added indoor plumbing to score housing burden

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

@@ -366,6 +366,7 @@ class ScoreETL(ExtractTransformLoad):
 
         numeric_columns = [
             field_names.HOUSING_BURDEN_FIELD,
+            field_names.NO_KITCHEN_OR_INDOOR_PLUMBING_FIELD,
             field_names.TOTAL_POP_FIELD,
             field_names.MEDIAN_INCOME_AS_PERCENT_OF_STATE_FIELD,
             field_names.ASTHMA_FIELD,
@@ -416,7 +417,6 @@ class ScoreETL(ExtractTransformLoad):
             field_names.HEALTHY_FOOD_FIELD,
             field_names.IMPENETRABLE_SURFACES_FIELD,
             field_names.UST_FIELD,
-            # We have to pass this boolean here in order to include it in ag value loss percentiles.
             field_names.AGRICULTURAL_VALUE_BOOL_FIELD,
             field_names.POVERTY_LESS_THAN_200_FPL_IMPUTED_FIELD,
         ]
@@ -473,9 +473,7 @@ class ScoreETL(ExtractTransformLoad):
             set(numeric_columns)
         ), "You have a double-entered column in the numeric columns list"
 
-        df_copy[numeric_columns] = df_copy[numeric_columns].apply(
+        df_copy[numeric_columns] = df_copy[numeric_columns].apply(pd.to_numeric)
-            pd.to_numeric
-        )
 
         # Convert all columns to numeric and do math
         # Note that we have a few special conditions here, that we handle explicitly.

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

@@ -176,6 +176,10 @@ LOW_INCOME_THRESHOLD = "Exceeds FPL200 threshold"
 
 # Housing
 HOUSING_BURDEN_FIELD = "Housing burden (percent)"
+NO_KITCHEN_OR_INDOOR_PLUMBING_FIELD = (
+    "Share of homes with no kitchen or indoor plumbing (percent)"
+)
+
 HT_INDEX_FIELD = (
     "Housing + Transportation Costs % Income for the Regional Typical Household"
 )
@@ -419,6 +423,10 @@ LEAD_PAINT_MEDIAN_HOUSE_VALUE_LOW_INCOME_FIELD = (
     f"percentile and is low income?"
 )
 HOUSING_BURDEN_LOW_INCOME_FIELD = f"Greater than or equal to the {PERCENTILE}th percentile for housing burden and is low income?"
+NO_KITCHEN_OR_INDOOR_PLUMBING_LOW_INCOME_FIELD = (
+    f"Greater than or equal to the {PERCENTILE}th percentile for "
+    + "share of homes with no kitchen or indoor plumbing and is low income?"
+)
 
 # Remediation and Reduction of Legacy Pollution
 RMP_LOW_INCOME_FIELD = f"Greater than or equal to the {PERCENTILE}th percentile for proximity to RMP sites and is low income?"
@@ -624,6 +632,11 @@ LEAD_PAINT_PROXY_PCTILE_THRESHOLD = (
 HOUSING_BURDEN_PCTILE_THRESHOLD = (
     f"Greater than or equal to the {PERCENTILE}th percentile for housing burden"
 )
+NO_KITCHEN_OR_INDOOR_PLUMBING_PCTILE_THRESHOLD = (
+    f"Greater than or equal to the {PERCENTILE}th percentile for share "
+    "of homes without indoor plumbing or a kitchen"
+)
+
 RMP_PCTILE_THRESHOLD = (
     f"Greater than or equal to the {PERCENTILE}th percentile for RMP proximity"
 )

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

@@ -315,9 +315,10 @@ class ScoreNarwhal(Score):
             field_names.LEAD_PAINT_MEDIAN_HOUSE_VALUE_LOW_INCOME_FIELD,
             field_names.HOUSING_BURDEN_LOW_INCOME_FIELD,
             field_names.HISTORIC_REDLINING_SCORE_EXCEEDED_LOW_INCOME_FIELD,
+            field_names.NO_KITCHEN_OR_INDOOR_PLUMBING_LOW_INCOME_FIELD,
         ]
 
-        # # design question -- should read in scalar with threshold here instead?
+        # Historic disinvestment
         self.df[
             field_names.HISTORIC_REDLINING_SCORE_EXCEEDED_LOW_INCOME_FIELD
         ] = (
@@ -325,6 +326,18 @@ class ScoreNarwhal(Score):
             & self.df[field_names.FPL_200_SERIES_IMPUTED_AND_ADJUSTED]
         )
 
+        # Kitchen / plumbing
+        self.df[field_names.NO_KITCHEN_OR_INDOOR_PLUMBING_PCTILE_THRESHOLD] = (
+            self.df[field_names.NO_KITCHEN_OR_INDOOR_PLUMBING_FIELD]
+            >= self.ENVIRONMENTAL_BURDEN_THRESHOLD
+        )
+
+        self.df[field_names.NO_KITCHEN_OR_INDOOR_PLUMBING_LOW_INCOME_FIELD] = (
+            self.df[field_names.NO_KITCHEN_OR_INDOOR_PLUMBING_PCTILE_THRESHOLD]
+            & self.df[field_names.FPL_200_SERIES_IMPUTED_AND_ADJUSTED]
+        )
+
+        # Lead paint
         self.df[field_names.LEAD_PAINT_PROXY_PCTILE_THRESHOLD] = (
             self.df[
                 field_names.LEAD_PAINT_FIELD
@@ -339,6 +352,12 @@ class ScoreNarwhal(Score):
             <= self.MEDIAN_HOUSE_VALUE_THRESHOLD
         )
 
+        self.df[field_names.LEAD_PAINT_MEDIAN_HOUSE_VALUE_LOW_INCOME_FIELD] = (
+            self.df[field_names.LEAD_PAINT_PROXY_PCTILE_THRESHOLD]
+            & self.df[field_names.FPL_200_SERIES_IMPUTED_AND_ADJUSTED]
+        )
+
+        # Housing burden
         self.df[field_names.HOUSING_BURDEN_PCTILE_THRESHOLD] = (
             self.df[
                 field_names.HOUSING_BURDEN_FIELD
@@ -346,29 +365,22 @@ class ScoreNarwhal(Score):
             ]
             >= self.ENVIRONMENTAL_BURDEN_THRESHOLD
         )
-
-        self.df[field_names.HOUSING_THREHSOLD_EXCEEDED] = (
-            self.df[field_names.LEAD_PAINT_PROXY_PCTILE_THRESHOLD]
-            | self.df[field_names.HOUSING_BURDEN_PCTILE_THRESHOLD]
-        )
-
-        # series by series indicators
-        self.df[field_names.LEAD_PAINT_MEDIAN_HOUSE_VALUE_LOW_INCOME_FIELD] = (
-            self.df[field_names.LEAD_PAINT_PROXY_PCTILE_THRESHOLD]
-            & self.df[field_names.FPL_200_SERIES_IMPUTED_AND_ADJUSTED]
-        )
-
         self.df[field_names.HOUSING_BURDEN_LOW_INCOME_FIELD] = (
             self.df[field_names.HOUSING_BURDEN_PCTILE_THRESHOLD]
             & self.df[field_names.FPL_200_SERIES_IMPUTED_AND_ADJUSTED]
         )
 
+        # any of the burdens
+        self.df[field_names.HOUSING_THREHSOLD_EXCEEDED] = self.df[
+            housing_eligibility_columns
+        ].any(axis="columns")
+
         self._increment_total_eligibility_exceeded(
             housing_eligibility_columns,
             skip_fips=constants.DROP_FIPS_FROM_NON_WTD_THRESHOLDS,
         )
 
-        return self.df[housing_eligibility_columns].any(axis="columns")
+        return self.df[field_names.HOUSING_THREHSOLD_EXCEEDED]
 
     def _pollution_factor(self) -> bool:
         # Proximity to Risk Management Plan sites is > X