rPFW1DrgOuPO8RKe5Msx5HnQo8N+9RqQ+THue28+Oy6vqv+YzMM2pYT7P9wDukeT0JN9MMlWNmRanYc7zkcAzkqyl68nsBfMTmubRTL+/ezUy3ftJi1WSZwArgYctdCyaW0k2At4KHLLAoah/m9D91LwP3a9TpyX5o6q6diGD0pw7CDi2qt6S5E+ADyW5b1XdstCBaWmyRnvmpn0c/OA8STah+3nqZ/MSnebKMOeZJI8CXgE8sapumqfYNHemO89bAPcFvpLkMmAv4GRviBw5w3ye1wInV9Vvq+pS4Pt0ibdGxzDn+VDgJICq+gawGbDdvESn+TLU9/d8MdGeuWEeB38ycHAbfgrwpbLD8lEz7XlO8gDgKLok2/aco2nK81xV11XVdlW1S1XtQtcW/4lVtWphwtUsDfN3+1N0tdkk2Y6uKckl8xij1t8w5/lHwCMBktybLtG+al6jVN9OBp7Veh/ZC7iuqn68UMHYdGSGJnscfJLXAquq6mTg/XQ/R11M12D/wIWLWLMx5Hn+F2Bz4KPtXtcfVdUTFyxozdiQ51kjbsjzfArwmCQXAjcD/1hV/hI5QoY8zy8G3pvkH+hujDzEirDRkuR4un+Kt2tt7V8NbApQVe+ha3v/OOBi4JfAsxcm0o5PhpQkSZJ6YNMRSZIkqQcm2pIkSVIPTLQlSZKkHphoS5IkST0w0ZYkSZJ6YKItLUJJbk5yTpILknw0yR0XIIZ9kuw9MP7cJM9qw8cmecoM1/fCseWnmOfIJC9pw4ck2XFg2mWtf+PeJPnKKDyMJsnLx43/ouftvXAhrsE+zGZf2mfhs33FtNgl2SPJ43pa9wlJfDCQliwTbWlx+lVV7VFV9wV+Azx3mIXak0jnyj7ArYl2Vb2nqj44mxW1uJ4DfGQGix0C7DjdTKNk/PlZj/P18ulnmVMvBKZNTpNsvD4bmePrdzIvZIh9GXVzfCz3oOuXuA/vBl7a07qlBWeiLS1+XwPunuROSY5JcmaS7yTZH26t+T05yZeALybZPMkHkpyf5Lwkf9nme0ySbyQ5u9WSb97KL0vymlZ+fpJ7JdmFLrn/h1az/meDtc2DkjwoyVeTnJXklCQ7TLAPjwDOrqp1bZm/SfLtJOcm+fj4GsZWW74S+HDb/h3apBcMxtnm3TbJp9q+fjPJ/Vr5beJtvw7s0oZfleSiJF9Pcvy4/XpqO8bfT/JnE52QJP/UYjg3yRtb2a214Um2S/fI9onOz/jxqc7rJ5J8PskPkry5lb8RuEM7Lh8eF9cHkzxpYPzDY+sbKNunxfqxJN9r86RNe2SL4fwW0+2T/B3dPzxfTvLlCY7FZUnelOTsduymus7e3NZ9ZpK7t/Jjk7wnybeANyfZte3zWUm+NnCen9rO4blJTmtlGyf5l3YtnZfk8Kn2caJ9mSLefduyZwN/Mcl1sEuL8ez22ruV75DktPzuV6nfu46S/HOL+4IkR4+dg3HzjB2bVe16fPwQ+/21JCcDF7b5/rVt47wkL2jzTfiZbcfsTRm4/tM9YfG1wAFtfw5Ismc7Zt9JckaSe7bl75jkpCQXJvlkkm/ld5+JCY8z3d+3R2V+/smS5l9V+fLla5G9gF+0902ATwP/B/i/wDNa+dbA94E70dX8rgW2bdPeBLx9YF3bANsBpwF3amX/BPxzG74MeEEb/lvgfW34SOAlA+u5dRw4FngK3dO4zgC2b+UH0D2Nbfz+vGZsG238zgPDrx/Y/uA2vgKsHJhvsjj/HXh1G34EcM4k8V8A7AL8MXAO3aOXtwB+MG6bb2nDjwP+Z4J92a/t8x3b+Lbj423H+7I2PP78jB+f6rxeAmzVYv0hsHzw+pjgenkY8Kk2vBVwKbDJuHn3Aa4DdqKrbPkG8KdtG2uAe7T5Pgi8cODYbzfJtXoZ8NKB/Z7qOntFG34W8NmBa+mzwMZt/IvAbm34wcCX2vD5wLKx49TeDwNe2YZvD6wC7jrZPo7fl8niHTgWuwEBThqLd9y+3xHYrA3vRvf0QeiePji2rxsDW0yw7LYDwx8CnjDBPMcCn2/7sBvddbPZNPt9I3DXNu3/AB8buwaAbZniM8sk1z/dtfjOgbi2HFjno4CPt+GXAEe14fsC6+j+YZ70umjjpwIP6uNvqS9fC/3yP0hpcbpDknPa8NeA99N9OT4xv6t93QxY0YZPrapr2vCjgAPHVlRVP281YbsDp7eKs9vRJR9jPtHez2KS2rtJ3JPuC/XUtt6NgR9PMN8OwHcHxu+b5PV0ieXmdI9MHsZEcf4p8JcAVfWlJHdOsuUU63gI8Omq+jXw6ySfmWIbu0yw/KOAD1TVL9s2r5lgnvFOHTff4PhjmPy8frGqrgNI92jwnekSwAlV1VeTvCvJ9nTH5OPVfkUY58yqWtvWew7dft4AXFpV32/zHAc8D3j7EPt3Ynvfi6mvs+MH3t82UP7Rqrq51XLuDXx0oIL39u39dODYJCfxu3P0GOB++d39AlvRJaS/mWQfvz4u7snivRfdsfhBW/4/6ZLb8TYF3plkD7rHtt+jlX8bOCbJpnT/+JwzwbIPT/JSumR9W2A1MP5aBDipqm4BfpDkkhbbdPt9aSt/FPCesWugqq5Jcl+m/sxOd/2Pbe+4dG2rqx0H6D6L/9a2dUGS81r5dNfFlXS/NJw1yfakkWWiLS1Ov6qqPQYL2k/Lf1lVF40rfzBdLdZUQpfcHTTJ9Jva+83M7O9CgNVV9SfTzPcrugRyzLHAk6rq3CSH0NXEDWMmca7jts3jNptsxvXYxmTbG7+t8edncHyq83rTQNGw8XwQeAbdP1vPnmSe2ax3KmP7M911VpMMjy2/EXDt+GsfoKqe247JnwNnJXlQ294Lquo2/6gl2Yfh9nHCeFviPIx/AH4K3L/F/usW62lJHtpiPTbJW2vg/oYkmwHvovsFZE2SI5n8+qwJxqfa72H+Fkz1mR3m+n8d8OWqenK65lhfGWKbU10Xm9H9jZCWHNtoS6PjFLo2ymPtaR8wyXyn0tVE0ubbBvgm8JD8rl3snZLcY5Llx9xA17RiKhcB2yf5k7beTZPcZ4L5vgvcfWB8C+DHrcbv6euxfehq/J/etr8PcHVVXU/XROCBrfyBdD+tQ1cz+oQkm7Ua1McPsY1BpwLPTmtXnmTbVn4Z8KA2PJMeWYY9r4N+247dRI6lu+GPqrpwBnFcBOwydo0AzwS+2oaHPRfTXWcHDLx/Y/zC7bxdmuSpbfkkuX8b3rWqvlVV/wxcBSynO3b/Z+xYJLlHkjtNE+PgvkwW7/fasdi1zTdZgrgV8ONW4/xMutphkuwM/LSq3gu8j3YdDhhLqq9u1+BU18tTk2zUYrkb3Xkadr9PBQ4fa//crtVhP7ODxp//rYDL2/AhA+WnA09r690d+KNWPt11cQ+6pl3SkmOiLY2O19H9RHtektVtfCKvB7ZJu3EMeHhVXUX3hXh8+zl37OfxqXwGeHLazZATzVBVv6FLEt7UtnUOAz2VDPhv4KED468CvkX3xfy9SbZ/LPCe3PZmyIkcCTyo7dcbgYNb+ceBbduxej5d22eq6tvAycB5La7z6drzDqWqPt+WX9WaJIw1+fhXuuTnO3RtUoc17HkddHSb/8PjJ1TVT+n+sfnADGKgNaV5Nl2zjfOBW4D3DGzv85ngZshx65juOtumlf89XW3wRJ4OHNqup9XA2M2c/5LuRsoL6JpRnUuXxF4InN3Kj2L62vlb92WyeNuxOAz4r3Q3Q145ybreBRzcYr0Xv6tN3gc4t10LB9CaU4ypqmuB99Ill6fQNTWZzI+AM+mu1ee22Ibd7/e15c9rMf7VDD6zg74M7N4+iwcAbwb+X9u/we2+iy6Jv5Du79Bq4Lqproskd6H7Be8n08QgjaRUjf9VSpLmXpJP0t0094NFEMvmVfWLVit9GnBYVZ290HHNhbZP5wMPHGvfvRik64VlZVVdvdCxjIokx9LdhPmxhY5lGOm6d9y0qn7dauD/B7hnS+4nW+YfgOur6v3zFac0n2yjLWm+HEF3U+SCJ9rA0e2n7c2A45ZQkv0ouhtn37aYkmxtMO5I13XipnTtsv92qiS7uZau1xVpSbJGW5IkSeqBbbQlSZKkHphoS5IkST0w0ZYkSZJ6YKItSZIk9cBEW5IkSeqBibYkSZLUg/8PFaPnaE7JsQEAAAAASUVORK5CYII=\n", - "text/plain": [ - "
      " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(12, 8))\n", - "plt.title(\"Distribution of Percentiles for Housing Burden (Score L)\")\n", - "# Set x-axis label\n", - "plt.xlabel('Percentile (although currently not represented as a percentage)')\n", - "# Set y-axis label\n", - "plt.ylabel('Relative Frequency in Support')\n", - "\n", - "sns.histplot(non_null_df[\"current_methodology_percentile_rank\"])" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "False 65733\n", - "True 7323\n", - "Name: current_threshold_exceeded, dtype: int64" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "non_null_df[\"current_threshold_exceeded\"].value_counts()" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "False 66255\n", - "True 6801\n", - "Name: new_threshold_exceeded, dtype: int64" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "non_null_df[\"new_threshold_exceeded\"].value_counts()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Score L Criterion as burden but not Calenvironscreen" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(2794, 15)" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# this includes uineligible np.nan values too\n", - "predicate_1 = (non_null_df['current_threshold_exceeded'] == True) & (non_null_df['new_threshold_exceeded'] != True)\n", - "\n", - "non_null_df[predicate_1].shape" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "# Where Score L is considered burdened but not in CalEnviroScreen\n", - "score_l_considered_burdened = non_null_df[predicate_1]" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [], - "source": [ - "import us\n", - "\n", - "mapping = us.states.mapping('fips', 'abbr')" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "metadata": {}, - "outputs": [], - "source": [ - "# Inserted after the basic stats definition.\n", - "# Load demographic data\n", - "import pathlib\n", - "\n", - "DATA_DIR = pathlib.Path.cwd().parent / \"data\"\n", - "COMPARISON_OUTPUTS_DIR = DATA_DIR / \"comparison_outputs\"\n", - "\n", - "demographics_path = DATA_DIR / \"dataset\" / \"census_acs_2019\" / \"usa.csv\"\n", - "\n", - "demographics_df = pd.read_csv(\n", - " demographics_path,\n", - " dtype={\"GEOID10_TRACT\": \"string\"},\n", - ")\n", - "\n", - "# Set some field names\n", - "BLACK_FIELD_NAME = \"Black or African American alone\"\n", - "AMERICAN_INDIAN_FIELD_NAME = \"American Indian and Alaska Native alone\"\n", - "ASIAN_FIELD_NAME = \"Asian alone\"\n", - "HAWAIIAN_FIELD_NAME = \"Native Hawaiian and Other Pacific alone\"\n", - "TWO_OR_MORE_RACES_FIELD_NAME = \"Two or more races\"\n", - "NON_HISPANIC_WHITE_FIELD_NAME = \"Non-Hispanic White\"\n", - "HISPANIC_FIELD_NAME = \"Hispanic or Latino\"\n", - "PERCENT_PREFIX = \"Percent \"\n", - "\n", - "RE_OUTPUT_FIELDS = [\n", - " BLACK_FIELD_NAME,\n", - " AMERICAN_INDIAN_FIELD_NAME,\n", - " ASIAN_FIELD_NAME,\n", - " HAWAIIAN_FIELD_NAME,\n", - " TWO_OR_MORE_RACES_FIELD_NAME,\n", - " NON_HISPANIC_WHITE_FIELD_NAME,\n", - " HISPANIC_FIELD_NAME,\n", - "]\n", - "\n", - "RE_PERCENT_OUTPUT_FIELDS = [PERCENT_PREFIX + field for field in RE_OUTPUT_FIELDS]\n", - "\n", - "columns_to_keep = (\n", - " [\"GEOID10_TRACT\"]\n", - " + RE_OUTPUT_FIELDS\n", - " + RE_PERCENT_OUTPUT_FIELDS\n", - " + ['Percent of individuals < 200% Federal Poverty Line', \n", - " 'Median value ($) of owner-occupied housing units',\n", - " 'Percent individuals age 25 or over with less than high school degree',\n", - " 'Percent enrollment in college or graduate school',\n", - " 'Linguistic isolation (percent)']\n", - ")\n", - "\n", - "\n", - "def highlight_deviations(s):\n", - " # unbiased estimate of variance N-1\n", - " # \n", - " is_greater_than_std = s > np.var(s, ddof=1) \n", - " return ['color: pink; background-color:#7272FE'\n", - " if cell else '' for cell in is_greater_than_std]\n", - "# Join the demographics in.\n", - "merged_df_score_l = score_l_considered_burdened.merge(\n", - " demographics_df[columns_to_keep],\n", - " left_on=\"FIPS_tract_id\",\n", - " right_on=\"GEOID10_TRACT\",\n", - " how=\"inner\"\n", - ")\n", - "\n", - "# these are not converted into percent 0 - 100 scale\n", - "percent_cols = [x for x in merged_df_score_l.columns if \n", - " 'Percent' in x or '(percent)' in x\n", - " ]\n", - "\n", - "merged_df_score_l[\n", - " percent_cols] = merged_df_score_l[\n", - " percent_cols].apply(lambda x: x * 100)\n", - "\n", - "for idx, row in merged_df_score_l.iterrows():\n", - " current_row = str(merged_df_score_l.loc[idx, 'state'])\n", - " state = mapping.get(current_row, None)\n", - " merged_df_score_l.loc[idx, 'state_name'] = state\n", - "\n", - "grouped_stats_score_l = merged_df_score_l.groupby([\"state_name\"]).agg({\n", - " 'GEOID10_TRACT': ['nunique'],\n", - " 'Percent of individuals < 200% Federal Poverty Line': [np.median, np.std],\n", - " 'Median value ($) of owner-occupied housing units': [np.median, np.std],\n", - " 'Percent individuals age 25 or over with less than high school degree': [np.median, np.std],\n", - " 'Percent enrollment in college or graduate school': [np.median, np.std],\n", - " 'Percent Black or African American alone': [np.median, np.std],\n", - " 'Percent American Indian and Alaska Native alone': [np.median, np.std],\n", - " 'Percent Non-Hispanic White': [np.median, np.std], \n", - " 'Linguistic isolation (percent)': [np.median, np.std],\n", - " 'Percent Hispanic or Latino': [np.median, np.std],\n", - " 'hbrd_rank': [np.median, np.std],\n", - " 'current_methodology_percent': [np.median, np.std],\n", - " 'current_summed_methodology': [np.median, np.std, np.sum]\n", - "}).reset_index()\n", - "\n", - "\n", - "grouped_stats_score_l.columns = [' '.join(col).strip() for \n", - " col in grouped_stats_score_l.columns.values]\n", - "\n", - "grouped_stats_score_l = grouped_stats_score_l[[x for x in grouped_stats_score_l \n", - " if \"std\" in x and \n", - " \"Percent\" in x] + [\"state_name\", \"current_summed_methodology std\"]]\n", - "\n", - "grouped_stats_score_l.set_index(\"state_name\", inplace=True)\n", - "\n", - "grouped_stats_score_l = grouped_stats_score_l.rename(columns={\n", - "'Percent of individuals < 200% Federal Poverty Line std'\n", - " : 'Percent of individuals < 200% Federal Poverty Line (variance across all tracts)', \n", - "'Percent individuals age 25 or over with less than high school degree std':\n", - "'Percent individuals age 25 or over with less than high school degree (variance across all tracts)', \n", - "'Percent enrollment in college or graduate school std'\n", - " :'Percent enrollment in college or graduate school (variance across all tracts)',\n", - "'Percent Black or African American alone std':\n", - " 'Percent Black or African American alone (variance across all tracts)',\n", - "'Percent American Indian and Alaska Native alone std':\n", - " 'Percent American Indian and Alaska Native alone (variance across all tracts)',\n", - "'Percent Non-Hispanic White std':\n", - " 'Percent Non-Hispanic White (variance across all tracts)',\n", - "'Percent Hispanic or Latino std':\n", - " 'Percent Hispanic or Latino (variance across all tracts)',\n", - "'GEOID10_TRACT std': \"Total Number of Unique Tracts\",\n", - "\"current_summed_methodology std\": \"Variance: Owned and Rented Burdened Households (Current Aggregation Methodology)\" \n", - "})" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - 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      Percent of individuals < 200% Federal Poverty Line (variance across all tracts) Percent individuals age 25 or over with less than high school degree (variance across all tracts) Percent enrollment in college or graduate school (variance across all tracts) Percent Black or African American alone (variance across all tracts) Percent American Indian and Alaska Native alone (variance across all tracts) Percent Non-Hispanic White (variance across all tracts) Percent Hispanic or Latino (variance across all tracts) Variance: Owned and Rented Burdened Households (Current Aggregation Methodology)
      state_name
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      AR10.1273822.66812218.67282227.7118780.12874825.89808710.756406209.143292
      AZ10.48630112.74623710.3841386.6239423.51664820.54689822.871205256.224598
      CA12.54990614.1888196.08685410.5307091.36845315.57186223.690174256.209407
      CO10.72246311.7362953.46381710.5055131.94547714.86618420.168076332.327502
      CT15.4655938.91864515.30907715.9804311.01436918.55163816.610853308.514106
      DC8.9614335.3547259.02522215.4035560.7624436.21962111.580841205.735892
      DE14.2870355.8710341.56110539.3161720.52842123.04221416.399730186.876965
      FL11.71419010.51745311.32950928.0765451.09883722.57614327.968331336.712451
      GA12.45407210.85212310.96298428.4691641.32924915.98492420.115423397.215028
      HI13.35362410.2720674.0151811.5860140.42443512.9178917.466775234.634661
      IAnannannannannannannannan
      ID2.0042312.74820411.3604490.1750570.03751222.36983319.631435659.730627
      IL13.00677012.07145813.09798136.5910410.84575519.74513633.788104262.443072
      IN15.3557848.29698131.63286628.6826540.64220827.2180187.313774427.114975
      KS0.10507714.3019960.84360312.4118430.0746610.4800656.454849308.298557
      KY6.1812539.63441938.65725911.5304270.63568112.38533910.232876336.240655
      LA12.02631010.23624214.05315420.9227320.56039514.9023549.279719305.110126
      MA11.8921448.97738813.07543620.1184301.21005322.39920720.572927253.379191
      MD13.5407198.7182238.77979027.5353702.60351119.68505414.383766259.769712
      MEnannannannannannannannan
      MI12.20623010.4148208.34425532.7719881.27446723.47476015.933231243.462106
      MN12.56187812.6860212.7824326.1173582.9595475.9763347.635743372.645274
      MO13.8011316.15923328.60334032.6358640.68476827.4057878.255299432.829801
      MS12.68907614.9179303.42723331.2764920.19627018.8615279.070286261.432326
      NC9.10814411.82430927.54812321.3700150.74342520.20992815.247821347.903709
      NE13.80154131.9109415.9804825.4757500.31478626.53988535.131407280.014285
      NH13.9054945.8392682.0024436.8648770.04557919.5199989.904841107.145929
      NJ12.75198710.9174277.62736629.7655130.82817020.25006326.757247250.668671
      NV8.89042013.9335229.2205818.6184451.54116212.81608720.475023281.744159
      NY15.36734910.4824829.36076927.2982931.15913723.47295024.695955355.029729
      OH12.1150137.56311217.61184528.1326830.52045422.3101308.078694232.867404
      OK16.72299012.33166526.92975810.8128092.69235522.09779821.618860282.164508
      OR8.0491247.1061452.3363716.0591801.66420816.59098217.607034339.545074
      PA13.3208948.62153212.54875929.8588660.51165324.16744325.464024244.351269
      RI9.49766610.1851297.8270827.2403601.64801316.32554418.332274162.327648
      SC5.6265638.2081854.63801121.8335600.29813120.6981224.274111230.949161
      SDnannannannannannannannan
      TN11.4898329.05737018.85942833.1676140.21238125.72851913.148975353.619736
      TX10.94271113.8208356.52205124.0810240.96374813.66037726.095366338.926031
      UT26.41286911.33158359.4145470.1276361.37704631.89093334.367822372.645274
      VA8.7697258.29822421.72351924.5266960.35460118.39252814.595742336.832852
      WA15.50537412.2179543.1541858.7101850.98104520.33376922.762020268.429693
      WI9.36911614.4960399.63347333.2129541.19530515.49568722.123829215.928941
      " - ], - "text/plain": [ - "" - ] - }, - "execution_count": 48, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "grouped_stats_score_l.style.bar(\n", - " subset=grouped_stats_score_l.columns, align='mid', color=['#d65f5f', '#5fba7d'])" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['Percent of individuals < 200% Federal Poverty Line (variance across all tracts)',\n", - " 'Percent individuals age 25 or over with less than high school degree (variance across all tracts)',\n", - " 'Percent enrollment in college or graduate school (variance across all tracts)',\n", - " 'Percent Black or African American alone (variance across all tracts)',\n", - " 'Percent American Indian and Alaska Native alone (variance across all tracts)',\n", - " 'Percent Non-Hispanic White (variance across all tracts)',\n", - " 'Percent Hispanic or Latino (variance across all tracts)'],\n", - " dtype='object')" - ] - }, - "execution_count": 46, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "grouped_stats_score_l.columns" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If the samples are samples from the population - a big assumption - in question and the sampling follows rules and things we take the mean:\n", - "\n", - "The mean of the median distribution (an order statistic) is the best estimate for the median. It's the best least unbiased estimator. The answer is not the median of that distribution.\n", - "\n", - "The answer is also not if the mean is an estimate of the median. Completely unrelated.\n", - "\n", - "Even if the original population is skewed, the distribution of a sampling statistic will be normalized - recall the the central limit theorem for more details\n", - "\n", - "The standard error of that mean should give you what you want to know to confidently make statements of the true population median across all states" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "metadata": {}, - "outputs": [], - "source": [ - "list_of_columns = list(grouped_stats_score_l.columns)\n", - "values_1 = list(grouped_stats_score_l[list_of_columns].var())" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "metadata": {}, - "outputs": [], - "source": [ - "values_1.extend([True, False])" - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[17.256903160392767,\n", - " 21.04537908818882,\n", - " 134.1363662670021,\n", - " 126.27122759971488,\n", - " 0.6672345565394615,\n", - " 37.71963763383144,\n", - " 66.32917757533399,\n", - " 8194.046353700449,\n", - " True,\n", - " False]" - ] - }, - "execution_count": 54, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "values_1" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# CalenviroScreen Burden" - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "metadata": {}, - "outputs": [], - "source": [ - "predicate_2 = (non_null_df['current_threshold_exceeded'] == False) & (non_null_df['new_threshold_exceeded'] == True)\n", - "\n", - "cal_ej_screen_burdened = non_null_df[predicate_2]" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "metadata": {}, - "outputs": [], - "source": [ - "# Join the demographics in.\n", - "merged_df_score_ejcreen = cal_ej_screen_burdened.merge(\n", - " demographics_df[columns_to_keep],\n", - " left_on=\"FIPS_tract_id\",\n", - " right_on=\"GEOID10_TRACT\",\n", - " how=\"inner\"\n", - ")\n", - "\n", - "# these are not converted into percent 0 - 100 scale\n", - "percent_cols = [x for x in merged_df_score_ejcreen.columns if \n", - " 'Percent' in x or '(percent)' in x\n", - " ]\n", - "\n", - "merged_df_score_ejcreen[\n", - " percent_cols] = merged_df_score_ejcreen[\n", - " percent_cols].apply(lambda x: x * 100)\n", - "\n", - "for idx, row in merged_df_score_ejcreen.iterrows():\n", - " current_row = str(merged_df_score_ejcreen.loc[idx, 'state'])\n", - " state = mapping.get(current_row, None)\n", - " merged_df_score_ejcreen.loc[idx, 'state_name'] = state\n", - "\n", - "grouped_stats_score_ej_screen = merged_df_score_ejcreen.groupby([\"state_name\"]).agg({\n", - " 'GEOID10_TRACT': 'nunique',\n", - " 'Percent of individuals < 200% Federal Poverty Line': [np.median, np.std],\n", - " 'Median value ($) of owner-occupied housing units': [np.median, np.std],\n", - " 'Percent individuals age 25 or over with less than high school degree': [np.median, np.std],\n", - " 'Percent enrollment in college or graduate school': [np.median, np.std],\n", - " 'Percent Black or African American alone': [np.median, np.std],\n", - " 'Percent American Indian and Alaska Native alone': [np.median, np.std],\n", - " 'Percent Non-Hispanic White': [np.median, np.std], \n", - " 'Linguistic isolation (percent)': [np.median, np.std],\n", - " 'Percent Hispanic or Latino': [np.median, np.std],\n", - " 'hbrd_rank': [np.median, np.std],\n", - " 'current_methodology_percent': [np.median, np.std],\n", - " 'current_summed_methodology': [np.median, np.std, np.sum]\n", - "}).reset_index()\n", - "\n", - "\n", - "grouped_stats_score_ej_screen.columns = [' '.join(col).strip() for \n", - " col in grouped_stats_score_ej_screen.columns.values]\n", - "\n", - "grouped_stats_score_ej_screen = grouped_stats_score_ej_screen[[x for x in grouped_stats_score_ej_screen \n", - " if \"std\" in x and \n", - " \"Percent\" in x] + [\n", - " 'current_summed_methodology std', \"state_name\"]]\n", - "\n", - "grouped_stats_score_ej_screen.set_index(\"state_name\", inplace=True)\n", - "\n", - "grouped_stats_score_ej_screen = grouped_stats_score_ej_screen.rename(columns={\n", - "'Percent of individuals < 200% Federal Poverty Line std'\n", - " : 'Percent of individuals < 200% Federal Poverty Line (variance across all tracts)', \n", - "'Percent individuals age 25 or over with less than high school degree std':\n", - "'Percent individuals age 25 or over with less than high school degree (variance across all tracts)', \n", - "'Percent enrollment in college or graduate school std'\n", - " :'Percent enrollment in college or graduate school (variance across all tracts)',\n", - "'Percent Black or African American alone std':\n", - " 'Percent Black or African American alone (variance across all tracts)',\n", - "'Percent American Indian and Alaska Native alone std':\n", - " 'Percent American Indian and Alaska Native alone (variance across all tracts)',\n", - "'Percent Non-Hispanic White std':\n", - " 'Percent Non-Hispanic White (variance across all tracts)',\n", - "'Percent Hispanic or Latino std':\n", - " 'Percent Hispanic or Latino (variance across all tracts)',\n", - "'GEOID10_TRACT std': \"Total Number of Unique Tracts\",\n", - "\"current_summed_methodology std\": \"Variance: Owned and Rented Burdened Households (Current Aggregation Methodology)\" \n", - "})" - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - 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      Percent of individuals < 200% Federal Poverty Line (variance across all tracts) Percent individuals age 25 or over with less than high school degree (variance across all tracts) Percent enrollment in college or graduate school (variance across all tracts) Percent Black or African American alone (variance across all tracts) Percent American Indian and Alaska Native alone (variance across all tracts) Percent Non-Hispanic White (variance across all tracts) Percent Hispanic or Latino (variance across all tracts) Variance: Owned and Rented Burdened Households (Current Aggregation Methodology)
      state_name
      AK13.5208954.6158804.2234584.3227036.65945920.0792344.363742259.106717
      AL10.1257099.43909011.87227526.5421480.83524822.6118357.001758248.330726
      AR12.2811277.97593612.49504330.4768521.37149824.5994229.514320192.426182
      AZ12.94603615.44714215.4685395.6017624.02709024.93921327.758437248.404072
      CA12.48787312.92223312.46106712.2229760.78997225.34539323.401828249.579452
      CO12.35071910.42057413.9550047.7471673.14779624.31513022.744562263.838721
      CT6.7124125.5024532.33533514.6558330.22190616.9565169.128131273.860001
      DC5.8399412.6326368.64727228.3252980.00000016.5552377.31129473.539105
      DE8.0807264.02487713.09328625.0022760.60191119.2637947.124579206.473727
      FL12.2917658.44330811.20660323.9858080.65069826.29181330.672446263.872563
      GA10.4063078.46970711.37910022.8698920.86607917.9370229.939746291.040808
      HI8.9537414.1198164.2304635.9033930.56751720.4145927.826224195.415711
      IA10.6074428.33517112.6225669.3462530.88537915.0409028.480330244.410783
      ID11.2198327.88150518.5971851.2030501.1828579.9681779.736982324.961525
      IL14.02664910.14091813.75296437.2686190.69828125.75099823.486043247.083538
      IN10.23474410.47114015.46295527.7684280.56284526.95004711.500188339.918101
      KS10.06677910.86042513.58243319.5236832.08630025.58568917.249613243.666729
      KY11.9794358.3891998.83623024.4643100.66438324.7822795.415088223.379490
      LA13.2152656.15507212.23361924.2692900.14663420.0156253.790697157.788981
      MA10.35913310.15197018.93774424.0692560.34804626.09247119.800689197.928531
      MD12.83582410.58696710.32020631.0951210.68726223.98853315.032420207.664458
      ME13.6851197.09951912.2894574.3925630.7656785.4006561.556741268.108982
      MI11.7871557.96843114.67324736.1008520.79445830.9276216.316568222.209551
      MN13.7230109.73920013.17228214.2117962.74510024.7992419.580859236.647262
      MO13.9268548.2688399.00413132.2743330.52247731.76636714.184567221.671125
      MS11.1289209.30394911.95488223.5507580.45735521.3353993.288823233.658743
      MT10.1196073.55941714.8467921.4298254.7610375.5483371.904990284.061423
      NC12.8847969.59422715.49908424.7842521.98152524.2130738.841596299.583580
      ND9.0216545.28919819.1264517.7113681.65374212.2069473.783639202.671137
      NE13.83825012.06991614.02089110.9277511.36988820.15295318.238305228.345148
      NH10.9903316.7456577.7440674.4394210.27998012.9056677.710063247.971051
      NJ12.6624214.3743193.45742334.1256230.06294227.42514423.794351121.752090
      NM13.70379411.9573498.5821043.01509011.55453618.85365621.865586219.885214
      NV13.59147514.48744810.43166116.1433581.00101223.91402322.030263163.780341
      NY13.7905198.05265814.37737635.5811511.50513123.62573621.544354149.887672
      OH12.3180277.7056789.50973430.8671980.76522827.5289878.507085189.476546
      OK15.69907810.37080313.94597822.1339273.63328720.90681617.779218211.075700
      OR9.3063176.6891317.9253633.8157641.43971613.33158110.691046268.643525
      PA14.8946478.6300379.49527434.0938900.87689230.97381418.200768237.806669
      RI11.1460194.43561732.9181877.4125470.24121623.03692416.407429257.420168
      SC12.2309019.14053115.99466228.2009570.54191424.2052958.326607299.090600
      SD10.7622696.35075418.3440696.48264617.75961717.5134713.146443231.027858
      TN13.1345978.46220318.73246931.3897120.72445627.5355738.915858260.655771
      TX13.57846114.43543610.95081321.9048950.88537922.32722328.187346326.165285
      UT11.6880579.50557111.6145782.3520142.32385718.71842216.466781255.040636
      VA12.3738958.15155213.04557525.4253110.55860122.04100414.043601239.139561
      VT10.1802755.43820421.6095604.0731870.98581510.9814191.490384224.502561
      WA12.4970078.33919710.2603047.7532621.63672519.16534113.734670298.822930
      WI13.73896810.50000612.71303831.8592611.82321429.77229624.227007151.094338
      WV13.3136746.10720617.04539811.0913880.34496712.5864163.074087227.309836
      WY8.3126187.08893814.9262381.7731463.09863210.2336869.157789255.577994
      " - ], - "text/plain": [ - "" - ] - }, - "execution_count": 58, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "grouped_stats_score_ej_screen.style.bar(\n", - " subset=grouped_stats_score_ej_screen.columns, align='mid', color=['#d65f5f', '#5fba7d'])" - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(2272, 36)" - ] - }, - "execution_count": 59, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "merged_df_score_ejcreen.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "metadata": {}, - "outputs": [], - "source": [ - "values_2 = list(grouped_stats_score_ej_screen[list_of_columns].mean())" - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "metadata": {}, - "outputs": [], - "source": [ - "values_2.extend([False, True])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Both Met Criterion" - ] - }, - { - "cell_type": "code", - "execution_count": 62, - "metadata": {}, - "outputs": [], - "source": [ - "predicate_3 = (non_null_df['current_threshold_exceeded'] == True) & (non_null_df['new_threshold_exceeded'] == True)\n", - "\n", - "union_df = non_null_df[predicate_3]" - ] - }, - { - "cell_type": "code", - "execution_count": 63, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(4529, 15)" - ] - }, - "execution_count": 63, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "union_df.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 66, - "metadata": {}, - "outputs": [], - "source": [ - "# Join the demographics in.\n", - "merged_df_both_met = union_df.merge(\n", - " demographics_df[columns_to_keep],\n", - " left_on=\"FIPS_tract_id\",\n", - " right_on=\"GEOID10_TRACT\",\n", - " how=\"inner\"\n", - ")\n", - "\n", - "# these are not converted into percent 0 - 100 scale\n", - "percent_cols = [x for x in merged_df_both_met.columns if \n", - " 'Percent' in x or '(percent)' in x\n", - " ]\n", - "\n", - "merged_df_both_met[\n", - " percent_cols] = merged_df_both_met[\n", - " percent_cols].apply(lambda x: x * 100)\n", - "\n", - "for idx, row in merged_df_both_met.iterrows():\n", - " current_row = str(merged_df_both_met.loc[idx, 'state'])\n", - " state = mapping.get(current_row, None)\n", - " merged_df_both_met.loc[idx, 'state_name'] = state\n", - "\n", - "grouped_stats_both_met = merged_df_both_met.groupby([\"state_name\"]).agg({\n", - " 'GEOID10_TRACT': 'nunique',\n", - " 'Percent of individuals < 200% Federal Poverty Line': [np.median, np.std],\n", - " 'Median value ($) of owner-occupied housing units': [np.median, np.std],\n", - " 'Percent individuals age 25 or over with less than high school degree': [np.median, np.std],\n", - " 'Percent enrollment in college or graduate school': [np.median, np.std],\n", - " 'Percent Black or African American alone': [np.median, np.std],\n", - " 'Percent American Indian and Alaska Native alone': [np.median, np.std],\n", - " 'Percent Non-Hispanic White': [np.median, np.std], \n", - " 'Linguistic isolation (percent)': [np.median, np.std],\n", - " 'Percent Hispanic or Latino': [np.median, np.std],\n", - " 'hbrd_rank': [np.median, np.std],\n", - " 'current_methodology_percent': [np.median, np.std],\n", - " 'current_summed_methodology': [np.median, np.std, np.sum]\n", - "}).reset_index()\n", - "\n", - "\n", - "grouped_stats_both_met.columns = [' '.join(col).strip() for \n", - " col in grouped_stats_both_met.columns.values]\n", - "\n", - "grouped_stats_both_met = grouped_stats_both_met[[x for x in grouped_stats_both_met \n", - " if \"std\" in x and \n", - " \"Percent\" in x] + [\n", - " 'current_summed_methodology std', \"state_name\"]]\n", - "\n", - "grouped_stats_both_met.set_index(\"state_name\", inplace=True)\n", - "\n", - "grouped_stats_both_met = grouped_stats_both_met.rename(columns={\n", - " 'Percent of individuals < 200% Federal Poverty Line std'\n", - " : 'Percent of individuals < 200% Federal Poverty Line (variance across all tracts)', \n", - "'Percent individuals age 25 or over with less than high school degree std':\n", - "'Percent individuals age 25 or over with less than high school degree (variance across all tracts)', \n", - "'Percent enrollment in college or graduate school std'\n", - " :'Percent enrollment in college or graduate school (variance across all tracts)',\n", - "'Percent Black or African American alone std':\n", - " 'Percent Black or African American alone (variance across all tracts)',\n", - "'Percent American Indian and Alaska Native alone std':\n", - " 'Percent American Indian and Alaska Native alone (variance across all tracts)',\n", - "'Percent Non-Hispanic White std':\n", - " 'Percent Non-Hispanic White (variance across all tracts)',\n", - "'Percent Hispanic or Latino std':\n", - " 'Percent Hispanic or Latino (variance across all tracts)',\n", - "'GEOID10_TRACT std': \"Total Number of Unique Tracts\",\n", - "\"current_summed_methodology std\": \"Variance: Owned and Rented Burdened Households (Current Aggregation Methodology)\" \n", - "})" - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - 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" \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
