adding tree equity score to the data pipeline (#398)

* adding tree equity score to the downloading pipeline so it can be easily compared as a reference index!

* removed redundant dependencies

data/data-pipeline/etl/runner.py

@@ -16,6 +16,11 @@ def etl_runner(dataset_to_run: str = None) -> None:
 
     # this list comes from YAMLs
     dataset_list = [
+        {
+            "name": "tree_equity_score",
+            "module_dir": "tree_equity_score",
+            "class_name": "TreeEquityScoreETL",
+        },
         {
             "name": "census_acs",
             "module_dir": "census_acs",

data/data-pipeline/etl/sources/tree_equity_score/README.md

@@ -0,0 +1,3 @@
+# Tree Equity Score
+
+The Tree Equity Score was built by American Forest to assess how equitably trees were planted in a city. More information, checkout [https://treeequityscore.org](https://treeequityscore.org).

data/data-pipeline/etl/sources/tree_equity_score/etl.py

@@ -0,0 +1,42 @@
+import pandas as pd
+import geopandas as gpd
+
+from etl.base import ExtractTransformLoad
+from utils import get_module_logger
+import os
+
+logger = get_module_logger(__name__)
+
+
+class TreeEquityScoreETL(ExtractTransformLoad):
+    def __init__(self):
+        self.TES_URL = "https://national-tes-data-share.s3.amazonaws.com/national_tes_share/"
+        self.TES_CSV = self.TMP_PATH / "tes_2021_data.csv"
+        self.CSV_PATH = self.DATA_PATH / "dataset" / "tree_equity_score"
+        self.df: gpd.GeoDataFrame
+        self.states =  ["al", "az", "ar", "ca", "co", "ct", "de", "dc", "fl", 
+        "ga", "id", "il", "in", "ia", "ks", "ky", "la", "me", 
+        "md", "ma", "mi", "mn", "ms", "mo", "mt", "ne", "nv", "nh", 
+        "nj", "nm", "ny", "nc", "nd", "oh", "ok", "or", "pa", 
+        "ri", "sc", "sd", "tn", "tx", "ut", "vt", "va", "wa", "wv", "wi", "wy"]
+
+    def extract(self) -> None:
+        logger.info(f"Downloading Tree Equity Score Data")
+        for state in self.states:
+            super().extract(
+                f"{self.TES_URL}{state}.zip.zip",
+                f"{self.TMP_PATH}/{state}",
+            )
+
+    def transform(self) -> None:
+        logger.info(f"Transforming Tree Equity Score Data")
+        tes_state_dfs = []
+        for state in self.states:
+            tes_state_dfs.append(gpd.read_file(f"{self.TMP_PATH}/{state}/{state}.shp"))
+        self.df = gpd.GeoDataFrame(pd.concat(tes_state_dfs), crs=tes_state_dfs[0].crs)
+
+    def load(self) -> None:
+        logger.info(f"Saving Tree Equity Score GeoJSON")
+        # write nationwide csv
+        self.CSV_PATH.mkdir(parents=True, exist_ok=True)
+        self.df.to_file(self.CSV_PATH / "tes_conus.geojson", driver='GeoJSON')