Merge branch 'main' into justice40-tool-iss-719

data/data-pipeline/data_pipeline/etl/constants.py

@@ -59,6 +59,11 @@ DATASET_LIST = [
         "module_dir": "doe_energy_burden",
         "class_name": "DOEEnergyBurden",
     },
+    {
+        "name": "geocorr",
+        "module_dir": "geocorr",
+        "class_name": "GeoCorrETL",
+    },
 ]
 CENSUS_INFO = {
     "name": "census",

data/data-pipeline/data_pipeline/etl/score/etl_score.py

@@ -80,6 +80,9 @@ class ScoreETL(ExtractTransformLoad):
 
         self.SCORE_CSV_PATH: Path = self.DATA_PATH / "score" / "csv" / "full"
 
+        # Urban Rural Map
+        self.URBAN_HERUISTIC_FIELD_NAME = "Urban Heuristic Flag"
+
         # dataframes
         self.df: pd.DataFrame
         self.ejscreen_df: pd.DataFrame
@@ -91,6 +94,7 @@ class ScoreETL(ExtractTransformLoad):
         self.cdc_life_expectancy_df: pd.DataFrame
         self.doe_energy_burden_df: pd.DataFrame
         self.national_risk_index_df: pd.DataFrame
+        self.geocorr_urban_rural_df: pd.DataFrame
 
     def data_sets(self) -> list:
         # Define a named tuple that will be used for each data set input.
@@ -197,6 +201,11 @@ class ScoreETL(ExtractTransformLoad):
                 renamed_field=self.RISK_INDEX_EXPECTED_ANNUAL_LOSS_SCORE_FIELD_NAME,
                 bucket=None,
             ),
+            DataSet(
+                input_field=self.URBAN_HERUISTIC_FIELD_NAME,
+                renamed_field=self.URBAN_HERUISTIC_FIELD_NAME,
+                bucket=None,
+            ),
             # The following data sets have buckets, because they're used in Score C
             DataSet(
                 input_field="CANCER",
@@ -386,6 +395,16 @@ class ScoreETL(ExtractTransformLoad):
             low_memory=False,
         )
 
+        # Load GeoCorr Urban Rural Map
+        geocorr_urban_rural_csv = (
+            self.DATA_PATH / "dataset" / "geocorr" / "usa.csv"
+        )
+        self.geocorr_urban_rural_df = pd.read_csv(
+            geocorr_urban_rural_csv,
+            dtype={self.GEOID_TRACT_FIELD_NAME: "string"},
+            low_memory=False,
+        )
+
     def _join_cbg_dfs(self, census_block_group_dfs: list) -> pd.DataFrame:
         logger.info("Joining Census Block Group dataframes")
         census_block_group_df = functools.reduce(
@@ -619,6 +638,15 @@ class ScoreETL(ExtractTransformLoad):
         df["Score G"] = df["Score G (communities)"].astype(int)
         df["Score G (percentile)"] = df["Score G"]
 
+        df["Score H (communities)"] = (
+            (df[self.MEDIAN_INCOME_AS_PERCENT_OF_AMI_FIELD_NAME] < 0.8)
+            & (df[self.HIGH_SCHOOL_FIELD_NAME] > high_school_cutoff_threshold_2)
+        ) | (
+            (df[self.POVERTY_LESS_THAN_200_FPL_FIELD_NAME] > 0.40)
+            & (df[self.HIGH_SCHOOL_FIELD_NAME] > high_school_cutoff_threshold_2)
+        )
+        df["Score H"] = df["Score H (communities)"].astype(int)
+
         df["Score I (communities)"] = (
             (df[self.MEDIAN_INCOME_AS_PERCENT_OF_AMI_FIELD_NAME] < 0.7)
             & (df[self.HIGH_SCHOOL_FIELD_NAME] > high_school_cutoff_threshold)
@@ -629,20 +657,10 @@ class ScoreETL(ExtractTransformLoad):
         df["Score I"] = df["Score I (communities)"].astype(int)
         df["Score I (percentile)"] = df["Score I"]
 
-        df["Score H (communities)"] = (
-            (df[self.MEDIAN_INCOME_AS_PERCENT_OF_AMI_FIELD_NAME] < 0.8)
-            & (df[self.HIGH_SCHOOL_FIELD_NAME] > high_school_cutoff_threshold_2)
-        ) | (
-            (df[self.POVERTY_LESS_THAN_200_FPL_FIELD_NAME] > 0.40)
-            & (df[self.HIGH_SCHOOL_FIELD_NAME] > high_school_cutoff_threshold_2)
-        )
-        df["Score H"] = df["Score H (communities)"].astype(int)
-
         df["NMTC (communities)"] = (
             (df[self.MEDIAN_INCOME_AS_PERCENT_OF_AMI_FIELD_NAME] < 0.8)
         ) | (df[self.POVERTY_LESS_THAN_100_FPL_FIELD_NAME] > 0.20)
 
-
         df["Score K (communities)"] = (
             (df[self.MEDIAN_INCOME_AS_PERCENT_OF_AMI_FIELD_NAME] < 0.8)
             & (df[self.HIGH_SCHOOL_FIELD_NAME] > high_school_cutoff_threshold_2)
@@ -673,6 +691,7 @@ class ScoreETL(ExtractTransformLoad):
             self.cdc_places_df,
             self.cdc_life_expectancy_df,
             self.doe_energy_burden_df,
+            self.geocorr_urban_rural_df,
         ]
         census_tract_df = self._join_tract_dfs(census_tract_dfs)
 

