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ETL Classes for Data Sets (#260)
* first commit * checkpoint * checkpoint * first extract module 🎉 * completed census acs etl class * completed ejscreen etl * completed etl * score generation ready * improving census load and separation * score generation working 🎉 * completed etls * new score generation * PR reviews * run specific etl; starting docstrings * docstrings work * more docstrings * completed docstrings * adding pyenv version * more reasonable poetry req for python * PR comments
This commit is contained in:
Jorge Escobar
GitHub
parent
69ef32485c
commit
842312f69f
33 changed files with 2628 additions and 2872 deletions
3
.gitignore
vendored
3
.gitignore
vendored
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@ -145,3 +145,6 @@ node_modules
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# serverless
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.serverless
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# pyenv
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.python-version
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@ -4,13 +4,16 @@
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We use Docker to install the necessary libraries in a container that can be run in any operating system.
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To build the docker container the first time, make sure you have Docker running on your machine and that you're in the root directory of the repository. Then run `docker-compose build`.
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To build the docker container the first time, make sure you're in the root directory of the repository and run `docker-compose build`.
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You can then run the following commands. You probably want to run these in a new terminal tab or window, because they may take a long time to run, especially the last one that generates the census data.
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After that, to run commands type the following:
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- Get help: `docker run --rm -it j40_score /bin/sh -c "python3 application.py" python3 application.py --help"`
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- Get help: `docker run --rm -it j40_score /bin/sh -c "python3 application.py --help"`
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- Clean up the census data directories: `docker run --rm -it j40_score /bin/sh -c "python3 application.py census-cleanup"`
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- Clean up the data directories: `docker run --rm -it j40_score /bin/sh -c "python3 application.py data-cleanup"`
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- Generate census data (this may take 2-3 hours): `docker run --rm -it j40_score /bin/sh -c "python3 application.py census-data-download"`
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- Generate census data: `docker run --rm -it j40_score /bin/sh -c "python3 application.py census-data-download"`
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- Run all ETL processes: `docker run --rm -it j40_score /bin/sh -c "python3 application.py etl-run"`
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- Generate Score: `docker run --rm -it j40_score /bin/sh -c "python3 application.py score-run"`
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## Log visualization
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@ -24,6 +27,8 @@ If you want to visualize logs while running a command, the following temporary w
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You can run the Python code locally without Docker to develop, using Poetry. However, to generate the census data you will need the [GDAL library](https://github.com/OSGeo/gdal) installed locally. Also to generate tiles for a local map, you will need [Mapbox tippeanoe](https://github.com/mapbox/tippecanoe)
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Note: If you are using Windows, please follow [these instructions](https://stackoverflow.com/questions/56958421/pip-install-geopandas-on-windows) to install Geopandas locally. If you want to install TippeCanoe, [follow these instrcutions](https://github.com/GISupportICRC/ArcGIS2Mapbox#installing-tippecanoe-on-windows).
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- Start a terminal
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- Make sure you have Python 3.9 installed: `python -V` or `python3 -V`
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- We use [Poetry](https://python-poetry.org/) for managing dependencies and building the application. Please follow the instructions on their site to download.
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@ -1,26 +1,31 @@
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from config import settings
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import click
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from pathlib import Path
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import sys
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from config import settings
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from etl.sources.census.etl_utils import reset_data_directories as census_reset
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from utils import remove_files_from_dir, remove_all_from_dir, get_module_logger
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from utils import (
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get_module_logger,
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data_folder_cleanup,
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score_folder_cleanup,
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temp_folder_cleanup,
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)
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from etl.sources.census.etl import download_census_csvs
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from etl.runner import etl_runner, score_generate
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settings.APP_ROOT = Path.cwd()
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logger = get_module_logger(__name__)
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@click.group()
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def cli():
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"""Defines a click group for the commands below"""
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pass
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@cli.command(
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help="Clean up all data folders",
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help="Clean up all census data folders",
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)
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def data_cleanup():
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def census_cleanup():
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"""CLI command to clean up the census data folder"""
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data_path = settings.APP_ROOT / "data"
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@ -28,32 +33,59 @@ def data_cleanup():
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logger.info(f"Initializing all census data")
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census_reset(data_path)
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# dataset directory
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logger.info(f"Initializing all dataset directoriees")
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remove_all_from_dir(data_path / "dataset")
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logger.info("Cleaned up all census data files")
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# score directory
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logger.info(f"Initializing all score data")
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remove_files_from_dir(data_path / "score" / "csv", ".csv")
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remove_files_from_dir(data_path / "score" / "geojson", ".json")
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# cleanup tmp dir
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logger.info(f"Initializing all temp directoriees")
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remove_all_from_dir(data_path / "tmp")
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@cli.command(
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help="Clean up all data folders",
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)
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def data_cleanup():
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"""CLI command to clean up the all the data folders"""
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logger.info("Cleaned up all data files")
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data_folder_cleanup()
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score_folder_cleanup()
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temp_folder_cleanup()
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logger.info("Cleaned up all data folders")
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@cli.command(
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help="Census data download",
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)
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def census_data_download():
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"""CLI command to download all census shape files from the Census FTP and extract the geojson
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to generate national and by state Census Block Group CSVs"""
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logger.info("Downloading census data")
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data_path = settings.APP_ROOT / "data"
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download_census_csvs(data_path)
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logger.info("Completed downloading census data")
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exit()
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@cli.command(
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help="Run all ETL processes or a specific one",
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)
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@click.option("-d", "--dataset", required=False, type=str)
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def etl_run(dataset: str):
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"""Run a specific or all ETL processes
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Args:
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dataset (str): Name of the ETL module to be run (optional)
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Returns:
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None
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"""
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etl_runner(dataset)
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@cli.command(
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help="Generate Score",
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)
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def score_run():
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"""CLI command to generate the score"""
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score_generate()
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if __name__ == "__main__":
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@ -1,4 +1,5 @@
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from dynaconf import Dynaconf
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from pathlib import Path
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settings = Dynaconf(
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envvar_prefix="DYNACONF",
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@ -6,6 +7,9 @@ settings = Dynaconf(
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environments=True,
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)
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# set root dir
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settings.APP_ROOT = Path.cwd()
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# To set an environment use:
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# Linux/OSX: export ENV_FOR_DYNACONF=staging
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# Windows: set ENV_FOR_DYNACONF=staging
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63
score/etl/base.py
Normal file
63
score/etl/base.py
Normal file
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@ -0,0 +1,63 @@
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from pathlib import Path
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import pathlib
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from config import settings
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from utils import unzip_file_from_url, remove_all_from_dir
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class ExtractTransformLoad(object):
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"""
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A class used to instantiate an ETL object to retrieve and process data from
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datasets.
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Attributes:
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DATA_PATH (pathlib.Path): Local path where all data will be stored
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TMP_PATH (pathlib.Path): Local path where temporary data will be stored
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GEOID_FIELD_NAME (str): The common column name for a Census Block Group identifier
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GEOID_TRACT_FIELD_NAME (str): The common column name for a Census Tract identifier
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"""
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DATA_PATH: Path = settings.APP_ROOT / "data"
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TMP_PATH: Path = DATA_PATH / "tmp"
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GEOID_FIELD_NAME: str = "GEOID10"
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GEOID_TRACT_FIELD_NAME: str = "GEOID10_TRACT"
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def get_yaml_config(self) -> None:
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"""Reads the YAML configuration file for the dataset and stores
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the properies in the instance (upcoming feature)"""
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pass
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def check_ttl(self) -> None:
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"""Checks if the ETL process can be run based on a the TLL value on the
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YAML config (upcoming feature)"""
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pass
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def extract(
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self, source_url: str = None, extract_path: Path = None
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) -> None:
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"""Extract the data from
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a remote source. By default it provides code to get the file from a source url,
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unzips it and stores it on an extract_path."""
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# this can be accessed via super().extract()
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if source_url and extract_path:
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unzip_file_from_url(source_url, self.TMP_PATH, extract_path)
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def transform(self) -> None:
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"""Transform the data extracted into a format that can be consumed by the
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score generator"""
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raise NotImplementedError
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def load(self) -> None:
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"""Saves the transformed data in the specified local data folder or remote AWS S3
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bucket"""
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raise NotImplementedError
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def cleanup(self) -> None:
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"""Clears out any files stored in the TMP folder"""
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remove_all_from_dir(self.TMP_PATH)
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99
score/etl/runner.py
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99
score/etl/runner.py
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import importlib
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from etl.score.etl import ScoreETL
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def etl_runner(dataset_to_run: str = None) -> None:
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"""Runs all etl processes or a specific one
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Args:
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dataset_to_run (str): Run a specific ETL process. If missing, runs all processes (optional)
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Returns:
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None
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"""
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# this list comes from YAMLs
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dataset_list = [
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{
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"name": "census_acs",
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"module_dir": "census_acs",
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"class_name": "CensusACSETL",
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},
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{"name": "ejscreen", "module_dir": "ejscreen", "class_name": "EJScreenETL"},
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{
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"name": "housing_and_transportation",
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"module_dir": "housing_and_transportation",
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"class_name": "HousingTransportationETL",
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},
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{
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"name": "hud_housing",
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"module_dir": "hud_housing",
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"class_name": "HudHousingETL",
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},
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{
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"name": "calenviroscreen",
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"module_dir": "calenviroscreen",
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"class_name": "CalEnviroScreenETL",
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},
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{"name": "hud_recap", "module_dir": "hud_recap", "class_name": "HudRecapETL"},
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]
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if dataset_to_run:
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dataset_element = next(
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(item for item in dataset_list if item["name"] == dataset_to_run), None
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)
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if not dataset_list:
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raise ValueError("Invalid dataset name")
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else:
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# reset the list to just the dataset
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dataset_list = [dataset_element]
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# Run the ETLs for the dataset_list
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for dataset in dataset_list:
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etl_module = importlib.import_module(f"etl.sources.{dataset['module_dir']}.etl")
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etl_class = getattr(etl_module, dataset["class_name"])
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etl_instance = etl_class()
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# run extract
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etl_instance.extract()
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# run transform
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etl_instance.transform()
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# run load
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etl_instance.load()
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# cleanup
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etl_instance.cleanup()
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# update the front end JSON/CSV of list of data sources
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pass
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def score_generate() -> None:
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"""Generates the score and saves it on the local data directory
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Args:
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None
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Returns:
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None
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"""
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score = ScoreETL()
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# run extract
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score.extract()
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# run transform
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score.transform()
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# run load
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score.load()
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def _find_dataset_index(dataset_list, key, value):
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for i, element in enumerate(dataset_list):
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if element[key] == value:
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return i
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return -1
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0
score/etl/score/__init__.py
Normal file
0
score/etl/score/__init__.py
Normal file
389
score/etl/score/etl.py
Normal file
389
score/etl/score/etl.py
Normal file
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import collections
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import functools
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import pandas as pd
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from etl.base import ExtractTransformLoad
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from utils import get_module_logger
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from etl.sources.census.etl_utils import get_state_fips_codes
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logger = get_module_logger(__name__)
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class ScoreETL(ExtractTransformLoad):
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def __init__(self):
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# Define some global parameters
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self.BUCKET_SOCIOECONOMIC = "Socioeconomic Factors"
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self.BUCKET_SENSITIVE = "Sensitive populations"
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self.BUCKET_ENVIRONMENTAL = "Environmental effects"
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self.BUCKET_EXPOSURES = "Exposures"
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self.BUCKETS = [
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self.BUCKET_SOCIOECONOMIC,
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self.BUCKET_SENSITIVE,
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self.BUCKET_ENVIRONMENTAL,
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self.BUCKET_EXPOSURES,
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]
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# A few specific field names
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# TODO: clean this up, I name some fields but not others.
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self.UNEMPLOYED_FIELD_NAME = "Unemployed civilians (percent)"
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self.LINGUISTIC_ISOLATION_FIELD_NAME = "Linguistic isolation (percent)"
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self.HOUSING_BURDEN_FIELD_NAME = "Housing burden (percent)"
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self.POVERTY_FIELD_NAME = "Poverty (Less than 200% of federal poverty line)"
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self.HIGH_SCHOOL_FIELD_NAME = (
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"Percent individuals age 25 or over with less than high school degree"
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)
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# There's another aggregation level (a second level of "buckets").
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self.AGGREGATION_POLLUTION = "Pollution Burden"
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self.AGGREGATION_POPULATION = "Population Characteristics"
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self.PERCENTILE_FIELD_SUFFIX = " (percentile)"
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self.MIN_MAX_FIELD_SUFFIX = " (min-max normalized)"
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self.SCORE_CSV_PATH = self.DATA_PATH / "score" / "csv"
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# dataframes
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self.df: pd.DataFrame
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self.ejscreen_df: pd.DataFrame
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self.census_df: pd.DataFrame
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self.housing_and_transportation_df: pd.DataFrame
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self.hud_housing_df: pd.DataFrame
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def extract(self) -> None:
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# EJSCreen csv Load
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ejscreen_csv = self.DATA_PATH / "dataset" / "ejscreen_2020" / "usa.csv"
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self.ejscreen_df = pd.read_csv(
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ejscreen_csv, dtype={"ID": "string"}, low_memory=False
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)
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self.ejscreen_df.rename(columns={"ID": self.GEOID_FIELD_NAME}, inplace=True)
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# Load census data
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census_csv = self.DATA_PATH / "dataset" / "census_acs_2019" / "usa.csv"
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self.census_df = pd.read_csv(
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census_csv, dtype={self.GEOID_FIELD_NAME: "string"}, low_memory=False
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)
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# Load housing and transportation data
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housing_and_transportation_index_csv = (
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self.DATA_PATH / "dataset" / "housing_and_transportation_index" / "usa.csv"
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)
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self.housing_and_transportation_df = pd.read_csv(
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housing_and_transportation_index_csv,
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dtype={self.GEOID_FIELD_NAME: "string"},
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low_memory=False,
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)
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# Load HUD housing data
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hud_housing_csv = self.DATA_PATH / "dataset" / "hud_housing" / "usa.csv"
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self.hud_housing_df = pd.read_csv(
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hud_housing_csv,
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dtype={self.GEOID_TRACT_FIELD_NAME: "string"},
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low_memory=False,
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)
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def transform(self) -> None:
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logger.info(f"Transforming Score Data")
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# Join all the data sources that use census block groups
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census_block_group_dfs = [
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self.ejscreen_df,
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self.census_df,
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self.housing_and_transportation_df,
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]
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census_block_group_df = functools.reduce(
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lambda left, right: pd.merge(
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left=left, right=right, on=self.GEOID_FIELD_NAME, how="outer"
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),
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census_block_group_dfs,
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)
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# Sanity check the join.
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if len(census_block_group_df[self.GEOID_FIELD_NAME].str.len().unique()) != 1:
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raise ValueError(
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f"One of the input CSVs uses {self.GEOID_FIELD_NAME} with a different length."
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)
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# Join all the data sources that use census tracts
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# TODO: when there's more than one data source using census tract, reduce/merge them here.
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census_tract_df = self.hud_housing_df
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# Calculate the tract for the CBG data.
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census_block_group_df[self.GEOID_TRACT_FIELD_NAME] = census_block_group_df[
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self.GEOID_FIELD_NAME
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].str[0:11]
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self.df = census_block_group_df.merge(
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census_tract_df, on=self.GEOID_TRACT_FIELD_NAME
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)
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|
||||
if len(census_block_group_df) > 220333:
|
||||
raise ValueError("Too many rows in the join.")
|
||||
|
||||
# Define a named tuple that will be used for each data set input.
|
||||
DataSet = collections.namedtuple(
|
||||
typename="DataSet", field_names=["input_field", "renamed_field", "bucket"]
|
||||
)
|
||||
|
||||
data_sets = [
|
||||
# The following data sets have `bucket=None`, because it's not used in the bucket based score ("Score C").
|
||||
DataSet(
|
||||
input_field=self.GEOID_FIELD_NAME,
|
||||
# Use the name `GEOID10` to enable geoplatform.gov's workflow.
|
||||
renamed_field=self.GEOID_FIELD_NAME,
|
||||
bucket=None,
|
||||
),
|
||||
DataSet(
|
||||
input_field=self.HOUSING_BURDEN_FIELD_NAME,
|
||||
renamed_field=self.HOUSING_BURDEN_FIELD_NAME,
|
||||
bucket=None,
|
||||
),
|
||||
DataSet(
|
||||
input_field="ACSTOTPOP", renamed_field="Total population", bucket=None
|
||||
),
|
||||
# The following data sets have buckets, because they're used in the score
|
||||
DataSet(
|
||||
input_field="CANCER",
|
||||
renamed_field="Air toxics cancer risk",
|
||||
bucket=self.BUCKET_EXPOSURES,
|
||||
),
|
||||
DataSet(
|
||||
input_field="RESP",
|
||||
renamed_field="Respiratory hazard index",
|
||||
bucket=self.BUCKET_EXPOSURES,
|
||||
),
|
||||
DataSet(
|
||||
input_field="DSLPM",
|
||||
renamed_field="Diesel particulate matter",
|
||||
bucket=self.BUCKET_EXPOSURES,
|
||||
),
|
||||
DataSet(
|
||||
input_field="PM25",
|
||||
renamed_field="Particulate matter (PM2.5)",
|
||||
bucket=self.BUCKET_EXPOSURES,
|
||||
),
|
||||
DataSet(
|
||||
input_field="OZONE", renamed_field="Ozone", bucket=self.BUCKET_EXPOSURES
|
||||
),
|
||||
DataSet(
|
||||
input_field="PTRAF",
|
||||
renamed_field="Traffic proximity and volume",
|
||||
bucket=self.BUCKET_EXPOSURES,
|
||||
),
|
||||
DataSet(
|
||||
input_field="PRMP",
|
||||
renamed_field="Proximity to RMP sites",
|
||||
bucket=self.BUCKET_ENVIRONMENTAL,
|
||||
),
|
||||
DataSet(
|
||||
input_field="PTSDF",
|
||||
renamed_field="Proximity to TSDF sites",
|
||||
bucket=self.BUCKET_ENVIRONMENTAL,
|
||||
),
|
||||
DataSet(
|
||||
input_field="PNPL",
|
||||
renamed_field="Proximity to NPL sites",
|
||||
bucket=self.BUCKET_ENVIRONMENTAL,
|
||||
),
|
||||
DataSet(
|
||||
input_field="PWDIS",
|
||||
renamed_field="Wastewater discharge",
|
||||
bucket=self.BUCKET_ENVIRONMENTAL,
|
||||
),
|
||||
DataSet(
|
||||
input_field="PRE1960PCT",
|
||||
renamed_field="Percent pre-1960s housing (lead paint indicator)",
|
||||
bucket=self.BUCKET_ENVIRONMENTAL,
|
||||
),
|
||||
DataSet(
|
||||
input_field="UNDER5PCT",
|
||||
renamed_field="Individuals under 5 years old",
|
||||
bucket=self.BUCKET_SENSITIVE,
|
||||
),
|
||||
DataSet(
|
||||
input_field="OVER64PCT",
|
||||
renamed_field="Individuals over 64 years old",
|
||||
bucket=self.BUCKET_SENSITIVE,
|
||||
),
|
||||
DataSet(
|
||||
input_field=self.LINGUISTIC_ISOLATION_FIELD_NAME,
|
||||
renamed_field=self.LINGUISTIC_ISOLATION_FIELD_NAME,
|
||||
bucket=self.BUCKET_SENSITIVE,
|
||||
),
|
||||
DataSet(
|
||||
input_field="LINGISOPCT",
|
||||
renamed_field="Percent of households in linguistic isolation",
|
||||
bucket=self.BUCKET_SOCIOECONOMIC,
|
||||
),
|
||||
DataSet(
|
||||
input_field="LOWINCPCT",
|
||||
renamed_field=self.POVERTY_FIELD_NAME,
|
||||
bucket=self.BUCKET_SOCIOECONOMIC,
|
||||
),
|
||||
DataSet(
|
||||
input_field="LESSHSPCT",
|
||||
renamed_field=self.HIGH_SCHOOL_FIELD_NAME,
|
||||
bucket=self.BUCKET_SOCIOECONOMIC,
|
||||
),
|
||||
DataSet(
|
||||
input_field=self.UNEMPLOYED_FIELD_NAME,
|
||||
renamed_field=self.UNEMPLOYED_FIELD_NAME,
|
||||
bucket=self.BUCKET_SOCIOECONOMIC,
|
||||
),
|
||||
DataSet(
|
||||
input_field="ht_ami",
|
||||
renamed_field="Housing + Transportation Costs % Income for the Regional Typical Household",
|
||||
bucket=self.BUCKET_SOCIOECONOMIC,
|
||||
),
|
||||
]
|
||||
|
||||
# Rename columns:
|
||||
renaming_dict = {
|
||||
data_set.input_field: data_set.renamed_field for data_set in data_sets
|
||||
}
|
||||
|
||||
self.df.rename(
|
||||
columns=renaming_dict,
|
||||
inplace=True,
|
||||
errors="raise",
|
||||
)
|
||||
|
||||
columns_to_keep = [data_set.renamed_field for data_set in data_sets]
|
||||
self.df = self.df[columns_to_keep]
|
||||
|
||||
# Convert all columns to numeric.
|
||||
for data_set in data_sets:
|
||||
# Skip GEOID_FIELD_NAME, because it's a string.
|
||||
if data_set.renamed_field == self.GEOID_FIELD_NAME:
|
||||
continue
|
||||
self.df[f"{data_set.renamed_field}"] = pd.to_numeric(
|
||||
self.df[data_set.renamed_field]
|
||||
)
|
||||
|
||||
# calculate percentiles
|
||||
for data_set in data_sets:
|
||||
self.df[
|
||||
f"{data_set.renamed_field}{self.PERCENTILE_FIELD_SUFFIX}"
|
||||
] = self.df[data_set.renamed_field].rank(pct=True)
|
||||
|
||||
# Math:
|
||||
# (
|
||||
# Observed value
|
||||
# - minimum of all values
|
||||
# )
|
||||
# divided by
|
||||
# (
|
||||
# Maximum of all values
|
||||
# - minimum of all values
|
||||
# )
|
||||
for data_set in data_sets:
|
||||
# Skip GEOID_FIELD_NAME, because it's a string.
|
||||
if data_set.renamed_field == self.GEOID_FIELD_NAME:
|
||||
continue
|
||||
|
||||
min_value = self.df[data_set.renamed_field].min(skipna=True)
|
||||
|
||||
max_value = self.df[data_set.renamed_field].max(skipna=True)
|
||||
|
||||
logger.info(
|
||||
f"For data set {data_set.renamed_field}, the min value is {min_value} and the max value is {max_value}."
|
||||
)
|
||||
|
||||
self.df[f"{data_set.renamed_field}{self.MIN_MAX_FIELD_SUFFIX}"] = (
|
||||
self.df[data_set.renamed_field] - min_value
|
||||
) / (max_value - min_value)
|
||||
|
||||
# Graph distributions and correlations.
|
||||
min_max_fields = [
|
||||
f"{data_set.renamed_field}{self.MIN_MAX_FIELD_SUFFIX}"
|
||||
for data_set in data_sets
|
||||
if data_set.renamed_field != self.GEOID_FIELD_NAME
|
||||
]
|
||||
|
||||
# Calculate score "A" and score "B"
|
||||
self.df["Score A"] = self.df[
|
||||
[
|
||||
"Poverty (Less than 200% of federal poverty line) (percentile)",
|
||||
"Percent individuals age 25 or over with less than high school degree (percentile)",
|
||||
]
|
||||
].mean(axis=1)
|
||||
self.df["Score B"] = (
|
||||
self.df["Poverty (Less than 200% of federal poverty line) (percentile)"]
|
||||
* self.df[
|
||||
"Percent individuals age 25 or over with less than high school degree (percentile)"
|
||||
]
|
||||
)
|
||||
|
||||
# Calculate "CalEnviroScreen for the US" score
|
||||
# Average all the percentile values in each bucket into a single score for each of the four buckets.
|
||||
for bucket in self.BUCKETS:
|
||||
fields_in_bucket = [
|
||||
f"{data_set.renamed_field}{self.PERCENTILE_FIELD_SUFFIX}"
|
||||
for data_set in data_sets
|
||||
if data_set.bucket == bucket
|
||||
]
|
||||
self.df[f"{bucket}"] = self.df[fields_in_bucket].mean(axis=1)
|
||||
|
||||
# Combine the score from the two Exposures and Environmental Effects buckets into a single score called "Pollution Burden". The math for this score is: (1.0 * Exposures Score + 0.5 * Environment Effects score) / 1.5.
|
||||
self.df[self.AGGREGATION_POLLUTION] = (
|
||||
1.0 * self.df[f"{self.BUCKET_EXPOSURES}"]
|
||||
+ 0.5 * self.df[f"{self.BUCKET_ENVIRONMENTAL}"]
|
||||
) / 1.5
|
||||
|
||||
# Average the score from the two Sensitive populations and Socioeconomic factors buckets into a single score called "Population Characteristics".
|
||||
self.df[self.AGGREGATION_POPULATION] = self.df[
|
||||
[f"{self.BUCKET_SENSITIVE}", f"{self.BUCKET_SOCIOECONOMIC}"]
|
||||
].mean(axis=1)
|
||||
|
||||
# Multiply the "Pollution Burden" score and the "Population Characteristics" together to produce the cumulative impact score.
|
||||
self.df["Score C"] = (
|
||||
self.df[self.AGGREGATION_POLLUTION] * self.df[self.AGGREGATION_POPULATION]
|
||||
)
|
||||
|
||||
if len(census_block_group_df) > 220333:
|
||||
raise ValueError("Too many rows in the join.")
|
||||
|
||||
fields_to_use_in_score = [
|
||||
self.UNEMPLOYED_FIELD_NAME,
|
||||
self.LINGUISTIC_ISOLATION_FIELD_NAME,
|
||||
self.HOUSING_BURDEN_FIELD_NAME,
|
||||
self.POVERTY_FIELD_NAME,
|
||||
self.HIGH_SCHOOL_FIELD_NAME,
|
||||
]
|
||||
|
||||
fields_min_max = [
|
||||
f"{field}{self.MIN_MAX_FIELD_SUFFIX}" for field in fields_to_use_in_score
|
||||
]
|
||||
fields_percentile = [
|
||||
f"{field}{self.PERCENTILE_FIELD_SUFFIX}" for field in fields_to_use_in_score
|
||||
]
|
||||
|
||||
# Calculate "Score D", which uses min-max normalization
|
||||
# and calculate "Score E", which uses percentile normalization for the same fields
|
||||
self.df["Score D"] = self.df[fields_min_max].mean(axis=1)
|
||||
self.df["Score E"] = self.df[fields_percentile].mean(axis=1)
|
||||
|
||||
# Calculate correlations
|
||||
self.df[fields_min_max].corr()
|
||||
|
||||
# Create percentiles for the scores
|
||||
for score_field in ["Score A", "Score B", "Score C", "Score D", "Score E"]:
|
||||
self.df[f"{score_field}{self.PERCENTILE_FIELD_SUFFIX}"] = self.df[
|
||||
score_field
|
||||
].rank(pct=True)
|
||||
self.df[f"{score_field} (top 25th percentile)"] = (
|
||||
self.df[f"{score_field}{self.PERCENTILE_FIELD_SUFFIX}"] >= 0.75
|
||||
)
|
||||
|
||||
def load(self) -> None:
|
||||
logger.info(f"Saving Score CSVs")
|
||||
|
||||
# write nationwide csv
|
||||
self.df.to_csv(self.SCORE_CSV_PATH / f"usa.csv", index=False)
|
||||
|
||||
# write per state csvs
|
||||
for states_fips in get_state_fips_codes(self.DATA_PATH):
|
||||
logger.info(f"Generating data{states_fips} csv")
|
||||
df1 = self.df[self.df["GEOID10"].str[:2] == states_fips]
|
||||
# we need to name the file data01.csv for ogr2ogr csv merge to work
|
||||
df1.to_csv(self.SCORE_CSV_PATH / f"data{states_fips}.csv", index=False)
|
||||
0
score/etl/sources/calenviroscreen/__init__.py
Normal file
0
score/etl/sources/calenviroscreen/__init__.py
Normal file
69
score/etl/sources/calenviroscreen/etl.py
Normal file
69
score/etl/sources/calenviroscreen/etl.py
Normal file
|
|
@ -0,0 +1,69 @@
|
|||
import pandas as pd
|
||||
|
||||
from etl.base import ExtractTransformLoad
|
||||
from utils import get_module_logger
|
||||
|
||||
logger = get_module_logger(__name__)
|
||||
|
||||
|
||||
class CalEnviroScreenETL(ExtractTransformLoad):
|
||||
def __init__(self):
|
||||
self.CALENVIROSCREEN_FTP_URL = "https://justice40-data.s3.amazonaws.com/CalEnviroScreen/CalEnviroScreen_4.0_2021.zip"
|
||||
self.CALENVIROSCREEN_CSV = self.TMP_PATH / "CalEnviroScreen_4.0_2021.csv"
|
||||
self.CSV_PATH = self.DATA_PATH / "dataset" / "calenviroscreen4"
|
||||
|
||||
# Definining some variable names
|
||||
self.CALENVIROSCREEN_SCORE_FIELD_NAME = "calenviroscreen_score"
|
||||
self.CALENVIROSCREEN_PERCENTILE_FIELD_NAME = "calenviroscreen_percentile"
|
||||
self.CALENVIROSCREEN_PRIORITY_COMMUNITY_FIELD_NAME = (
|
||||
"calenviroscreen_priority_community"
|
||||
)
|
||||
|
||||
# Choosing constants.
|
||||
# None of these numbers are final, but just for the purposes of comparison.
|
||||
self.CALENVIROSCREEN_PRIORITY_COMMUNITY_THRESHOLD = 75
|
||||
|
||||
self.df: pd.DataFrame
|
||||
|
||||
def extract(self) -> None:
|
||||
logger.info(f"Downloading CalEnviroScreen Data")
|
||||
super().extract(
|
||||
self.CALENVIROSCREEN_FTP_URL,
|
||||
self.TMP_PATH,
|
||||
)
|
||||
|
||||
def transform(self) -> None:
|
||||
logger.info(f"Transforming CalEnviroScreen Data")
|
||||
|
||||
# Data from https://calenviroscreen-oehha.hub.arcgis.com/#Data, specifically:
|
||||
# https://oehha.ca.gov/media/downloads/calenviroscreen/document/calenviroscreen40resultsdatadictionaryd12021.zip
|
||||
# Load comparison index (CalEnviroScreen 4)
|
||||
self.df = pd.read_csv(
|
||||
self.CALENVIROSCREEN_CSV, dtype={"Census Tract": "string"}
|
||||
)
|
||||
|
||||
self.df.rename(
|
||||
columns={
|
||||
"Census Tract": self.GEOID_TRACT_FIELD_NAME,
|
||||
"DRAFT CES 4.0 Score": self.CALENVIROSCREEN_SCORE_FIELD_NAME,
|
||||
"DRAFT CES 4.0 Percentile": self.CALENVIROSCREEN_PERCENTILE_FIELD_NAME,
|
||||
},
|
||||
inplace=True,
|
||||
)
|
||||
|
||||
# Add a leading "0" to the Census Tract to match our format in other data frames.
|
||||
self.df[self.GEOID_TRACT_FIELD_NAME] = (
|
||||
"0" + self.df[self.GEOID_TRACT_FIELD_NAME]
|
||||
)
|
||||
|
||||
# Calculate the top K% of prioritized communities
|
||||
self.df[self.CALENVIROSCREEN_PRIORITY_COMMUNITY_FIELD_NAME] = (
|
||||
self.df[self.CALENVIROSCREEN_PERCENTILE_FIELD_NAME]
|
||||
>= self.CALENVIROSCREEN_PRIORITY_COMMUNITY_THRESHOLD
|
||||
)
|
||||
|
||||
def load(self) -> None:
|
||||
logger.info(f"Saving CalEnviroScreen CSV")
|
||||
# write nationwide csv
|
||||
self.CSV_PATH.mkdir(parents=True, exist_ok=True)
|
||||
self.df.to_csv(self.CSV_PATH / f"data06.csv", index=False)
|
||||
|
|
@ -10,84 +10,102 @@ logger = get_module_logger(__name__)
|
|||
|
||||
|
||||
def download_census_csvs(data_path: Path) -> None:
|
||||
"""Download all census shape files from the Census FTP and extract the geojson
|
||||
to generate national and by state Census Block Group CSVs
|
||||
|
||||
Args:
|
||||
data_path (pathlib.Path): Name of the directory where the files and directories will
|
||||
be created
|
||||
|
||||
Returns:
|
||||
None
|
||||
"""
|
||||
|
||||
# the fips_states_2010.csv is generated from data here
|
||||
# https://www.census.gov/geographies/reference-files/time-series/geo/tallies.html
|
||||
state_fips_codes = get_state_fips_codes(data_path)
|
||||
geojson_dir_path = data_path / "census" / "geojson"
|
||||
|
||||
for fips in state_fips_codes:
|
||||
# check if file exists
|
||||
shp_file_path = data_path / "census" / "shp" / fips / f"tl_2010_{fips}_bg10.shp"
|
||||
shp_file_path = (
|
||||
data_path / "census" / "shp" / fips / f"tl_2010_{fips}_bg10.shp"
|
||||
)
|
||||
|
||||
logger.info(f"Checking if {fips} file exists")
|
||||
if not os.path.isfile(shp_file_path):
|
||||
logger.info(f"Downloading {fips}")
|
||||
|
||||
logger.info(f"Downloading and extracting {fips} shape file")
|
||||
# 2020 tiger data is here: https://www2.census.gov/geo/tiger/TIGER2020/BG/
|
||||
# But using 2010 for now
|
||||
cbg_state_url = f"https://www2.census.gov/geo/tiger/TIGER2010/BG/2010/tl_2010_{fips}_bg10.zip"
|
||||
unzip_file_from_url(
|
||||
cbg_state_url, data_path / "tmp", data_path / "census" / "shp" / fips
|
||||
cbg_state_url,
|
||||
data_path / "tmp",
|
||||
data_path / "census" / "shp" / fips,
|
||||
)
|
||||
|
||||
geojson_dir_path = data_path / "census" / "geojson"
|
||||
cmd = (
|
||||
"ogr2ogr -f GeoJSON data/census/geojson/"
|
||||
+ fips
|
||||
+ ".json data/census/shp/"
|
||||
+ fips
|
||||
+ "/tl_2010_"
|
||||
+ fips
|
||||
+ "_bg10.shp"
|
||||
)
|
||||
os.system(cmd)
|
||||
|
||||
cmd = (
|
||||
"ogr2ogr -f GeoJSON data/census/geojson/"
|
||||
+ fips
|
||||
+ ".json data/census/shp/"
|
||||
+ fips
|
||||
+ "/tl_2010_"
|
||||
+ fips
|
||||
+ "_bg10.shp"
|
||||
)
|
||||
os.system(cmd)
|
||||
# generate CBG CSV table for pandas
|
||||
## load in memory
|
||||
cbg_national = [] # in-memory global list
|
||||
cbg_per_state: dict = {} # in-memory dict per state
|
||||
for file in os.listdir(geojson_dir_path):
|
||||
if file.endswith(".json"):
|
||||
logger.info(f"Ingesting geoid10 for file {file}")
|
||||
with open(geojson_dir_path / file) as f:
|
||||
geojson = json.load(f)
|
||||
for feature in geojson["features"]:
|
||||
geoid10 = feature["properties"]["GEOID10"]
|
||||
cbg_national.append(str(geoid10))
|
||||
geoid10_state_id = geoid10[:2]
|
||||
if not cbg_per_state.get(geoid10_state_id):
|
||||
cbg_per_state[geoid10_state_id] = []
|
||||
cbg_per_state[geoid10_state_id].append(geoid10)
|
||||
|
||||
# generate CBG CSV table for pandas
|
||||
## load in memory
|
||||
cbg_national = [] # in-memory global list
|
||||
cbg_per_state: dict = {} # in-memory dict per state
|
||||
for file in os.listdir(geojson_dir_path):
|
||||
if file.endswith(".json"):
|
||||
logger.info(f"Ingesting geoid10 for file {file}")
|
||||
with open(geojson_dir_path / file) as f:
|
||||
geojson = json.load(f)
|
||||
for feature in geojson["features"]:
|
||||
geoid10 = feature["properties"]["GEOID10"]
|
||||
cbg_national.append(str(geoid10))
|
||||
geoid10_state_id = geoid10[:2]
|
||||
if not cbg_per_state.get(geoid10_state_id):
|
||||
cbg_per_state[geoid10_state_id] = []
|
||||
cbg_per_state[geoid10_state_id].append(geoid10)
|
||||
|
||||
csv_dir_path = data_path / "census" / "csv"
|
||||
## write to individual state csv
|
||||
for state_id in cbg_per_state:
|
||||
geoid10_list = cbg_per_state[state_id]
|
||||
with open(
|
||||
csv_dir_path / f"{state_id}.csv", mode="w", newline=""
|
||||
) as cbg_csv_file:
|
||||
cbg_csv_file_writer = csv.writer(
|
||||
cbg_csv_file,
|
||||
delimiter=",",
|
||||
quotechar='"',
|
||||
quoting=csv.QUOTE_MINIMAL,
|
||||
)
|
||||
|
||||
for geoid10 in geoid10_list:
|
||||
cbg_csv_file_writer.writerow(
|
||||
[
|
||||
geoid10,
|
||||
]
|
||||
)
|
||||
|
||||
## write US csv
|
||||
with open(csv_dir_path / "us.csv", mode="w", newline="") as cbg_csv_file:
|
||||
csv_dir_path = data_path / "census" / "csv"
|
||||
## write to individual state csv
|
||||
for state_id in cbg_per_state:
|
||||
geoid10_list = cbg_per_state[state_id]
|
||||
with open(
|
||||
csv_dir_path / f"{state_id}.csv", mode="w", newline=""
|
||||
) as cbg_csv_file:
|
||||
cbg_csv_file_writer = csv.writer(
|
||||
cbg_csv_file, delimiter=",", quotechar='"', quoting=csv.QUOTE_MINIMAL
|
||||
cbg_csv_file,
|
||||
delimiter=",",
|
||||
quotechar='"',
|
||||
quoting=csv.QUOTE_MINIMAL,
|
||||
)
|
||||
for geoid10 in cbg_national:
|
||||
|
||||
for geoid10 in geoid10_list:
|
||||
cbg_csv_file_writer.writerow(
|
||||
[
|
||||
geoid10,
|
||||
]
|
||||
)
|
||||
|
||||
logger.info("Census block groups downloading complete")
|
||||
## write US csv
|
||||
with open(csv_dir_path / "us.csv", mode="w", newline="") as cbg_csv_file:
|
||||
cbg_csv_file_writer = csv.writer(
|
||||
cbg_csv_file,
|
||||
delimiter=",",
|
||||
quotechar='"',
|
||||
quoting=csv.QUOTE_MINIMAL,
|
||||
)
|
||||
for geoid10 in cbg_national:
|
||||
cbg_csv_file_writer.writerow(
|
||||
[
|
||||
geoid10,
|
||||
]
|
||||
)
|
||||
|
||||
logger.info("Census block groups downloading complete")
|
||||
|
|
|
|||
0
score/etl/sources/census_acs/__init__.py
Normal file
0
score/etl/sources/census_acs/__init__.py
Normal file
98
score/etl/sources/census_acs/etl.py
Normal file
98
score/etl/sources/census_acs/etl.py
Normal file
|
|
@ -0,0 +1,98 @@
|
|||
import pandas as pd
|
||||
import censusdata
|
||||
|
||||
from etl.base import ExtractTransformLoad
|
||||
from etl.sources.census.etl_utils import get_state_fips_codes
|
||||
from utils import get_module_logger
|
||||
|
||||
logger = get_module_logger(__name__)
|
||||
|
||||
|
||||
class CensusACSETL(ExtractTransformLoad):
|
||||
def __init__(self):
|
||||
self.ACS_YEAR = 2019
|
||||
self.OUTPUT_PATH = self.DATA_PATH / "dataset" / f"census_acs_{self.ACS_YEAR}"
|
||||
self.UNEMPLOYED_FIELD_NAME = "Unemployed civilians (percent)"
|
||||
self.LINGUISTIC_ISOLATION_FIELD_NAME = "Linguistic isolation (percent)"
|
||||
self.LINGUISTIC_ISOLATION_TOTAL_FIELD_NAME = "Linguistic isolation (total)"
|
||||
self.LINGUISTIC_ISOLATION_FIELDS = [
|
||||
"C16002_001E",
|
||||
"C16002_004E",
|
||||
"C16002_007E",
|
||||
"C16002_010E",
|
||||
"C16002_013E",
|
||||
]
|
||||
self.df: pd.DataFrame
|
||||
|
||||
def _fips_from_censusdata_censusgeo(self, censusgeo: censusdata.censusgeo) -> str:
|
||||
"""Create a FIPS code from the proprietary censusgeo index."""
