Territories in the 65th percentile of low income are added is disadvantaged

data/data-pipeline/data_pipeline/application.py

@@ -33,6 +33,30 @@ dataset_cli_help = "Grab the data from either 'local' for local access or 'aws'
 
 LOG_LINE_WIDTH = 60
 
+use_cache_option = click.option(
+    "-u",
+    "--use-cache",
+    is_flag=True,
+    default=False,
+    help="Check if data source has been downloaded already, and if it has, use the cached version of the data source.",
+)
+
+dataset_option = click.option(
+    "-d",
+    "--dataset",
+    required=False,
+    type=str,
+    help=dataset_cli_help,
+)
+
+data_source_option = click.option(
+    "-s",
+    "--data-source",
+    default="local",
+    required=False,
+    type=str,
+    help=dataset_cli_help,
+)
 
 @click.group()
 def cli():
@@ -51,7 +75,6 @@ def census_cleanup():
     census_reset(data_path)
 
     log_goodbye()
-    sys.exit()
 
 
 @cli.command(help="Clean up all data folders")
@@ -70,7 +93,6 @@ def data_cleanup():
     geo_score_folder_cleanup()
 
     log_goodbye()
-    sys.exit()
 
 
 @cli.command(
@@ -82,13 +104,7 @@ def data_cleanup():
     is_flag=True,
     help="Upload to AWS S3 a zipped archive of the census data.",
 )
-@click.option(
-    "-u",
-    "--use-cache",
-    is_flag=True,
-    default=False,
-    help="Check if data source has been downloaded already, and if it has, use the cached version of the data source.",
-)
+@use_cache_option
 def census_data_download(zip_compress, use_cache):
     """CLI command to download all census shape files from the Census FTP and extract the geojson
     to generate national and by state Census Block Group CSVs"""
@@ -105,18 +121,10 @@ def census_data_download(zip_compress, use_cache):
         zip_census_data()
 
     log_goodbye()
-    sys.exit()
 
 
 @cli.command(help="Retrieve census data from source")
-@click.option(
-    "-s",
-    "--data-source",
-    default="local",
-    required=False,
-    type=str,
-    help=dataset_cli_help,
-)
+@data_source_option
 def pull_census_data(data_source: str):
 
     log_title("Pull Census Data")
@@ -126,26 +134,13 @@ def pull_census_data(data_source: str):
     check_census_data_source(data_path, data_source)
 
     log_goodbye()
-    sys.exit()
 
 
 @cli.command(
     help="Run all ETL processes or a specific one",
 )
-@click.option(
-    "-d",
-    "--dataset",
-    required=False,
-    type=str,
-    help=dataset_cli_help,
-)
-@click.option(
-    "-u",
-    "--use-cache",
-    is_flag=True,
-    default=False,
-    help="Check if data source has been downloaded already, and if it has, use the cached version of the data source.",
-)
+@dataset_option
+@use_cache_option
 def etl_run(dataset: str, use_cache: bool):
     """Run a specific or all ETL processes
 
@@ -161,7 +156,6 @@ def etl_run(dataset: str, use_cache: bool):
     etl_runner(dataset, use_cache)
 
     log_goodbye()
-    sys.exit()
 
 
 @cli.command(
@@ -178,19 +172,12 @@ def score_run():
     score_generate()
 
     log_goodbye()
-    sys.exit()
 
 
 @cli.command(
     help="Run ETL + Score Generation",
 )
-@click.option(
-    "-u",
-    "--use-cache",
-    is_flag=True,
-    default=False,
-    help="Check if data source has been downloaded already, and if it has, use the cached version of the data source.",
-)
+@use_cache_option
 def score_full_run(use_cache: bool):
     """CLI command to run ETL and generate the score in one command"""
     log_title("Score Full Run", "Run ETL and Generate Score (no tiles)")
@@ -207,20 +194,12 @@ def score_full_run(use_cache: bool):
     score_generate()
 
     log_goodbye()
-    sys.exit()
 
