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Ingest census data directly, add unemployment to the score (#214)
* Ingest two data sources and add to score Co-authored-by: Jorge Escobar <jorge.e.escobar@omb.eop.gov>
This commit is contained in:
Lucas Merrill Brown
GitHub
parent
f12ab4d3b7
commit
589ec483e3
7 changed files with 395 additions and 26 deletions
152
score/ipython/census_etl.ipynb
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152
score/ipython/census_etl.ipynb
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@ -0,0 +1,152 @@
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "0491828b",
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"import censusdata\n",
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"import csv\n",
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"from pathlib import Path\n",
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"import os\n",
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"\n",
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"ACS_YEAR = 2019\n",
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"\n",
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"DATA_PATH = Path.cwd().parent / \"data\"\n",
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"FIPS_CSV_PATH = DATA_PATH / \"fips_states_2010.csv\"\n",
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"OUTPUT_PATH = DATA_PATH / \"dataset\" / f\"census_acs_{ACS_YEAR}\"\n",
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"\n",
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"GEOID_FIELD_NAME = \"GEOID10\"\n",
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"UNEMPLOYED_FIELD_NAME = \"Unemployed Civilians (fraction)\"\n",
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"\n",
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"# Some display settings to make pandas outputs more readable.\n",
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"pd.set_option(\"display.expand_frame_repr\", False)\n",
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"pd.set_option(\"display.precision\", 2)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "654f25a1",
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"metadata": {
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"scrolled": true
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},
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"outputs": [],
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"source": [
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"# Following the tutorial at https://jtleider.github.io/censusdata/example1.html.\n",
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"# Full list of fields is at https://www2.census.gov/programs-surveys/acs/summary_file/2019/documentation/user_tools/ACS2019_Table_Shells.xlsx\n",
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"censusdata.printtable(censusdata.censustable(src=\"acs5\", year=ACS_YEAR, table=\"B23025\"))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "8999cea4",
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"metadata": {
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"scrolled": false
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},
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"outputs": [],
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"source": [
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"def fips_from_censusdata_censusgeo(censusgeo: censusdata.censusgeo) -> str:\n",
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" \"\"\"Create a FIPS code from the proprietary censusgeo index.\"\"\"\n",
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" fips = \"\".join([value for (key, value) in censusgeo.params()])\n",
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" return fips\n",
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"\n",
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"\n",
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"dfs = []\n",
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"with open(FIPS_CSV_PATH) as csv_file:\n",
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" csv_reader = csv.reader(csv_file, delimiter=\",\")\n",
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" line_count = 0\n",
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"\n",
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" for row in csv_reader:\n",
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" if line_count == 0:\n",
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" line_count += 1\n",
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" else:\n",
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" fips = row[0].strip()\n",
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" print(f\"Downloading data for state/territory with FIPS code {fips}\")\n",
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"\n",
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" dfs.append(\n",
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" censusdata.download(\n",
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" src=\"acs5\",\n",
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" year=ACS_YEAR,\n",
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" geo=censusdata.censusgeo(\n",
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" [(\"state\", fips), (\"county\", \"*\"), (\"block group\", \"*\")]\n",
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" ),\n",
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" var=[\"B23025_005E\", \"B23025_003E\"],\n",
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" )\n",
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" )\n",
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"\n",
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"df = pd.concat(dfs)\n",
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"\n",
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"df[GEOID_FIELD_NAME] = df.index.to_series().apply(func=fips_from_censusdata_censusgeo)\n",
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"\n",
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"df.head()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "803cce31",
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"metadata": {
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"scrolled": false
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},
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"outputs": [],
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"source": [
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"# Calculate percent unemployment.\n",
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"# TODO: remove small-sample data that should be `None` instead of a high-variance fraction.\n",
