Adding island area indicators to the tiles (#1213)

This updates the backend to produce tile data with island indicators / island fields. 

Contains:
- new tile codes for island data
- threshold column that specifies number of thresholds to show
- ui experience column that specifies which ui experience to show

TODO: Drop the logger info message from main :)

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

@@ -77,6 +77,29 @@ PERCENT_PREFIXES_SUFFIXES = [
 ]
 TILES_ROUND_NUM_DECIMALS = 2
 
+# The following constants and fields get used by the front end to change the side panel.
+# The islands, Puerto Rico and the nation all have different
+# data available, and as a consequence, show a different number of fields.
+
+# Controlling Tile user experience columns
+THRESHOLD_COUNT_TO_SHOW_FIELD_NAME = "Thresholds"
+TILES_ISLAND_AREAS_THRESHOLD_COUNT = 4
+TILES_PUERTO_RICO_THRESHOLD_COUNT = 5
+TILES_NATION_THRESHOLD_COUNT = 21
+
+# Note that the FIPS code is a string
+# The FIPS codes listed are:
+# 60: American Samoa, 66: Guam, 69: N. Mariana Islands, 78: US Virgin Islands
+TILES_ISLAND_AREA_FIPS_CODES = ["60", "66", "69", "78"]
+TILES_PUERTO_RICO_FIPS_CODE = ["72"]
+
+# Constant to reflect UI Experience version
+# "Nation" referring to 50 states and DC is from Census
+USER_INTERFACE_EXPERIENCE_FIELD_NAME = "UI Experience"
+NATION_USER_EXPERIENCE = "Nation"
+PUERTO_RICO_USER_EXPERIENCE = "Puerto Rico"
+ISLAND_AREAS_USER_EXPERIENCE = "Island Areas"
+
 # FEMA rounding columns
 FEMA_ROUND_NUM_COLUMNS = [
     field_names.EXPECTED_BUILDING_LOSS_RATE_FIELD,
@@ -166,9 +189,20 @@ TILES_SCORE_COLUMNS = {
     field_names.FPL_200_AND_COLLEGE_ATTENDANCE_SERIES: "FPL200S",
     field_names.THRESHOLD_COUNT: "TC",
     field_names.ISLAND_AREAS_UNEMPLOYMENT_LOW_HS_EDUCATION_FIELD: "IAULHSE",
-    field_names.ISLAND_AREAS_POVERTY_LOW_HS_EDUCATION_FIELD: "ISPLHSE",
+    field_names.ISLAND_AREAS_POVERTY_LOW_HS_EDUCATION_FIELD: "IAPLHSE",
     field_names.ISLAND_AREAS_LOW_MEDIAN_INCOME_LOW_HS_EDUCATION_FIELD: "IALMILHSE",
     field_names.ISLAND_AREAS_LOW_HS_EDUCATION_FIELD: "IALHE",
+    # Percentiles for Island areas' workforce columns
+    field_names.LOW_CENSUS_DECENNIAL_AREA_MEDIAN_INCOME_PERCENT_FIELD_2009
+    + field_names.PERCENTILE_FIELD_SUFFIX: "IALMILHSE_PFS",
+    field_names.CENSUS_DECENNIAL_POVERTY_LESS_THAN_100_FPL_FIELD_2009
+    + field_names.ISLAND_AREAS_PERCENTILE_ADJUSTMENT_FIELD
+    + field_names.PERCENTILE_FIELD_SUFFIX: "IAPLHSE_PFS",
+    field_names.CENSUS_DECENNIAL_UNEMPLOYMENT_FIELD_2009
+    + field_names.ISLAND_AREAS_PERCENTILE_ADJUSTMENT_FIELD
+    + field_names.PERCENTILE_FIELD_SUFFIX: "IAULHSE_PFS",
+    # Percentage of HS Degree completion for Islands
+    field_names.CENSUS_DECENNIAL_HIGH_SCHOOL_ED_FIELD_2009: "IAHSEF",
 }
 