data/data-pipeline/data_pipeline/etl/sources/geocorr/etl.py

@@ -0,0 +1,70 @@
+import pandas as pd
+
+from data_pipeline.config import settings
+from data_pipeline.etl.base import ExtractTransformLoad
+from data_pipeline.utils import (
+    get_module_logger,
+    unzip_file_from_url,
+)
+
+logger = get_module_logger(__name__)
+
+
+class GeoCorrETL(ExtractTransformLoad):
+    def __init__(self):
+        self.OUTPUT_PATH = self.DATA_PATH / "dataset" / "geocorr"
+
+        # Need to change hyperlink to S3
+        self.GEOCORR_PLACES_URL = "https://justice40-data.s3.amazonaws.com/data-sources/geocorr_urban_rural.csv.zip"
+        self.GEOCORR_GEOID_FIELD_NAME = "GEOID10_TRACT"
+        self.URBAN_HERUISTIC_FIELD_NAME = "Urban Heuristic Flag"
+
+        self.df: pd.DataFrame
+
+    def extract(self) -> None:
+        logger.info(
+            "Starting to download 2MB GeoCorr Urban Rural Census Tract Map file."
+        )
+        unzip_file_from_url(
+            file_url=settings.AWS_JUSTICE40_DATASOURCES_URL
+            + "/geocorr_urban_rural.csv.zip",
+            download_path=self.TMP_PATH,
+            unzipped_file_path=self.TMP_PATH / "geocorr",
+        )
+
+        self.df = pd.read_csv(
+            filepath_or_buffer=self.TMP_PATH
+            / "geocorr"
+            / "geocorr_urban_rural.csv",
+            dtype={
+                self.GEOCORR_GEOID_FIELD_NAME: "string",
+            },
+            low_memory=False,
+        )
+
+    def transform(self) -> None:
+        logger.info("Starting GeoCorr Urban Rural Map transform")
+
+        self.df.rename(
+            columns={
+                "urban_heuristic_flag": self.URBAN_HERUISTIC_FIELD_NAME,
+            },
+            inplace=True,
+        )
+
+        pass
+
+        # Put in logic from Jupyter Notebook transform when we switch in the hyperlink to Geocorr
+
+    def load(self) -> None:
+        logger.info("Saving GeoCorr Urban Rural Map Data")
+
+        # mkdir census
+        self.OUTPUT_PATH.mkdir(parents=True, exist_ok=True)
+
+        self.df.to_csv(path_or_buf=self.OUTPUT_PATH / "usa.csv", index=False)
+
+    def validate(self) -> None:
+        logger.info("Validating GeoCorr Urban Rural Map Data")
+
+        pass

data/data-pipeline/data_pipeline/etl/sources/national_risk_index/etl.py

@@ -75,7 +75,7 @@ class NationalRiskIndexETL(ExtractTransformLoad):
         # Reduce columns.
         # Note: normally we wait until writing to CSV for this step, but since the file is so huge,
         # move this up here for performance reasons.
-        df_nri = df_nri[ # pylint: disable=unsubscriptable-object
+        df_nri = df_nri[  # pylint: disable=unsubscriptable-object
             [self.RISK_INDEX_EXPECTED_ANNUAL_LOSS_SCORE_FIELD_NAME, TRACT_COL]
         ]
 