      Percent of individuals < 200% Federal Poverty Line (variance across all tracts) Percent individuals age 25 or over with less than high school degree (variance across all tracts) Percent enrollment in college or graduate school (variance across all tracts) Percent Black or African American alone (variance across all tracts) Percent American Indian and Alaska Native alone (variance across all tracts) Percent Non-Hispanic White (variance across all tracts) Percent Hispanic or Latino (variance across all tracts) Variance: Owned and Rented Burdened Households (Current Aggregation Methodology)
      state_name
      AK5.3625646.8667304.7137434.3592154.4619644.4644355.648686200.852350
      AL10.7623168.75984926.35758929.5426670.98336126.1700446.445044241.026314
      AR8.9555066.66183021.33549829.0461090.37664226.2302117.373333386.784673
      AZ9.73477013.66752921.3850005.9293293.03109918.39565521.505953264.572097
      CA12.83947815.51909415.96530212.1085541.27480819.06573826.046888300.670483
      CO12.01350812.05723122.89590411.3011222.01156524.48495721.863004381.523930
      CT10.32209710.49818115.64198717.9451221.12603517.17725319.111784294.901340
      DC9.3531635.74429221.80751322.1817550.86537516.1064024.547917187.708717
      DE10.0443189.20793232.72001229.5699300.57255127.48149015.873234110.123365
      FL11.80758410.35941914.81064531.2447870.95524816.99004233.349320409.998796
      GA11.57912611.21756521.46972328.0520591.59884618.90761516.634868413.546272
      HI7.4881053.8861266.7377307.3025100.60096815.7939438.150455267.260404
      IA9.36744012.73321333.96088919.5374932.64652822.30231911.377196527.841747
      ID13.13546212.42705923.0836620.4243812.92402321.73182923.764339974.017967
      IL14.3652669.96104818.64186334.7107510.66795819.92963824.171428300.228779
      IN10.50257010.00958926.20874229.4082040.53110628.71921910.778399331.278547
      KS9.34344811.56321631.57152727.8183531.68067327.07746718.532743424.193116
      KY10.35354110.49134629.52427731.1849620.88558828.09197214.085954289.382905
      LA11.4858708.78654315.56270320.0085070.52787615.5208567.542826305.445267
      MA13.41589211.85625021.68614525.5766490.76330423.18901327.384592261.976125
      MD14.6115979.26090917.77445828.4309710.75564417.88354715.157175308.477828
      ME5.9839948.77885825.8059009.0165650.59817912.2362582.361052354.752227
      MI11.36461310.10472224.60437835.7407050.74998029.1399419.723557270.032843
      MN11.15587710.70212225.20580220.8830182.01106526.1278687.197320373.996267
      MO10.1373248.59236422.71200535.0982820.52610329.59930010.088432324.569103
      MS9.8548166.81183224.15529523.4494680.63894320.7101783.181963235.058305
      MT4.1152677.63392531.6545370.4667366.59305412.0427156.102896260.969730
      NC10.39089710.50162823.54481724.3778191.16606423.25954913.346942342.769565
      ND13.0655865.24549334.2102783.5110705.1049305.1058512.359395297.654162
      NE8.3504558.89322621.94854823.0753370.58544821.6620615.582370202.499665
      NH11.68352711.31140537.6397094.9983370.40383716.84282713.079522419.520096
      NJ12.20321710.4736627.61094430.1069500.81180221.22933426.540235275.002256
      NM11.39786511.01139526.6015502.9630154.72071110.61652116.064664411.970186
      NV10.15712510.3952777.1345889.7867161.24139215.75248018.739589211.298685
      NY13.4887379.8989868.69703226.8675331.60941228.82092326.174584370.123164
      OH10.8258229.17883826.33289232.2192661.05261028.1839019.513140261.267771
      OK11.69098411.77816229.82343025.8110253.28294922.03832216.282388313.721214
      OR11.6723628.56043126.4025504.4384731.99947311.49711711.710487390.637347
      PA12.48221511.95400023.38347330.6640601.10783725.26876226.123990265.380774
      RI9.2925748.2442448.29339111.7344091.08468929.76975526.781181253.312496
      SC10.63383910.50319425.39678729.2218930.61737724.66912612.992547230.947955
      SDnannannannannannannannan
      TN9.8587978.62066818.12872027.4530930.41115820.79116111.842790344.209468
      TX10.61348414.75916922.11476422.9734540.89891119.66871226.558668366.266727
      UT13.79132210.33027933.7965306.0216411.63139823.35843817.502232212.805390
      VA14.13061611.28611523.74317932.1256570.98427522.72260716.743508315.503703
      VT9.5745265.94732923.2458653.3390030.0269205.2288001.019496268.424260
      WA13.3306109.59060328.3964268.2699622.26794619.26657612.581541510.235920
      WI12.64560711.62231827.07744336.8088050.89691030.23744923.002879303.044555
      WV14.1959708.05431033.53055314.3051990.32436612.9871622.963561256.028444
      WY4.9240080.13808228.2316830.6643800.1981444.3871991.966222361.222000
      " - ], - "text/plain": [ - "" - ] - }, - "execution_count": 68, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "grouped_stats_both_met.style.bar(\n", - " subset=grouped_stats_both_met.columns, align='mid', color=['#d65f5f', '#5fba7d'])" - ] - }, - { - "cell_type": "code", - "execution_count": 67, - "metadata": {}, - "outputs": [], - "source": [ - "values_3 = list(grouped_stats_both_met[list_of_columns].mean())" - ] - }, - { - "cell_type": "code", - "execution_count": 243, - "metadata": {}, - "outputs": [], - "source": [ - "values_3.extend([True, True])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Neither Met Criterion " - ] - }, - { - "cell_type": "code", - "execution_count": 244, - "metadata": {}, - "outputs": [], - "source": [ - "predicate_4 = (non_null_df[\n", - " 'current_threshold_exceeded'] == False) & (\n", - " non_null_df['new_threshold_exceeded'] == False)\n", - "\n", - "negation_union_df = non_null_df[predicate_4]" - ] - }, - { - "cell_type": "code", - "execution_count": 245, - "metadata": {}, - "outputs": [], - "source": [ - "# Join the demographics in.\n", - "merged_df_negation_union = negation_union_df.merge(\n", - " demographics_df[columns_to_keep],\n", - " left_on=\"FIPS_tract_id\",\n", - " right_on=\"GEOID10_TRACT\",\n", - " how=\"inner\"\n", - ")\n", - "\n", - "# these are not converted into percent 0 - 100 scale\n", - "percent_cols = [x for x in merged_df_negation_union.columns if \n", - " 'Percent' in x or '(percent)' in x\n", - " ]\n", - "\n", - "merged_df_negation_union[\n", - " percent_cols] = merged_df_negation_union[\n", - " percent_cols].apply(lambda x: x * 100)\n", - "\n", - "for idx, row in merged_df_negation_union.iterrows():\n", - " current_row = str(merged_df_negation_union.loc[idx, 'state'])\n", - " state = mapping.get(current_row, None)\n", - " merged_df_negation_union.loc[idx, 'state_name'] = state\n", - "\n", - "grouped_stats_both_not_met = merged_df_negation_union.groupby([\"state_name\"]).agg({\n", - " 'GEOID10_TRACT': 'nunique',\n", - " 'Percent of individuals < 200% Federal Poverty Line': [np.median, np.std],\n", - " 'Median value ($) of owner-occupied housing units': [np.median, np.std],\n", - " 'Percent individuals age 25 or over with less than high school degree': [np.median, np.std],\n", - " 'Percent enrollment in college or graduate school': [np.median, np.std],\n", - " 'Percent Black or African American alone': [np.median, np.std],\n", - " 'Percent American Indian and Alaska Native alone': [np.median, np.std],\n", - " 'Percent Non-Hispanic White': [np.median, np.std], \n", - " 'Linguistic isolation (percent)': [np.median, np.std],\n", - " 'Percent Hispanic or Latino': [np.median, np.std],\n", - " 'hbrd_rank': [np.median, np.std],\n", - " 'current_methodology_percent': [np.median, np.std],\n", - " 'current_summed_methodology': [np.median, np.std, np.sum]\n", - "}).reset_index()\n", - "\n", - "\n", - "grouped_stats_both_not_met.columns = [' '.join(col).strip() for \n", - " col in grouped_stats_both_not_met.columns.values]\n", - "\n", - "grouped_stats_both_not_met = grouped_stats_both_not_met[[x for x in grouped_stats_both_not_met \n", - " if \"median\" in x and \n", - " \"Percent\" in x] + [\"GEOID10_TRACT nunique\", \n", - " 'current_summed_methodology sum', \"state_name\"]]\n", - "\n", - "grouped_stats_both_not_met.set_index(\"state_name\", inplace=True)\n", - "\n", - "grouped_stats_both_not_met = grouped_stats_both_not_met.rename(columns={\n", - "'Percent of individuals < 200% Federal Poverty Line median'\n", - " : 'Percent of individuals < 200% Federal Poverty Line (median across all tracts)', \n", - "'Percent individuals age 25 or over with less than high school degree median':\n", - "'Percent individuals age 25 or over with less than high school degree (median across all tracts)', \n", - "'Percent enrollment in college or graduate school median'\n", - " :'Percent enrollment in college or graduate school (median across all tracts)',\n", - "'Percent Black or African American alone median':\n", - " 'Percent Black or African American alone (median across all tracts)',\n", - "'Percent American Indian and Alaska Native alone median':\n", - " 'Percent American Indian and Alaska Native alone (median across all tracts)',\n", - "'Percent Non-Hispanic White median':\n", - " 'Percent Non-Hispanic White (median across all tracts)',\n", - "'Percent Hispanic or Latino median':\n", - " 'Percent Hispanic or Latino (median across all tracts)',\n", - "'GEOID10_TRACT nunique': \"Total Number of Unique Tracts\",\n", - "\"current_summed_methodology sum\": \"Total Owned and Rented Burdened Households (Current Aggregation Methodology)\" \n", - "})" - ] - }, - { - "cell_type": "code", - "execution_count": 246, - "metadata": {}, - "outputs": [], - "source": [ - "values_4 = list(grouped_stats_both_not_met[list_of_columns].mean())\n", - "\n", - "values_4.extend([False, False])" - ] - }, - { - "cell_type": "code", - "execution_count": 247, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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      Percent of individuals < 200% Federal Poverty Line (median across all tracts)Percent individuals age 25 or over with less than high school degree (median across all tracts)Percent enrollment in college or graduate school (median across all tracts)Percent Black or African American alone (median across all tracts)Percent American Indian and Alaska Native alone (median across all tracts)Percent Non-Hispanic White (median across all tracts)Percent Hispanic or Latino (median across all tracts)Total Number of Unique TractsTotal Owned and Rented Burdened Households (Current Aggregation Methodology)
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      Percent of individuals < 200% Federal Poverty Line (median across all tracts) Percent individuals age 25 or over with less than high school degree (median across all tracts) Percent enrollment in college or graduate school (median across all tracts) Percent Black or African American alone (median across all tracts) Percent American Indian and Alaska Native alone (median across all tracts) Percent Non-Hispanic White (median across all tracts) Percent Hispanic or Latino (median across all tracts) Total Number of Unique Tracts Total Owned and Rented Burdened Households (Current Aggregation Methodology)
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      " - ], - "text/plain": [ - "" - ] - }, - "execution_count": 279, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "def style_negative(v, props=''):\n", - " return props if v < 0 else None\n", - "\n", - "diff_style = diff.style.applymap(style_negative, props='color:red;')\\\n", - " .applymap(lambda v: 'opacity: 20%;' if (v < 0.0) and (v > 0.0) else None)\n", - "diff_style" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.2" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/data/data-pipeline/data_pipeline/ipython/hud_eda_se_12_27-highlevel-summarization.ipynb b/data/data-pipeline/data_pipeline/ipython/hud_eda_se_12_27-highlevel-summarization.ipynb deleted file mode 100644 index 9c671466..00000000 --- a/data/data-pipeline/data_pipeline/ipython/hud_eda_se_12_27-highlevel-summarization.ipynb +++ /dev/null @@ -1,3434 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Methodology to address fundamental problem 1 itemized in Issue 1024 - follow-up compare tabulations and relative household burden. This time I extend the 12-11 notebook to look at how the percentile ranks affects the proportion of tracts considered as burdened versus the current methodology." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Indicator reviewed: \n", - "\n", - "Socioeconomic Factors Indicator reviewed\n", - "* [Extreme Housing Burden](#housingburden)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Packages" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import math\n", - "import numpy as np\n", - "import os\n", - "import pandas as pd\n", - "\n", - "import seaborn as sns\n", - "import matplotlib.pyplot as plt\n", - "import pandas as pd\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### ETL process for acquiring relevant tables" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### NOTE: If you ran the ETL Process to acquire Table 8 in the other notebook of this draft PR you do not need to run the ETL cell block again" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# Copy and adapt certain sections of code from data_pipeline.utils \n", - "\n", - "def download_hud_dataset():\n", - " DOWNLOAD_FILENAME = \"HUD_ZIPPED.csv\"\n", - " HOUSING_FTP_URL = \"https://www.huduser.gov/portal/datasets/cp/2014thru2018-140-csv.zip\" \n", - " response = requests.get(HOUSING_FTP_URL, verify=True)\n", - " if response.status_code == 200:\n", - " file_contents = response.content\n", - " else:\n", - " sys.exit(\n", - " f\"HTTP response {response.status_code} from url {file_url}. Info: {response.content}\"\n", - " )\n", - "\n", - " # Write the contents to disk.\n", - " file = open(DOWNLOAD_FILENAME, \"wb\")\n", - " file.write(file_contents)\n", - " file.close()\n", - " \n", - "def extract_zipped_download(zip_file_path, unzipped_path):\n", - " with zipfile.ZipFile(zip_file_path, \"r\") as zip_ref:\n", - " zip_ref.extractall(unzipped_path)\n", - " # cleanup temporary file\n", - " os.remove(zip_file_path)\n", - " \n", - "def up_one_directory(path):\n", - " try:\n", - " # from Python 3.6\n", - " parent_dir = Path(path).parents[1]\n", - " # for Python 3.4/3.5, use str to convert the path to string\n", - " # parent_dir = str(Path(path).parents[1])\n", - " shutil.move(path, parent_dir)\n", - " except IndexError:\n", - " # no upper directory\n", - " pass\n", - "\n", - "CURRENT_DIRECTORY = os.getcwd()\n", - "download_hud_dataset()\n", - "extract_zipped_download(CURRENT_DIRECTORY + \"/HUD_ZIPPED.csv\", CURRENT_DIRECTORY) \n", - "up_one_directory(CURRENT_DIRECTORY + \"/140/Table8.csv\")\n", - "shutil.rmtree(\"./140/\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Extreme Housing Burden \n", - "\n", - "The Extreme Housing Burden indicator represents the proportion of low-income households that have to spend more than half their income on rent. These households experience higher levels of stress, report lower health, and may delay medical treatment because of its high cost.\n", - "\n", - "The Extreme Housing Burden indicator measures the percent of households in a census tract that are:\n", - "\n", - "1. Making less than 80% of the Area Median Family Income as determined by the Department of Housing and Urban Development (HUD), and\n", - "2. Paying greater than 50% of their income to housing costs. \n", - "\n", - "This data is sourced from the 2014-2018 Comprehensive Housing Affordability Strategy dataset from the Department of Housing and Urban Development (HUD) using the census tract geographic summary level, and contains cost burdens for households by percent HUD-adjusted median family income (HAMFI) category. This data can be found [here](https://www.huduser.gov/portal/datasets/cp.html). \n", - "\n", - "Because CHAS data is based on American Communities Survey (ACS) estimates, which come from a sample of the population, they may be unreliable if based on a small sample or population size.\n", - "\n", - "The standard error and relative standard error were used to evaluate the reliability of each estimate using CalEnviroScreen’s methodology. \n", - "\n", - "Census tract estimates that met either of the following criteria were considered reliable and included in the analysis [(CalEnviroScreen, 2017, page 129)](https://oehha.ca.gov/media/downloads/calenviroscreen/report/ces3report.pdf ):\n", - "\n", - "- Relative standard error less than 50 (meaning the standard error was less than half of the estimate), OR \n", - "- Standard error less than the mean standard error of all census tract estimates \n", - "\n", - "Formulas for calculating the standard error of sums, proportions, and ratio come from the [American Communities Survey Office](https://www2.census.gov/programs-surveys/acs/tech_docs/accuracy/MultiyearACSAccuracyofData2013.pdf).\n", - "\n", - "Note that this code creates a score and rank by state, for every state." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The relevant variables in table 8 of the CHAS dataset are the following (CHAS data dictionary available [here](https://www.huduser.gov/portal/datasets/cp/CHAS-data-dictionary-14-18.xlsx)):\n", - "\n", - "| Name | Label |\n", - "|---------|-----------------------------------------------------|\n", - "|T1_est1 | Total Occupied housing units | \n", - "|T8_est10 | Owner occupied less than or equal to 30% of HAMFI cost burden greater than 50% |\n", - "|T8_est23 |Owner occupied greater than 30% but less than or equal to 50% of HAMFI\tcost burden greater than 50%|\n", - "|T8_est36 |Owner occupied\tgreater than 50% but less than or equal to 80% of HAMFI\tcost burden greater than 50%|\n", - "|T8_est76 | Renter occupied less than or equal to 30% of HAMFI cost burden greater than 50% |\n", - "|T8_est89 |Renter occupied\tgreater than 30% but less than or equal to 50% of HAMFI\tcost burden greater than 50%|\n", - "|T8_est102|Renter occupied\tgreater than 50% but less than or equal to 80% of HAMFI\tcost burden greater than 50%|\n", - " " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Below I also propose an alternate means for ranking census tracts\n", - "### These steps are outlined and commented below" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/sarahluw/.pyenv/versions/3.6.2/envs/my-virtual-env-3.6.2/lib/python3.6/site-packages/pandas/core/series.py:726: RuntimeWarning: invalid value encountered in sqrt\n", - " result = getattr(ufunc, method)(*inputs, **kwargs)\n", - "/Users/sarahluw/.pyenv/versions/3.6.2/envs/my-virtual-env-3.6.2/lib/python3.6/site-packages/pandas/core/indexing.py:670: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " iloc._setitem_with_indexer(indexer, value)\n" - ] - } - ], - "source": [ - "# Read in the data from https://www.huduser.gov/portal/datasets/cp.html\n", - "housing = pd.read_csv(\"Table8.csv\", \n", - " encoding = \"ISO-8859-1\", \n", - " dtype = {'Tract_ID': object, 'st': object, 'geoid': object})\n", - "\n", - "# Remove data for states that aren't included in the census (e.g. American Samoa, Guam, etc.):\n", - "housing.drop(housing.loc[housing['st'] == '72'].index, inplace = True)\n", - "\n", - "# Combine owner and renter occupied low-income households that make less than 80% of HAMFI into one variable\n", - "housing['summed'] = (housing['T8_est10'] + \n", - " housing['T8_est23'] + \n", - " housing['T8_est36'] + \n", - " housing['T8_est76'] + \n", - " housing['T8_est89'] + \n", - " housing['T8_est102'])\n", - "\n", - "# Create a variable for the standard error of the summed variables\n", - "housing['summed_se'] = np.sqrt((housing['T8_moe10'] / 1.645)**2 + \n", - " (housing['T8_moe23'] / 1.645)**2 + \n", - " (housing['T8_moe36'] / 1.645)**2 + \n", - " (housing['T8_moe76'] / 1.645)**2 + \n", - " (housing['T8_moe89'] / 1.645)**2 + \n", - " (housing['T8_moe102'] / 1.645)**2)\n", - "\n", - "# Remove the first 7 digits in the FIPS Census Tract ID \n", - "housing['geoid'] = housing['geoid'].str[-11:]\n", - "\n", - "# Find the estimate of the proportion of the population that is heavily rent burdened\n", - "housing['hbrd_score'] = housing['summed'] / housing['T8_est1']\n", - "\n", - "# Change rates where the population is 0 to nan\n", - "housing['hbrd_score'].replace(np.inf, np.nan, inplace = True)\n", - "\n", - "# Create function for calculating the standard error, using the proportions standard error formula\n", - "# if the value under the radical is negative, use the ratio standard error formula\n", - "def se_prop(x, y, se_x, moe_y): \n", - " se_y = moe_y / 1.645\n", - " test = se_x**2 - (((x**2)/(y**2))*((se_y)**2))\n", - " se = np.where(test < 0,\n", - " (1/y) * np.sqrt(se_x**2 + (((x**2)/(y**2))*(se_y**2))), \n", - " (1/y) * np.sqrt(se_x**2 - (((x**2)/(y**2))*(se_y**2))))\n", - " return se\n", - "\n", - "housing['se'] = se_prop(housing['summed'], housing['T8_est1'], housing['summed_se'], housing['T8_moe1'])\n", - "\n", - "# Calculate the relative standard error\n", - "housing['rse'] = housing['se'] / housing['hbrd_score']*100\n", - "\n", - "# Change infinite rse's where the housing burden is 0 to np.nan\n", - "housing['rse'].replace(np.inf, np.nan, inplace = True)\n", - "\n", - "# Calculate the mean standard error for each state\n", - "housing['mean_state_se'] = np.zeros(len(housing))\n", - "\n", - "for state in housing['st'].unique():\n", - " mean_se = np.mean(housing[housing['st'] == state]['se'])\n", - " housing['mean_state_se'].loc[housing['st'] == state] = mean_se\n", - " \n", - "# Find census tract estimates that meet both of the following criteria and are thus considered unreliable estimates: \n", - "# RSE less than 50 AND\n", - "# SE less than the mean state SE or housing burdened low income households\n", - "# Convert these scores to nan\n", - "housing.loc[(housing['rse'] >= 50) & (housing['rse'] >= housing['mean_state_se']), 'hbrd_score'] = np.nan\n", - "\n", - "# Rename columns\n", - "housing = housing.rename(columns = {'geoid' :'FIPS_tract_id',\n", - " 'st' : 'state'\n", - " })\n", - "\n", - "# Calculate percentile rank for census tracts with a score above 0, set percentile to 0 if score is 0, for each state\n", - "housing['hbrd_rank'] = housing[\n", - " housing['hbrd_score'] != 0][['hbrd_score',\n", - " 'state']].groupby('state').rank( \n", - " na_option = 'keep', \n", - " pct = True) * 100\n", - "\n", - "housing.loc[housing['hbrd_score'] == 0, 'hbrd_rank'] = 0\n", - "\n", - "# Create final housing burden df\n", - "housingburden = housing.copy()" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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      5 rows Ă— 280 columns

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      " - ], - "text/plain": [ - " source sumlevel FIPS_tract_id \\\n", - "0 2014thru2018 140 01001020100 \n", - "1 2014thru2018 140 01001020200 \n", - "2 2014thru2018 140 01001020300 \n", - "3 2014thru2018 140 01001020400 \n", - "4 2014thru2018 140 01001020500 \n", - "\n", - " name state cnty tract T8_est1 \\\n", - "0 Census Tract 201, Autauga County, Alabama 01 1 20100 765 \n", - "1 Census Tract 202, Autauga County, Alabama 01 1 20200 720 \n", - "2 Census Tract 203, Autauga County, Alabama 01 1 20300 1295 \n", - "3 Census Tract 204, Autauga County, Alabama 01 1 20400 1640 \n", - "4 Census Tract 205, Autauga County, Alabama 01 1 20500 4175 \n", - "\n", - " T8_est2 T8_est3 ... T8_moe131 T8_moe132 T8_moe133 summed summed_se \\\n", - "0 570 50 ... 12 12 12 80 31.721807 \n", - "1 465 65 ... 12 12 12 138 45.531874 \n", - "2 840 60 ... 12 12 12 170 53.722921 \n", - "3 1260 15 ... 12 12 12 145 46.288510 \n", - "4 2320 175 ... 17 17 17 595 147.221693 \n", - "\n", - " hbrd_score se rse mean_state_se hbrd_rank \n", - "0 0.104575 0.041032 39.237314 0.036604 46.298077 \n", - "1 0.191667 0.061614 32.146659 0.036604 83.269231 \n", - "2 0.131274 0.040927 31.176999 0.036604 63.653846 \n", - "3 0.088415 0.027822 31.467397 0.036604 34.615385 \n", - "4 0.142515 0.034760 24.390193 0.036604 68.221154 \n", - "\n", - "[5 rows x 280 columns]" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "housingburden.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(73056, 280)" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "housingburden.shape" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### As desired we see a uniform distribution for the percentile rank for burdened households" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Now we compute for a baseline comparison " - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# Owner occupied numerator fields\n", - "OWNER_OCCUPIED_NUMERATOR_FIELDS = [\n", - " # Column Name\n", - " # Line_Type\n", - " # Tenure\n", - " # Household income\n", - " # Cost burden\n", - " # Facilities\n", - " \"T8_est7\",\n", - " # Subtotal\n", - " # Owner occupied\n", - " # less than or equal to 30% of HAMFI\n", - " # greater than 30% but less than or equal to 50%\n", - " # All\n", - " \"T8_est10\",\n", - " # Subtotal\n", - " # Owner occupied\n", - " # less than or equal to 30% of HAMFI\n", - " # greater than 50%\n", - " # All\n", - " \"T8_est20\",\n", - " \n", - " # Subtotal\n", - " # Owner occupied\n", - " # greater than 30% but less than or equal to 50% of HAMFI\n", - " # greater than 30% but less than or equal to 50%\n", - " # All\n", - " \"T8_est23\",\n", - " # Subtotal\n", - " # Owner occupied\n", - " # greater than 30% but less than or equal to 50% of HAMFI\n", - " # greater than 50%\n", - " # All\n", - " \"T8_est33\",\n", - " # Subtotal\n", - " # Owner occupied\n", - " # greater than 50% but less than or equal to 80% of HAMFI\n", - " # greater than 30% but less than or equal to 50%\n", - " # All\n", - " \"T8_est36\",\n", - " # Subtotal\n", - " # Owner occupied\n", - " # greater than 50% but less than or equal to 80% of HAMFI\n", - " # greater than 50%\n", - " # All\n", - "]\n", - "\n", - "# These rows have the values where HAMFI was not computed, b/c of no or negative income.\n", - "OWNER_OCCUPIED_NOT_COMPUTED_FIELDS = [\n", - " # Column Name\n", - " # Line_Type\n", - " # Tenure\n", - " # Household income\n", - " # Cost burden\n", - " # Facilities\n", - " \"T8_est13\",\n", - " # Subtotal\n", - " # Owner occupied\n", - " # less than or equal to 30% of HAMFI\n", - " # not computed (no/negative income)\n", - " # All\n", - " \"T8_est26\",\n", - " # Subtotal\n", - " # Owner occupied\n", - " # greater than 30% but less than or equal to 50% of HAMFI\n", - " # not computed (no/negative income)\n", - " # All\n", - " \"T8_est39\",\n", - " # Subtotal\n", - " # Owner occupied\n", - " # greater than 50% but less than or equal to 80% of HAMFI\n", - " # not computed (no/negative income)\n", - " # All\n", - " \"T8_est52\",\n", - " # Subtotal\n", - " # Owner occupied\n", - " # greater than 80% but less than or equal to 100% of HAMFI\n", - " # not computed (no/negative income)\n", - " # All\n", - " \"T8_est65\",\n", - " # Subtotal\n", - " # Owner occupied\n", - " # greater than 100% of HAMFI\n", - " # not computed (no/negative income)\n", - " # All\n", - "]\n", - "\n", - "OWNER_OCCUPIED_POPULATION_FIELD = \"T8_est2\"\n", - "# Subtotal\n", - "# Owner occupied\n", - "# All\n", - "# All\n", - "# All\n", - "\n", - "OWNER_OCCUPIED_POPULATION_HAMFI_FIELD = \"T8_est3\"\n", - "# Subtotal\n", - "# Owner occupied \n", - "# All\n", - "# All\n", - "# All\n", - "\n", - "# Renter occupied numerator fields\n", - "RENTER_OCCUPIED_NUMERATOR_FIELDS = [\n", - " # Column Name\n", - " # Line_Type\n", - " # Tenure\n", - " # Household income\n", - " # Cost burden\n", - " # Facilities\n", - " \"T8_est73\",\n", - " # Subtotal\n", - " # Renter occupied\n", - " # less than or equal to 30% of HAMFI\n", - " # greater than 30% but less than or equal to 50%\n", - " # All\n", - " \"T8_est76\",\n", - " # Subtotal\n", - " # Renter occupied\n", - " # less than or equal to 30% of HAMFI\n", - " # greater than 50%\n", - " # All\n", - " \"T8_est86\",\n", - " # Subtotal\n", - " # Renter occupied\n", - " # greater than 30% but less than or equal to 50% of HAMFI\n", - " # greater than 30% but less than or equal to 50%\n", - " # All\n", - " \"T8_est89\",\n", - " # Subtotal\n", - " # Renter occupied\n", - " # greater than 30% but less than or equal to 50% of HAMFI\n", - " # greater than 50%\n", - " # All\n", - " \"T8_est99\",\n", - " # Subtotal\n", - " # Renter occupied\tgreater than 50% but less than or equal to 80% of HAMFI\n", - " # greater than 30% but less than or equal to 50%\n", - " # All\n", - " \"T8_est102\",\n", - " # Subtotal\n", - " # Renter occupied\n", - " # greater than 50% but less than or equal to 80% of HAMFI\n", - " # greater than 50%\n", - " # All\n", - "]\n", - "\n", - "# These rows have the values where HAMFI was not computed, b/c of no or negative income.\n", - "RENTER_OCCUPIED_NOT_COMPUTED_FIELDS = [\n", - " # Column Name\n", - " # Line_Type\n", - " # Tenure\n", - " # Household income\n", - " # Cost burden\n", - " # Facilities\n", - " \"T8_est79\",\n", - " # Subtotal\n", - " # Renter occupied\tless than or equal to 30% of HAMFI\n", - " # not computed (no/negative income)\n", - " # All\n", - " \"T8_est92\",\n", - " # Subtotal\n", - " # Renter occupied\tgreater than 30% but less than or equal to 50% of HAMFI\n", - " # not computed (no/negative income)\n", - " # All\n", - " \"T8_est105\",\n", - " # Subtotal\n", - " # Renter occupied\n", - " # greater than 50% but less than or equal to 80% of HAMFI\n", - " # not computed (no/negative income)\n", - " # All\n", - " \"T8_est118\",\n", - " # Subtotal\n", - " # Renter occupied\tgreater than 80% but less than or equal to 100% of HAMFI\n", - " # not computed (no/negative income)\n", - " # All\n", - " \"T8_est131\",\n", - " # Subtotal\n", - " # Renter occupied\n", - " # greater than 100% of HAMFI\n", - " # not computed (no/negative income)\n", - " # All\n", - "]\n", - "\n", - "# T8_est68\tSubtotalRenter occupied\tAll\tAll\tAll\n", - "RENTER_OCCUPIED_POPULATION_FIELD = \"T8_est68\"" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "housingburden[\"current_summed_methodology\"] = housingburden[\n", - " OWNER_OCCUPIED_NUMERATOR_FIELDS\n", - "].sum(axis=1) + housingburden[RENTER_OCCUPIED_NUMERATOR_FIELDS].sum(axis=1)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "housingburden[\"current_methodology_denominator\"] = (\n", - " housingburden[OWNER_OCCUPIED_POPULATION_FIELD]\n", - " + housingburden[RENTER_OCCUPIED_POPULATION_FIELD]\n", - " - housingburden[OWNER_OCCUPIED_NOT_COMPUTED_FIELDS].sum(axis=1)\n", - " - housingburden[RENTER_OCCUPIED_NOT_COMPUTED_FIELDS].sum(axis=1)\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "housingburden[\"current_methodology_denominator_sans_not_computed\"] = (\n", - " housingburden[OWNER_OCCUPIED_POPULATION_FIELD]\n", - " + housingburden[RENTER_OCCUPIED_POPULATION_FIELD]\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "housingburden[\"current_methodology_percent\"] = np.round(\n", - " (housingburden[\"current_summed_methodology\"] / housingburden[\"current_methodology_denominator\"] ), 2) * 100" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Now we construct the distribution of differences in the number of owned and rented burdened households\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Percentiles Comparison" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "final_df = housingburden[['FIPS_tract_id', 'state','hbrd_rank','hbrd_score', 'summed', \n", - " 'current_summed_methodology', 'T8_est1', \n", - " \"current_methodology_denominator_sans_not_computed\",\n", - " 'current_methodology_denominator', 'current_methodology_percent']]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### First notice here that **T8_est1** and **current_methodology_denominator** should represent same or similar aggregates. In general, we cen see that the current computation performed results in a differerntial that undercounts the total occupied and rental households." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/sarahluw/.pyenv/versions/3.6.2/envs/my-virtual-env-3.6.2/lib/python3.6/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " \n" - ] - } - ], - "source": [ - "final_df[\"differences_aggregate_denominator\"] = (\n", - " final_df[\"current_methodology_denominator\"] - final_df[\"T8_est1\"] \n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/sarahluw/.pyenv/versions/3.6.2/envs/my-virtual-env-3.6.2/lib/python3.6/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " \n" - ] - } - ], - "source": [ - "final_df[\"differences_aggregate_denominator_sans_not_computed\"] = (\n", - " final_df[\"current_methodology_denominator\"] - final_df[\"T8_est1\"] \n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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      " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(12, 8))\n", - "plt.title('Distribution of differences between aggregate totals that normalizes tabulation of poverty households')\n", - "# Set x-axis label\n", - "plt.xlabel('Aggregate differences in total owner and renter occupied low-income households < 80%')\n", - "# Set y-axis label\n", - "plt.ylabel('Relative Frequency in Support')\n", - "\n", - "sns.histplot(final_df[\"differences_aggregate_denominator\"])" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
      " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(12, 8))\n", - "plt.title('Distribution of differences between aggregate totals that normalizes tabulation of poverty households (with removal of not computed fields) ')\n", - "# Set x-axis label\n", - "plt.xlabel('Aggregate differences in total owner and renter occupied low-income households')\n", - "# Set y-axis label\n", - "plt.ylabel('Relative Frequency in Support')\n", - "\n", - "sns.histplot(final_df[\"differences_aggregate_denominator_sans_not_computed\"])" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/sarahluw/.pyenv/versions/3.6.2/envs/my-virtual-env-3.6.2/lib/python3.6/site-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " after removing the cwd from sys.path.\n" - ] - } - ], - "source": [ - "final_df[\"current_methodology_percentile_rank\"] = final_df[\"current_methodology_percent\"].rank(\n", - " pct=True,\n", - " # Set ascending to the parameter value.\n", - " ascending=True,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/sarahluw/.pyenv/versions/3.6.2/envs/my-virtual-env-3.6.2/lib/python3.6/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " \"\"\"Entry point for launching an IPython kernel.\n", - "/Users/sarahluw/.pyenv/versions/3.6.2/envs/my-virtual-env-3.6.2/lib/python3.6/site-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " after removing the cwd from sys.path.\n" - ] - } - ], - "source": [ - "final_df[\"new_threshold_exceeded\"] = (final_df['hbrd_rank'] >= 90)\n", - "\n", - "final_df[\"current_threshold_exceeded\"] = (final_df[\n", - " 'current_methodology_percentile_rank'] >= 0.90)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Only include non-NA tracts for comparison purposes" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "# first save NA tracts that were considered unreliable\n", - "ineligible_tracts = list(final_df[final_df[\"hbrd_rank\"].isna()][\"FIPS_tract_id\"].values)" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "5243" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "len(ineligible_tracts)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### How many tracts are ineligible according to CalEnvironScreen but are considerd in Score L?\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "final_current_methodology = final_df[final_df[\"current_methodology_percentile_rank\"] >= 0.90]" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(7323, 15)" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "final_current_methodology.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(53, 15)" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# 53 tracts\n", - "final_current_methodology[\n", - " final_current_methodology.FIPS_tract_id.isin(ineligible_tracts)].shape" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "non_null_df = final_df.copy()" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(7323, 15)" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# number of tracks eligible\n", - "non_null_df[non_null_df[\"current_methodology_percentile_rank\"] >= 0.90].shape" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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      " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(12, 8))\n", - "plt.title(\"Distribution of Percentiles for Housing Burden (Score L)\")\n", - "# Set x-axis label\n", - "plt.xlabel('Percentile (although currently not represented as a percentage)')\n", - "# Set y-axis label\n", - "plt.ylabel('Relative Frequency in Support')\n", - "\n", - "sns.histplot(non_null_df[\"current_methodology_percentile_rank\"])" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "False 65733\n", - "True 7323\n", - "Name: current_threshold_exceeded, dtype: int64" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "non_null_df[\"current_threshold_exceeded\"].value_counts()" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "False 66255\n", - "True 6801\n", - "Name: new_threshold_exceeded, dtype: int64" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "non_null_df[\"new_threshold_exceeded\"].value_counts()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Score L Criterion as burden but not Calvironscreen" - ] - }, - { - "cell_type": "code", - "execution_count": 217, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(2794, 15)" - ] - }, - "execution_count": 217, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# this includes uineligible np.nan values too\n", - "predicate_1 = (non_null_df['current_threshold_exceeded'] == True) & (non_null_df['new_threshold_exceeded'] != True)\n", - "\n", - "non_null_df[predicate_1].shape" - ] - }, - { - "cell_type": "code", - "execution_count": 218, - "metadata": {}, - "outputs": [], - "source": [ - "# Where Score L is considered burdened but not in CalEnviroScreen\n", - "score_l_considered_burdened = non_null_df[predicate_1]" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [], - "source": [ - "import us\n", - "\n", - "mapping = us.states.mapping('fips', 'abbr')" - ] - }, - { - "cell_type": "code", - "execution_count": 219, - "metadata": {}, - "outputs": [], - "source": [ - "# Inserted after the basic stats definition.\n", - "# Load demographic data\n", - "import pathlib\n", - "\n", - "DATA_DIR = pathlib.Path.cwd().parent / \"data\"\n", - "COMPARISON_OUTPUTS_DIR = DATA_DIR / \"comparison_outputs\"\n", - "\n", - "demographics_path = DATA_DIR / \"dataset\" / \"census_acs_2019\" / \"usa.csv\"\n", - "\n", - "demographics_df = pd.read_csv(\n", - " demographics_path,\n", - " dtype={\"GEOID10_TRACT\": \"string\"},\n", - ")\n", - "\n", - "# Set some field names\n", - "BLACK_FIELD_NAME = \"Black or African American alone\"\n", - "AMERICAN_INDIAN_FIELD_NAME = \"American Indian and Alaska Native alone\"\n", - "ASIAN_FIELD_NAME = \"Asian alone\"\n", - "HAWAIIAN_FIELD_NAME = \"Native Hawaiian and Other Pacific alone\"\n", - "TWO_OR_MORE_RACES_FIELD_NAME = \"Two or more races\"\n", - "NON_HISPANIC_WHITE_FIELD_NAME = \"Non-Hispanic White\"\n", - "HISPANIC_FIELD_NAME = \"Hispanic or Latino\"\n", - "PERCENT_PREFIX = \"Percent \"\n", - "\n", - "RE_OUTPUT_FIELDS = [\n", - " BLACK_FIELD_NAME,\n", - " AMERICAN_INDIAN_FIELD_NAME,\n", - " ASIAN_FIELD_NAME,\n", - " HAWAIIAN_FIELD_NAME,\n", - " TWO_OR_MORE_RACES_FIELD_NAME,\n", - " NON_HISPANIC_WHITE_FIELD_NAME,\n", - " HISPANIC_FIELD_NAME,\n", - "]\n", - "\n", - "RE_PERCENT_OUTPUT_FIELDS = [PERCENT_PREFIX + field for field in RE_OUTPUT_FIELDS]\n", - "\n", - "columns_to_keep = (\n", - " [\"GEOID10_TRACT\"]\n", - " + RE_OUTPUT_FIELDS\n", - " + RE_PERCENT_OUTPUT_FIELDS\n", - " + ['Percent of individuals < 200% Federal Poverty Line', \n", - " 'Median value ($) of owner-occupied housing units',\n", - " 'Percent individuals age 25 or over with less than high school degree',\n", - " 'Percent enrollment in college or graduate school',\n", - " 'Linguistic isolation (percent)']\n", - ")\n", - "\n", - "\n", - "def highlight_medians(s):\n", - " # highlight if the current median is greater than the median of medians for that series\n", - " is_greater_than_median = s > s.median()\n", - " return ['color: pink; background-color:#7272FE'\n", - " if cell else '' for cell in is_greater_than_median]\n", - "# Join the demographics in.\n", - "merged_df_score_l = score_l_considered_burdened.merge(\n", - " demographics_df[columns_to_keep],\n", - " left_on=\"FIPS_tract_id\",\n", - " right_on=\"GEOID10_TRACT\",\n", - " how=\"inner\"\n", - ")\n", - "\n", - "# these are not converted into percent 0 - 100 scale\n", - "percent_cols = [x for x in merged_df_score_l.columns if \n", - " 'Percent' in x or '(percent)' in x\n", - " ]\n", - "\n", - "merged_df_score_l[\n", - " percent_cols] = merged_df_score_l[\n", - " percent_cols].apply(lambda x: x * 100)\n", - "\n", - "for idx, row in merged_df_score_l.iterrows():\n", - " current_row = str(merged_df_score_l.loc[idx, 'state'])\n", - " state = mapping.get(current_row, None)\n", - " merged_df_score_l.loc[idx, 'state_name'] = state\n", - "\n", - "grouped_stats_score_l = merged_df_score_l.groupby([\"state_name\"]).agg({\n", - " 'GEOID10_TRACT': 'nunique',\n", - " 'Percent of individuals < 200% Federal Poverty Line': [np.median, np.std],\n", - " 'Median value ($) of owner-occupied housing units': [np.median, np.std],\n", - " 'Percent individuals age 25 or over with less than high school degree': [np.median, np.std],\n", - " 'Percent enrollment in college or graduate school': [np.median, np.std],\n", - " 'Percent Black or African American alone': [np.median, np.std],\n", - " 'Percent American Indian and Alaska Native alone': [np.median, np.std],\n", - " 'Percent Non-Hispanic White': [np.median, np.std], \n", - " 'Linguistic isolation (percent)': [np.median, np.std],\n", - " 'Percent Hispanic or Latino': [np.median, np.std],\n", - " 'hbrd_rank': [np.median, np.std],\n", - " 'current_methodology_percent': [np.median, np.std],\n", - " 'current_summed_methodology': [np.median, np.std, np.sum]\n", - "}).reset_index()\n", - "\n", - "\n", - "grouped_stats_score_l.columns = [' '.join(col).strip() for \n", - " col in grouped_stats_score_l.columns.values]\n", - "\n", - "grouped_stats_score_l = grouped_stats_score_l[[x for x in grouped_stats_score_l \n", - " if \"median\" in x and \n", - " \"Percent\" in x] + [\"GEOID10_TRACT nunique\", \n", - " 'current_summed_methodology sum', \"state_name\"]]\n", - "\n", - "grouped_stats_score_l.set_index(\"state_name\", inplace=True)\n", - "\n", - "grouped_stats_score_l = grouped_stats_score_l.rename(columns={\n", - "'Percent of individuals < 200% Federal Poverty Line median'\n", - " : 'Percent of individuals < 200% Federal Poverty Line (median across all tracts)', \n", - "'Percent individuals age 25 or over with less than high school degree median':\n", - "'Percent individuals age 25 or over with less than high school degree (median across all tracts)', \n", - "'Percent enrollment in college or graduate school median'\n", - " :'Percent enrollment in college or graduate school (median across all tracts)',\n", - "'Percent Black or African American alone median':\n", - " 'Percent Black or African American alone (median across all tracts)',\n", - "'Percent American Indian and Alaska Native alone median':\n", - " 'Percent American Indian and Alaska Native alone (median across all tracts)',\n", - "'Percent Non-Hispanic White median':\n", - " 'Percent Non-Hispanic White (median across all tracts)',\n", - "'Percent Hispanic or Latino median':\n", - " 'Percent Hispanic or Latino (median across all tracts)',\n", - "'GEOID10_TRACT nunique': \"Total Number of Unique Tracts\",\n", - "\"current_summed_methodology sum\": \"Total Owned and Rented Burdened Households (Current Aggregation Methodology)\" \n", - "})" - ] - }, - { - "cell_type": "code", - "execution_count": 220, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['Percent of individuals < 200% Federal Poverty Line (median across all tracts)',\n", - " 'Percent individuals age 25 or over with less than high school degree (median across all tracts)',\n", - " 'Percent enrollment in college or graduate school (median across all tracts)',\n", - " 'Percent Black or African American alone (median across all tracts)',\n", - " 'Percent American Indian and Alaska Native alone (median across all tracts)',\n", - " 'Percent Non-Hispanic White (median across all tracts)',\n", - " 'Percent Hispanic or Latino (median across all tracts)',\n", - " 'Total Number of Unique Tracts',\n", - " 'Total Owned and Rented Burdened Households (Current Aggregation Methodology)'],\n", - " dtype='object')" - ] - }, - "execution_count": 220, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "grouped_stats_score_l.columns" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If the samples are samples from the population - a big assumption - in question and the sampling follows rules and things we take the mean:\n", - "\n", - "The mean of the median distribution (an order statistic) is the best estimate for the median. It's the best least unbiased estimator. The answer is not the median of that distribution.\n", - "\n", - "The answer is also not if the mean is an estimate of the median. Completely unrelated.\n", - "\n", - "Even if the original population is skewed, the distribution of a sampling statistic will be normalized - recall the the central limit theorem for more details\n", - "\n", - "The standard error of that mean should give you what you want to know to confidently make statements of the true population median across all states" - ] - }, - { - "cell_type": "code", - "execution_count": 221, - "metadata": {}, - "outputs": [], - "source": [ - "list_of_columns = list(grouped_stats_score_l.columns)\n", - "values_1 = list(grouped_stats_score_l[list_of_columns].mean())" - ] - }, - { - "cell_type": "code", - "execution_count": 222, - "metadata": {}, - "outputs": [], - "source": [ - "values_1.extend([True, False])" - ] - }, - { - "cell_type": "code", - "execution_count": 223, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[57.05272445515469,\n", - " 19.383812769449793,\n", - " 8.132533443213486,\n", - " 33.69280603472747,\n", - " 1.2213947311592683,\n", - " 31.32188596954498,\n", - " 19.378347515748676,\n", - " 62.08888888888889,\n", - " 40949.2,\n", - " True,\n", - " False]" - ] - }, - "execution_count": 223, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "values_1" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# CalenviroScreen Burden" - ] - }, - { - "cell_type": "code", - "execution_count": 224, - "metadata": {}, - "outputs": [], - "source": [ - "predicate_2 = (non_null_df['current_threshold_exceeded'] == False) & (non_null_df['new_threshold_exceeded'] == True)\n", - "\n", - "cal_ej_screen_burdened = non_null_df[predicate_2]" - ] - }, - { - "cell_type": "code", - "execution_count": 225, - "metadata": {}, - "outputs": [], - "source": [ - "# Join the demographics in.\n", - "merged_df_score_ejcreen = cal_ej_screen_burdened.merge(\n", - " demographics_df[columns_to_keep],\n", - " left_on=\"FIPS_tract_id\",\n", - " right_on=\"GEOID10_TRACT\",\n", - " how=\"inner\"\n", - ")\n", - "\n", - "# these are not converted into percent 0 - 100 scale\n", - "percent_cols = [x for x in merged_df_score_ejcreen.columns if \n", - " 'Percent' in x or '(percent)' in x\n", - " ]\n", - "\n", - "merged_df_score_ejcreen[\n", - " percent_cols] = merged_df_score_ejcreen[\n", - " percent_cols].apply(lambda x: x * 100)\n", - "\n", - "for idx, row in merged_df_score_ejcreen.iterrows():\n", - " current_row = str(merged_df_score_ejcreen.loc[idx, 'state'])\n", - " state = mapping.get(current_row, None)\n", - " merged_df_score_ejcreen.loc[idx, 'state_name'] = state\n", - "\n", - "grouped_stats_score_ej_screen = merged_df_score_ejcreen.groupby([\"state_name\"]).agg({\n", - " 'GEOID10_TRACT': 'nunique',\n", - " 'Percent of individuals < 200% Federal Poverty Line': [np.median, np.std],\n", - " 'Median value ($) of owner-occupied housing units': [np.median, np.std],\n", - " 'Percent individuals age 25 or over with less than high school degree': [np.median, np.std],\n", - " 'Percent enrollment in college or graduate school': [np.median, np.std],\n", - " 'Percent Black or African American alone': [np.median, np.std],\n", - " 'Percent American Indian and Alaska Native alone': [np.median, np.std],\n", - " 'Percent Non-Hispanic White': [np.median, np.std], \n", - " 'Linguistic isolation (percent)': [np.median, np.std],\n", - " 'Percent Hispanic or Latino': [np.median, np.std],\n", - " 'hbrd_rank': [np.median, np.std],\n", - " 'current_methodology_percent': [np.median, np.std],\n", - " 'current_summed_methodology': [np.median, np.std, np.sum]\n", - "}).reset_index()\n", - "\n", - "\n", - "grouped_stats_score_ej_screen.columns = [' '.join(col).strip() for \n", - " col in grouped_stats_score_ej_screen.columns.values]\n", - "\n", - "grouped_stats_score_ej_screen = grouped_stats_score_ej_screen[[x for x in grouped_stats_score_ej_screen \n", - " if \"median\" in x and \n", - " \"Percent\" in x] + [\"GEOID10_TRACT nunique\", \n", - " 'current_summed_methodology sum', \"state_name\"]]\n", - "\n", - "grouped_stats_score_ej_screen.set_index(\"state_name\", inplace=True)\n", - "\n", - "grouped_stats_score_ej_screen = grouped_stats_score_ej_screen.rename(columns={\n", - "'Percent of individuals < 200% Federal Poverty Line median'\n", - " : 'Percent of individuals < 200% Federal Poverty Line (median across all tracts)', \n", - "'Percent individuals age 25 or over with less than high school degree median':\n", - "'Percent individuals age 25 or over with less than high school degree (median across all tracts)', \n", - "'Percent enrollment in college or graduate school median'\n", - " :'Percent enrollment in college or graduate school (median across all tracts)',\n", - "'Percent Black or African American alone median':\n", - " 'Percent Black or African American alone (median across all tracts)',\n", - "'Percent American Indian and Alaska Native alone median':\n", - " 'Percent American Indian and Alaska Native alone (median across all tracts)',\n", - "'Percent Non-Hispanic White median':\n", - " 'Percent Non-Hispanic White (median across all tracts)',\n", - "'Percent Hispanic or Latino median':\n", - " 'Percent Hispanic or Latino (median across all tracts)',\n", - "'GEOID10_TRACT nunique': \"Total Number of Unique Tracts\",\n", - "\"current_summed_methodology sum\": \"Total Owned and Rented Burdened Households (Current Aggregation Methodology)\" \n", - "})" - ] - }, - { - "cell_type": "code", - "execution_count": 235, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(2272, 36)" - ] - }, - "execution_count": 235, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "merged_df_score_ejcreen.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 236, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "2272" - ] - }, - "execution_count": 236, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "grouped_stats_score_ej_screen['Total Number of Unique Tracts'].sum()" - ] - }, - { - "cell_type": "code", - "execution_count": 237, - "metadata": {}, - "outputs": [], - "source": [ - "values_2 = list(grouped_stats_score_ej_screen[list_of_columns].mean())" - ] - }, - { - "cell_type": "code", - "execution_count": 238, - "metadata": {}, - "outputs": [], - "source": [ - "values_2.extend([False, True])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Both Met Criterion" - ] - }, - { - "cell_type": "code", - "execution_count": 239, - "metadata": {}, - "outputs": [], - "source": [ - "predicate_3 = (non_null_df['current_threshold_exceeded'] == True) & (non_null_df['new_threshold_exceeded'] == True)\n", - "\n", - "union_df = non_null_df[predicate_3]" - ] - }, - { - "cell_type": "code", - "execution_count": 240, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(4529, 15)" - ] - }, - "execution_count": 240, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "union_df.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 241, - "metadata": {}, - "outputs": [], - "source": [ - "# Join the demographics in.\n", - "merged_df_both_met = union_df.merge(\n", - " demographics_df[columns_to_keep],\n", - " left_on=\"FIPS_tract_id\",\n", - " right_on=\"GEOID10_TRACT\",\n", - " how=\"inner\"\n", - ")\n", - "\n", - "# these are not converted into percent 0 - 100 scale\n", - "percent_cols = [x for x in merged_df_both_met.columns if \n", - " 'Percent' in x or '(percent)' in x\n", - " ]\n", - "\n", - "merged_df_both_met[\n", - " percent_cols] = merged_df_both_met[\n", - " percent_cols].apply(lambda x: x * 100)\n", - "\n", - "for idx, row in merged_df_both_met.iterrows():\n", - " current_row = str(merged_df_both_met.loc[idx, 'state'])\n", - " state = mapping.get(current_row, None)\n", - " merged_df_both_met.loc[idx, 'state_name'] = state\n", - "\n", - "grouped_stats_both_met = merged_df_both_met.groupby([\"state_name\"]).agg({\n", - " 'GEOID10_TRACT': 'nunique',\n", - " 'Percent of individuals < 200% Federal Poverty Line': [np.median, np.std],\n", - " 'Median value ($) of owner-occupied housing units': [np.median, np.std],\n", - " 'Percent individuals age 25 or over with less than high school degree': [np.median, np.std],\n", - " 'Percent enrollment in college or graduate school': [np.median, np.std],\n", - " 'Percent Black or African American alone': [np.median, np.std],\n", - " 'Percent American Indian and Alaska Native alone': [np.median, np.std],\n", - " 'Percent Non-Hispanic White': [np.median, np.std], \n", - " 'Linguistic isolation (percent)': [np.median, np.std],\n", - " 'Percent Hispanic or Latino': [np.median, np.std],\n", - " 'hbrd_rank': [np.median, np.std],\n", - " 'current_methodology_percent': [np.median, np.std],\n", - " 'current_summed_methodology': [np.median, np.std, np.sum]\n", - "}).reset_index()\n", - "\n", - "\n", - "grouped_stats_both_met.columns = [' '.join(col).strip() for \n", - " col in grouped_stats_both_met.columns.values]\n", - "\n", - "grouped_stats_both_met = grouped_stats_both_met[[x for x in grouped_stats_both_met \n", - " if \"median\" in x and \n", - " \"Percent\" in x] + [\"GEOID10_TRACT nunique\", \n", - " 'current_summed_methodology sum', \"state_name\"]]\n", - "\n", - "grouped_stats_both_met.set_index(\"state_name\", inplace=True)\n", - "\n", - "grouped_stats_both_met = grouped_stats_both_met.rename(columns={\n", - "'Percent of individuals < 200% Federal Poverty Line median'\n", - " : 'Percent of individuals < 200% Federal Poverty Line (median across all tracts)', \n", - "'Percent individuals age 25 or over with less than high school degree median':\n", - "'Percent individuals age 25 or over with less than high school degree (median across all tracts)', \n", - "'Percent enrollment in college or graduate school median'\n", - " :'Percent enrollment in college or graduate school (median across all tracts)',\n", - "'Percent Black or African American alone median':\n", - " 'Percent Black or African American alone (median across all tracts)',\n", - "'Percent American Indian and Alaska Native alone median':\n", - " 'Percent American Indian and Alaska Native alone (median across all tracts)',\n", - "'Percent Non-Hispanic White median':\n", - " 'Percent Non-Hispanic White (median across all tracts)',\n", - "'Percent Hispanic or Latino median':\n", - " 'Percent Hispanic or Latino (median across all tracts)',\n", - "'GEOID10_TRACT nunique': \"Total Number of Unique Tracts\",\n", - "\"current_summed_methodology sum\": \"Total Owned and Rented Burdened Households (Current Aggregation Methodology)\" \n", - "})" - ] - }, - { - "cell_type": "code", - "execution_count": 242, - "metadata": {}, - "outputs": [], - "source": [ - "values_3 = list(grouped_stats_both_met[list_of_columns].mean())" - ] - }, - { - "cell_type": "code", - "execution_count": 243, - "metadata": {}, - "outputs": [], - "source": [ - "values_3.extend([True, True])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Neither Met Criterion " - ] - }, - { - "cell_type": "code", - "execution_count": 244, - "metadata": {}, - "outputs": [], - "source": [ - "predicate_4 = (non_null_df[\n", - " 'current_threshold_exceeded'] == False) & (\n", - " non_null_df['new_threshold_exceeded'] == False)\n", - "\n", - "negation_union_df = non_null_df[predicate_4]" - ] - }, - { - "cell_type": "code", - "execution_count": 245, - "metadata": {}, - "outputs": [], - "source": [ - "# Join the demographics in.\n", - "merged_df_negation_union = negation_union_df.merge(\n", - " demographics_df[columns_to_keep],\n", - " left_on=\"FIPS_tract_id\",\n", - " right_on=\"GEOID10_TRACT\",\n", - " how=\"inner\"\n", - ")\n", - "\n", - "# these are not converted into percent 0 - 100 scale\n", - "percent_cols = [x for x in merged_df_negation_union.columns if \n", - " 'Percent' in x or '(percent)' in x\n", - " ]\n", - "\n", - "merged_df_negation_union[\n", - " percent_cols] = merged_df_negation_union[\n", - " percent_cols].apply(lambda x: x * 100)\n", - "\n", - "for idx, row in merged_df_negation_union.iterrows():\n", - " current_row = str(merged_df_negation_union.loc[idx, 'state'])\n", - " state = mapping.get(current_row, None)\n", - " merged_df_negation_union.loc[idx, 'state_name'] = state\n", - "\n", - "grouped_stats_both_not_met = merged_df_negation_union.groupby([\"state_name\"]).agg({\n", - " 'GEOID10_TRACT': 'nunique',\n", - " 'Percent of individuals < 200% Federal Poverty Line': [np.median, np.std],\n", - " 'Median value ($) of owner-occupied housing units': [np.median, np.std],\n", - " 'Percent individuals age 25 or over with less than high school degree': [np.median, np.std],\n", - " 'Percent enrollment in college or graduate school': [np.median, np.std],\n", - " 'Percent Black or African American alone': [np.median, np.std],\n", - " 'Percent American Indian and Alaska Native alone': [np.median, np.std],\n", - " 'Percent Non-Hispanic White': [np.median, np.std], \n", - " 'Linguistic isolation (percent)': [np.median, np.std],\n", - " 'Percent Hispanic or Latino': [np.median, np.std],\n", - " 'hbrd_rank': [np.median, np.std],\n", - " 'current_methodology_percent': [np.median, np.std],\n", - " 'current_summed_methodology': [np.median, np.std, np.sum]\n", - "}).reset_index()\n", - "\n", - "\n", - "grouped_stats_both_not_met.columns = [' '.join(col).strip() for \n", - " col in grouped_stats_both_not_met.columns.values]\n", - "\n", - "grouped_stats_both_not_met = grouped_stats_both_not_met[[x for x in grouped_stats_both_not_met \n", - " if \"median\" in x and \n", - " \"Percent\" in x] + [\"GEOID10_TRACT nunique\", \n", - " 'current_summed_methodology sum', \"state_name\"]]\n", - "\n", - "grouped_stats_both_not_met.set_index(\"state_name\", inplace=True)\n", - "\n", - "grouped_stats_both_not_met = grouped_stats_both_not_met.rename(columns={\n", - "'Percent of individuals < 200% Federal Poverty Line median'\n", - " : 'Percent of individuals < 200% Federal Poverty Line (median across all tracts)', \n", - "'Percent individuals age 25 or over with less than high school degree median':\n", - "'Percent individuals age 25 or over with less than high school degree (median across all tracts)', \n", - "'Percent enrollment in college or graduate school median'\n", - " :'Percent enrollment in college or graduate school (median across all tracts)',\n", - "'Percent Black or African American alone median':\n", - " 'Percent Black or African American alone (median across all tracts)',\n", - "'Percent American Indian and Alaska Native alone median':\n", - " 'Percent American Indian and Alaska Native alone (median across all tracts)',\n", - "'Percent Non-Hispanic White median':\n", - " 'Percent Non-Hispanic White (median across all tracts)',\n", - "'Percent Hispanic or Latino median':\n", - " 'Percent Hispanic or Latino (median across all tracts)',\n", - "'GEOID10_TRACT nunique': \"Total Number of Unique Tracts\",\n", - "\"current_summed_methodology sum\": \"Total Owned and Rented Burdened Households (Current Aggregation Methodology)\" \n", - "})" - ] - }, - { - "cell_type": "code", - "execution_count": 246, - "metadata": {}, - "outputs": [], - "source": [ - "values_4 = list(grouped_stats_both_not_met[list_of_columns].mean())\n", - "\n", - "values_4.extend([False, False])" - ] - }, - { - "cell_type": "code", - "execution_count": 247, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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      Percent of individuals < 200% Federal Poverty Line (median across all tracts)Percent individuals age 25 or over with less than high school degree (median across all tracts)Percent enrollment in college or graduate school (median across all tracts)Percent Black or African American alone (median across all tracts)Percent American Indian and Alaska Native alone (median across all tracts)Percent Non-Hispanic White (median across all tracts)Percent Hispanic or Latino (median across all tracts)Total Number of Unique TractsTotal Owned and Rented Burdened Households (Current Aggregation Methodology)
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      Percent of individuals < 200% Federal Poverty Line (median across all tracts) Percent individuals age 25 or over with less than high school degree (median across all tracts) Percent enrollment in college or graduate school (median across all tracts) Percent Black or African American alone (median across all tracts) Percent American Indian and Alaska Native alone (median across all tracts) Percent Non-Hispanic White (median across all tracts) Percent Hispanic or Latino (median across all tracts) Total Number of Unique Tracts Total Owned and Rented Burdened Households (Current Aggregation Methodology)
      state_name
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      AR5.4320521.6018613.94275420.3190490.102459-23.263854-0.896646-35.000000-16843.000000
      AZ13.59487111.979424-0.2720401.361388-0.915677-8.9533618.757832-39.000000-23253.000000
      CA15.71759816.799587-0.7029510.2476470.415820-21.87051940.216194783.000000542144.000000
