Add NATA cancer risk and respiratory hazard to definition L (#1001)

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

@@ -322,7 +322,7 @@ class ScoreETL(ExtractTransformLoad):
             field_names.FEMA_RISK_FIELD,
             field_names.URBAN_HEURISTIC_FIELD,
             field_names.AIR_TOXICS_CANCER_RISK_FIELD,
-            field_names.RESPITORY_HAZARD_FIELD,
+            field_names.RESPIRATORY_HAZARD_FIELD,
             field_names.DIESEL_FIELD,
             field_names.PM25_FIELD,
             field_names.OZONE_FIELD,

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

@@ -8,6 +8,12 @@ logger = get_module_logger(__name__)
 
 
 class EJSCREENETL(ExtractTransformLoad):
+    """Load EJSCREEN data.
+
+    Data dictionary:
+        https://gaftp.epa.gov/EJSCREEN/2019/2019_EJSCREEN_columns_explained.csv
+    """
+
     def __init__(self):
         self.EJSCREEN_FTP_URL = "https://edap-arcgiscloud-data-commons.s3.amazonaws.com/EJSCREEN2020/EJSCREEN_Tract_2020_USPR.csv.zip"
         self.EJSCREEN_CSV = self.TMP_PATH / "EJSCREEN_Tract_2020_USPR.csv"
@@ -19,7 +25,7 @@ class EJSCREENETL(ExtractTransformLoad):
             field_names.TOTAL_POP_FIELD,
             # pylint: disable=duplicate-code
             field_names.AIR_TOXICS_CANCER_RISK_FIELD,
-            field_names.RESPITORY_HAZARD_FIELD,
+            field_names.RESPIRATORY_HAZARD_FIELD,
             field_names.DIESEL_FIELD,
             field_names.PM25_FIELD,
             field_names.OZONE_FIELD,
@@ -61,7 +67,7 @@ class EJSCREENETL(ExtractTransformLoad):
                 # but I think that's the direction we'd like to move all ETL classes. - LMB
                 "ACSTOTPOP": field_names.TOTAL_POP_FIELD,
                 "CANCER": field_names.AIR_TOXICS_CANCER_RISK_FIELD,
-                "RESP": field_names.RESPITORY_HAZARD_FIELD,
+                "RESP": field_names.RESPIRATORY_HAZARD_FIELD,
                 "DSLPM": field_names.DIESEL_FIELD,
                 "PM25": field_names.PM25_FIELD,
                 "OZONE": field_names.OZONE_FIELD,

data/data-pipeline/data_pipeline/ipython/scoring_comparison.ipynb

@@ -479,7 +479,7 @@
     "comparison_fields = [\n",
     "    field_names.POVERTY_LESS_THAN_100_FPL_FIELD,\n",
     "    field_names.POVERTY_LESS_THAN_200_FPL_FIELD,\n",
-    "    field_names.MEDIAN_INCOME_PERCENT_AMI_FIELD,\n",
+    "    field_names.MEDIAN_INCOME_AS_PERCENT_OF_AMI_FIELD,\n",
     "    field_names.LINGUISTIC_ISO_FIELD,\n",
     "    field_names.UNEMPLOYMENT_FIELD,\n",
     "    field_names.HIGH_SCHOOL_ED_FIELD,\n",

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

@@ -89,6 +89,7 @@ RMP_FIELD = "Proximity to Risk Management Plan (RMP) facilities"
 TSDF_FIELD = "Proximity to TSDF sites"
 NPL_FIELD = "Proximity to NPL sites"
 AIR_TOXICS_CANCER_RISK_FIELD = "Air toxics cancer risk"
+RESPIRATORY_HAZARD_FIELD = "Respiratory hazard index"
 