 
 @cli.command(
     help="Run etl_score_post to create score csv, tile csv, and downloadable zip"
 )
-@click.option(
-    "-s",
-    "--data-source",
-    default="local",
-    required=False,
-    type=str,
-    help=dataset_cli_help,
-)
+@data_source_option
 def generate_score_post(data_source: str):
     """CLI command to generate score, tile, and downloadable files
 
@@ -244,18 +223,10 @@ def generate_score_post(data_source: str):
     score_post(data_source)
 
     log_goodbye()
-    sys.exit()
 
 
 @cli.command(help="Generate GeoJSON files with scores baked in")
-@click.option(
-    "-s",
-    "--data-source",
-    default="local",
-    required=False,
-    type=str,
-    help=dataset_cli_help,
-)
+@data_source_option
 def geo_score(data_source: str):
     """CLI command to combine score with GeoJSON data and generate low and high files
 
@@ -280,7 +251,6 @@ def geo_score(data_source: str):
     score_geo(data_source=data_source)
 
     log_goodbye()
-    sys.exit()
 
 
 @cli.command(
@@ -304,7 +274,6 @@ def generate_map_tiles(generate_tribal_layer):
     generate_tiles(data_path, generate_tribal_layer)
 
     log_goodbye()
-    sys.exit()
 
 
 @cli.command(
@@ -316,21 +285,8 @@ def generate_map_tiles(generate_tribal_layer):
     is_flag=True,
     help="Check if data run has been run before, and don't run it if so.",
 )
-@click.option(
-    "-s",
-    "--data-source",
-    default="local",
-    required=False,
-    type=str,
-    help=dataset_cli_help,
-)
-@click.option(
-    "-u",
-    "--use-cache",
-    is_flag=True,
-    default=False,
-    help="Check if data source has been downloaded already, and if it has, use the cached version of the data source.",
-)
+@data_source_option
+@use_cache_option
 def data_full_run(check: bool, data_source: str, use_cache: bool):
     """CLI command to run ETL, score, JSON combine and generate tiles in one command
 
@@ -388,19 +344,12 @@ def data_full_run(check: bool, data_source: str, use_cache: bool):
     call(cmd, shell=True)
 
     log_goodbye()
-    sys.exit()
 
 
 @cli.command(
     help="Print data sources for all ETL processes (or a specific one)",
 )
-@click.option(
-    "-d",
-    "--dataset",
-    required=False,
-    type=str,
-    help=dataset_cli_help,
-)
+@dataset_option
 def print_data_sources(dataset: str):
     """Print data sources for all ETL processes (or a specific one)
 
@@ -421,26 +370,13 @@ def print_data_sources(dataset: str):
         log_info(s)
 
     log_goodbye()
-    sys.exit()
 
 
 @cli.command(
     help="Fetch data sources for all ETL processes (or a specific one)",
 )
-@click.option(
-    "-d",
-    "--dataset",
-    required=False,
-    type=str,
-    help=dataset_cli_help,
-)
-@click.option(
-    "-u",
-    "--use-cache",
-    is_flag=True,
-    default=False,
-    help="Check if data source has been downloaded already, and if it has, use the cached version of the data source.",
-)
+@dataset_option
+@use_cache_option
 def extract_data_sources(dataset: str, use_cache: bool):
     """Extract and cache data source(s) for all ETL processes (or a specific one)
 
@@ -457,19 +393,12 @@ def extract_data_sources(dataset: str, use_cache: bool):
     extract_ds(dataset, use_cache)
 
     log_goodbye()
-    sys.exit()
 
 
 @cli.command(
     help="Clear data source cache for all ETL processes (or a specific one)",
 )
-@click.option(
-    "-d",
-    "--dataset",
-    required=False,
-    type=str,
-    help=dataset_cli_help,
-)
+@dataset_option
 def clear_data_source_cache(dataset: str):
     """Clear data source(s) cache for all ETL processes (or a specific one)
 
@@ -485,8 +414,32 @@ def clear_data_source_cache(dataset: str):
     clear_ds_cache(dataset)
 
     log_goodbye()
-    sys.exit()
 