data/data-pipeline/data_pipeline/ipython/scoring_comparison.ipynb

@@ -71,6 +71,7 @@
     "GEOID_STATE_FIELD_NAME = \"GEOID10_STATE\"\n",
     "COUNTRY_FIELD_NAME = \"Country\"\n",
     "CENSUS_BLOCK_GROUP_POPULATION_FIELD = \"Total population\"\n",
+    "URBAN_HEURISTIC_FIELD = \"Urban Heuristic Flag\"\n",
     "\n",
     "CEJST_SCORE_FIELD = \"cejst_score\"\n",
     "CEJST_PERCENTILE_FIELD = \"cejst_percentile\"\n",
@@ -124,6 +125,7 @@
     "    \"Percent of individuals < 200% Federal Poverty Line\",\n",
     "    \"Life expectancy (years)\",\n",
     "    \"Energy burden\",\n",
+    "    URBAN_HEURISTIC_FIELD,\n",
     "]:\n",
     "    print(f\"\\n~~~~Analysis for field `{field}`~~~~\")\n",
     "    print(cejst_df[field].describe())\n",
@@ -230,7 +232,7 @@
     ")\n",
     "\n",
     "\n",
-    "if len(merged_df) > 220335:\n",
+    "if len(merged_df) > 220405:\n",
     "    raise ValueError(f\"Too many rows in the join: {len(merged_df)}\")\n",
     "\n",
     "merged_df.head()\n",
@@ -273,21 +275,16 @@
     "        other_census_tract_fields_to_keep=[],\n",
     "    ),\n",
     "    Index(\n",
+    "        method_name=\"Score I\",\n",
+    "        priority_communities_field=\"Score I (communities)\",\n",
+    "        other_census_tract_fields_to_keep=[],\n",
+    "    ),\n",
+    "    Index(\n",
     "        method_name=\"NMTC\",\n",
     "        priority_communities_field=\"NMTC (communities)\",\n",
     "        other_census_tract_fields_to_keep=[],\n",
     "    ),\n",
     "    Index(\n",
-    "        method_name=\"NMTC modified\",\n",
-    "        priority_communities_field=\"NMTC modified (communities)\",\n",
-    "        other_census_tract_fields_to_keep=[],\n",
-    "    ),\n",
-    "    Index(\n",
-    "        method_name=\"Score F\",\n",
-    "        priority_communities_field=\"Score F (communities)\",\n",
-    "        other_census_tract_fields_to_keep=[],\n",
-    "    ),\n",
-    "    Index(\n",
     "        method_name=\"Score A\",\n",
     "        priority_communities_field=\"Score A (top 25th percentile)\",\n",
     "        other_census_tract_fields_to_keep=[],\n",
@@ -308,6 +305,11 @@
     "        other_census_tract_fields_to_keep=[],\n",
     "    ),\n",
     "    Index(\n",
+    "        method_name=\"Score F\",\n",
+    "        priority_communities_field=\"Score F (communities)\",\n",
+    "        other_census_tract_fields_to_keep=[],\n",
+    "    ),\n",
+    "    Index(\n",
     "        method_name=\"Poverty\",\n",
     "        priority_communities_field=\"Poverty (Less than 200% of federal poverty line) (top 25th percentile)\",\n",
     "        other_census_tract_fields_to_keep=[],\n",
@@ -365,6 +367,8 @@
     "        summary_dict = {}\n",
     "        summary_dict[COUNTRY_FIELD_NAME] = frame[COUNTRY_FIELD_NAME].unique()[0]\n",
     "\n",
+    "        summary_dict[\"Analysis grouped by\"] = geography_field\n",
+    "\n",
     "        if geography_field == COUNTRY_FIELD_NAME:\n",
     "            summary_dict[GEOID_STATE_FIELD_NAME] = \"00\"\n",
     "            summary_dict[\"Geography name\"] = \"(Entire USA)\"\n",
@@ -389,9 +393,12 @@
     "            summary_dict[\"Geography name\"] = division_id\n",
     "\n",
     "        total_cbgs_in_geography = len(frame)\n",
-    "        total_population_in_geography = frame[\n",
+    "        total_population_in_geography = frame[CENSUS_BLOCK_GROUP_POPULATION_FIELD].sum()\n",
-    "            CENSUS_BLOCK_GROUP_POPULATION_FIELD\n",
+    "\n",
-    "        ].sum()\n",
+    "        if geography_field == URBAN_HEURISTIC_FIELD:\n",
+    "            urban_flag = frame[URBAN_HEURISTIC_FIELD].unique()[0]\n",
+    "            summary_dict[\"Urban vs Rural\"] = \"Urban\" if urban_flag else \"Rural\"\n",
+    "            summary_dict[\"Geography name\"] = summary_dict[\"Urban vs Rural\"]\n",
     "\n",
     "        for priority_communities_field in priority_communities_fields:\n",
     "            summary_dict[f\"{priority_communities_field}{POPULATION_SUFFIX}\"] = frame[\n",
@@ -465,13 +472,24 @@
     "        lambda frame: calculate_state_comparison(frame, geography_field=\"division\")\n",
     "    )\n",
     "\n",
-    "    # Combine the three\n",
+    "    # Next, run the comparison by urban/rural\n",
+    "    urban_grouped_df = df.groupby(URBAN_HEURISTIC_FIELD)\n",
+    "\n",
+    "    # Run the comparison function on the groups.\n",
+    "    urban_grouped_df = urban_grouped_df.progress_apply(\n",
+    "        lambda frame: calculate_state_comparison(\n",
+    "            frame, geography_field=URBAN_HEURISTIC_FIELD\n",
+    "        )\n",
+    "    )\n",
+    "\n",
+    "    # Combine the five\n",
     "    combined_df = pd.concat(\n",
     "        [\n",
     "            usa_distribution_df,\n",
     "            state_distribution_df,\n",
     "            region_distribution_df,\n",
     "            division_distribution_df,\n",
+    "            urban_grouped_df,\n",
     "        ]\n",
     "    )\n",
     "\n",
@@ -565,15 +583,17 @@
     "    priority_communities_fields=fields_to_analyze,\n",
     ")\n",
     "\n",
+    "file_prefix = \"Priority CBGs – Different geographic groupings\"\n",
+    "\n",
     "state_distribution_df.to_csv(\n",
-    "    path_or_buf=COMPARISON_OUTPUTS_DIR / \"Priority CBGs by state.csv\",\n",
+    "    path_or_buf=COMPARISON_OUTPUTS_DIR / f\"{file_prefix}.csv\",\n",
     "    na_rep=\"\",\n",
     "    index=False,\n",
     ")\n",
     "\n",
     "write_state_distribution_excel(\n",
     "    state_distribution_df=state_distribution_df,\n",
-    "    file_path=COMPARISON_OUTPUTS_DIR / \"Priority CBGs by state.xlsx\",\n",
+    "    file_path=COMPARISON_OUTPUTS_DIR / f\"{file_prefix}.xlsx\",\n",
     ")\n",
     "\n",
     "state_distribution_df.head()"
@@ -625,10 +645,10 @@
     "\n",
     "    criteria_description_field_name = \"Description of criteria\"\n",
     "    comparison_df[criteria_description_field_name] = comparison_df.apply(\n",
-    "        func=lambda row: f\"CBGs that are {'not' if row[method_a_priority_census_block_groups_field] is False else ''} \" + \n",
+    "        func=lambda row: f\"CBGs that are {'not' if row[method_a_priority_census_block_groups_field] is False else ''} \"\n",
-    "        f\"prioritized by {method_a_priority_census_block_groups_field} \" + \n",
+    "        + f\"prioritized by {method_a_priority_census_block_groups_field} \"\n",
-    "        f\"and are {'not' if row[method_b_priority_census_block_groups_field] is False else ''} \" + \n",
+    "        + f\"and are {'not' if row[method_b_priority_census_block_groups_field] is False else ''} \"\n",
-    "        f\"prioritized by {method_b_priority_census_block_groups_field}\",\n",
+    "        + f\"prioritized by {method_b_priority_census_block_groups_field}\",\n",
     "        axis=1,\n",
     "    )\n",
     "\n",
@@ -636,7 +656,7 @@
     "    new_column_order = [criteria_description_field_name] + [\n",
     "        col for col in comparison_df.columns if col != criteria_description_field_name\n",
     "    ]\n",
-    "    \n",
+    "\n",
     "    comparison_df = comparison_df[new_column_order]\n",
     "\n",
     "    # Rename fields to reflect the mean aggregation\n",
@@ -763,6 +783,7 @@
     "    \"Linguistic isolation (percent)\",\n",
     "    \"Unemployed civilians (percent)\",\n",
     "    \"Median household income in the past 12 months\",\n",
+    "    URBAN_HEURISTIC_FIELD,\n",
     "]\n",
     "\n",
     "for (index_a, index_b) in itertools.combinations(census_block_group_indices, 2):\n",