 # columns to round floats to 2 decimals
@@ -202,6 +236,18 @@ TILES_SCORE_FLOAT_COLUMNS = [
     field_names.TSDF_FIELD + field_names.PERCENTILE_FIELD_SUFFIX,
     field_names.TRAFFIC_FIELD + field_names.PERCENTILE_FIELD_SUFFIX,
     field_names.UNEMPLOYMENT_FIELD + field_names.PERCENTILE_FIELD_SUFFIX,
+    # Percentiles for Island areas' workforce columns
+    # To be clear: the island areas pull from 2009 census. PR does not.
+    field_names.LOW_CENSUS_DECENNIAL_AREA_MEDIAN_INCOME_PERCENT_FIELD_2009
+    + field_names.PERCENTILE_FIELD_SUFFIX,
+    field_names.CENSUS_DECENNIAL_POVERTY_LESS_THAN_100_FPL_FIELD_2009
+    + field_names.ISLAND_AREAS_PERCENTILE_ADJUSTMENT_FIELD
+    + field_names.PERCENTILE_FIELD_SUFFIX,
+    field_names.CENSUS_DECENNIAL_UNEMPLOYMENT_FIELD_2009
+    + field_names.ISLAND_AREAS_PERCENTILE_ADJUSTMENT_FIELD
+    + field_names.PERCENTILE_FIELD_SUFFIX,
+    # Island areas HS degree attainment rate
+    field_names.CENSUS_DECENNIAL_HIGH_SCHOOL_ED_FIELD_2009,
     field_names.LOW_HS_EDUCATION_LOW_HIGHER_ED_FIELD,
     field_names.ISLAND_AREAS_LOW_HS_EDUCATION_FIELD,
     field_names.WASTEWATER_FIELD + field_names.PERCENTILE_FIELD_SUFFIX,
@@ -297,4 +343,12 @@ DOWNLOADABLE_SCORE_COLUMNS = [
     field_names.ISLAND_AREAS_UNEMPLOYMENT_LOW_HS_EDUCATION_FIELD,
     field_names.ISLAND_AREAS_POVERTY_LOW_HS_EDUCATION_FIELD,
     field_names.ISLAND_AREAS_LOW_MEDIAN_INCOME_LOW_HS_EDUCATION_FIELD,
+    field_names.LOW_CENSUS_DECENNIAL_AREA_MEDIAN_INCOME_PERCENT_FIELD_2009
+    + field_names.PERCENTILE_FIELD_SUFFIX,
+    field_names.CENSUS_DECENNIAL_POVERTY_LESS_THAN_100_FPL_FIELD_2009
+    + field_names.ISLAND_AREAS_PERCENTILE_ADJUSTMENT_FIELD
+    + field_names.PERCENTILE_FIELD_SUFFIX,
+    field_names.CENSUS_DECENNIAL_UNEMPLOYMENT_FIELD_2009
+    + field_names.ISLAND_AREAS_PERCENTILE_ADJUSTMENT_FIELD
+    + field_names.PERCENTILE_FIELD_SUFFIX,
 ]

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

@@ -63,7 +63,7 @@ class PostScoreETL(ExtractTransformLoad):
             score_path, dtype={self.GEOID_TRACT_FIELD_NAME: "string"}
         )
 
-        # Convert total population to an int:
+        # Convert total population to an int
         df["Total population"] = df["Total population"].astype(
             int, errors="ignore"
         )
@@ -234,6 +234,36 @@ class PostScoreETL(ExtractTransformLoad):
             axis=0,
         )
 
+        logger.info("Adding fields for island areas and Puerto Rico")
+        # The below operation constructs variables for the front end.
+        # Since the Island Areas, Puerto Rico, and the nation all have a different
+        # set of available data, each has its own user experience.
+
+        # First, we identify which user experience -- Puerto Rico, islands, or nation --
+        # a row pertains to using the FIPS codes
+        fips_code_series = score_tiles[field_names.GEOID_TRACT_FIELD].str[:2]
+        score_tiles[constants.USER_INTERFACE_EXPERIENCE_FIELD_NAME] = np.where(
+            fips_code_series.isin(constants.TILES_PUERTO_RICO_FIPS_CODE),
+            constants.PUERTO_RICO_USER_EXPERIENCE,
+            np.where(
+                fips_code_series.isin(constants.TILES_ISLAND_AREA_FIPS_CODES),
+                constants.ISLAND_AREAS_USER_EXPERIENCE,
+                constants.NATION_USER_EXPERIENCE,
+            ),
+        )
+
+        # Next, we determine how many thresholds the front end should show, entirely
+        # based on the variable for user interface experience.
+        score_tiles[constants.THRESHOLD_COUNT_TO_SHOW_FIELD_NAME] = score_tiles[
+            constants.USER_INTERFACE_EXPERIENCE_FIELD_NAME
+        ].map(
+            {
+                constants.PUERTO_RICO_USER_EXPERIENCE: constants.TILES_PUERTO_RICO_THRESHOLD_COUNT,
+                constants.ISLAND_AREAS_USER_EXPERIENCE: constants.TILES_ISLAND_AREAS_THRESHOLD_COUNT,
+                constants.NATION_USER_EXPERIENCE: constants.TILES_NATION_THRESHOLD_COUNT,
+            }
+        )
+
         # create indexes
         score_tiles = score_tiles.rename(
             columns=constants.TILES_SCORE_COLUMNS,
@@ -306,6 +336,7 @@ class PostScoreETL(ExtractTransformLoad):
             transformed_states,
             transformed_score,
         )
+
         self.output_score_tiles_df = self._create_tile_data(
             output_score_county_state_merged_df
         )