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 088794e3..5ce509a7 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 (ipykernel)", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -657,7 +657,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.4" + "version": "3.6.2" } }, "nbformat": 4, 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 new file mode 100644 index 00000000..82da3edb --- /dev/null +++ b/data/data-pipeline/data_pipeline/ipython/hud_eda_reference_12_27_2021.ipynb @@ -0,0 +1,1063 @@ +{ + "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": [ + "
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numerator_predenominator_predenominator_post
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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": [ + "
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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
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" + ], + "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 7d13e42c..deff9367 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 (ipykernel)", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -1541,7 +1541,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.9" + "version": "3.6.2" } }, "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 1fb5df81..1eda2136 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 Hosuing Only')\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", @@ -785,147 +785,6 @@ "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, @@ -2617,18 +2476,11 @@ "source": [ "seg_austin_2013.statistic" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -2642,7 +2494,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.4" + "version": "3.6.2" } }, "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 new file mode 100644 index 00000000..d8af856a --- /dev/null +++ b/data/data-pipeline/data_pipeline/ipython/hud_eda_se_12_26_2011_relative_differences_between_methodologies-ranking-percentile-comparison-difference-presentation.ipynb @@ -0,0 +1,2992 @@ +{ + "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", + " 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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 new file mode 100644 index 00000000..d99d8979 --- /dev/null +++ b/data/data-pipeline/data_pipeline/ipython/hud_eda_se_12_26_2011_relative_differences_between_methodologies-ranking-percentile-ejscreen-burden.ipynb @@ -0,0 +1,1952 @@ +{ + "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", + " 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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 new file mode 100644 index 00000000..4cbb98d5 --- /dev/null +++ b/data/data-pipeline/data_pipeline/ipython/hud_eda_se_12_26_2011_relative_differences_between_methodologies-ranking-percentile-score-l-burdened.ipynb @@ -0,0 +1,1651 @@ +{ + "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", + " 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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
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
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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 index 2605a915..e8e8391d 100644 --- 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 @@ -1039,7 +1039,8 @@ ], "source": [ "# 53 tracts\n", - "final_current_methodology[final_current_methodology.FIPS_tract_id.isin(ineligible_tracts)].shape" + "final_current_methodology[\n", + " final_current_methodology.FIPS_tract_id.isin(ineligible_tracts)].shape" ] }, { @@ -1074,22 +1075,22 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 85, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 25, + "execution_count": 85, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -1102,7 +1103,7 @@ ], "source": [ "plt.figure(figsize=(12, 8))\n", - "plt.title(\"Distribution of Percentiles (Score L)\")\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", @@ -1372,7 +1373,39 @@ }, { "cell_type": "code", - "execution_count": 30, + "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": [ @@ -1423,6 +1456,4427 @@ " '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", + " subset=grouped_stats_score_l.columns, align='mid', color=['#d65f5f', '#5fba7d'])" + ] + }, + { + "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", + " 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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
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
WA39.87985215.3468824.6728977.2115381.14379145.10117118.53614399168
WI67.78936118.8295175.52861355.8699100.02455812.6329797.870680136719
" + ], + "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
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
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
WA39.87985215.3468824.6728977.2115381.14379145.10117118.53614399168
WI67.78936118.8295175.52861355.8699100.02455812.6329797.870680136719