|
||||
fips = "".join([value for (key, value) in censusgeo.params()])
|
||||
return fips
|
||||
|
||||
def extract(self) -> None:
|
||||
dfs = []
|
||||
for fips in get_state_fips_codes(self.DATA_PATH):
|
||||
logger.info(f"Downloading data for state/territory with FIPS code {fips}")
|
||||
|
||||
dfs.append(
|
||||
censusdata.download(
|
||||
src="acs5",
|
||||
year=self.ACS_YEAR,
|
||||
geo=censusdata.censusgeo(
|
||||
[("state", fips), ("county", "*"), ("block group", "*")]
|
||||
),
|
||||
var=[
|
||||
# Emploment fields
|
||||
"B23025_005E",
|
||||
"B23025_003E",
|
||||
]
|
||||
+ self.LINGUISTIC_ISOLATION_FIELDS,
|
||||
)
|
||||
)
|
||||
|
||||
self.df = pd.concat(dfs)
|
||||
|
||||
self.df[self.GEOID_FIELD_NAME] = self.df.index.to_series().apply(
|
||||
func=self._fips_from_censusdata_censusgeo
|
||||
)
|
||||
|
||||
def transform(self) -> None:
|
||||
logger.info(f"Starting Census ACS Transform")
|
||||
|
||||
# Calculate percent unemployment.
|
||||
# TODO: remove small-sample data that should be `None` instead of a high-variance fraction.
|
||||
self.df[self.UNEMPLOYED_FIELD_NAME] = self.df.B23025_005E / self.df.B23025_003E
|
||||
|
||||
# Calculate linguistic isolation.
|
||||
individual_limited_english_fields = [
|
||||
"C16002_004E",
|
||||
"C16002_007E",
|
||||
"C16002_010E",
|
||||
"C16002_013E",
|
||||
]
|
||||
|
||||
self.df[self.LINGUISTIC_ISOLATION_TOTAL_FIELD_NAME] = self.df[
|
||||
individual_limited_english_fields
|
||||
].sum(axis=1, skipna=True)
|
||||
self.df[self.LINGUISTIC_ISOLATION_FIELD_NAME] = (
|
||||
self.df[self.LINGUISTIC_ISOLATION_TOTAL_FIELD_NAME].astype(float)
|
||||
/ self.df["C16002_001E"]
|
||||
)
|
||||
|
||||
self.df[self.LINGUISTIC_ISOLATION_FIELD_NAME].describe()
|
||||
|
||||
def load(self) -> None:
|
||||
logger.info(f"Saving Census ACS Data")
|
||||
|
||||
# mkdir census
|
||||
self.OUTPUT_PATH.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
columns_to_include = [
|
||||
self.GEOID_FIELD_NAME,
|
||||
self.UNEMPLOYED_FIELD_NAME,
|
||||
self.LINGUISTIC_ISOLATION_FIELD_NAME,
|
||||
]
|
||||
|
||||
self.df[columns_to_include].to_csv(
|
||||
path_or_buf=self.OUTPUT_PATH / "usa.csv", index=False
|
||||
)
|
||||
|
|
@ -1 +1,39 @@
|
|||
# https://gaftp.epa.gov/EJSCREEN/2020/EJSCREEN_2020_StatePctile.csv.zip
|
||||
import pandas as pd
|
||||
|
||||
from etl.base import ExtractTransformLoad
|
||||
from utils import get_module_logger
|
||||
|
||||
logger = get_module_logger(__name__)
|
||||
|
||||
|
||||
class EJScreenETL(ExtractTransformLoad):
|
||||
def __init__(self):
|
||||
self.EJSCREEN_FTP_URL = (
|
||||
"https://gaftp.epa.gov/EJSCREEN/2020/EJSCREEN_2020_StatePctile.csv.zip"
|
||||
)
|
||||
self.EJSCREEN_CSV = self.TMP_PATH / "EJSCREEN_2020_StatePctile.csv"
|
||||
self.CSV_PATH = self.DATA_PATH / "dataset" / "ejscreen_2020"
|
||||
self.df: pd.DataFrame
|
||||
|
||||
def extract(self) -> None:
|
||||
logger.info(f"Downloading EJScreen Data")
|
||||
super().extract(
|
||||
self.EJSCREEN_FTP_URL,
|
||||
self.TMP_PATH,
|
||||
)
|
||||
|
||||
def transform(self) -> None:
|
||||
logger.info(f"Transforming EJScreen Data")
|
||||
self.df = pd.read_csv(
|
||||
self.EJSCREEN_CSV,
|
||||
dtype={"ID": "string"},
|
||||
# EJSCREEN writes the word "None" for NA data.
|
||||
na_values=["None"],
|
||||
low_memory=False,
|
||||
)
|
||||
|
||||
def load(self) -> None:
|
||||
logger.info(f"Saving EJScreen CSV")
|
||||
# write nationwide csv
|
||||
self.CSV_PATH.mkdir(parents=True, exist_ok=True)
|
||||
self.df.to_csv(self.CSV_PATH / f"usa.csv", index=False)
|
||||
|
|
|
|||
0
score/etl/sources/housing_and_transportation/__init__.py
Normal file
0
score/etl/sources/housing_and_transportation/__init__.py
Normal file
55
score/etl/sources/housing_and_transportation/etl.py
Normal file
55
score/etl/sources/housing_and_transportation/etl.py
Normal file
|
|
@ -0,0 +1,55 @@
|
|||
import pandas as pd
|
||||
|
||||
from etl.base import ExtractTransformLoad
|
||||
from etl.sources.census.etl_utils import get_state_fips_codes
|
||||
from utils import get_module_logger, unzip_file_from_url
|
||||
|
||||
logger = get_module_logger(__name__)
|
||||
|
||||
|
||||
class HousingTransportationETL(ExtractTransformLoad):
|
||||
def __init__(self):
|
||||
self.HOUSING_FTP_URL = (
|
||||
"https://htaindex.cnt.org/download/download.php?focus=blkgrp&geoid="
|
||||
)
|
||||
self.OUTPUT_PATH = (
|
||||
self.DATA_PATH / "dataset" / "housing_and_transportation_index"
|
||||
)
|
||||
self.df: pd.DataFrame
|
||||
|
||||
def extract(self) -> None:
|
||||
# Download each state / territory individually
|
||||
dfs = []
|
||||
zip_file_dir = self.TMP_PATH / "housing_and_transportation_index"
|
||||
for fips in get_state_fips_codes(self.DATA_PATH):
|
||||
logger.info(
|
||||
f"Downloading housing data for state/territory with FIPS code {fips}"
|
||||
)
|
||||
unzip_file_from_url(
|
||||
f"{self.HOUSING_FTP_URL}{fips}", self.TMP_PATH, zip_file_dir
|
||||
)
|
||||
|
||||
# New file name:
|
||||
tmp_csv_file_path = zip_file_dir / f"htaindex_data_blkgrps_{fips}.csv"
|
||||
tmp_df = pd.read_csv(filepath_or_buffer=tmp_csv_file_path)
|
||||
|
||||
dfs.append(tmp_df)
|
||||
|
||||
self.df = pd.concat(dfs)
|
||||
|
||||
self.df.head()
|
||||
|
||||
def transform(self) -> None:
|
||||
logger.info(f"Transforming Housing and Transportation Data")
|
||||
|
||||
# Rename and reformat block group ID
|
||||
self.df.rename(columns={"blkgrp": self.GEOID_FIELD_NAME}, inplace=True)
|
||||
self.df[self.GEOID_FIELD_NAME] = self.df[self.GEOID_FIELD_NAME].str.replace(
|
||||
'"', ""
|
||||
)
|
||||
|
||||
def load(self) -> None:
|
||||
logger.info(f"Saving Housing and Transportation Data")
|
||||
|
||||
self.OUTPUT_PATH.mkdir(parents=True, exist_ok=True)
|
||||
self.df.to_csv(path_or_buf=self.OUTPUT_PATH / "usa.csv", index=False)
|
||||
0
score/etl/sources/hud_housing/__init__.py
Normal file
0
score/etl/sources/hud_housing/__init__.py
Normal file
180
score/etl/sources/hud_housing/etl.py
Normal file
180
score/etl/sources/hud_housing/etl.py
Normal file
|
|
@ -0,0 +1,180 @@
|
|||
import pandas as pd
|
||||
|
||||
from etl.base import ExtractTransformLoad
|
||||
from etl.sources.census.etl_utils import get_state_fips_codes
|
||||
from utils import get_module_logger, unzip_file_from_url, remove_all_from_dir
|
||||
|
||||
logger = get_module_logger(__name__)
|
||||
|
||||
|
||||
class HudHousingETL(ExtractTransformLoad):
|
||||
def __init__(self):
|
||||
self.OUTPUT_PATH = self.DATA_PATH / "dataset" / "hud_housing"
|
||||
self.GEOID_TRACT_FIELD_NAME = "GEOID10_TRACT"
|
||||
self.HOUSING_FTP_URL = (
|
||||
"https://www.huduser.gov/portal/datasets/cp/2012thru2016-140-csv.zip"
|
||||
)
|
||||
self.HOUSING_ZIP_FILE_DIR = self.TMP_PATH / "hud_housing"
|
||||
|
||||
# We measure households earning less than 80% of HUD Area Median Family Income by county
|
||||
# and paying greater than 30% of their income to housing costs.
|
||||
self.HOUSING_BURDEN_FIELD_NAME = "Housing burden (percent)"
|
||||
self.HOUSING_BURDEN_NUMERATOR_FIELD_NAME = "HOUSING_BURDEN_NUMERATOR"
|
||||
self.HOUSING_BURDEN_DENOMINATOR_FIELD_NAME = "HOUSING_BURDEN_DENOMINATOR"
|
||||
|
||||
# Note: some variable definitions.
|
||||
# HUD-adjusted median family income (HAMFI).
|
||||
# The four housing problems are: incomplete kitchen facilities, incomplete plumbing facilities, more than 1 person per room, and cost burden greater than 30%.
|
||||
# Table 8 is the desired table.
|
||||
|
||||
self.df: pd.DataFrame
|
||||
|
||||
def extract(self) -> None:
|
||||
logger.info(f"Extracting HUD Housing Data")
|
||||
super().extract(
|
||||
self.HOUSING_FTP_URL,
|
||||
self.HOUSING_ZIP_FILE_DIR,
|
||||
)
|
||||
|
||||
def transform(self) -> None:
|
||||
logger.info(f"Transforming HUD Housing Data")
|
||||
|
||||
# New file name:
|
||||
tmp_csv_file_path = (
|
||||
self.HOUSING_ZIP_FILE_DIR
|
||||
/ "2012thru2016-140-csv"
|
||||
/ "2012thru2016-140-csv"
|
||||
/ "140"
|
||||
/ "Table8.csv"
|
||||
)
|
||||
self.df = pd.read_csv(filepath_or_buffer=tmp_csv_file_path)
|
||||
|
||||
# Rename and reformat block group ID
|
||||
self.df.rename(columns={"geoid": self.GEOID_TRACT_FIELD_NAME}, inplace=True)
|
||||
|
||||
# The CHAS data has census tract ids such as `14000US01001020100`
|
||||
# Whereas the rest of our data uses, for the same tract, `01001020100`.
|
||||
# the characters before `US`:
|
||||
self.df[self.GEOID_TRACT_FIELD_NAME] = self.df[
|
||||
self.GEOID_TRACT_FIELD_NAME
|
||||
].str.replace(r"^.*?US", "", regex=True)
|
||||
|
||||
# Calculate housing burden
|
||||
# This is quite a number of steps. It does not appear to be accessible nationally in a simpler format, though.
|
||||
# See "CHAS data dictionary 12-16.xlsx"
|
||||
|
||||
# Owner occupied numerator fields
|
||||
OWNER_OCCUPIED_NUMERATOR_FIELDS = [
|
||||
# Key: Column Name Line_Type Tenure Household income Cost burden Facilities
|
||||
# T8_est7 Subtotal Owner occupied less than or equal to 30% of HAMFI greater than 30% but less than or equal to 50% All
|
||||
"T8_est7",
|
||||
# T8_est10 Subtotal Owner occupied less than or equal to 30% of HAMFI greater than 50% All
|
||||
"T8_est10",
|
||||
# T8_est20 Subtotal Owner occupied greater than 30% but less than or equal to 50% of HAMFI greater than 30% but less than or equal to 50% All
|
||||
"T8_est20",
|
||||
# T8_est23 Subtotal Owner occupied greater than 30% but less than or equal to 50% of HAMFI greater than 50% All
|
||||
"T8_est23",
|
||||
# T8_est33 Subtotal Owner occupied greater than 50% but less than or equal to 80% of HAMFI greater than 30% but less than or equal to 50% All
|
||||
"T8_est33",
|
||||
# T8_est36 Subtotal Owner occupied greater than 50% but less than or equal to 80% of HAMFI greater than 50% All
|
||||
"T8_est36",
|
||||
]
|
||||
|
||||
# These rows have the values where HAMFI was not computed, b/c of no or negative income.
|
||||
OWNER_OCCUPIED_NOT_COMPUTED_FIELDS = [
|
||||
# Key: Column Name Line_Type Tenure Household income Cost burden Facilities
|
||||
# T8_est13 Subtotal Owner occupied less than or equal to 30% of HAMFI not computed (no/negative income) All
|
||||
"T8_est13",
|
||||
# T8_est26 Subtotal Owner occupied greater than 30% but less than or equal to 50% of HAMFI not computed (no/negative income) All
|
||||
"T8_est26",
|
||||
# T8_est39 Subtotal Owner occupied greater than 50% but less than or equal to 80% of HAMFI not computed (no/negative income) All
|
||||
"T8_est39",
|
||||
# T8_est52 Subtotal Owner occupied greater than 80% but less than or equal to 100% of HAMFI not computed (no/negative income) All
|
||||
"T8_est52",
|
||||
# T8_est65 Subtotal Owner occupied greater than 100% of HAMFI not computed (no/negative income) All
|
||||
"T8_est65",
|
||||
]
|
||||
|
||||
# T8_est2 Subtotal Owner occupied All All All
|
||||
OWNER_OCCUPIED_POPULATION_FIELD = "T8_est2"
|
||||
|
||||
# Renter occupied numerator fields
|
||||
RENTER_OCCUPIED_NUMERATOR_FIELDS = [
|
||||
# Key: Column Name Line_Type Tenure Household income Cost burden Facilities
|
||||
# T8_est73 Subtotal Renter occupied less than or equal to 30% of HAMFI greater than 30% but less than or equal to 50% All
|
||||
"T8_est73",
|
||||
# T8_est76 Subtotal Renter occupied less than or equal to 30% of HAMFI greater than 50% All
|
||||
"T8_est76",
|
||||
# T8_est86 Subtotal Renter occupied greater than 30% but less than or equal to 50% of HAMFI greater than 30% but less than or equal to 50% All
|
||||
"T8_est86",
|
||||
# T8_est89 Subtotal Renter occupied greater than 30% but less than or equal to 50% of HAMFI greater than 50% All
|
||||
"T8_est89",
|
||||
# T8_est99 Subtotal Renter occupied greater than 50% but less than or equal to 80% of HAMFI greater than 30% but less than or equal to 50% All
|
||||
"T8_est99",
|
||||
# T8_est102 Subtotal Renter occupied greater than 50% but less than or equal to 80% of HAMFI greater than 50% All
|
||||
"T8_est102",
|
||||
]
|
||||
|
||||
# These rows have the values where HAMFI was not computed, b/c of no or negative income.
|
||||
RENTER_OCCUPIED_NOT_COMPUTED_FIELDS = [
|
||||
# Key: Column Name Line_Type Tenure Household income Cost burden Facilities
|
||||
# T8_est79 Subtotal Renter occupied less than or equal to 30% of HAMFI not computed (no/negative income) All
|
||||
"T8_est79",
|
||||
# T8_est92 Subtotal Renter occupied greater than 30% but less than or equal to 50% of HAMFI not computed (no/negative income) All
|
||||
"T8_est92",
|
||||
# T8_est105 Subtotal Renter occupied greater than 50% but less than or equal to 80% of HAMFI not computed (no/negative income) All
|
||||
"T8_est105",
|
||||
# T8_est118 Subtotal Renter occupied greater than 80% but less than or equal to 100% of HAMFI not computed (no/negative income) All
|
||||
"T8_est118",
|
||||
# T8_est131 Subtotal Renter occupied greater than 100% of HAMFI not computed (no/negative income) All
|
||||
"T8_est131",
|
||||
]
|
||||
|
||||
# T8_est68 Subtotal Renter occupied All All All
|
||||
RENTER_OCCUPIED_POPULATION_FIELD = "T8_est68"
|
||||
|
||||
# Math:
|
||||
# (
|
||||
# # of Owner Occupied Units Meeting Criteria
|
||||
# + # of Renter Occupied Units Meeting Criteria
|
||||
# )
|
||||
# divided by
|
||||
# (
|
||||
# Total # of Owner Occupied Units
|
||||
# + Total # of Renter Occupied Units
|
||||
# - # of Owner Occupied Units with HAMFI Not Computed
|
||||
# - # of Renter Occupied Units with HAMFI Not Computed
|
||||
# )
|
||||
|
||||
self.df[self.HOUSING_BURDEN_NUMERATOR_FIELD_NAME] = self.df[
|
||||
OWNER_OCCUPIED_NUMERATOR_FIELDS
|
||||
].sum(axis=1) + self.df[RENTER_OCCUPIED_NUMERATOR_FIELDS].sum(axis=1)
|
||||
|
||||
self.df[self.HOUSING_BURDEN_DENOMINATOR_FIELD_NAME] = (
|
||||
self.df[OWNER_OCCUPIED_POPULATION_FIELD]
|
||||
+ self.df[RENTER_OCCUPIED_POPULATION_FIELD]
|
||||
- self.df[OWNER_OCCUPIED_NOT_COMPUTED_FIELDS].sum(axis=1)
|
||||
- self.df[RENTER_OCCUPIED_NOT_COMPUTED_FIELDS].sum(axis=1)
|
||||
)
|
||||
|
||||
# TODO: add small sample size checks
|
||||
self.df[self.HOUSING_BURDEN_FIELD_NAME] = self.df[
|
||||
self.HOUSING_BURDEN_NUMERATOR_FIELD_NAME
|
||||
].astype(float) / self.df[self.HOUSING_BURDEN_DENOMINATOR_FIELD_NAME].astype(
|
||||
float
|
||||
)
|
||||
|
||||
def load(self) -> None:
|
||||
logger.info(f"Saving HUD Housing Data")
|
||||
|
||||
self.OUTPUT_PATH.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
# Drop unnecessary fields
|
||||
self.df[
|
||||
[
|
||||
self.GEOID_TRACT_FIELD_NAME,
|
||||
self.HOUSING_BURDEN_NUMERATOR_FIELD_NAME,
|
||||
self.HOUSING_BURDEN_DENOMINATOR_FIELD_NAME,
|
||||
self.HOUSING_BURDEN_FIELD_NAME,
|
||||
]
|
||||
].to_csv(path_or_buf=self.OUTPUT_PATH / "usa.csv", index=False)
|
||||
0
score/etl/sources/hud_recap/__init__.py
Normal file
0
score/etl/sources/hud_recap/__init__.py
Normal file
63
score/etl/sources/hud_recap/etl.py
Normal file
63
score/etl/sources/hud_recap/etl.py
Normal file
|
|
@ -0,0 +1,63 @@
|
|||
import pandas as pd
|
||||
import requests
|
||||
|
||||
from etl.base import ExtractTransformLoad
|
||||
from utils import get_module_logger
|
||||
|
||||
logger = get_module_logger(__name__)
|
||||
|
||||
|
||||
class HudRecapETL(ExtractTransformLoad):
|
||||
def __init__(self):
|
||||
self.HUD_RECAP_CSV_URL = "https://opendata.arcgis.com/api/v3/datasets/56de4edea8264fe5a344da9811ef5d6e_0/downloads/data?format=csv&spatialRefId=4326"
|
||||
self.HUD_RECAP_CSV = (
|
||||
self.TMP_PATH
|
||||
/ "Racially_or_Ethnically_Concentrated_Areas_of_Poverty__R_ECAPs_.csv"
|
||||
)
|
||||
self.CSV_PATH = self.DATA_PATH / "dataset" / "hud_recap"
|
||||
|
||||
# Definining some variable names
|
||||
self.HUD_RECAP_PRIORITY_COMMUNITY_FIELD_NAME = "hud_recap_priority_community"
|
||||
|
||||
self.df: pd.DataFrame
|
||||
|
||||
def extract(self) -> None:
|
||||
logger.info(f"Downloading HUD Recap Data")
|
||||
download = requests.get(self.HUD_RECAP_CSV_URL, verify=None)
|
||||
file_contents = download.content
|
||||
csv_file = open(self.HUD_RECAP_CSV, "wb")
|
||||
csv_file.write(file_contents)
|
||||
csv_file.close()
|
||||
|
||||
def transform(self) -> None:
|
||||
logger.info(f"Transforming HUD Recap Data")
|
||||
|
||||
# Load comparison index (CalEnviroScreen 4)
|
||||
self.df = pd.read_csv(self.HUD_RECAP_CSV, dtype={"Census Tract": "string"})
|
||||
|
||||
self.df.rename(
|
||||
columns={
|
||||
"GEOID": self.GEOID_TRACT_FIELD_NAME,
|
||||
# Interestingly, there's no data dictionary for the RECAP data that I could find.
|
||||
# However, this site (http://www.schousing.com/library/Tax%20Credit/2020/QAP%20Instructions%20(2).pdf)
|
||||
# suggests:
|
||||
# "If RCAP_Current for the tract in which the site is located is 1, the tract is an R/ECAP. If RCAP_Current is 0, it is not."