+@cli.command(
+    help="Generate scoring and tiles",
+)
+@click.pass_context
+def full_post_etl(ctx):
+    """Generate scoring and tiles"""
+    ctx.invoke(score_run)
+    ctx.invoke(generate_score_post, data_source=None)
+    ctx.invoke(geo_score, data_source=None)
+    ctx.invoke(generate_map_tiles, generate_tribal_layer=False)
+
+
+@cli.command(
+    help="Run all downloads, extracts, and generate scores and tiles",
+)
+@use_cache_option
+@click.pass_context
+def full_run(ctx, use_cache):
+    """Run all downloads, ETLs, and generate scores and tiles"""
+    if not use_cache:
+        ctx.invoke(data_cleanup)
+    ctx.invoke(census_data_download, zip_compress=False, use_cache=use_cache)
+    ctx.invoke(extract_data_sources, dataset=None, use_cache=use_cache)
+    ctx.invoke(etl_run, dataset=None, use_cache=use_cache)
+    ctx.invoke(full_post_etl)
 
 def log_title(title: str, subtitle: str = None):
     """Logs a title in our fancy title format"""

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

@@ -472,6 +472,7 @@ class ScoreETL(ExtractTransformLoad):
             field_names.EXPECTED_POPULATION_LOSS_RATE_FIELD,
             field_names.CENSUS_DECENNIAL_HIGH_SCHOOL_ED_FIELD_2009,
             field_names.CENSUS_DECENNIAL_POVERTY_LESS_THAN_100_FPL_FIELD_2009,
+            field_names.CENSUS_DECENNIAL_POVERTY_LESS_THAN_200_FPL_FIELD_2009,
             field_names.CENSUS_DECENNIAL_UNEMPLOYMENT_FIELD_2009,
             field_names.CENSUS_UNEMPLOYMENT_FIELD_2010,
             field_names.CENSUS_POVERTY_LESS_THAN_100_FPL_FIELD_2010,

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

@@ -187,6 +187,9 @@ CENSUS_DECENNIAL_MEDIAN_INCOME_2009 = "Median household income in 2009 ($)"
 CENSUS_DECENNIAL_POVERTY_LESS_THAN_100_FPL_FIELD_2009 = (
     "Percentage households below 100% of federal poverty line in 2009"
 )
+CENSUS_DECENNIAL_POVERTY_LESS_THAN_200_FPL_FIELD_2009 = (
+    "Percentage households below 200% of federal poverty line in 2009"
+)
 CENSUS_DECENNIAL_HIGH_SCHOOL_ED_FIELD_2009 = "Percent individuals age 25 or over with less than high school degree in 2009"
 CENSUS_DECENNIAL_UNEMPLOYMENT_FIELD_2009 = "Unemployment (percent) in 2009"
 CENSUS_DECENNIAL_TOTAL_POPULATION_FIELD_2009 = "Total population in 2009"
@@ -226,6 +229,10 @@ COMBINED_POVERTY_LESS_THAN_100_FPL_FIELD_2010 = (
     "Percentage households below 100% of federal poverty line in 2009 (island areas) "
     "and 2010 (states and PR)"
 )
+COMBINED_POVERTY_LESS_THAN_200_FPL_FIELD_2010 = (
+    "Percentage households below 200% of federal poverty line in 2009 (island areas) "
+    "and 2010 (states and PR)"
+)
 
 # Urban Rural Map
 URBAN_HEURISTIC_FIELD = "Urban Heuristic Flag"

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

@@ -1013,6 +1013,47 @@ class ScoreNarwhal(Score):
             self.df[field_names.SCORE_N_COMMUNITIES],
         )
 