" + ], + "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", 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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", @@ -1761,54 +6215,6 @@ "merged_df.shape" ] }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 35, - "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": "code", "execution_count": 36, @@ -1829,15 +6235,9 @@ " '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": 37, - "metadata": {}, - "outputs": [], - "source": [ + "}).reset_index()\n", + "\n", + "\n", "grouped_stats.columns = [' '.join(col).strip() for col in grouped_stats.columns.values]" ] }, @@ -1884,617 +6284,2985 @@ }, { "cell_type": "code", - "execution_count": 39, + "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\", \"state_name\"]]" + " \"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": 40, + "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": [ - "grouped_stats_states.set_index(\"state_name\", inplace=True)" + "styled = grouped_stats_states.style.apply(highlight_medians)" ] }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 82, + "metadata": {}, + "outputs": [], + "source": [ + "bar_styled = grouped_stats_states.style.bar(\n", + " subset=grouped_stats_states.columns, align='mid', color=['#d65f5f', '#5fba7d'])" + ] + }, + { + "cell_type": "code", + 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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
AK33.07046910.5860986.5016045.09620012.63370352.24087210.26414212AK33.07046910.5860986.5016045.09620012.63370352.24087210.264142125829
AL59.38757516.9137957.56397874.4757010.00000016.8269372.38608256AL59.38757516.9137957.56397874.4757010.00000016.8269372.3860825629636
AR60.04119517.9231865.21064355.7103060.00000034.8001015.39374343AR60.04119517.9231865.21064355.7103060.00000034.8001015.3937434321417
AZ57.70065126.5266437.0756255.6077602.75893326.17866056.66125899AZ57.70065126.5266437.0756255.6077602.75893326.17866056.6612589961251
CA51.26895231.9902329.1743124.0784490.4722289.84325065.462248897CA51.26895231.9902329.1743124.0784490.4722289.84325065.462248897610306
CO46.30058814.2493257.9066043.8809621.09258646.52824034.48032772CO46.30058814.2493257.9066043.8809621.09258646.52824034.4803277247552
CT50.17689818.9679858.59463123.3867030.00000027.06270836.69563058CT50.17689818.9679858.59463123.3867030.00000027.06270836.6956305840980
DC50.41085415.4830456.94641390.3525770.0000003.9939143.97022317DC50.41085415.4830456.94641390.3525770.0000003.9939143.9702231711105
DE42.44744711.2353687.16747548.9519210.47055836.9102358.75762914DE42.44744711.2353687.16747548.9519210.47055836.9102358.757629147502
FL51.10930217.0430947.58889422.7603000.00000022.10462426.459155234FL51.10930217.0430947.58889422.7603000.00000022.10462426.459155234162836
GA58.09138817.4952208.78173866.0021550.00000016.7491174.751314121GA58.09138817.4952208.78173866.0021550.00000016.7491174.75131412178491
HI34.6405239.9914248.0929962.0005260.00000011.6621984.94736817HI34.6405239.9914248.0929962.0005260.00000011.6621984.947368178425
IA45.76192710.9478677.41301111.2195120.02554969.9254357.93507755IA45.76192710.9478677.41301111.2195120.02554969.9254357.9350775526664
ID52.5401366.0718259.1437310.5292651.06339583.4355838.60478525ID52.5401366.0718259.1437310.5292651.06339583.4355838.6047852518487
IL55.34478318.7627467.25308638.0509940.0000009.25583112.976765203IL55.34478318.7627467.25308638.0509940.0000009.25583112.976765203108186
IN58.68210818.0456815.95164435.0849010.00000039.4594119.06376482IN58.68210818.0456815.95164435.0849010.00000039.4594119.0637648246470
KS56.97732518.2332798.18659411.9044760.45919356.78561816.89101050KS56.97732518.2332798.18659411.9044760.45919356.78561816.8910105023873
KY56.32823418.1950865.83430618.9645030.00000063.1455404.23620071KY56.32823418.1950865.83430618.9645030.00000063.1455404.2362007136328
LA61.15131620.0523317.09914376.7875130.00000017.4779322.48051059LA61.15131620.0523317.09914376.7875130.00000017.4779322.4805105930785
MA47.83000120.9422967.76858611.8248780.00000034.79487327.95240884MA47.83000120.9422967.76858611.8248780.00000034.79487327.9524088457782
MD39.75211215.3517317.62244149.9278960.00000022.1150727.09447088MD39.75211215.3517317.62244149.9278960.00000022.1150727.0944708846574
ME42.4021237.4773148.4269661.3470680.36040490.6806281.30890127ME42.4021237.4773148.4269661.3470680.36040490.6806281.3089012715361
MI60.18348616.2672816.96378869.9781660.00000017.6454671.912181145MI60.18348616.2672816.96378869.9781660.00000017.6454671.91218114567070
MN42.50854112.09616810.21069716.9693820.54782058.1542808.03099791MN42.50854112.09616810.21069716.9693820.54782058.1542808.0309979148964
MO55.19430615.2006196.15305936.8479800.00000035.8847104.61922152MO55.19430615.2006196.15305936.8479800.00000035.8847104.6192215226962
MS58.97967617.0385406.71198072.4340180.00000025.2270430.70575543MS58.97967617.0385406.71198072.4340180.00000025.2270430.7057554320260
MT45.0789855.35533213.3026610.1291611.84455488.8817273.65063820MT45.0789855.35533213.3026610.1291611.84455488.8817273.6506382013069
NC56.81125419.2268577.08257542.4155180.25968433.41225210.714959109NC56.81125419.2268577.08257542.4155180.25968433.41225210.71495910970145