      CO5.2679630.8162070.0893705.3386490.149809-24.42590310.133080-34.000000-17296.000000
      CT20.3379166.8652140.574592-7.9523960.000000-9.3842333.73425348.00000033786.000000
      DC20.2256199.277701-10.58936465.9467630.000000-49.789447-6.12477713.0000009673.000000
      DE1.370275-1.231413-4.499021-27.8068720.5818989.5720516.704117-8.000000-2816.000000
      FL12.7986417.913089-0.46445013.4531160.000000-19.5679552.96004488.00000071042.000000
      GA0.6239272.733393-0.7999511.511527-0.093487-9.6807102.172740-3.00000012615.000000
      HI4.8971419.1014610.265372-0.896060-0.094805-35.9393850.3791457.0000004771.000000
      IA10.953716-0.708470-4.742017-1.6234740.73201219.552402-7.997991-53.000000-25766.000000
      ID4.7221459.6434937.189788-0.311804-1.015539-15.99256919.816490-23.000000-16597.000000
      IL5.1553795.303919-0.6307145.0376000.000000-25.0494806.05043593.00000054402.000000
      IN2.1136622.4372930.7196577.3157130.0000001.3477450.492553-36.000000-16444.000000
      KS5.264502-7.5629430.62746012.2458900.4666341.465143-0.964424-46.000000-21169.000000
      KY8.7944366.6839700.862572-0.5751220.578035-6.1331015.455541-57.000000-27542.000000
      LA8.3431173.4948501.3988047.9643770.000000-11.833643-1.60731027.00000019739.000000
      MA8.9367727.7326661.0050288.4156980.000000-19.35966715.17047538.00000028222.000000
      MD9.1005303.424516-1.34189921.8483530.000000-10.271062-2.199229-26.000000-8900.000000
      ME12.5650099.122611-3.922820-0.880265-0.439467-5.913473-0.866007-25.000000-14441.000000
      MI6.4537851.0685880.6898326.7088500.000000-8.072578-1.350049-23.000000-6868.000000
      MN15.92096517.7407803.14160722.4983201.758343-38.33239622.509297-87.000000-46742.000000
      MO4.976759-0.085646-3.57740847.1020900.000000-35.919908-2.378652-30.000000-13248.000000
      MS8.4848791.040226-0.57274424.8631840.000000-22.9510000.051741-27.000000-12524.000000
      MT0.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.000000
      NC8.15338411.292289-2.8119826.4963310.078513-19.50466811.151508-71.000000-45011.000000
      ND0.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.000000
      NE7.48434337.676269-9.271518-7.5629470.634817-37.67089358.015686-27.000000-13452.000000
      NH11.3336927.394888-1.6955912.0796890.000000-14.2165729.124264-16.000000-11851.000000
      NJ5.9273278.713014-2.337923-10.3411420.000000-5.62311725.764242151.000000102626.000000
      NM0.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.000000
      NV7.4854532.632552-0.783104-0.5003270.0769852.5120228.68748512.00000014693.000000
      NY12.3690888.434444-0.870792-10.4370370.0000004.47756715.929982573.000000407383.000000
      OH5.1664312.6264700.16094516.3221080.000000-16.4005980.628654-21.000000355.000000
      OK17.2678635.687201-2.7335255.227426-1.254840-4.62973913.684527-60.000000-25920.000000
      OR10.3127027.677240-0.6675781.177527-0.110343-15.16862316.277201-35.000000-26320.000000
      PA9.5685676.789819-1.1000452.7685510.000000-7.1880586.730014-63.000000-33293.000000
      RI28.09766518.885313-28.0646809.5690740.344864-42.40429539.7406705.0000004138.000000
      SC3.7095082.3040213.76689219.0487500.000000-12.7341471.243649-43.000000-22081.000000
      SD0.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.000000
      TN8.6929494.908793-1.48778131.1622240.000000-26.6405142.135541-31.000000-13021.000000
      TX8.2296324.785835-0.0184457.6089030.000000-6.99150311.472775-103.000000-58670.000000
      UT12.4011359.2794802.393714-1.0952252.211103-27.44416833.552393-31.000000-18553.000000
      VA12.7479834.210496-2.29480120.5814870.000000-12.1926752.823834-48.000000-24962.000000
      VT0.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.000000
      WA0.8368433.285992-2.8539851.3650100.242227-14.7086995.595930-84.000000-61277.000000
      WI15.5521194.422237-0.79604740.591511-0.526995-20.282972-3.363747-17.000000-8795.000000
      WV0.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.000000
      WY0.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.000000
      " - ], - "text/plain": [ - "" - ] - }, - "execution_count": 279, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "def style_negative(v, props=''):\n", - " return props if v < 0 else None\n", - "\n", - "diff_style = diff.style.applymap(style_negative, props='color:red;')\\\n", - " .applymap(lambda v: 'opacity: 20%;' if (v < 0.0) and (v > 0.0) else None)\n", - "diff_style" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.2" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/data/data-pipeline/data_pipeline/ipython/hud_eda_se_12_27_2011_relative_differences_between_methodologies-ranking-percentile.ipynb b/data/data-pipeline/data_pipeline/ipython/hud_eda_se_12_27_2011_relative_differences_between_methodologies-ranking-percentile.ipynb deleted file mode 100644 index 3ca6fd6a..00000000 --- a/data/data-pipeline/data_pipeline/ipython/hud_eda_se_12_27_2011_relative_differences_between_methodologies-ranking-percentile.ipynb +++ /dev/null @@ -1,5701 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Methodology to address fundamental problem 1 itemized in Issue 1024 - follow-up compare tabulations and relative household burden. This time I extend the 12-11 notebook to look at how the percentile ranks affects the proportion of tracts considered as burdened versus the current methodology." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Indicator reviewed: \n", - "\n", - "Socioeconomic Factors Indicator reviewed\n", - "* [Extreme Housing Burden](#housingburden)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Packages" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import math\n", - "import numpy as np\n", - "import os\n", - "import pandas as pd\n", - "\n", - "import seaborn as sns\n", - "import matplotlib.pyplot as plt\n", - "import pandas as pd\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### ETL process for acquiring relevant tables" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### NOTE: If you ran the ETL Process to acquire Table 8 in the other notebook of this draft PR you do not need to run the ETL cell block again" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# Copy and adapt certain sections of code from data_pipeline.utils \n", - "\n", - "def download_hud_dataset():\n", - " DOWNLOAD_FILENAME = \"HUD_ZIPPED.csv\"\n", - " HOUSING_FTP_URL = \"https://www.huduser.gov/portal/datasets/cp/2014thru2018-140-csv.zip\" \n", - " response = requests.get(HOUSING_FTP_URL, verify=True)\n", - " if response.status_code == 200:\n", - " file_contents = response.content\n", - " else:\n", - " sys.exit(\n", - " f\"HTTP response {response.status_code} from url {file_url}. Info: {response.content}\"\n", - " )\n", - "\n", - " # Write the contents to disk.\n", - " file = open(DOWNLOAD_FILENAME, \"wb\")\n", - " file.write(file_contents)\n", - " file.close()\n", - " \n", - "def extract_zipped_download(zip_file_path, unzipped_path):\n", - " with zipfile.ZipFile(zip_file_path, \"r\") as zip_ref:\n", - " zip_ref.extractall(unzipped_path)\n", - " # cleanup temporary file\n", - " os.remove(zip_file_path)\n", - " \n", - "def up_one_directory(path):\n", - " try:\n", - " # from Python 3.6\n", - " parent_dir = Path(path).parents[1]\n", - " # for Python 3.4/3.5, use str to convert the path to string\n", - " # parent_dir = str(Path(path).parents[1])\n", - " shutil.move(path, parent_dir)\n", - " except IndexError:\n", - " # no upper directory\n", - " pass\n", - "\n", - "CURRENT_DIRECTORY = os.getcwd()\n", - "download_hud_dataset()\n", - "extract_zipped_download(CURRENT_DIRECTORY + \"/HUD_ZIPPED.csv\", CURRENT_DIRECTORY) \n", - "up_one_directory(CURRENT_DIRECTORY + \"/140/Table8.csv\")\n", - "shutil.rmtree(\"./140/\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Extreme Housing Burden \n", - "\n", - "The Extreme Housing Burden indicator represents the proportion of low-income households that have to spend more than half their income on rent. These households experience higher levels of stress, report lower health, and may delay medical treatment because of its high cost.\n", - "\n", - "The Extreme Housing Burden indicator measures the percent of households in a census tract that are:\n", - "\n", - "1. Making less than 80% of the Area Median Family Income as determined by the Department of Housing and Urban Development (HUD), and\n", - "2. Paying greater than 50% of their income to housing costs. \n", - "\n", - "This data is sourced from the 2014-2018 Comprehensive Housing Affordability Strategy dataset from the Department of Housing and Urban Development (HUD) using the census tract geographic summary level, and contains cost burdens for households by percent HUD-adjusted median family income (HAMFI) category. This data can be found [here](https://www.huduser.gov/portal/datasets/cp.html). \n", - "\n", - "Because CHAS data is based on American Communities Survey (ACS) estimates, which come from a sample of the population, they may be unreliable if based on a small sample or population size.\n", - "\n", - "The standard error and relative standard error were used to evaluate the reliability of each estimate using CalEnviroScreen’s methodology. \n", - "\n", - "Census tract estimates that met either of the following criteria were considered reliable and included in the analysis [(CalEnviroScreen, 2017, page 129)](https://oehha.ca.gov/media/downloads/calenviroscreen/report/ces3report.pdf ):\n", - "\n", - "- Relative standard error less than 50 (meaning the standard error was less than half of the estimate), OR \n", - "- Standard error less than the mean standard error of all census tract estimates \n", - "\n", - "Formulas for calculating the standard error of sums, proportions, and ratio come from the [American Communities Survey Office](https://www2.census.gov/programs-surveys/acs/tech_docs/accuracy/MultiyearACSAccuracyofData2013.pdf).\n", - "\n", - "Note that this code creates a score and rank by state, for every state." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The relevant variables in table 8 of the CHAS dataset are the following (CHAS data dictionary available [here](https://www.huduser.gov/portal/datasets/cp/CHAS-data-dictionary-14-18.xlsx)):\n", - "\n", - "| Name | Label |\n", - "|---------|-----------------------------------------------------|\n", - "|T1_est1 | Total Occupied housing units | \n", - "|T8_est10 | Owner occupied less than or equal to 30% of HAMFI cost burden greater than 50% |\n", - "|T8_est23 |Owner occupied greater than 30% but less than or equal to 50% of HAMFI\tcost burden greater than 50%|\n", - "|T8_est36 |Owner occupied\tgreater than 50% but less than or equal to 80% of HAMFI\tcost burden greater than 50%|\n", - "|T8_est76 | Renter occupied less than or equal to 30% of HAMFI cost burden greater than 50% |\n", - "|T8_est89 |Renter occupied\tgreater than 30% but less than or equal to 50% of HAMFI\tcost burden greater than 50%|\n", - "|T8_est102|Renter occupied\tgreater than 50% but less than or equal to 80% of HAMFI\tcost burden greater than 50%|\n", - " " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Below I also propose an alternate means for ranking census tracts\n", - "### These steps are outlined and commented below" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/sarahluw/.pyenv/versions/3.6.2/envs/my-virtual-env-3.6.2/lib/python3.6/site-packages/pandas/core/series.py:726: RuntimeWarning: invalid value encountered in sqrt\n", - " result = getattr(ufunc, method)(*inputs, **kwargs)\n", - "/Users/sarahluw/.pyenv/versions/3.6.2/envs/my-virtual-env-3.6.2/lib/python3.6/site-packages/pandas/core/indexing.py:670: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " iloc._setitem_with_indexer(indexer, value)\n" - ] - } - ], - "source": [ - "# Read in the data from https://www.huduser.gov/portal/datasets/cp.html\n", - "housing = pd.read_csv(\"Table8.csv\", \n", - " encoding = \"ISO-8859-1\", \n", - " dtype = {'Tract_ID': object, 'st': object, 'geoid': object})\n", - "\n", - "# Remove data for states that aren't included in the census (e.g. American Samoa, Guam, etc.):\n", - "housing.drop(housing.loc[housing['st'] == '72'].index, inplace = True)\n", - "\n", - "# Combine owner and renter occupied low-income households that make less than 80% of HAMFI into one variable\n", - "housing['summed'] = (housing['T8_est10'] + \n", - " housing['T8_est23'] + \n", - " housing['T8_est36'] + \n", - " housing['T8_est76'] + \n", - " housing['T8_est89'] + \n", - " housing['T8_est102'])\n", - "\n", - "# Create a variable for the standard error of the summed variables\n", - "housing['summed_se'] = np.sqrt((housing['T8_moe10'] / 1.645)**2 + \n", - " (housing['T8_moe23'] / 1.645)**2 + \n", - " (housing['T8_moe36'] / 1.645)**2 + \n", - " (housing['T8_moe76'] / 1.645)**2 + \n", - " (housing['T8_moe89'] / 1.645)**2 + \n", - " (housing['T8_moe102'] / 1.645)**2)\n", - "\n", - "# Remove the first 7 digits in the FIPS Census Tract ID \n", - "housing['geoid'] = housing['geoid'].str[-11:]\n", - "\n", - "# Find the estimate of the proportion of the population that is heavily rent burdened\n", - "housing['hbrd_score'] = housing['summed'] / housing['T8_est1']\n", - "\n", - "# Change rates where the population is 0 to nan\n", - "housing['hbrd_score'].replace(np.inf, np.nan, inplace = True)\n", - "\n", - "# Create function for calculating the standard error, using the proportions standard error formula\n", - "# if the value under the radical is negative, use the ratio standard error formula\n", - "def se_prop(x, y, se_x, moe_y): \n", - " se_y = moe_y / 1.645\n", - " test = se_x**2 - (((x**2)/(y**2))*((se_y)**2))\n", - " se = np.where(test < 0,\n", - " (1/y) * np.sqrt(se_x**2 + (((x**2)/(y**2))*(se_y**2))), \n", - " (1/y) * np.sqrt(se_x**2 - (((x**2)/(y**2))*(se_y**2))))\n", - " return se\n", - "\n", - "housing['se'] = se_prop(housing['summed'], housing['T8_est1'], housing['summed_se'], housing['T8_moe1'])\n", - "\n", - "# Calculate the relative standard error\n", - "housing['rse'] = housing['se'] / housing['hbrd_score']*100\n", - "\n", - "# Change infinite rse's where the housing burden is 0 to np.nan\n", - "housing['rse'].replace(np.inf, np.nan, inplace = True)\n", - "\n", - "# Calculate the mean standard error for each state\n", - "housing['mean_state_se'] = np.zeros(len(housing))\n", - "\n", - "for state in housing['st'].unique():\n", - " mean_se = np.mean(housing[housing['st'] == state]['se'])\n", - " housing['mean_state_se'].loc[housing['st'] == state] = mean_se\n", - " \n", - "# Find census tract estimates that meet both of the following criteria and are thus considered unreliable estimates: \n", - "# RSE less than 50 AND\n", - "# SE less than the mean state SE or housing burdened low income households\n", - "# Convert these scores to nan\n", - "housing.loc[(housing['rse'] >= 50) & (housing['rse'] >= housing['mean_state_se']), 'hbrd_score'] = np.nan\n", - "\n", - "# Rename columns\n", - "housing = housing.rename(columns = {'geoid' :'FIPS_tract_id',\n", - " 'st' : 'state'\n", - " })\n", - "\n", - "# Calculate percentile rank for census tracts with a score above 0, set percentile to 0 if score is 0, for each state\n", - "housing['hbrd_rank'] = housing[\n", - " housing['hbrd_score'] != 0][['hbrd_score',\n", - " 'state']].groupby('state').rank( \n", - " na_option = 'keep', \n", - " pct = True) * 100\n", - "\n", - "housing.loc[housing['hbrd_score'] == 0, 'hbrd_rank'] = 0\n", - "\n", - "# Create final housing burden df\n", - "housingburden = housing.copy()" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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      sourcesumlevelFIPS_tract_idnamestatecntytractT8_est1T8_est2T8_est3...T8_moe131T8_moe132T8_moe133summedsummed_sehbrd_scoresersemean_state_sehbrd_rank
      02014thru201814001001020100Census Tract 201, Autauga County, Alabama0112010076557050...1212128031.7218070.1045750.04103239.2373140.03660446.298077
      12014thru201814001001020200Census Tract 202, Autauga County, Alabama0112020072046565...12121213845.5318740.1916670.06161432.1466590.03660483.269231
      22014thru201814001001020300Census Tract 203, Autauga County, Alabama01120300129584060...12121217053.7229210.1312740.04092731.1769990.03660463.653846
      32014thru201814001001020400Census Tract 204, Autauga County, Alabama011204001640126015...12121214546.2885100.0884150.02782231.4673970.03660434.615385
      42014thru201814001001020500Census Tract 205, Autauga County, Alabama0112050041752320175...171717595147.2216930.1425150.03476024.3901930.03660468.221154
      \n", - "

      5 rows Ă— 280 columns

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      " - ], - "text/plain": [ - " source sumlevel FIPS_tract_id \\\n", - "0 2014thru2018 140 01001020100 \n", - "1 2014thru2018 140 01001020200 \n", - "2 2014thru2018 140 01001020300 \n", - "3 2014thru2018 140 01001020400 \n", - "4 2014thru2018 140 01001020500 \n", - "\n", - " name state cnty tract T8_est1 \\\n", - "0 Census Tract 201, Autauga County, Alabama 01 1 20100 765 \n", - "1 Census Tract 202, Autauga County, Alabama 01 1 20200 720 \n", - "2 Census Tract 203, Autauga County, Alabama 01 1 20300 1295 \n", - "3 Census Tract 204, Autauga County, Alabama 01 1 20400 1640 \n", - "4 Census Tract 205, Autauga County, Alabama 01 1 20500 4175 \n", - "\n", - " T8_est2 T8_est3 ... T8_moe131 T8_moe132 T8_moe133 summed summed_se \\\n", - "0 570 50 ... 12 12 12 80 31.721807 \n", - "1 465 65 ... 12 12 12 138 45.531874 \n", - "2 840 60 ... 12 12 12 170 53.722921 \n", - "3 1260 15 ... 12 12 12 145 46.288510 \n", - "4 2320 175 ... 17 17 17 595 147.221693 \n", - "\n", - " hbrd_score se rse mean_state_se hbrd_rank \n", - "0 0.104575 0.041032 39.237314 0.036604 46.298077 \n", - "1 0.191667 0.061614 32.146659 0.036604 83.269231 \n", - "2 0.131274 0.040927 31.176999 0.036604 63.653846 \n", - "3 0.088415 0.027822 31.467397 0.036604 34.615385 \n", - "4 0.142515 0.034760 24.390193 0.036604 68.221154 \n", - "\n", - "[5 rows x 280 columns]" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "housingburden.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(73056, 280)" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "housingburden.shape" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### As desired we see a uniform distribution for the percentile rank for burdened households" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Now we compute for a baseline comparison " - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# Owner occupied numerator fields\n", - "OWNER_OCCUPIED_NUMERATOR_FIELDS = [\n", - " # Column Name\n", - " # Line_Type\n", - " # Tenure\n", - " # Household income\n", - " # Cost burden\n", - " # Facilities\n", - " \"T8_est7\",\n", - " # Subtotal\n", - " # Owner occupied\n", - " # less than or equal to 30% of HAMFI\n", - " # greater than 30% but less than or equal to 50%\n", - " # All\n", - " \"T8_est10\",\n", - " # Subtotal\n", - " # Owner occupied\n", - " # less than or equal to 30% of HAMFI\n", - " # greater than 50%\n", - " # All\n", - " \"T8_est20\",\n", - " \n", - " # Subtotal\n", - " # Owner occupied\n", - " # greater than 30% but less than or equal to 50% of HAMFI\n", - " # greater than 30% but less than or equal to 50%\n", - " # All\n", - " \"T8_est23\",\n", - " # Subtotal\n", - " # Owner occupied\n", - " # greater than 30% but less than or equal to 50% of HAMFI\n", - " # greater than 50%\n", - " # All\n", - " \"T8_est33\",\n", - " # Subtotal\n", - " # Owner occupied\n", - " # greater than 50% but less than or equal to 80% of HAMFI\n", - " # greater than 30% but less than or equal to 50%\n", - " # All\n", - " \"T8_est36\",\n", - " # Subtotal\n", - " # Owner occupied\n", - " # greater than 50% but less than or equal to 80% of HAMFI\n", - " # greater than 50%\n", - " # All\n", - "]\n", - "\n", - "# These rows have the values where HAMFI was not computed, b/c of no or negative income.\n", - "OWNER_OCCUPIED_NOT_COMPUTED_FIELDS = [\n", - " # Column Name\n", - " # Line_Type\n", - " # Tenure\n", - " # Household income\n", - " # Cost burden\n", - " # Facilities\n", - " \"T8_est13\",\n", - " # Subtotal\n", - " # Owner occupied\n", - " # less than or equal to 30% of HAMFI\n", - " # not computed (no/negative income)\n", - " # All\n", - " \"T8_est26\",\n", - " # Subtotal\n", - " # Owner occupied\n", - " # greater than 30% but less than or equal to 50% of HAMFI\n", - " # not computed (no/negative income)\n", - " # All\n", - " \"T8_est39\",\n", - " # Subtotal\n", - " # Owner occupied\n", - " # greater than 50% but less than or equal to 80% of HAMFI\n", - " # not computed (no/negative income)\n", - " # All\n", - " \"T8_est52\",\n", - " # Subtotal\n", - " # Owner occupied\n", - " # greater than 80% but less than or equal to 100% of HAMFI\n", - " # not computed (no/negative income)\n", - " # All\n", - " \"T8_est65\",\n", - " # Subtotal\n", - " # Owner occupied\n", - " # greater than 100% of HAMFI\n", - " # not computed (no/negative income)\n", - " # All\n", - "]\n", - "\n", - "OWNER_OCCUPIED_POPULATION_FIELD = \"T8_est2\"\n", - "# Subtotal\n", - "# Owner occupied\n", - "# All\n", - "# All\n", - "# All\n", - "\n", - "OWNER_OCCUPIED_POPULATION_HAMFI_FIELD = \"T8_est3\"\n", - "# Subtotal\n", - "# Owner occupied \n", - "# All\n", - "# All\n", - "# All\n", - "\n", - "# Renter occupied numerator fields\n", - "RENTER_OCCUPIED_NUMERATOR_FIELDS = [\n", - " # Column Name\n", - " # Line_Type\n", - " # Tenure\n", - " # Household income\n", - " # Cost burden\n", - " # Facilities\n", - " \"T8_est73\",\n", - " # Subtotal\n", - " # Renter occupied\n", - " # less than or equal to 30% of HAMFI\n", - " # greater than 30% but less than or equal to 50%\n", - " # All\n", - " \"T8_est76\",\n", - " # Subtotal\n", - " # Renter occupied\n", - " # less than or equal to 30% of HAMFI\n", - " # greater than 50%\n", - " # All\n", - " \"T8_est86\",\n", - " # Subtotal\n", - " # Renter occupied\n", - " # greater than 30% but less than or equal to 50% of HAMFI\n", - " # greater than 30% but less than or equal to 50%\n", - " # All\n", - " \"T8_est89\",\n", - " # Subtotal\n", - " # Renter occupied\n", - " # greater than 30% but less than or equal to 50% of HAMFI\n", - " # greater than 50%\n", - " # All\n", - " \"T8_est99\",\n", - " # Subtotal\n", - " # Renter occupied\tgreater than 50% but less than or equal to 80% of HAMFI\n", - " # greater than 30% but less than or equal to 50%\n", - " # All\n", - " \"T8_est102\",\n", - " # Subtotal\n", - " # Renter occupied\n", - " # greater than 50% but less than or equal to 80% of HAMFI\n", - " # greater than 50%\n", - " # All\n", - "]\n", - "\n", - "# These rows have the values where HAMFI was not computed, b/c of no or negative income.\n", - "RENTER_OCCUPIED_NOT_COMPUTED_FIELDS = [\n", - " # Column Name\n", - " # Line_Type\n", - " # Tenure\n", - " # Household income\n", - " # Cost burden\n", - " # Facilities\n", - " \"T8_est79\",\n", - " # Subtotal\n", - " # Renter occupied\tless than or equal to 30% of HAMFI\n", - " # not computed (no/negative income)\n", - " # All\n", - " \"T8_est92\",\n", - " # Subtotal\n", - " # Renter occupied\tgreater than 30% but less than or equal to 50% of HAMFI\n", - " # not computed (no/negative income)\n", - " # All\n", - " \"T8_est105\",\n", - " # Subtotal\n", - " # Renter occupied\n", - " # greater than 50% but less than or equal to 80% of HAMFI\n", - " # not computed (no/negative income)\n", - " # All\n", - " \"T8_est118\",\n", - " # Subtotal\n", - " # Renter occupied\tgreater than 80% but less than or equal to 100% of HAMFI\n", - " # not computed (no/negative income)\n", - " # All\n", - " \"T8_est131\",\n", - " # Subtotal\n", - " # Renter occupied\n", - " # greater than 100% of HAMFI\n", - " # not computed (no/negative income)\n", - " # All\n", - "]\n", - "\n", - "# T8_est68\tSubtotalRenter occupied\tAll\tAll\tAll\n", - "RENTER_OCCUPIED_POPULATION_FIELD = \"T8_est68\"" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "housingburden[\"current_summed_methodology\"] = housingburden[\n", - " OWNER_OCCUPIED_NUMERATOR_FIELDS\n", - "].sum(axis=1) + housingburden[RENTER_OCCUPIED_NUMERATOR_FIELDS].sum(axis=1)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "housingburden[\"current_methodology_denominator\"] = (\n", - " housingburden[OWNER_OCCUPIED_POPULATION_FIELD]\n", - " + housingburden[RENTER_OCCUPIED_POPULATION_FIELD]\n", - " - housingburden[OWNER_OCCUPIED_NOT_COMPUTED_FIELDS].sum(axis=1)\n", - " - housingburden[RENTER_OCCUPIED_NOT_COMPUTED_FIELDS].sum(axis=1)\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "housingburden[\"current_methodology_denominator_sans_not_computed\"] = (\n", - " housingburden[OWNER_OCCUPIED_POPULATION_FIELD]\n", - " + housingburden[RENTER_OCCUPIED_POPULATION_FIELD]\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "housingburden[\"current_methodology_percent\"] = np.round(\n", - " (housingburden[\"current_summed_methodology\"] / housingburden[\"current_methodology_denominator\"] ), 2) * 100" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Now we construct the distribution of differences in the number of owned and rented burdened households\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Percentiles Comparison" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "final_df = housingburden[['FIPS_tract_id', 'state','hbrd_rank','hbrd_score', 'summed', \n", - " 'current_summed_methodology', 'T8_est1', \n", - " \"current_methodology_denominator_sans_not_computed\",\n", - " 'current_methodology_denominator', 'current_methodology_percent']]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### First notice here that **T8_est1** and **current_methodology_denominator** should represent same or similar aggregates. In general, we cen see that the current computation performed results in a differerntial that undercounts the total occupied and rental households." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/sarahluw/.pyenv/versions/3.6.2/envs/my-virtual-env-3.6.2/lib/python3.6/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " \n" - ] - } - ], - "source": [ - "final_df[\"differences_aggregate_denominator\"] = (\n", - " final_df[\"current_methodology_denominator\"] - final_df[\"T8_est1\"] \n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/sarahluw/.pyenv/versions/3.6.2/envs/my-virtual-env-3.6.2/lib/python3.6/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " \n" - ] - } - ], - "source": [ - "final_df[\"differences_aggregate_denominator_sans_not_computed\"] = (\n", - " final_df[\"current_methodology_denominator\"] - final_df[\"T8_est1\"] \n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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zdX+tEhGbRcRzekx/H+UL5nMR8WCAiNggInbuY133UbrPfDYiHl5bSZ8WEatTtv3FEbFzLX9AlPuablhbCXetJ7e7KP2c7xtjVU+OiJdFuavDO+o8Z1K6dtwWEe+PiDXqeh5X/0GE7vv/15SfJbcFzsrMSykntKdSfoKkj+V+hbKvH1Hra15E7DpeffWwFqXl+R8RsS0l0R11HKUOnx4Rq1FaP5oJxrHAByNibkRsQEkSxtJzn2TmnykXIO0XEatFxNOAF3fEeRel9W4O5bPS1PnZHq8OO3XO/1tKkvS+iJgdEdvXeI7pMf2alC+lmwAi4jWUlsplRMRTovzyMJvypfwPeh9/Ez1nrUU5nm+p+6QzoZzsOXAqzjPj6bsOm2qr9fGULomdjTJfoRyjW9Vp14mIl9fh5dkPa1ESq5uAVSPiY0Bn63mv8waUbiT/rx6Xu1D+Ae9mvDq5Adiwfj67ORp4TUQ8qZ4X/xP4bWb+qcf0Yzka+Eg936xP6aI20X3/0YiYU/fHaygXqfaz7GOAnYA3sqT1G8Y4p4wRwzKfgcy8Bjib0vL9vcYvKBM11nf6mPlCRPxrRMyr32uLavF9k9zGUccC/xwRO9Z1vptyHP66x7Sj5/MNKd3FRmN7dETsUI+hf7Dkxg7TzgR88H4UEbdR/hP8MOV+o6/pMe0WlDso3E65qOHLmfnLOu6/KB/yRRHxngms/yhKv7rrKV0e3gaQmbdQ+lt9nfIf5R2UnxlHfbf+/VtEnNdluYfVZZ9OuUDnHzQO+gl6a13/lZRfBr5dlz+uzPwZpe/vKZTuO6dMMoZR8ykXrl1G6Rd5HEv/XNbp/XW9Z0b52fx/KUlqP95DuXvB2ZSflz9J6c95NaW15EOUL6+rKcnIKvX1LkrrwELKl98bx1jH8ZQuKyPAvwEvy8x7akLyIuBJlP13M+VYWKfOt8z+r91fzgMuzcy76/jfAH/OepvIPpb7BUqr9S/q5+JMSgI/GW8C9q/L+RjlJEyN41LKcXUMpXXkdko/wLvqJPtTjverKPvsuMa4ZYyzTwBeTen/P3oHje80lnck5SfRaynH1Zks7VBgy/rZ/mEfddipc/67KQn3C+q8XwbmZ+bvekx/GeVuPL+hfME/nnJXgm7WpvzTOcKSO8z8d49pvwDsHhEjEXFQj2ma/oNywd0twE8oF4E1TfYcOGrS55nxTLAOm7amnC8+F+UhMLdHxO11mT+gnBOOqeeWSyj7FJZvP5xI6Sr3+zrvP1i2q0PX80Yd93bK8bWIctz/sNtK+6iTUyh3uLg+Im7uMv//UvoBf4/yGd4M2KPHNo7n45R/ki+inHPPq2UTcRrlXH8y8OnM/EU/y64NO7+hXGT/nUb5eOeUbnp9po6g1O943U/G0vM7vY98YRfg0nrsfoFy3c7fJ7mN1HVeQfkV7IuU89iLKbd1vrvL5P9BOZavojSiNethdcpFmjdT8qAHU66pmXajd9iQpKFVW3QWAVtk5lVdxr+R8qXRqzVvouv7DvC7zJz0LzKSpl9EbMKSu8BM6BfetkTEsymtzY9Ik7oZwxZwSUMpIl5cfzJek3KLwIupF0BGxMMi4hlRuhk9mvLz5g+WY11PidJdaZX6s/yuTL4vpiT1pXbPeDvwdZPvmcUEXNKw2pUlD8fYgtLCPfoFtRrlDje3UX4KP57SVWOyHkq5K8ztwEGU+/yfvxzLk6QxRcRjKb/sPYzSFVMzyMC6oETEYZR+jDdm5uMa5W8F3ky5wPAnmfm+Wv5Byq1s7qU89e3EWr4LpU/RLMp/eAfW8k0p/TsfRLl36r/16BskSZIkrTAG2QJ+OKVj/v0i4rmUVqknZrm126dr+ZaUiyu2qvN8uV4xO4tyn9MXUG619Ko6LZSLUz6XmZtTLhTZe4DbIkmSJE2JgSXgmXk6yz5s5o2UpxbeVae5sZbvChyTmXfVC6QWUG51ti2wIDOvrK3bx1DudxmUJ4EdV+c/gvLUNEmSJGmFtmrL63sU8KyI+ATlFjfvycyzKQ+mad6a6xqWPO3o6o7yp1K6nSxqXJHcnH4ZEbEP5dGkrLnmmk9+zGPGenCgJEmStHzOPffcmzNzXrdxbSfgqwLrAdsBTwGOjYhJPVhhIjLzEOAQgG222SbPOeecQa9SkiRJK7GI+HOvcW0n4NcA3693IjgrIu4D1qfc2L35GNENWfK40W7lfwPWjYhVayt4c3pJkiRphdX2bQh/CDwXICIeRbkV2M2UJ+PtERGr17ubbEF5HPPZwBb10airUS7UPKEm8L8Edq/L3ZNyGzFJkiRphTawFvCIOBrYHlg/Iq4B9qU86vSwiLgEuBvYsybTl0bEsZTHNC8G3lwfx0xEvIXy6NxZwGH1EdNQHgF+TER8HDif8nhlSZIkaYW20j2K3j7gkiRJGrSIODczt+k2zidhSpIkSS0yAZckSZJaZAIuSZIktcgEXJIkSWqRCbgkSZLUIhNwSZIkqUUm4JIkSVKLTMAlSZKkFpmAS5IkSS0yAZckSZJaZAIuSZIktcgEXJIkSWqRCbgkSZLUIhNwSZIkqUWrTncAkiRJ0vLITEZGRgCYO3cuETHNEY3NFnBJkiTNaCMjI8w/+GTmH3zy/Yn4iswWcEmSJM14s+esPd0h9M0WcEmSJKlFJuCSJElSi0zAJUmSpBaZgEuSJEktMgGXJEmSWmQCLkmSJLXIBFySJElqkQm4JEmS1CITcEmSJKlFJuCSJElSi0zAJUmSpBaZgEuSJEktMgGXJEmSWmQCLkmSJLXIBFySJElqkQm4JEmS1CITcEmSJKlFJuCSJElSi0zAJUmSpBaZgEuSJEktMgGXJEmSWmQCLkmSJLXIBFySJElqkQm4JEmS1CITcEmSJKlFJuCSJElSi0zAJUmSpBaZgEuSJEktGlgCHhGHRcSNEXFJl3HvjoiMiPXr+4iIgyJiQURcFBFbN6bdMyL+UF97NsqfHBEX13kOiogY1LZIkiRJU2WQLeCHA7t0FkbERsBOwF8axS8AtqivfYCD67TrAfsCTwW2BfaNiLl1noOB1zfmW2ZdkiRJ0opmYAl4Zp4OLOwy6nPA+4BslO0KHJnFmcC6EfEwYGfgpMxcmJkjwEnALnXc2pl5ZmYmcCSw26C2RZIkSZoqrfYBj4hdgWsz88KOURsAVzfeX1PLxiq/pku5JEmStEJbta0VRcQc4EOU7ietioh9KF1b2HjjjdtevSRJknS/NlvANwM2BS6MiD8BGwLnRcRDgWuBjRrTbljLxirfsEt5V5l5SGZuk5nbzJs3bwo2RZIkSZqc1hLwzLw4Mx+cmZtk5iaUbiNbZ+b1wAnA/Ho3lO2AWzLzOuBEYKeImFsvvtwJOLGOuzUitqt3P5kPHN/WtkiSJEmTNcjbEB4N/AZ4dERcExF7jzH5T4ErgQXA14A3AWTmQuAA4Oz62r+WUaf5ep3nj8DPBrEdkiRJ0lQaWB/wzHzVOOM3aQwn8OYe0x0GHNal/BzgccsXpSRJktQun4QpSZIktcgEXJIkSWqRCbgkSZLUIhNwSZIkqUUm4JIkSVKLTMAlSZKkFpmAS5IkSS0yAZckSZJaZAIuSZIktcgEXJIkSWqRCbgkSZLUIhNwSZIkqUUm4JIkSVKLTMAlSZKkFpmAS5IkSS0yAZckSZJaZAIuSZIktcgEXJIkSWqRCbgkSZLUIhNwSZIkqUUm4JIkSVKLTMAlSZKkFpmAS5IkSS0yAZckSZJaZAIuSZIktcgEXJIkSWqRCbgkSZLUIhNwSZIkqUUm4JIkSVKLTMAlSZKkFpmAS5IkSS0yAZckSZJaZAIuSZIktcgEXJIkSWqRCbgkSZLUIhNwSZIkqUUm4JIkSVKLTMAlSZKkFpmAS5IkSS0yAZckSZJaZAIuSZIktcgEXJIkSWqRCbgkSZLUIhNwSZIkqUUm4JIkSVKLBpaAR8RhEXFjRFzSKPvviPhdRFwUET+IiHUb4z4YEQsi4oqI2LlRvkstWxARH2iUbxoRv63l34mI1Qa1LZIkSdJUGWQL+OHALh1lJwGPy8wnAL8HPggQEVsCewBb1Xm+HBGzImIW8D/AC4AtgVfVaQE+CXwuMzcHRoC9B7gtkiRJ0pQYWAKemacDCzvKfpGZi+vbM4EN6/CuwDGZeVdmXgUsALatrwWZeWVm3g0cA+waEQHsABxX5z8C2G1Q2yJJkiRNlensA/5a4Gd1eAPg6sa4a2pZr/IHAYsayfxouSRJkrRCm5YEPCI+DCwGvtXS+vaJiHMi4pybbrqpjVVKkiRJXbWegEfEXsCLgFdnZtbia4GNGpNtWMt6lf8NWDciVu0o7yozD8nMbTJzm3nz5k3JdkiSJEmT0WoCHhG7AO8DXpKZdzZGnQDsERGrR8SmwBbAWcDZwBb1jierUS7UPKEm7r8Edq/z7wkc39Z2SJIkSZM1yNsQHg38Bnh0RFwTEXsDXwLWAk6KiAsi4isAmXkpcCxwGfBz4M2ZeW/t4/0W4ETgcuDYOi3A+4F3RcQCSp/wQwe1LZIkSdJUWXX8SSYnM1/VpbhnkpyZnwA+0aX8p8BPu5RfSblLiiRJkjRj+CRMSZIkqUUm4JIkSVKLTMAlSZKkFpmAS5IkSS0yAZckSZJaZAIuSZIktcgEXJIkSWqRCbgkSZLUIhNwSZIkqUUm4JIkSVKLTMAlSZKkFpmAS5IkSS0yAZckSZJaZAIuSZIktcgEXJIkSWqRCbgkSZLUIhNwSZIkqUUm4JIkSVKLTMAlSZKkFpmAS5IkSS0yAZckSZJaZAIuSZIktcgEXJIkSWqRCbgkSZLUIhNwSZIkqUUm4JIkSVKLTMAlSZKkFpmAS5IkSS0yAZckSZJaNG4CHhEv76dMkiRJ0vj6aQH/YJ9lkiRJksaxaq8REfEC4IXABhFxUGPU2sDiQQcmSZIkDaOeCTjwV+Ac4CXAuY3y24B3DjIoSZIkaVj1TMAz88KIuATYOTOPaDEmSZIkaWiN2Qc8M+8FNoqI1VqKR5IkSRpqY3VBGXUV8KuIOAG4Y7QwMz87sKgkSZKkIdVPAv7H+loFWGuw4UiSJEnDbdwEPDP/AyAiHljf3z7ooCRJkqRh1c+DeB4XEecDlwKXRsS5EbHV4EOTJEmShk8/D+I5BHhXZj4iMx8BvBv42mDDkiRJkoZTPwn4mpn5y9E3mXkqsObAIpIkSZKGWD8XYV4ZER8Fjqrv/xW4cnAhSZIkScOrnxbw1wLzgO/X17xaJkmSJGmC+rkLygjwtohYB7gvM28bfFiSJEnScOrnLihPiYiLgQuBiyPiwoh48uBDkyRJkoZPP11QDgXelJmbZOYmwJuBb4w3U0QcFhE3RsQljbL1IuKkiPhD/Tu3lkdEHBQRCyLioojYujHPnnX6P0TEno3yJ0fExXWegyIiJrDdkiRJ0rToJwG/NzPPGH2Tmf8HLO5jvsOBXTrKPgCcnJlbACfX9wAvALaor32Ag6Ek7MC+wFOBbYF9R5P2Os3rG/N1rkuSJEla4fSTgJ8WEV+NiO0j4jkR8WXg1IjYutlS3SkzTwcWdhTvChxRh48AdmuUH5nFmcC6EfEwYGfgpMxcWPuinwTsUsetnZlnZmYCRzaWJUmSJK2w+rkN4RPr3307yv8JSGCHCazvIZl5XR2+HnhIHd4AuLox3TW1bKzya7qUS5IkSSu0fu6C8txBrDgzMyJyEMvuFBH7ULq2sPHGG7exSkmSJKmrfu6C8qB6keN5EXFuRHwhIh40yfXdULuPUP/eWMuvBTZqTLdhLRurfMMu5V1l5iGZuU1mbjNv3rxJhi5JkiQtv376gB8D3AT8C7B7Hf7OJNd3AjB6J5M9geMb5fPr3VC2A26pXVVOBHaKiLn14sudgBPruFsjYrt695P5jWVJkiRJK6x++oA/LDMPaLz/eES8cryZIuJoYHtg/Yi4htKH/EDg2IjYG/gz8Io6+U+BFwILgDuB1wBk5sKIOAA4u063f2aOXtj5JsqdVtYAflZfkiRJ0gqtnwT8FxGxB3Bsfb87pWV6TJn5qh6jduwybVLuL95tOYcBh3UpPwd43HhxSJIkSSuSfrqgvB74NnB3fR0DvCEibouIWwcZnCRJkjRs+rkLylptBCJJkiStDMZNwCPi2d3K64N2JEmSJE1AP33A39sYfgDlkfDnMrEH8EiSJEmivy4oL26+j4iNgM8PKiBJkiRpmPVzEWana4DHTnUgkiRJ0sqgnz7gXwRGHxm/CvAk4LwBxiRJkiQNrX76gJ/TGF4MHJ2ZvxpQPJIkSdJQ66cP+BEAETGb8uCbawcdlCRJkjSsevYBj4ivRMRWdXgd4ELgSOD8iOj1lEtJkiRJYxjrIsxnZealdfg1wO8z8/HAk4H3DTwySZIkaQiNlYDf3Rh+PvBDgMy8fpABSZIkScNsrAR8UUS8KCL+CXgG8HOAiFgVWKON4CRJkqRhM9ZFmG8ADgIeCryj0fK9I/CTQQcmSZIkDaOeCXhm/h7YpUv5icCJgwxKkiRJGlaTeRKmJEmSpEkyAZckSZJaZAIuSZIktWjcJ2FGxOrAvwCbNKfPzP0HF5YkSZI0nMZNwIHjgVuAc4G7BhuOJEmSNNz6ScA3zMxl7oYiSZIkaeL66QP+64h4/MAjkSRJklYC/bSAPxPYKyKuonRBCSAz8wkDjUySJEkaQv0k4C8YeBSSJEnSSqJnAh4Ra2fmrcBtLcYjSZIkDbWxWsC/DbyIcveTpHQ9GZXAIwcYlyRJkjSUeibgmfmi+nfT9sKRJEmShptPwpQkSZJaZAIuSZIktcgEXJIkSWrRuAl4RHwmIrZqIxhJkiRp2PXTAn45cEhE/DYi/j0i1hl0UJIkSdKwGjcBz8yvZ+YzgPnAJsBFEfHtiHjuoIOTJEmShk1ffcAjYhbwmPq6GbgQeFdEHDPA2CRJkqShM+6j6CPic5QH8pwC/GdmnlVHfTIirhhkcJIkSdKwGTcBBy4CPpKZd3QZt+0UxyNJkiQNtX66oCyikahHxLoRsRtAZt4ymLAkSZKk4dRPAr5vM9HOzEXAvgOLSJIkSRpi/STg3abpp+uKJEmSpA79JODnRMRnI2Kz+voscO6gA5MkSZKGUT8J+FuBu4Hv1NddwJsHGZQkSZI0rMbtSlLvfvKBFmKRJEmShl4/9wF/FPAeylMw758+M3cYXFiSJEnScOrnYsrvAl8Bvg7cO9hwJEmSpOHWTwK+ODMPHngkkiRJ0kqgn4swfxQRb4qIh0XEeqOvgUcmSZIkDaF+EvA9gfcCv6bcfvBc4JzlWWlEvDMiLo2ISyLi6Ih4QERsGhG/jYgFEfGdiFitTrt6fb+gjt+ksZwP1vIrImLn5YlJkiRJasO4CXhmbtrl9cjJrjAiNgDeBmyTmY8DZgF7AJ8EPpeZmwMjwN51lr2BkVr+uTodEbFlnW8rYBfgyxExa7JxSZIkSW0YNwGPiDkR8ZGIOKS+3yIiXrSc610VWCMiVgXmANcBOwDH1fFHALvV4V3re+r4HSMiavkxmXlXZl4FLAC2Xc64JEmSpIHqpwvKNygP4nl6fX8t8PHJrjAzrwU+DfyFknjfQunWsigzF9fJrgE2qMMbAFfXeRfX6R/ULO8yjyRJkrRC6icB3ywzPwXcA5CZdwIx2RVGxFxK6/WmwMOBNSldSAYmIvaJiHMi4pybbrppkKuSJEmSxtRPAn53RKwBJEBEbEZ5HP1kPQ+4KjNvysx7gO8DzwDWrV1SADaktLRT/25U170qsA7wt2Z5l3mWkpmHZOY2mbnNvHnzliN0SZIkafn0k4DvC/wc2CgivgWcDLxvOdb5F2C72rc8gB2By4BfArvXafYEjq/DJ9T31PGnZGbW8j3qXVI2BbYAzlqOuCRJkqSBG/dBPJl5UkScB2xH6Xry9sy8ebIrzMzfRsRxwHnAYuB84BDgJ8AxEfHxWnZoneVQ4KiIWAAspNz5hMy8NCKOpSTvi4E3Z6ZP6pQkSdIKbdwEPCKeXQdvq3+3jAgy8/TJrjQz96W0rDddSZe7mGTmP4CX91jOJ4BPTDYOSZIkqW39PIr+vY3hB1CS5HMptw2UJEmSNAH9dEF5cfN9RGwEfH5QAUmSJEnDrJ+LMDtdAzx2qgORJEmSVgb99AH/IvUWhJSE/UmUCyglSZIkTVA/fcDPaQwvBo7OzF8NKB5JkiRpqPXTB/yINgKRJEmSVgb9dEG5mCVdUJYaBWRmPmHKo5IkSZKGVD9dUH5W/x5V/766/j146sORJEmSxpaZjIyMMHfuXMqD1WeWfu6C8vzMfF9mXlxfHwB2ysw/Z+afBx2gJEmS1DQyMsIen/4hIyMj0x3KpPSTgEdEPKPx5ul9zidJkiQNxOw5a013CJPWTxeUvYHDImKd+n4R8NqBRSRJkiQNsX7ugnIu8MTRBDwzbxl4VJIkSdKQGrcrSUQ8JCIOBY7JzFsiYsuI2LuF2CRJkqSh009f7sOBE4GH1/e/B94xoHgkSZKkodZPAr5+Zh4L3AeQmYuBewcalSRJkjSk+knA74iIB1EfxhMR2wH2A5ckSZImoZ+7oLwLOAHYLCJ+BcwDdh9oVJIkSdKQGjMBj4hZwHPq69GUx89fkZn3tBCbJEmSNHTG7IKSmfcCr8rMxZl5aWZeYvItSZIkTV4/XVB+FRFfAr4D3DFamJnnDSwqSZIkaUj1k4A/qf7dv1GWwA5THo0kSZI05Hom4BHxlsz8UmY+NyK2ysxL2wxMkiRJGkZj9QF/bWP4qEEHIkmSJK0M+rkPOJS7n0iSJElaTmP1AV83Il5KSdLXjoiXNUdm5vcHGpkkSZI0hMZKwE8DXlKHTwde3BiXgAm4JEmSNEE9E/DMfE2bgUiSJEkrg35uQyhJkiS1LjMZGRkBYO7cuUQMx2WJ/V6EKUmSJLVqZGSE+QefzPyDT74/ER8GtoBLkiRphTV7ztrTHcKUG7cFPCLmRMRHI+Jr9f0WEfGiwYcmSZIkDZ9+uqB8A7gLeFp9fy3w8YFFJEmSJA2xfhLwzTLzU8A9AJl5Jz6YR5IkSZqUfhLwuyNiDcq9v4mIzSgt4pIkSZImqJ+LMPcDfg5sFBHfAp4B7DXAmCRJkqShNW4Cnpm/iIhzge0oXU/enpk3DzwySZIkaQiNm4BHxI+AbwMnZOYdgw9JkiRJGl799AH/NPAs4LKIOC4ido+IBww4LkmSJGko9dMF5TTgtIiYBewAvB44DBi+u6JLkiRJA9bXkzDrXVBeDLwS2Bo4YpBBSZIkScOqnz7gxwLbUu6E8iXgtMy8b9CBSZIkScOonxbwQ4FXZea9gw5GkiRJGnY9E/CI2CEzTwHWBHaNWPrhl5n5/QHHJkmSJA2dsVrAnwOcQun73SkBE3BJkiRpgnom4Jm5bx3cPzOvao6LiE0HGpUkSZI0pPq5D/j3upQdtzwrjYh16z3FfxcRl0fE0yJivYg4KSL+UP/OrdNGRBwUEQsi4qKI2LqxnD3r9H+IiD2XJyZJkiSpDWP1AX8MsBWwTkS8rDFqbWB5H8TzBeDnmbl7RKwGzAE+BJycmQdGxAeADwDvB14AbFFfTwUOBp4aEesB+wLbULrEnBsRJ2TmyHLGJkmSJA3MWH3AHw28CFiXpfuB30Z5GM+kRMQ6wLOBvQAy827g7ojYFdi+TnYEcColAd8VODIzEziztp4/rE57UmYurMs9CdgFOHqysUmSJEmDNlYf8OOB4yPiaZn5mylc56bATcA3IuKJwLnA24GHZOZ1dZrrgYfU4Q2AqxvzX1PLepVLkiRJK6x+7gN+fkS8mdId5f6uJ5n52uVY59bAWzPztxHxBUp3k/tlZkZETnL5y4iIfYB9ADbeeOOpWqwkSZI0Yf1chHkU8FBgZ+A0YENKN5TJuga4JjN/W98fR0nIb6hdS6h/b6zjrwU2asy/YS3rVb6MzDwkM7fJzG3mzZu3HKFLkiRJy6efBHzzzPwocEdmHgH8M+ViyEnJzOuBqyPi0bVoR+Ay4ARg9E4mewLH1+ETgPn1bijbAbfUrionAjtFxNx6x5SdapkkSZK0wuqnC8o99e+iiHgcpX/2g5dzvW8FvlXvgHIl8BrKPwPHRsTewJ+BV9Rpfwq8EFgA3FmnJTMXRsQBwNl1uv1HL8iUJEmSVlT9JOCH1Bbmj1Jaox8IfGx5VpqZF1BuH9hpxy7TJvDmHss5DDhseWKRJEmS2jRuAp6ZX6+DpwGPHGw4kiRJ0nAb60E87xprxsz87NSHI0mSJA23sVrA12otCkmSJGklMdaDeP6jzUAkSZKklcG4tyGMiEdFxMkRcUl9/4SI+MjgQ5MkSZKGTz/3Af8a8EHq7Qgz8yJgj0EGJUmSpJVPZrJw4UIWLlxIuRHecOrnNoRzMvOsiGiWLR5QPJIkSVpJjYyMMP/gkwE48o3L3J16aPSTgN8cEZsBCRARuwPXDTQqSZIkrZRmz1l7ukMYuH4S8DcDhwCPiYhrgauAVw80KkmSJGlIjdsHPDOvzMznAfOAxwDPAZ456MAkSZI03Eb7fA9zf+9ueibgEbF2RHwwIr4UEc8H7gT2BBYAr2grQEmSJA2nkZER9vj0DxkZGZnuUFo1VheUo4AR4DfA64EPAwG8NDMvGHxokiRJGnaz56x8z34cKwF/ZGY+HiAivk658HLjzPxHK5FJkiRJQ2isPuD3jA5k5r3ANSbfkiRJ0vIZqwX8iRFxax0OYI36PoDMzOG/R4wkSZI0xXom4Jk5q81AJEmSpJVBP4+ilyRJkjRFTMAlSZKkFpmAS5IkSS0yAZckSZJaZAIuSZIktcgEXJIkSWqRCbgkSZLUIhNwSZIkqUUm4JIkSVKLTMAlSZKkFpmAS5IkSS0yAZckSZJaZAIuSZIktcgEXJIkSWqRCbgkSZLUIhNwSZIkqUUm4JIkSVKLTMAlSZKkFpmAS5IkSS0yAZckSZJaZAIuSZIktcgEXJIkSWqRCbgkSZLUIhNwSZIkqUUm4JIkSVKLTMAlSZKkFpmAS5IkSS0yAZckSZJaZAIuSZIktWjaEvCImBUR50fEj+v7TSPitxGxICK+ExGr1fLV6/sFdfwmjWV8sJZfERE7T9OmSJIkSX2bzhbwtwOXN95/EvhcZm4OjAB71/K9gZFa/rk6HRGxJbAHsBWwC/DliJjVUuySJEnSpExLAh4RGwL/DHy9vg9gB+C4OskRwG51eNf6njp+xzr9rsAxmXlXZl4FLAC2bWUDJEmSpEmarhbwzwPvA+6r7x8ELMrMxfX9NcAGdXgD4GqAOv6WOv395V3mkSRJklZIrSfgEfEi4MbMPLfFde4TEedExDk33XRTW6uVJEmSljEdLeDPAF4SEX8CjqF0PfkCsG5ErFqn2RC4tg5fC2wEUMevA/ytWd5lnqVk5iGZuU1mbjNv3ryp3RpJkiRpAlpPwDPzg5m5YWZuQrmI8pTMfDXwS2D3OtmewPF1+IT6njr+lMzMWr5HvUvKpsAWwFktbYYkSZI0KauOP0lr3g8cExEfB84HDq3lhwJHRcQCYCElaSczL42IY4HLgMXAmzPz3vbDliRJkvo3rQl4Zp4KnFqHr6TLXUwy8x/Ay3vM/wngE4OLUJIkSZpaPglTkiRJapEJuCRJktQiE3BJkiSpRSbgkiRJUotMwCVJkqQWmYBLkiRJLTIBlyRJklpkAi5JkiS1aEV6EqYkSZKGUGYyMjICwNy5c4mIaY5oetkCLkmSpIEaGRlh/sEnM//gk+9PxFdmtoBLkiRp4GbPWXu6Q1hh2AIuSZIktcgEXJIkSWqRCbgkSZLUIhNwSZIkqUVehClJkqQp1XnbQS3NBFySJElTavS2gwBHvnHHaY5mxWMCLkmSpCnnbQd7sw+4JEmS1CITcEmSJKlFJuCSJElSi0zAJUmSpBaZgEuSJGm5ZCYLFy4kM6c7lBnBBFySJEnLZWRkhD0+/cP77/2tsZmAS5IkabnNnrPWdIcwY5iAS5IkSS0yAZckSZJaZAIuSZIktcgEXJIkSWqRCbgkSZLUIhNwSZIkqUUm4JIkSVKLTMAlSZKkFpmAS5IkSS0yAZckSZJaZAIuSZIktcgEXJIkSWqRCbgkSZLUIhNwSZIkqUUm4JIkSVKLTMAlSZKkFpmAS5IkSS0yAZckSZJatOp0ByBJkqSZITMZGRkBYO7cuUTENEfU3Yoepy3gkiRJ6svIyAjzDz6Z+QeffH+CuyJa0eO0BVySJEl9mz1n7ekOoS8rcpytt4BHxEYR8cuIuCwiLo2It9fy9SLipIj4Q/07t5ZHRBwUEQsi4qKI2LqxrD3r9H+IiD3b3hZJkiRpoqajC8pi4N2ZuSWwHfDmiNgS+ABwcmZuAZxc3wO8ANiivvYBDoaSsAP7Ak8FtgX2HU3aJUmSpBVV6wl4Zl6XmefV4duAy4ENgF2BI+pkRwC71eFdgSOzOBNYNyIeBuwMnJSZCzNzBDgJ2KW9LZEkSZImblovwoyITYB/An4LPCQzr6ujrgceUoc3AK5uzHZNLetV3m09+0TEORFxzk033TR1GyBJkjTEMpOFCxeycOFCMnO6wxka05aAR8QDge8B78jMW5vjsuzhKdvLmXlIZm6TmdvMmzdvqhYrSZI01Fb0u4nMVNOSgEfEbEry/a3M/H4tvqF2LaH+vbGWXwts1Jh9w1rWq1ySJElTZPactVfoO4rMRNNxF5QADgUuz8zPNkadAIzeyWRP4PhG+fx6N5TtgFtqV5UTgZ0iYm69+HKnWiZJkiStsKbjPuDPAP4NuDgiLqhlHwIOBI6NiL2BPwOvqON+CrwQWADcCbwGIDMXRsQBwNl1uv0zc2ErWyBJkiRNUusJeGb+H9DreaA7dpk+gTf3WNZhwGFTF50kSZI0WD6KXpIkSWqRCbgkSZLUIhNwSZIkqUUm4JIkSVKLTMAlSZKkFpmAS5IkSS0yAZckSZJaZAIuSZIkMpOFCxdSHsGiQTIBlyRJEiMjI+zx6R8yMjIy3aEMvel4FL0kSZKmUWben2jPnTuXiPKQ8tlz1prOsFYatoBLkiStZEZGRph/8MnMP/hkW7yngQm4JEnSSmj2nLWZPWft6Q6jq9EW+mHtj24CLkmSpBXKPXfezj5fPYVFixZNdygDYQIuSZKkFc7sOQ+c7hAGxgRckiRJapEJuCRJktQib0MoSZI05DpvO6jpZQIuSZI05EZvOwhw5Bt3nOZoZAIuSZK0ElhRbzm4MrIPuCRJktQiE3BJkiSpRXZBkSRJ0govM1m4cOH9wzOZCbgkSdIQGb3jydy5c4mI6Q5nyixatIi3H3MeAF/YY+tpjmb52AVFkiRpiIyMjLDHp394/20Hh0FmsmjRImbPWXsoLiY1AZckSRoys+esNd0hTKl77ryddx5+Bvfes3i6Q5kSJuCSJEla4a06Z83pDmHKmIBLkiRJLfIiTEmSpBmq8xHzw3TR5TCzBVySJGmGGn3E/PyDTx6qiy4na/QfkhX9NoUm4JIkSTPYsNwZZCrcc+ft7PPVU1i0aNF0hzImu6BIkiTNEJ1dTrSs2XMeON0hjMsEXJIkaYYY7XICcOQbd5zmaDRZdkGRJEmaQWZyl5OZ0kd70EzAJUmSVkCZycKFC4cqWR3to72yXzBqAi5JkrQCGsZHysPM6KM9aCbgkiRJK4BuLd7D9kh5FSbgkiRJK4BhbfHWskzAJUmSWjRW3+5ha/H2osvuTMAlSZJatDK1dM+UB+O0zQRckiSpZcPW0j0WL7pclgm4JEnSJPTqStJZPoy3E9TyMQGXJEmahF5dSTrLV6YuJ+qPCbgkSVIfJnKbwM7yYety4sWVy8cEXJIkrdT67UpiS/YSPtFy+ZiAS5KkoTSaQHcm1/0m1t3Kh60le3lM98WVo63wM7F//YxPwCNil4i4IiIWRMQHpjseSZLUjvEudhwZGWH+wScz/+CTl0qiJ5JYr2wJ90zqWnLP3+/gzUedzfyDT55xtzmc0Ql4RMwC/gd4AbAl8KqI2HJ6o5IkSdA7QR6vRbrf+fq52HH2nLWZPWftZWJb2RLrXjoT7nv+fseM6lqy2py1WHWNtUzAW7YtsCAzr8zMu4FjgF2nOSZJ0pDqlgh26z/cOd1Uztdt3hV1vm4Jcj8t0v3OBzP3Ysd+u0/02yLdOV2/83Xryz3dXUvGk5lLJdz33Hk77zz8DO69Z/H0BTVBq053AMtpA+DqxvtrgKdOUyxjWrhw4XSHIElaTiMjI7zx0NMAOHjv5zB37lxGRkZ43Zd+ytff8kLmzp3bdTpgyuYbna4574o8X7e/vYbH+9tt+J47b1tqumXf3zrh+Sa2/MnN9/eF17PPV09h1qqzljomuk33mi/8iG+8/cWNaZbepnvuvJW777yd13zhd3zj7S8G6Hu+e/5++1Jli/9+G/ctXjxmHY7Od+89ndMtW/ej0y1atGi551t85x3cvcZt3HfP33nLV89l9uprMmu1Nbjvnr8DcPedZVxzvhVVzIQ+Pr1ExO7ALpn5uvr+34CnZuZbOqbbB9invn00cEWrga7Y1gdunu4gVkLW+/Sx7qeH9T49rPfpY91PjxWp3h+RmfO6jZjpLeDXAhs13m9Yy5aSmYcAh7QV1EwSEedk5jbTHcfKxnqfPtb99LDep4f1Pn2s++kxU+p9pvcBPxvYIiI2jYjVgD2AE6Y5JkmSJKmnGd0CnpmLI+ItwInALOCwzLx0msOSJEmSeprRCThAZv4U+Ol0xzGD2TVneljv08e6nx7W+/Sw3qePdT89ZkS9z+iLMCVJkqSZZqb3AZckSZJmFBPwlUhEvDUifhcRl0bEpxrlH4yIBRFxRUTs3CjfpZYtiIgPTE/UM19E7BcR10bEBfX1wsY4637AIuLdEZERsX59HxFxUK3biyJi68a0e0bEH+prz+mLemaLiANq3V4QEb+IiIfXcut+gCLiv+s5/qKI+EFErNsY57lmQCLi5fV79b6I2KZjnPXeohlVr6NPsPI13C/gucD/AqvX9w+uf7cELgRWBzYF/ki5oHVWHX4ksFqdZsvp3o6Z+AL2A97Tpdy6H3zdb0S5SPvPwPq17IXAz4AAtgN+W8vXA66sf+fW4bnTvQ0z8QWs3Rh+G/AV676Vet8JWLUOfxL4ZB32XDPYen8s5RkjpwLbNMqt93b3w4yqV1vAVx5vBA7MzLsAMvPGWr4rcExm3pWZVwELgG3ra0FmXpmZdwPH1Gk1daz7wfsc8D6gebHLrsCRWZwJrBsRDwN2Bk7KzIWZOQKcBOzSesRDIDObj59bkyX1b90PUGb+IjNHn8V9JuXZGOC5ZqAy8/LM7PaAP+u9XTOqXk3AVx6PAp4VEb+NiNMi4im1fAPg6sZ019SyXuWanLfUn4UPi4i5tcy6H6CI2BW4NjMv7BhlvbcgIj4REVcDrwY+Vout+/a8lvJrA1jv08V6b9eMqtcZfxtCLRER/ws8tMuoD1P29XqUn32fAhwbEY9sMbyhNk7dHwwcQGkFPAD4DOXLUctpnHr/EOUneQ3AWHWfmcdn5oeBD0fEB4G3APu2GuCQGq/e6zQfBhYD32oztmHWT71LE2ECPkQy83m9xkXEG4HvZ+kodVZE3AesD1xL6Sc7asNaxhjl6jBW3TdFxNeAH9e31v1y6lXvEfF4Sp/LCyMCSh2eFxHb0rverwW27yg/dcqDHhL9HvOUJPCnlATcul9O49V7ROwFvAjYsZ7vwXPNcpvA8d5kvbdrrPpe4dgFZeXxQ8qFmETEoygXKNwMnADsERGrR8SmwBbAWcDZwBYRsWlErAbsUafVBNU+rqNeClxSh637AcnMizPzwZm5SWZuQvkpcuvMvJ5Sl/PrHTm2A27JzOsoF2vuFBFzazehnWqZJigitmi83RX4XR227gcoInahXPPwksy8szHKc830sN7bNaPq1RbwlcdhwGERcQlwN7BnbR25NCKOBS6j/GT55sy8FyAi3kL5EpwFHJaZl05P6DPepyLiSZQuKH8C3gCQmdb99Pgp5W4cC4A7gdcAZObCiDiAchIH2D8zF05PiDPegRHxaOA+yh1o/r2WW/eD9SXKHTdOqr/8nJmZ/+65ZrAi4qXAF4F5wE8i4oLM3Nl6b1dmLp5J9eqTMCVJkqQW2QVFkiRJapEJuCRJktQiE3BJkiSpRSbgkiRJUotMwCVJkqQWmYBrxoqI3SIiI+Ix0x1LLxHxoeWcf/uI+HEdfklEfKAOz4uI30bE+RHxrIh4eURcHhG/nIq42xIRX4+ILScw/fYR8fQ+ptutn+VGxH4R8Z5+1z/TRMThEbH7AJb7pIh44VQvtw3Nz9EE5vlTRKzfb/lyxLZNRBw0VcubahFxakRsMw3rndBxHBGb1Fvudhs3sG2IiE9FxKX1XHxQ1HtBRsSTI+LiiFjQUf7JiLgoIo5sLONfI+Idg4hPKxYTcM1krwL+r/6dEhEx1ffGX64EvCkzT8jMA+vbHYGLM/OfMvMMYG/g9Zn53H6WNYDtnJTMfF1mXjaBWbYHxk3Agd2AvhP7FUVEzJqGdU7mWHgS5X7eg17PlOv4HK1QMvOczHzbdMehsUXEel3Kng48A3gC8DjgKcBz6uiDgddTHsSzBbBLRKxDeTjYE4C7I+LxEbEG5d74/zP4rdB0MwHXjBQRDwSeSUk892iUrxIRX46I30XESRHx09GWk4h4YS0/t7ZCjLYs7xcRR0XEr4Cjauvy9yLi7Pp6Rp1uXl3mpbXl9s+jrV8R8cO63EsjYp9adiCwRkRcEBHfqmX/GhFn1bKvdku4ImKXGud5wMsa5XtFxJfqQ30+Bexal7NvrYtDI+K/I2JW/Xt2bV15Q51/+4g4IyJOAC4bZ7pTI+K4Gse3Gi02T4mIX0fEhXU71hpjOQ+LiNNrjJdExLO6bOv9rVERcXtEfKIu+8yIeEjHtJtQHujyzrrMZ9WWrlPqek+OiI3rF+FLgP+u020WEa+v8V1Y9+2ccY6vbsudFRFXRbFuRNwbEc+u058eEVvUY+mwul1XRsTbGsvsuu/rdn8mIi4EntYRR9e4o7QIHlT3xZWx5BiPeoxcERH/Czy4x/adGhGfj4hzgLdHaaE7LcoxfGLUp7fW6T5Z4/59rfPVgP2BV9ZteWVErFm3+6wov8rs2jhmT4iIU4CTu8TxrnpsXBKNVr+ImF/r/sKIOKqWPSQiflDLLoyIp0dHS2dEvCci9mvE/oXG8bdtI6Yv1eFen/UHRcQvon7WgRjreOm1LRHx3tFjICI+V+uBiNgh6jmhYxnNX7zGOpa61c8yx2wtPzwiDo7ymbqyruOwKK20hzeWuVNE/CYizouI70Y5x3bz8ubxUOd9QER8I0or7/kRMfrU5fvrur7/cV3/rBrXJXWed9bxm0XEz+txeEYs/evms6P78f7fjeW8skudrhERx9Tt/QGwRi3vGsMY+/cBEfHqKL8ydvuVIoEHUJ4yvTowG7ghymdp7cw8sz787khKA8F9wOyICGAOcA/wHuCLmXnPWLFoSGSmL18z7gW8Gji0Dv8aeHId3p3ytL1VgIcCI7XsAcDVwKZ1uqOBH9fh/YBzgTXq+28Dz6zDGwOX1+EvAR+sw7tQTrjr1/fr1b9rUB41/6D6/vZGzI8FfgTMru+/DMzv2K7ROLegfOkf24hzL+BLncP1/anANnV4H+AjdXh14BxgU0rr8R2NOhhruluADWs9/oaS4K8GXAk8pc6zNuVpur2W827gw7V8FrBWl/3YjDuBF9fhT40us2P6/YD3NN7/iPJUV4DXAj+sw4cDuzeme1Bj+OPAW7str4/l/hzYCngR5amNH67bfFVjeb+uZesDf6N8Effc93W7X9HjOO8V9+HAd+v+2RJYUMtfBpxU6/vhwKJmPXTU+5fr8Owa87z6/pWUJ8iNTveZOvxC4H97HH//CfxrHV4X+D2wZp3uGurnoyOGJwMX1+keCFwK/FOt39+z7GfrO8A7GsfTOsAmwCWNZb4H2K8R+9fq8LNHp2Ppz1Gvz/pBwMfq8D/T+Kx3bMOf6n7utS3bAd+t055BeQz5bGBf4A1dlrc9S5+Xuh1LvepnrM/CMZTzya7ArcDjKcfOuZRfM9YHTgfWrPO8f3T7uxw33Y6Hd7PkmHkM8BfKuez+uq7jfly38cnASY3ydevfk4Et6vBTgVPGOd7/hSXH+0Pqeh9G47gA3tWI7QmUJ1Ju0yuGLtv8RMpTLhdQvgO27jZdnfbTlM/cLcAnatk2o/VU3z+rsY/fB1wAfKbG/eNey/Y1fK8V4idBaRJeBXyhDh9T359LSRS/m5n3AdfHkj7RjwGuzMyr6vujKYnjqBMy8+91+HnAlqVhAoC1Y0mL+0sBMvPnETHSmP9tUR5HDLARJYH+W0fMO1JO+mfXZa8B3NgxzWMoydwfACLimx1x9mMn4AmxpM/kOjWeu4GzGnUw3nTX1BguoHyh3QJcl5ln1zq4tY7vtZyzgcMiYjYlGbhgnLjvpnxBQ9mXz+9jW5/Gkl8JjqIk7t08LiI+TkkOH0h5VPFklnsGJZnbFPgvys/Kp7HkEeoAP8nMu4C7IuJGSmIw1r6/F/jeJOL+YT3OL4slvxY8Gzg6y+Ou/zra4trDd+rfR1N+Mh99fPks4LrGdN+vf8+lHAfd7AS8JJb0p38AJaGFkuR0e6z8M4EfZOYdABHxfUpykpTP8M1QHlNfp98BmF/L7gVuiYi5Y2wflM85mXl6RKwdEet2jO/1WX82df9n5k86Puvd9NqWg4EnR8TawF3AeZSE7FlAP11Nuh1LO9C9fsb6LPwoMzMiLgZuyMyLa5yXUvbphpTE9le1Llaj/OPdTbfj4ZmUJJXM/F1E/Bl41BjbdSXwyIj4IvAT4Be13p8OfLexP1ZvzNPteH8mS473GyLiNErXj4sa8z2b2mKdmRdFxOi4ZWLoDDIi3kX55/K9lH/U7+q1QRGxOeUf7Q1r0Un1F4K/95onMz9F3U9Rfmn5WES8jvJ5uigzP95rXs18JuCacaL0v9sBeHxEJCVhyIh473Is9o7G8CrAdpn5j4719opne8oX+dMy886IOJWSgCwzKXBEZn5wOeLsR1BaSpdKMmucd/Q5XfOL5l7GPld0XU5d1rMpLYiHR8RnM/PIZeZe4p7M0izUxzon6nBgt8y8MCL2orTCTcbpwBsprcsfo3wxb09JzEd1q7ux9v0/agIx0bib6xm3i0QXo8dCAJdm5tN6TDe6nrH2SQD/kplXLFUY8VSWPuam2mKW7krZ+bnLcd5P6LM+UZl5T0RcRWkJ/jUlMXwusDlwef2nfd86+eu6LGIin8OxjC7nvo5l3leXeS/lH6V+rqfp53gY1XX/ZOZIRDwR2JnSrewVwDuARZn5pHHWC5M73pfSI4bXdkz2TcqvDm8AnhsR3wB+lpmLuyzypcCZmXk7QET8jPJP0VEsScqpw9c2Z4yIf6rbdAXwX5m5c+3Ss8VoY4yGj33ANRPtDhyVmY/IzE0ycyPgKkqr0q+Af4nSF/whLElYrqC0dmxS3y/TV7DhF8BbR99E6XNNXfYratlOwGjr2zrASE2+H0P52XnUPbUFGMrPq7tHxIPrMtaLiEd0rPt3wCYRsVl9P5kLTE8E3ji63oh4VESsuRzTjboCeFhEPKVOv1aUC+u6Lqdu2w2Z+TXg68DWk9iWTrcBazXe/5ol1wC8miWJcOd0awHX1Rhf3cd6ei33LEor3X01abuA8uV8+jjL62ffdzPRuE+n9M2eVfue9nNR7hXAvIh4Wo1tdkRsNc48nfV7IvDW2p91NKEYzxnAbhExpx53L61lp1D6GT+oLmv0greTKf/8jPbfXQe4AXhwlD7bq1O6BjW9sk7/TOCWzLylY3yvz/rpwP+rZS9gyWd9otsyOu49dZlnUJK987P4QWY+qb7OGWcdo3rVT69jth9nAs+orbjUz+9YLdidzqjrpM63MeW4+hPwpHo+3ggY7Ye/PrBKZn4P+AilW8etwFUR8fI6TdQEebz1jh7v8yit3Wd1TNPcl4+jdEPpGkPnwjPzxsz8ZGY+Dvg85bvn97VlvNNfgOdExKr18/ocSpem64BbI2K7+vmYDxzfMe8BwEcpyf7odUH3UfqGa0iZgGsmehXwg46y79Xy71H6nF5Gab04j/LF+3fgTcDPI+JcSgLR+WU86m3ANlEuZrqM8oUJ8B/ATlEu+no5cH1dzs+BVSPicuBAypfZqEOAiyLiW1nu9vERys+tF1H6Lj6sueKa1O0D/CTKRZidXVT68fW6/efVWL9K95aqfqcbje1uSkLzxSgXDJ5EadHqtZztgQsj4vw63xe6LXeCfgS8NOpFmJTk6TW1Pv8NeHud7hjgvVEuCNuM8uX2W8o/Ub/rYz1dl1t/gr6aJfv4DEoievFYC+tn3/cw0bh/APyBsj+OpHc3gmZsd1MSi0/W/XoB499p5peUrhsXRLnw7QBK8nBRlG4NB/Sx3vMoLfxnUbbx65l5fmZeCnwCOK3G89k6y9sprZAXU7o/bJnlYrX96zJOYtk6+kc9/r5CuWC701if9WfXbXkZJbma8LbU0WdQ9vVvMvMG4B9MLDnuXFev+un1WehnmTdRWumPrvP/htIdrl9fBlap++Y7wF71s/IrSuPIZZRuIOfV6TcATo3Sve2bwOgvQ68G9q7bdSmlz/pYfkD5VeFCyj8m78vM6zumORh4YD0/7085dsaKoavMPD0z96T0mb+oyyTHAX+knAsuBC7MzB/VcW+inCcX1Gl+NjpTROwGnJOZf83MRcAFtR4fkJkXjrP9msFiyS++0nCIiAdm5u21hegs4BmZeX2jPCi3efpDZn5uAstdHbg3MxfX1sKDx/i5VNI0itIV7D0TaFmWpNbYB1zD6MdRLrZaDTig0SLy+ojYs5afT2mpnYiNgWMjYhXKBYOvn6J4JUnSSsQWcEmSJKlF9gGXJEmSWmQCLkmSJLXIBFySJElqkQm4JEmS1CITcEmSJKlFJuCSJElSi/4/7weBX9A3W+0AAAAASUVORK5CYII=\n", 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      " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(12, 8))\n", - "plt.title('Distribution of differences between aggregate totals that normalizes tabulation of poverty households')\n", - "# Set x-axis label\n", - "plt.xlabel('Aggregate differences in total owner and renter occupied low-income households < 80%')\n", - "# Set y-axis label\n", - "plt.ylabel('Relative Frequency in Support')\n", - "\n", - "sns.histplot(final_df[\"differences_aggregate_denominator\"])" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
      " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(12, 8))\n", - "plt.title('Distribution of differences between aggregate totals that normalizes tabulation of poverty households (with removal of not computed fields) ')\n", - "# Set x-axis label\n", - "plt.xlabel('Aggregate differences in total owner and renter occupied low-income households')\n", - "# Set y-axis label\n", - "plt.ylabel('Relative Frequency in Support')\n", - "\n", - "sns.histplot(final_df[\"differences_aggregate_denominator_sans_not_computed\"])" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/sarahluw/.pyenv/versions/3.6.2/envs/my-virtual-env-3.6.2/lib/python3.6/site-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " after removing the cwd from sys.path.\n" - ] - } - ], - "source": [ - "final_df[\"current_methodology_percentile_rank\"] = final_df[\"current_methodology_percent\"].rank(\n", - " pct=True,\n", - " # Set ascending to the parameter value.\n", - " ascending=True,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/sarahluw/.pyenv/versions/3.6.2/envs/my-virtual-env-3.6.2/lib/python3.6/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " \"\"\"Entry point for launching an IPython kernel.\n", - "/Users/sarahluw/.pyenv/versions/3.6.2/envs/my-virtual-env-3.6.2/lib/python3.6/site-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " after removing the cwd from sys.path.\n" - ] - } - ], - "source": [ - "final_df[\"new_threshold_exceeded\"] = (final_df['hbrd_rank'] >= 90)\n", - "\n", - "final_df[\"current_threshold_exceeded\"] = (final_df[\n", - " 'current_methodology_percentile_rank'] >= 0.90)" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(73056, 15)" - ] - }, - "execution_count": 46, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "final_df.shape" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Only include non-NA tracts for comparison purposes" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "# first save NA tracts that were considered unreliable\n", - "ineligible_tracts = list(final_df[final_df[\"hbrd_rank\"].isna()][\"FIPS_tract_id\"].values)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### How many tracts are ineligible according to CalEnvironScreen but are considerd in Score L?\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "metadata": {}, - "outputs": [], - "source": [ - "final_current_methodology = final_df[final_df[\"current_threshold_exceeded\"] == True]" - ] - }, - { - "cell_type": "code", - "execution_count": 62, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(7323, 15)" - ] - }, - "execution_count": 62, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "final_current_methodology.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 67, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "3145" - ] - }, - "execution_count": 67, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# 53 tracts\n", - "final_current_methodology[\n", - " final_current_methodology.FIPS_tract_id.isin(ineligible_tracts)]['current_summed_methodology'].sum()" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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      " - ], - "text/plain": [ - " FIPS_tract_id state hbrd_rank hbrd_score summed \\\n", - "0 01001020100 01 46.298077 0.104575 80 \n", - "1 01001020200 01 83.269231 0.191667 138 \n", - "2 01001020300 01 63.653846 0.131274 170 \n", - "3 01001020400 01 34.615385 0.088415 145 \n", - "4 01001020500 01 68.221154 0.142515 595 \n", - "\n", - " current_summed_methodology T8_est1 \\\n", - "0 174 765 \n", - "1 177 720 \n", - "2 279 1295 \n", - "3 274 1640 \n", - "4 885 4175 \n", - "\n", - " current_methodology_denominator_sans_not_computed \\\n", - "0 765 \n", - "1 720 \n", - "2 1295 \n", - "3 1635 \n", - "4 4175 \n", - "\n", - " current_methodology_denominator current_methodology_percent \\\n", - "0 765 23.0 \n", - "1 720 25.0 \n", - "2 1291 22.0 \n", - "3 1635 17.0 \n", - "4 4135 21.0 \n", - "\n", - " differences_aggregate_denominator \\\n", - "0 0 \n", - "1 0 \n", - "2 -4 \n", - "3 -5 \n", - "4 -40 \n", - "\n", - " differences_aggregate_denominator_sans_not_computed \\\n", - "0 0 \n", - "1 0 \n", - "2 -4 \n", - "3 -5 \n", - "4 -40 \n", - "\n", - " current_methodology_percentile_rank new_threshold_exceeded \\\n", - "0 0.512833 False \n", - "1 0.575315 False \n", - "2 0.479242 False \n", - "3 0.289696 False \n", - "4 0.444306 False \n", - "\n", - " current_threshold_exceeded \n", - "0 False \n", - "1 False \n", - "2 False \n", - "3 False \n", - "4 False " - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "final_current_methodology.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [], - "source": [ - "# Inserted after the basic stats definition.\n", - "# Load demographic data\n", - "import pathlib\n", - "\n", - "DATA_DIR = pathlib.Path.cwd().parent / \"data\"\n", - "COMPARISON_OUTPUTS_DIR = DATA_DIR / \"comparison_outputs\"\n", - "\n", - "demographics_path = DATA_DIR / \"dataset\" / \"census_acs_2019\" / \"usa.csv\"\n", - "\n", - "demographics_df = pd.read_csv(\n", - " demographics_path,\n", - " dtype={\"GEOID10_TRACT\": \"string\"},\n", - ")\n", - "\n", - "# Set some field names\n", - "BLACK_FIELD_NAME = \"Black or African American alone\"\n", - "AMERICAN_INDIAN_FIELD_NAME = \"American Indian and Alaska Native alone\"\n", - "ASIAN_FIELD_NAME = \"Asian alone\"\n", - "HAWAIIAN_FIELD_NAME = \"Native Hawaiian and Other Pacific alone\"\n", - "TWO_OR_MORE_RACES_FIELD_NAME = \"Two or more races\"\n", - "NON_HISPANIC_WHITE_FIELD_NAME = \"Non-Hispanic White\"\n", - "HISPANIC_FIELD_NAME = \"Hispanic or Latino\"\n", - "PERCENT_PREFIX = \"Percent \"\n", - "\n", - "RE_OUTPUT_FIELDS = [\n", - " BLACK_FIELD_NAME,\n", - " AMERICAN_INDIAN_FIELD_NAME,\n", - " ASIAN_FIELD_NAME,\n", - " HAWAIIAN_FIELD_NAME,\n", - " TWO_OR_MORE_RACES_FIELD_NAME,\n", - " NON_HISPANIC_WHITE_FIELD_NAME,\n", - " HISPANIC_FIELD_NAME,\n", - "]\n", - "\n", - "RE_PERCENT_OUTPUT_FIELDS = [PERCENT_PREFIX + field for field in RE_OUTPUT_FIELDS]\n", - "\n", - "columns_to_keep = (\n", - " [\"GEOID10_TRACT\"]\n", - " + RE_OUTPUT_FIELDS\n", - " + RE_PERCENT_OUTPUT_FIELDS\n", - " + ['Percent of individuals < 200% Federal Poverty Line', \n", - " 'Median value ($) of owner-occupied housing units',\n", - " 'Percent individuals age 25 or over with less than high school degree',\n", - " 'Percent enrollment in college or graduate school',\n", - " 'Linguistic isolation (percent)']\n", - ")\n", - "\n", - "# Join the demographics in.\n", - "merged_df = final_current_methodology.merge(\n", - " demographics_df[columns_to_keep],\n", - " left_on=\"FIPS_tract_id\",\n", - " right_on=\"GEOID10_TRACT\",\n", - " how=\"inner\"\n", - ")\n", - "\n", - "\n", - "# Group bys.\n", - "demographics_directory = COMPARISON_OUTPUTS_DIR / \"demographics_basic_stats\"\n", - "demographics_directory.mkdir(parents=True, exist_ok=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "# these are not converted into percent 0 - 100 scale\n", - "percent_cols = [x for x in merged_df.columns if \n", - " 'Percent' in x or '(percent)' in x\n", - " ]\n", - "\n", - "merged_df[\n", - " percent_cols] = merged_df[\n", - " percent_cols].apply(lambda x: x * 100)" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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      FIPS_tract_idstatehbrd_rankhbrd_scoresummedcurrent_summed_methodologyT8_est1current_methodology_denominator_sans_not_computedcurrent_methodology_denominatorcurrent_methodology_percent...Percent Asian alonePercent Native Hawaiian and Other Pacific alonePercent Two or more racesPercent Non-Hispanic WhitePercent Hispanic or LatinoPercent of individuals < 200% Federal Poverty LineMedian value ($) of owner-occupied housing unitsPercent individuals age 25 or over with less than high school degreePercent enrollment in college or graduate schoolLinguistic isolation (percent)
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      5 rows Ă— 35 columns

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      " - ], - "text/plain": [ - " FIPS_tract_id state hbrd_rank hbrd_score summed \\\n", - "0 01015002101 01 99.519231 0.363184 365 \n", - "1 01045020700 01 94.711538 0.252174 319 \n", - "2 01047956400 01 96.923077 0.282158 340 \n", - "3 01047956500 01 96.442308 0.272401 380 \n", - "4 01069040600 01 92.884615 0.240000 180 \n", - "\n", - " current_summed_methodology T8_est1 \\\n", - "0 483 1005 \n", - "1 539 1265 \n", - "2 625 1205 \n", - "3 625 1395 \n", - "4 345 750 \n", - "\n", - " current_methodology_denominator_sans_not_computed \\\n", - "0 1000 \n", - "1 1265 \n", - "2 1200 \n", - "3 1395 \n", - "4 750 \n", - "\n", - " current_methodology_denominator current_methodology_percent ... \\\n", - "0 970 50.0 ... \n", - "1 1230 44.0 ... \n", - "2 1180 53.0 ... \n", - "3 1340 47.0 ... \n", - "4 691 50.0 ... \n", - "\n", - " Percent Asian alone Percent Native Hawaiian and Other Pacific alone \\\n", - "0 0.030057 0.4208 \n", - "1 3.998741 0.0000 \n", - "2 1.986755 0.0000 \n", - "3 0.000000 0.0000 \n", - "4 0.308483 0.0000 \n", - "\n", - " Percent Two or more races Percent Non-Hispanic White \\\n", - "0 0.480914 71.145176 \n", - "1 3.369018 30.321159 \n", - "2 0.397351 6.721854 \n", - "3 0.000000 2.659747 \n", - "4 1.850900 12.750643 \n", - "\n", - " Percent Hispanic or Latino \\\n", - "0 0.510971 \n", - "1 0.000000 \n", - "2 0.000000 \n", - "3 0.000000 \n", - "4 0.771208 \n", - "\n", - " Percent of individuals < 200% Federal Poverty Line \\\n", - "0 61.035565 \n", - "1 72.012882 \n", - "2 81.459854 \n", - "3 83.522543 \n", - "4 77.870216 \n", - "\n", - " Median value ($) of owner-occupied housing units \\\n", - "0 153500 \n", - "1 76200 \n", - "2 59000 \n", - "3 46000 \n", - "4 52100 \n", - "\n", - " Percent individuals age 25 or over with less than high school degree \\\n", - "0 14.425163 \n", - "1 20.528911 \n", - "2 30.427046 \n", - "3 29.666197 \n", - "4 24.772727 \n", - "\n", - " Percent enrollment in college or graduate school \\\n", - "0 68.001261 \n", - "1 3.720930 \n", - "2 15.017513 \n", - "3 3.272871 \n", - "4 4.795852 \n", - "\n", - " Linguistic isolation (percent) \n", - "0 0.000000 \n", - "1 2.184087 \n", - "2 1.475694 \n", - "3 0.000000 \n", - "4 1.041667 \n", - "\n", - "[5 rows x 35 columns]" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "merged_df.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [], - "source": [ - "import us\n", - "\n", - "mapping = us.states.mapping('fips', 'abbr')\n", - "\n", - "for idx, row in merged_df.iterrows():\n", - " current_row = str(merged_df.loc[idx, 'state'])\n", - " state = mapping.get(current_row, None)\n", - " merged_df.loc[idx, 'state_name'] = state" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(7323, 36)" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "merged_df.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [], - "source": [ - "grouped_stats = merged_df.groupby([\"state_name\"]).agg({\n", - " 'GEOID10_TRACT': 'nunique',\n", - " 'Percent of individuals < 200% Federal Poverty Line': [np.median, np.std],\n", - " 'Median value ($) of owner-occupied housing units': [np.median, np.std],\n", - " 'Percent individuals age 25 or over with less than high school degree': [np.median, np.std],\n", - " 'Percent enrollment in college or graduate school': [np.median, np.std],\n", - " 'Percent Black or African American alone': [np.median, np.std],\n", - " 'Percent American Indian and Alaska Native alone': [np.median, np.std],\n", - " 'Percent Non-Hispanic White': [np.median, np.std], \n", - " 'Linguistic isolation (percent)': [np.median, np.std],\n", - " 'Percent Hispanic or Latino': [np.median, np.std],\n", - " 'hbrd_rank': [np.median, np.std],\n", - " 'current_methodology_percent': [np.median, np.std],\n", - " 'current_summed_methodology': [np.median, np.std, np.sum]\n", - "}).reset_index()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [], - "source": [ - "grouped_stats.columns = [' '.join(col).strip() for col in grouped_stats.columns.values]" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['state_name', 'GEOID10_TRACT nunique',\n", - " 'Percent of individuals < 200% Federal Poverty Line median',\n", - " 'Percent of individuals < 200% Federal Poverty Line std',\n", - " 'Median value ($) of owner-occupied housing units median',\n", - " 'Median value ($) of owner-occupied housing units std',\n", - " 'Percent individuals age 25 or over with less than high school degree median',\n", - " 'Percent individuals age 25 or over with less than high school degree std',\n", - " 'Percent enrollment in college or graduate school median',\n", - " 'Percent enrollment in college or graduate school std',\n", - " 'Percent Black or African American alone median',\n", - " 'Percent Black or African American alone std',\n", - " 'Percent American Indian and Alaska Native alone median',\n", - " 'Percent American Indian and Alaska Native alone std',\n", - " 'Percent Non-Hispanic White median', 'Percent Non-Hispanic White std',\n", - " 'Linguistic isolation (percent) median',\n", - " 'Linguistic isolation (percent) std',\n", - " 'Percent Hispanic or Latino median', 'Percent Hispanic or Latino std',\n", - " 'hbrd_rank median', 'hbrd_rank std',\n", - " 'current_methodology_percent median', 'current_methodology_percent std',\n", - " 'current_summed_methodology median', 'current_summed_methodology std',\n", - " 'current_summed_methodology sum'],\n", - " dtype='object')" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "grouped_stats.columns" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [], - "source": [ - "grouped_stats_states = grouped_stats[[x for x in grouped_stats \n", - " if \"median\" in x and \n", - " \"Percent\" in x] + [\"GEOID10_TRACT nunique\", \n", - " 'current_summed_methodology sum', \"state_name\"]]" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [], - "source": [ - "grouped_stats_states.set_index(\"state_name\", inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [], - "source": [ - "grouped_stats_states = grouped_stats_states.rename(columns={\n", - "'Percent of individuals < 200% Federal Poverty Line median'\n", - " : 'Percent of individuals < 200% Federal Poverty Line (median across all tracts)', \n", - "'Percent individuals age 25 or over with less than high school degree median':\n", - "'Percent individuals age 25 or over with less than high school degree (median across all tracts)', \n", - "'Percent enrollment in college or graduate school median'\n", - " :'Percent enrollment in college or graduate school (median across all tracts)',\n", - "'Percent Black or African American alone median':\n", - " 'Percent Black or African American alone (median across all tracts)',\n", - "'Percent American Indian and Alaska Native alone median':\n", - " 'Percent American Indian and Alaska Native alone (median across all tracts)',\n", - "'Percent Non-Hispanic White median':\n", - " 'Percent Non-Hispanic White (median across all tracts)',\n", - "'Percent Hispanic or Latino median':\n", - " 'Percent Hispanic or Latino (median across all tracts)',\n", - "'GEOID10_TRACT nunique': \"Total Number of Unique Tracts\",\n", - "\"current_summed_methodology sum\": \"Total Owned and Rented Burdened Households (Current Aggregation Methodology)\" \n", - "})" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "metadata": {}, - "outputs": [], - "source": [ - "bar_styled_current = grouped_stats_states.style.bar(\n", - " subset=grouped_stats_states.columns, align='mid', color=['#d65f5f', '#5fba7d'])" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - 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      Percent of individuals < 200% Federal Poverty Line (median across all tracts) Percent individuals age 25 or over with less than high school degree (median across all tracts) Percent enrollment in college or graduate school (median across all tracts) Percent Black or African American alone (median across all tracts) Percent American Indian and Alaska Native alone (median across all tracts) Percent Non-Hispanic White (median across all tracts) Percent Hispanic or Latino (median across all tracts) Total Number of Unique Tracts Total Owned and Rented Burdened Households (Current Aggregation Methodology)