+    def _mark_territory_dacs(self) -> None:
+        """Territory tracts that are flagged as low income are Score N communities.
+        """
+        self.df[field_names.SCORE_N_COMMUNITIES] = np.where(
+            self.df[field_names.GEOID_TRACT_FIELD]
+                .str.startswith(tuple(constants.TILES_ISLAND_AREA_FIPS_CODES)) &
+            self.df[field_names.FPL_200_SERIES_IMPUTED_AND_ADJUSTED],
+            True,
+            self.df[field_names.SCORE_N_COMMUNITIES],
+        )
+
+    def _mark_poverty_flag(self) -> None:
+        """Combine poverty less than 200% for territories and update the income flag."""
+        # First we set the low income flag for non-territories by themselves, this
+        # way we don't change the original outcome if we include territories.
+        self.df[field_names.FPL_200_SERIES_IMPUTED_AND_ADJUSTED] = (
+            self.df[
+                # UPDATE: Pull the imputed poverty statistic
+                field_names.POVERTY_LESS_THAN_200_FPL_IMPUTED_FIELD
+                + field_names.PERCENTILE_FIELD_SUFFIX
+            ]
+            >= self.LOW_INCOME_THRESHOLD
+        )
+
+        # Now we set the low income flag only for territories, but we need to rank them
+        # with all other tracts.
+        (
+            self.df,
+            island_areas_poverty_200_criteria_field_name,
+        ) = self._combine_island_areas_with_states_and_set_thresholds(
+            df=self.df,
+            column_from_island_areas=field_names.CENSUS_DECENNIAL_POVERTY_LESS_THAN_200_FPL_FIELD_2009,
+            column_from_decennial_census=field_names.POVERTY_LESS_THAN_200_FPL_IMPUTED_FIELD,
+            combined_column_name=field_names.COMBINED_POVERTY_LESS_THAN_200_FPL_FIELD_2010,
+            threshold_cutoff_for_island_areas=self.LOW_INCOME_THRESHOLD,
+        )
+        self.df.loc[self.df[field_names.GEOID_TRACT_FIELD].str.startswith(tuple(constants.TILES_ISLAND_AREA_FIPS_CODES)),
+                    field_names.FPL_200_SERIES_IMPUTED_AND_ADJUSTED] = (
+            self.df[island_areas_poverty_200_criteria_field_name] >= self.LOW_INCOME_THRESHOLD
+        )
+
     def _get_percent_of_tract_that_is_dac(self) -> float:
         """Per the October 7th compromise (#1988),
         tracts can be partially DACs if some portion of the tract is tribal land.
@@ -1034,14 +1075,7 @@ class ScoreNarwhal(Score):
         logger.debug("Adding Score Narhwal")
         self.df[field_names.THRESHOLD_COUNT] = 0
 
-        self.df[field_names.FPL_200_SERIES_IMPUTED_AND_ADJUSTED] = (
-            self.df[
-                # UPDATE: Pull the imputed poverty statistic
-                field_names.POVERTY_LESS_THAN_200_FPL_IMPUTED_FIELD
-                + field_names.PERCENTILE_FIELD_SUFFIX
-            ]
-            >= self.LOW_INCOME_THRESHOLD
-        )
+        self._mark_poverty_flag()
 
         self.df[field_names.N_CLIMATE] = self._climate_factor()
         self.df[field_names.N_ENERGY] = self._energy_factor()
@@ -1065,6 +1099,7 @@ class ScoreNarwhal(Score):
         self.df[field_names.CATEGORY_COUNT] = self.df[factors].sum(axis=1)
         self.df[field_names.SCORE_N_COMMUNITIES] = self.df[factors].any(axis=1)
         self._mark_tribal_dacs()
+        self._mark_territory_dacs()
         self.df[
             field_names.SCORE_N_COMMUNITIES
             + field_names.PERCENTILE_FIELD_SUFFIX

data/data-pipeline/data_pipeline/tests/score/test_score_narwhal_methods.py

@@ -2,22 +2,20 @@
 import pandas as pd
 import pytest
 from data_pipeline.config import settings
+from data_pipeline.etl.score import constants
 from data_pipeline.etl.score.etl_score import ScoreETL
 from data_pipeline.score import field_names
+from data_pipeline.score.score_narwhal import ScoreNarwhal
 from data_pipeline.utils import get_module_logger
 
+
 logger = get_module_logger(__name__)
 
+TEST_DATA_FOLDER = settings.APP_ROOT / "tests" / "score" / "test_utils" / "data"
 
 @pytest.fixture
 def toy_score_df(scope="module"):
-    return pd.read_csv(
-        settings.APP_ROOT
-        / "tests"
-        / "score"
-        / "test_utils"
-        / "data"
-        / "test_drop_tracts_from_percentile.csv",
+    return pd.read_csv(TEST_DATA_FOLDER / "test_drop_tracts_from_percentile.csv",
         dtype={field_names.GEOID_TRACT_FIELD: str},
     )
 