|
||||
"RCAP_Current": self.HUD_RECAP_PRIORITY_COMMUNITY_FIELD_NAME,
|
||||
},
|
||||
inplace=True,
|
||||
)
|
||||
|
||||
# Convert to boolean
|
||||
self.df[self.HUD_RECAP_PRIORITY_COMMUNITY_FIELD_NAME] = self.df[
|
||||
self.HUD_RECAP_PRIORITY_COMMUNITY_FIELD_NAME
|
||||
].astype("bool")
|
||||
|
||||
self.df[self.HUD_RECAP_PRIORITY_COMMUNITY_FIELD_NAME].value_counts()
|
||||
|
||||
self.df.sort_values(by=self.GEOID_TRACT_FIELD_NAME, inplace=True)
|
||||
|
||||
def load(self) -> None:
|
||||
logger.info(f"Saving HUD Recap CSV")
|
||||
# write nationwide csv
|
||||
self.CSV_PATH.mkdir(parents=True, exist_ok=True)
|
||||
self.df.to_csv(self.CSV_PATH / f"usa.csv", index=False)
|
||||
|
|
@ -1,141 +0,0 @@
|
|||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "20aa3891",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from pathlib import Path\n",
|
||||
"import numpy as np\n",
|
||||
"import pandas as pd\n",
|
||||
"import csv\n",
|
||||
"import sys\n",
|
||||
"import os\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 etl.sources.census.etl_utils import get_state_fips_codes\n",
|
||||
"from utils import unzip_file_from_url, remove_all_from_dir\n",
|
||||
"\n",
|
||||
"DATA_PATH = Path.cwd().parent / \"data\"\n",
|
||||
"TMP_PATH = DATA_PATH / \"tmp\"\n",
|
||||
"CALENVIROSCREEN_FTP_URL = \"https://justice40-data.s3.amazonaws.com/CalEnviroScreen/CalEnviroScreen_4.0_2021.zip\"\n",
|
||||
"CSV_PATH = DATA_PATH / \"dataset\" / \"calenviroscreen4\"\n",
|
||||
"\n",
|
||||
"# Definining some variable names\n",
|
||||
"CALENVIROSCREEN_SCORE_FIELD_NAME = \"calenviroscreen_score\"\n",
|
||||
"CALENVIROSCREEN_PERCENTILE_FIELD_NAME = \"calenviroscreen_percentile\"\n",
|
||||
"CALENVIROSCREEN_PRIORITY_COMMUNITY_FIELD_NAME = \"calenviroscreen_priority_community\"\n",
|
||||
"GEOID_TRACT_FIELD_NAME = \"GEOID10_TRACT\"\n",
|
||||
"\n",
|
||||
"# Choosing constants.\n",
|
||||
"# None of these numbers are final, but just for the purposes of comparison.\n",
|
||||
"CALENVIROSCREEN_PRIORITY_COMMUNITY_THRESHOLD = 75\n",
|
||||
"\n",
|
||||
"print(DATA_PATH)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "cc3fb9ec",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# download file from ejscreen ftp\n",
|
||||
"unzip_file_from_url(CALENVIROSCREEN_FTP_URL, TMP_PATH, TMP_PATH)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "15f66756",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Data from https://calenviroscreen-oehha.hub.arcgis.com/#Data, specifically:\n",
|
||||
"# https://oehha.ca.gov/media/downloads/calenviroscreen/document/calenviroscreen40resultsdatadictionaryd12021.zip\n",
|
||||
"calenviroscreen_4_csv_name = \"CalEnviroScreen_4.0_2021.csv\"\n",
|
||||
"calenviroscreen_data_path = TMP_PATH.joinpath(calenviroscreen_4_csv_name)\n",
|
||||
"\n",
|
||||
"# Load comparison index (CalEnviroScreen 4)\n",
|
||||
"calenviroscreen_df = pd.read_csv(\n",
|
||||
" calenviroscreen_data_path, dtype={\"Census Tract\": \"string\"}\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"calenviroscreen_df.rename(\n",
|
||||
" columns={\n",
|
||||
" \"Census Tract\": GEOID_TRACT_FIELD_NAME,\n",
|
||||
" \"DRAFT CES 4.0 Score\": CALENVIROSCREEN_SCORE_FIELD_NAME,\n",
|
||||
" \"DRAFT CES 4.0 Percentile\": CALENVIROSCREEN_PERCENTILE_FIELD_NAME,\n",
|
||||
" },\n",
|
||||
" inplace=True,\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"# Add a leading \"0\" to the Census Tract to match our format in other data frames.\n",
|
||||
"\n",
|
||||
"calenviroscreen_df[GEOID_TRACT_FIELD_NAME] = (\n",
|
||||
" \"0\" + calenviroscreen_df[GEOID_TRACT_FIELD_NAME]\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"# Calculate the top K% of prioritized communities\n",
|
||||
"calenviroscreen_df[CALENVIROSCREEN_PRIORITY_COMMUNITY_FIELD_NAME] = (\n",
|
||||
" calenviroscreen_df[CALENVIROSCREEN_PERCENTILE_FIELD_NAME]\n",
|
||||
" >= CALENVIROSCREEN_PRIORITY_COMMUNITY_THRESHOLD\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"calenviroscreen_df.head()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "9fa2077a",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# write csv\n",
|
||||
"CSV_PATH.mkdir(parents=True, exist_ok=True)\n",
|
||||
"\n",
|
||||
"# Matching other conventions in the ETL scripts, write only for the state (FIPS code 06).\n",
|
||||
"calenviroscreen_df.to_csv(CSV_PATH / \"data06.csv\", index=False)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "81b977f8",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# cleanup\n",
|
||||
"remove_all_from_dir(TMP_PATH)"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.7.1"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
|
|
@ -1,203 +0,0 @@
|
|||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "0491828b",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import pandas as pd\n",
|
||||
"import censusdata\n",
|
||||
"import csv\n",
|
||||
"from pathlib import Path\n",
|
||||
"import os\n",
|
||||
"import sys\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 etl.sources.census.etl_utils import get_state_fips_codes\n",
|
||||
"\n",
|
||||
"ACS_YEAR = 2019\n",
|
||||
"\n",
|
||||
"DATA_PATH = Path.cwd().parent / \"data\"\n",
|
||||
"OUTPUT_PATH = DATA_PATH / \"dataset\" / f\"census_acs_{ACS_YEAR}\"\n",
|
||||
"\n",
|
||||
"GEOID_FIELD_NAME = \"GEOID10\"\n",
|
||||
"UNEMPLOYED_FIELD_NAME = \"Unemployed civilians (percent)\"\n",
|
||||
"LINGUISTIC_ISOLATION_FIELD_NAME = \"Linguistic isolation (percent)\"\n",
|
||||
"LINGUISTIC_ISOLATION_TOTAL_FIELD_NAME = \"Linguistic isolation (total)\"\n",
|
||||
"\n",
|
||||
"LINGUISTIC_ISOLATION_FIELDS = [\n",
|
||||
" \"C16002_001E\",\n",
|
||||
" \"C16002_004E\",\n",
|
||||
" \"C16002_007E\",\n",
|
||||
" \"C16002_010E\",\n",
|
||||
" \"C16002_013E\",\n",
|
||||
"]\n",
|
||||
"\n",
|
||||
"# Some display settings to make pandas outputs more readable.\n",
|
||||
"pd.set_option(\"display.expand_frame_repr\", False)\n",
|
||||
"pd.set_option(\"display.precision\", 2)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "64df0b63",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# For variable discovery, if necessary.\n",
|
||||
"# censusdata.search(\n",
|
||||
"# \"acs5\", 2019, \"label\", \"Limited English speaking\"\n",
|
||||
"# )"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "654f25a1",
|
||||
"metadata": {
|
||||
"scrolled": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Following the tutorial at https://jtleider.github.io/censusdata/example1.html.\n",
|
||||
"# Full list of fields is at https://www2.census.gov/programs-surveys/acs/summary_file/2019/documentation/user_tools/ACS2019_Table_Shells.xlsx\n",
|
||||
"censusdata.printtable(censusdata.censustable(src=\"acs5\", year=ACS_YEAR, table=\"B23025\"))\n",
|
||||
"censusdata.printtable(censusdata.censustable(src=\"acs5\", year=ACS_YEAR, table=\"C16002\"))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "8999cea4",
|
||||
"metadata": {
|
||||
"scrolled": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def fips_from_censusdata_censusgeo(censusgeo: censusdata.censusgeo) -> str:\n",
|
||||
" \"\"\"Create a FIPS code from the proprietary censusgeo index.\"\"\"\n",
|
||||
" fips = \"\".join([value for (key, value) in censusgeo.params()])\n",
|
||||
" return fips\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"dfs = []\n",
|
||||
"for fips in get_state_fips_codes(DATA_PATH):\n",
|
||||
" print(f\"Downloading data for state/territory with FIPS code {fips}\")\n",
|
||||
"\n",
|
||||
" dfs.append(\n",
|
||||
" censusdata.download(\n",
|
||||
" src=\"acs5\",\n",
|
||||
" year=ACS_YEAR,\n",
|
||||
" geo=censusdata.censusgeo(\n",
|
||||
" [(\"state\", fips), (\"county\", \"*\"), (\"block group\", \"*\")]\n",
|
||||
" ),\n",
|
||||
" var=[\n",
|
||||
" # Emploment fields\n",
|
||||
" \"B23025_005E\",\n",
|
||||
" \"B23025_003E\",\n",
|
||||
" ]\n",
|
||||
" + LINGUISTIC_ISOLATION_FIELDS,\n",
|
||||
" )\n",
|
||||
" )\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"df = pd.concat(dfs)\n",
|
||||
"\n",
|
||||
"df[GEOID_FIELD_NAME] = df.index.to_series().apply(func=fips_from_censusdata_censusgeo)\n",
|
||||
"\n",
|
||||
"df.head()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "803cce31",
|
||||
"metadata": {
|
||||
"scrolled": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Calculate percent unemployment.\n",
|
||||
"# TODO: remove small-sample data that should be `None` instead of a high-variance fraction.\n",
|
||||
"df[UNEMPLOYED_FIELD_NAME] = df.B23025_005E / df.B23025_003E\n",
|
||||
"\n",
|
||||
"df[UNEMPLOYED_FIELD_NAME].describe()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "e475472c",
|
||||
"metadata": {
|
||||
"scrolled": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Calculate linguistic isolation.\n",
|
||||
"individual_limited_english_fields = [\n",
|
||||
" \"C16002_004E\",\n",
|
||||
" \"C16002_007E\",\n",
|
||||
" \"C16002_010E\",\n",
|
||||
" \"C16002_013E\",\n",
|
||||
"]\n",
|
||||
"\n",
|
||||
"df[LINGUISTIC_ISOLATION_TOTAL_FIELD_NAME] = df[individual_limited_english_fields].sum(\n",
|
||||
" axis=1, skipna=True\n",
|
||||
")\n",
|
||||
"df[LINGUISTIC_ISOLATION_FIELD_NAME] = (\n",
|
||||
" df[LINGUISTIC_ISOLATION_TOTAL_FIELD_NAME].astype(float) / df[\"C16002_001E\"]\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"df[LINGUISTIC_ISOLATION_FIELD_NAME].describe()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "2a269bb1",
|
||||
"metadata": {
|
||||
"scrolled": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# mkdir census\n",
|
||||
"OUTPUT_PATH.mkdir(parents=True, exist_ok=True)\n",
|
||||
"\n",
|
||||
"columns_to_include = [\n",
|
||||
" GEOID_FIELD_NAME,\n",
|
||||
" UNEMPLOYED_FIELD_NAME,\n",
|
||||
" LINGUISTIC_ISOLATION_FIELD_NAME,\n",
|
||||
"]\n",
|
||||
"\n",
|
||||
"df[columns_to_include].to_csv(path_or_buf=OUTPUT_PATH / \"usa.csv\", index=False)"
|
||||
]
|
||||
}
|
||||
],
|
||||
"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": "3.7.1"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
|
|
@ -1,123 +0,0 @@
|
|||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "20aa3891",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from pathlib import Path\n",
|
||||
"import numpy as np\n",
|
||||
"import pandas as pd\n",
|
||||
"import csv\n",
|
||||
"import sys\n",
|
||||
"import os\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 etl.sources.census.etl_utils import get_state_fips_codes\n",
|
||||
"from utils import unzip_file_from_url, remove_all_from_dir\n",
|
||||
"\n",
|
||||
"DATA_PATH = Path.cwd().parent / \"data\"\n",
|
||||
"TMP_PATH = DATA_PATH / \"tmp\"\n",
|
||||
"EJSCREEN_FTP_URL = (\n",
|
||||
" \"https://gaftp.epa.gov/EJSCREEN/2020/EJSCREEN_2020_StatePctile.csv.zip\"\n",
|
||||
")\n",
|
||||
"EJSCREEN_CSV = TMP_PATH / \"EJSCREEN_2020_StatePctile.csv\"\n",
|
||||
"CSV_PATH = DATA_PATH / \"dataset\" / \"ejscreen_2020\"\n",
|
||||
"print(DATA_PATH)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "cc3fb9ec",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# download file from ejscreen ftp\n",
|
||||
"unzip_file_from_url(EJSCREEN_FTP_URL, TMP_PATH, TMP_PATH)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "b25738bb",
|
||||
"metadata": {
|
||||
"scrolled": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df = pd.read_csv(\n",
|
||||
" EJSCREEN_CSV,\n",
|
||||
" dtype={\"ID\": \"string\"},\n",
|
||||
" # EJSCREEN writes the word \"None\" for NA data.\n",
|
||||
" na_values=[\"None\"],\n",
|
||||
" low_memory=False,\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "9fa2077a",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# write nationwide csv\n",
|
||||
"CSV_PATH.mkdir(parents=True, exist_ok=True)\n",
|
||||
"df.to_csv(CSV_PATH / f\"usa.csv\", index=False)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "5e5cc12a",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# write per state csvs\n",
|
||||
"for fips in get_state_fips_codes(DATA_PATH):\n",
|
||||
" print(f\"Generating data{fips} csv\")\n",
|
||||
" df1 = df[df.ID.str[:2] == fips]\n",
|
||||
" # we need to name the file data01.csv for ogr2ogr csv merge to work\n",
|
||||
" df1.to_csv(CSV_PATH / f\"data{fips}.csv\", index=False)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "81b977f8",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# cleanup\n",
|
||||
"remove_all_from_dir(TMP_PATH)"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.7.1"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
|
|
@ -1,116 +0,0 @@
|
|||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "c21b63a3",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import pandas as pd\n",
|
||||
"import censusdata\n",
|
||||
"import csv\n",
|
||||
"from pathlib import Path\n",
|
||||
"import os\n",
|
||||
"import sys\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 etl.sources.census.etl_utils import get_state_fips_codes\n",
|
||||
"from utils import unzip_file_from_url, remove_all_from_dir\n",
|
||||
"\n",
|
||||
"ACS_YEAR = 2019\n",
|
||||
"\n",
|
||||
"DATA_PATH = Path.cwd().parent / \"data\"\n",
|
||||
"TMP_PATH = DATA_PATH / \"tmp\"\n",
|
||||
"HOUSING_FTP_URL = \"https://htaindex.cnt.org/download/download.php?focus=blkgrp&geoid=\"\n",
|
||||
"OUTPUT_PATH = DATA_PATH / \"dataset\" / \"housing_and_transportation_index\"\n",
|
||||
"\n",
|
||||
"GEOID_FIELD_NAME = \"GEOID10\""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "6696bc66",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Download each state / territory individually\n",
|
||||
"dfs = []\n",
|
||||
"zip_file_dir = TMP_PATH / \"housing_and_transportation_index\"\n",
|
||||
"for fips in get_state_fips_codes(DATA_PATH):\n",
|
||||
" print(f\"Downloading housing data for state/territory with FIPS code {fips}\")\n",
|
||||
" unzip_file_from_url(f\"{HOUSING_FTP_URL}{fips}\", TMP_PATH, zip_file_dir)\n",
|
||||
"\n",
|
||||
" # New file name:\n",
|
||||
" tmp_csv_file_path = zip_file_dir / f\"htaindex_data_blkgrps_{fips}.csv\"\n",
|
||||
" tmp_df = pd.read_csv(filepath_or_buffer=tmp_csv_file_path)\n",
|
||||
"\n",
|
||||
" dfs.append(tmp_df)\n",
|
||||
"\n",
|
||||
"df = pd.concat(dfs)\n",
|
||||
"\n",
|
||||
"df.head()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "244e0d03",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Rename and reformat block group ID\n",
|
||||
"df.rename(columns={\"blkgrp\": GEOID_FIELD_NAME}, inplace=True)\n",
|
||||
"df[GEOID_FIELD_NAME] = df[GEOID_FIELD_NAME].str.replace('\"', \"\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "8275c1ef",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"OUTPUT_PATH.mkdir(parents=True, exist_ok=True)\n",
|
||||
"\n",
|
||||
"df.to_csv(path_or_buf=OUTPUT_PATH / \"usa.csv\", index=False)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "ef5bb862",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# cleanup\n",
|
||||
"remove_all_from_dir(TMP_PATH)"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.7.1"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
|
|
@ -1,274 +0,0 @@
|
|||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "c21b63a3",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import pandas as pd\n",
|
||||
"import censusdata\n",
|
||||
"import csv\n",
|
||||
"from pathlib import Path\n",
|
||||
"import os\n",
|
||||
"import re\n",
|
||||
"import sys\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 etl.sources.census.etl_utils import get_state_fips_codes\n",
|
||||
"from utils import unzip_file_from_url, remove_all_from_dir\n",
|
||||
"\n",
|
||||
"DATA_PATH = Path.cwd().parent / \"data\"\n",
|
||||
"TMP_PATH = DATA_PATH / \"tmp\"\n",
|
||||
"OUTPUT_PATH = DATA_PATH / \"dataset\" / \"hud_housing\"\n",
|
||||
"\n",
|
||||
"GEOID_TRACT_FIELD_NAME = \"GEOID10_TRACT\"\n",
|
||||
"\n",
|
||||
"# We measure households earning less than 80% of HUD Area Median Family Income by county\n",
|
||||
"# and paying greater than 30% of their income to housing costs.\n",
|
||||
"HOUSING_BURDEN_FIELD_NAME = \"Housing burden (percent)\"\n",
|
||||
"HOUSING_BURDEN_NUMERATOR_FIELD_NAME = \"HOUSING_BURDEN_NUMERATOR\"\n",
|
||||
"HOUSING_BURDEN_DENOMINATOR_FIELD_NAME = \"HOUSING_BURDEN_DENOMINATOR\"\n",
|
||||
"\n",
|
||||
"# Note: some variable definitions.\n",
|
||||
"# HUD-adjusted median family income (HAMFI).\n",
|
||||
"# The four housing problems are: incomplete kitchen facilities, incomplete plumbing facilities, more than 1 person per room, and cost burden greater than 30%.\n",
|
||||
"# Table 8 is the desired table."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "6696bc66",
|
||||
"metadata": {
|
||||
"scrolled": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Download the data.\n",
|
||||
"dfs = []\n",
|
||||
"zip_file_dir = TMP_PATH / \"hud_housing\"\n",
|
||||
"\n",
|
||||
"print(f\"Downloading 225MB housing data\")\n",
|
||||
"unzip_file_from_url(\n",
|
||||
" \"https://www.huduser.gov/portal/datasets/cp/2012thru2016-140-csv.zip\",\n",
|
||||
" TMP_PATH,\n",
|
||||
" zip_file_dir,\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "3e954589",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# New file name:\n",
|
||||
"tmp_csv_file_path = (\n",
|
||||
" zip_file_dir\n",
|
||||
" / \"2012thru2016-140-csv\"\n",
|
||||
" / \"2012thru2016-140-csv\"\n",
|
||||
" / \"140\"\n",
|
||||
" / \"Table8.csv\"\n",
|
||||
")\n",
|
||||
"df = pd.read_csv(filepath_or_buffer=tmp_csv_file_path)\n",
|
||||
"\n",
|
||||
"df.head()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "244e0d03",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Rename and reformat block group ID\n",
|
||||
"df.rename(columns={\"geoid\": GEOID_TRACT_FIELD_NAME}, inplace=True)\n",
|
||||
"\n",
|
||||
"# The CHAS data has census tract ids such as `14000US01001020100`\n",
|
||||
"# Whereas the rest of our data uses, for the same tract, `01001020100`.\n",
|
||||
"# the characters before `US`:\n",
|
||||
"df[GEOID_TRACT_FIELD_NAME] = df[GEOID_TRACT_FIELD_NAME].str.replace(\n",
|
||||