ND43.2432436.99017916.4667397.1492562.12194877.9402937.50226713ND43.2432436.99017916.4667397.1492562.12194877.9402937.502267138858
NE55.4258249.50728713.6915898.2790700.53516864.62978310.86384829NE55.4258249.50728713.6915898.2790700.53516864.62978310.8638482914442
NH39.67807813.1355246.5123402.7739100.00000086.0097285.64457124NH39.67807813.1355246.5123402.7739100.00000086.0097285.6445712415941
NJ46.34573320.8433017.07307525.0784090.00000012.42603643.496410165NJ46.34573320.8433017.07307525.0784090.00000012.42603643.496410165110008
NM53.60195416.7253529.3477502.7661803.11614729.44009657.23481727NM53.60195416.7253529.3477502.7661803.11614729.44009657.2348172714087
NV56.08303622.5641365.47547911.8682720.55708528.14339243.94739948NV56.08303622.5641365.47547911.8682720.55708528.14339243.9473994830773
NY47.53685724.0067428.26279025.8594920.00000011.30094728.649040603NY47.53685724.0067428.26279025.8594920.00000011.30094728.649040603419587
OH64.85730319.1609985.71620761.0523220.00000027.2796353.455150157OH64.85730319.1609985.71620761.0523220.00000027.2796353.45515015767453
OK57.61886716.7902917.02701213.9696743.42083848.52185013.89988570OK57.61886716.7902917.02701213.9696743.42083848.52185013.8998857028634
OR45.05710911.4072497.8693561.7578830.74264567.68737214.95778051OR45.05710911.4072497.8693561.7578830.74264567.68737214.9577805139136
PA54.89406815.2657607.53012031.8856820.00000027.3596186.151832165PA54.89406815.2657607.53012031.8856820.00000027.3596186.15183216587243
RI55.85143720.0353277.63214712.3701610.40053429.26877933.46851719RI55.85143720.0353277.63214712.3701610.40053429.26877933.4685171911532
SC53.74211015.1065456.65387146.2740380.00000039.1304354.81528767SC53.74211015.1065456.65387146.2740380.00000039.1304354.8152876735985
SD47.8048787.6506438.2104653.6046323.99484575.2877454.77278519SD47.8048787.6506438.2104653.6046323.99484575.2877454.7727851911063
TN55.41666718.7124467.28065853.9672130.00000035.9933964.44444473TN55.41666718.7124467.28065853.9672130.00000035.9933964.4444447345269
TX56.93950225.4102206.78726514.3435370.00000014.62720549.238149309TX56.93950225.4102206.78726514.3435370.00000014.62720549.238149309204844
UT45.8097409.5987419.4932431.4449290.38485264.48710320.42816633UT45.8097409.5987419.4932431.4449290.38485264.48710320.4281663319743
VA44.75079214.1815067.65054136.8306090.00000032.1986966.607595102VA44.75079214.1815067.65054136.8306090.00000032.1986966.60759510265128
VT43.1128236.8371867.7231700.9235940.05055693.7005651.60550515VT43.1128236.8371867.7231700.9235940.05055693.7005651.605505158061
WA39.37531712.5430817.4483175.8803020.90327159.32113613.893070102WA39.37531712.5430817.4483175.8803020.90327159.32113613.89307010279613
WI57.28758215.1812696.31025423.5638920.34694422.6303328.54177843WI57.28758215.1812696.31025423.5638920.34694422.6303328.5417784322233
WV52.37579511.7412986.8748136.8052340.00000082.4479241.63657126WV52.37579511.7412986.8748136.8052340.00000082.4479241.6365712611981
WY33.4140924.8757176.5539660.5780351.17455181.37824211.8082799WY33.4140924.8757176.5539660.5780351.17455181.37824211.80827996319
" ], "text/plain": [ - "" + "" ] }, - "execution_count": 41, + "execution_count": 86, "metadata": {}, "output_type": "execute_result" } ], "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: pink; background-color:darkblue' \n", - " if cell else '' for cell in is_greater_than_median]\n", - "grouped_stats_states.style.apply(highlight_medians)" + "bar_styled" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "metadata": {}, + "outputs": [], + "source": [ + "styled.to_excel(\"final_comparison_5013.xlsx\")" + ] + }, + { + "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})" ] }, { 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 new file mode 100644 index 00000000..caa4202d --- /dev/null +++ b/data/data-pipeline/data_pipeline/ipython/hud_eda_se_12_27-highlevel-summarization-variance.ipynb @@ -0,0 +1,8783 @@ +{ + "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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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 × 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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\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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\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, + 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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
AKnannannannannannannannan
AL13.0964206.64498618.76860422.9591790.22308719.3302968.007999270.819158
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", 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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", + " 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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
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)
state_name
AK0.985223-0.2745783.206568-1.0852588.721076-6.803172-1.778285-10.0-4179.0
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