      state_name
      AK33.5630814.72049712.9471179.1497881.80169654.25664515.93450554355
      AL68.48575720.8499346.43335081.3486750.00000011.4537442.6930897739724
      AR69.92193516.7912988.45213861.8012420.00000026.0964126.8036532717542
      AZ65.06783929.3746577.4409586.5374512.66804628.07220654.6225449972365
      CA54.70668534.3274509.2552735.4245820.4227739.31244665.59206415711118566
      CO54.39716814.8644748.9972855.9617380.88097639.04591734.9917738267865
      CT56.11766923.8203107.33219625.3492090.00000020.59689844.74430013092624
      DC56.07303515.0242336.56211390.3525770.0000002.6914453.0153123119666
      DE63.46053316.4743593.82322054.9120550.00000013.6556409.678878136789
      FL59.62050222.3035957.39398330.5580420.00000011.21951231.551965489404962
      GA63.29436819.0977448.05778577.7303370.0000008.8122613.894967181128381
      HI32.5389557.77891511.1966824.7138050.08060232.68789012.9153273723247
      IA64.78127515.07535012.51371910.3818920.48242765.5791676.0342422618522
      ID55.41781711.1411329.9626600.1237840.03374484.03072210.46188365539
      IL60.02592918.8374136.78045472.0207250.0000004.1192627.936508395216364
      IN66.91881020.4819285.99128533.6233790.00000040.1370537.34684510360903
      KS67.14503917.4478469.52460517.1006560.45163546.02890411.8972292211898
      KY66.56535019.5135146.68640520.5160880.17412958.8274043.5996494525912
      LA66.40522922.5030576.89149684.3454790.0000009.0414402.57997913168097
      MA52.54704022.6301128.31295812.7292640.00000026.18653432.157058179126065
      MD48.92450418.3716086.90322678.5337060.0000006.4959405.55632310561295
      ME56.25199910.33816415.0468388.6062940.25570079.1746953.84495296324
      MI68.67178818.8376086.55197980.1555560.00000011.0456241.723390238115230
      MN62.41092618.55396111.82453921.0200000.99866833.7200007.6535294127230
      MO62.72878517.0533176.37825874.0643120.00000015.8671592.39397110159221
      MS72.36992220.4418396.24003989.3578150.0000006.9696820.8862363419441
      MT70.2715365.30973524.4362870.7421152.33904084.3738394.94720453674
      NC65.60890921.5845997.27422052.6391250.09921218.47358013.05467413490512
      ND63.9458708.21194144.7966138.0742201.52481975.8589924.51572643472
      NE66.21621617.7252586.54942444.9063230.66740828.31788112.8344482310386
      NH52.39637316.9408905.6714996.2700570.09115873.36734711.550422137642
      NJ52.10125323.0245946.98450527.4983670.0000009.41362941.214623349234656
      NM69.63271520.9867458.6654484.1156517.46268727.19468360.9599651713686
      NV62.55195327.1322824.49305414.2746910.97469925.89886045.9683647552971
      NY52.15211925.1457128.25986125.4619020.00000010.35892229.1591321047733619
      OH68.81663619.1576136.39610459.1313740.00000026.5644723.700967258127780
      OK67.96461411.8125609.57112715.1002653.30342841.67786410.4853663820564
      OR56.14510716.2112319.2308542.4935560.94765660.44508916.6929224642271
      PA64.91580820.2090596.88259131.8856820.00000020.68850311.699164243150476
      RI56.08674223.8420117.01026915.9936910.40053424.03886847.8496872919900
      SC64.52610722.6471936.95862060.8434510.00000020.5010154.3873755632258
      SD67.18661519.15672620.4197603.67893716.59219860.49514612.2930412678
      TN68.15398121.6494856.41582281.8941500.00000011.1824772.83273410567617
      TX64.91242227.3404266.74217317.6723760.20536512.10420749.275139364289510
      UT65.5114488.05418341.9867630.8028840.63826470.76361915.9404382012154
      VA55.79073518.3931627.61407456.4785790.00000020.2898555.35714312381557
      VT57.8788308.54915334.5207395.7162340.00000084.4970942.85378543483
      WA51.07172312.2996306.7691485.8148200.82304553.72920314.0073365551433
      WI68.17221017.5824186.38587758.4980060.04599313.6226355.59414312065589
      WV74.48717912.50000017.80141811.3311330.00000076.7795441.606145135794
      WY70.2755915.06649851.2195120.3748830.37488383.3801948.99698731558
      " - ], - "text/plain": [ - "" - ] - }, - "execution_count": 60, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bar_styled_current" - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "metadata": {}, - "outputs": [], - "source": [ - "def highlight_medians(s):\n", - " # highliht if the current median is greater than the median of medians for that series\n", - " \n", - " is_greater_than_median = s > s.median()\n", - " return ['color: red; background-color:#7272FE' \n", - " if cell else '' for cell in is_greater_than_median]\n", - "\n", - "styled = grouped_stats_states.style.apply(highlight_medians)" - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "metadata": {}, - "outputs": [], - "source": [ - "styled.to_excel(\"current_methodology_7323.xlsx\")" - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "metadata": {}, - "outputs": [], - "source": [ - "# bar_styled_current.to_excel(\"current_bar.xlsx\")" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "7323" - ] - }, - "execution_count": 47, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "grouped_stats_states[\"Total Number of Unique Tracts\"].sum()" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 49, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
      " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# Compute the correlation matrix\n", - "import seaborn as sns\n", - "corr = merged_df[[\"hbrd_rank\", \n", - " \"current_methodology_percentile_rank\"] + percent_cols].corr()\n", - "\n", - "# Generate a mask for the upper triangle\n", - "mask = np.triu(np.ones_like(corr, dtype=bool))\n", - "\n", - "# Set up the matplotlib figure\n", - "f, ax = plt.subplots(figsize=(15, 12))\n", - "\n", - "# Generate a custom diverging colormap\n", - "cmap = sns.diverging_palette(230, 20, as_cmap=True)\n", - "\n", - "# Draw the heatmap with the mask and correct aspect ratio\n", - "sns.heatmap(corr, mask=mask, cmap=cmap, vmax=.3, center=0,\n", - " square=True, linewidths=.5, cbar_kws={\"shrink\": .5})" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In the previous, comparing both methodologies. \n", - "\n", - "1. " - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.2" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/data/data-pipeline/data_pipeline/ipython/hud_eda_se_12_27_reference.ipynb b/data/data-pipeline/data_pipeline/ipython/hud_eda_se_12_27_reference.ipynb deleted file mode 100644 index 3efdbb67..00000000 --- a/data/data-pipeline/data_pipeline/ipython/hud_eda_se_12_27_reference.ipynb +++ /dev/null @@ -1,865 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Indicator reviewed: \n", - "\n", - "Socioeconomic Factors Indicator reviewed\n", - "* [Extreme Housing Burden](#housingburden)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Packages" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import math\n", - "import numpy as np\n", - "import os\n", - "import pandas as pd\n", - "\n", - "import seaborn as sns\n", - "import matplotlib.pyplot as plt\n", - "import pandas as pd\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Extreme Housing Burden \n", - "\n", - "The Extreme Housing Burden indicator represents the proportion of low-income households that have to spend more than half their income on rent. These households experience higher levels of stress, report lower health, and may delay medical treatment because of its high cost.\n", - "\n", - "The Extreme Housing Burden indicator measures the percent of households in a census tract that are:\n", - "\n", - "1. Making less than 80% of the Area Median Family Income as determined by the Department of Housing and Urban Development (HUD), and\n", - "2. Paying greater than 50% of their income to housing costs. \n", - "\n", - "This data is sourced from the 2014-2018 Comprehensive Housing Affordability Strategy dataset from the Department of Housing and Urban Development (HUD) using the census tract geographic summary level, and contains cost burdens for households by percent HUD-adjusted median family income (HAMFI) category. This data can be found [here](https://www.huduser.gov/portal/datasets/cp.html). \n", - "\n", - "Because CHAS data is based on American Communities Survey (ACS) estimates, which come from a sample of the population, they may be unreliable if based on a small sample or population size.\n", - "\n", - "The standard error and relative standard error were used to evaluate the reliability of each estimate using CalEnviroScreen’s methodology. \n", - "\n", - "Census tract estimates that met either of the following criteria were considered reliable and included in the analysis [(CalEnviroScreen, 2017, page 129)](https://oehha.ca.gov/media/downloads/calenviroscreen/report/ces3report.pdf ):\n", - "\n", - "- Relative standard error less than 50 (meaning the standard error was less than half of the estimate), OR \n", - "- Standard error less than the mean standard error of all census tract estimates \n", - "\n", - "Formulas for calculating the standard error of sums, proportions, and ratio come from the [American Communities Survey Office](https://www2.census.gov/programs-surveys/acs/tech_docs/accuracy/MultiyearACSAccuracyofData2013.pdf).\n", - "\n", - "Note that this code creates a score and rank by state, for every state." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The relevant variables in table 8 of the CHAS dataset are the following (CHAS data dictionary available [here](https://www.huduser.gov/portal/datasets/cp/CHAS-data-dictionary-14-18.xlsx)):\n", - "\n", - "| Name | Label |\n", - "|---------|-----------------------------------------------------|\n", - "|T1_est1 | Total Occupied housing units | \n", - "|T8_est10 | Owner occupied less than or equal to 30% of HAMFI cost burden greater than 50% |\n", - "|T8_est23 |Owner occupied greater than 30% but less than or equal to 50% of HAMFI\tcost burden greater than 50%|\n", - "|T8_est36 |Owner occupied\tgreater than 50% but less than or equal to 80% of HAMFI\tcost burden greater than 50%|\n", - "|T8_est76 | Renter occupied less than or equal to 30% of HAMFI cost burden greater than 50% |\n", - "|T8_est89 |Renter occupied\tgreater than 30% but less than or equal to 50% of HAMFI\tcost burden greater than 50%|\n", - "|T8_est102|Renter occupied\tgreater than 50% but less than or equal to 80% of HAMFI\tcost burden greater than 50%|\n", - " " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Below I also propose an alternate means for ranking census tracts\n", - "### These steps are outlined and commented below" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/sarahluw/.pyenv/versions/3.6.2/envs/my-virtual-env-3.6.2/lib/python3.6/site-packages/pandas/core/series.py:726: RuntimeWarning: invalid value encountered in sqrt\n", - " result = getattr(ufunc, method)(*inputs, **kwargs)\n", - "/Users/sarahluw/.pyenv/versions/3.6.2/envs/my-virtual-env-3.6.2/lib/python3.6/site-packages/pandas/core/indexing.py:670: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " iloc._setitem_with_indexer(indexer, value)\n" - ] - } - ], - "source": [ - "# Read in the data from https://www.huduser.gov/portal/datasets/cp.html\n", - "housing = pd.read_csv(\"Table8.csv\", \n", - " encoding = \"ISO-8859-1\", \n", - " dtype = {'Tract_ID': object, 'st': object, 'geoid': object})\n", - "\n", - "# Remove data for states that aren't included in the census (e.g. American Samoa, Guam, etc.):\n", - "housing.drop(housing.loc[housing['st'] == '72'].index, inplace = True)\n", - "\n", - "# Combine owner and renter occupied low-income households that make less than 80% of HAMFI into one variable\n", - "housing['summed'] = (housing['T8_est10'] + \n", - " housing['T8_est23'] + \n", - " housing['T8_est36'] + \n", - " housing['T8_est76'] + \n", - " housing['T8_est89'] + \n", - " housing['T8_est102'])\n", - "\n", - "# Create a variable for the standard error of the summed variables\n", - "housing['summed_se'] = np.sqrt((housing['T8_moe10'] / 1.645)**2 + \n", - " (housing['T8_moe23'] / 1.645)**2 + \n", - " (housing['T8_moe36'] / 1.645)**2 + \n", - " (housing['T8_moe76'] / 1.645)**2 + \n", - " (housing['T8_moe89'] / 1.645)**2 + \n", - " (housing['T8_moe102'] / 1.645)**2)\n", - "\n", - "# Remove the first 7 digits in the FIPS Census Tract ID \n", - "housing['geoid'] = housing['geoid'].str[-11:]\n", - "\n", - "# Find the estimate of the proportion of the population that is heavily rent burdened\n", - "housing['hbrd_score'] = housing['summed'] / housing['T8_est1']\n", - "\n", - "# Change rates where the population is 0 to nan\n", - "housing['hbrd_score'].replace(np.inf, np.nan, inplace = True)\n", - "\n", - "# Create function for calculating the standard error, using the proportions standard error formula\n", - "# if the value under the radical is negative, use the ratio standard error formula\n", - "def se_prop(x, y, se_x, moe_y): \n", - " se_y = moe_y / 1.645\n", - " test = se_x**2 - (((x**2)/(y**2))*((se_y)**2))\n", - " se = np.where(test < 0,\n", - " (1/y) * np.sqrt(se_x**2 + (((x**2)/(y**2))*(se_y**2))), \n", - " (1/y) * np.sqrt(se_x**2 - (((x**2)/(y**2))*(se_y**2))))\n", - " return se\n", - "\n", - "housing['se'] = se_prop(housing['summed'], housing['T8_est1'], housing['summed_se'], housing['T8_moe1'])\n", - "\n", - "# Calculate the relative standard error\n", - "housing['rse'] = housing['se'] / housing['hbrd_score']*100\n", - "\n", - "# Change infinite rse's where the housing burden is 0 to np.nan\n", - "housing['rse'].replace(np.inf, np.nan, inplace = True)\n", - "\n", - "# Calculate the mean standard error for each state\n", - "housing['mean_state_se'] = np.zeros(len(housing))\n", - "\n", - "for state in housing['st'].unique():\n", - " mean_se = np.mean(housing[housing['st'] == state]['se'])\n", - " housing['mean_state_se'].loc[housing['st'] == state] = mean_se\n", - " \n", - "# Find census tract estimates that meet both of the following criteria and are thus considered unreliable estimates: \n", - "# RSE less than 50 AND\n", - "# SE less than the mean state SE or housing burdened low income households\n", - "# Convert these scores to nan\n", - "housing.loc[(housing['rse'] >= 50) & (housing['rse'] >= housing['mean_state_se']), 'hbrd_score'] = np.nan\n", - "\n", - "# Rename columns\n", - "housing = housing.rename(columns = {'geoid' :'FIPS_tract_id',\n", - " 'st' : 'state'\n", - " })\n", - "\n", - "# Calculate percentile rank for census tracts with a score above 0, set percentile to 0 if score is 0, for each state\n", - "housing['hbrd_rank'] = housing[\n", - " housing['hbrd_score'] != 0][['hbrd_score',\n", - " 'state']].groupby('state').rank( \n", - " na_option = 'keep', \n", - " pct = True) * 100\n", - "\n", - "housing.loc[housing['hbrd_score'] == 0, 'hbrd_rank'] = 0\n", - "\n", - "# Create final housing burden df\n", - "housingburden = housing.copy()" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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      sourcesumlevelFIPS_tract_idnamestatecntytractT8_est1T8_est2T8_est3...T8_moe131T8_moe132T8_moe133summedsummed_sehbrd_scoresersemean_state_sehbrd_rank
      02014thru201814001001020100Census Tract 201, Autauga County, Alabama0112010076557050...1212128031.7218070.1045750.04103239.2373140.03660446.298077
      12014thru201814001001020200Census Tract 202, Autauga County, Alabama0112020072046565...12121213845.5318740.1916670.06161432.1466590.03660483.269231
      22014thru201814001001020300Census Tract 203, Autauga County, Alabama01120300129584060...12121217053.7229210.1312740.04092731.1769990.03660463.653846
      32014thru201814001001020400Census Tract 204, Autauga County, Alabama011204001640126015...12121214546.2885100.0884150.02782231.4673970.03660434.615385
      42014thru201814001001020500Census Tract 205, Autauga County, Alabama0112050041752320175...171717595147.2216930.1425150.03476024.3901930.03660468.221154
      \n", - "

      5 rows Ă— 280 columns

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      " - ], - "text/plain": [ - " source sumlevel FIPS_tract_id \\\n", - "0 2014thru2018 140 01001020100 \n", - "1 2014thru2018 140 01001020200 \n", - "2 2014thru2018 140 01001020300 \n", - "3 2014thru2018 140 01001020400 \n", - "4 2014thru2018 140 01001020500 \n", - "\n", - " name state cnty tract T8_est1 \\\n", - "0 Census Tract 201, Autauga County, Alabama 01 1 20100 765 \n", - "1 Census Tract 202, Autauga County, Alabama 01 1 20200 720 \n", - "2 Census Tract 203, Autauga County, Alabama 01 1 20300 1295 \n", - "3 Census Tract 204, Autauga County, Alabama 01 1 20400 1640 \n", - "4 Census Tract 205, Autauga County, Alabama 01 1 20500 4175 \n", - "\n", - " T8_est2 T8_est3 ... T8_moe131 T8_moe132 T8_moe133 summed summed_se \\\n", - "0 570 50 ... 12 12 12 80 31.721807 \n", - "1 465 65 ... 12 12 12 138 45.531874 \n", - "2 840 60 ... 12 12 12 170 53.722921 \n", - "3 1260 15 ... 12 12 12 145 46.288510 \n", - "4 2320 175 ... 17 17 17 595 147.221693 \n", - "\n", - " hbrd_score se rse mean_state_se hbrd_rank \n", - "0 0.104575 0.041032 39.237314 0.036604 46.298077 \n", - "1 0.191667 0.061614 32.146659 0.036604 83.269231 \n", - "2 0.131274 0.040927 31.176999 0.036604 63.653846 \n", - "3 0.088415 0.027822 31.467397 0.036604 34.615385 \n", - "4 0.142515 0.034760 24.390193 0.036604 68.221154 \n", - "\n", - "[5 rows x 280 columns]" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "housingburden.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(73056, 280)" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "housingburden.shape" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### As desired we see a uniform distribution for the percentile rank for burdened households" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Now we compute for a baseline comparison " - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# Owner occupied numerator fields\n", - "OWNER_OCCUPIED_NUMERATOR_FIELDS = [\n", - " # Column Name\n", - " # Line_Type\n", - " # Tenure\n", - " # Household income\n", - " # Cost burden\n", - " # Facilities\n", - " \"T8_est7\",\n", - " # Subtotal\n", - " # Owner occupied\n", - " # less than or equal to 30% of HAMFI\n", - " # greater than 30% but less than or equal to 50%\n", - " # All\n", - " \"T8_est10\",\n", - " # Subtotal\n", - " # Owner occupied\n", - " # less than or equal to 30% of HAMFI\n", - " # greater than 50%\n", - " # All\n", - " \"T8_est20\",\n", - " \n", - " # Subtotal\n", - " # Owner occupied\n", - " # greater than 30% but less than or equal to 50% of HAMFI\n", - " # greater than 30% but less than or equal to 50%\n", - " # All\n", - " \"T8_est23\",\n", - " # Subtotal\n", - " # Owner occupied\n", - " # greater than 30% but less than or equal to 50% of HAMFI\n", - " # greater than 50%\n", - " # All\n", - " \"T8_est33\",\n", - " # Subtotal\n", - " # Owner occupied\n", - " # greater than 50% but less than or equal to 80% of HAMFI\n", - " # greater than 30% but less than or equal to 50%\n", - " # All\n", - " \"T8_est36\",\n", - " # Subtotal\n", - " # Owner occupied\n", - " # greater than 50% but less than or equal to 80% of HAMFI\n", - " # greater than 50%\n", - " # All\n", - "]\n", - "\n", - "# These rows have the values where HAMFI was not computed, b/c of no or negative income.\n", - "OWNER_OCCUPIED_NOT_COMPUTED_FIELDS = [\n", - " # Column Name\n", - " # Line_Type\n", - " # Tenure\n", - " # Household income\n", - " # Cost burden\n", - " # Facilities\n", - " \"T8_est13\",\n", - " # Subtotal\n", - " # Owner occupied\n", - " # less than or equal to 30% of HAMFI\n", - " # not computed (no/negative income)\n", - " # All\n", - " \"T8_est26\",\n", - " # Subtotal\n", - " # Owner occupied\n", - " # greater than 30% but less than or equal to 50% of HAMFI\n", - " # not computed (no/negative income)\n", - " # All\n", - " \"T8_est39\",\n", - " # Subtotal\n", - " # Owner occupied\n", - " # greater than 50% but less than or equal to 80% of HAMFI\n", - " # not computed (no/negative income)\n", - " # All\n", - " \"T8_est52\",\n", - " # Subtotal\n", - " # Owner occupied\n", - " # greater than 80% but less than or equal to 100% of HAMFI\n", - " # not computed (no/negative income)\n", - " # All\n", - " \"T8_est65\",\n", - " # Subtotal\n", - " # Owner occupied\n", - " # greater than 100% of HAMFI\n", - " # not computed (no/negative income)\n", - " # All\n", - "]\n", - "\n", - "OWNER_OCCUPIED_POPULATION_FIELD = \"T8_est2\"\n", - "# Subtotal\n", - "# Owner occupied\n", - "# All\n", - "# All\n", - "# All\n", - "\n", - "OWNER_OCCUPIED_POPULATION_HAMFI_FIELD = \"T8_est3\"\n", - "# Subtotal\n", - "# Owner occupied \n", - "# All\n", - "# All\n", - "# All\n", - "\n", - "# Renter occupied numerator fields\n", - "RENTER_OCCUPIED_NUMERATOR_FIELDS = [\n", - " # Column Name\n", - " # Line_Type\n", - " # Tenure\n", - " # Household income\n", - " # Cost burden\n", - " # Facilities\n", - " \"T8_est73\",\n", - " # Subtotal\n", - " # Renter occupied\n", - " # less than or equal to 30% of HAMFI\n", - " # greater than 30% but less than or equal to 50%\n", - " # All\n", - " \"T8_est76\",\n", - " # Subtotal\n", - " # Renter occupied\n", - " # less than or equal to 30% of HAMFI\n", - " # greater than 50%\n", - " # All\n", - " \"T8_est86\",\n", - " # Subtotal\n", - " # Renter occupied\n", - " # greater than 30% but less than or equal to 50% of HAMFI\n", - " # greater than 30% but less than or equal to 50%\n", - " # All\n", - " \"T8_est89\",\n", - " # Subtotal\n", - " # Renter occupied\n", - " # greater than 30% but less than or equal to 50% of HAMFI\n", - " # greater than 50%\n", - " # All\n", - " \"T8_est99\",\n", - " # Subtotal\n", - " # Renter occupied\tgreater than 50% but less than or equal to 80% of HAMFI\n", - " # greater than 30% but less than or equal to 50%\n", - " # All\n", - " \"T8_est102\",\n", - " # Subtotal\n", - " # Renter occupied\n", - " # greater than 50% but less than or equal to 80% of HAMFI\n", - " # greater than 50%\n", - " # All\n", - "]\n", - "\n", - "# These rows have the values where HAMFI was not computed, b/c of no or negative income.\n", - "RENTER_OCCUPIED_NOT_COMPUTED_FIELDS = [\n", - " # Column Name\n", - " # Line_Type\n", - " # Tenure\n", - " # Household income\n", - " # Cost burden\n", - " # Facilities\n", - " \"T8_est79\",\n", - " # Subtotal\n", - " # Renter occupied\tless than or equal to 30% of HAMFI\n", - " # not computed (no/negative income)\n", - " # All\n", - " \"T8_est92\",\n", - " # Subtotal\n", - " # Renter occupied\tgreater than 30% but less than or equal to 50% of HAMFI\n", - " # not computed (no/negative income)\n", - " # All\n", - " \"T8_est105\",\n", - " # Subtotal\n", - " # Renter occupied\n", - " # greater than 50% but less than or equal to 80% of HAMFI\n", - " # not computed (no/negative income)\n", - " # All\n", - " \"T8_est118\",\n", - " # Subtotal\n", - " # Renter occupied\tgreater than 80% but less than or equal to 100% of HAMFI\n", - " # not computed (no/negative income)\n", - " # All\n", - " \"T8_est131\",\n", - " # Subtotal\n", - " # Renter occupied\n", - " # greater than 100% of HAMFI\n", - " # not computed (no/negative income)\n", - " # All\n", - "]\n", - "\n", - "# T8_est68\tSubtotalRenter occupied\tAll\tAll\tAll\n", - "RENTER_OCCUPIED_POPULATION_FIELD = \"T8_est68\"" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "housingburden[\"current_summed_methodology\"] = housingburden[\n", - " OWNER_OCCUPIED_NUMERATOR_FIELDS\n", - "].sum(axis=1) + housingburden[RENTER_OCCUPIED_NUMERATOR_FIELDS].sum(axis=1)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "housingburden[\"current_methodology_denominator\"] = (\n", - " housingburden[OWNER_OCCUPIED_POPULATION_FIELD]\n", - " + housingburden[RENTER_OCCUPIED_POPULATION_FIELD]\n", - " - housingburden[OWNER_OCCUPIED_NOT_COMPUTED_FIELDS].sum(axis=1)\n", - " - housingburden[RENTER_OCCUPIED_NOT_COMPUTED_FIELDS].sum(axis=1)\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "housingburden[\"current_methodology_denominator_sans_not_computed\"] = (\n", - " housingburden[OWNER_OCCUPIED_POPULATION_FIELD]\n", - " + housingburden[RENTER_OCCUPIED_POPULATION_FIELD]\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "housingburden[\"current_methodology_percent\"] = np.round(\n", - " (housingburden[\"current_summed_methodology\"] / housingburden[\"current_methodology_denominator\"] ), 2) * 100" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Now we construct the distribution of differences in the number of owned and rented burdened households\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Percentiles Comparison" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "final_df = housingburden[['FIPS_tract_id', 'state','hbrd_rank','hbrd_score', 'summed', \n", - " 'current_summed_methodology', 'T8_est1', \n", - " \"current_methodology_denominator_sans_not_computed\",\n", - " 'current_methodology_denominator', 'current_methodology_percent']]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### First notice here that **T8_est1** and **current_methodology_denominator** should represent same or similar aggregates. In general, we cen see that the current computation performed results in a differential that undercounts the total occupied and rental households." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/sarahluw/.pyenv/versions/3.6.2/envs/my-virtual-env-3.6.2/lib/python3.6/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " \n" - ] - } - ], - "source": [ - "final_df[\"differences_aggregate_denominator\"] = (\n", - " final_df[\"current_methodology_denominator\"] - final_df[\"T8_est1\"] \n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/sarahluw/.pyenv/versions/3.6.2/envs/my-virtual-env-3.6.2/lib/python3.6/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " \n" - ] - } - ], - "source": [ - "final_df[\"differences_aggregate_denominator_sans_not_computed\"] = (\n", - " final_df[\"current_methodology_denominator\"] - final_df[\"T8_est1\"] \n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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      " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(12, 8))\n", - "plt.title('Distribution of differences between aggregate totals that normalizes tabulation of poverty households')\n", - "# Set x-axis label\n", - "plt.xlabel('Aggregate differences in total owner and renter occupied low-income households < 80%')\n", - "# Set y-axis label\n", - "plt.ylabel('Relative Frequency in Support')\n", - "\n", - "sns.histplot(final_df[\"differences_aggregate_denominator\"])" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
      " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(12, 8))\n", - "plt.title('Distribution of differences between aggregate totals that normalizes tabulation of poverty households (with removal of not computed fields) ')\n", - "# Set x-axis label\n", - "plt.xlabel('Aggregate differences in total owner and renter occupied low-income households')\n", - "# Set y-axis label\n", - "plt.ylabel('Relative Frequency in Support')\n", - "\n", - "sns.histplot(final_df[\"differences_aggregate_denominator_sans_not_computed\"])" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.2" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/data/data-pipeline/data_pipeline/ipython/hud_notebook_tabulation_differences_12_27_2021.ipynb b/data/data-pipeline/data_pipeline/ipython/hud_notebook_tabulation_differences_12_27_2021.ipynb deleted file mode 100644 index 2ce5a35b..00000000 --- a/data/data-pipeline/data_pipeline/ipython/hud_notebook_tabulation_differences_12_27_2021.ipynb +++ /dev/null @@ -1,9194 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Extreme Housing Burden \n", - "\n", - "The Extreme Housing Burden indicator represents the proportion of low-income households that have to spend more than half their income on rent. These households experience higher levels of stress, report lower health, and may delay medical treatment because of its high cost.\n", - "\n", - "The Extreme Housing Burden indicator measures the percent of households in a census tract that are:\n", - "\n", - "1. Making less than 80% of the Area Median Family Income as determined by the Department of Housing and Urban Development (HUD), and\n", - "2. Paying greater than 50% of their income to housing costs. \n", - "\n", - "This data is sourced from the 2014-2018 Comprehensive Housing Affordability Strategy dataset from the Department of Housing and Urban Development (HUD) using the census tract geographic summary level, and contains cost burdens for households by percent HUD-adjusted median family income (HAMFI) category. This data can be found [here](https://www.huduser.gov/portal/datasets/cp.html). \n", - "\n", - "Because CHAS data is based on American Communities Survey (ACS) estimates, which come from a sample of the population, they may be unreliable if based on a small sample or population size.\n", - "\n", - "The standard error and relative standard error were used to evaluate the reliability of each estimate using CalEnviroScreen’s methodology. \n", - "\n", - "Census tract estimates that met either of the following criteria were considered reliable and included in the analysis [(CalEnviroScreen, 2017, page 129)](https://oehha.ca.gov/media/downloads/calenviroscreen/report/ces3report.pdf ):\n", - "\n", - "- Relative standard error less than 50 (meaning the standard error was less than half of the estimate), OR \n", - "- Standard error less than the mean standard error of all census tract estimates \n", - "\n", - "Formulas for calculating the standard error of sums, proportions, and ratio come from the [American Communities Survey Office](https://www2.census.gov/programs-surveys/acs/tech_docs/accuracy/MultiyearACSAccuracyofData2013.pdf).\n", - "\n", - "Note that this code creates a score and rank by state, for every state." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The relevant variables in table 8 of the CHAS dataset are the following (CHAS data dictionary available [here](https://www.huduser.gov/portal/datasets/cp/CHAS-data-dictionary-14-18.xlsx)):\n", - "\n", - "| Name | Label |\n", - "|---------|-----------------------------------------------------|\n", - "|T1_est1 | Total Occupied housing units | \n", - "|T8_est10 | Owner occupied less than or equal to 30% of HAMFI cost burden greater than 50% |\n", - "|T8_est23 |Owner occupied greater than 30% but less than or equal to 50% of HAMFI\tcost burden greater than 50%|\n", - "|T8_est36 |Owner occupied\tgreater than 50% but less than or equal to 80% of HAMFI\tcost burden greater than 50%|\n", - "|T8_est76 | Renter occupied less than or equal to 30% of HAMFI cost burden greater than 50% |\n", - "|T8_est89 |Renter occupied\tgreater than 30% but less than or equal to 50% of HAMFI\tcost burden greater than 50%|\n", - "|T8_est102|Renter occupied\tgreater than 50% but less than or equal to 80% of HAMFI\tcost burden greater than 50%|\n", - " " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Below I also propose an alternate means for ranking census tracts\n", - "### These steps are outlined and commented below" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/sarahluw/.pyenv/versions/3.6.2/envs/my-virtual-env-3.6.2/lib/python3.6/site-packages/pandas/core/series.py:726: RuntimeWarning: invalid value encountered in sqrt\n", - " result = getattr(ufunc, method)(*inputs, **kwargs)\n", - "/Users/sarahluw/.pyenv/versions/3.6.2/envs/my-virtual-env-3.6.2/lib/python3.6/site-packages/pandas/core/indexing.py:670: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " iloc._setitem_with_indexer(indexer, value)\n" - ] - } - ], - "source": [ - "# Read in the data from https://www.huduser.gov/portal/datasets/cp.html\n", - "housing = pd.read_csv(\"Table8.csv\", \n", - " encoding = \"ISO-8859-1\", \n", - " dtype = {'Tract_ID': object, 'st': object, 'geoid': object})\n", - "\n", - "# Remove data for states that aren't included in the census (e.g. American Samoa, Guam, etc.):\n", - "housing.drop(housing.loc[housing['st'] == '72'].index, inplace = True)\n", - "\n", - "# Combine owner and renter occupied low-income households that make less than 80% of HAMFI into one variable\n", - "housing['summed'] = (housing['T8_est10'] + \n", - " housing['T8_est23'] + \n", - " housing['T8_est36'] + \n", - " housing['T8_est76'] + \n", - " housing['T8_est89'] + \n", - " housing['T8_est102'])\n", - "\n", - "# Create a variable for the standard error of the summed variables\n", - "housing['summed_se'] = np.sqrt((housing['T8_moe10'] / 1.645)**2 + \n", - " (housing['T8_moe23'] / 1.645)**2 + \n", - " (housing['T8_moe36'] / 1.645)**2 + \n", - " (housing['T8_moe76'] / 1.645)**2 + \n", - " (housing['T8_moe89'] / 1.645)**2 + \n", - " (housing['T8_moe102'] / 1.645)**2)\n", - "\n", - "# Remove the first 7 digits in the FIPS Census Tract ID \n", - "housing['geoid'] = housing['geoid'].str[-11:]\n", - "\n", - "# Find the estimate of the proportion of the population that is heavily rent burdened\n", - "housing['hbrd_score'] = housing['summed'] / housing['T8_est1']\n", - "\n", - "# Change rates where the population is 0 to nan\n", - "housing['hbrd_score'].replace(np.inf, np.nan, inplace = True)\n", - "\n", - "# Create function for calculating the standard error, using the proportions standard error formula\n", - "# if the value under the radical is negative, use the ratio standard error formula\n", - "def se_prop(x, y, se_x, moe_y): \n", - " se_y = moe_y / 1.645\n", - " test = se_x**2 - (((x**2)/(y**2))*((se_y)**2))\n", - " se = np.where(test < 0,\n", - " (1/y) * np.sqrt(se_x**2 + (((x**2)/(y**2))*(se_y**2))), \n", - " (1/y) * np.sqrt(se_x**2 - (((x**2)/(y**2))*(se_y**2))))\n", - " return se\n", - "\n", - "housing['se'] = se_prop(housing['summed'], housing['T8_est1'], housing['summed_se'], housing['T8_moe1'])\n", - "\n", - "# Calculate the relative standard error\n", - "housing['rse'] = housing['se'] / housing['hbrd_score']*100\n", - "\n", - "# Change infinite rse's where the housing burden is 0 to np.nan\n", - "housing['rse'].replace(np.inf, np.nan, inplace = True)\n", - "\n", - "# Calculate the mean standard error for each state\n", - "housing['mean_state_se'] = np.zeros(len(housing))\n", - "\n", - "for state in housing['st'].unique():\n", - " mean_se = np.mean(housing[housing['st'] == state]['se'])\n", - " housing['mean_state_se'].loc[housing['st'] == state] = mean_se\n", - " \n", - "# Find census tract estimates that meet both of the following criteria and are thus considered unreliable estimates: \n", - "# RSE less than 50 AND\n", - "# SE less than the mean state SE or housing burdened low income households\n", - "# Convert these scores to nan\n", - "housing.loc[(housing['rse'] >= 50) & (housing['rse'] >= housing['mean_state_se']), 'hbrd_score'] = np.nan\n", - "\n", - "# Rename columns\n", - "housing = housing.rename(columns = {'geoid' :'FIPS_tract_id',\n", - " 'st' : 'state'\n", - " })\n", - "\n", - "# Calculate percentile rank for census tracts with a score above 0, set percentile to 0 if score is 0, for each state\n", - "housing['hbrd_rank'] = housing[\n", - " housing['hbrd_score'] != 0][['hbrd_score',\n", - " 'state']].groupby('state').rank( \n", - " na_option = 'keep', \n", - " pct = True) * 100\n", - "\n", - "housing.loc[housing['hbrd_score'] == 0, 'hbrd_rank'] = 0\n", - "\n", - "# Create final housing burden df\n", - "housingburden = housing.copy()" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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      sourcesumlevelFIPS_tract_idnamestatecntytractT8_est1T8_est2T8_est3...T8_moe131T8_moe132T8_moe133summedsummed_sehbrd_scoresersemean_state_sehbrd_rank
      02014thru201814001001020100Census Tract 201, Autauga County, Alabama0112010076557050...1212128031.7218070.1045750.04103239.2373140.03660446.298077
      12014thru201814001001020200Census Tract 202, Autauga County, Alabama0112020072046565...12121213845.5318740.1916670.06161432.1466590.03660483.269231
      22014thru201814001001020300Census Tract 203, Autauga County, Alabama01120300129584060...12121217053.7229210.1312740.04092731.1769990.03660463.653846
      32014thru201814001001020400Census Tract 204, Autauga County, Alabama011204001640126015...12121214546.2885100.0884150.02782231.4673970.03660434.615385
      42014thru201814001001020500Census Tract 205, Autauga County, Alabama0112050041752320175...171717595147.2216930.1425150.03476024.3901930.03660468.221154
      \n", - "

      5 rows Ă— 280 columns

      \n", - "
      " - ], - "text/plain": [ - " source sumlevel FIPS_tract_id \\\n", - "0 2014thru2018 140 01001020100 \n", - "1 2014thru2018 140 01001020200 \n", - "2 2014thru2018 140 01001020300 \n", - "3 2014thru2018 140 01001020400 \n", - "4 2014thru2018 140 01001020500 \n", - "\n", - " name state cnty tract T8_est1 \\\n", - "0 Census Tract 201, Autauga County, Alabama 01 1 20100 765 \n", - "1 Census Tract 202, Autauga County, Alabama 01 1 20200 720 \n", - "2 Census Tract 203, Autauga County, Alabama 01 1 20300 1295 \n", - "3 Census Tract 204, Autauga County, Alabama 01 1 20400 1640 \n", - "4 Census Tract 205, Autauga County, Alabama 01 1 20500 4175 \n", - "\n", - " T8_est2 T8_est3 ... T8_moe131 T8_moe132 T8_moe133 summed summed_se \\\n", - "0 570 50 ... 12 12 12 80 31.721807 \n", - "1 465 65 ... 12 12 12 138 45.531874 \n", - "2 840 60 ... 12 12 12 170 53.722921 \n", - "3 1260 15 ... 12 12 12 145 46.288510 \n", - "4 2320 175 ... 17 17 17 595 147.221693 \n", - "\n", - " hbrd_score se rse mean_state_se hbrd_rank \n", - "0 0.104575 0.041032 39.237314 0.036604 46.298077 \n", - "1 0.191667 0.061614 32.146659 0.036604 83.269231 \n", - "2 0.131274 0.040927 31.176999 0.036604 63.653846 \n", - "3 0.088415 0.027822 31.467397 0.036604 34.615385 \n", - "4 0.142515 0.034760 24.390193 0.036604 68.221154 \n", - "\n", - "[5 rows x 280 columns]" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "housingburden.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(73056, 280)" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "housingburden.shape" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### As desired we see a uniform distribution for the percentile rank for burdened households" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Now we compute for a baseline comparison " - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# Owner occupied numerator fields\n", - "OWNER_OCCUPIED_NUMERATOR_FIELDS = [\n", - " # Column Name\n", - " # Line_Type\n", - " # Tenure\n", - " # Household income\n", - " # Cost burden\n", - " # Facilities\n", - " \"T8_est7\",\n", - " # Subtotal\n", - " # Owner occupied\n", - " # less than or equal to 30% of HAMFI\n", - " # greater than 30% but less than or equal to 50%\n", - " # All\n", - " \"T8_est10\",\n", - " # Subtotal\n", - " # Owner occupied\n", - " # less than or equal to 30% of HAMFI\n", - " # greater than 50%\n", - " # All\n", - " \"T8_est20\",\n", - " \n", - " # Subtotal\n", - " # Owner occupied\n", - " # greater than 30% but less than or equal to 50% of HAMFI\n", - " # greater than 30% but less than or equal to 50%\n", - " # All\n", - " \"T8_est23\",\n", - " # Subtotal\n", - " # Owner occupied\n", - " # greater than 30% but less than or equal to 50% of HAMFI\n", - " # greater than 50%\n", - " # All\n", - " \"T8_est33\",\n", - " # Subtotal\n", - " # Owner occupied\n", - " # greater than 50% but less than or equal to 80% of HAMFI\n", - " # greater than 30% but less than or equal to 50%\n", - " # All\n", - " \"T8_est36\",\n", - " # Subtotal\n", - " # Owner occupied\n", - " # greater than 50% but less than or equal to 80% of HAMFI\n", - " # greater than 50%\n", - " # All\n", - "]\n", - "\n", - "# These rows have the values where HAMFI was not computed, b/c of no or negative income.\n", - "OWNER_OCCUPIED_NOT_COMPUTED_FIELDS = [\n", - " # Column Name\n", - " # Line_Type\n", - " # Tenure\n", - " # Household income\n", - " # Cost burden\n", - " # Facilities\n", - " \"T8_est13\",\n", - " # Subtotal\n", - " # Owner occupied\n", - " # less than or equal to 30% of HAMFI\n", - " # not computed (no/negative income)\n", - " # All\n", - " \"T8_est26\",\n", - " # Subtotal\n", - " # Owner occupied\n", - " # greater than 30% but less than or equal to 50% of HAMFI\n", - " # not computed (no/negative income)\n", - " # All\n", - " \"T8_est39\",\n", - " # Subtotal\n", - " # Owner occupied\n", - " # greater than 50% but less than or equal to 80% of HAMFI\n", - " # not computed (no/negative income)\n", - " # All\n", - " \"T8_est52\",\n", - " # Subtotal\n", - " # Owner occupied\n", - " # greater than 80% but less than or equal to 100% of HAMFI\n", - " # not computed (no/negative income)\n", - " # All\n", - " \"T8_est65\",\n", - " # Subtotal\n", - " # Owner occupied\n", - " # greater than 100% of HAMFI\n", - " # not computed (no/negative income)\n", - " # All\n", - "]\n", - "\n", - "OWNER_OCCUPIED_POPULATION_FIELD = \"T8_est2\"\n", - "# Subtotal\n", - "# Owner occupied\n", - "# All\n", - "# All\n", - "# All\n", - "\n", - "OWNER_OCCUPIED_POPULATION_HAMFI_FIELD = \"T8_est3\"\n", - "# Subtotal\n", - "# Owner occupied \n", - "# All\n", - "# All\n", - "# All\n", - "\n", - "# Renter occupied numerator fields\n", - "RENTER_OCCUPIED_NUMERATOR_FIELDS = [\n", - " # Column Name\n", - " # Line_Type\n", - " # Tenure\n", - " # Household income\n", - " # Cost burden\n", - " # Facilities\n", - " \"T8_est73\",\n", - " # Subtotal\n", - " # Renter occupied\n", - " # less than or equal to 30% of HAMFI\n", - " # greater than 30% but less than or equal to 50%\n", - " # All\n", - " \"T8_est76\",\n", - " # Subtotal\n", - " # Renter occupied\n", - " # less than or equal to 30% of HAMFI\n", - " # greater than 50%\n", - " # All\n", - " \"T8_est86\",\n", - " # Subtotal\n", - " # Renter occupied\n", - " # greater than 30% but less than or equal to 50% of HAMFI\n", - " # greater than 30% but less than or equal to 50%\n", - " # All\n", - " \"T8_est89\",\n", - " # Subtotal\n", - " # Renter occupied\n", - " # greater than 30% but less than or equal to 50% of HAMFI\n", - " # greater than 50%\n", - " # All\n", - " \"T8_est99\",\n", - " # Subtotal\n", - " # Renter occupied\tgreater than 50% but less than or equal to 80% of HAMFI\n", - " # greater than 30% but less than or equal to 50%\n", - " # All\n", - " \"T8_est102\",\n", - " # Subtotal\n", - " # Renter occupied\n", - " # greater than 50% but less than or equal to 80% of HAMFI\n", - " # greater than 50%\n", - " # All\n", - "]\n", - "\n", - "# These rows have the values where HAMFI was not computed, b/c of no or negative income.\n", - "RENTER_OCCUPIED_NOT_COMPUTED_FIELDS = [\n", - " # Column Name\n", - " # Line_Type\n", - " # Tenure\n", - " # Household income\n", - " # Cost burden\n", - " # Facilities\n", - " \"T8_est79\",\n", - " # Subtotal\n", - " # Renter occupied\tless than or equal to 30% of HAMFI\n", - " # not computed (no/negative income)\n", - " # All\n", - " \"T8_est92\",\n", - " # Subtotal\n", - " # Renter occupied\tgreater than 30% but less than or equal to 50% of HAMFI\n", - " # not computed (no/negative income)\n", - " # All\n", - " \"T8_est105\",\n", - " # Subtotal\n", - " # Renter occupied\n", - " # greater than 50% but less than or equal to 80% of HAMFI\n", - " # not computed (no/negative income)\n", - " # All\n", - " \"T8_est118\",\n", - " # Subtotal\n", - " # Renter occupied\tgreater than 80% but less than or equal to 100% of HAMFI\n", - " # not computed (no/negative income)\n", - " # All\n", - " \"T8_est131\",\n", - " # Subtotal\n", - " # Renter occupied\n", - " # greater than 100% of HAMFI\n", - " # not computed (no/negative income)\n", - " # All\n", - "]\n", - "\n", - "# T8_est68\tSubtotalRenter occupied\tAll\tAll\tAll\n", - "RENTER_OCCUPIED_POPULATION_FIELD = \"T8_est68\"" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "housingburden[\"current_summed_methodology\"] = housingburden[\n", - " OWNER_OCCUPIED_NUMERATOR_FIELDS\n", - "].sum(axis=1) + housingburden[RENTER_OCCUPIED_NUMERATOR_FIELDS].sum(axis=1)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "housingburden[\"current_methodology_denominator\"] = (\n", - " housingburden[OWNER_OCCUPIED_POPULATION_FIELD]\n", - " + housingburden[RENTER_OCCUPIED_POPULATION_FIELD]\n", - " - housingburden[OWNER_OCCUPIED_NOT_COMPUTED_FIELDS].sum(axis=1)\n", - " - housingburden[RENTER_OCCUPIED_NOT_COMPUTED_FIELDS].sum(axis=1)\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "housingburden[\"current_methodology_denominator_sans_not_computed\"] = (\n", - " housingburden[OWNER_OCCUPIED_POPULATION_FIELD]\n", - " + housingburden[RENTER_OCCUPIED_POPULATION_FIELD]\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "housingburden[\"current_methodology_percent\"] = np.round(\n", - " (housingburden[\"current_summed_methodology\"] / housingburden[\"current_methodology_denominator\"] ), 2) * 100" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Now we construct the distribution of differences in the number of owned and rented burdened households\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Percentiles Comparison" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "final_df = housingburden[['FIPS_tract_id', 'state','hbrd_rank','hbrd_score', 'summed', \n", - " 'current_summed_methodology', 'T8_est1', \n", - " \"current_methodology_denominator_sans_not_computed\",\n", - " 'current_methodology_denominator', 'current_methodology_percent']]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### First notice here that **T8_est1** and **current_methodology_denominator** should represent same or similar aggregates. In general, we cen see that the current computation performed results in a differerntial that undercounts the total occupied and rental households." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/sarahluw/.pyenv/versions/3.6.2/envs/my-virtual-env-3.6.2/lib/python3.6/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " \n" - ] - } - ], - "source": [ - "final_df[\"differences_aggregate_denominator\"] = (\n", - " final_df[\"current_methodology_denominator\"] - final_df[\"T8_est1\"] \n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/sarahluw/.pyenv/versions/3.6.2/envs/my-virtual-env-3.6.2/lib/python3.6/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " \n" - ] - } - ], - "source": [ - "final_df[\"differences_aggregate_denominator_sans_not_computed\"] = (\n", - " final_df[\"current_methodology_denominator\"] - final_df[\"T8_est1\"] \n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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      " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(12, 8))\n", - "plt.title('Distribution of differences between aggregate totals that normalizes tabulation of poverty households')\n", - "# Set x-axis label\n", - "plt.xlabel('Aggregate differences in total owner and renter occupied low-income households < 80%')\n", - "# Set y-axis label\n", - "plt.ylabel('Relative Frequency in Support')\n", - "\n", - "sns.histplot(final_df[\"differences_aggregate_denominator\"])" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
      " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(12, 8))\n", - "plt.title('Distribution of differences between aggregate totals that normalizes tabulation of poverty households (with removal of not computed fields) ')\n", - "# Set x-axis label\n", - "plt.xlabel('Aggregate differences in total owner and renter occupied low-income households')\n", - "# Set y-axis label\n", - "plt.ylabel('Relative Frequency in Support')\n", - "\n", - "sns.histplot(final_df[\"differences_aggregate_denominator_sans_not_computed\"])" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/sarahluw/.pyenv/versions/3.6.2/envs/my-virtual-env-3.6.2/lib/python3.6/site-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " after removing the cwd from sys.path.\n" - ] - } - ], - "source": [ - "final_df[\"current_methodology_percentile_rank\"] = final_df[\"current_methodology_percent\"].rank(\n", - " pct=True,\n", - " # Set ascending to the parameter value.\n", - " ascending=True,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/sarahluw/.pyenv/versions/3.6.2/envs/my-virtual-env-3.6.2/lib/python3.6/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " \"\"\"Entry point for launching an IPython kernel.\n", - "/Users/sarahluw/.pyenv/versions/3.6.2/envs/my-virtual-env-3.6.2/lib/python3.6/site-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " after removing the cwd from sys.path.\n" - ] - } - ], - "source": [ - "final_df[\"new_threshold_exceeded\"] = (final_df['hbrd_rank'] >= 90)\n", - "\n", - "final_df[\"current_threshold_exceeded\"] = (final_df[\n", - " 'current_methodology_percentile_rank'] >= 0.90)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Only include non-NA tracts for comparison purposes" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "# first save NA tracts that were considered unreliable\n", - "ineligible_tracts = list(final_df[final_df[\"hbrd_rank\"].isna()][\"FIPS_tract_id\"].values)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### How many tracts are ineligible according to CalEnvironScreen but are considerd in Score L?\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "final_current_methodology = final_df[final_df[\"current_methodology_percentile_rank\"] >= 0.90]" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(7323, 15)" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "final_current_methodology.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(53, 15)" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# 53 tracts\n", - "final_current_methodology[\n", - " final_current_methodology.FIPS_tract_id.isin(ineligible_tracts)].shape" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "non_null_df = final_df[~final_df[\"hbrd_rank\"].isna()]" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(7270, 15)" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# number of tracks eligible\n", - "non_null_df[non_null_df[\"current_methodology_percentile_rank\"] >= 0.90].shape" - ] - }, - { - "cell_type": "code", - "execution_count": 85, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 85, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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      FIPS_tract_idstatehbrd_rankhbrd_scoresummedcurrent_summed_methodologyT8_est1current_methodology_denominator_sans_not_computedcurrent_methodology_denominatorcurrent_methodology_percentdifferences_aggregate_denominatordifferences_aggregate_denominator_sans_not_computedcurrent_methodology_percentile_ranknew_threshold_exceededcurrent_threshold_exceeded
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      \n", - "
      " - ], - "text/plain": [ - " FIPS_tract_id state hbrd_rank hbrd_score summed \\\n", - "0 01001020100 01 46.298077 0.104575 80 \n", - "1 01001020200 01 83.269231 0.191667 138 \n", - "2 01001020300 01 63.653846 0.131274 170 \n", - "3 01001020400 01 34.615385 0.088415 145 \n", - "4 01001020500 01 68.221154 0.142515 595 \n", - "\n", - " current_summed_methodology T8_est1 \\\n", - "0 174 765 \n", - "1 177 720 \n", - "2 279 1295 \n", - "3 274 1640 \n", - "4 885 4175 \n", - "\n", - " current_methodology_denominator_sans_not_computed \\\n", - "0 765 \n", - "1 720 \n", - "2 1295 \n", - "3 1635 \n", - "4 4175 \n", - "\n", - " current_methodology_denominator current_methodology_percent \\\n", - "0 765 23.0 \n", - "1 720 25.0 \n", - "2 1291 22.0 \n", - "3 1635 17.0 \n", - "4 4135 21.0 \n", - "\n", - " differences_aggregate_denominator \\\n", - "0 0 \n", - "1 0 \n", - "2 -4 \n", - "3 -5 \n", - "4 -40 \n", - "\n", - " differences_aggregate_denominator_sans_not_computed \\\n", - "0 0 \n", - "1 0 \n", - "2 -4 \n", - "3 -5 \n", - "4 -40 \n", - "\n", - " current_methodology_percentile_rank new_threshold_exceeded \\\n", - "0 0.512833 False \n", - "1 0.575315 False \n", - "2 0.479242 False \n", - "3 0.289696 False \n", - "4 0.444306 False \n", - "\n", - " current_threshold_exceeded \n", - "0 False \n", - "1 False \n", - "2 False \n", - "3 False \n", - "4 False " - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "non_null_df.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [], - "source": [ - "net_difference = non_null_df[(non_null_df[\n", - " \"current_threshold_exceeded\"] != non_null_df[\"new_threshold_exceeded\"])]" - ] - }, - { - "cell_type": "code", - "execution_count": 89, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(2741, 15)" - ] - }, - "execution_count": 89, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "predicate_1 = (net_difference['current_threshold_exceeded'] == True) & (net_difference['new_threshold_exceeded'] == False)\n", - "\n", - "net_difference[predicate_1].shape" - ] - }, - { - "cell_type": "code", - "execution_count": 102, - "metadata": {}, - "outputs": [], - "source": [ - "# Where Score L is considered burdened but not in CalEnviroScreen\n", - "score_l_considered_burdened = net_difference[predicate_1]" - ] - }, - { - "cell_type": "code", - "execution_count": 109, - "metadata": {}, - "outputs": [], - "source": [ - "# Inserted after the basic stats definition.\n", - "# Load demographic data\n", - "import pathlib\n", - "\n", - "DATA_DIR = pathlib.Path.cwd().parent / \"data\"\n", - "COMPARISON_OUTPUTS_DIR = DATA_DIR / \"comparison_outputs\"\n", - "\n", - "demographics_path = DATA_DIR / \"dataset\" / \"census_acs_2019\" / \"usa.csv\"\n", - "\n", - "demographics_df = pd.read_csv(\n", - " demographics_path,\n", - " dtype={\"GEOID10_TRACT\": \"string\"},\n", - ")\n", - "\n", - "# Set some field names\n", - "BLACK_FIELD_NAME = \"Black or African American alone\"\n", - "AMERICAN_INDIAN_FIELD_NAME = \"American Indian and Alaska Native alone\"\n", - "ASIAN_FIELD_NAME = \"Asian alone\"\n", - "HAWAIIAN_FIELD_NAME = \"Native Hawaiian and Other Pacific alone\"\n", - "TWO_OR_MORE_RACES_FIELD_NAME = \"Two or more races\"\n", - "NON_HISPANIC_WHITE_FIELD_NAME = \"Non-Hispanic White\"\n", - "HISPANIC_FIELD_NAME = \"Hispanic or Latino\"\n", - "PERCENT_PREFIX = \"Percent \"\n", - "\n", - "RE_OUTPUT_FIELDS = [\n", - " BLACK_FIELD_NAME,\n", - " AMERICAN_INDIAN_FIELD_NAME,\n", - " ASIAN_FIELD_NAME,\n", - " HAWAIIAN_FIELD_NAME,\n", - " TWO_OR_MORE_RACES_FIELD_NAME,\n", - " NON_HISPANIC_WHITE_FIELD_NAME,\n", - " HISPANIC_FIELD_NAME,\n", - "]\n", - "\n", - "RE_PERCENT_OUTPUT_FIELDS = [PERCENT_PREFIX + field for field in RE_OUTPUT_FIELDS]\n", - "\n", - "columns_to_keep = (\n", - " [\"GEOID10_TRACT\"]\n", - " + RE_OUTPUT_FIELDS\n", - " + RE_PERCENT_OUTPUT_FIELDS\n", - " + ['Percent of individuals < 200% Federal Poverty Line', \n", - " 'Median value ($) of owner-occupied housing units',\n", - " 'Percent individuals age 25 or over with less than high school degree',\n", - " 'Percent enrollment in college or graduate school',\n", - " 'Linguistic isolation (percent)']\n", - ")\n", - "\n", - "\n", - "def highlight_medians(s):\n", - " # highlight if the current median is greater than the median of medians for that series\n", - " is_greater_than_median = s > s.median()\n", - " return ['color: pink; background-color:#7272FE'\n", - " if cell else '' for cell in is_greater_than_median]\n", - "# Join the demographics in.\n", - "merged_df_score_l = score_l_considered_burdened.merge(\n", - " demographics_df[columns_to_keep],\n", - " left_on=\"FIPS_tract_id\",\n", - " right_on=\"GEOID10_TRACT\",\n", - " how=\"inner\"\n", - ")\n", - "\n", - "# these are not converted into percent 0 - 100 scale\n", - "percent_cols = [x for x in merged_df.columns if \n", - " 'Percent' in x or '(percent)' in x\n", - " ]\n", - "\n", - "merged_df_score_l[\n", - " percent_cols] = merged_df_score_l[\n", - " percent_cols].apply(lambda x: x * 100)\n", - "\n", - "for idx, row in merged_df_score_l.iterrows():\n", - " current_row = str(merged_df_score_l.loc[idx, 'state'])\n", - " state = mapping.get(current_row, None)\n", - " merged_df_score_l.loc[idx, 'state_name'] = state\n", - "\n", - "grouped_stats_score_l = merged_df_score_l.groupby([\"state_name\"]).agg({\n", - " 'GEOID10_TRACT': 'nunique',\n", - " 'Percent of individuals < 200% Federal Poverty Line': [np.median, np.std],\n", - " 'Median value ($) of owner-occupied housing units': [np.median, np.std],\n", - " 'Percent individuals age 25 or over with less than high school degree': [np.median, np.std],\n", - " 'Percent enrollment in college or graduate school': [np.median, np.std],\n", - " 'Percent Black or African American alone': [np.median, np.std],\n", - " 'Percent American Indian and Alaska Native alone': [np.median, np.std],\n", - " 'Percent Non-Hispanic White': [np.median, np.std], \n", - " 'Linguistic isolation (percent)': [np.median, np.std],\n", - " 'Percent Hispanic or Latino': [np.median, np.std],\n", - " 'hbrd_rank': [np.median, np.std],\n", - " 'current_methodology_percent': [np.median, np.std],\n", - " 'current_summed_methodology': [np.median, np.std, np.sum]\n", - "}).reset_index()\n", - "\n", - "\n", - "grouped_stats_score_l.columns = [' '.join(col).strip() for \n", - " col in grouped_stats_score_l.columns.values]\n", - "\n", - "grouped_stats_score_l = grouped_stats_score_l[[x for x in grouped_stats_score_l \n", - " if \"median\" in x and \n", - " \"Percent\" in x] + [\"GEOID10_TRACT nunique\", \n", - " 'current_summed_methodology sum', \"state_name\"]]\n", - "\n", - "grouped_stats_score_l.set_index(\"state_name\", inplace=True)\n", - "\n", - "grouped_stats_score_l = grouped_stats_score_l.rename(columns={\n", - "'Percent of individuals < 200% Federal Poverty Line median'\n", - " : 'Percent of individuals < 200% Federal Poverty Line (median across all tracts)', \n", - "'Percent individuals age 25 or over with less than high school degree median':\n", - "'Percent individuals age 25 or over with less than high school degree (median across all tracts)', \n", - "'Percent enrollment in college or graduate school median'\n", - " :'Percent enrollment in college or graduate school (median across all tracts)',\n", - "'Percent Black or African American alone median':\n", - " 'Percent Black or African American alone (median across all tracts)',\n", - "'Percent American Indian and Alaska Native alone median':\n", - " 'Percent American Indian and Alaska Native alone (median across all tracts)',\n", - "'Percent Non-Hispanic White median':\n", - " 'Percent Non-Hispanic White (median across all tracts)',\n", - "'Percent Hispanic or Latino median':\n", - " 'Percent Hispanic or Latino (median across all tracts)',\n", - "'GEOID10_TRACT nunique': \"Total Number of Unique Tracts\",\n", - "\"current_summed_methodology sum\": \"Total Owned and Rented Burdened Households (Current Aggregation Methodology)\" \n", - "})" - ] - }, - { - "cell_type": "code", - "execution_count": 114, - "metadata": {}, - "outputs": [], - "source": [ - "bar_styled_score_l = grouped_stats_score_l.style.bar(\n", - 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      Percent of individuals < 200% Federal Poverty Line (median across all tracts) Percent individuals age 25 or over with less than high school degree (median across all tracts) Percent enrollment in college or graduate school (median across all tracts) Percent Black or African American alone (median across all tracts) Percent American Indian and Alaska Native alone (median across all tracts) Percent Non-Hispanic White (median across all tracts) Percent Hispanic or Latino (median across all tracts) Total Number of Unique Tracts Total Owned and Rented Burdened Households (Current Aggregation Methodology)
      state_name
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      AR62.78581218.6580529.05639172.6277140.10245911.6507574.49709742287
      AZ67.89663235.3178426.8035856.8779112.06493020.32014363.4241583018999
      CA52.18890133.3458789.1456794.1038560.4856049.28976866.540085840576225
      CO50.79802714.9253737.9512898.3552821.21033228.21264936.3037751915128
      CT52.66257019.7992198.79765422.8709680.00000026.31832436.7860945337383
      DC50.88139715.8562376.47948291.5753210.0000002.9838713.0153121510389
      DE43.17124710.0204503.49417627.0983210.94004641.69626113.62909732343
      FL54.78608320.4034317.37691126.8486920.00000017.74120526.870748161116939
      GA58.31884118.8155468.75099466.0021550.00000010.8166006.2990895945553
      HI35.98409812.5218038.3583681.7069770.0000009.5053015.326514126598
      IA56.50446910.3132162.7076929.8311820.74478689.4240320.0000001449
      ID56.68056415.42500816.0661010.2475690.05305068.36427927.7630421945
      IL57.13485219.1528407.23355841.4607730.0000006.83374815.93533614881294
      IN60.36199119.8126806.40301341.5031470.00000040.1332849.3698372315013
      KS61.78077210.6703358.65369823.7452410.87474356.91916715.92658721352
      KY63.82604324.7813416.68640518.5387130.57803560.2690749.63391174393
      LA63.48164921.2974307.83115077.6525200.00000015.0530502.0414484325262
      MA51.64444422.6301127.82520316.0096540.00000031.34865132.9847146143002
      MD47.03989717.6980206.49960865.9347550.00000012.3831335.8508603118837
      ME54.94023916.3678434.6161320.5553350.00000084.9662830.4760021460
      MI63.34328416.9279007.13058473.7889850.00000014.2468241.2779556130101
      MN58.24654729.02049613.32462039.2776002.30616319.89362430.51320121111
      MO57.44125315.1291513.85446776.9371730.00000016.0209422.467344116857