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"df[UNEMPLOYED_FIELD_NAME] = df.B23025_005E / df.B23025_003E\n",
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"\n",
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"df.head()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "2a269bb1",
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"metadata": {
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"scrolled": true
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},
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"outputs": [],
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"source": [
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"# mkdir census\n",
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"OUTPUT_PATH.mkdir(parents=True, exist_ok=True)\n",
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"\n",
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"columns_to_include = [GEOID_FIELD_NAME, UNEMPLOYED_FIELD_NAME]\n",
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"\n",
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"df[columns_to_include].to_csv(path_or_buf=OUTPUT_PATH / \"usa.csv\", index=False)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "91932af5",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.0"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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@ -69,6 +69,7 @@
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"outputs": [],
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"source": [
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"# write nationwide csv\n",
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"csv_path.mkdir(parents=True, exist_ok=True)\n",
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"df.to_csv(csv_path / f\"usa.csv\", index=False)"
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]
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},
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@ -94,6 +95,14 @@
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" # we need to name the file data01.csv for ogr2ogr csv merge to work\n",
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" df1.to_csv(csv_path / f\"data{fips}.csv\", index=False)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "81b977f8",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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@ -112,7 +121,7 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.1"
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"version": "3.9.0"
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}
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},
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"nbformat": 4,
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132
score/ipython/housing_and_transportation_etl.ipynb
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132
score/ipython/housing_and_transportation_etl.ipynb
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@ -0,0 +1,132 @@
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "c21b63a3",
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"import censusdata\n",
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"import csv\n",
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"import requests\n",
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"import zipfile\n",
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"\n",
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"from pathlib import Path\n",
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"\n",
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"ACS_YEAR = 2019\n",
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"\n",
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"DATA_PATH = Path.cwd().parent / \"data\"\n",
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"FIPS_CSV_PATH = DATA_PATH / \"fips_states_2010.csv\"\n",
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"OUTPUT_PATH = DATA_PATH / \"dataset\" / \"housing_and_transportation_index\"\n",
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"\n",
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"GEOID_FIELD_NAME = \"GEOID10\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "6696bc66",
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"metadata": {},
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"outputs": [],
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"source": [
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"# https://htaindex.cnt.org/download/download.php?focus=blkgrp&geoid=01\n",
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"\n",
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"# Download each state / territory individually\n",
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"dfs = []\n",
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"with open(FIPS_CSV_PATH) as csv_file:\n",
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" csv_reader = csv.reader(csv_file, delimiter=\",\")\n",
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" line_count = 0\n",
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"\n",
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" for row in csv_reader:\n",
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" if line_count == 0:\n",
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" line_count += 1\n",
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" else:\n",
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" fips = row[0].strip()\n",
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"\n",
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" print(f\"Downloading data for state/territory with FIPS code {fips}\")\n",
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"\n",
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" download = requests.get(\n",
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" f\"https://htaindex.cnt.org/download/download.php?focus=blkgrp&geoid={fips}\",\n",
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" verify=False,\n",
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" )\n",
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" file_contents = download.content\n",
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" zip_file_dir = DATA_PATH / \"tmp\" / \"housing_and_transportation_index\"\n",
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"\n",
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" # Make the directory if it doesn't exist\n",
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" zip_file_dir.mkdir(parents=True, exist_ok=True)\n",
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" zip_file_path = zip_file_dir / f\"{fips}-downloaded.zip\"\n",
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" zip_file = open(zip_file_path, \"wb\")\n",
