From d298f7dedbb4c52bd36fdedaee6cba6347363dcf Mon Sep 17 00:00:00 2001 From: Carlos Felix Date: Tue, 3 Dec 2024 16:11:56 -0500 Subject: [PATCH 01/28] Removed unused files --- .../data_pipeline/score/score_a.py | 19 - .../data_pipeline/score/score_b.py | 21 - .../data_pipeline/score/score_c.py | 102 -- .../data_pipeline/score/score_d.py | 34 - .../data_pipeline/score/score_f.py | 97 -- .../data_pipeline/score/score_g.py | 34 - .../data_pipeline/score/score_h.py | 32 - .../data_pipeline/score/score_i.py | 33 - .../data_pipeline/score/score_k.py | 33 - .../data_pipeline/score/score_l.py | 690 -------------- .../data_pipeline/score/score_m.py | 888 ------------------ 11 files changed, 1983 deletions(-) delete mode 100644 data/data-pipeline/data_pipeline/score/score_a.py delete mode 100644 data/data-pipeline/data_pipeline/score/score_b.py delete mode 100644 data/data-pipeline/data_pipeline/score/score_c.py delete mode 100644 data/data-pipeline/data_pipeline/score/score_d.py delete mode 100644 data/data-pipeline/data_pipeline/score/score_f.py delete mode 100644 data/data-pipeline/data_pipeline/score/score_g.py delete mode 100644 data/data-pipeline/data_pipeline/score/score_h.py delete mode 100644 data/data-pipeline/data_pipeline/score/score_i.py delete mode 100644 data/data-pipeline/data_pipeline/score/score_k.py delete mode 100644 data/data-pipeline/data_pipeline/score/score_l.py delete mode 100644 data/data-pipeline/data_pipeline/score/score_m.py diff --git a/data/data-pipeline/data_pipeline/score/score_a.py b/data/data-pipeline/data_pipeline/score/score_a.py deleted file mode 100644 index 0788f094..00000000 --- a/data/data-pipeline/data_pipeline/score/score_a.py +++ /dev/null @@ -1,19 +0,0 @@ -import data_pipeline.score.field_names as field_names -import pandas as pd -from data_pipeline.score.score import Score -from data_pipeline.utils import get_module_logger - -logger = get_module_logger(__name__) - - -class ScoreA(Score): - def add_columns(self) -> pd.DataFrame: - logger.debug("Adding Score A") - self.df[field_names.SCORE_A] = self.df[ - [ - field_names.POVERTY_FIELD + field_names.PERCENTILE_FIELD_SUFFIX, - field_names.HIGH_SCHOOL_ED_FIELD - + field_names.PERCENTILE_FIELD_SUFFIX, - ] - ].mean(axis=1) - return self.df diff --git a/data/data-pipeline/data_pipeline/score/score_b.py b/data/data-pipeline/data_pipeline/score/score_b.py deleted file mode 100644 index 2e980b3d..00000000 --- a/data/data-pipeline/data_pipeline/score/score_b.py +++ /dev/null @@ -1,21 +0,0 @@ -import data_pipeline.score.field_names as field_names -import pandas as pd -from data_pipeline.score.score import Score -from data_pipeline.utils import get_module_logger - -logger = get_module_logger(__name__) - - -class ScoreB(Score): - def add_columns(self) -> pd.DataFrame: - logger.debug("Adding Score B") - self.df[field_names.SCORE_B] = ( - self.df[ - field_names.POVERTY_FIELD + field_names.PERCENTILE_FIELD_SUFFIX - ] - * self.df[ - field_names.HIGH_SCHOOL_ED_FIELD - + field_names.PERCENTILE_FIELD_SUFFIX - ] - ) - return self.df diff --git a/data/data-pipeline/data_pipeline/score/score_c.py b/data/data-pipeline/data_pipeline/score/score_c.py deleted file mode 100644 index b91b7417..00000000 --- a/data/data-pipeline/data_pipeline/score/score_c.py +++ /dev/null @@ -1,102 +0,0 @@ -from collections import namedtuple - -import data_pipeline.score.field_names as field_names -import pandas as pd -from data_pipeline.score.score import Score -from data_pipeline.utils import get_module_logger - -logger = get_module_logger(__name__) - - -class ScoreC(Score): - def __init__(self, df: pd.DataFrame) -> None: - Bucket = namedtuple(typename="Bucket", field_names=["name", "fields"]) - - # Note: we use percentiles for every field below. - # To do so, we add the percentile suffix to all the field names. - self.BUCKET_SOCIOECONOMIC = Bucket( - field_names.C_SOCIOECONOMIC, - [ - field_names.HOUSEHOLDS_LINGUISTIC_ISO_FIELD - + field_names.PERCENTILE_FIELD_SUFFIX, - field_names.POVERTY_FIELD + field_names.PERCENTILE_FIELD_SUFFIX, - field_names.HIGH_SCHOOL_ED_FIELD - + field_names.PERCENTILE_FIELD_SUFFIX, - field_names.UNEMPLOYMENT_FIELD - + field_names.PERCENTILE_FIELD_SUFFIX, - field_names.HOUSING_BURDEN_FIELD - + field_names.PERCENTILE_FIELD_SUFFIX, - ], - ) - self.BUCKET_SENSITIVE = Bucket( - field_names.C_SENSITIVE, - [ - field_names.UNDER_5_FIELD + field_names.PERCENTILE_FIELD_SUFFIX, - field_names.OVER_64_FIELD + field_names.PERCENTILE_FIELD_SUFFIX, - field_names.LINGUISTIC_ISO_FIELD - + field_names.PERCENTILE_FIELD_SUFFIX, - ], - ) - self.BUCKET_ENVIRONMENTAL = Bucket( - field_names.C_ENVIRONMENTAL, - [ - field_names.RMP_FIELD + field_names.PERCENTILE_FIELD_SUFFIX, - field_names.TSDF_FIELD + field_names.PERCENTILE_FIELD_SUFFIX, - field_names.NPL_FIELD + field_names.PERCENTILE_FIELD_SUFFIX, - field_names.WASTEWATER_FIELD - + field_names.PERCENTILE_FIELD_SUFFIX, - field_names.LEAD_PAINT_FIELD - + field_names.PERCENTILE_FIELD_SUFFIX, - ], - ) - self.BUCKET_EXPOSURES = Bucket( - field_names.C_EXPOSURES, - [ - field_names.AIR_TOXICS_CANCER_RISK_FIELD - + field_names.PERCENTILE_FIELD_SUFFIX, - 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, - field_names.OZONE_FIELD + field_names.PERCENTILE_FIELD_SUFFIX, - field_names.TRAFFIC_FIELD + field_names.PERCENTILE_FIELD_SUFFIX, - ], - ) - self.BUCKETS = [ - self.BUCKET_SOCIOECONOMIC, - self.BUCKET_SENSITIVE, - self.BUCKET_ENVIRONMENTAL, - self.BUCKET_EXPOSURES, - ] - super().