" r\"^.*?US\", \"\", regex=True\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"df[GEOID_TRACT_FIELD_NAME].head()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "03250026",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Calculate housing burden\n",
|
||||
"# This is quite a number of steps. It does not appear to be accessible nationally in a simpler format, though.\n",
|
||||
"# See \"CHAS data dictionary 12-16.xlsx\"\n",
|
||||
"\n",
|
||||
"# Owner occupied numerator fields\n",
|
||||
"OWNER_OCCUPIED_NUMERATOR_FIELDS = [\n",
|
||||
" # Key: Column Name\tLine_Type\tTenure\tHousehold income\tCost burden\tFacilities\n",
|
||||
" # T8_est7\tSubtotal\tOwner occupied\tless than or equal to 30% of HAMFI\tgreater than 30% but less than or equal to 50%\tAll\n",
|
||||
" \"T8_est7\",\n",
|
||||
" # T8_est10\tSubtotal\tOwner occupied\tless than or equal to 30% of HAMFI\tgreater than 50%\tAll\n",
|
||||
" \"T8_est10\",\n",
|
||||
" # T8_est20\tSubtotal\tOwner occupied\tgreater than 30% but less than or equal to 50% of HAMFI\tgreater than 30% but less than or equal to 50%\tAll\n",
|
||||
" \"T8_est20\",\n",
|
||||
" # T8_est23\tSubtotal\tOwner occupied\tgreater than 30% but less than or equal to 50% of HAMFI\tgreater than 50%\tAll\n",
|
||||
" \"T8_est23\",\n",
|
||||
" # T8_est33\tSubtotal\tOwner occupied\tgreater than 50% but less than or equal to 80% of HAMFI\tgreater than 30% but less than or equal to 50%\tAll\n",
|
||||
" \"T8_est33\",\n",
|
||||
" # T8_est36\tSubtotal\tOwner occupied\tgreater than 50% but less than or equal to 80% of HAMFI\tgreater than 50%\tAll\n",
|
||||
" \"T8_est36\",\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",
|
||||
" # Key: Column Name\tLine_Type\tTenure\tHousehold income\tCost burden\tFacilities\n",
|
||||
" # T8_est13\tSubtotal\tOwner occupied\tless than or equal to 30% of HAMFI\tnot computed (no/negative income)\tAll\n",
|
||||
" \"T8_est13\",\n",
|
||||
" # T8_est26\tSubtotal\tOwner occupied\tgreater than 30% but less than or equal to 50% of HAMFI\tnot computed (no/negative income)\tAll\n",
|
||||
" \"T8_est26\",\n",
|
||||
" # T8_est39\tSubtotal\tOwner occupied\tgreater than 50% but less than or equal to 80% of HAMFI\tnot computed (no/negative income)\tAll\n",
|
||||
" \"T8_est39\",\n",
|
||||
" # T8_est52\tSubtotal\tOwner occupied\tgreater than 80% but less than or equal to 100% of HAMFI\tnot computed (no/negative income)\tAll\n",
|
||||
" \"T8_est52\",\n",
|
||||
" # T8_est65\tSubtotal\tOwner occupied\tgreater than 100% of HAMFI\tnot computed (no/negative income)\tAll\n",
|
||||
" \"T8_est65\",\n",
|
||||
"]\n",
|
||||
"\n",
|
||||
"# T8_est2\tSubtotal\tOwner occupied\tAll\tAll\tAll\n",
|
||||
"OWNER_OCCUPIED_POPULATION_FIELD = \"T8_est2\"\n",
|
||||
"\n",
|
||||
"# Renter occupied numerator fields\n",
|
||||
"RENTER_OCCUPIED_NUMERATOR_FIELDS = [\n",
|
||||
" # Key: Column Name\tLine_Type\tTenure\tHousehold income\tCost burden\tFacilities\n",
|
||||
" # T8_est73\tSubtotal\tRenter occupied\tless than or equal to 30% of HAMFI\tgreater than 30% but less than or equal to 50%\tAll\n",
|
||||
" \"T8_est73\",\n",
|
||||
" # T8_est76\tSubtotal\tRenter occupied\tless than or equal to 30% of HAMFI\tgreater than 50%\tAll\n",
|
||||
" \"T8_est76\",\n",
|
||||
" # T8_est86\tSubtotal\tRenter occupied\tgreater than 30% but less than or equal to 50% of HAMFI\tgreater than 30% but less than or equal to 50%\tAll\n",
|
||||
" \"T8_est86\",\n",
|
||||
" # T8_est89\tSubtotal\tRenter occupied\tgreater than 30% but less than or equal to 50% of HAMFI\tgreater than 50%\tAll\n",
|
||||
" \"T8_est89\",\n",
|
||||
" # T8_est99\tSubtotal\tRenter occupied\tgreater than 50% but less than or equal to 80% of HAMFI\tgreater than 30% but less than or equal to 50%\tAll\n",
|
||||
" \"T8_est99\",\n",
|
||||
" # T8_est102\tSubtotal\tRenter occupied\tgreater than 50% but less than or equal to 80% of HAMFI\tgreater than 50%\tAll\n",
|
||||
" \"T8_est102\",\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",
|
||||
" # Key: Column Name\tLine_Type\tTenure\tHousehold income\tCost burden\tFacilities\n",
|
||||
" # T8_est79\tSubtotal\tRenter occupied\tless than or equal to 30% of HAMFI\tnot computed (no/negative income)\tAll\n",
|
||||
" \"T8_est79\",\n",
|
||||
" # T8_est92\tSubtotal\tRenter occupied\tgreater than 30% but less than or equal to 50% of HAMFI\tnot computed (no/negative income)\tAll\n",
|
||||
" \"T8_est92\",\n",
|
||||
" # T8_est105\tSubtotal\tRenter occupied\tgreater than 50% but less than or equal to 80% of HAMFI\tnot computed (no/negative income)\tAll\n",
|
||||
" \"T8_est105\",\n",
|
||||
" # T8_est118\tSubtotal\tRenter occupied\tgreater than 80% but less than or equal to 100% of HAMFI\tnot computed (no/negative income)\tAll\n",
|
||||
" \"T8_est118\",\n",
|
||||
" # T8_est131\tSubtotal\tRenter occupied\tgreater than 100% of HAMFI\tnot computed (no/negative income)\tAll\n",
|
||||
" \"T8_est131\",\n",
|
||||
"]\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# T8_est68\tSubtotal\tRenter occupied\tAll\tAll\tAll\n",
|
||||
"RENTER_OCCUPIED_POPULATION_FIELD = \"T8_est68\"\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# Math:\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",
|
||||
"df[HOUSING_BURDEN_NUMERATOR_FIELD_NAME] = df[OWNER_OCCUPIED_NUMERATOR_FIELDS].sum(\n",
|
||||
" axis=1\n",
|
||||
") + df[RENTER_OCCUPIED_NUMERATOR_FIELDS].sum(axis=1)\n",
|
||||
"\n",
|
||||
"df[HOUSING_BURDEN_DENOMINATOR_FIELD_NAME] = (\n",
|
||||
" df[OWNER_OCCUPIED_POPULATION_FIELD]\n",
|
||||
" + df[RENTER_OCCUPIED_POPULATION_FIELD]\n",
|
||||
" - df[OWNER_OCCUPIED_NOT_COMPUTED_FIELDS].sum(axis=1)\n",
|
||||
" - df[RENTER_OCCUPIED_NOT_COMPUTED_FIELDS].sum(axis=1)\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"# TODO: add small sample size checks\n",
|
||||
"df[HOUSING_BURDEN_FIELD_NAME] = df[HOUSING_BURDEN_NUMERATOR_FIELD_NAME].astype(\n",
|
||||
" float\n",
|
||||
") / df[HOUSING_BURDEN_DENOMINATOR_FIELD_NAME].astype(float)\n",
|
||||
"\n",
|
||||
"df.head()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "8275c1ef",
|
||||
"metadata": {
|
||||
"scrolled": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"OUTPUT_PATH.mkdir(parents=True, exist_ok=True)\n",
|
||||
"\n",
|
||||
"# Drop unnecessary fields\n",
|
||||
"df[\n",
|
||||
" [\n",
|
||||
" GEOID_TRACT_FIELD_NAME,\n",
|
||||
" HOUSING_BURDEN_NUMERATOR_FIELD_NAME,\n",
|
||||
" HOUSING_BURDEN_DENOMINATOR_FIELD_NAME,\n",
|
||||
" HOUSING_BURDEN_FIELD_NAME,\n",
|
||||
" ]\n",
|
||||
"].to_csv(path_or_buf=OUTPUT_PATH / \"usa.csv\", index=False)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "ef5bb862",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# cleanup\n",
|
||||
"remove_all_from_dir(TMP_PATH)"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.7.1"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
|
|
@ -1,115 +0,0 @@
|
|||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "20aa3891",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from pathlib import Path\n",
|
||||
"import numpy as np\n",
|
||||
"import pandas as pd\n",
|
||||
"import csv\n",
|
||||
"import sys\n",
|
||||
"import os\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 etl.sources.census.etl_utils import get_state_fips_codes\n",
|
||||
"from utils import unzip_file_from_url, remove_all_from_dir\n",
|
||||
"\n",
|
||||
"DATA_PATH = Path.cwd().parent / \"data\"\n",
|
||||
"TMP_PATH = DATA_PATH / \"tmp\"\n",
|
||||
"HUD_RECAP_CSV_URL = \"https://opendata.arcgis.com/api/v3/datasets/56de4edea8264fe5a344da9811ef5d6e_0/downloads/data?format=csv&spatialRefId=4326\"\n",
|
||||
"CSV_PATH = DATA_PATH / \"dataset\" / \"hud_recap\"\n",
|
||||
"\n",
|
||||
"# Definining some variable names\n",
|
||||
"GEOID_TRACT_FIELD_NAME = \"GEOID10_TRACT\"\n",
|
||||
"HUD_RECAP_PRIORITY_COMMUNITY_FIELD_NAME = \"hud_recap_priority_community\""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "b9455da5",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Data from https://hudgis-hud.opendata.arcgis.com/datasets/HUD::racially-or-ethnically-concentrated-areas-of-poverty-r-ecaps/about\n",
|
||||
"df = pd.read_csv(HUD_RECAP_CSV_URL, dtype={\"GEOID\": \"string\"})\n",
|
||||
"df.head()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "ca63e66c",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Rename some fields\n",
|
||||
"df.rename(\n",
|
||||
" columns={\n",
|
||||
" \"GEOID\": GEOID_TRACT_FIELD_NAME,\n",
|
||||
" # Interestingly, there's no data dictionary for the RECAP data that I could find.\n",
|
||||
" # However, this site (http://www.schousing.com/library/Tax%20Credit/2020/QAP%20Instructions%20(2).pdf)\n",
|
||||
" # suggests:\n",
|
||||
" # \"If RCAP_Current for the tract in which the site is located is 1, the tract is an R/ECAP. If RCAP_Current is 0, it is not.\"\n",
|
||||
" \"RCAP_Current\": HUD_RECAP_PRIORITY_COMMUNITY_FIELD_NAME,\n",
|
||||
" },\n",
|
||||
" inplace=True,\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"# Convert to boolean\n",
|
||||
"df[HUD_RECAP_PRIORITY_COMMUNITY_FIELD_NAME] = df[\n",
|
||||
" HUD_RECAP_PRIORITY_COMMUNITY_FIELD_NAME\n",
|
||||
"].astype(\"bool\")\n",
|
||||
"\n",
|
||||
"df[HUD_RECAP_PRIORITY_COMMUNITY_FIELD_NAME].value_counts()\n",
|
||||
"\n",
|
||||
"df.sort_values(by=GEOID_TRACT_FIELD_NAME, inplace=True)\n",
|
||||
"df.head()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "9fa2077a",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# write csv\n",
|
||||
"CSV_PATH.mkdir(parents=True, exist_ok=True)\n",
|
||||
"\n",
|
||||
"# Drop unnecessary columns.\n",
|
||||
"df[[GEOID_TRACT_FIELD_NAME, HUD_RECAP_PRIORITY_COMMUNITY_FIELD_NAME]].to_csv(\n",
|
||||
" CSV_PATH / \"usa.csv\", index=False\n",
|
||||
")"
|
||||
]
|
||||
}
|
||||
],
|
||||
"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": "3.7.1"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
|
|
@ -1,619 +0,0 @@
|
|||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "a664f981",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Before running this notebook, you must run the following notebooks (in any order):\n",
|
||||
"# 1. `ejscreen_etl.ipynb`\n",
|
||||
"# 2. `census_etl.ipynb`\n",
|
||||
"# 3. `housing_and_transportation_etl.ipynb`\n",
|
||||
"# 4. `hud_housing_etl.ipynb`\n",
|
||||
"\n",
|
||||
"import collections\n",
|
||||
"import functools\n",
|
||||
"from pathlib import Path\n",
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"import pandas as pd\n",
|
||||
"import csv\n",
|
||||
"import os\n",
|
||||
"import sys\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 etl.sources.census.etl_utils import get_state_fips_codes\n",
|
||||
"\n",
|
||||
"# Define some global parameters\n",
|
||||
"GEOID_FIELD_NAME = \"GEOID10\"\n",
|
||||
"GEOID_TRACT_FIELD_NAME = \"GEOID10_TRACT\"\n",
|
||||
"BUCKET_SOCIOECONOMIC = \"Socioeconomic Factors\"\n",
|
||||
"BUCKET_SENSITIVE = \"Sensitive populations\"\n",
|
||||
"BUCKET_ENVIRONMENTAL = \"Environmental effects\"\n",
|
||||
"BUCKET_EXPOSURES = \"Exposures\"\n",
|
||||
"BUCKETS = [\n",
|
||||
" BUCKET_SOCIOECONOMIC,\n",
|
||||
" BUCKET_SENSITIVE,\n",
|
||||
" BUCKET_ENVIRONMENTAL,\n",
|
||||
" BUCKET_EXPOSURES,\n",
|
||||
"]\n",
|
||||
"\n",
|
||||
"# A few specific field names\n",
|
||||
"# TODO: clean this up, I name some fields but not others.\n",
|
||||
"UNEMPLOYED_FIELD_NAME = \"Unemployed civilians (percent)\"\n",
|
||||
"LINGUISTIC_ISOLATION_FIELD_NAME = \"Linguistic isolation (percent)\"\n",
|
||||
"HOUSING_BURDEN_FIELD_NAME = \"Housing burden (percent)\"\n",
|
||||
"POVERTY_FIELD_NAME = \"Poverty (Less than 200% of federal poverty line)\"\n",
|
||||
"HIGH_SCHOOL_FIELD_NAME = (\n",
|
||||
" \"Percent individuals age 25 or over with less than high school degree\"\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"# There's another aggregation level (a second level of \"buckets\").\n",
|
||||
"AGGREGATION_POLLUTION = \"Pollution Burden\"\n",
|
||||
"AGGREGATION_POPULATION = \"Population Characteristics\"\n",
|
||||
"\n",
|
||||
"PERCENTILE_FIELD_SUFFIX = \" (percentile)\"\n",
|
||||
"MIN_MAX_FIELD_SUFFIX = \" (min-max normalized)\"\n",
|
||||
"\n",
|
||||
"DATA_PATH = Path.cwd().parent / \"data\"\n",
|
||||
"SCORE_CSV_PATH = DATA_PATH / \"score\" / \"csv\"\n",
|
||||
"\n",
|
||||
"# Tell pandas to display all columns\n",
|
||||
"pd.set_option(\"display.max_columns\", None)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "7df430cb",
|
||||
"metadata": {
|
||||
"scrolled": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# EJSCreen csv Load\n",
|
||||
"ejscreen_csv = DATA_PATH / \"dataset\" / \"ejscreen_2020\" / \"usa.csv\"\n",
|
||||
"ejscreen_df = pd.read_csv(ejscreen_csv, dtype={\"ID\": \"string\"}, low_memory=False)\n",
|
||||
"ejscreen_df.rename(columns={\"ID\": GEOID_FIELD_NAME}, inplace=True)\n",
|
||||
"ejscreen_df.head()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "daba69fb",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Load census data\n",
|
||||
"census_csv = DATA_PATH / \"dataset\" / \"census_acs_2019\" / \"usa.csv\"\n",
|
||||
"census_df = pd.read_csv(\n",
|
||||
" census_csv, dtype={GEOID_FIELD_NAME: \"string\"}, low_memory=False\n",
|
||||
")\n",
|
||||
"census_df.head()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "144bdde2",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Load housing and transportation data\n",
|
||||
"housing_and_transportation_index_csv = (\n",
|
||||
" DATA_PATH / \"dataset\" / \"housing_and_transportation_index\" / \"usa.csv\"\n",
|
||||
")\n",
|
||||
"housing_and_transportation_df = pd.read_csv(\n",
|
||||
" housing_and_transportation_index_csv,\n",
|
||||
" dtype={GEOID_FIELD_NAME: \"string\"},\n",
|
||||
" low_memory=False,\n",
|
||||
")\n",
|
||||
"housing_and_transportation_df.head()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "a9202e5d",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Load HUD housing data\n",
|
||||
"hud_housing_csv = DATA_PATH / \"dataset\" / \"hud_housing\" / \"usa.csv\"\n",
|
||||
"hud_housing_df = pd.read_csv(\n",
|
||||
" hud_housing_csv,\n",
|
||||
" dtype={GEOID_TRACT_FIELD_NAME: \"string\"},\n",
|
||||
" low_memory=False,\n",
|
||||
")\n",
|
||||
"hud_housing_df.head()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "bf89efd8",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Join all the data sources that use census block groups\n",
|
||||
"census_block_group_dfs = [ejscreen_df, census_df, housing_and_transportation_df]\n",
|
||||
"\n",
|
||||
"census_block_group_df = functools.reduce(\n",
|
||||
" lambda left, right: pd.merge(\n",
|
||||
" left=left, right=right, on=GEOID_FIELD_NAME, how=\"outer\"\n",
|
||||
" ),\n",
|
||||
" census_block_group_dfs,\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"if len(census_block_group_df) > 220333:\n",
|
||||
" raise ValueError(\"Too many rows in the join.\")\n",
|
||||
"\n",
|
||||
"census_block_group_df.head()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "e79ec27a",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Sanity check the join.\n",
|
||||
"if len(census_block_group_df[GEOID_FIELD_NAME].str.len().unique()) != 1:\n",
|
||||
" raise ValueError(\n",
|
||||
" f\"One of the input CSVs uses {GEOID_FIELD_NAME} with a different length.\"\n",
|
||||
" )"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "3d0d2915",
|
||||
"metadata": {
|
||||
"scrolled": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Join all the data sources that use census tracts\n",
|
||||
"# TODO: when there's more than one data source using census tract, reduce/merge them here.\n",
|
||||
"census_tract_df = hud_housing_df\n",
|
||||
"\n",
|
||||
"# Calculate the tract for the CBG data.\n",
|
||||
"census_block_group_df[GEOID_TRACT_FIELD_NAME] = census_block_group_df[\n",
|
||||
" GEOID_FIELD_NAME\n",
|
||||
"].str[0:11]\n",
|
||||
"\n",
|
||||
"df = census_block_group_df.merge(census_tract_df, on=GEOID_TRACT_FIELD_NAME)\n",
|
||||
"\n",
|
||||
"if len(census_block_group_df) > 220333:\n",
|
||||
" raise ValueError(\"Too many rows in the join.\")\n",
|
||||
"\n",
|
||||
"df.head()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "b8567900",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Define a named tuple that will be used for each data set input.\n",
|
||||
"DataSet = collections.namedtuple(\n",
|
||||
" typename=\"DataSet\", field_names=[\"input_field\", \"renamed_field\", \"bucket\"]\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"data_sets = [\n",
|
||||
" # The following data sets have `bucket=None`, because it's not used in the bucket based score (\"Score C\").\n",
|
||||
" DataSet(\n",
|
||||
" input_field=GEOID_FIELD_NAME,\n",
|
||||
" # Use the name `GEOID10` to enable geoplatform.gov's workflow.\n",
|
||||
" renamed_field=GEOID_FIELD_NAME,\n",
|
||||
" bucket=None,\n",
|
||||
" ),\n",
|
||||
" DataSet(\n",
|
||||
" input_field=HOUSING_BURDEN_FIELD_NAME,\n",
|
||||
" renamed_field=HOUSING_BURDEN_FIELD_NAME,\n",
|
||||
" bucket=None,\n",
|
||||
" ),\n",
|
||||
" DataSet(input_field=\"ACSTOTPOP\", renamed_field=\"Total population\", bucket=None),\n",
|
||||
" # The following data sets have buckets, because they're used in the score\n",
|
||||
" DataSet(\n",
|
||||
" input_field=\"CANCER\",\n",
|
||||
" renamed_field=\"Air toxics cancer risk\",\n",
|
||||
" bucket=BUCKET_EXPOSURES,\n",
|
||||
" ),\n",
|
||||
" DataSet(\n",
|
||||
" input_field=\"RESP\",\n",
|
||||
" renamed_field=\"Respiratory hazard index\",\n",
|
||||
" bucket=BUCKET_EXPOSURES,\n",
|
||||
" ),\n",
|
||||
" DataSet(\n",
|
||||
" input_field=\"DSLPM\",\n",
|
||||
" renamed_field=\"Diesel particulate matter\",\n",
|
||||
" bucket=BUCKET_EXPOSURES,\n",
|
||||
" ),\n",
|
||||
" DataSet(\n",
|
||||
" input_field=\"PM25\",\n",
|
||||
" renamed_field=\"Particulate matter (PM2.5)\",\n",
|
||||
" bucket=BUCKET_EXPOSURES,\n",
|
||||
" ),\n",
|
||||
" DataSet(input_field=\"OZONE\", renamed_field=\"Ozone\", bucket=BUCKET_EXPOSURES),\n",
|
||||
" DataSet(\n",
|
||||
" input_field=\"PTRAF\",\n",
|
||||
" renamed_field=\"Traffic proximity and volume\",\n",
|
||||
" bucket=BUCKET_EXPOSURES,\n",
|
||||
" ),\n",
|
||||