data/data-pipeline/data_pipeline/ipython/urban_vs_rural.ipynb

@@ -0,0 +1,311 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "51412a14",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "import collections\n",
+    "from datetime import datetime\n",
+    "import functools\n",
+    "import itertools\n",
+    "import os\n",
+    "import pathlib\n",
+    "import requests\n",
+    "import string\n",
+    "import sys\n",
+    "import typing\n",
+    "import zipfile\n",
+    "\n",
+    "import IPython\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import pypandoc\n",
+    "\n",
+    "from tqdm.notebook import tqdm_notebook\n",
+    "\n",
+    "module_path = os.path.abspath(os.path.join(\"../..\"))\n",
+    "if module_path not in sys.path:\n",
+    "    sys.path.append(module_path)\n",
+    "\n",
+    "from data_pipeline.utils import remove_all_from_dir, get_excel_column_name\n",
+    "from data_pipeline.etl.sources.census.etl_utils import get_state_information\n",
+    "\n",
+    "# Turn on TQDM for pandas so that we can have progress bars when running `apply`.\n",
+    "tqdm_notebook.pandas()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e3234c61",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "# Suppress scientific notation in pandas (this shows up for census tract IDs)\n",
+    "pd.options.display.float_format = \"{:.2f}\".format\n",
+    "\n",
+    "# Set some global parameters\n",
+    "DATA_DIR = pathlib.Path.cwd().parent / \"data\"\n",
+    "TEMP_DATA_DIR = DATA_DIR / \"tmp\"\n",
+    "COMPARISON_OUTPUTS_DIR = DATA_DIR / \"comparison_outputs\"\n",
+    "\n",
+    "## I (Vincent) created this manually locally. Will need to change potentially when putting into official ETL scripts\n",
+    "GEOCORR_DATA_DIR = DATA_DIR / \"geocorr\"\n",
+    "\n",
+    "# Make the dirs if they don't exist\n",
+    "TEMP_DATA_DIR.mkdir(parents=True, exist_ok=True)\n",
+    "COMPARISON_OUTPUTS_DIR.mkdir(parents=True, exist_ok=True)\n",
+    "\n",
+    "CEJST_PRIORITY_COMMUNITY_THRESHOLD = 0.75\n",
+    "\n",
+    "# Name fields using variables. (This makes it easy to reference the same fields frequently without using strings\n",
+    "# and introducing the risk of misspelling the field name.)\n",
+    "\n",
+    "GEOID_FIELD_NAME = \"GEOID10\"\n",
+    "GEOID_TRACT_FIELD_NAME = \"GEOID10_TRACT\"\n",
+    "GEOID_STATE_FIELD_NAME = \"GEOID10_STATE\"\n",
+    "GEOID_CBG_FIELD_NAME = \"GEOID10_CBG\"\n",
+    "COUNTRY_FIELD_NAME = \"Country\"\n",
+    "CENSUS_BLOCK_GROUP_POPULATION_FIELD = \"Total population\"\n",
+    "\n",
+    "CEJST_SCORE_FIELD = \"cejst_score\"\n",
+    "CEJST_PERCENTILE_FIELD = \"cejst_percentile\"\n",
+    "CEJST_PRIORITY_COMMUNITY_FIELD = \"cejst_priority_community\"\n",
+    "\n",
+    "# Define some suffixes\n",
+    "POPULATION_SUFFIX = \" (priority population)\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "376f5b2e",
+   "metadata": {},
+   "source": [
+    "## Mapping Census Block Group to Urban and Rural Indicators using Geocorr Data\n",
+    "\n",
+    "The end result is a dataframe `urban_rural_map`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4147c081",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "geocorr_urban_rural_map = pd.read_csv(\n",
+    "    os.path.join(GEOCORR_DATA_DIR, 'geocorr2014_2125804280.csv'),\n",
+    "    encoding = \"ISO-8859-1\",\n",
+    "    skiprows=[1],\n",
+    "    dtype='str',\n",
+    ")\n",
+    "\n",
+    "geocorr_urban_rural_map['pop10'] = pd.to_numeric(geocorr_urban_rural_map['pop10'])\n",
+    "geocorr_urban_rural_map['afact'] = pd.to_numeric(geocorr_urban_rural_map['afact'])\n",
+    "\n",
+    "geocorr_urban_rural_map[GEOID_TRACT_FIELD_NAME] = geocorr_urban_rural_map['county'] + geocorr_urban_rural_map['tract'] # + geocorr_urban_rural_map['bg']\n",
+    "geocorr_urban_rural_map[GEOID_TRACT_FIELD_NAME] = geocorr_urban_rural_map[GEOID_TRACT_FIELD_NAME].str.replace('.', '', regex=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "78276a83",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "geocorr_urban_rural_map[GEOID_TRACT_FIELD_NAME].str.len().value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f2890779",
+   "metadata": {},
+   "source": [
+    "We want to see that the length of the derived Census Block Group is always 12 digits. Census Tracts are always 11 digits"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fd89f6c8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "geocorr_urban_rural_map = geocorr_urban_rural_map[[\n",
+    "    GEOID_TRACT_FIELD_NAME,\n",
+    "    'ur',\n",
+    "    'ua',\n",
+    "    'cntyname',\n",
+    "    'uaname',\n",
+    "    'pop10',\n",
+    "    'afact'\n",
+    "]]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e597d7e2",
+   "metadata": {},
+   "source": [
+    "Checking Primary Key"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "29929046",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "geocorr_urban_rural_map.groupby([GEOID_TRACT_FIELD_NAME, 'ur', 'ua'], dropna=False).size().sort_values(ascending=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9e4c0c3f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "geocorr_urban_rural_map.loc[geocorr_urban_rural_map[GEOID_TRACT_FIELD_NAME] == '36117020302']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d52761e8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "total_geo_population = geocorr_urban_rural_map.groupby(GEOID_TRACT_FIELD_NAME).agg({'pop10': np.sum}).reset_index()\n",
+    "total_geo_population.rename(columns={'pop10': 'total_population'}, inplace=True)\n",
+    "total_geo_population.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "38225b78",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "geocorr_urban_rural_with_total_pop_map = geocorr_urban_rural_map.groupby([GEOID_TRACT_FIELD_NAME, 'ur']).agg({'pop10': np.sum}).reset_index()\n",
+    "geocorr_urban_rural_with_total_pop_map = geocorr_urban_rural_with_total_pop_map.merge(total_geo_population, how='inner', on=GEOID_TRACT_FIELD_NAME)\n",
+    "geocorr_urban_rural_with_total_pop_map.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "41b9448a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "geocorr_urban_rural_with_total_pop_map['afact'] = geocorr_urban_rural_with_total_pop_map['pop10'] / geocorr_urban_rural_with_total_pop_map['total_population']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "eb4ddb9b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "geocorr_urban_rural_with_total_pop_map.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1e03d1e6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "geocorr_urban_rural_with_total_pop_map.loc[geocorr_urban_rural_with_total_pop_map[GEOID_TRACT_FIELD_NAME] == '01001020200']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1d976cb5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "urban_rural_map = geocorr_urban_rural_with_total_pop_map.pivot(index=GEOID_TRACT_FIELD_NAME, columns='ur', values=['pop10', 'afact'])\n",
+    "urban_rural_map.columns = ['_'.join(col).strip() for col in urban_rural_map.columns.values]\n",
+    "urban_rural_map.reset_index(inplace=True)\n",
+    "urban_rural_map['urban_heuristic_flag'] = 0\n",
+    "mask = urban_rural_map['afact_U'] >= 0.5\n",
+    "urban_rural_map.loc[mask, 'urban_heuristic_flag'] = 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0f3a0993",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "urban_rural_map.rename(\n",
+    "    columns={\n",
+    "        'pop10_R': 'population_in_rural_areas',\n",
+    "        'pop10_U': 'population_in_urban_areas',\n",
+    "        'afact_R': 'perc_population_in_rural_areas',\n",
+    "        'afact_U': 'perc_population_in_urban_areas',\n",
+    "    }, \n",
+    "    inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ba10f07c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "urban_rural_map.head(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "56098d7b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "urban_rural_map.to_csv(\n",
+    "    path_or_buf=GEOCORR_DATA_DIR / \"urban_rural_map.csv\", na_rep=\"\", index=False\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

data/data-pipeline/poetry.lock

@@ -38,7 +38,7 @@ tests = ["coverage[toml] (>=5.0.2)", "hypothesis", "pytest"]
 
 [[package]]
 name = "astroid"
-version = "2.7.3"
+version = "2.8.0"
 description = "An abstract syntax tree for Python with inference support."
 category = "dev"
 optional = false
@@ -47,7 +47,7 @@ python-versions = "~=3.6"
 [package.dependencies]
 lazy-object-proxy = ">=1.4.0"
 typed-ast = {version = ">=1.4.0,<1.5", markers = "implementation_name == \"cpython\" and python_version < \"3.8\""}
-typing-extensions = {version = ">=3.7.4", markers = "python_version < \"3.8\""}
+typing-extensions = {version = ">=3.10", markers = "python_version < \"3.10\""}
 wrapt = ">=1.11,<1.13"
 
 [[package]]
@@ -169,7 +169,7 @@ pycparser = "*"
 
 [[package]]
 name = "charset-normalizer"
-version = "2.0.5"
+version = "2.0.6"
 description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet."
 category = "main"
 optional = false
@@ -275,7 +275,7 @@ python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
 
 [[package]]
 name = "distlib"
-version = "0.3.2"
+version = "0.3.3"
 description = "Distribution utilities"
 category = "dev"
 optional = false
@@ -592,7 +592,7 @@ qtconsole = "*"
 
 [[package]]
 name = "jupyter-client"
-version = "7.0.2"
+version = "7.0.3"
 description = "Jupyter protocol implementation and client libraries"
 category = "main"
 optional = false
@@ -673,14 +673,14 @@ test = ["nbformat", "nose", "pip", "requests", "mock"]
 
 [[package]]
 name = "jupyter-core"
-version = "4.7.1"
+version = "4.8.1"
 description = "Jupyter core package. A base package on which Jupyter projects rely."
 category = "main"
 optional = false
 python-versions = ">=3.6"
 
 [package.dependencies]
-pywin32 = {version = ">=1.0", markers = "sys_platform == \"win32\""}
+pywin32 = {version = ">=1.0", markers = "sys_platform == \"win32\" and platform_python_implementation != \"PyPy\""}
 traitlets = "*"
 
 [[package]]
@@ -956,7 +956,7 @@ python-versions = ">=3.5"
 
 [[package]]
 name = "notebook"
-version = "6.4.3"
+version = "6.4.4"
 description = "A web-based notebook environment for interactive computing"
 category = "main"
 optional = false
@@ -993,7 +993,7 @@ python-versions = ">=3.7"
 
 [[package]]
 name = "openpyxl"
-version = "3.0.8"
+version = "3.0.9"
 description = "A Python library to read/write Excel 2010 xlsx/xlsm files"
 category = "dev"
 optional = false
@@ -1183,19 +1183,20 @@ python-versions = ">=3.5"
 
 [[package]]
 name = "pylint"
-version = "2.10.2"
+version = "2.11.1"
 description = "python code static checker"
 category = "dev"
 optional = false
 python-versions = "~=3.6"
 
 [package.dependencies]
-astroid = ">=2.7.2,<2.8"
+astroid = ">=2.8.0,<2.9"
 colorama = {version = "*", markers = "sys_platform == \"win32\""}
 isort = ">=4.2.5,<6"
 mccabe = ">=0.6,<0.7"
 platformdirs = ">=2.2.0"
 toml = ">=0.7.1"
+typing-extensions = {version = ">=3.10.0", markers = "python_version < \"3.10\""}
 
 [[package]]
 name = "pypandoc"
@@ -1215,7 +1216,7 @@ python-versions = ">=2.6, !=3.0.*, !=3.1.*, !=3.2.*"
 
 [[package]]
 name = "pyproj"
-version = "3.2.0"
+version = "3.2.1"
 description = "Python interface to PROJ (cartographic projections and coordinate transformations library)"
 category = "main"
 optional = false
@@ -1313,7 +1314,7 @@ python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*"
 
 [[package]]
 name = "pyzmq"
-version = "22.2.1"
+version = "22.3.0"
 description = "Python bindings for 0MQ"
 category = "main"
 optional = false
@@ -1488,7 +1489,7 @@ python-versions = ">= 3.5"
 
 [[package]]
 name = "tox"
-version = "3.24.3"
+version = "3.24.4"
 description = "tox is a generic virtualenv management and test command line tool"
 category = "dev"
 optional = false
@@ -1546,7 +1547,7 @@ python-versions = "*"
 
 [[package]]
 name = "types-requests"
-version = "2.25.6"
+version = "2.25.8"
 description = "Typing stubs for requests"
 category = "main"
 optional = false
@@ -1586,7 +1587,7 @@ jellyfish = "0.6.1"
 
