j40-cejst-2/data/data-pipeline/data_pipeline/etl/sources/tribal/etl.py
Lucas Merrill Brown aca226165c
Issue 1900: Tribal overlap with Census tracts (#1903)
* working notebook

* updating notebook

* wip

* fixing broken tests

* adding tribal overlap files

* WIP

* WIP

* WIP, calculated count and names

* working

* partial cleanup

* partial cleanup

* updating field names

* fixing bug

* removing pyogrio

* removing unused imports

* updating test fixtures to be more realistic

* cleaning up notebook

* fixing black

* fixing flake8 errors

* adding tox instructions

* updating etl_score

* suppressing warning

* Use projected CRSes, ignore geom types (#1900)

I looked into this a bit, and in general the geometry type mismatch
changes very little about the calculation; we have a mix of
multipolygons and polygons. The fastest thing to do is just not keep
geom type; I did some runs with it set to both True and False, and
they're the same within 9 digits of precision. Logically we just want to
overlaps, regardless of how the actual geometries are encoded between
the frames, so we can in this case ignore the geom types and feel OKAY.

I also moved to projected CRSes, since we are actually trying to do area
calculations and so like, we should. Again, the change is small in
magnitude but logically more sound.

* Readd CDC dataset config (#1900)

* adding comments to fips code

* delete unnecessary loggers

Co-authored-by: matt bowen <matthew.r.bowen@omb.eop.gov>
2022-09-20 14:53:12 -04:00