@@ -83,3 +81,44 @@ def test_drop_all_tracts(toy_score_df):
         toy_score_df,
         drop_tracts=toy_score_df[field_names.GEOID_TRACT_FIELD].to_list(),
     ), "Percentile in score fails when we drop all tracts"
+
+
+def test_mark_territory_dacs():
+    test_data = pd.read_csv(TEST_DATA_FOLDER / "test_mark_territory_dacs.csv",
+        dtype={field_names.GEOID_TRACT_FIELD: str},
+    )
+    # Sanity check on the input data
+    assert not test_data[field_names.SCORE_N_COMMUNITIES].all()
+
+    scorer = ScoreNarwhal(test_data)
+    scorer._mark_territory_dacs()
+    territory_filter = test_data[field_names.GEOID_TRACT_FIELD].str.startswith(tuple(constants.TILES_ISLAND_AREA_FIPS_CODES))
+    # Check territories are set to true
+    expected_new_dacs_filter = (
+        test_data[field_names.GEOID_TRACT_FIELD].isin(['60050951100', '66010951100', '69110001101', '78010990000'])
+    )
+    assert test_data.loc[expected_new_dacs_filter, field_names.SCORE_N_COMMUNITIES].all()
+    # Non-territories are still false
+    assert not test_data.loc[~expected_new_dacs_filter, field_names.SCORE_N_COMMUNITIES].all()
+
+
+def test_mark_poverty_flag():
+    test_data = pd.read_csv(TEST_DATA_FOLDER / "test_mark_poverty_flag.csv",
+        dtype={field_names.GEOID_TRACT_FIELD: str},
+    )
+    # Sanity check on the input data
+    assert not test_data[field_names.FPL_200_SERIES_IMPUTED_AND_ADJUSTED].all()
+
+    scorer = ScoreNarwhal(test_data)
+    scorer._mark_poverty_flag()
+    expected_low_income_filter = (
+        test_data[field_names.GEOID_TRACT_FIELD].isin(['36087011302', '66010951100', '78010990000'])
+    )
+    # Three tracts are set to true
+    assert (
+        test_data[expected_low_income_filter][field_names.FPL_200_SERIES_IMPUTED_AND_ADJUSTED].all()
+    )
+    # Everything else is false
+    assert (
+        not test_data[~expected_low_income_filter][field_names.FPL_200_SERIES_IMPUTED_AND_ADJUSTED].all()
+    )

data/data-pipeline/data_pipeline/tests/score/test_utils/data/test_mark_poverty_flag.csv

@@ -0,0 +1,8 @@
+GEOID10_TRACT,Percentage households below 200% of federal poverty line in 2009,"Percent of individuals below 200% Federal Poverty Line, imputed and adjusted","Percent of individuals below 200% Federal Poverty Line, imputed and adjusted (percentile)",Is low income (imputed and adjusted)?
+01071950300,,0.1,0.1,False
+36087011302,,0.7,0.7,False
+72119130701,,0.5,0.5,False
+60050951100,0.1,,,False
+66010951100,0.7,,,False
+69110001100,0.5,,,False
+78010990000,0.9,,,False

data/data-pipeline/data_pipeline/tests/score/test_utils/data/test_mark_territory_dacs.csv

@@ -0,0 +1,9 @@
+GEOID10_TRACT,Is low income (imputed and adjusted)?,Definition N (communities)
+01071950300,True,False
+36087011302,False,False
+72119130701,True,False
+60050951100,True,False
+66010951100,True,False
+69110001100,False,False
+69110001101,True,False
+78010990000,True,False

data/data-pipeline/pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "justice40-data-pipeline"
-version = "0.1.0"
+version = "1.0.1"
 description = "ETL, Score and Map Generation of Justice 40 Tool"
 authors = ["Justice40 Engineering <j40-engineering@lists.usds.gov>"]
 keywords = ["justice40", "environmental_justice", "python", "etl"]