tribal tiles fix (#1874)

* Alaska tribal points fix (#1821)

* tribal tiles fix

* disabling child opportunity

* lint

* removing COI

* removing commented out code

data/data-pipeline/data_pipeline/etl/constants.py

@@ -95,12 +95,6 @@ DATASET_LIST = [
         "class_name": "GeoCorrETL",
         "is_memory_intensive": False,
     },
-    {
-        "name": "child_opportunity_index",
-        "module_dir": "child_opportunity_index",
-        "class_name": "ChildOpportunityIndex",
-        "is_memory_intensive": False,
-    },
     {
         "name": "mapping_inequality",
         "module_dir": "mapping_inequality",

data/data-pipeline/data_pipeline/etl/score/etl_score.py

@@ -45,7 +45,7 @@ class ScoreETL(ExtractTransformLoad):
         self.persistent_poverty_df: pd.DataFrame
         self.census_decennial_df: pd.DataFrame
         self.census_2010_df: pd.DataFrame
-        self.child_opportunity_index_df: pd.DataFrame
+        # self.child_opportunity_index_df: pd.DataFrame
         self.hrs_df: pd.DataFrame
         self.dot_travel_disadvantage_df: pd.DataFrame
         self.fsf_flood_df: pd.DataFrame
@@ -192,19 +192,6 @@ class ScoreETL(ExtractTransformLoad):
             low_memory=False,
         )
 
-        # Load COI data
-        child_opportunity_index_csv = (
-            constants.DATA_PATH
-            / "dataset"
-            / "child_opportunity_index"
-            / "usa.csv"
-        )
-        self.child_opportunity_index_df = pd.read_csv(
-            child_opportunity_index_csv,
-            dtype={self.GEOID_TRACT_FIELD_NAME: "string"},
-            low_memory=False,
-        )
-
         # Load HRS data
         hrs_csv = (
             constants.DATA_PATH / "dataset" / "historic_redlining" / "usa.csv"
@@ -368,7 +355,6 @@ class ScoreETL(ExtractTransformLoad):
             self.census_acs_median_incomes_df,
             self.census_decennial_df,
             self.census_2010_df,
-            self.child_opportunity_index_df,
             self.hrs_df,
             self.dot_travel_disadvantage_df,
             self.fsf_flood_df,
@@ -455,9 +441,6 @@ class ScoreETL(ExtractTransformLoad):
             field_names.CENSUS_UNEMPLOYMENT_FIELD_2010,
             field_names.CENSUS_POVERTY_LESS_THAN_100_FPL_FIELD_2010,
             field_names.CENSUS_DECENNIAL_TOTAL_POPULATION_FIELD_2009,
-            field_names.EXTREME_HEAT_FIELD,
-            field_names.HEALTHY_FOOD_FIELD,
-            field_names.IMPENETRABLE_SURFACES_FIELD,
             field_names.UST_FIELD,
             field_names.DOT_TRAVEL_BURDEN_FIELD,
             field_names.FUTURE_FLOOD_RISK_FIELD,
@@ -509,10 +492,6 @@ class ScoreETL(ExtractTransformLoad):
             # This low field will not exist yet, it is only calculated for the
             # percentile.
             # TODO: This will come from the YAML dataset config
-            ReversePercentile(
-                field_name=field_names.READING_FIELD,
-                low_field_name=field_names.LOW_READING_FIELD,
-            ),
             ReversePercentile(
                 field_name=field_names.MEDIAN_INCOME_AS_PERCENT_OF_AMI_FIELD,
                 low_field_name=field_names.LOW_MEDIAN_INCOME_AS_PERCENT_OF_AMI_FIELD,

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

@@ -59,7 +59,7 @@ class TribalETL(ExtractTransformLoad):
         )
 
         bia_national_lar_df.rename(
-            columns={"TSAID": "tribalId", "LARName": "landAreaName"},
+            columns={"LARID": "tribalId", "LARName": "landAreaName"},
             inplace=True,
         )
 
@@ -154,7 +154,9 @@ class TribalETL(ExtractTransformLoad):
 
         # load the geojsons
         bia_national_lar_geojson = (
-            self.GEOJSON_BASE_PATH / "bia_national_lar" / "BIA_TSA.json"
+            self.GEOJSON_BASE_PATH
+            / "bia_national_lar"
+            / "BIA_National_LAR.json"
         )
         bia_aian_supplemental_geojson = (
             self.GEOJSON_BASE_PATH

data/data-pipeline/data_pipeline/score/field_names.py

@@ -318,21 +318,6 @@ MARYLAND_EJSCREEN_SCORE_FIELD: str = "Maryland Environmental Justice Score"
 MARYLAND_EJSCREEN_BURDENED_THRESHOLD_FIELD: str = (
     "Maryland EJSCREEN Priority Community"
 )
-# Child Opportunity Index data
-# Summer days with maximum temperature above 90F.
-EXTREME_HEAT_FIELD = "Summer days above 90F"
-
-# Percentage households without a car located further than a half-mile from the
-# nearest supermarket.
-HEALTHY_FOOD_FIELD = "Percent low access to healthy food"
-
-# Percentage impenetrable surface areas such as rooftops, roads or parking lots.
-IMPENETRABLE_SURFACES_FIELD = "Percent impenetrable surface areas"
-
-# Percentage third graders scoring proficient on standardized reading tests,
-# converted to NAEP scale score points.
-READING_FIELD = "Third grade reading proficiency"
-LOW_READING_FIELD = "Low third grade reading proficiency"
 
 # Alternative energy-related definition of DACs
 ENERGY_RELATED_COMMUNITIES_DEFINITION_ALTERNATIVE = (