 [[package]]
 name = "virtualenv"
-version = "20.7.2"
+version = "20.8.0"
 description = "Virtual Python Environment builder"
 category = "dev"
 optional = false
@@ -1687,8 +1688,8 @@ argon2-cffi = [
     {file = "argon2_cffi-21.1.0-pp37-pypy37_pp73-win_amd64.whl", hash = "sha256:566ffb581bbd9db5562327aee71b2eda24a1c15b23a356740abe3c011bbe0dcb"},
 ]
 astroid = [
-    {file = "astroid-2.7.3-py3-none-any.whl", hash = "sha256:dc1e8b28427d6bbef6b8842b18765ab58f558c42bb80540bd7648c98412af25e"},
+    {file = "astroid-2.8.0-py3-none-any.whl", hash = "sha256:dcc06f6165f415220013801642bd6c9808a02967070919c4b746c6864c205471"},
-    {file = "astroid-2.7.3.tar.gz", hash = "sha256:3b680ce0419b8a771aba6190139a3998d14b413852506d99aff8dc2bf65ee67c"},
+    {file = "astroid-2.8.0.tar.gz", hash = "sha256:fe81f80c0b35264acb5653302ffbd935d394f1775c5e4487df745bf9c2442708"},
 ]
 atomicwrites = [
     {file = "atomicwrites-1.4.0-py2.py3-none-any.whl", hash = "sha256:6d1784dea7c0c8d4a5172b6c620f40b6e4cbfdf96d783691f2e1302a7b88e197"},
@@ -1769,8 +1770,8 @@ cffi = [
     {file = "cffi-1.14.6.tar.gz", hash = "sha256:c9a875ce9d7fe32887784274dd533c57909b7b1dcadcc128a2ac21331a9765dd"},
 ]
 charset-normalizer = [
-    {file = "charset-normalizer-2.0.5.tar.gz", hash = "sha256:7098e7e862f6370a2a8d1a6398cd359815c45d12626267652c3f13dec58e2367"},
+    {file = "charset-normalizer-2.0.6.tar.gz", hash = "sha256:5ec46d183433dcbd0ab716f2d7f29d8dee50505b3fdb40c6b985c7c4f5a3591f"},
-    {file = "charset_normalizer-2.0.5-py3-none-any.whl", hash = "sha256:fa471a601dfea0f492e4f4fca035cd82155e65dc45c9b83bf4322dfab63755dd"},
+    {file = "charset_normalizer-2.0.6-py3-none-any.whl", hash = "sha256:5d209c0a931f215cee683b6445e2d77677e7e75e159f78def0db09d68fafcaa6"},
 ]
 click = [
     {file = "click-8.0.1-py3-none-any.whl", hash = "sha256:fba402a4a47334742d782209a7c79bc448911afe1149d07bdabdf480b3e2f4b6"},
@@ -1825,8 +1826,8 @@ defusedxml = [
     {file = "defusedxml-0.7.1.tar.gz", hash = "sha256:1bb3032db185915b62d7c6209c5a8792be6a32ab2fedacc84e01b52c51aa3e69"},
 ]
 distlib = [
-    {file = "distlib-0.3.2-py2.py3-none-any.whl", hash = "sha256:23e223426b28491b1ced97dc3bbe183027419dfc7982b4fa2f05d5f3ff10711c"},
+    {file = "distlib-0.3.3-py2.py3-none-any.whl", hash = "sha256:c8b54e8454e5bf6237cc84c20e8264c3e991e824ef27e8f1e81049867d861e31"},
-    {file = "distlib-0.3.2.zip", hash = "sha256:106fef6dc37dd8c0e2c0a60d3fca3e77460a48907f335fa28420463a6f799736"},
+    {file = "distlib-0.3.3.zip", hash = "sha256:d982d0751ff6eaaab5e2ec8e691d949ee80eddf01a62eaa96ddb11531fe16b05"},
 ]
 dparse = [
     {file = "dparse-0.5.1-py3-none-any.whl", hash = "sha256:e953a25e44ebb60a5c6efc2add4420c177f1d8404509da88da9729202f306994"},
@@ -1920,8 +1921,8 @@ jupyter = [
     {file = "jupyter-1.0.0.zip", hash = "sha256:3e1f86076bbb7c8c207829390305a2b1fe836d471ed54be66a3b8c41e7f46cc7"},
 ]
 jupyter-client = [
-    {file = "jupyter_client-7.0.2-py3-none-any.whl", hash = "sha256:37a30c13d3655b819add61c830594090af7fca40cd2d74f41cad9e2e12118501"},
+    {file = "jupyter_client-7.0.3-py3-none-any.whl", hash = "sha256:b07ceecb8f845f908bbd0f78bb17c0abac7b393de9d929bd92190e36c24c201e"},
-    {file = "jupyter_client-7.0.2.tar.gz", hash = "sha256:0c6cabd07e003a2e9692394bf1ae794188ad17d2e250ed747232d7a473aa772c"},
+    {file = "jupyter_client-7.0.3.tar.gz", hash = "sha256:bb58e3218d74e072673948bd1e2a6bb3b65f32447b3e8c143eeca16b946ee230"},
 ]
 jupyter-console = [
     {file = "jupyter_console-6.4.0-py3-none-any.whl", hash = "sha256:7799c4ea951e0e96ba8260575423cb323ea5a03fcf5503560fa3e15748869e27"},
@@ -1936,8 +1937,8 @@ jupyter-contrib-nbextensions = [
     {file = "jupyter_contrib_nbextensions-0.5.1.tar.gz", hash = "sha256:eecd28ecc2fc410226c0a3d4932ed2fac4860ccf8d9e9b1b29548835a35b22ab"},
 ]
 jupyter-core = [
-    {file = "jupyter_core-4.7.1-py3-none-any.whl", hash = "sha256:8c6c0cac5c1b563622ad49321d5ec47017bd18b94facb381c6973a0486395f8e"},
+    {file = "jupyter_core-4.8.1-py3-none-any.whl", hash = "sha256:8dd262ec8afae95bd512518eb003bc546b76adbf34bf99410e9accdf4be9aa3a"},
-    {file = "jupyter_core-4.7.1.tar.gz", hash = "sha256:79025cb3225efcd36847d0840f3fc672c0abd7afd0de83ba8a1d3837619122b4"},
+    {file = "jupyter_core-4.8.1.tar.gz", hash = "sha256:ef210dcb4fca04de07f2ead4adf408776aca94d17151d6f750ad6ded0b91ea16"},
 ]
 jupyter-highlight-selected-word = [
     {file = "jupyter_highlight_selected_word-0.2.0-py2.py3-none-any.whl", hash = "sha256:9545dfa9cb057eebe3a5795604dcd3a5294ea18637e553f61a0b67c1b5903c58"},
@@ -2222,8 +2223,8 @@ nest-asyncio = [
     {file = "nest_asyncio-1.5.1.tar.gz", hash = "sha256:afc5a1c515210a23c461932765691ad39e8eba6551c055ac8d5546e69250d0aa"},
 ]
 notebook = [
-    {file = "notebook-6.4.3-py3-none-any.whl", hash = "sha256:b50eafa8208d5db966efd1caa4076b4dfc51815e02a805b32ecd717e9e6cc071"},
+    {file = "notebook-6.4.4-py3-none-any.whl", hash = "sha256:33488bdcc5cbef23c3cfa12cd51b0b5459a211945b5053d17405980611818149"},
-    {file = "notebook-6.4.3.tar.gz", hash = "sha256:e6b6dfed36b00cf950f63c0d42e947c101d4258aec21624de62b9e0c11ed5c0d"},
+    {file = "notebook-6.4.4.tar.gz", hash = "sha256:26b0095c568e307a310fd78818ad8ebade4f00462dada4c0e34cbad632b9085d"},
 ]
 numpy = [
     {file = "numpy-1.21.1-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:38e8648f9449a549a7dfe8d8755a5979b45b3538520d1e735637ef28e8c2dc50"},
@@ -2256,8 +2257,8 @@ numpy = [
     {file = "numpy-1.21.1.zip", hash = "sha256:dff4af63638afcc57a3dfb9e4b26d434a7a602d225b42d746ea7fe2edf1342fd"},
 ]
 openpyxl = [
-    {file = "openpyxl-3.0.8-py2.py3-none-any.whl", hash = "sha256:52150a09b660fe444af7abe2592b156c14e324526b1968a57705525547317a7f"},
+    {file = "openpyxl-3.0.9-py2.py3-none-any.whl", hash = "sha256:8f3b11bd896a95468a4ab162fc4fcd260d46157155d1f8bfaabb99d88cfcf79f"},
-    {file = "openpyxl-3.0.8.tar.gz", hash = "sha256:4f2770348c029ce9433316ced8f91ed37d2a605e654f8bfdc93a3524561a8ce2"},
+    {file = "openpyxl-3.0.9.tar.gz", hash = "sha256:40f568b9829bf9e446acfffce30250ac1fa39035124d55fc024025c41481c90f"},
 ]
 packaging = [