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
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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 new file mode 100644 index 00000000..9c671466 --- /dev/null +++ b/data/data-pipeline/data_pipeline/ipython/hud_eda_se_12_27-highlevel-summarization.ipynb @@ -0,0 +1,3434 @@ +{ + "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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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 × 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", + "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": 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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" + ], + "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", + "
Percent of individuals < 200% Federal Poverty Line (mean of median across all states) 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 (mean of median across all states) Percent American Indian and Alaska Native alone (mean of median across all states) Percent Non-Hispanic White (mean of median across all states) Percent Hispanic or Latino (mean of median across all states) Total Number of Tracts - Mean of median across all states Total Owned and Rented Burdened Households (Current Aggregation Methodology)- Mean of median across all states
Score L Relative Housing Burden for all households met burden threshold CalEnviroScreen Housing Burden Met Burden Threshold (Ranked Percentile)
TrueFalse57.0519.388.1333.691.2231.3219.3862.0940949.20
FalseTrue47.0813.228.6923.060.7748.2412.5544.5524719.10
TrueTrue62.3317.6911.8233.460.9234.5015.8688.8060757.90
FalseFalse27.748.795.855.730.5475.927.461244.33443680.57
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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
AK33.56308110.4072409.4856384.55357121.27976248.8392869.3750001825
AL63.33585420.2736177.89540879.2360900.0000008.4367791.986055147107
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" + ], + "text/plain": [ + " Percent of individuals < 200% Federal Poverty Line (median across all tracts) \\\n", + "state_name \n", + "AK 33.563081 \n", + "AL 63.335854 \n", + "\n", + " Percent individuals age 25 or over with less than high school degree (median across all tracts) \\\n", + "state_name \n", + "AK 10.407240 \n", + "AL 20.273617 \n", + "\n", + " Percent enrollment in college or graduate school (median across all tracts) \\\n", + "state_name \n", + "AK 9.485638 \n", + "AL 7.895408 \n", + "\n", + " Percent Black or African American alone (median across all tracts) \\\n", + "state_name \n", + "AK 4.553571 \n", + "AL 79.236090 \n", + "\n", + " Percent American Indian and Alaska Native alone (median across all tracts) \\\n", + "state_name \n", + "AK 21.279762 \n", + "AL 0.000000 \n", + "\n", + " Percent Non-Hispanic White (median across all tracts) \\\n", + "state_name \n", + "AK 48.839286 \n", + "AL 8.436779 \n", + "\n", + " Percent Hispanic or Latino (median across all tracts) \\\n", + "state_name \n", + "AK 9.375000 \n", + "AL 1.986055 \n", + "\n", + " Total Number of Unique Tracts \\\n", + "state_name \n", + "AK 1 \n", + "AL 14 \n", + "\n", + " Total Owned and Rented Burdened Households (Current Aggregation Methodology) \n", + "state_name \n", + "AK 825 \n", + "AL 7107 " + ] + }, + "execution_count": 272, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_1.head(2)" + ] + }, + { + "cell_type": "code", + "execution_count": 273, + "metadata": {}, + "outputs": [], + "source": [ + "candidate_columns = ['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)']" + ] + }, + { + "cell_type": "code", + "execution_count": 276, + "metadata": {}, + "outputs": [], + "source": [ + "diff = (df_1[candidate_columns] - df_2[candidate_columns]).fillna(0)" + ] + }, + { + "cell_type": "code", + "execution_count": 277, + "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
AK0.985223-0.2745783.206568-1.0852588.721076-6.803172-1.778285-10.0-4179.0
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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"state_name \n", + "AK 8.721076 \n", + "AL 0.000000 \n", + "AR 0.102459 \n", + "AZ -0.915677 \n", + "CA 0.415820 \n", + "\n", + " Percent Non-Hispanic White (median across all tracts) \\\n", + "state_name \n", + "AK -6.803172 \n", + "AL -9.011147 \n", + "AR -23.263854 \n", + "AZ -8.953361 \n", + "CA -21.870519 \n", + "\n", + " Percent Hispanic or Latino (median across all tracts) \\\n", + "state_name \n", + "AK -1.778285 \n", + "AL -0.630017 \n", + "AR -0.896646 \n", + "AZ 8.757832 \n", + "CA 40.216194 \n", + "\n", + " Total Number of Unique Tracts \\\n", + "state_name \n", + "AK -10.0 \n", + "AL -28.0 \n", + "AR -35.0 \n", + "AZ -39.0 \n", + "CA 783.0 \n", + "\n", + " Total Owned and Rented Burdened Households (Current Aggregation Methodology) \n", + "state_name \n", + "AK -4179.0 \n", + "AL -15422.0 \n", + "AR -16843.0 \n", + "AZ -23253.0 \n", + "CA 542144.0 " + ] + }, + "execution_count": 277, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "diff.head()" 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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