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" zip_file.write(file_contents)\n",
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" zip_file.close()\n",
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"\n",
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" with zipfile.ZipFile(zip_file_path, \"r\") as zip_ref:\n",
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" zip_ref.extractall(zip_file_dir)\n",
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"\n",
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" # New file name:\n",
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" tmp_csv_file_path = zip_file_dir / f\"htaindex_data_blkgrps_{fips}.csv\"\n",
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" tmp_df = pd.read_csv(filepath_or_buffer=tmp_csv_file_path)\n",
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"\n",
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" dfs.append(tmp_df)\n",
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"\n",
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"df = pd.concat(dfs)\n",
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"\n",
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"df.head()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "244e0d03",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Rename and reformat block group ID\n",
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"df.rename(columns={\"blkgrp\": GEOID_FIELD_NAME}, inplace=True)\n",
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"df[GEOID_FIELD_NAME] = df[GEOID_FIELD_NAME].str.replace('\"', \"\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "8275c1ef",
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"metadata": {},
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"outputs": [],
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"source": [
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"OUTPUT_PATH.mkdir(parents=True, exist_ok=True)\n",
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"\n",
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"df.to_csv(path_or_buf=OUTPUT_PATH / \"usa.csv\", index=False)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "ef5bb862",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.0"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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"metadata": {},
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"outputs": [],
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"source": [
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"# Before running this notebook, you must run the notebook `ejscreen_etl.ipynb`.\n",
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"# Before running this notebook, you must run the following notebooks (in any order):\n",
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"# 1. `ejscreen_etl.ipynb`\n",
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"# 2. `census_etl.ipynb`\n",
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"# 3. `housing_and_transportation_etl.ipynb`\n",
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"\n",
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"import collections\n",
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"import functools\n",
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"from pathlib import Path\n",
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"import pandas as pd\n",
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"import csv\n",
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"\n",
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"# Define some global parameters\n",
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"GEOID_FIELD_NAME = \"GEOID10\"\n",
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"BUCKET_SOCIOECONOMIC = \"Socioeconomic Factors\"\n",
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"BUCKET_SENSITIVE = \"Sensitive populations\"\n",
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"BUCKET_ENVIRONMENTAL = \"Environmental effects\"\n",
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@ -51,7 +56,62 @@
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"source": [
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"# EJSCreen csv Load\n",
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"ejscreen_csv = data_path / \"dataset\" / \"ejscreen_2020\" / \"usa.csv\"\n",
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"df = pd.read_csv(ejscreen_csv, dtype={\"ID\": \"string\"}, low_memory=False)\n",
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"ejscreen_df = pd.read_csv(ejscreen_csv, dtype={\"ID\": \"string\"}, low_memory=False)\n",
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"ejscreen_df.rename(columns={\"ID\": GEOID_FIELD_NAME}, inplace=True)\n",
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"ejscreen_df.head()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "daba69fb",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Load census data\n",
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"census_csv = data_path / \"dataset\" / \"census_acs_2019\" / \"usa.csv\"\n",
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"census_df = pd.read_csv(\n",
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" census_csv, dtype={GEOID_FIELD_NAME: \"string\"}, low_memory=False\n",
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")\n",
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"census_df.head()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "144bdde2",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Load housing and transportation data\n",
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"housing_and_transportation_index_csv = (\n",
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" data_path / \"dataset\" / \"housing_and_transportation_index\" / \"usa.csv\"\n",
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")\n",
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"housing_and_transportation_df = pd.read_csv(\n",
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" housing_and_transportation_index_csv,\n",
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" dtype={GEOID_FIELD_NAME: \"string\"},\n",
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" low_memory=False,\n",
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")\n",
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"housing_and_transportation_df.head()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "bf89efd8",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Join all the data sources that use census block groups\n",
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"dfs = [ejscreen_df, census_df, housing_and_transportation_df]\n",
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"\n",