     {file = "packaging-21.0-py3-none-any.whl", hash = "sha256:c86254f9220d55e31cc94d69bade760f0847da8000def4dfe1c6b872fd14ff14"},
@@ -2402,8 +2403,8 @@ pygments = [
     {file = "Pygments-2.10.0.tar.gz", hash = "sha256:f398865f7eb6874156579fdf36bc840a03cab64d1cde9e93d68f46a425ec52c6"},
 ]
 pylint = [
-    {file = "pylint-2.10.2-py3-none-any.whl", hash = "sha256:e178e96b6ba171f8ef51fbce9ca30931e6acbea4a155074d80cc081596c9e852"},
+    {file = "pylint-2.11.1-py3-none-any.whl", hash = "sha256:0f358e221c45cbd4dad2a1e4b883e75d28acdcccd29d40c76eb72b307269b126"},
-    {file = "pylint-2.10.2.tar.gz", hash = "sha256:6758cce3ddbab60c52b57dcc07f0c5d779e5daf0cf50f6faacbef1d3ea62d2a1"},
+    {file = "pylint-2.11.1.tar.gz", hash = "sha256:2c9843fff1a88ca0ad98a256806c82c5a8f86086e7ccbdb93297d86c3f90c436"},
 ]
 pypandoc = [
     {file = "pypandoc-1.6.4-py3-none-win_amd64.whl", hash = "sha256:080903342d8cca6d953835c103b0f280a6cb66a6a20102692143a138b046c44f"},
@@ -2414,32 +2415,26 @@ pyparsing = [
     {file = "pyparsing-2.4.7.tar.gz", hash = "sha256:c203ec8783bf771a155b207279b9bccb8dea02d8f0c9e5f8ead507bc3246ecc1"},
 ]
 pyproj = [
-    {file = "pyproj-3.2.0-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:85b7f67a3606b8a846691effd80c187597ac4e2733ae818f3e3e8be33edcb582"},
+    {file = "pyproj-3.2.1-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:ce554616880ab59110af9baa2948b4442d2961e20390df00cea49782b7c779fe"},
-    {file = "pyproj-3.2.0-cp37-cp37m-manylinux2010_x86_64.whl", hash = "sha256:72e0c4409a0c2f83ba448ecdf6accc9615d3e4069b8cad2a2a37a464225d0582"},
+    {file = "pyproj-3.2.1-cp37-cp37m-manylinux2010_x86_64.whl", hash = "sha256:40ed2a66d93af811abac9fd2581685a2aade22a6753501f2f9760893ee6b0828"},
-    {file = "pyproj-3.2.0-cp37-cp37m-manylinux_2_12_i686.manylinux2010_i686.whl", hash = "sha256:e95ad0255610b719e257150c4f989da51e025dea9a4d686b072c68f99ded538d"},
+    {file = "pyproj-3.2.1-cp37-cp37m-manylinux_2_12_i686.manylinux2010_i686.whl", hash = "sha256:8e6821a472f03e3604413b562536e05cb7926c3bd85bfc423c88c4909871f692"},
-    {file = "pyproj-3.2.0-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:1563a854eff7b6ed430fb968f8f711d13fc02d587afb1dc93a88e5cda6d36462"},
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-    {file = "pyzmq-22.2.1-pp37-pypy37_pp73-manylinux_2_12_i686.manylinux2010_i686.whl", hash = "sha256:021e22a8c58ab294bd4b96448a2ca4e716e1d76600192ff84c33d71edb1fbd37"},
+    {file = "pyzmq-22.3.0-pp37-pypy37_pp73-manylinux_2_12_i686.manylinux2010_i686.whl", hash = "sha256:80e043a89c6cadefd3a0712f8a1322038e819ebe9dbac7eca3bce1721bcb63bf"},
-    {file = "pyzmq-22.2.1-pp37-pypy37_pp73-manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:200ac096cee5499964c90687306a7244b79ef891f773ed4cf15019fd1f3df330"},
+    {file = "pyzmq-22.3.0-pp37-pypy37_pp73-manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:1621e7a2af72cced1f6ec8ca8ca91d0f76ac236ab2e8828ac8fe909512d566cb"},
-    {file = "pyzmq-22.2.1-pp37-pypy37_pp73-win_amd64.whl", hash = "sha256:b3f57bee62e36be5c97712de32237c5589caee0d1154c2ad01a888accfae20bc"},
+    {file = "pyzmq-22.3.0-pp37-pypy37_pp73-win_amd64.whl", hash = "sha256:d6157793719de168b199194f6b6173f0ccd3bf3499e6870fac17086072e39115"},
-    {file = "pyzmq-22.2.1.tar.gz", hash = "sha256:6d18c76676771fd891ca8e0e68da0bbfb88e30129835c0ade748016adb3b6242"},
+    {file = "pyzmq-22.3.0.tar.gz", hash = "sha256:8eddc033e716f8c91c6a2112f0a8ebc5e00532b4a6ae1eb0ccc48e027f9c671c"},
 ]
 qtconsole = [
     {file = "qtconsole-5.1.1-py3-none-any.whl", hash = "sha256:73994105b0369bb99f4164df4a131010f3c7b33a7b5169c37366358d8744675b"},
@@ -2725,8 +2720,8 @@ tornado = [
     {file = "tornado-6.1.tar.gz", hash = "sha256:33c6e81d7bd55b468d2e793517c909b139960b6c790a60b7991b9b6b76fb9791"},
 ]
 tox = [
-    {file = "tox-3.24.3-py2.py3-none-any.whl", hash = "sha256:9fbf8e2ab758b2a5e7cb2c72945e4728089934853076f67ef18d7575c8ab6b88"},
+    {file = "tox-3.24.4-py2.py3-none-any.whl", hash = "sha256:5e274227a53dc9ef856767c21867377ba395992549f02ce55eb549f9fb9a8d10"},
-    {file = "tox-3.24.3.tar.gz", hash = "sha256:c6c4e77705ada004283610fd6d9ba4f77bc85d235447f875df9f0ba1bc23b634"},
+    {file = "tox-3.24.4.tar.gz", hash = "sha256:c30b57fa2477f1fb7c36aa1d83292d5c2336cd0018119e1b1c17340e2c2708ca"},
 ]
 tqdm = [
     {file = "tqdm-4.62.0-py2.py3-none-any.whl", hash = "sha256:706dea48ee05ba16e936ee91cb3791cd2ea6da348a0e50b46863ff4363ff4340"},
@@ -2769,8 +2764,8 @@ typed-ast = [
     {file = "typed_ast-1.4.3.tar.gz", hash = "sha256:fb1bbeac803adea29cedd70781399c99138358c26d05fcbd23c13016b7f5ec65"},
 ]
 types-requests = [
-    {file = "types-requests-2.25.6.tar.gz", hash = "sha256:e21541c0f55c066c491a639309159556dd8c5833e49fcde929c4c47bdb0002ee"},
+    {file = "types-requests-2.25.8.tar.gz", hash = "sha256:225ac2e86549b6ef3a8a44bf955f80b4955855704a15d2883d8445c8df637242"},
-    {file = "types_requests-2.25.6-py3-none-any.whl", hash = "sha256:a5a305b43ea57bf64d6731f89816946a405b591eff6de28d4c0fd58422cee779"},
+    {file = "types_requests-2.25.8-py3-none-any.whl", hash = "sha256:26e90866bcd773d76b316de7e6bd6e24641f9e1653cf27241c533886600f6824"},
 ]
 typing-extensions = [
     {file = "typing_extensions-3.10.0.2-py2-none-any.whl", hash = "sha256:d8226d10bc02a29bcc81df19a26e56a9647f8b0a6d4a83924139f4a8b01f17b7"},
@@ -2785,8 +2780,8 @@ us = [
     {file = "us-2.0.2.tar.gz", hash = "sha256:cb11ad0d43deff3a1c3690c74f0c731cff5b862c73339df2edd91133e1496fbc"},
 ]
 virtualenv = [
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+    {file = "virtualenv-20.8.0-py2.py3-none-any.whl", hash = "sha256:a4b987ec31c3c9996cf1bc865332f967fe4a0512c41b39652d6224f696e69da5"},
-    {file = "virtualenv-20.7.2.tar.gz", hash = "sha256:9ef4e8ee4710826e98ff3075c9a4739e2cb1040de6a2a8d35db0055840dc96a0"},
+    {file = "virtualenv-20.8.0.tar.gz", hash = "sha256:4da4ac43888e97de9cf4fdd870f48ed864bbfd133d2c46cbdec941fed4a25aef"},
 ]
 wcwidth = [
     {file = "wcwidth-0.2.5-py2.py3-none-any.whl", hash = "sha256:beb4802a9cebb9144e99086eff703a642a13d6a0052920003a230f3294bbe784"},