AK0.985223-0.2745783.206568-1.0852588.721076-6.803172-1.778285-10.000000-4179.000000
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
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 new file mode 100644 index 00000000..3ca6fd6a --- /dev/null +++ b/data/data-pipeline/data_pipeline/ipython/hud_eda_se_12_27_2011_relative_differences_between_methodologies-ranking-percentile.ipynb @@ -0,0 +1,5701 @@ +{ + "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 × 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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\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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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": 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)
0010150021010199.5192310.3631843654831005100097050.0...0.0300570.42080.48091471.1451760.51097161.03556515350014.42516368.0012610.000000
1010450207000194.7115380.25217431953912651265123044.0...3.9987410.00003.36901830.3211590.00000072.0128827620020.5289113.7209302.184087
2010479564000196.9230770.28215834062512051200118053.0...1.9867550.00000.3973516.7218540.00000081.4598545900030.42704615.0175131.475694
3010479565000196.4423080.27240138062513951395134047.0...0.0000000.00000.0000002.6597470.00000083.5225434600029.6661973.2728710.000000
4010690406000192.8846150.24000018034575075069150.0...0.3084830.00001.85090012.7506430.77120877.8702165210024.7727274.7958521.041667
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5 rows × 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, + "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", + 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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 new file mode 100644 index 00000000..3efdbb67 --- /dev/null +++ b/data/data-pipeline/data_pipeline/ipython/hud_eda_se_12_27_reference.ipynb @@ -0,0 +1,865 @@ +{ + "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 × 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 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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\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\"])" + ] + } + ], + "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 new file mode 100644 index 00000000..2ce5a35b --- /dev/null +++ b/data/data-pipeline/data_pipeline/ipython/hud_notebook_tabulation_differences_12_27_2021.ipynb @@ -0,0 +1,9194 @@ +{ + "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 × 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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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": 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
0010010201000146.2980770.1045758017476576576523.0000.512833FalseFalse
1010010202000183.2692310.19166713817772072072025.0000.575315FalseFalse
2010010203000163.6538460.13127417027912951295129122.0-4-40.479242FalseFalse
3010010204000134.6153850.08841514527416401635163517.0-5-50.289696FalseFalse
4010010205000168.2211540.14251559588541754175413521.0-40-400.444306FalseFalse
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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": [ + "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", + " subset=grouped_stats_score_l.columns, align='mid', color=['#d65f5f', '#5fba7d'])" + ] + }, + { + "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", + 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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
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
WA39.87985215.3468824.6728977.2115381.14379145.10117118.53614399168
WI67.78936118.8295175.52861355.8699100.02455812.6329797.870680136719
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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
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
WA39.87985215.3468824.6728977.2115381.14379145.10117118.53614399168
WI67.78936118.8295175.52861355.8699100.02455812.6329797.870680136719
" + ], + "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", 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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
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5 rows × 35 columns

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" + ], + "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'])" + ] + }, + { + "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", + " 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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
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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 e5e02974..997b8cc7 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 (ipykernel)", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -1287,7 +1287,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.6" + "version": "3.6.2" } }, "nbformat": 4,