207 lines
6.2 KiB
Python

from pathlib import Path
import geopandas as gpd
import pandas as pd
from data_pipeline.etl.base import ExtractTransformLoad
from data_pipeline.score import field_names
from data_pipeline.utils import get_module_logger, unzip_file_from_url
logger = get_module_logger(__name__)
class TribalETL(ExtractTransformLoad):
def __init__(self):
self.GEOJSON_BASE_PATH = self.DATA_PATH / "tribal" / "geojson"
self.CSV_BASE_PATH = self.DATA_PATH / "tribal" / "csv"
self.NATIONAL_TRIBAL_GEOJSON_PATH = self.GEOJSON_BASE_PATH / "usa.json"
self.USA_TRIBAL_DF_LIST = []
def extract(self) -> None:
"""Extract the tribal geojson zip files from Justice40 S3 data folder
Returns:
None
"""
logger.info("Downloading Tribal Data")
bia_geojson_url = "https://justice40-data.s3.amazonaws.com/data-sources/BIA_National_LAR_json.zip"
alaska_geojson_url = "https://justice40-data.s3.amazonaws.com/data-sources/Alaska_Native_Villages_json.zip"
unzip_file_from_url(
bia_geojson_url,
self.TMP_PATH,
self.DATA_PATH / "tribal" / "geojson" / "bia_national_lar",
)
unzip_file_from_url(
alaska_geojson_url,
self.TMP_PATH,
self.DATA_PATH / "tribal" / "geojson" / "alaska_native_villages",
)
pass
def _transform_bia_national_lar(self, tribal_geojson_path: Path) -> None:
"""Transform the Tribal BIA National Lar Geodataframe and appends it to the
national Tribal Dataframe List
Args:
tribal_geojson_path (Path): the Path to the Tribal Geojson
Returns:
None
"""
bia_national_lar_df = gpd.read_file(tribal_geojson_path)
bia_national_lar_df.drop(
["OBJECTID", "GISAcres", "Shape_Length", "Shape_Area"],
axis=1,
inplace=True,
)
bia_national_lar_df.rename(
columns={
"LARID": field_names.TRIBAL_ID,
"LARName": field_names.TRIBAL_LAND_AREA_NAME,
},
inplace=True,
)
self.USA_TRIBAL_DF_LIST.append(bia_national_lar_df)
def _transform_bia_aian_supplemental(
self, tribal_geojson_path: Path
) -> None:
"""Transform the Tribal BIA Supplemental Geodataframe and appends it to the
national Tribal Dataframe List
Args:
tribal_geojson_path (Path): the Path to the Tribal Geojson
Returns:
None
"""
bia_aian_supplemental_df = gpd.read_file(tribal_geojson_path)
bia_aian_supplemental_df.drop(
["GISAcres", "Source", "Shape_Length", "Shape_Area"],
axis=1,
inplace=True,
)
bia_aian_supplemental_df.rename(
columns={
"OBJECTID": field_names.TRIBAL_ID,
"Land_Area_": field_names.TRIBAL_LAND_AREA_NAME,
},
inplace=True,
)
self.USA_TRIBAL_DF_LIST.append(bia_aian_supplemental_df)
def _transform_bia_tsa(self, tribal_geojson_path: Path) -> None:
"""Transform the Tribal BIA TSA Geodataframe and appends it to the
national Tribal Dataframe List
Args:
tribal_geojson_path (Path): the Path to the Tribal Geojson
Returns:
None
"""
bia_tsa_df = gpd.read_file(tribal_geojson_path)
bia_tsa_df.drop(
["OBJECTID", "GISAcres", "Shape_Length", "Shape_Area"],
axis=1,
inplace=True,
)
bia_tsa_df.rename(
columns={
"TSAID": field_names.TRIBAL_ID,
"LARName": field_names.TRIBAL_LAND_AREA_NAME,
},
inplace=True,
)
self.USA_TRIBAL_DF_LIST.append(bia_tsa_df)
def _transform_alaska_native_villages(
self, tribal_geojson_path: Path
) -> None:
"""Transform the Alaska Native Villages Geodataframe and appends it to the
national Tribal Dataframe List
Args:
tribal_geojson_path (Path): the Path to the Tribal Geojson
Returns:
None
"""
alaska_native_villages_df = gpd.read_file(tribal_geojson_path)
alaska_native_villages_df.rename(
columns={
"GlobalID": field_names.TRIBAL_ID,
"TRIBALOFFICENAME": field_names.TRIBAL_LAND_AREA_NAME,
},
inplace=True,
)
self.USA_TRIBAL_DF_LIST.append(alaska_native_villages_df)
def transform(self) -> None:
"""Transform the tribal geojson files to generate national CSVs and GeoJSONs
Returns:
None
"""
logger.info("Transforming Tribal Data")
# load the geojsons
bia_national_lar_geojson = (
self.GEOJSON_BASE_PATH
/ "bia_national_lar"
/ "BIA_National_LAR.json"
)
bia_aian_supplemental_geojson = (
self.GEOJSON_BASE_PATH
/ "bia_national_lar"
/ "BIA_AIAN_Supplemental.json"
)
bia_tsa_geojson_geojson = (
self.GEOJSON_BASE_PATH / "bia_national_lar" / "BIA_TSA.json"
)
alaska_native_villages_geojson = (
self.GEOJSON_BASE_PATH
/ "alaska_native_villages"
/ "AlaskaNativeVillages.gdb.geojson"
)
self._transform_bia_national_lar(bia_national_lar_geojson)
self._transform_bia_aian_supplemental(bia_aian_supplemental_geojson)
self._transform_bia_tsa(bia_tsa_geojson_geojson)
self._transform_alaska_native_villages(alaska_native_villages_geojson)
def load(self) -> None:
"""Create tribal national CSV and GeoJSON
Returns:
None
"""
logger.info("Saving Tribal GeoJson and CSV")
usa_tribal_df = gpd.GeoDataFrame(
pd.concat(self.USA_TRIBAL_DF_LIST, ignore_index=True)
)
usa_tribal_df = usa_tribal_df.to_crs(
"+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs"
)
logger.info("Writing national geojson file")
usa_tribal_df.to_file(
self.NATIONAL_TRIBAL_GEOJSON_PATH, driver="GeoJSON"
)