" DataSet(\n",
|
||||
" input_field=\"PRMP\",\n",
|
||||
" renamed_field=\"Proximity to RMP sites\",\n",
|
||||
" bucket=BUCKET_ENVIRONMENTAL,\n",
|
||||
" ),\n",
|
||||
" DataSet(\n",
|
||||
" input_field=\"PTSDF\",\n",
|
||||
" renamed_field=\"Proximity to TSDF sites\",\n",
|
||||
" bucket=BUCKET_ENVIRONMENTAL,\n",
|
||||
" ),\n",
|
||||
" DataSet(\n",
|
||||
" input_field=\"PNPL\",\n",
|
||||
" renamed_field=\"Proximity to NPL sites\",\n",
|
||||
" bucket=BUCKET_ENVIRONMENTAL,\n",
|
||||
" ),\n",
|
||||
" DataSet(\n",
|
||||
" input_field=\"PWDIS\",\n",
|
||||
" renamed_field=\"Wastewater discharge\",\n",
|
||||
" bucket=BUCKET_ENVIRONMENTAL,\n",
|
||||
" ),\n",
|
||||
" DataSet(\n",
|
||||
" input_field=\"PRE1960PCT\",\n",
|
||||
" renamed_field=\"Percent pre-1960s housing (lead paint indicator)\",\n",
|
||||
" bucket=BUCKET_ENVIRONMENTAL,\n",
|
||||
" ),\n",
|
||||
" DataSet(\n",
|
||||
" input_field=\"UNDER5PCT\",\n",
|
||||
" renamed_field=\"Individuals under 5 years old\",\n",
|
||||
" bucket=BUCKET_SENSITIVE,\n",
|
||||
" ),\n",
|
||||
" DataSet(\n",
|
||||
" input_field=\"OVER64PCT\",\n",
|
||||
" renamed_field=\"Individuals over 64 years old\",\n",
|
||||
" bucket=BUCKET_SENSITIVE,\n",
|
||||
" ),\n",
|
||||
" DataSet(\n",
|
||||
" input_field=LINGUISTIC_ISOLATION_FIELD_NAME,\n",
|
||||
" renamed_field=LINGUISTIC_ISOLATION_FIELD_NAME,\n",
|
||||
" bucket=BUCKET_SENSITIVE,\n",
|
||||
" ),\n",
|
||||
" DataSet(\n",
|
||||
" input_field=\"LINGISOPCT\",\n",
|
||||
" renamed_field=\"Percent of households in linguistic isolation\",\n",
|
||||
" bucket=BUCKET_SOCIOECONOMIC,\n",
|
||||
" ),\n",
|
||||
" DataSet(\n",
|
||||
" input_field=\"LOWINCPCT\",\n",
|
||||
" renamed_field=POVERTY_FIELD_NAME,\n",
|
||||
" bucket=BUCKET_SOCIOECONOMIC,\n",
|
||||
" ),\n",
|
||||
" DataSet(\n",
|
||||
" input_field=\"LESSHSPCT\",\n",
|
||||
" renamed_field=HIGH_SCHOOL_FIELD_NAME,\n",
|
||||
" bucket=BUCKET_SOCIOECONOMIC,\n",
|
||||
" ),\n",
|
||||
" DataSet(\n",
|
||||
" input_field=UNEMPLOYED_FIELD_NAME,\n",
|
||||
" renamed_field=UNEMPLOYED_FIELD_NAME,\n",
|
||||
" bucket=BUCKET_SOCIOECONOMIC,\n",
|
||||
" ),\n",
|
||||
" DataSet(\n",
|
||||
" input_field=\"ht_ami\",\n",
|
||||
" renamed_field=\"Housing + Transportation Costs % Income for the Regional Typical Household\",\n",
|
||||
" bucket=BUCKET_SOCIOECONOMIC,\n",
|
||||
" ),\n",
|
||||
"]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "e152a655",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Rename columns:\n",
|
||||
"renaming_dict = {data_set.input_field: data_set.renamed_field for data_set in data_sets}\n",
|
||||
"\n",
|
||||
"df.rename(\n",
|
||||
" columns=renaming_dict,\n",
|
||||
" inplace=True,\n",
|
||||
" errors=\"raise\",\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"columns_to_keep = [data_set.renamed_field for data_set in data_sets]\n",
|
||||
"df = df[columns_to_keep]\n",
|
||||
"\n",
|
||||
"df.head()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "1280cbd4",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Convert all columns to numeric.\n",
|
||||
"for data_set in data_sets:\n",
|
||||
" # Skip GEOID_FIELD_NAME, because it's a string.\n",
|
||||
" if data_set.renamed_field == GEOID_FIELD_NAME:\n",
|
||||
" continue\n",
|
||||
" df[f\"{data_set.renamed_field}\"] = pd.to_numeric(df[data_set.renamed_field])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "27677132",
|
||||
"metadata": {
|
||||
"scrolled": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Calculate percentiles for each data set.\n",
|
||||
"for data_set in data_sets:\n",
|
||||
" df[f\"{data_set.renamed_field}{PERCENTILE_FIELD_SUFFIX}\"] = df[\n",
|
||||
" data_set.renamed_field\n",
|
||||
" ].rank(pct=True)\n",
|
||||
"\n",
|
||||
"df.head()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "f2088013",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Calculate min-max for each data set.\n",
|
||||
"# Math:\n",
|
||||
"# (\n",
|
||||
"# Observed value\n",
|
||||
"# - minimum of all values\n",
|
||||
"# )\n",
|
||||
"# divided by\n",
|
||||
"# (\n",
|
||||
"# Maximum of all values\n",
|
||||
"# - minimum of all values\n",
|
||||
"# )\n",
|
||||
"for data_set in data_sets:\n",
|
||||
" # Skip GEOID_FIELD_NAME, because it's a string.\n",
|
||||
" if data_set.renamed_field == GEOID_FIELD_NAME:\n",
|
||||
" continue\n",
|
||||
"\n",
|
||||
" min_value = df[data_set.renamed_field].min(skipna=True)\n",
|
||||
"\n",
|
||||
" max_value = df[data_set.renamed_field].max(skipna=True)\n",
|
||||
"\n",
|
||||
" print(\n",
|
||||
" f\"For data set {data_set.renamed_field}, the min value is {min_value} and the max value is {max_value}.\"\n",
|
||||
" )\n",
|
||||
"\n",
|
||||
" df[f\"{data_set.renamed_field}{MIN_MAX_FIELD_SUFFIX}\"] = (\n",
|
||||
" df[data_set.renamed_field] - min_value\n",
|
||||
" ) / (max_value - min_value)\n",
|
||||
"\n",
|
||||
"df.head()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "f4eec326",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Graph distributions and correlations.\n",
|
||||
"min_max_fields = [\n",
|
||||
" f\"{data_set.renamed_field}{MIN_MAX_FIELD_SUFFIX}\"\n",
|
||||
" for data_set in data_sets\n",
|
||||
" if data_set.renamed_field != GEOID_FIELD_NAME\n",
|
||||
"]\n",
|
||||
"df.hist(\n",
|
||||
" column=min_max_fields, layout=(len(min_max_fields), 1), figsize=(10, 30), bins=30\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"plt.tight_layout()\n",
|
||||
"\n",
|
||||
"plt.show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "1f7b864f",
|
||||
"metadata": {
|
||||
"scrolled": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Calculate score \"A\" and score \"B\"\n",
|
||||
"df[\"Score A\"] = df[\n",
|
||||
" [\n",
|
||||
" \"Poverty (Less than 200% of federal poverty line) (percentile)\",\n",
|
||||
" \"Percent individuals age 25 or over with less than high school degree (percentile)\",\n",
|
||||
" ]\n",
|
||||
"].mean(axis=1)\n",
|
||||
"df[\"Score B\"] = (\n",
|
||||
" df[\"Poverty (Less than 200% of federal poverty line) (percentile)\"]\n",
|
||||
" * df[\n",
|
||||
" \"Percent individuals age 25 or over with less than high school degree (percentile)\"\n",
|
||||
" ]\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "0c107baf",
|
||||
"metadata": {
|
||||
"scrolled": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Calculate \"CalEnviroScreen for the US\" score\n",
|
||||
"# Average all the percentile values in each bucket into a single score for each of the four buckets.\n",
|
||||
"for bucket in BUCKETS:\n",
|
||||
" fields_in_bucket = [\n",
|
||||
" f\"{data_set.renamed_field}{PERCENTILE_FIELD_SUFFIX}\"\n",
|
||||
" for data_set in data_sets\n",
|
||||
" if data_set.bucket == bucket\n",
|
||||
" ]\n",
|
||||
" df[f\"{bucket}\"] = df[fields_in_bucket].mean(axis=1)\n",
|
||||
"\n",
|
||||
"# Combine the score from the two Exposures and Environmental Effects buckets into a single score called \"Pollution Burden\". The math for this score is: (1.0 * Exposures Score + 0.5 * Environment Effects score) / 1.5.\n",
|
||||
"df[AGGREGATION_POLLUTION] = (\n",
|
||||
" 1.0 * df[f\"{BUCKET_EXPOSURES}\"] + 0.5 * df[f\"{BUCKET_ENVIRONMENTAL}\"]\n",
|
||||
") / 1.5\n",
|
||||
"\n",
|
||||
"# Average the score from the two Sensitive populations and Socioeconomic factors buckets into a single score called \"Population Characteristics\".\n",
|
||||
"df[AGGREGATION_POPULATION] = df[\n",
|
||||
" [f\"{BUCKET_SENSITIVE}\", f\"{BUCKET_SOCIOECONOMIC}\"]\n",
|
||||
"].mean(axis=1)\n",
|
||||
"\n",
|
||||
"# Multiply the \"Pollution Burden\" score and the \"Population Characteristics\" together to produce the cumulative impact score.\n",
|
||||
"df[\"Score C\"] = df[AGGREGATION_POLLUTION] * df[AGGREGATION_POPULATION]\n",
|
||||
"\n",
|
||||
"df.head()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "f70106f5",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Calculate scores D and E.\n",
|
||||
"fields_to_use_in_score_d_and_e = [\n",
|
||||
" UNEMPLOYED_FIELD_NAME,\n",
|
||||
" LINGUISTIC_ISOLATION_FIELD_NAME,\n",
|
||||
" HOUSING_BURDEN_FIELD_NAME,\n",
|
||||
" POVERTY_FIELD_NAME,\n",
|
||||
" HIGH_SCHOOL_FIELD_NAME,\n",
|
||||
"]\n",
|
||||
"\n",
|
||||
"fields_min_max = [\n",
|
||||
" f\"{field}{MIN_MAX_FIELD_SUFFIX}\" for field in fields_to_use_in_score_d_and_e\n",
|
||||
"]\n",
|
||||
"fields_percentile = [\n",
|
||||
" f\"{field}{PERCENTILE_FIELD_SUFFIX}\" for field in fields_to_use_in_score_d_and_e\n",
|
||||
"]\n",
|
||||
"\n",
|
||||
"# Calculate \"Score D\", which uses min-max normalization\n",
|
||||
"# and calculate \"Score E\", which uses percentile normalization for the same fields\n",
|
||||
"df[\"Score D\"] = df[fields_min_max].mean(axis=1)\n",
|
||||
"df[\"Score E\"] = df[fields_percentile].mean(axis=1)\n",
|
||||
"\n",
|
||||
"print(df[\"Score D\"].describe())\n",
|
||||
"print(df[\"Score E\"].describe())"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "a02e5bac",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Graph distributions\n",
|
||||
"df.hist(\n",
|
||||
" column=fields_min_max, layout=(len(fields_min_max), 1), figsize=(10, 30), bins=30\n",
|
||||
")\n",
|
||||
"plt.tight_layout()\n",
|
||||
"plt.show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "a0e608c8",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Calculate correlations\n",
|
||||
"df[fields_min_max].corr()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "729aed12",
|
||||
"metadata": {
|
||||
"scrolled": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Create percentiles for the scores\n",
|
||||
"for score_field in [\"Score A\", \"Score B\", \"Score C\", \"Score D\", \"Score E\"]:\n",
|
||||
" df[f\"{score_field}{PERCENTILE_FIELD_SUFFIX}\"] = df[score_field].rank(pct=True)\n",
|
||||
" df[f\"{score_field} (top 25th percentile)\"] = (\n",
|
||||
" df[f\"{score_field}{PERCENTILE_FIELD_SUFFIX}\"] >= 0.75\n",
|
||||
" )\n",
|
||||
"df.head()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "b3a65af4",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# write nationwide csv\n",
|
||||
"df.to_csv(SCORE_CSV_PATH / f\"usa.csv\", index=False)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "58ddd8b3",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# write per state csvs\n",
|
||||
"for states_fips in get_state_fips_codes(DATA_PATH):\n",
|
||||
" print(f\"Generating data{states_fips} csv\")\n",
|
||||
" df1 = df[df[\"GEOID10\"].str[:2] == states_fips]\n",
|
||||
" # 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)"
|
||||
]
|
||||
}
|
||||
],
|
||||
"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": "3.7.1"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
451
score/poetry.lock
generated
451
score/poetry.lock
generated
|
|
@ -76,6 +76,8 @@ mypy-extensions = ">=0.4.3"
|
|||
pathspec = ">=0.8.1,<1"
|
||||
regex = ">=2020.1.8"
|
||||
toml = ">=0.10.1"
|
||||
typed-ast = {version = ">=1.4.2", markers = "python_version < \"3.8\""}
|
||||
typing-extensions = {version = ">=3.7.4", markers = "python_version < \"3.8\""}
|
||||
|
||||
[package.extras]
|
||||
colorama = ["colorama (>=0.4.3)"]
|
||||
|
|
@ -118,7 +120,7 @@ python-versions = "*"
|
|||
|
||||
[[package]]
|
||||
name = "cffi"
|
||||
version = "1.14.5"
|
||||
version = "1.14.6"
|
||||
description = "Foreign Function Interface for Python calling C code."
|
||||
category = "main"
|
||||
optional = false
|
||||
|
|
@ -145,6 +147,7 @@ python-versions = ">=3.6"
|
|||
|
||||
[package.dependencies]
|
||||
colorama = {version = "*", markers = "platform_system == \"Windows\""}
|
||||
importlib-metadata = {version = "*", markers = "python_version < \"3.8\""}
|
||||
|
||||
[[package]]
|
||||
name = "colorama"
|
||||
|
|
@ -154,6 +157,14 @@ category = "main"
|
|||
optional = false
|
||||
python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
|
||||
|
||||
[[package]]
|
||||
name = "debugpy"
|
||||
version = "1.3.0"
|
||||
description = "An implementation of the Debug Adapter Protocol for Python"
|
||||
category = "main"
|
||||
optional = false
|
||||
python-versions = ">=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*"
|
||||
|
||||
[[package]]
|
||||
name = "decorator"
|
||||
version = "5.0.9"
|
||||
|
|
@ -203,27 +214,45 @@ category = "main"
|
|||
optional = false
|
||||
python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
|
||||
|
||||
[[package]]
|
||||
name = "importlib-metadata"
|
||||
version = "3.10.1"
|
||||
description = "Read metadata from Python packages"
|
||||
category = "main"
|
||||
optional = false
|
||||
python-versions = ">=3.6"
|
||||
|
||||
[package.dependencies]
|
||||
typing-extensions = {version = ">=3.6.4", markers = "python_version < \"3.8\""}
|
||||
zipp = ">=0.5"
|
||||
|
||||
[package.extras]
|
||||
docs = ["sphinx", "jaraco.packaging (>=8.2)", "rst.linker (>=1.9)"]
|
||||
testing = ["pytest (>=4.6)", "pytest-checkdocs (>=2.4)", "pytest-flake8", "pytest-cov", "pytest-enabler (>=1.0.1)", "packaging", "pep517", "pyfakefs", "flufl.flake8", "pytest-black (>=0.3.7)", "pytest-mypy", "importlib-resources (>=1.3)"]
|
||||
|
||||
[[package]]
|
||||
name = "ipykernel"
|
||||
version = "5.5.5"
|
||||
version = "6.0.1"
|
||||
description = "IPython Kernel for Jupyter"
|
||||
category = "main"
|
||||
optional = false
|
||||
python-versions = ">=3.5"
|
||||
python-versions = ">=3.7"
|
||||
|
||||
[package.dependencies]
|
||||
appnope = {version = "*", markers = "platform_system == \"Darwin\""}
|
||||
ipython = ">=5.0.0"
|
||||
debugpy = ">=1.0.0"
|
||||
importlib-metadata = {version = "<4", markers = "python_version < \"3.8.0\""}
|
||||
ipython = ">=7.23.1"
|
||||
jupyter-client = "*"
|
||||
matplotlib-inline = {version = ">=0.1.0,<0.2.0appnope", markers = "platform_system == \"Darwin\""}
|
||||
tornado = ">=4.2"
|
||||
traitlets = ">=4.1.0"
|
||||
|
||||
[package.extras]
|
||||
test = ["pytest (!=5.3.4)", "pytest-cov", "flaky", "nose", "jedi (<=0.17.2)"]
|
||||
test = ["pytest (!=5.3.4)", "pytest-cov", "flaky", "nose", "ipyparallel"]
|
||||
|
||||
[[package]]
|
||||
name = "ipython"
|
||||
version = "7.24.1"
|
||||
version = "7.25.0"
|
||||
description = "IPython: Productive Interactive Computing"
|
||||
category = "main"
|
||||
optional = false
|
||||
|
|
@ -319,6 +348,7 @@ python-versions = "*"
|
|||
|
||||
[package.dependencies]
|
||||
attrs = ">=17.4.0"
|
||||
importlib-metadata = {version = "*", markers = "python_version < \"3.8\""}
|
||||
pyrsistent = ">=0.14.0"
|
||||
six = ">=1.11.0"
|
||||
|
||||
|
|
@ -547,6 +577,7 @@ python-versions = ">=3.5"
|
|||
[package.dependencies]
|
||||
mypy-extensions = ">=0.4.3,<0.5.0"
|
||||
toml = "*"
|
||||
typed-ast = {version = ">=1.4.0,<1.5.0", markers = "python_version < \"3.8\""}
|
||||
typing-extensions = ">=3.7.4"
|
||||
|
||||
[package.extras]
|
||||
|
|
@ -676,30 +707,30 @@ python-versions = ">=3.7"
|
|||
|
||||
[[package]]
|
||||
name = "packaging"
|
||||
version = "20.9"
|
||||
version = "21.0"
|
||||
description = "Core utilities for Python packages"
|
||||
category = "main"
|
||||
optional = false
|
||||
python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
|
||||
python-versions = ">=3.6"
|
||||
|
||||
[package.dependencies]
|
||||
pyparsing = ">=2.0.2"
|
||||
|
||||
[[package]]
|
||||
name = "pandas"
|
||||
version = "1.2.5"
|
||||
version = "1.3.0"
|
||||
description = "Powerful data structures for data analysis, time series, and statistics"
|
||||
category = "main"
|
||||
optional = false
|
||||
python-versions = ">=3.7.1"
|
||||
|
||||
[package.dependencies]
|
||||
numpy = ">=1.16.5"
|
||||
numpy = ">=1.17.3"
|
||||
python-dateutil = ">=2.7.3"
|
||||
pytz = ">=2017.3"
|
||||
|
||||
[package.extras]
|
||||
test = ["pytest (>=5.0.1)", "pytest-xdist", "hypothesis (>=3.58)"]
|
||||
test = ["hypothesis (>=3.58)", "pytest (>=6.0)", "pytest-xdist"]
|
||||
|
||||
[[package]]
|
||||
name = "pandocfilters"
|
||||
|
|
@ -812,11 +843,11 @@ python-versions = ">=2.6, !=3.0.*, !=3.1.*, !=3.2.*"
|
|||
|
||||
[[package]]
|
||||
name = "pyrsistent"
|
||||
version = "0.17.3"
|
||||
version = "0.18.0"
|
||||
description = "Persistent/Functional/Immutable data structures"
|
||||
category = "main"
|
||||
optional = false
|
||||
python-versions = ">=3.5"
|
||||
python-versions = ">=3.6"
|
||||
|
||||
[[package]]
|
||||
name = "python-dateutil"
|
||||
|
|
@ -847,7 +878,7 @@ python-versions = "*"
|
|||
|
||||
[[package]]
|
||||
name = "pywinpty"
|
||||
version = "1.1.2"
|
||||
version = "1.1.3"
|
||||
description = "Pseudo terminal support for Windows from Python."
|
||||
category = "main"
|
||||
optional = false
|
||||
|
|
@ -875,7 +906,7 @@ py = {version = "*", markers = "implementation_name == \"pypy\""}
|
|||
|
||||
[[package]]
|
||||
name = "qtconsole"
|
||||
version = "5.1.0"
|
||||
version = "5.1.1"
|
||||
description = "Jupyter Qt console"
|
||||
category = "main"
|
||||
optional = false
|
||||
|
|
@ -905,7 +936,7 @@ python-versions = "*"
|
|||
|
||||
[[package]]
|
||||
name = "regex"
|
||||
version = "2021.4.4"
|
||||
version = "2021.7.6"
|
||||
description = "Alternative regular expression module, to replace re."
|
||||
category = "dev"
|
||||
optional = false
|
||||
|
|
@ -1005,6 +1036,14 @@ ipython-genutils = "*"
|
|||
[package.extras]
|
||||
test = ["pytest"]
|
||||
|
||||
[[package]]
|
||||
name = "typed-ast"
|
||||
version = "1.4.3"
|
||||
description = "a fork of Python 2 and 3 ast modules with type comment support"
|
||||
category = "dev"
|
||||
optional = false
|
||||
python-versions = "*"
|
||||
|
||||
[[package]]
|
||||
name = "types-requests"
|
||||
version = "2.25.0"
|
||||
|
|
@ -1017,13 +1056,13 @@ python-versions = "*"
|
|||
name = "typing-extensions"
|
||||
version = "3.10.0.0"
|
||||
description = "Backported and Experimental Type Hints for Python 3.5+"
|
||||
category = "dev"
|
||||
category = "main"
|
||||
optional = false
|
||||
python-versions = "*"
|
||||
|
||||
[[package]]
|
||||
name = "urllib3"
|
||||
version = "1.26.5"
|
||||
version = "1.26.6"
|
||||
description = "HTTP library with thread-safe connection pooling, file post, and more."
|
||||
category = "main"
|
||||
optional = false
|
||||
|
|
@ -1061,10 +1100,22 @@ python-versions = "*"
|
|||
[package.dependencies]
|
||||
notebook = ">=4.4.1"
|
||||
|
||||
[[package]]
|
||||
name = "zipp"
|
||||
version = "3.5.0"
|
||||
description = "Backport of pathlib-compatible object wrapper for zip files"
|
||||
category = "main"
|
||||
optional = false
|
||||
python-versions = ">=3.6"
|
||||
|
||||
[package.extras]
|
||||
docs = ["sphinx", "jaraco.packaging (>=8.2)", "rst.linker (>=1.9)"]
|
||||
testing = ["pytest (>=4.6)", "pytest-checkdocs (>=2.4)", "pytest-flake8", "pytest-cov", "pytest-enabler (>=1.0.1)", "jaraco.itertools", "func-timeout", "pytest-black (>=0.3.7)", "pytest-mypy"]
|
||||
|
||||
[metadata]
|
||||
lock-version = "1.1"
|
||||
python-versions = "^3.9"
|
||||
content-hash = "93144f580490e6f83370e233e8259e884973752d42460e632eff508240339db8"
|
||||
python-versions = "^3.7.1"
|
||||
content-hash = "52ad5ba35e7d93fa44ce1b0040073618f990a30bdd90b8828e7b3270bbaf8385"
|
||||
|
||||
[metadata.files]
|
||||
appdirs = [
|
||||
|
|
@ -1127,55 +1178,46 @@ certifi = [
|
|||
{file = "certifi-2021.5.30.tar.gz", hash = "sha256:2bbf76fd432960138b3ef6dda3dde0544f27cbf8546c458e60baf371917ba9ee"},
|
||||
]
|
||||
cffi = [
|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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]
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{file = "decorator-5.0.9-py3-none-any.whl", hash = "sha256:6e5c199c16f7a9f0e3a61a4a54b3d27e7dad0dbdde92b944426cb20914376323"},
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{file = "decorator-5.0.9.tar.gz", hash = "sha256:72ecfba4320a893c53f9706bebb2d55c270c1e51a28789361aa93e4a21319ed5"},
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|
|
@ -1209,13 +1309,17 @@ idna = [
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{file = "ipython-7.25.0.tar.gz", hash = "sha256:54bbd1fe3882457aaf28ae060a5ccdef97f212a741754e420028d4ec5c2291dc"},
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ipython-genutils = [
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@ -1452,28 +1556,29 @@ numpy = [
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{file = "packaging-21.0.tar.gz", hash = "sha256:7dc96269f53a4ccec5c0670940a4281106dd0bb343f47b7471f779df49c2fbe7"},
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pandas = [
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{file = "pandas-1.2.5.tar.gz", hash = "sha256:14abb8ea73fce8aebbb1fb44bec809163f1c55241bcc1db91c2c780e97265033"},
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{file = "pandas-1.3.0-cp38-cp38-win32.whl", hash = "sha256:98efc2d4983d5bb47662fe2d97b2c81b91566cb08b266490918b9c7d74a5ef64"},
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{file = "pandas-1.3.0-cp38-cp38-win_amd64.whl", hash = "sha256:e6b75091fa54a53db3927b4d1bc997c23c5ba6f87acdfe1ee5a92c38c6b2ed6a"},
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{file = "pandas-1.3.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:798675317d0e4863a92a9a6bc5bd2490b5f6fef8c17b95f29e2e33f28bef9eca"},
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|
||||
{file = "pandas-1.3.0-cp39-cp39-win_amd64.whl", hash = "sha256:b10d7910ae9d7920a5ff7816d794d99acbc361f7b16a0f017d4fa83ced8cb55e"},
|
||||
{file = "pandas-1.3.0.tar.gz", hash = "sha256:c554e6c9cf2d5ea1aba5979cc837b3649539ced0e18ece186f055450c86622e2"},
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||||
]
|
||||
pandocfilters = [
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||||
{file = "pandocfilters-1.4.3.tar.gz", hash = "sha256:bc63fbb50534b4b1f8ebe1860889289e8af94a23bff7445259592df25a3906eb"},
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|
|
@ -1523,7 +1628,27 @@ pyparsing = [
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pyrsistent = [
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|
||||
{file = "pyrsistent-0.18.0-cp37-cp37m-win_amd64.whl", hash = "sha256:097b96f129dd36a8c9e33594e7ebb151b1515eb52cceb08474c10a5479e799f2"},
|
||||
{file = "pyrsistent-0.18.0-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:772e94c2c6864f2cd2ffbe58bb3bdefbe2a32afa0acb1a77e472aac831f83427"},
|
||||
{file = "pyrsistent-0.18.0-cp38-cp38-manylinux1_i686.whl", hash = "sha256:c1a9ff320fa699337e05edcaae79ef8c2880b52720bc031b219e5b5008ebbdef"},
|
||||
{file = "pyrsistent-0.18.0-cp38-cp38-manylinux1_x86_64.whl", hash = "sha256:cd3caef37a415fd0dae6148a1b6957a8c5f275a62cca02e18474608cb263640c"},
|
||||
{file = "pyrsistent-0.18.0-cp38-cp38-win32.whl", hash = "sha256:e79d94ca58fcafef6395f6352383fa1a76922268fa02caa2272fff501c2fdc78"},
|
||||
{file = "pyrsistent-0.18.0-cp38-cp38-win_amd64.whl", hash = "sha256:a0c772d791c38bbc77be659af29bb14c38ced151433592e326361610250c605b"},
|
||||
{file = "pyrsistent-0.18.0-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:d5ec194c9c573aafaceebf05fc400656722793dac57f254cd4741f3c27ae57b4"},
|
||||
{file = "pyrsistent-0.18.0-cp39-cp39-manylinux1_i686.whl", hash = "sha256:6b5eed00e597b5b5773b4ca30bd48a5774ef1e96f2a45d105db5b4ebb4bca680"},
|
||||
{file = "pyrsistent-0.18.0-cp39-cp39-manylinux1_x86_64.whl", hash = "sha256:48578680353f41dca1ca3dc48629fb77dfc745128b56fc01096b2530c13fd426"},
|
||||
{file = "pyrsistent-0.18.0-cp39-cp39-win32.whl", hash = "sha256:f3ef98d7b76da5eb19c37fda834d50262ff9167c65658d1d8f974d2e4d90676b"},
|
||||
{file = "pyrsistent-0.18.0-cp39-cp39-win_amd64.whl", hash = "sha256:404e1f1d254d314d55adb8d87f4f465c8693d6f902f67eb6ef5b4526dc58e6ea"},
|
||||
{file = "pyrsistent-0.18.0.tar.gz", hash = "sha256:773c781216f8c2900b42a7b638d5b517bb134ae1acbebe4d1e8f1f41ea60eb4b"},
|
||||
]
|
||||
python-dateutil = [
|
||||
{file = "python-dateutil-2.8.1.tar.gz", hash = "sha256:73ebfe9dbf22e832286dafa60473e4cd239f8592f699aa5adaf10050e6e1823c"},
|
||||
|
|
@ -1546,11 +1671,11 @@ pywin32 = [
|
|||
{file = "pywin32-301-cp39-cp39-win_amd64.whl", hash = "sha256:87604a4087434cd814ad8973bd47d6524bd1fa9e971ce428e76b62a5e0860fdf"},
|
||||
]
|
||||
pywinpty = [
|
||||
{file = "pywinpty-1.1.2-cp36-none-win_amd64.whl", hash = "sha256:7bb1b8380bc71bf04a983e803746b1ea7b8a91765723a82e108df81538b258c1"},
|
||||
{file = "pywinpty-1.1.2-cp37-none-win_amd64.whl", hash = "sha256:951f1b988c2407e9bd0c5c9b199f588673769abf0c8cb4724a01bc0666b97b0a"},
|
||||
{file = "pywinpty-1.1.2-cp38-none-win_amd64.whl", hash = "sha256:b3a38a0afb63b639ca4f78f67f4f8caa78ca470bd71b146480ef37d86cc99823"},
|
||||
{file = "pywinpty-1.1.2-cp39-none-win_amd64.whl", hash = "sha256:eac78a3ff69ce443ad9f67620bc60469f6354b18388570c63af6fc643beae498"},
|
||||
{file = "pywinpty-1.1.2.tar.gz", hash = "sha256:f1718838e1c7c700e5f0b79d5d5e05243ff583313ff88e47bb94318ba303e565"},
|
||||
{file = "pywinpty-1.1.3-cp36-none-win_amd64.whl", hash = "sha256:81dc6f16d917b756e06fc58943e9750d59dbefc0ffd2086871d3fa5f33824446"},
|
||||
{file = "pywinpty-1.1.3-cp37-none-win_amd64.whl", hash = "sha256:54557887e712ea3215ab0d9f089ed55a6cc8d826cd5d1e340d75300654c9663f"},
|
||||
{file = "pywinpty-1.1.3-cp38-none-win_amd64.whl", hash = "sha256:f5e25197397f1fef0362caf3eb89f25441827a1e48bf15827c27021592fd2160"},
|
||||
{file = "pywinpty-1.1.3-cp39-none-win_amd64.whl", hash = "sha256:b767276224f86b7560eb9173ba7956758cafcdfab97bb33837d42d2a0f1dbf67"},
|
||||
{file = "pywinpty-1.1.3.tar.gz", hash = "sha256:3a1d57b338390333812a5eed31c93c7d8ba82b131078063703e731946d90c9f2"},
|
||||
]
|
||||
pyyaml = [
|
||||
{file = "PyYAML-5.4.1-cp27-cp27m-macosx_10_9_x86_64.whl", hash = "sha256:3b2b1824fe7112845700f815ff6a489360226a5609b96ec2190a45e62a9fc922"},
|
||||
|
|
@ -1618,55 +1743,55 @@ pyzmq = [
|
|||
{file = "pyzmq-22.1.0.tar.gz", hash = "sha256:7040d6dd85ea65703904d023d7f57fab793d7ffee9ba9e14f3b897f34ff2415d"},
|
||||
]
|
||||
qtconsole = [
|
||||
{file = "qtconsole-5.1.0-py3-none-any.whl", hash = "sha256:3a2adecc43ff201a08972fb2179df22e7b3a08d71b9ed680f46ad1bfd4fb9132"},
|
||||
{file = "qtconsole-5.1.0.tar.gz", hash = "sha256:12c734494901658787339dea9bbd82f3dc0d5e394071377a1c77b4a0954d7d8b"},
|
||||
{file = "qtconsole-5.1.1-py3-none-any.whl", hash = "sha256:73994105b0369bb99f4164df4a131010f3c7b33a7b5169c37366358d8744675b"},
|
||||
{file = "qtconsole-5.1.1.tar.gz", hash = "sha256:bbc34bca14f65535afcb401bc74b752bac955e5313001ba640383f7e5857dc49"},
|
||||
]
|
||||
qtpy = [
|
||||
{file = "QtPy-1.9.0-py2.py3-none-any.whl", hash = "sha256:fa0b8363b363e89b2a6f49eddc162a04c0699ae95e109a6be3bb145a913190ea"},
|
||||
{file = "QtPy-1.9.0.tar.gz", hash = "sha256:2db72c44b55d0fe1407be8fba35c838ad0d6d3bb81f23007886dc1fc0f459c8d"},
|
||||
]
|
||||
regex = [
|
||||
{file = "regex-2021.4.4-cp36-cp36m-macosx_10_9_x86_64.whl", hash = "sha256:619d71c59a78b84d7f18891fe914446d07edd48dc8328c8e149cbe0929b4e000"},
|
||||
{file = "regex-2021.4.4-cp36-cp36m-manylinux1_i686.whl", hash = "sha256:47bf5bf60cf04d72bf6055ae5927a0bd9016096bf3d742fa50d9bf9f45aa0711"},
|
||||
{file = "regex-2021.4.4-cp36-cp36m-manylinux1_x86_64.whl", hash = "sha256:281d2fd05555079448537fe108d79eb031b403dac622621c78944c235f3fcf11"},
|
||||
{file = "regex-2021.4.4-cp36-cp36m-manylinux2010_i686.whl", hash = "sha256:bd28bc2e3a772acbb07787c6308e00d9626ff89e3bfcdebe87fa5afbfdedf968"},
|
||||
{file = "regex-2021.4.4-cp36-cp36m-manylinux2010_x86_64.whl", hash = "sha256:7c2a1af393fcc09e898beba5dd59196edaa3116191cc7257f9224beaed3e1aa0"},
|
||||
{file = "regex-2021.4.4-cp36-cp36m-manylinux2014_aarch64.whl", hash = "sha256:c38c71df845e2aabb7fb0b920d11a1b5ac8526005e533a8920aea97efb8ec6a4"},
|
||||
{file = "regex-2021.4.4-cp36-cp36m-manylinux2014_i686.whl", hash = "sha256:96fcd1888ab4d03adfc9303a7b3c0bd78c5412b2bfbe76db5b56d9eae004907a"},
|
||||
{file = "regex-2021.4.4-cp36-cp36m-manylinux2014_x86_64.whl", hash = "sha256:ade17eb5d643b7fead300a1641e9f45401c98eee23763e9ed66a43f92f20b4a7"},
|
||||
{file = "regex-2021.4.4-cp36-cp36m-win32.whl", hash = "sha256:e8e5b509d5c2ff12f8418006d5a90e9436766133b564db0abaec92fd27fcee29"},
|
||||
{file = "regex-2021.4.4-cp36-cp36m-win_amd64.whl", hash = "sha256:11d773d75fa650cd36f68d7ca936e3c7afaae41b863b8c387a22aaa78d3c5c79"},
|
||||
{file = "regex-2021.4.4-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:d3029c340cfbb3ac0a71798100ccc13b97dddf373a4ae56b6a72cf70dfd53bc8"},
|
||||
{file = "regex-2021.4.4-cp37-cp37m-manylinux1_i686.whl", hash = "sha256:18c071c3eb09c30a264879f0d310d37fe5d3a3111662438889ae2eb6fc570c31"},
|
||||
{file = "regex-2021.4.4-cp37-cp37m-manylinux1_x86_64.whl", hash = "sha256:4c557a7b470908b1712fe27fb1ef20772b78079808c87d20a90d051660b1d69a"},
|
||||
{file = "regex-2021.4.4-cp37-cp37m-manylinux2010_i686.whl", hash = "sha256:01afaf2ec48e196ba91b37451aa353cb7eda77efe518e481707e0515025f0cd5"},
|
||||
{file = "regex-2021.4.4-cp37-cp37m-manylinux2010_x86_64.whl", hash = "sha256:3a9cd17e6e5c7eb328517969e0cb0c3d31fd329298dd0c04af99ebf42e904f82"},
|
||||
{file = "regex-2021.4.4-cp37-cp37m-manylinux2014_aarch64.whl", hash = "sha256:90f11ff637fe8798933fb29f5ae1148c978cccb0452005bf4c69e13db951e765"},
|
||||
{file = "regex-2021.4.4-cp37-cp37m-manylinux2014_i686.whl", hash = "sha256:919859aa909429fb5aa9cf8807f6045592c85ef56fdd30a9a3747e513db2536e"},
|
||||
{file = "regex-2021.4.4-cp37-cp37m-manylinux2014_x86_64.whl", hash = "sha256:339456e7d8c06dd36a22e451d58ef72cef293112b559010db3d054d5560ef439"},
|
||||
{file = "regex-2021.4.4-cp37-cp37m-win32.whl", hash = "sha256:67bdb9702427ceddc6ef3dc382455e90f785af4c13d495f9626861763ee13f9d"},
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||||
{file = "regex-2021.4.4-cp37-cp37m-win_amd64.whl", hash = "sha256:32e65442138b7b76dd8173ffa2cf67356b7bc1768851dded39a7a13bf9223da3"},
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||||
{file = "regex-2021.4.4-cp38-cp38-manylinux2010_i686.whl", hash = "sha256:741a9647fcf2e45f3a1cf0e24f5e17febf3efe8d4ba1281dcc3aa0459ef424dc"},
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||||
{file = "regex-2021.4.4-cp38-cp38-manylinux2014_aarch64.whl", hash = "sha256:e512d8ef5ad7b898cdb2d8ee1cb09a8339e4f8be706d27eaa180c2f177248a10"},
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{file = "regex-2021.4.4-cp38-cp38-manylinux2014_i686.whl", hash = "sha256:980d7be47c84979d9136328d882f67ec5e50008681d94ecc8afa8a65ed1f4a6f"},
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||||
{file = "regex-2021.4.4-cp38-cp38-manylinux2014_x86_64.whl", hash = "sha256:ce15b6d103daff8e9fee13cf7f0add05245a05d866e73926c358e871221eae87"},
|
||||
{file = "regex-2021.4.4-cp38-cp38-win32.whl", hash = "sha256:a91aa8619b23b79bcbeb37abe286f2f408d2f2d6f29a17237afda55bb54e7aac"},
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||||
{file = "regex-2021.4.4-cp38-cp38-win_amd64.whl", hash = "sha256:c0502c0fadef0d23b128605d69b58edb2c681c25d44574fc673b0e52dce71ee2"},
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||||
{file = "regex-2021.4.4-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:598585c9f0af8374c28edd609eb291b5726d7cbce16be6a8b95aa074d252ee17"},
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||||
{file = "regex-2021.4.4-cp39-cp39-manylinux1_i686.whl", hash = "sha256:ee54ff27bf0afaf4c3b3a62bcd016c12c3fdb4ec4f413391a90bd38bc3624605"},
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||||
{file = "regex-2021.4.4-cp39-cp39-manylinux1_x86_64.whl", hash = "sha256:7d9884d86dd4dd489e981d94a65cd30d6f07203d90e98f6f657f05170f6324c9"},
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||||
{file = "regex-2021.4.4-cp39-cp39-manylinux2010_i686.whl", hash = "sha256:bf5824bfac591ddb2c1f0a5f4ab72da28994548c708d2191e3b87dd207eb3ad7"},
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||||
{file = "regex-2021.4.4-cp39-cp39-manylinux2010_x86_64.whl", hash = "sha256:563085e55b0d4fb8f746f6a335893bda5c2cef43b2f0258fe1020ab1dd874df8"},
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||||
{file = "regex-2021.4.4-cp39-cp39-manylinux2014_aarch64.whl", hash = "sha256:b9c3db21af35e3b3c05764461b262d6f05bbca08a71a7849fd79d47ba7bc33ed"},
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||||
{file = "regex-2021.4.4-cp39-cp39-manylinux2014_i686.whl", hash = "sha256:3916d08be28a1149fb97f7728fca1f7c15d309a9f9682d89d79db75d5e52091c"},
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||||
{file = "regex-2021.4.4-cp39-cp39-manylinux2014_x86_64.whl", hash = "sha256:fd45ff9293d9274c5008a2054ecef86a9bfe819a67c7be1afb65e69b405b3042"},
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||||
{file = "regex-2021.4.4-cp39-cp39-win32.whl", hash = "sha256:fa4537fb4a98fe8fde99626e4681cc644bdcf2a795038533f9f711513a862ae6"},
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||||
{file = "regex-2021.4.4-cp39-cp39-win_amd64.whl", hash = "sha256:97f29f57d5b84e73fbaf99ab3e26134e6687348e95ef6b48cfd2c06807005a07"},
|
||||
{file = "regex-2021.4.4.tar.gz", hash = "sha256:52ba3d3f9b942c49d7e4bc105bb28551c44065f139a65062ab7912bef10c9afb"},
|
||||
{file = "regex-2021.7.6-cp36-cp36m-macosx_10_9_x86_64.whl", hash = "sha256:e6a1e5ca97d411a461041d057348e578dc344ecd2add3555aedba3b408c9f874"},
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||||
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|
||||
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||||
{file = "regex-2021.7.6-cp36-cp36m-manylinux2010_i686.whl", hash = "sha256:ed693137a9187052fc46eedfafdcb74e09917166362af4cc4fddc3b31560e93d"},
|
||||
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||||
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||||
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||||
{file = "regex-2021.7.6-cp36-cp36m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e5983c19d0beb6af88cb4d47afb92d96751fb3fa1784d8785b1cdf14c6519407"},
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||||
{file = "regex-2021.7.6-cp36-cp36m-win32.whl", hash = "sha256:c92831dac113a6e0ab28bc98f33781383fe294df1a2c3dfd1e850114da35fd5b"},
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||||
{file = "regex-2021.7.6-cp36-cp36m-win_amd64.whl", hash = "sha256:791aa1b300e5b6e5d597c37c346fb4d66422178566bbb426dd87eaae475053fb"},
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||||
{file = "regex-2021.7.6-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:59506c6e8bd9306cd8a41511e32d16d5d1194110b8cfe5a11d102d8b63cf945d"},
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{file = "regex-2021.7.6-cp37-cp37m-manylinux2010_x86_64.whl", hash = "sha256:db2b7df831c3187a37f3bb80ec095f249fa276dbe09abd3d35297fc250385694"},
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||||
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||||
{file = "regex-2021.7.6-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:2ddeabc7652024803666ea09f32dd1ed40a0579b6fbb2a213eba590683025895"},
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||||
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|
||||
{file = "regex-2021.7.6-cp38-cp38-manylinux1_x86_64.whl", hash = "sha256:cbe23b323988a04c3e5b0c387fe3f8f363bf06c0680daf775875d979e376bd26"},
|
||||
{file = "regex-2021.7.6-cp38-cp38-manylinux2010_i686.whl", hash = "sha256:0eb2c6e0fcec5e0f1d3bcc1133556563222a2ffd2211945d7b1480c1b1a42a6f"},
|
||||
{file = "regex-2021.7.6-cp38-cp38-manylinux2010_x86_64.whl", hash = "sha256:1c78780bf46d620ff4fff40728f98b8afd8b8e35c3efd638c7df67be2d5cddbf"},
|
||||
{file = "regex-2021.7.6-cp38-cp38-manylinux2014_i686.whl", hash = "sha256:bc84fb254a875a9f66616ed4538542fb7965db6356f3df571d783f7c8d256edd"},
|
||||
{file = "regex-2021.7.6-cp38-cp38-manylinux2014_x86_64.whl", hash = "sha256:598c0a79b4b851b922f504f9f39a863d83ebdfff787261a5ed061c21e67dd761"},
|
||||
{file = "regex-2021.7.6-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:875c355360d0f8d3d827e462b29ea7682bf52327d500a4f837e934e9e4656068"},
|
||||
{file = "regex-2021.7.6-cp38-cp38-win32.whl", hash = "sha256:e586f448df2bbc37dfadccdb7ccd125c62b4348cb90c10840d695592aa1b29e0"},