__init__(df) - - # "CalEnviroScreen for the US" score - def add_columns(self) -> pd.DataFrame: - logger.debug("Adding Score C") - # Average all the percentile values in each bucket into a single score for each of the four buckets. - for bucket in self.BUCKETS: - self.df[bucket.name] = self.df[bucket.fields].mean(axis=1) - - # Combine the score from the two Exposures and Environmental Effects buckets - # into a single score called "Pollution Burden". - # The math for this score is: - # (1.0 * Exposures Score + 0.5 * Environment Effects score) / 1.5. - self.df[field_names.AGGREGATION_POLLUTION_FIELD] = ( - 1.0 * self.df[self.BUCKET_EXPOSURES.name] - + 0.5 * self.df[self.BUCKET_ENVIRONMENTAL.name] - ) / 1.5 - - # Average the score from the two Sensitive populations and - # Socioeconomic factors buckets into a single score called - # "Population Characteristics". - self.df[field_names.AGGREGATION_POPULATION_FIELD] = self.df[ - [self.BUCKET_SENSITIVE.name, self.BUCKET_SOCIOECONOMIC.name] - ].mean(axis=1) - - # Multiply the "Pollution Burden" score and the "Population Characteristics" - # together to produce the cumulative impact score. - self.df[field_names.SCORE_C] = ( - self.df[field_names.AGGREGATION_POLLUTION_FIELD] - * self.df[field_names.AGGREGATION_POPULATION_FIELD] - ) - return self.df diff --git a/data/data-pipeline/data_pipeline/score/score_d.py b/data/data-pipeline/data_pipeline/score/score_d.py deleted file mode 100644 index 08e55f01..00000000 --- a/data/data-pipeline/data_pipeline/score/score_d.py +++ /dev/null @@ -1,34 +0,0 @@ -import data_pipeline.score.field_names as field_names -import pandas as pd -from data_pipeline.score.score import Score -from data_pipeline.utils import get_module_logger - -logger = get_module_logger(__name__) - - -class ScoreD(Score): - def add_columns(self) -> pd.DataFrame: - logger.debug("Adding Scores D and E") - fields_to_use_in_score = [ - field_names.UNEMPLOYMENT_FIELD, - field_names.LINGUISTIC_ISO_FIELD, - field_names.HOUSING_BURDEN_FIELD, - field_names.POVERTY_FIELD, - field_names.HIGH_SCHOOL_ED_FIELD, - ] - - fields_min_max = [ - f"{field}{field_names.MIN_MAX_FIELD_SUFFIX}" - for field in fields_to_use_in_score - ] - fields_percentile = [ - f"{field}{field_names.PERCENTILE_FIELD_SUFFIX}" - for field in fields_to_use_in_score - ] - - # Calculate "Score D", which uses min-max normalization - # and calculate "Score E", which uses percentile normalization for the same fields - self.df[field_names.SCORE_D] = self.df[fields_min_max].mean(axis=1) - self.df[field_names.SCORE_E] = self.df[fields_percentile].mean(axis=1) - - return self.df diff --git a/data/data-pipeline/data_pipeline/score/score_f.py b/data/data-pipeline/data_pipeline/score/score_f.py deleted file mode 100644 index be38e514..00000000 --- a/data/data-pipeline/data_pipeline/score/score_f.py +++ /dev/null @@ -1,97 +0,0 @@ -import data_pipeline.score.field_names as field_names -import pandas as pd -from data_pipeline.score.score import Score -from data_pipeline.utils import get_module_logger - -logger = get_module_logger(__name__) - - -class ScoreF(Score): - # TODO Make variables and constants clearer (meaning and type) - - def add_columns(self) -> pd.DataFrame: - logger.debug("Adding Score F") - ami_and_high_school_field = "Low AMI, Low HS graduation" - meets_socio_field = "Meets socioeconomic criteria" - meets_burden_field = "Meets burden criteria" - - self.df[ami_and_high_school_field] = ( - self.df[field_names.MEDIAN_INCOME_AS_PERCENT_OF_STATE_FIELD] < 0.80 - ) & (self.df[field_names.HIGH_SCHOOL_ED_FIELD] > 0.2) - - self.df[meets_socio_field] = ( - self.df[ami_and_high_school_field] - | (self.df[field_names.POVERTY_FIELD] > 0.40) - | (self.df[field_names.LINGUISTIC_ISO_FIELD] > 0.10) - | (self.df[field_names.HIGH_SCHOOL_ED_FIELD] > 0.4) - ) - - self.df[meets_burden_field] = ( - ( - self.df[ - field_names.PM25_FIELD + field_names.PERCENTILE_FIELD_SUFFIX - ] - > 0.9 - ) - | ( - self.df[ - field_names.RESPIRATORY_HAZARD_FIELD - + field_names.PERCENTILE_FIELD_SUFFIX - ] - > 0.9 - ) - | ( - self.df[ - field_names.TRAFFIC_FIELD - + field_names.PERCENTILE_FIELD_SUFFIX - ] - > 0.9 - ) - | ( - self.df[ - field_names.LEAD_PAINT_FIELD - + field_names.PERCENTILE_FIELD_SUFFIX - ] - > 0.9 - ) - | ( - self.df[ - field_names.RMP_FIELD + field_names.PERCENTILE_FIELD_SUFFIX - ] - > 0.9 - ) - | ( - self.df[ - field_names.ASTHMA_FIELD - + field_names.PERCENTILE_FIELD_SUFFIX - ] - > 0.9 - ) - | ( - self.df[ - field_names.HEART_DISEASE_FIELD - + field_names.PERCENTILE_FIELD_SUFFIX - ] - > 0.9 - ) - | ( - self.df[ - field_names.CANCER_FIELD - + field_names.PERCENTILE_FIELD_SUFFIX - ] - > 0.9 - ) - | ( - self.df[ - field_names.DIABETES_FIELD - + field_names.PERCENTILE_FIELD_SUFFIX - ] - > 0.9 - ) - ) - - self.df[field_names.SCORE_F_COMMUNITIES] = ( - self.df[meets_socio_field] & self.df[meets_burden_field] - ) - - return self.df diff --git a/data/data-pipeline/data_pipeline/score/score_g.py b/data/data-pipeline/data_pipeline/score/score_g.py deleted file mode 100644 index d4e10726..00000000 --- a/data/data-pipeline/data_pipeline/score/score_g.py +++ /dev/null @@ -1,34 +0,0 @@ -import data_pipeline.score.field_names as field_names -import pandas as pd -from data_pipeline.score.score import Score -from data_pipeline.utils import get_module_logger - -logger = get_module_logger(__name__) - - -class ScoreG(Score): - def add_columns(self) -> pd.DataFrame: - logger.debug("Adding Score G") - - high_school_cutoff_threshold = 0.05 - - # Score G is now modified NMTC - self.df[field_names.SCORE_G_COMMUNITIES] = ( - (self.df[field_names.MEDIAN_INCOME_AS_PERCENT_OF_AMI_FIELD] < 0.8) - & ( - self.df[field_names.HIGH_SCHOOL_ED_FIELD] - > high_school_cutoff_threshold - ) - ) | ( - (self.df[field_names.POVERTY_LESS_THAN_100_FPL_FIELD] > 0.20) - & ( - self.df[field_names.HIGH_SCHOOL_ED_FIELD] - > high_school_cutoff_threshold - ) - ) - self.df[field_names.SCORE_G] = self.df[ - field_names.SCORE_G_COMMUNITIES - ].astype(int) - self.df["Score G (percentile)"] = self.df[field_names.SCORE_G] - - return self.df diff --git a/data/data-pipeline/data_pipeline/score/score_h.py b/data/data-pipeline/data_pipeline/score/score_h.py deleted file mode 100644 index 56168c53..00000000 --- a/data/data-pipeline/data_pipeline/score/score_h.py +++ /dev/null @@ -1,32 +0,0 @@ -import data_pipeline.score.field_names as field_names -import pandas as pd -from data_pipeline.score.score import Score -from data_pipeline.utils import get_module_logger - -logger = get_module_logger(__name__) - - -class ScoreH(Score): - def add_columns(self) -> pd.DataFrame: - logger.debug("Adding Score H") - - high_school_cutoff_threshold = 0.06 - - self.df[field_names.SCORE_H_COMMUNITIES] = ( - (self.df[field_names.MEDIAN_INCOME_AS_PERCENT_OF_AMI_FIELD] < 