Adding ej screen areas of concern

data/data-pipeline/data_pipeline/etl/sources/ejscreen_areas_of_concern/README.md

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+# EJ Screen Areas of Concern Data
+
+Note, this dataset is actually not public, so the data file must be stored locally in order for this to run

data/data-pipeline/data_pipeline/etl/sources/ejscreen_areas_of_concern/etl.py

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+import pandas as pd
+
+from data_pipeline.etl.base import ExtractTransformLoad
+from data_pipeline.utils import get_module_logger
+
+logger = get_module_logger(__name__)
+
+
+class EJScreenAreasOfConcernETL(ExtractTransformLoad):
+    def __init__(self):
+        # self.EJSCREEN_FTP_URL = "https://gaftp.epa.gov/EJSCREEN/2019/EJSCREEN_2019_StatePctile.csv.zip"
+        # self.EJSCREEN_CSV = self.TMP_PATH / "EJSCREEN_2019_StatePctiles.csv"
+        self.CSV_PATH = self.DATA_PATH / "dataset" / "ejscreen_2019"
+        self.df: pd.DataFrame
+
+    def extract(self) -> None:
+        logger.info("Downloading EJScreen Data")
+        super().extract(
+            self.EJSCREEN_FTP_URL,
+            self.TMP_PATH,
+            verify=False,  # EPA EJScreen end point has certificate issues often
+        )
+
+    def transform(self) -> None:
+        logger.info("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("Saving EJScreen CSV")
+        # write nationwide csv
+        self.CSV_PATH.mkdir(parents=True, exist_ok=True)
+        self.df.to_csv(self.CSV_PATH / "usa.csv", index=False)