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"df = functools.reduce(\n",
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" lambda left, right: pd.merge(\n",
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" left=left, right=right, on=GEOID_FIELD_NAME, how=\"outer\"\n",
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" ),\n",
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" dfs,\n",
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")\n",
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"\n",
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"df.head()"
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]
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},
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@ -70,9 +130,10 @@
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"data_sets = [\n",
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" # The following data sets have `bucket=None`, because it's not used in the score.\n",
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" DataSet(\n",
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" input_field=\"ID\", \n",
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" input_field=GEOID_FIELD_NAME,\n",
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" # Use the name `GEOID10` to enable geoplatform.gov's workflow.\n",
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" renamed_field=\"GEOID10\", bucket=None\n",
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" renamed_field=GEOID_FIELD_NAME,\n",
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" bucket=None,\n",
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" ),\n",
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" DataSet(input_field=\"ACSTOTPOP\", renamed_field=\"Total population\", bucket=None),\n",
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" # The following data sets have buckets, because they're used in the score\n",
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@ -152,6 +213,17 @@
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" renamed_field=\"Percent individuals age 25 or over with less than high school degree\",\n",
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" bucket=BUCKET_SOCIOECONOMIC,\n",
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" ),\n",
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" DataSet(\n",
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" input_field=\"Unemployed Civilians (fraction)\",\n",
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" # Following EJSCREEN conventions, where fractional data is named as a percent.\n",
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" renamed_field=\"Unemployed Civilians (percent)\",\n",
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" bucket=BUCKET_SOCIOECONOMIC,\n",
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" ),\n",
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" DataSet(\n",
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" input_field=\"ht_ami\",\n",
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" renamed_field=\"Housing + Transportation Costs % Income for the Regional Typical Household\",\n",
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" bucket=BUCKET_SOCIOECONOMIC,\n",
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" ),\n",
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"]"
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]
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},
|
||||
|
|
@ -305,6 +377,14 @@
|
|||
" # we need to name the file data01.csv for ogr2ogr csv merge to work\n",
|
||||
" df1.to_csv(score_csv_path / f\"data{states_fips}.csv\", index=False)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "167ebba3",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
|
|
@ -323,7 +403,7 @@
|
|||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.7.1"
|
||||
"version": "3.9.0"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
|
|
|
|||
|
|
@ -71,10 +71,10 @@
|
|||
"# Rename unclear name \"id\" to \"census_block_group_id\", as well as other renamings.\n",
|
||||
"cejst_df.rename(\n",
|
||||
" columns={\n",
|
||||
" \"ID\": CENSUS_BLOCK_GROUP_ID_FIELD,\n",
|
||||
" \"ACSTOTPOP\": CENSUS_BLOCK_GROUP_POPULATION_FIELD,\n",
|
||||
" \"score_a\": CEJST_SCORE_FIELD,\n",
|
||||
" \"score_a_percentile\": CEJST_PERCENTILE_FIELD,\n",
|
||||
" \"GEOID10\": CENSUS_BLOCK_GROUP_ID_FIELD,\n",
|
||||
" \"Total population\": CENSUS_BLOCK_GROUP_POPULATION_FIELD,\n",
|
||||
" \"Score C\": CEJST_SCORE_FIELD,\n",
|
||||
" \"Score C (percentile)\": CEJST_PERCENTILE_FIELD,\n",
|
||||
" },\n",
|
||||
" inplace=True,\n",
|
||||
" errors=\"raise\",\n",
|
||||
|
|
@ -110,10 +110,13 @@
|
|||
"# Data from https://calenviroscreen-oehha.hub.arcgis.com/#Data, specifically:\n",
|
||||
"# https://oehha.ca.gov/media/downloads/calenviroscreen/document/calenviroscreen40resultsdatadictionaryd12021.zip\n",
|
||||
"\n",
|
||||
"download = requests.get(\"https://justice40-data.s3.amazonaws.com/CalEnviroScreen/CalEnviroScreen_4.0_2021.zip\", verify=False)\n",
|
||||
"download = requests.get(\n",
|
||||
" \"https://justice40-data.s3.amazonaws.com/CalEnviroScreen/CalEnviroScreen_4.0_2021.zip\",\n",
|
||||
" verify=False,\n",
|
||||
")\n",
|
||||
"file_contents = download.content\n",
|
||||
"zip_file_path = TEMP_DATA_DIR\n",
|
||||
"zip_file = open(zip_file_path / \"downloaded.zip\", \"wb\")\n",
|
||||
"zip_file = open(zip_file_path / \"downloaded.zip\", \"wb\")\n",
|
||||
"zip_file.write(file_contents)\n",
|
||||
"zip_file.close()"
|
||||
]
|
||||
|
|
@ -298,11 +301,11 @@
|
|||
"id": "0c534966",
|
||||
"metadata": {
|
||||
"variables": {
|
||||
"all_100_sum": "1168",
|
||||
"all_100_sum_percent": "59%",
|
||||
"at_least_one_sum": "1817",
|
||||
"at_least_one_sum_percent": "92%",
|
||||
"cejst_cbgs_ca_only": "6987",
|
||||
"all_100_sum": "1373",
|
||||
"all_100_sum_percent": "69%",
|
||||
"at_least_one_sum": "1866",
|
||||
"at_least_one_sum_percent": "94%",
|
||||
"cejst_cbgs_ca_only": "10849",
|
||||
"ces_tracts_count": "1983"
|
||||
}
|
||||
},
|
||||
|
|
@ -319,14 +322,6 @@
|
|||
"\n",
|
||||
"Out of every CalEnviroScreen Disadvantaged Community census tract, {{all_100_sum}} ({{all_100_sum_percent}}) of these census tracts have 100% of the included census block groups within them considered priority communities by the current version of the CEJST score."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "db3c7d38",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
|
|
@ -345,7 +340,7 @@
|
|||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.9.0"
|
||||
"version": "3.7.1"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
|
|
|
|||
|
|
@ -1,3 +1,4 @@
|
|||
censusdata
|
||||
ipython
|
||||
jupyter
|
||||
jupyter_contrib_nbextensions
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue