j40-cejst-2/data/data-pipeline/data_pipeline/etl/sources/census/etl.py
Nat Hillard 9f3b2f056b
Fixes #467: (#470)
If the census download task is run more than once,
us.csv doubles in size and all data is removed from dataframe
2021-08-05 16:20:18 -04:00

134 lines
4.6 KiB
Python

import csv
import json
import os
from pathlib import Path
import subprocess
import geopandas as gpd
from data_pipeline.utils import get_module_logger, unzip_file_from_url
from .etl_utils import get_state_fips_codes
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 and GeoJSONs
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: Path = (
data_path / "census" / "shp" / fips / f"tl_2010_{fips}_bg10.shp"
)
geojson_file_path = data_path / "census" / "geojson" / f"{fips}.json"
logger.info(f"Checking if {fips} shp file exists")
if not shp_file_path.is_file():
logger.info(
f"{fips} shp file does not exist. Downloading and extracting 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,
)
logger.info(f"Checking if {fips} geoJSON file exists ")
if not geojson_file_path.is_file():
logger.info(
f"GeoJSON file {fips} does not exist. Converting shp to geoJSON"
)
cmd = [
"ogr2ogr",
"-f",
"GeoJSON",
str(geojson_file_path),
str(shp_file_path),
]
subprocess.run(cmd, check=True)
# 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
logger.info("Writing national us.csv file")
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,
]
)
## create national geojson
logger.info("Generating national geojson file")
usa_df = gpd.GeoDataFrame()
for file_name in geojson_dir_path.rglob("*.json"):
logger.info(f"Ingesting {file_name}")
state_gdf = gpd.read_file(file_name)
usa_df = usa_df.append(state_gdf)
usa_df = usa_df.to_crs("+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs")
logger.info("Writing national geojson file")
usa_df.to_file(geojson_dir_path / "us.json", driver="GeoJSON")
logger.info("Census block groups downloading complete")