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      " - ], - "text/plain": [ - "" - ] - }, - "execution_count": 115, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bar_styled_score_l" - ] - }, - { - "cell_type": "code", - "execution_count": 116, - "metadata": {}, - "outputs": [], - "source": [ - "styled_score_l = grouped_stats_score_l.style.apply(highlight_medians)" - ] - }, - { - "cell_type": "code", - "execution_count": 120, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - 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      Percent of individuals < 200% Federal Poverty Line (median across all tracts) Percent individuals age 25 or over with less than high school degree (median across all tracts) Percent enrollment in college or graduate school (median across all tracts) Percent Black or African American alone (median across all tracts) Percent American Indian and Alaska Native alone (median across all tracts) Percent Non-Hispanic White (median across all tracts) Percent Hispanic or Latino (median across all tracts) Total Number of Unique Tracts Total Owned and Rented Burdened Households (Current Aggregation Methodology)
      state_name
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      AR62.78581218.6580529.05639172.6277140.10245911.6507574.49709742287
      AZ67.89663235.3178426.8035856.8779112.06493020.32014363.4241583018999
      CA52.18890133.3458789.1456794.1038560.4856049.28976866.540085840576225
      CO50.79802714.9253737.9512898.3552821.21033228.21264936.3037751915128
      CT52.66257019.7992198.79765422.8709680.00000026.31832436.7860945337383
      DC50.88139715.8562376.47948291.5753210.0000002.9838713.0153121510389
      DE43.17124710.0204503.49417627.0983210.94004641.69626113.62909732343
      FL54.78608320.4034317.37691126.8486920.00000017.74120526.870748161116939
      GA58.31884118.8155468.75099466.0021550.00000010.8166006.2990895945553
      HI35.98409812.5218038.3583681.7069770.0000009.5053015.326514126598
      IA56.50446910.3132162.7076929.8311820.74478689.4240320.0000001449
      ID56.68056415.42500816.0661010.2475690.05305068.36427927.7630421945
      IL57.13485219.1528407.23355841.4607730.0000006.83374815.93533614881294
      IN60.36199119.8126806.40301341.5031470.00000040.1332849.3698372315013
      KS61.78077210.6703358.65369823.7452410.87474356.91916715.92658721352
      KY63.82604324.7813416.68640518.5387130.57803560.2690749.63391174393
      LA63.48164921.2974307.83115077.6525200.00000015.0530502.0414484325262
      MA51.64444422.6301127.82520316.0096540.00000031.34865132.9847146143002
      MD47.03989717.6980206.49960865.9347550.00000012.3831335.8508603118837
      ME54.94023916.3678434.6161320.5553350.00000084.9662830.4760021460
      MI63.34328416.9279007.13058473.7889850.00000014.2468241.2779556130101
      MN58.24654729.02049613.32462039.2776002.30616319.89362430.51320121111
      MO57.44125315.1291513.85446776.9371730.00000016.0209422.467344116857
      MS66.20366018.0787666.21738094.8784050.0000003.9307200.71454183868
      NC63.94933428.7051484.70401746.3207550.29252018.76923120.4315281912567
      NE62.76567147.1637694.5857990.8211921.11258328.31788167.8145701495
      NH49.84622518.8016185.0095024.6453460.00000073.45290213.49966742045
      NJ46.91510721.5162877.05805024.0194120.00000012.37740146.025136158106317
      NV57.43692523.2876545.03340711.7136650.59562428.48294844.7116753022733
      NY47.65609724.2640698.25713525.7262290.00000011.39643228.944737588413485
      OH67.32936920.1264705.74308270.9250590.00000020.0292753.6286546833904
      OK73.17715222.3287674.36324918.1390372.18978144.16058427.37226351357
      OR53.86003618.5121927.3918682.8698860.68837953.60466429.77829686408
      PA61.39221119.7604796.88259133.0663620.00000024.41661911.6991645126975
      RI61.53964725.7508446.40419516.7547030.45375222.64266349.720052127835
      SC56.54455916.8884439.84424061.0905330.00000030.1401225.667862126952
      TN61.55836322.1389936.12393780.8630390.00000011.1824776.2540722116124
      TX61.08414228.2543896.78503518.8251950.00000012.01619055.03788910373087
      UT57.19201718.71345011.7765140.5047032.59233841.47740353.6820371595
      VA52.26472116.4648916.13181653.9263980.00000021.9739919.3340062720083
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      " - ], - "text/plain": [ - "" - ] - }, - "execution_count": 120, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "styled_score_l" - ] - }, - { - "cell_type": "code", - "execution_count": 118, - "metadata": {}, - "outputs": [], - "source": [ - "styled_score_l.to_excel(\"score_l_burdened_not_calenviroscreen.xlsx\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# CalenviroScreen Burden" - ] - }, - { - "cell_type": "code", - "execution_count": 122, - "metadata": {}, - "outputs": [], - "source": [ - "predicate_2 = (net_difference['current_threshold_exceeded'] == False) & (net_difference['new_threshold_exceeded'] == True)\n", - "\n", - "cal_ej_screen_burdened = net_difference[predicate_2]" - ] - }, - { - "cell_type": "code", - "execution_count": 123, - "metadata": {}, - "outputs": [], - "source": [ - "# Join the demographics in.\n", - "merged_df_score_ejcreen = cal_ej_screen_burdened.merge(\n", - " demographics_df[columns_to_keep],\n", - " left_on=\"FIPS_tract_id\",\n", - " right_on=\"GEOID10_TRACT\",\n", - " how=\"inner\"\n", - ")\n", - "\n", - "# these are not converted into percent 0 - 100 scale\n", - "percent_cols = [x for x in merged_df_score_ejcreen.columns if \n", - " 'Percent' in x or '(percent)' in x\n", - " ]\n", - "\n", - "merged_df_score_ejcreen[\n", - " percent_cols] = merged_df_score_ejcreen[\n", - " percent_cols].apply(lambda x: x * 100)\n", - "\n", - "for idx, row in merged_df_score_ejcreen.iterrows():\n", - " current_row = str(merged_df_score_ejcreen.loc[idx, 'state'])\n", - " state = mapping.get(current_row, None)\n", - " merged_df_score_ejcreen.loc[idx, 'state_name'] = state\n", - "\n", - "grouped_stats_score_ej_screen = merged_df_score_ejcreen.groupby([\"state_name\"]).agg({\n", - " 'GEOID10_TRACT': 'nunique',\n", - " 'Percent of individuals < 200% Federal Poverty Line': [np.median, np.std],\n", - " 'Median value ($) of owner-occupied housing units': [np.median, np.std],\n", - " 'Percent individuals age 25 or over with less than high school degree': [np.median, np.std],\n", - " 'Percent enrollment in college or graduate school': [np.median, np.std],\n", - " 'Percent Black or African American alone': [np.median, np.std],\n", - " 'Percent American Indian and Alaska Native alone': [np.median, np.std],\n", - " 'Percent Non-Hispanic White': [np.median, np.std], \n", - " 'Linguistic isolation (percent)': [np.median, np.std],\n", - " 'Percent Hispanic or Latino': [np.median, np.std],\n", - " 'hbrd_rank': [np.median, np.std],\n", - " 'current_methodology_percent': [np.median, np.std],\n", - " 'current_summed_methodology': [np.median, np.std, np.sum]\n", - "}).reset_index()\n", - "\n", - "\n", - "grouped_stats_score_ej_screen.columns = [' '.join(col).strip() for \n", - " col in grouped_stats_score_ej_screen.columns.values]\n", - "\n", - "grouped_stats_score_ej_screen = grouped_stats_score_ej_screen[[x for x in grouped_stats_score_ej_screen \n", - " if \"median\" in x and \n", - " \"Percent\" in x] + [\"GEOID10_TRACT nunique\", \n", - " 'current_summed_methodology sum', \"state_name\"]]\n", - "\n", - "grouped_stats_score_ej_screen.set_index(\"state_name\", inplace=True)\n", - "\n", - "grouped_stats_score_ej_screen = grouped_stats_score_ej_screen.rename(columns={\n", - "'Percent of individuals < 200% Federal Poverty Line median'\n", - " : 'Percent of individuals < 200% Federal Poverty Line (median across all tracts)', \n", - "'Percent individuals age 25 or over with less than high school degree median':\n", - "'Percent individuals age 25 or over with less than high school degree (median across all tracts)', \n", - "'Percent enrollment in college or graduate school median'\n", - " :'Percent enrollment in college or graduate school (median across all tracts)',\n", - "'Percent Black or African American alone median':\n", - " 'Percent Black or African American alone (median across all tracts)',\n", - "'Percent American Indian and Alaska Native alone median':\n", - " 'Percent American Indian and Alaska Native alone (median across all tracts)',\n", - "'Percent Non-Hispanic White median':\n", - " 'Percent Non-Hispanic White (median across all tracts)',\n", - "'Percent Hispanic or Latino median':\n", - " 'Percent Hispanic or Latino (median across all tracts)',\n", - "'GEOID10_TRACT nunique': \"Total Number of Unique Tracts\",\n", - "\"current_summed_methodology sum\": \"Total Owned and Rented Burdened Households (Current Aggregation Methodology)\" \n", - "})" - ] - }, - { - "cell_type": "code", - "execution_count": 124, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(2272, 36)" - ] - }, - "execution_count": 124, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "merged_df_score_ejcreen.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 126, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "2272" - ] - }, - "execution_count": 126, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "grouped_stats_score_ej_screen['Total Number of Unique Tracts'].sum()" - ] - }, - { - "cell_type": "code", - "execution_count": 127, - "metadata": {}, - "outputs": [], - "source": [ - "bar_styled_ej_screen = grouped_stats_score_ej_screen.style.bar(\n", - " subset=grouped_stats_score_ej_screen.columns, align='mid', color=['#d65f5f', '#5fba7d'])" - ] - }, - { - "cell_type": "code", - "execution_count": 128, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - 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      Percent of individuals < 200% Federal Poverty Line (median across all tracts) Percent individuals age 25 or over with less than high school degree (median across all tracts) Percent enrollment in college or graduate school (median across all tracts) Percent Black or African American alone (median across all tracts) Percent American Indian and Alaska Native alone (median across all tracts) Percent Non-Hispanic White (median across all tracts) Percent Hispanic or Latino (median across all tracts) Total Number of Unique Tracts Total Owned and Rented Burdened Households (Current Aggregation Methodology)
      state_name
      AK32.57785710.6818186.2790705.63882912.55868555.64245811.153285115004
      AL57.12393815.9896757.56397873.0238130.00000017.4479262.6160734222529
      AR57.35376017.0561915.11363652.3086650.00000034.9146115.3937433919130
      AZ54.30176223.3384187.0756255.5165242.98060729.27350454.6663266942252
      CA36.47130316.5462909.8486303.8562090.06978431.16028726.3238925734081
      CO45.53006314.1091667.8619193.0166341.06052352.63855226.1706955332424
      CT32.32465412.9340058.22306230.8233640.00000035.70255733.05184153597
      DC30.6557776.57853617.06884625.6285580.00000052.7733189.1400892716
      DE41.80097211.2518637.99319754.9051940.35814832.1242106.924979115159
      FL41.98744212.4903437.84136213.3955750.00000037.30916023.9107057345897
      GA57.69491416.0821539.55094664.4906280.09348720.4973104.1263496232938
      HI31.0869573.4203428.0929962.6030370.09480545.4446854.94736851827
      IA45.55075311.0216867.44970911.4546560.01277569.8716297.9979915426215
      ID51.9584195.7815158.8763120.5593731.06859084.3568487.9465512417542
      IL51.97947213.8489217.86427136.4231740.00000031.8832289.8849025526892
      IN58.24832917.3753875.68335634.1874340.00000038.7855398.8772855931457
      KS56.51626918.2332798.02623811.4993510.40810955.45402516.8910104822521
      KY55.03160718.0973715.82383319.1138350.00000066.4021754.1783716431935
      LA55.13853217.8025806.43234669.6881430.00000026.8866943.648757165523
      MA42.70767314.8974456.8201757.5939560.00000050.70831817.8142402314780
      MD37.93936714.2735047.84150744.0864020.00000022.6541958.0500895727737
      ME42.3752307.2452328.5389521.4356000.43946790.8797561.3420082614901
      MI56.88949915.8593116.44075267.0801350.00000022.3194022.6280058436969
      MN42.32558111.27971710.18301316.7792790.54782058.2260208.0039048947853
      MO52.46449515.2147977.43187429.8350820.00000051.9408504.8459964120105
      MS57.71878117.0385406.79012370.0152210.00000026.8817200.6628003516392
      MT45.0789855.35533213.3026610.1291611.84455488.8817273.6506382013069
      NC55.79595017.4128607.51599939.8244240.21400738.2738999.2800209057578
      ND43.2432436.99017916.4667397.1492562.12194877.9402937.502267138858
      NE55.2813289.48750013.8573178.3841390.47776665.9887749.7988842813947
      NH38.51253211.4067306.7050932.5656560.00000087.6694744.3754032013896
      NJ40.98778012.8032749.39597334.3605550.00000018.00051820.26089473691
      NM53.60195416.7253529.3477502.7661803.11614729.44009657.2348172714087
      NV49.95147220.6551015.81651112.2139930.51863925.97092636.024189188040
      NY35.28700915.8296259.12792736.1632650.0000006.91886513.014754156102
      OH62.16293717.5000005.58213754.6029520.00000036.4298723.0000008933549
      OK55.90928916.6415667.09677412.9116123.44462148.79032313.6877356527277
      OR43.54733410.8349518.0594461.6923590.79872268.77328713.5010944332728
      PA51.82364412.9706607.98263630.2978110.00000031.6046774.96915011460268
      RI33.4419826.86553034.4688767.1856290.10888865.0469589.97938173697
      SC52.83505214.5844226.07734842.0417830.00000042.8742694.4242135529033
      SD47.8048787.6506438.2104653.6046323.99484575.2877454.7727851911063
      TN52.86541417.2302007.61171849.7008150.00000037.8229904.1185315229145
      TX52.85451023.4685546.80347911.2162920.00000019.00769443.565114206131757
      UT44.7908829.4339709.3828001.5999280.38123568.92157120.1296443219148
      VA39.51673812.2543958.42661733.3449110.00000034.1666676.5101727545045
      VT43.1128236.8371867.7231700.9235940.05055693.7005651.605505158061
      WA39.04300912.0608907.5268825.8465290.90156459.80987112.9402139370445
      WI52.23724214.4072806.32466015.2783990.55155332.91595111.2344273015514
      WV52.37579511.7412986.8748136.8052340.00000082.4479241.6365712611981
      WY33.4140924.8757176.5539660.5780351.17455181.37824211.80827996319
      " - ], - "text/plain": [ - "" - ] - }, - "execution_count": 128, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bar_styled_ej_screen" - ] - }, - { - "cell_type": "code", - "execution_count": 129, - "metadata": {}, - "outputs": [], - "source": [ - "styled_screen = grouped_stats_score_ej_screen.style.apply(highlight_medians)" - ] - }, - { - "cell_type": "code", - "execution_count": 131, - "metadata": {}, - "outputs": [], - "source": [ - "styled_screen.to_excel(\"calenviroscreen_summary.xlsx\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 50, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(5013, 15)" - ] - }, - "execution_count": 50, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "net_difference.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 87, - "metadata": {}, - "outputs": [], - "source": [ - "union_df = non_null_df[(non_null_df[\n", - " \"current_threshold_exceeded\"] == non_null_df[\"new_threshold_exceeded\"])]" - ] - }, - { - "cell_type": "code", - "execution_count": 88, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(62800, 15)" - ] - }, - "execution_count": 88, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "union_df.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [], - "source": [ - "# All together\n", - "\n", - "# Join the demographics in.\n", - "merged_df = net_difference.merge(\n", - " demographics_df[columns_to_keep],\n", - " left_on=\"FIPS_tract_id\",\n", - " right_on=\"GEOID10_TRACT\",\n", - " how=\"inner\"\n", - ")\n", - "\n", - "\n", - "# Group bys.\n", - "demographics_directory = COMPARISON_OUTPUTS_DIR / \"demographics_basic_stats\"\n", - "demographics_directory.mkdir(parents=True, exist_ok=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [], - "source": [ - "# these are not converted into percent 0 - 100 scale\n", - "percent_cols = [x for x in merged_df.columns if \n", - " 'Percent' in x or '(percent)' in x\n", - " ]\n", - "\n", - "merged_df[\n", - " percent_cols] = merged_df[\n", - " percent_cols].apply(lambda x: x * 100)" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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      FIPS_tract_idstatehbrd_rankhbrd_scoresummedcurrent_summed_methodologyT8_est1current_methodology_denominator_sans_not_computedcurrent_methodology_denominatorcurrent_methodology_percent...Percent Asian alonePercent Native Hawaiian and Other Pacific alonePercent Two or more racesPercent Non-Hispanic WhitePercent Hispanic or LatinoPercent of individuals < 200% Federal Poverty LineMedian value ($) of owner-occupied housing unitsPercent individuals age 25 or over with less than high school degreePercent enrollment in college or graduate schoolLinguistic isolation (percent)
      0010059507000192.0192310.23414614421861561560736.0...0.0000000.03.28731143.1952660.00000057.11094613750031.6770191.6191210.000000
      1010119521000196.1538460.26976717422264564564135.0...0.0000000.00.0000003.1032300.00000059.5946806690024.9512675.6472633.145695
      2010150002000191.3461540.22983928547512401245122539.0...1.0327020.00.38726341.1790026.19621349.9139417700013.2335334.2894061.862828
      3010150003000191.0576920.22950828048812201215117542.0...0.0000000.00.00000017.5010521.76693365.6289445130029.1840386.3500260.000000
      4010550010000198.4615380.30804613417743544043041.0...0.0000000.01.26671426.60098515.48205568.4882906540023.3372232.4108001.489362
      \n", - "

      5 rows Ă— 35 columns

      \n", - "
      " - ], - "text/plain": [ - " FIPS_tract_id state hbrd_rank hbrd_score summed \\\n", - "0 01005950700 01 92.019231 0.234146 144 \n", - "1 01011952100 01 96.153846 0.269767 174 \n", - "2 01015000200 01 91.346154 0.229839 285 \n", - "3 01015000300 01 91.057692 0.229508 280 \n", - "4 01055001000 01 98.461538 0.308046 134 \n", - "\n", - " current_summed_methodology T8_est1 \\\n", - "0 218 615 \n", - "1 222 645 \n", - "2 475 1240 \n", - "3 488 1220 \n", - "4 177 435 \n", - "\n", - " current_methodology_denominator_sans_not_computed \\\n", - "0 615 \n", - "1 645 \n", - "2 1245 \n", - "3 1215 \n", - "4 440 \n", - "\n", - " current_methodology_denominator current_methodology_percent ... \\\n", - "0 607 36.0 ... \n", - "1 641 35.0 ... \n", - "2 1225 39.0 ... \n", - "3 1175 42.0 ... \n", - "4 430 41.0 ... \n", - "\n", - " Percent Asian alone Percent Native Hawaiian and Other Pacific alone \\\n", - "0 0.000000 0.0 \n", - "1 0.000000 0.0 \n", - "2 1.032702 0.0 \n", - "3 0.000000 0.0 \n", - "4 0.000000 0.0 \n", - "\n", - " Percent Two or more races Percent Non-Hispanic White \\\n", - "0 3.287311 43.195266 \n", - "1 0.000000 3.103230 \n", - "2 0.387263 41.179002 \n", - "3 0.000000 17.501052 \n", - "4 1.266714 26.600985 \n", - "\n", - " Percent Hispanic or Latino \\\n", - "0 0.000000 \n", - "1 0.000000 \n", - "2 6.196213 \n", - "3 1.766933 \n", - "4 15.482055 \n", - "\n", - " Percent of individuals < 200% Federal Poverty Line \\\n", - "0 57.110946 \n", - "1 59.594680 \n", - "2 49.913941 \n", - "3 65.628944 \n", - "4 68.488290 \n", - "\n", - " Median value ($) of owner-occupied housing units \\\n", - "0 137500 \n", - "1 66900 \n", - "2 77000 \n", - "3 51300 \n", - "4 65400 \n", - "\n", - " Percent individuals age 25 or over with less than high school degree \\\n", - "0 31.677019 \n", - "1 24.951267 \n", - "2 13.233533 \n", - "3 29.184038 \n", - "4 23.337223 \n", - "\n", - " Percent enrollment in college or graduate school \\\n", - "0 1.619121 \n", - "1 5.647263 \n", - "2 4.289406 \n", - "3 6.350026 \n", - "4 2.410800 \n", - "\n", - " Linguistic isolation (percent) \n", - "0 0.000000 \n", - "1 3.145695 \n", - "2 1.862828 \n", - "3 0.000000 \n", - "4 1.489362 \n", - "\n", - "[5 rows x 35 columns]" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "merged_df.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [], - "source": [ - "import us\n", - "\n", - "mapping = us.states.mapping('fips', 'abbr')\n", - "\n", - "for idx, row in merged_df.iterrows():\n", - " current_row = str(merged_df.loc[idx, 'state'])\n", - " state = mapping.get(current_row, None)\n", - " merged_df.loc[idx, 'state_name'] = state" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(5013, 36)" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "merged_df.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [], - "source": [ - "grouped_stats = merged_df.groupby([\"state_name\"]).agg({\n", - " 'GEOID10_TRACT': 'nunique',\n", - " 'Percent of individuals < 200% Federal Poverty Line': [np.median, np.std],\n", - " 'Median value ($) of owner-occupied housing units': [np.median, np.std],\n", - " 'Percent individuals age 25 or over with less than high school degree': [np.median, np.std],\n", - " 'Percent enrollment in college or graduate school': [np.median, np.std],\n", - " 'Percent Black or African American alone': [np.median, np.std],\n", - " 'Percent American Indian and Alaska Native alone': [np.median, np.std],\n", - " 'Percent Non-Hispanic White': [np.median, np.std], \n", - " 'Linguistic isolation (percent)': [np.median, np.std],\n", - " 'Percent Hispanic or Latino': [np.median, np.std],\n", - " 'hbrd_rank': [np.median, np.std],\n", - " 'current_methodology_percent': [np.median, np.std],\n", - " 'current_summed_methodology': [np.median, np.std, np.sum]\n", - "}).reset_index()\n", - "\n", - "\n", - "grouped_stats.columns = [' '.join(col).strip() for col in grouped_stats.columns.values]" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['state_name', 'GEOID10_TRACT nunique',\n", - " 'Percent of individuals < 200% Federal Poverty Line median',\n", - " 'Percent of individuals < 200% Federal Poverty Line std',\n", - " 'Median value ($) of owner-occupied housing units median',\n", - " 'Median value ($) of owner-occupied housing units std',\n", - " 'Percent individuals age 25 or over with less than high school degree median',\n", - " 'Percent individuals age 25 or over with less than high school degree std',\n", - " 'Percent enrollment in college or graduate school median',\n", - " 'Percent enrollment in college or graduate school std',\n", - " 'Percent Black or African American alone median',\n", - " 'Percent Black or African American alone std',\n", - " 'Percent American Indian and Alaska Native alone median',\n", - " 'Percent American Indian and Alaska Native alone std',\n", - " 'Percent Non-Hispanic White median', 'Percent Non-Hispanic White std',\n", - " 'Linguistic isolation (percent) median',\n", - " 'Linguistic isolation (percent) std',\n", - " 'Percent Hispanic or Latino median', 'Percent Hispanic or Latino std',\n", - " 'hbrd_rank median', 'hbrd_rank std',\n", - " 'current_methodology_percent median', 'current_methodology_percent std',\n", - " 'current_summed_methodology median', 'current_summed_methodology std',\n", - " 'current_summed_methodology sum'],\n", - " dtype='object')" - ] - }, - "execution_count": 38, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "grouped_stats.columns" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": {}, - "outputs": [], - "source": [ - "grouped_stats_states = grouped_stats[[x for x in grouped_stats \n", - " if \"median\" in x and \n", - " \"Percent\" in x] + [\"GEOID10_TRACT nunique\", \n", - " 'current_summed_methodology sum', \"state_name\"]]\n", - "\n", - "grouped_stats_states.set_index(\"state_name\", inplace=True)\n", - "\n", - "grouped_stats_states = grouped_stats_states.rename(columns={\n", - "'Percent of individuals < 200% Federal Poverty Line median'\n", - " : 'Percent of individuals < 200% Federal Poverty Line (median across all tracts)', \n", - "'Percent individuals age 25 or over with less than high school degree median':\n", - "'Percent individuals age 25 or over with less than high school degree (median across all tracts)', \n", - "'Percent enrollment in college or graduate school median'\n", - " :'Percent enrollment in college or graduate school (median across all tracts)',\n", - "'Percent Black or African American alone median':\n", - " 'Percent Black or African American alone (median across all tracts)',\n", - "'Percent American Indian and Alaska Native alone median':\n", - " 'Percent American Indian and Alaska Native alone (median across all tracts)',\n", - "'Percent Non-Hispanic White median':\n", - " 'Percent Non-Hispanic White (median across all tracts)',\n", - "'Percent Hispanic or Latino median':\n", - " 'Percent Hispanic or Latino (median across all tracts)',\n", - "'GEOID10_TRACT nunique': \"Total Number of Unique Tracts\",\n", - "\"current_summed_methodology sum\": \"Total Owned and Rented Burdened Households (Current Aggregation Methodology)\" \n", - "})" - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "5013" - ] - }, - "execution_count": 68, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "grouped_stats_states[\"Total Number of Unique Tracts\"].sum()" - ] - }, - { - "cell_type": "code", - "execution_count": 80, - "metadata": {}, - "outputs": [], - "source": [ - "styled = grouped_stats_states.style.apply(highlight_medians)" - ] - }, - { - "cell_type": "code", - "execution_count": 82, - "metadata": {}, - "outputs": [], - "source": [ - "bar_styled = grouped_stats_states.style.bar(\n", - " subset=grouped_stats_states.columns, align='mid', color=['#d65f5f', '#5fba7d'])" - 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      Percent of individuals < 200% Federal Poverty Line (median across all tracts) Percent individuals age 25 or over with less than high school degree (median across all tracts) Percent enrollment in college or graduate school (median across all tracts) Percent Black or African American alone (median across all tracts) Percent American Indian and Alaska Native alone (median across all tracts) Percent Non-Hispanic White (median across all tracts) Percent Hispanic or Latino (median across all tracts) Total Number of Unique Tracts Total Owned and Rented Burdened Households (Current Aggregation Methodology)
      state_name
      AK33.07046910.5860986.5016045.09620012.63370352.24087210.264142125829
      AL59.38757516.9137957.56397874.4757010.00000016.8269372.3860825629636
      AR60.04119517.9231865.21064355.7103060.00000034.8001015.3937434321417
      AZ57.70065126.5266437.0756255.6077602.75893326.17866056.6612589961251
      CA51.26895231.9902329.1743124.0784490.4722289.84325065.462248897610306
      CO46.30058814.2493257.9066043.8809621.09258646.52824034.4803277247552
      CT50.17689818.9679858.59463123.3867030.00000027.06270836.6956305840980
      DC50.41085415.4830456.94641390.3525770.0000003.9939143.9702231711105
      DE42.44744711.2353687.16747548.9519210.47055836.9102358.757629147502
      FL51.10930217.0430947.58889422.7603000.00000022.10462426.459155234162836
      GA58.09138817.4952208.78173866.0021550.00000016.7491174.75131412178491
      HI34.6405239.9914248.0929962.0005260.00000011.6621984.947368178425
      IA45.76192710.9478677.41301111.2195120.02554969.9254357.9350775526664
      ID52.5401366.0718259.1437310.5292651.06339583.4355838.6047852518487
      IL55.34478318.7627467.25308638.0509940.0000009.25583112.976765203108186
      IN58.68210818.0456815.95164435.0849010.00000039.4594119.0637648246470
      KS56.97732518.2332798.18659411.9044760.45919356.78561816.8910105023873
      KY56.32823418.1950865.83430618.9645030.00000063.1455404.2362007136328
      LA61.15131620.0523317.09914376.7875130.00000017.4779322.4805105930785
      MA47.83000120.9422967.76858611.8248780.00000034.79487327.9524088457782
      MD39.75211215.3517317.62244149.9278960.00000022.1150727.0944708846574
      ME42.4021237.4773148.4269661.3470680.36040490.6806281.3089012715361
      MI60.18348616.2672816.96378869.9781660.00000017.6454671.91218114567070
      MN42.50854112.09616810.21069716.9693820.54782058.1542808.0309979148964
      MO55.19430615.2006196.15305936.8479800.00000035.8847104.6192215226962
      MS58.97967617.0385406.71198072.4340180.00000025.2270430.7057554320260
      MT45.0789855.35533213.3026610.1291611.84455488.8817273.6506382013069
      NC56.81125419.2268577.08257542.4155180.25968433.41225210.71495910970145
      ND43.2432436.99017916.4667397.1492562.12194877.9402937.502267138858
      NE55.4258249.50728713.6915898.2790700.53516864.62978310.8638482914442
      NH39.67807813.1355246.5123402.7739100.00000086.0097285.6445712415941
      NJ46.34573320.8433017.07307525.0784090.00000012.42603643.496410165110008
      NM53.60195416.7253529.3477502.7661803.11614729.44009657.2348172714087
      NV56.08303622.5641365.47547911.8682720.55708528.14339243.9473994830773
      NY47.53685724.0067428.26279025.8594920.00000011.30094728.649040603419587
      OH64.85730319.1609985.71620761.0523220.00000027.2796353.45515015767453
      OK57.61886716.7902917.02701213.9696743.42083848.52185013.8998857028634
      OR45.05710911.4072497.8693561.7578830.74264567.68737214.9577805139136
      PA54.89406815.2657607.53012031.8856820.00000027.3596186.15183216587243
      RI55.85143720.0353277.63214712.3701610.40053429.26877933.4685171911532
      SC53.74211015.1065456.65387146.2740380.00000039.1304354.8152876735985
      SD47.8048787.6506438.2104653.6046323.99484575.2877454.7727851911063
      TN55.41666718.7124467.28065853.9672130.00000035.9933964.4444447345269
      TX56.93950225.4102206.78726514.3435370.00000014.62720549.238149309204844
      UT45.8097409.5987419.4932431.4449290.38485264.48710320.4281663319743
      VA44.75079214.1815067.65054136.8306090.00000032.1986966.60759510265128
      VT43.1128236.8371867.7231700.9235940.05055693.7005651.605505158061
      WA39.37531712.5430817.4483175.8803020.90327159.32113613.89307010279613
      WI57.28758215.1812696.31025423.5638920.34694422.6303328.5417784322233
      WV52.37579511.7412986.8748136.8052340.00000082.4479241.6365712611981
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\n", - "text/plain": [ - "
      " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# Compute the correlation matrix\n", - "import seaborn as sns\n", - "corr = merged_df[[\"hbrd_rank\", \n", - " \"current_methodology_percentile_rank\"] + percent_cols].corr()\n", - "\n", - "# Generate a mask for the upper triangle\n", - "mask = np.triu(np.ones_like(corr, dtype=bool))\n", - "\n", - "# Set up the matplotlib figure\n", - "f, ax = plt.subplots(figsize=(15, 12))\n", - "\n", - "# Generate a custom diverging colormap\n", - "cmap = sns.diverging_palette(230, 20, as_cmap=True)\n", - "\n", - "# Draw the heatmap with the mask and correct aspect ratio\n", - "sns.heatmap(corr, mask=mask, cmap=cmap, vmax=.3, center=0,\n", - " square=True, linewidths=.5, cbar_kws={\"shrink\": .5})" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.2" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/data/data-pipeline/data_pipeline/ipython/scoring_comparison.ipynb b/data/data-pipeline/data_pipeline/ipython/scoring_comparison.ipynb index 997b8cc7..e5e02974 100644 --- a/data/data-pipeline/data_pipeline/ipython/scoring_comparison.ipynb +++ b/data/data-pipeline/data_pipeline/ipython/scoring_comparison.ipynb @@ -1273,7 +1273,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -1287,7 +1287,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.2" + "version": "3.9.6" } }, "nbformat": 4,

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