data/data-pipeline/requirements.txt

@@ -7,7 +7,7 @@ bleach==4.1.0; python_version >= "3.7"
 censusdata==1.15; python_version >= "2.7"
 certifi==2021.5.30; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.7"
 cffi==1.14.6; implementation_name == "pypy" and python_version >= "3.6"
-charset-normalizer==2.0.5; python_full_version >= "3.6.0" and python_version >= "3"
+charset-normalizer==2.0.6; python_full_version >= "3.6.0" and python_version >= "3"
 click-plugins==1.1.1; python_version >= "3.6"
 click==8.0.1; python_version >= "3.6"
 cligj==0.7.2; python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.3.0" and python_version < "4" and python_version >= "3.6"
@@ -30,11 +30,11 @@ jedi==0.18.0; python_version >= "3.7"
 jellyfish==0.6.1
 jinja2==3.0.1; python_version >= "3.7"
 jsonschema==3.2.0; python_version >= "3.5"
-jupyter-client==7.0.2; python_full_version >= "3.6.1" and python_version >= "3.7"
+jupyter-client==7.0.3; python_full_version >= "3.6.1" and python_version >= "3.7"
 jupyter-console==6.4.0; python_version >= "3.6"
 jupyter-contrib-core==0.3.3
 jupyter-contrib-nbextensions==0.5.1
-jupyter-core==4.7.1; python_full_version >= "3.6.1" and python_version >= "3.7"
+jupyter-core==4.8.1; python_full_version >= "3.6.1" and python_version >= "3.7"
 jupyter-highlight-selected-word==0.2.0
 jupyter-latex-envs==1.4.6
 jupyter-nbextensions-configurator==0.4.1
@@ -52,7 +52,7 @@ nbclient==0.5.4; python_full_version >= "3.6.1" and python_version >= "3.7"
 nbconvert==6.1.0; python_version >= "3.7"
 nbformat==5.1.3; python_full_version >= "3.6.1" and python_version >= "3.7"
 nest-asyncio==1.5.1; python_full_version >= "3.6.1" and python_version >= "3.7"
-notebook==6.4.3; python_version >= "3.6"
+notebook==6.4.4; python_version >= "3.6"
 numpy==1.21.1; python_version >= "3.7"
 packaging==21.0; python_version >= "3.7"
 pandas==1.3.3; python_full_version >= "3.7.1"
@@ -69,14 +69,14 @@ pycparser==2.20; python_version >= "3.6" and python_full_version < "3.0.0" or py
 pygments==2.10.0; python_version >= "3.7"
 pypandoc==1.6.4
 pyparsing==2.4.7; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.3.0" and python_version >= "3.7"
-pyproj==3.2.0; python_version >= "3.7"
+pyproj==3.2.1; python_version >= "3.7"
 pyrsistent==0.18.0; python_version >= "3.6"
 python-dateutil==2.8.2; python_full_version >= "3.7.1" and python_version >= "3.7"
 pytz==2021.1; python_full_version >= "3.7.1" and python_version >= "2.7"
-pywin32==301; sys_platform == "win32" and python_version >= "3.6"
+pywin32==301; sys_platform == "win32" and platform_python_implementation != "PyPy" and python_version >= "3.6"
 pywinpty==1.1.4; os_name == "nt" and python_version >= "3.6"
 pyyaml==5.4.1; python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0"
-pyzmq==22.2.1; python_full_version >= "3.6.1" and python_version >= "3.7"
+pyzmq==22.3.0; python_full_version >= "3.6.1" and python_version >= "3.7"
 qtconsole==5.1.1; python_version >= "3.6"
 qtpy==1.11.1; python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.6"
 requests==2.26.0; (python_version >= "2.7" and python_full_version < "3.0.0") or (python_full_version >= "3.6.0")
@@ -88,7 +88,7 @@ testpath==0.5.0; python_version >= "3.7"
 tornado==6.1; python_full_version >= "3.6.1" and python_version >= "3.7"
 tqdm==4.62.0; (python_version >= "2.7" and python_full_version < "3.0.0") or (python_full_version >= "3.4.0")
 traitlets==5.1.0; python_full_version >= "3.6.1" and python_version >= "3.7"
-types-requests==2.25.6
+types-requests==2.25.8
 typing-extensions==3.10.0.2; python_version < "3.8" and python_version >= "3.6"
 urllib3==1.26.6; python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version < "4" and python_version >= "2.7"
 us==2.0.2