|
||||
{file = "regex-2021.7.6-cp38-cp38-win_amd64.whl", hash = "sha256:2fe5e71e11a54e3355fa272137d521a40aace5d937d08b494bed4529964c19c4"},
|
||||
{file = "regex-2021.7.6-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:6110bab7eab6566492618540c70edd4d2a18f40ca1d51d704f1d81c52d245026"},
|
||||
{file = "regex-2021.7.6-cp39-cp39-manylinux1_i686.whl", hash = "sha256:4f64fc59fd5b10557f6cd0937e1597af022ad9b27d454e182485f1db3008f417"},
|
||||
{file = "regex-2021.7.6-cp39-cp39-manylinux1_x86_64.whl", hash = "sha256:89e5528803566af4df368df2d6f503c84fbfb8249e6631c7b025fe23e6bd0cde"},
|
||||
{file = "regex-2021.7.6-cp39-cp39-manylinux2010_i686.whl", hash = "sha256:2366fe0479ca0e9afa534174faa2beae87847d208d457d200183f28c74eaea59"},
|
||||
{file = "regex-2021.7.6-cp39-cp39-manylinux2010_x86_64.whl", hash = "sha256:f9392a4555f3e4cb45310a65b403d86b589adc773898c25a39184b1ba4db8985"},
|
||||
{file = "regex-2021.7.6-cp39-cp39-manylinux2014_i686.whl", hash = "sha256:2bceeb491b38225b1fee4517107b8491ba54fba77cf22a12e996d96a3c55613d"},
|
||||
{file = "regex-2021.7.6-cp39-cp39-manylinux2014_x86_64.whl", hash = "sha256:f98dc35ab9a749276f1a4a38ab3e0e2ba1662ce710f6530f5b0a6656f1c32b58"},
|
||||
{file = "regex-2021.7.6-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:319eb2a8d0888fa6f1d9177705f341bc9455a2c8aca130016e52c7fe8d6c37a3"},
|
||||
{file = "regex-2021.7.6-cp39-cp39-win32.whl", hash = "sha256:eaf58b9e30e0e546cdc3ac06cf9165a1ca5b3de8221e9df679416ca667972035"},
|
||||
{file = "regex-2021.7.6-cp39-cp39-win_amd64.whl", hash = "sha256:4c9c3155fe74269f61e27617529b7f09552fbb12e44b1189cebbdb24294e6e1c"},
|
||||
{file = "regex-2021.7.6.tar.gz", hash = "sha256:8394e266005f2d8c6f0bc6780001f7afa3ef81a7a2111fa35058ded6fce79e4d"},
|
||||
]
|
||||
requests = [
|
||||
{file = "requests-2.25.1-py2.py3-none-any.whl", hash = "sha256:c210084e36a42ae6b9219e00e48287def368a26d03a048ddad7bfee44f75871e"},
|
||||
|
|
@ -1739,6 +1864,38 @@ traitlets = [
|
|||
{file = "traitlets-5.0.5-py3-none-any.whl", hash = "sha256:69ff3f9d5351f31a7ad80443c2674b7099df13cc41fc5fa6e2f6d3b0330b0426"},
|
||||
{file = "traitlets-5.0.5.tar.gz", hash = "sha256:178f4ce988f69189f7e523337a3e11d91c786ded9360174a3d9ca83e79bc5396"},
|
||||
]
|
||||
typed-ast = [
|
||||
{file = "typed_ast-1.4.3-cp35-cp35m-manylinux1_i686.whl", hash = "sha256:2068531575a125b87a41802130fa7e29f26c09a2833fea68d9a40cf33902eba6"},
|
||||
{file = "typed_ast-1.4.3-cp35-cp35m-manylinux1_x86_64.whl", hash = "sha256:c907f561b1e83e93fad565bac5ba9c22d96a54e7ea0267c708bffe863cbe4075"},
|
||||
{file = "typed_ast-1.4.3-cp35-cp35m-manylinux2014_aarch64.whl", hash = "sha256:1b3ead4a96c9101bef08f9f7d1217c096f31667617b58de957f690c92378b528"},
|
||||
{file = "typed_ast-1.4.3-cp35-cp35m-win32.whl", hash = "sha256:dde816ca9dac1d9c01dd504ea5967821606f02e510438120091b84e852367428"},
|
||||
{file = "typed_ast-1.4.3-cp35-cp35m-win_amd64.whl", hash = "sha256:777a26c84bea6cd934422ac2e3b78863a37017618b6e5c08f92ef69853e765d3"},
|
||||
{file = "typed_ast-1.4.3-cp36-cp36m-macosx_10_9_x86_64.whl", hash = "sha256:f8afcf15cc511ada719a88e013cec87c11aff7b91f019295eb4530f96fe5ef2f"},
|
||||
{file = "typed_ast-1.4.3-cp36-cp36m-manylinux1_i686.whl", hash = "sha256:52b1eb8c83f178ab787f3a4283f68258525f8d70f778a2f6dd54d3b5e5fb4341"},
|
||||
{file = "typed_ast-1.4.3-cp36-cp36m-manylinux1_x86_64.whl", hash = "sha256:01ae5f73431d21eead5015997ab41afa53aa1fbe252f9da060be5dad2c730ace"},
|
||||
{file = "typed_ast-1.4.3-cp36-cp36m-manylinux2014_aarch64.whl", hash = "sha256:c190f0899e9f9f8b6b7863debfb739abcb21a5c054f911ca3596d12b8a4c4c7f"},
|
||||
{file = "typed_ast-1.4.3-cp36-cp36m-win32.whl", hash = "sha256:398e44cd480f4d2b7ee8d98385ca104e35c81525dd98c519acff1b79bdaac363"},
|
||||
{file = "typed_ast-1.4.3-cp36-cp36m-win_amd64.whl", hash = "sha256:bff6ad71c81b3bba8fa35f0f1921fb24ff4476235a6e94a26ada2e54370e6da7"},
|
||||
{file = "typed_ast-1.4.3-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:0fb71b8c643187d7492c1f8352f2c15b4c4af3f6338f21681d3681b3dc31a266"},
|
||||
{file = "typed_ast-1.4.3-cp37-cp37m-manylinux1_i686.whl", hash = "sha256:760ad187b1041a154f0e4d0f6aae3e40fdb51d6de16e5c99aedadd9246450e9e"},
|
||||
{file = "typed_ast-1.4.3-cp37-cp37m-manylinux1_x86_64.whl", hash = "sha256:5feca99c17af94057417d744607b82dd0a664fd5e4ca98061480fd8b14b18d04"},
|
||||
{file = "typed_ast-1.4.3-cp37-cp37m-manylinux2014_aarch64.whl", hash = "sha256:95431a26309a21874005845c21118c83991c63ea800dd44843e42a916aec5899"},
|
||||
{file = "typed_ast-1.4.3-cp37-cp37m-win32.whl", hash = "sha256:aee0c1256be6c07bd3e1263ff920c325b59849dc95392a05f258bb9b259cf39c"},
|
||||
{file = "typed_ast-1.4.3-cp37-cp37m-win_amd64.whl", hash = "sha256:9ad2c92ec681e02baf81fdfa056fe0d818645efa9af1f1cd5fd6f1bd2bdfd805"},
|
||||
{file = "typed_ast-1.4.3-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:b36b4f3920103a25e1d5d024d155c504080959582b928e91cb608a65c3a49e1a"},
|
||||
{file = "typed_ast-1.4.3-cp38-cp38-manylinux1_i686.whl", hash = "sha256:067a74454df670dcaa4e59349a2e5c81e567d8d65458d480a5b3dfecec08c5ff"},
|
||||
{file = "typed_ast-1.4.3-cp38-cp38-manylinux1_x86_64.whl", hash = "sha256:7538e495704e2ccda9b234b82423a4038f324f3a10c43bc088a1636180f11a41"},
|
||||
{file = "typed_ast-1.4.3-cp38-cp38-manylinux2014_aarch64.whl", hash = "sha256:af3d4a73793725138d6b334d9d247ce7e5f084d96284ed23f22ee626a7b88e39"},
|
||||
{file = "typed_ast-1.4.3-cp38-cp38-win32.whl", hash = "sha256:f2362f3cb0f3172c42938946dbc5b7843c2a28aec307c49100c8b38764eb6927"},
|
||||
{file = "typed_ast-1.4.3-cp38-cp38-win_amd64.whl", hash = "sha256:dd4a21253f42b8d2b48410cb31fe501d32f8b9fbeb1f55063ad102fe9c425e40"},
|
||||
{file = "typed_ast-1.4.3-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:f328adcfebed9f11301eaedfa48e15bdece9b519fb27e6a8c01aa52a17ec31b3"},
|
||||
{file = "typed_ast-1.4.3-cp39-cp39-manylinux1_i686.whl", hash = "sha256:2c726c276d09fc5c414693a2de063f521052d9ea7c240ce553316f70656c84d4"},
|
||||
{file = "typed_ast-1.4.3-cp39-cp39-manylinux1_x86_64.whl", hash = "sha256:cae53c389825d3b46fb37538441f75d6aecc4174f615d048321b716df2757fb0"},
|
||||
{file = "typed_ast-1.4.3-cp39-cp39-manylinux2014_aarch64.whl", hash = "sha256:b9574c6f03f685070d859e75c7f9eeca02d6933273b5e69572e5ff9d5e3931c3"},
|
||||
{file = "typed_ast-1.4.3-cp39-cp39-win32.whl", hash = "sha256:209596a4ec71d990d71d5e0d312ac935d86930e6eecff6ccc7007fe54d703808"},
|
||||
{file = "typed_ast-1.4.3-cp39-cp39-win_amd64.whl", hash = "sha256:9c6d1a54552b5330bc657b7ef0eae25d00ba7ffe85d9ea8ae6540d2197a3788c"},
|
||||
{file = "typed_ast-1.4.3.tar.gz", hash = "sha256:fb1bbeac803adea29cedd70781399c99138358c26d05fcbd23c13016b7f5ec65"},
|
||||
]
|
||||
types-requests = [
|
||||
{file = "types-requests-2.25.0.tar.gz", hash = "sha256:ee0d0c507210141b7d5b8639cc43eaa726084178775db2a5fb06fbf85c185808"},
|
||||
{file = "types_requests-2.25.0-py3-none-any.whl", hash = "sha256:fa5c1e5e832ff6193507d8da7e1159281383908ee193a2f4b37bc08140b51844"},
|
||||
|
|
@ -1749,8 +1906,8 @@ typing-extensions = [
|
|||
{file = "typing_extensions-3.10.0.0.tar.gz", hash = "sha256:50b6f157849174217d0656f99dc82fe932884fb250826c18350e159ec6cdf342"},
|
||||
]
|
||||
urllib3 = [
|
||||
{file = "urllib3-1.26.5-py2.py3-none-any.whl", hash = "sha256:753a0374df26658f99d826cfe40394a686d05985786d946fbe4165b5148f5a7c"},
|
||||
{file = "urllib3-1.26.5.tar.gz", hash = "sha256:a7acd0977125325f516bda9735fa7142b909a8d01e8b2e4c8108d0984e6e0098"},
|
||||
{file = "urllib3-1.26.6-py2.py3-none-any.whl", hash = "sha256:39fb8672126159acb139a7718dd10806104dec1e2f0f6c88aab05d17df10c8d4"},
|
||||
{file = "urllib3-1.26.6.tar.gz", hash = "sha256:f57b4c16c62fa2760b7e3d97c35b255512fb6b59a259730f36ba32ce9f8e342f"},
|
||||
]
|
||||
wcwidth = [
|
||||
{file = "wcwidth-0.2.5-py2.py3-none-any.whl", hash = "sha256:beb4802a9cebb9144e99086eff703a642a13d6a0052920003a230f3294bbe784"},
|
||||
|
|
@ -1764,3 +1921,7 @@ widgetsnbextension = [
|
|||
{file = "widgetsnbextension-3.5.1-py2.py3-none-any.whl", hash = "sha256:bd314f8ceb488571a5ffea6cc5b9fc6cba0adaf88a9d2386b93a489751938bcd"},
|
||||
{file = "widgetsnbextension-3.5.1.tar.gz", hash = "sha256:079f87d87270bce047512400efd70238820751a11d2d8cb137a5a5bdbaf255c7"},
|
||||
]
|
||||
zipp = [
|
||||
{file = "zipp-3.5.0-py3-none-any.whl", hash = "sha256:957cfda87797e389580cb8b9e3870841ca991e2125350677b2ca83a0e99390a3"},
|
||||
{file = "zipp-3.5.0.tar.gz", hash = "sha256:f5812b1e007e48cff63449a5e9f4e7ebea716b4111f9c4f9a645f91d579bf0c4"},
|
||||
]
|
||||
|
|
|
|||
|
|
@ -5,7 +5,7 @@ description = "ETL and Generation of Justice 40 Score"
|
|||
authors = ["Your Name <you@example.com>"]
|
||||
|
||||
[tool.poetry.dependencies]
|
||||
python = "^3.9"
|
||||
python = "^3.7.1"
|
||||
ipython = "^7.24.1"
|
||||
jupyter = "^1.0.0"
|
||||
jupyter-contrib-nbextensions = "^0.5.1"
|
||||
|
|
|
|||
|
|
@ -1,4 +1,4 @@
|
|||
appnope==0.1.2; sys_platform == "darwin" and python_version >= "3.7" and platform_system == "Darwin"
|
||||
appnope==0.1.2; sys_platform == "darwin" and python_version >= "3.7"
|
||||
argon2-cffi==20.1.0; python_version >= "3.6"
|
||||
async-generator==1.10; python_full_version >= "3.6.1" and python_version >= "3.7"
|
||||
attrs==21.2.0; python_version >= "3.5" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "3.5"
|
||||
|
|
@ -6,18 +6,20 @@ backcall==0.2.0; python_version >= "3.7"
|
|||
bleach==3.3.0; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "3.7"
|
||||
censusdata==1.13; python_version >= "2.7"
|
||||
certifi==2021.5.30; python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "2.7"
|
||||
cffi==1.14.5; implementation_name == "pypy" and python_version >= "3.6"
|
||||
cffi==1.14.6; implementation_name == "pypy" and python_version >= "3.6"
|
||||
chardet==4.0.0; python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "2.7"
|
||||
click==8.0.1; python_version >= "3.6"
|
||||
colorama==0.4.4; python_version >= "3.7" and python_full_version < "3.0.0" and sys_platform == "win32" and platform_system == "Windows" or sys_platform == "win32" and python_version >= "3.7" and python_full_version >= "3.5.0" and platform_system == "Windows"
|
||||
debugpy==1.3.0; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "3.7"
|
||||
decorator==5.0.9; python_version >= "3.7"
|
||||
defusedxml==0.7.1; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "3.7"
|
||||
dynaconf==3.1.4
|
||||
entrypoints==0.3; python_version >= "3.7"
|
||||
idna==2.10; python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "2.7"
|
||||
ipykernel==5.5.5; python_version >= "3.6"
|
||||
importlib-metadata==3.10.1; python_version < "3.8" and python_version >= "3.7"
|
||||
ipykernel==6.0.1; python_version >= "3.7"
|
||||
ipython-genutils==0.2.0; python_version >= "3.7"
|
||||
ipython==7.24.1; python_version >= "3.7"
|
||||
ipython==7.25.0; python_version >= "3.7"
|
||||
ipywidgets==7.6.3
|
||||
jedi==0.18.0; python_version >= "3.7"
|
||||
jinja2==3.0.1; python_version >= "3.7"
|
||||
|
|
@ -35,7 +37,7 @@ jupyterlab-pygments==0.1.2; python_version >= "3.7"
|
|||
jupyterlab-widgets==1.0.0; python_version >= "3.6"
|
||||
lxml==4.6.3; python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.5.0"
|
||||
markupsafe==2.0.1; python_version >= "3.7"
|
||||
matplotlib-inline==0.1.2; python_version >= "3.7"
|
||||
matplotlib-inline==0.1.2; platform_system == "Darwin" and python_version >= "3.7"
|
||||
mistune==0.8.4; python_version >= "3.7"
|
||||
nbclient==0.5.3; python_full_version >= "3.6.1" and python_version >= "3.7"
|
||||
nbconvert==6.1.0; python_version >= "3.7"
|
||||
|
|
@ -43,8 +45,8 @@ 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==6.4.0; python_version >= "3.6"
|
||||
numpy==1.21.0; python_version >= "3.7"
|
||||
packaging==20.9; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "3.7"
|
||||
pandas==1.2.5; python_full_version >= "3.7.1"
|
||||
packaging==21.0; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "3.7"
|
||||
pandas==1.3.0; python_full_version >= "3.7.1"
|
||||
pandocfilters==1.4.3; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" and python_version >= "3.7"
|
||||
parso==0.8.2; python_version >= "3.7"
|
||||
pexpect==4.8.0; sys_platform != "win32" and python_version >= "3.7"
|
||||
|
|
@ -56,24 +58,26 @@ py==1.10.0; python_version >= "3.6" and python_full_version < "3.0.0" and implem
|
|||
pycparser==2.20; python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" and python_version >= "3.6"
|
||||
pygments==2.9.0; python_version >= "3.7"
|
||||
pyparsing==2.4.7; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "3.7"
|
||||
pyrsistent==0.17.3; python_version >= "3.5"
|
||||
python-dateutil==2.8.1; python_full_version >= "3.7.1" and python_version >= "3.5"
|
||||
pyrsistent==0.18.0; python_version >= "3.6"
|
||||
python-dateutil==2.8.1; 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"
|
||||
pywinpty==1.1.2; os_name == "nt" and python_version >= "3.6"
|
||||
pywinpty==1.1.3; 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==22.1.0; python_full_version >= "3.6.1" and python_version >= "3.6"
|
||||
qtconsole==5.1.0; python_version >= "3.6"
|
||||
pyzmq==22.1.0; python_full_version >= "3.6.1" and python_version >= "3.7"
|
||||
qtconsole==5.1.1; python_version >= "3.6"
|
||||
qtpy==1.9.0; python_version >= "3.6"
|
||||
requests==2.25.1; (python_version >= "2.7" and python_full_version < "3.0.0") or (python_full_version >= "3.5.0")
|
||||
send2trash==1.7.1; python_version >= "3.6"
|
||||
six==1.16.0; python_full_version >= "3.7.1" and python_version >= "3.7" and (python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.3.0" and python_version >= "3.6") and (python_version >= "3.5" and python_full_version < "3.0.0" or python_full_version >= "3.3.0" and python_version >= "3.5")
|
||||
terminado==0.10.1; python_version >= "3.6"
|
||||
testpath==0.5.0; python_version >= "3.7"
|
||||
tornado==6.1; python_full_version >= "3.6.1" and python_version >= "3.6"
|
||||
tornado==6.1; python_full_version >= "3.6.1" and python_version >= "3.7"
|
||||
traitlets==5.0.5; python_full_version >= "3.6.1" and python_version >= "3.7"
|
||||
types-requests==2.25.0
|
||||
urllib3==1.26.5; python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version < "4" and python_version >= "2.7"
|
||||
typing-extensions==3.10.0.0; 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 >= "3.5.0" and python_version < "4" and python_version >= "2.7"
|
||||
wcwidth==0.2.5; python_full_version >= "3.6.1" and python_version >= "3.7"
|
||||
webencodings==0.5.1; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "3.7"
|
||||
widgetsnbextension==3.5.1
|
||||
zipp==3.5.0; python_version < "3.8" and python_version >= "3.6"
|
||||
|
|
|
|||
|
|
@ -17,13 +17,6 @@ def generate_tiles(data_path: Path) -> None:
|
|||
if os.path.exists(mvt_tiles_path):
|
||||
shutil.rmtree(mvt_tiles_path)
|
||||
|
||||
# Merge scores into json
|
||||
|
||||
if os.name == "nt":
|
||||
pwd = "%cd%"
|
||||
else:
|
||||
pwd = "${PWD}"
|
||||
|
||||
# remove existing score json files
|
||||
score_geojson_dir = data_path / "score" / "geojson"
|
||||
files_in_directory = os.listdir(score_geojson_dir)
|
||||
|
|
@ -36,12 +29,9 @@ def generate_tiles(data_path: Path) -> None:
|
|||
state_fips_codes = get_state_fips_codes()
|
||||
for fips in state_fips_codes:
|
||||
cmd = (
|
||||
'docker run --rm -v "'
|
||||
+ pwd
|
||||
+ '"/:/home '
|
||||
+ "osgeo/gdal:alpine-small-latest ogr2ogr -f GeoJSON "
|
||||
+ f"-sql \"SELECT * FROM tl_2010_{fips}_bg10 LEFT JOIN '/home/data/score/csv/data{fips}.csv'.data{fips} ON tl_2010_{fips}_bg10.GEOID10 = data{fips}.ID\" "
|
||||
+ f"/home/data/score/geojson/{fips}.json /home/data/census/shp/{fips}/tl_2010_{fips}_bg10.dbf"
|
||||
"ogr2ogr -f GeoJSON "
|
||||
+ f"-sql \"SELECT * FROM tl_2010_{fips}_bg10 LEFT JOIN 'data/score/csv/data{fips}.csv'.data{fips} ON tl_2010_{fips}_bg10.GEOID10 = data{fips}.ID\" "
|
||||
+ f"data/score/geojson/{fips}.json data/census/shp/{fips}/tl_2010_{fips}_bg10.dbf"
|
||||
)
|
||||
os.system(cmd)
|
||||
|
||||
|
|
@ -51,7 +41,7 @@ def generate_tiles(data_path: Path) -> None:
|
|||
geojson_path = data_path / "score" / "geojson"
|
||||
for file in os.listdir(geojson_path):
|
||||
if file.endswith(".json"):
|
||||
geojson_list += f"/home/data/score/geojson/{file} "
|
||||
geojson_list += f"data/score/geojson/{file} "
|
||||
|
||||
if geojson_list == "":
|
||||
logging.error(
|
||||
|
|
@ -59,28 +49,15 @@ def generate_tiles(data_path: Path) -> None:
|
|||
)
|
||||
|
||||
# generate mbtiles file
|
||||
# PWD is different for Windows
|
||||
if os.name == "nt":
|
||||
pwd = "%cd%"
|
||||
else:
|
||||
pwd = "${PWD}"
|
||||
cmd = (
|
||||
'docker run --rm -it -v "'
|
||||
+ pwd
|
||||
+ '"/:/home klokantech/tippecanoe tippecanoe --drop-densest-as-needed -zg -o /home/data/tiles/block2010.mbtiles --extend-zooms-if-still-dropping -l cbg2010 -s_srs EPSG:4269 -t_srs EPSG:4326 '
|
||||
"tippecanoe --drop-densest-as-needed -zg -o /home/data/tiles/block2010.mbtiles --extend-zooms-if-still-dropping -l cbg2010 -s_srs EPSG:4269 -t_srs EPSG:4326 "
|
||||
+ geojson_list
|
||||
)
|
||||
os.system(cmd)
|
||||
|
||||
# PWD is different for Windows
|
||||
if os.name == "nt":
|
||||
pwd = "%cd%"
|
||||
else:
|
||||
pwd = "${PWD}"
|
||||
# generate mvts
|
||||
cmd = (
|
||||
'docker run --rm -it -v "'
|
||||
+ pwd
|
||||
+ '"/:/home klokantech/tippecanoe tippecanoe --drop-densest-as-needed --no-tile-compression -zg -e /home/data/tiles/mvt '
|
||||
"tippecanoe --drop-densest-as-needed --no-tile-compression -zg -e /home/data/tiles/mvt "
|
||||
+ geojson_list
|
||||
)
|
||||
os.system(cmd)
|
||||
|
|
|
|||
2105
score/utils.py
2105
score/utils.py
File diff suppressed because it is too large
Load diff
Loading…
Add table
Reference in a new issue