 # Housing
 HOUSING_BURDEN_FIELD = "Housing burden (percent)"
@@ -104,7 +105,6 @@ DIABETES_FIELD = "Diagnosed diabetes among adults aged >=18 years"
 ASTHMA_FIELD = "Current asthma among adults aged >=18 years"
 HEART_DISEASE_FIELD = "Coronary heart disease among adults aged >=18 years"
 LIFE_EXPECTANCY_FIELD = "Life expectancy (years)"
-RESPITORY_HAZARD_FIELD = "Respiratory hazard index"
 CANCER_FIELD = "Cancer (excluding skin cancer) among adults aged >=18 years"
 HEALTH_INSURANCE_FIELD = (
     "Current lack of health insurance among adults aged 18-64 years"
@@ -260,6 +260,8 @@ IMPENETRABLE_SURFACES_LOW_INCOME_FIELD = (
 RMP_LOW_INCOME_FIELD = f"At or above the {PERCENTILE}th percentile for proximity to RMP sites and is low income"
 SUPERFUND_LOW_INCOME_FIELD = f"At or above the {PERCENTILE}th percentile for proximity to superfund sites and is low income"
 HAZARDOUS_WASTE_LOW_INCOME_FIELD = f"At or above the {PERCENTILE}th percentile for proximity to hazardous waste facilities and is low income"
+AIR_TOXICS_CANCER_RISK_LOW_INCOME_FIELD = f"At or above the {PERCENTILE}th percentile for air toxics cancer risk and is low income"
+RESPIRATORY_HAZARD_LOW_INCOME_FIELD = f"At or above the {PERCENTILE}th percentile for respiratory hazard index and is low income"
 
 # Critical Clean Water and Waste Infrastructure
 WASTEWATER_DISCHARGE_LOW_INCOME_FIELD = f"At or above the {PERCENTILE}th percentile for wastewater discharge and is low income"

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

@@ -54,7 +54,7 @@ class ScoreC(Score):
             [
                 field_names.AIR_TOXICS_CANCER_RISK_FIELD
                 + field_names.PERCENTILE_FIELD_SUFFIX,
-                field_names.RESPITORY_HAZARD_FIELD
+                field_names.RESPIRATORY_HAZARD_FIELD
                 + field_names.PERCENTILE_FIELD_SUFFIX,
                 field_names.DIESEL_FIELD + field_names.PERCENTILE_FIELD_SUFFIX,
                 field_names.PM25_FIELD + field_names.PERCENTILE_FIELD_SUFFIX,

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

@@ -36,7 +36,7 @@ class ScoreF(Score):
             )
             | (
                 self.df[
-                    field_names.RESPITORY_HAZARD_FIELD
+                    field_names.RESPIRATORY_HAZARD_FIELD
                     + field_names.PERCENTILE_FIELD_SUFFIX
                 ]
                 > 0.9

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

@@ -407,6 +407,8 @@ class ScoreL(Score):
             field_names.RMP_LOW_INCOME_FIELD,
             field_names.SUPERFUND_LOW_INCOME_FIELD,
             field_names.HAZARDOUS_WASTE_LOW_INCOME_FIELD,
+            field_names.AIR_TOXICS_CANCER_RISK_LOW_INCOME_FIELD,
+            field_names.RESPIRATORY_HAZARD_LOW_INCOME_FIELD,
         ]
 
         rmp_sites_threshold = (
@@ -426,6 +428,22 @@ class ScoreL(Score):
             >= self.ENVIRONMENTAL_BURDEN_THRESHOLD
         )
 
+        air_toxics_cancer_risk_threshold = (
+            self.df[
+                field_names.AIR_TOXICS_CANCER_RISK_FIELD
+                + field_names.PERCENTILE_FIELD_SUFFIX
+            ]
+            >= self.ENVIRONMENTAL_BURDEN_THRESHOLD
+        )
+
+        respiratory_hazard_risk_threshold = (
+            self.df[
+                field_names.RESPIRATORY_HAZARD_FIELD
+                + field_names.PERCENTILE_FIELD_SUFFIX
+            ]
+            >= self.ENVIRONMENTAL_BURDEN_THRESHOLD
+        )
+
         # individual series-by-series
         self.df[field_names.RMP_LOW_INCOME_FIELD] = (
             rmp_sites_threshold & self.df[field_names.FPL_200_SERIES]
@@ -436,6 +454,14 @@ class ScoreL(Score):
         self.df[field_names.HAZARDOUS_WASTE_LOW_INCOME_FIELD] = (
             tsdf_sites_threshold & self.df[field_names.FPL_200_SERIES]
         )
+        self.df[field_names.AIR_TOXICS_CANCER_RISK_LOW_INCOME_FIELD] = (
+            air_toxics_cancer_risk_threshold
+            & self.df[field_names.FPL_200_SERIES]
+        )
+        self.df[field_names.RESPIRATORY_HAZARD_LOW_INCOME_FIELD] = (
+            respiratory_hazard_risk_threshold
+            & self.df[field_names.FPL_200_SERIES]
+        )
 
         self._increment_total_eligibility_exceeded(
             pollution_eligibility_columns