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70 lines
2.6 KiB
Python
70 lines
2.6 KiB
Python
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import pandas as pd
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from data_pipeline.etl.base import ExtractTransformLoad
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from data_pipeline.utils import get_module_logger
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from data_pipeline.score import field_names
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from data_pipeline.config import settings
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logger = get_module_logger(__name__)
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class MichiganEnviroScreenETL(ExtractTransformLoad):
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"""Michigan EJ Screen class that ingests dataset represented
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here: https://www.arcgis.com/apps/webappviewer/index.html?id=dc4f0647dda34959963488d3f519fd24
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This class ingests the data presented in "Assessing the State of Environmental
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Justice in Michigan." Please see the README in this module for further details.
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"""
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def __init__(self):
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self.MICHIGAN_EJSCREEN_S3_URL = (
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settings.AWS_JUSTICE40_DATASOURCES_URL
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+ "/michigan_ejscore_12212021.csv"
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)
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self.CSV_PATH = self.DATA_PATH / "dataset" / "michigan_ejscreen"
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self.MICHIGAN_EJSCREEN_PRIORITY_COMMUNITY_THRESHOLD: float = 0.75
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self.COLUMNS_TO_KEEP = [
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self.GEOID_TRACT_FIELD_NAME,
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field_names.MICHIGAN_EJSCREEN_SCORE_FIELD,
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field_names.MICHIGAN_EJSCREEN_PERCENTILE_FIELD,
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field_names.MICHIGAN_EJSCREEN_PRIORITY_COMMUNITY_FIELD,
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]
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self.df: pd.DataFrame
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def extract(self) -> None:
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logger.info("Downloading Michigan EJSCREEN Data")
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self.df = pd.read_csv(
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filepath_or_buffer=self.MICHIGAN_EJSCREEN_S3_URL,
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dtype={"GEO_ID": "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("Transforming Michigan EJSCREEN Data")
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self.df.rename(
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columns={
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"GEO_ID": self.GEOID_TRACT_FIELD_NAME,
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"EJ_Score_Cal_Min": field_names.MICHIGAN_EJSCREEN_SCORE_FIELD,
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"Pct_CalMin": field_names.MICHIGAN_EJSCREEN_PERCENTILE_FIELD,
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},
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inplace=True,
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)
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# Calculate the top quartile of prioritized communities
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# Please see pg. 104 - 109 from source:
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# pg. https://deepblue.lib.umich.edu/bitstream/handle/2027.42/149105/AssessingtheStateofEnvironmentalJusticeinMichigan_344.pdf
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self.df[field_names.MICHIGAN_EJSCREEN_PRIORITY_COMMUNITY_FIELD] = (
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self.df[field_names.MICHIGAN_EJSCREEN_PERCENTILE_FIELD]
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>= self.MICHIGAN_EJSCREEN_PRIORITY_COMMUNITY_THRESHOLD
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)
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def load(self) -> None:
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logger.info("Saving Michigan Environmental Screening Tool to CSV")
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# write nationwide csv
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self.CSV_PATH.mkdir(parents=True, exist_ok=True)
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self.df[self.COLUMNS_TO_KEEP].to_csv(
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self.CSV_PATH / "michigan_ejscreen.csv", index=False
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)
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