0.8) - & ( - self.df[field_names.HIGH_SCHOOL_ED_FIELD] - > high_school_cutoff_threshold - ) - ) | ( - (self.df[field_names.POVERTY_LESS_THAN_200_FPL_FIELD] > 0.40) - & ( - self.df[field_names.HIGH_SCHOOL_ED_FIELD] - > high_school_cutoff_threshold - ) - ) - self.df[field_names.SCORE_H] = self.df[ - field_names.SCORE_H_COMMUNITIES - ].astype(int) - - return self.df diff --git a/data/data-pipeline/data_pipeline/score/score_i.py b/data/data-pipeline/data_pipeline/score/score_i.py deleted file mode 100644 index 25f2c098..00000000 --- a/data/data-pipeline/data_pipeline/score/score_i.py +++ /dev/null @@ -1,33 +0,0 @@ -import data_pipeline.score.field_names as field_names -import pandas as pd -from data_pipeline.score.score import Score -from data_pipeline.utils import get_module_logger - -logger = get_module_logger(__name__) - - -class ScoreI(Score): - def add_columns(self) -> pd.DataFrame: - logger.debug("Adding Score I") - - high_school_cutoff_threshold = 0.05 - - self.df[field_names.SCORE_I_COMMUNITIES] = ( - (self.df[field_names.MEDIAN_INCOME_AS_PERCENT_OF_AMI_FIELD] < 0.7) - & ( - self.df[field_names.HIGH_SCHOOL_ED_FIELD] - > high_school_cutoff_threshold - ) - ) | ( - (self.df[field_names.POVERTY_LESS_THAN_200_FPL_FIELD] > 0.50) - & ( - self.df[field_names.HIGH_SCHOOL_ED_FIELD] - > high_school_cutoff_threshold - ) - ) - self.df[field_names.SCORE_I] = self.df[ - field_names.SCORE_I_COMMUNITIES - ].astype(int) - self.df["Score I (percentile)"] = self.df[field_names.SCORE_I] - - return self.df diff --git a/data/data-pipeline/data_pipeline/score/score_k.py b/data/data-pipeline/data_pipeline/score/score_k.py deleted file mode 100644 index db05344c..00000000 --- a/data/data-pipeline/data_pipeline/score/score_k.py +++ /dev/null @@ -1,33 +0,0 @@ -import data_pipeline.score.field_names as field_names -import pandas as pd -from data_pipeline.score.score import Score -from data_pipeline.utils import get_module_logger - -logger = get_module_logger(__name__) - - -class ScoreK(Score): - def add_columns(self) -> pd.DataFrame: - logger.debug("Adding Score K") - - high_school_cutoff_threshold = 0.06 - - self.df[field_names.SCORE_K] = ( - (self.df[field_names.MEDIAN_INCOME_AS_PERCENT_OF_AMI_FIELD] < 0.8) - ) | (self.df[field_names.POVERTY_LESS_THAN_100_FPL_FIELD] > 0.20) - - self.df[field_names.SCORE_K_COMMUNITIES] = ( - (self.df[field_names.MEDIAN_INCOME_AS_PERCENT_OF_AMI_FIELD] < 0.8) - & ( - self.df[field_names.HIGH_SCHOOL_ED_FIELD] - > high_school_cutoff_threshold - ) - ) | ( - (self.df[field_names.POVERTY_LESS_THAN_100_FPL_FIELD] > 0.20) - & ( - self.df[field_names.HIGH_SCHOOL_ED_FIELD] - > high_school_cutoff_threshold - ) - ) - - return self.df diff --git a/data/data-pipeline/data_pipeline/score/score_l.py b/data/data-pipeline/data_pipeline/score/score_l.py deleted file mode 100644 index 322b9129..00000000 --- a/data/data-pipeline/data_pipeline/score/score_l.py +++ /dev/null @@ -1,690 +0,0 @@ -import data_pipeline.score.field_names as field_names -import numpy as np -import pandas as pd -from data_pipeline.score.score import Score -from data_pipeline.utils import get_module_logger - -logger = get_module_logger(__name__) - - -class ScoreL(Score): - def __init__(self, df: pd.DataFrame) -> None: - self.LOW_INCOME_THRESHOLD: float = 0.65 - self.ENVIRONMENTAL_BURDEN_THRESHOLD: float = 0.90 - self.MEDIAN_HOUSE_VALUE_THRESHOLD: float = 0.90 - self.LACK_OF_HIGH_SCHOOL_MINIMUM_THRESHOLD: float = 0.10 - - super().__init__(df) - - def _combine_island_areas_with_states_and_set_thresholds( - self, - df: pd.DataFrame, - column_from_island_areas: str, - column_from_decennial_census: str, - combined_column_name: str, - threshold_cutoff_for_island_areas: float, - ) -> (pd.DataFrame, str): - """Steps to set thresholds for island areas. - - This function is fairly logically complicated. It takes the following steps: - - 1. Combine the two different fields into a single field. - 2. Calculate the 90th percentile cutoff raw value for the combined field. - 3. Create a boolean series that is true for any census tract in the island - areas (and only the island areas) that exceeds this cutoff. - - For step one, it combines data that is either the island area's Decennial Census - value in 2009 or the state's value in 5-year ACS ending in 2010. - - This will be used to generate the percentile cutoff for the 90th percentile. - - The stateside decennial census stopped asking economic comparisons, - so this is as close to apples-to-apples as we get. We use 5-year ACS for data - robustness over 1-year ACS. - """ - # Create the combined field. - # TODO: move this combined field percentile calculation to `etl_score`, - # since most other percentile logic is there. - # There should only be one entry in either 2009 or 2019 fields, not one in both. - # But just to be safe, we take the mean and ignore null values so if there - # *were* entries in both, this result would make sense. - df[combined_column_name] = df[ - [column_from_island_areas, column_from_decennial_census] - ].mean(axis=1, skipna=True) - - logger.debug( - f"Combined field `{combined_column_name}` has " - f"{df[combined_column_name].isnull().sum()} " - f"({df[combined_column_name].isnull().sum() * 100 / len(df):.2f}%) " - f"missing values for census tracts. " - ) - - # Calculate the percentile threshold raw value. - raw_threshold = np.nanquantile( - a=df[combined_column_name], q=threshold_cutoff_for_island_areas - ) - - logger.debug( - f"For combined field `{combined_column_name}`, " - f"the {threshold_cutoff_for_island_areas*100:.0f} percentile cutoff is a " - f"raw value of {raw_threshold:.3f}." - ) - - threshold_column_name = ( - f"{column_from_island_areas} exceeds " - f"{threshold_cutoff_for_island_areas*100:.0f}th percentile" - ) - - df[threshold_column_name] = ( - df[column_from_island_areas] >= raw_threshold - ) - - percent_of_tracts_highlighted = ( - 100 - * df[threshold_column_name].sum() - / df[column_from_island_areas].notnull().sum() - ) - - logger.info( - f"For `{threshold_column_name}`, " - f"{df[threshold_column_name].sum()} (" - f"{percent_of_tracts_highlighted:.2f}% of tracts that have non-null data " - f"in the column) have a value of TRUE." - ) - - return df, threshold_column_name - - def _create_low_income_threshold(self, df: pd.DataFrame) -> pd.Series: - """ - Returns a pandas series (really a numpy array) - of booleans based on the condition of the FPL at 200% - is at or more than some established threshold - """ - return ( - df[ - field_names.POVERTY_LESS_THAN_200_FPL_FIELD - + field_names.PERCENTILE_FIELD_SUFFIX - ] - >= self.LOW_INCOME_THRESHOLD - ) - - def _increment_total_eligibility_exceeded( - self, columns_for_subset: list - ) -> None: - """ - Increments the total eligible factors for a given tract - """ - - self.df[field_names.THRESHOLD_COUNT] += self.df[columns_for_subset].sum( - axis=1, skipna=True - ) - - def _climate_factor(self) -> bool: - # In Xth percentile or above for FEMA’s Risk Index (Source: FEMA - # AND - # Low income: In Nth percentile or above for percent of block group population - # of households where household income is less than or equal to twice the federal - # poverty level. Source: Census's American Community Survey] - - climate_eligibility_columns = [ - field_names.EXPECTED_POPULATION_LOSS_RATE_LOW_INCOME_FIELD, - field_names.EXPECTED_AGRICULTURE_LOSS_RATE_LOW_INCOME_FIELD, - field_names.EXPECTED_BUILDING_LOSS_RATE_LOW_INCOME_FIELD, - ] - - expected_population_loss_threshold = ( - self.df[ - field_names.EXPECTED_POPULATION_LOSS_RATE_FIELD - + field_names.PERCENTILE_FIELD_SUFFIX - ] - >= self.ENVIRONMENTAL_BURDEN_THRESHOLD - ) - - expected_agriculture_loss_threshold = ( - self.df[ - field_names.EXPECTED_AGRICULTURE_LOSS_RATE_FIELD - + field_names.PERCENTILE_FIELD_SUFFIX - ] - >= self.ENVIRONMENTAL_BURDEN_THRESHOLD - ) - - expected_building_loss_threshold = ( - self.df[ - field_names.EXPECTED_BUILDING_LOSS_RATE_FIELD - + field_names.PERCENTILE_FIELD_SUFFIX - ] - >= self.ENVIRONMENTAL_BURDEN_THRESHOLD - ) - - self.df[field_names.EXPECTED_POPULATION_LOSS_RATE_LOW_INCOME_FIELD] = ( - expected_population_loss_threshold - & self.df[field_names.FPL_200_SERIES] - ) - - self.df[field_names.EXPECTED_AGRICULTURE_LOSS_RATE_LOW_INCOME_FIELD] = ( - expected_agriculture_loss_threshold - & self.df[field_names.FPL_200_SERIES] - ) - - self.df[field_names.EXPECTED_BUILDING_LOSS_RATE_LOW_INCOME_FIELD] = ( - expected_building_loss_threshold - & self.df[field_names.FPL_200_SERIES] - ) - - self._increment_total_eligibility_exceeded(climate_eligibility_columns) - - return self.df[climate_eligibility_columns].any(axis="columns") - - def _energy_factor(self) -> bool: - # In Xth percentile or above for DOE’s energy cost burden score (Source: LEAD Score) - # AND - # Low income: In Nth percentile or above for percent of block group population - # of households where household income is less than or equal to twice the federal - # poverty level. Source: Census's American Community Survey] - - energy_eligibility_columns = [ - field_names.PM25_EXPOSURE_LOW_INCOME_FIELD, - field_names.ENERGY_BURDEN_LOW_INCOME_FIELD, - ] - - energy_burden_threshold = ( - self.df[ - field_names.ENERGY_BURDEN_FIELD - + field_names.PERCENTILE_FIELD_SUFFIX - ] - >= self.ENVIRONMENTAL_BURDEN_THRESHOLD - ) - - pm25_threshold = ( - self.df[ - field_names.PM25_FIELD + field_names.PERCENTILE_FIELD_SUFFIX - ] - >= self.ENVIRONMENTAL_BURDEN_THRESHOLD - ) - - self.df[field_names.PM25_EXPOSURE_LOW_INCOME_FIELD] = ( - pm25_threshold & self.df[field_names.FPL_200_SERIES] - ) - - self.df[field_names.ENERGY_BURDEN_LOW_INCOME_FIELD] = ( - energy_burden_threshold & self.df[field_names.FPL_200_SERIES] - ) - - self._increment_total_eligibility_exceeded(energy_eligibility_columns) - - return self.df[energy_eligibility_columns].any(axis="columns") - - def _transportation_factor(self) -> bool: - # In Xth percentile or above for diesel particulate matter (Source: EPA National Air Toxics Assessment (NATA) - # or - # In Xth percentile or above for PM 2.5 (Source: EPA, Office of Air and Radiation (OAR) fusion of model and monitor data)] - # or - # In Xth percentile or above traffic proximity and volume (Source: 2017 U.S. Department of Transportation (DOT) traffic data - # AND - # Low income: In Nth percentile or above for percent of block group population - # of households where household income is less than or equal to twice the federal - # poverty level. Source: Census's American Community Survey] - - transportion_eligibility_columns = [ - field_names.DIESEL_PARTICULATE_MATTER_LOW_INCOME_FIELD, - field_names.TRAFFIC_PROXIMITY_LOW_INCOME_FIELD, - ] - - diesel_threshold = ( - self.df[ - field_names.DIESEL_FIELD + field_names.PERCENTILE_FIELD_SUFFIX - ] - >= self.ENVIRONMENTAL_BURDEN_THRESHOLD - ) - - traffic_threshold = ( - self.df[ - field_names.TRAFFIC_FIELD + field_names.PERCENTILE_FIELD_SUFFIX - ] - >= self.ENVIRONMENTAL_BURDEN_THRESHOLD - ) - - self.df[field_names.DIESEL_PARTICULATE_MATTER_LOW_INCOME_FIELD] = ( - diesel_threshold & self.df[field_names.FPL_200_SERIES] - ) - - self.df[field_names.TRAFFIC_PROXIMITY_LOW_INCOME_FIELD] = ( - traffic_threshold & self.df[field_names.FPL_200_SERIES] - ) - - self._increment_total_eligibility_exceeded( - transportion_eligibility_columns - ) - - return self.df[transportion_eligibility_columns].any(axis="columns") - - def _housing_factor(self) -> bool: - # ( - # In Xth percentile or above for lead paint (Source: Census's American Community Survey’s - # percent of housing units built pre-1960, used as an indicator of potential lead paint exposure in homes) - # AND - # In Yth percentile or below for Median House Value (Source: Census's American Community Survey) - # ) - # or - # In Xth percentile or above for housing cost burden (Source: HUD's Comprehensive Housing Affordability Strategy dataset - # AND - # Low income: In Nth percentile or above for percent of block group population - # of households where household income is less than or equal to twice the federal - # poverty level. Source: Census's American Community Survey] - - housing_eligibility_columns = [ - field_names.LEAD_PAINT_MEDIAN_HOUSE_VALUE_LOW_INCOME_FIELD, - field_names.HOUSING_BURDEN_LOW_INCOME_FIELD, - ] - - lead_paint_median_home_value_threshold = ( - self.df[ - field_names.LEAD_PAINT_FIELD - + field_names.PERCENTILE_FIELD_SUFFIX - ] - >= self.ENVIRONMENTAL_BURDEN_THRESHOLD - ) & ( - self.df[ - field_names.MEDIAN_HOUSE_VALUE_FIELD - + field_names.PERCENTILE_FIELD_SUFFIX - ] - <= self.MEDIAN_HOUSE_VALUE_THRESHOLD - ) - - housing_burden_threshold = ( - self.df[ - field_names.HOUSING_BURDEN_FIELD - + field_names.PERCENTILE_FIELD_SUFFIX - ] - >= self.ENVIRONMENTAL_BURDEN_THRESHOLD - ) - - # series by series indicators - self.df[field_names.LEAD_PAINT_MEDIAN_HOUSE_VALUE_LOW_INCOME_FIELD] = ( - lead_paint_median_home_value_threshold - & self.df[field_names.FPL_200_SERIES] - ) - - self.df[field_names.HOUSING_BURDEN_LOW_INCOME_FIELD] = ( - housing_burden_threshold & self.df[field_names.FPL_200_SERIES] - ) - - self._increment_total_eligibility_exceeded(housing_eligibility_columns) - - return self.df[housing_eligibility_columns].any(axis="columns") - - def _pollution_factor(self) -> bool: - # Proximity to Risk Management Plan sites is > X - # AND - # Low income: In Nth percentile or above for percent of block group population - # of households where household income is less than or equal to twice the federal - # poverty level. Source: Census's American Community Survey] - - pollution_eligibility_columns = [ - field_names.RMP_LOW_INCOME_FIELD, - field_names.SUPERFUND_LOW_INCOME_FIELD, - field_names.HAZARDOUS_WASTE_LOW_INCOME_FIELD, - ] - - rmp_sites_threshold = ( - self.df[field_names.RMP_FIELD + field_names.PERCENTILE_FIELD_SUFFIX] - >= self.ENVIRONMENTAL_BURDEN_THRESHOLD - ) - - npl_sites_threshold = ( - self.df[field_names.NPL_FIELD + field_names.PERCENTILE_FIELD_SUFFIX] - >= self.ENVIRONMENTAL_BURDEN_THRESHOLD - ) - - tsdf_sites_threshold = ( - self.df[ - field_names.TSDF_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] - ) - self.df[field_names.SUPERFUND_LOW_INCOME_FIELD] = ( - npl_sites_threshold & self.df[field_names.FPL_200_SERIES] - ) - self.df[field_names.HAZARDOUS_WASTE_LOW_INCOME_FIELD] = ( - tsdf_sites_threshold & self.df[field_names.FPL_200_SERIES] - ) - - self._increment_total_eligibility_exceeded( - pollution_eligibility_columns - ) - - return self.df[pollution_eligibility_columns].any(axis="columns") - - def _water_factor(self) -> bool: - # In Xth percentile or above for wastewater discharge (Source: EPA Risk-Screening Environmental Indicators (RSEI) Model) - # AND - # Low income: In Nth percentile or above for percent of block group population - # of households where household income is less than or equal to twice the federal - # poverty level. Source: Census's American Community Survey] - - wastewater_threshold = ( - self.df[ - field_names.WASTEWATER_FIELD - + field_names.PERCENTILE_FIELD_SUFFIX - ] - >= self.ENVIRONMENTAL_BURDEN_THRESHOLD - ) - - self.df[field_names.WASTEWATER_DISCHARGE_LOW_INCOME_FIELD] = ( - wastewater_threshold & self.df[field_names.FPL_200_SERIES] - ) - - self._increment_total_eligibility_exceeded( - [field_names.WASTEWATER_DISCHARGE_LOW_INCOME_FIELD] - ) - - return self.df[field_names.WASTEWATER_DISCHARGE_LOW_INCOME_FIELD] - - def _health_factor(self) -> bool: - # In Xth percentile or above for diabetes (Source: CDC Places) - # or - # In Xth percentile or above for asthma (Source: CDC Places) - # or - # In Xth percentile or above for heart disease - # or - # In Xth percentile or above for low life expectancy (Source: CDC Places) - # AND - # Low income: In Nth percentile or above for percent of block group population - # of households where household income is less than or equal to twice the federal - # poverty level. Source: Census's American Community Survey] - - health_eligibility_columns = [ - field_names.DIABETES_LOW_INCOME_FIELD, - field_names.ASTHMA_LOW_INCOME_FIELD, - field_names.HEART_DISEASE_LOW_INCOME_FIELD, - field_names.LOW_LIFE_EXPECTANCY_LOW_INCOME_FIELD, - ] - - diabetes_threshold = ( - self.df[ - field_names.DIABETES_FIELD + field_names.PERCENTILE_FIELD_SUFFIX - ] - >= self.ENVIRONMENTAL_BURDEN_THRESHOLD - ) - - asthma_threshold = ( - self.df[ - field_names.ASTHMA_FIELD + field_names.PERCENTILE_FIELD_SUFFIX - ] - >= self.ENVIRONMENTAL_BURDEN_THRESHOLD - ) - - heart_disease_threshold = ( - self.df[ - field_names.HEART_DISEASE_FIELD - + field_names.PERCENTILE_FIELD_SUFFIX - ] - >= self.ENVIRONMENTAL_BURDEN_THRESHOLD - ) - - low_life_expectancy_threshold = ( - self.df[ - field_names.LOW_LIFE_EXPECTANCY_FIELD - + field_names.PERCENTILE_FIELD_SUFFIX - ] - >= self.ENVIRONMENTAL_BURDEN_THRESHOLD - ) - - self.df[field_names.DIABETES_LOW_INCOME_FIELD] = ( - diabetes_threshold & self.df[field_names.FPL_200_SERIES] - ) - self.df[field_names.ASTHMA_LOW_INCOME_FIELD] = ( - asthma_threshold & self.df[field_names.FPL_200_SERIES] - ) - self.df[field_names.HEART_DISEASE_LOW_INCOME_FIELD] = ( - heart_disease_threshold & self.df[field_names.FPL_200_SERIES] - ) - self.df[field_names.LOW_LIFE_EXPECTANCY_LOW_INCOME_FIELD] = ( - low_life_expectancy_threshold & self.df[field_names.FPL_200_SERIES] - ) - - self._increment_total_eligibility_exceeded(health_eligibility_columns) - - return self.df[health_eligibility_columns].any(axis="columns") - - def _workforce_factor(self) -> bool: - # Where unemployment is above Xth percentile - # or - # Where median income as a percent of area median income is above Xth percentile - # or - # Where the percent of households at or below 100% of the federal poverty level - # is above Xth percentile - # or - # Where linguistic isolation is above Xth percentile - # AND - # Where the high school degree achievement rates for adults 25 years and older - # is less than Y% - # (necessary to screen out university tracts) - - # Workforce criteria for states fields. - workforce_eligibility_columns = [ - field_names.UNEMPLOYMENT_LOW_HS_EDUCATION_FIELD, - field_names.POVERTY_LOW_HS_EDUCATION_FIELD, - field_names.LINGUISTIC_ISOLATION_LOW_HS_EDUCATION_FIELD, - field_names.LOW_MEDIAN_INCOME_LOW_HS_EDUCATION_FIELD, - ] - - self.df[field_names.LOW_HS_EDUCATION_FIELD] = ( - self.df[field_names.HIGH_SCHOOL_ED_FIELD] - >= self.LACK_OF_HIGH_SCHOOL_MINIMUM_THRESHOLD - ) - - unemployment_threshold = ( - self.df[ - field_names.UNEMPLOYMENT_FIELD - + field_names.PERCENTILE_FIELD_SUFFIX - ] - >= self.ENVIRONMENTAL_BURDEN_THRESHOLD - ) - - low_median_income_threshold = ( - self.df[ - field_names.LOW_MEDIAN_INCOME_AS_PERCENT_OF_AMI_FIELD - + field_names.PERCENTILE_FIELD_SUFFIX - ] - >= self.ENVIRONMENTAL_BURDEN_THRESHOLD - ) - - linguistic_isolation_threshold = ( - self.df[ - field_names.LINGUISTIC_ISO_FIELD - + field_names.PERCENTILE_FIELD_SUFFIX - ] - >= self.ENVIRONMENTAL_BURDEN_THRESHOLD - ) - - poverty_threshold = ( - self.df[ - field_names.POVERTY_LESS_THAN_100_FPL_FIELD - + field_names.PERCENTILE_FIELD_SUFFIX - ] - >= self.ENVIRONMENTAL_BURDEN_THRESHOLD - ) - - self.df[field_names.LINGUISTIC_ISOLATION_LOW_HS_EDUCATION_FIELD] = ( - linguistic_isolation_threshold - & self.df[field_names.LOW_HS_EDUCATION_FIELD] - ) - - self.df[field_names.POVERTY_LOW_HS_EDUCATION_FIELD] = ( - poverty_threshold & self.df[field_names.LOW_HS_EDUCATION_FIELD] - ) - - self.df[field_names.LOW_MEDIAN_INCOME_LOW_HS_EDUCATION_FIELD] = ( - low_median_income_threshold - & self.df[field_names.LOW_HS_EDUCATION_FIELD] - ) - - self.df[field_names.UNEMPLOYMENT_LOW_HS_EDUCATION_FIELD] = ( - unemployment_threshold & self.df[field_names.LOW_HS_EDUCATION_FIELD] - ) - - workforce_combined_criteria_for_states = self.df[ - workforce_eligibility_columns - ].any(axis="columns") - - self._increment_total_eligibility_exceeded( - workforce_eligibility_columns - ) - - # Now, calculate workforce criteria for island territories. - island_areas_workforce_eligibility_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, - ] - - # First, combine unemployment. - ( - self.df, - island_areas_unemployment_criteria_field_name, - ) = self._combine_island_areas_with_states_and_set_thresholds( - df=self.df, - column_from_island_areas=field_names.CENSUS_DECENNIAL_UNEMPLOYMENT_FIELD_2009, - column_from_decennial_census=field_names.CENSUS_UNEMPLOYMENT_FIELD_2010, - combined_column_name=field_names.COMBINED_UNEMPLOYMENT_2010, - threshold_cutoff_for_island_areas=self.ENVIRONMENTAL_BURDEN_THRESHOLD, - ) - - # Next, combine poverty. - ( - self.df, - island_areas_poverty_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_100_FPL_FIELD_2009, - column_from_decennial_census=field_names.CENSUS_POVERTY_LESS_THAN_100_FPL_FIELD_2010, - combined_column_name=field_names.COMBINED_POVERTY_LESS_THAN_100_FPL_FIELD_2010, - threshold_cutoff_for_island_areas=self.ENVIRONMENTAL_BURDEN_THRESHOLD, - ) - - # Also check whether low area median income is 90th percentile or higher - # within the islands. - island_areas_low_median_income_as_a_percent_of_ami_criteria_field_name = ( - f"{field_names.LOW_CENSUS_DECENNIAL_AREA_MEDIAN_INCOME_PERCENT_FIELD_2009} exceeds " - f"{field_names.PERCENTILE}th percentile" - ) - self.df[ - island_areas_low_median_income_as_a_percent_of_ami_criteria_field_name - ] = ( - self.df[ - field_names.LOW_CENSUS_DECENNIAL_AREA_MEDIAN_INCOME_PERCENT_FIELD_2009 - + field_names.PERCENTILE_FIELD_SUFFIX - ] - >= self.ENVIRONMENTAL_BURDEN_THRESHOLD - ) - - self.df[field_names.ISLAND_AREAS_LOW_HS_EDUCATION_FIELD] = ( - self.df[field_names.CENSUS_DECENNIAL_HIGH_SCHOOL_ED_FIELD_2009] - >= self.LACK_OF_HIGH_SCHOOL_MINIMUM_THRESHOLD - ) - - self.df[ - field_names.ISLAND_AREAS_UNEMPLOYMENT_LOW_HS_EDUCATION_FIELD - ] = ( - self.df[island_areas_unemployment_criteria_field_name] - & self.df[field_names.ISLAND_AREAS_LOW_HS_EDUCATION_FIELD] - ) - - self.df[field_names.ISLAND_AREAS_POVERTY_LOW_HS_EDUCATION_FIELD] = ( - self.df[island_areas_poverty_criteria_field_name] - & self.df[field_names.ISLAND_AREAS_LOW_HS_EDUCATION_FIELD] - ) - - self.df[ - field_names.ISLAND_AREAS_LOW_MEDIAN_INCOME_LOW_HS_EDUCATION_FIELD - ] = ( - self.df[ - island_areas_low_median_income_as_a_percent_of_ami_criteria_field_name - ] - & self.df[field_names.ISLAND_AREAS_LOW_HS_EDUCATION_FIELD] - ) - - workforce_combined_criteria_for_island_areas = self.df[ - island_areas_workforce_eligibility_columns - ].any(axis="columns") - - self._increment_total_eligibility_exceeded( - island_areas_workforce_eligibility_columns - ) - - percent_of_island_tracts_highlighted = ( - 100 - * workforce_combined_criteria_for_island_areas.sum() - # Choosing a random column from island areas to calculate the denominator. - / self.df[field_names.CENSUS_DECENNIAL_UNEMPLOYMENT_FIELD_2009] - .notnull() - .sum() - ) - - logger.debug( - f"For workforce criteria in island areas, " - f"{workforce_combined_criteria_for_island_areas.sum()} (" - f"{percent_of_island_tracts_highlighted:.2f}% of tracts that have non-null data " - f"in the column) have a value of TRUE." - ) - - # A tract is included if it meets either the states tract criteria or the - # island areas tract criteria. - return ( - workforce_combined_criteria_for_states - | workforce_combined_criteria_for_island_areas - ) - - def add_columns(self) -> pd.DataFrame: - logger.debug("Adding Score L") - - self.df[field_names.THRESHOLD_COUNT] = 0 - self.df[field_names.FPL_200_SERIES] = self._create_low_income_threshold( - self.df - ) - self.df[field_names.L_CLIMATE] = self._climate_factor() - self.df[field_names.L_ENERGY] = self._energy_factor() - self.df[field_names.L_TRANSPORTATION] = self._transportation_factor() - self.df[field_names.L_HOUSING] = self._housing_factor() - self.df[field_names.L_POLLUTION] = self._pollution_factor() - self.df[field_names.L_WATER] = self._water_factor() - self.df[field_names.L_HEALTH] = self._health_factor() - self.df[field_names.L_WORKFORCE] = self._workforce_factor() - - factors = [ - field_names.L_CLIMATE, - field_names.L_ENERGY, - field_names.L_TRANSPORTATION, - field_names.L_HOUSING, - field_names.L_POLLUTION, - field_names.L_WATER, - field_names.L_HEALTH, - field_names.L_WORKFORCE, - ] - self.df[field_names.SCORE_L_COMMUNITIES] = self.df[factors].any(axis=1) - - # Note: this is purely used for comparison tool analysis, and can be removed at a later date. - LMB. - non_workforce_factors = [ - field_names.L_CLIMATE, - field_names.L_ENERGY, - field_names.L_TRANSPORTATION, - field_names.L_HOUSING, - field_names.L_POLLUTION, - field_names.L_WATER, - field_names.L_HEALTH, - ] - self.df[field_names.L_NON_WORKFORCE] = self.df[ - non_workforce_factors - ].any(axis=1) - - self.df[ - field_names.SCORE_L + field_names.PERCENTILE_FIELD_SUFFIX - ] = self.df[field_names.SCORE_L_COMMUNITIES].astype(int) - - return self.df diff --git a/data/data-pipeline/data_pipeline/score/score_m.py b/data/data-pipeline/data_pipeline/score/score_m.py deleted file mode 100644 index a8751c2a..00000000 --- a/data/data-pipeline/data_pipeline/score/score_m.py +++ /dev/null @@ -1,888 +0,0 @@ -from typing import Tuple - -import data_pipeline.etl.score.constants as constants -import data_pipeline.score.field_names as field_names -import numpy as np -import pandas as pd -from data_pipeline.score.score import Score -from data_pipeline.utils import get_module_logger - -logger = get_module_logger(__name__) - - -class ScoreM(Score): - """Very similar to Score L, with a few minor modifications.""" - - def __init__(self, df: pd.DataFrame) -> None: - self.LOW_INCOME_THRESHOLD: float = 0.65 - self.MAX_COLLEGE_ATTENDANCE_THRESHOLD: float = 0.20 - self.ENVIRONMENTAL_BURDEN_THRESHOLD: float = 0.90 - self.MEDIAN_HOUSE_VALUE_THRESHOLD: float = 0.90 - self.LACK_OF_HIGH_SCHOOL_MINIMUM_THRESHOLD: float = 0.10 - - super().__init__(df) - - def _combine_island_areas_with_states_and_set_thresholds( - self, - df: pd.DataFrame, - column_from_island_areas: str, - column_from_decennial_census: str, - combined_column_name: str, - threshold_cutoff_for_island_areas: float, - ) -> Tuple[pd.DataFrame, str]: - """Steps to set thresholds for island areas. - - This function is fairly logically complicated. It takes the following steps: - - 1. Combine the two different fields into a single field. - 2. Calculate the 90th percentile for the combined field. - 3. Create a boolean series that is true for any census tract in the island - areas (and only the island areas) that exceeds this percentile. - - For step one, it combines data that is either the island area's Decennial Census - value in 2009 or the state's value in 5-year ACS ending in 2010. - - This will be used to generate the percentile cutoff for the 90th percentile. - - The stateside decennial census stopped asking economic comparisons, - so this is as close to apples-to-apples as we get. We use 5-year ACS for data - robustness over 1-year ACS. - """ - # Create the combined field. - # TODO: move this combined field percentile calculation to `etl_score`, - # since most other percentile logic is there. - # There should only be one entry in either 2009 or 2019 fields, not one in both. - # But just to be safe, we take the mean and ignore null values so if there - # *were* entries in both, this result would make sense. - df[combined_column_name] = df[ - [column_from_island_areas, column_from_decennial_census] - ].mean(axis=1, skipna=True) - - # Create a percentile field for use in the Islands / PR visualization - # TODO: move this code - # In the code below, percentiles are constructed based on the combined column - # of census and island data, but only reported for the island areas (where there - # is no other comprehensive percentile information) - return_series_name = ( - column_from_island_areas - + field_names.ISLAND_AREAS_PERCENTILE_ADJUSTMENT_FIELD - + field_names.PERCENTILE_FIELD_SUFFIX - ) - df[return_series_name] = np.where( - df[column_from_decennial_census].isna(), - df[combined_column_name].rank(pct=True), - np.nan, - ) - - threshold_column_name = ( - f"{column_from_island_areas} exceeds " - f"{threshold_cutoff_for_island_areas*100:.0f}th percentile" - ) - - df[threshold_column_name] = ( - df[return_series_name] >= threshold_cutoff_for_island_areas - ) - - return df, threshold_column_name - - def _create_low_income_and_low_college_attendance_threshold( - self, df: pd.DataFrame - ) -> pd.Series: - """ - Returns a pandas series (really a numpy array) - of booleans based on the condition of the FPL at 200% - is at or more than some established threshold - """ - - return (df[field_names.LOW_INCOME_THRESHOLD]) & ( - df[field_names.COLLEGE_ATTENDANCE_LESS_THAN_20_FIELD] - | ( - # If college attendance data is null for this tract, just rely on the - # poverty data - df[field_names.COLLEGE_ATTENDANCE_FIELD].isna() - ) - ) - - def _increment_total_eligibility_exceeded( - self, columns_for_subset: list, skip_fips: tuple = () - ) -> None: - """ - Increments the total eligible factors for a given tract - - The new skip_fips argument specifies which (if any) fips codes to - skip over for incrementing. - This allows us to essentially skip data we think is of limited veracity, - without overriding any values in the data. - THIS IS A TEMPORARY FIX. - """ - if skip_fips: - self.df[field_names.THRESHOLD_COUNT] += np.where( - self.df[field_names.GEOID_TRACT_FIELD].str.startswith( - skip_fips - ), - 0, - self.df[columns_for_subset].sum(axis=1, skipna=True), - ) - else: - self.df[field_names.THRESHOLD_COUNT] += self.df[ - columns_for_subset - ].sum(axis=1, skipna=True) - - def _climate_factor(self) -> bool: - # In Xth percentile or above for FEMA’s Risk Index (Source: FEMA - # AND - # Low income: In Nth percentile or above for percent of block group population - # of households where household income is less than or equal to twice the federal - # poverty level and there is low higher ed attendance - # Source: Census's American Community Survey - - climate_eligibility_columns = [ - field_names.EXPECTED_POPULATION_LOSS_RATE_LOW_INCOME_LOW_HIGHER_ED_FIELD, - field_names.EXPECTED_AGRICULTURE_LOSS_RATE_LOW_INCOME_LOW_HIGHER_ED_FIELD, - field_names.EXPECTED_BUILDING_LOSS_RATE_LOW_INCOME_LOW_HIGHER_ED_FIELD, - ] - - self.df[ - field_names.EXPECTED_POPULATION_LOSS_EXCEEDS_PCTILE_THRESHOLD - ] = ( - self.df[ - field_names.EXPECTED_POPULATION_LOSS_RATE_FIELD - + field_names.PERCENTILE_FIELD_SUFFIX - ] - >= self.ENVIRONMENTAL_BURDEN_THRESHOLD - ) - - self.df[ - field_names.EXPECTED_AGRICULTURAL_LOSS_EXCEEDS_PCTILE_THRESHOLD - ] = ( - self.df[ - field_names.EXPECTED_AGRICULTURE_LOSS_RATE_FIELD - + field_names.PERCENTILE_FIELD_SUFFIX - ] - >= self.ENVIRONMENTAL_BURDEN_THRESHOLD - ) - - self.df[field_names.EXPECTED_BUILDING_LOSS_EXCEEDS_PCTILE_THRESHOLD] = ( - self.df[ - field_names.EXPECTED_BUILDING_LOSS_RATE_FIELD - + field_names.PERCENTILE_FIELD_SUFFIX - ] - >= self.ENVIRONMENTAL_BURDEN_THRESHOLD - ) - - self.df[field_names.CLIMATE_THRESHOLD_EXCEEDED] = ( - self.df[ - field_names.EXPECTED_POPULATION_LOSS_EXCEEDS_PCTILE_THRESHOLD - ] - | self.df[ - field_names.EXPECTED_AGRICULTURAL_LOSS_EXCEEDS_PCTILE_THRESHOLD - ] - | self.df[ - field_names.EXPECTED_BUILDING_LOSS_EXCEEDS_PCTILE_THRESHOLD - ] - ) - - self.df[ - field_names.EXPECTED_POPULATION_LOSS_RATE_LOW_INCOME_LOW_HIGHER_ED_FIELD - ] = ( - self.df[ - field_names.EXPECTED_POPULATION_LOSS_EXCEEDS_PCTILE_THRESHOLD - ] - & self.df[field_names.FPL_200_AND_COLLEGE_ATTENDANCE_SERIES] - ) - - self.df[ - field_names.EXPECTED_AGRICULTURE_LOSS_RATE_LOW_INCOME_LOW_HIGHER_ED_FIELD - ] = ( - self.df[ - field_names.EXPECTED_AGRICULTURAL_LOSS_EXCEEDS_PCTILE_THRESHOLD - ] - & self.df[field_names.FPL_200_AND_COLLEGE_ATTENDANCE_SERIES] - ) - - self.df[ - field_names.EXPECTED_BUILDING_LOSS_RATE_LOW_INCOME_LOW_HIGHER_ED_FIELD - ] = ( - self.df[field_names.EXPECTED_BUILDING_LOSS_EXCEEDS_PCTILE_THRESHOLD] - & self.df[field_names.FPL_200_AND_COLLEGE_ATTENDANCE_SERIES] - ) - - self._increment_total_eligibility_exceeded( - climate_eligibility_columns, - skip_fips=constants.DROP_FIPS_FROM_NON_WTD_THRESHOLDS, - ) - - return self.df[climate_eligibility_columns].any(axis="columns") - - def _energy_factor(self) -> bool: - # In Xth percentile or above for DOE’s energy cost burden score (Source: LEAD Score) - # AND - # Low income: In Nth percentile or above for percent of block group population - # of households where household income is less than or equal to twice the federal - # poverty level and has low higher ed attendance. - # Source: Census's American Community Survey - - energy_eligibility_columns = [ - field_names.PM25_EXPOSURE_LOW_INCOME_LOW_HIGHER_ED_FIELD, - field_names.ENERGY_BURDEN_LOW_INCOME_LOW_HIGHER_ED_FIELD, - ] - - self.df[field_names.ENERGY_BURDEN_EXCEEDS_PCTILE_THRESHOLD] = ( - self.df[ - field_names.ENERGY_BURDEN_FIELD - + field_names.PERCENTILE_FIELD_SUFFIX - ] - >= self.ENVIRONMENTAL_BURDEN_THRESHOLD - ) - - self.df[field_names.PM25_EXCEEDS_PCTILE_THRESHOLD] = ( - self.df[ - field_names.PM25_FIELD + field_names.PERCENTILE_FIELD_SUFFIX - ] - >= self.ENVIRONMENTAL_BURDEN_THRESHOLD - ) - - self.df[field_names.ENERGY_THRESHOLD_EXCEEDED] = ( - self.df[field_names.ENERGY_BURDEN_EXCEEDS_PCTILE_THRESHOLD] - | self.df[field_names.PM25_EXCEEDS_PCTILE_THRESHOLD] - ) - - self.df[field_names.PM25_EXPOSURE_LOW_INCOME_LOW_HIGHER_ED_FIELD] = ( - self.df[field_names.PM25_EXCEEDS_PCTILE_THRESHOLD] - & self.df[field_names.FPL_200_AND_COLLEGE_ATTENDANCE_SERIES] - ) - - self.df[field_names.ENERGY_BURDEN_LOW_INCOME_LOW_HIGHER_ED_FIELD] = ( - self.df[field_names.ENERGY_BURDEN_EXCEEDS_PCTILE_THRESHOLD] - & self.df[field_names.FPL_200_AND_COLLEGE_ATTENDANCE_SERIES] - ) - - self._increment_total_eligibility_exceeded( - energy_eligibility_columns, - skip_fips=constants.DROP_FIPS_FROM_NON_WTD_THRESHOLDS, - ) - - return self.df[energy_eligibility_columns].any(axis="columns") - - def _transportation_factor(self) -> bool: - # In Xth percentile or above for diesel particulate matter (Source: EPA National Air Toxics Assessment (NATA) - # or - # In Xth percentile or above for PM 2.5 (Source: EPA, Office of Air and Radiation (OAR) fusion of model and monitor data)] - # or - # In Xth percentile or above traffic proximity and volume (Source: 2017 U.S. Department of Transportation (DOT) traffic data - # AND - # Low income: In Nth percentile or above for percent of block group population - # of households where household income is less than or equal to twice the federal - # poverty level and has a low percent of higher ed students. - # Source: Census's American Community Survey - - transportion_eligibility_columns = [ - field_names.DIESEL_PARTICULATE_MATTER_LOW_INCOME_LOW_HIGHER_ED_FIELD, - field_names.TRAFFIC_PROXIMITY_LOW_INCOME_LOW_HIGHER_ED_FIELD, - ] - - self.df[field_names.DIESEL_EXCEEDS_PCTILE_THRESHOLD] = ( - self.df[ - field_names.DIESEL_FIELD + field_names.PERCENTILE_FIELD_SUFFIX - ] - >= self.ENVIRONMENTAL_BURDEN_THRESHOLD - ) - - self.df[field_names.TRAFFIC_PROXIMITY_PCTILE_THRESHOLD] = ( - self.df[ - field_names.TRAFFIC_FIELD + field_names.PERCENTILE_FIELD_SUFFIX - ] - >= self.ENVIRONMENTAL_BURDEN_THRESHOLD - ) - - self.df[field_names.TRAFFIC_THRESHOLD_EXCEEDED] = ( - self.df[field_names.TRAFFIC_PROXIMITY_PCTILE_THRESHOLD] - | self.df[field_names.DIESEL_EXCEEDS_PCTILE_THRESHOLD] - ) - - self.df[ - field_names.DIESEL_PARTICULATE_MATTER_LOW_INCOME_LOW_HIGHER_ED_FIELD - ] = ( - self.df[field_names.DIESEL_EXCEEDS_PCTILE_THRESHOLD] - & self.df[field_names.FPL_200_AND_COLLEGE_ATTENDANCE_SERIES] - ) - - self.df[ - field_names.TRAFFIC_PROXIMITY_LOW_INCOME_LOW_HIGHER_ED_FIELD - ] = ( - self.df[field_names.TRAFFIC_PROXIMITY_PCTILE_THRESHOLD] - & self.df[field_names.FPL_200_AND_COLLEGE_ATTENDANCE_SERIES] - ) - - self._increment_total_eligibility_exceeded( - transportion_eligibility_columns, - skip_fips=constants.DROP_FIPS_FROM_NON_WTD_THRESHOLDS, - ) - - return self.df[transportion_eligibility_columns].any(axis="columns") - - def _housing_factor(self) -> bool: - # ( - # In Xth percentile or above for lead paint (Source: Census's American Community Survey’s - # percent of housing units built pre-1960, used as an indicator of potential lead paint exposure in homes) - # AND - # In Yth percentile or below for Median House Value (Source: Census's American Community Survey) - # ) - # or - # In Xth percentile or above for housing cost burden (Source: HUD's Comprehensive Housing Affordability Strategy dataset - # AND - # Low income: In Nth percentile or above for percent of block group population - # of households where household income is less than or equal to twice the federal - # poverty level and has a low percent of higher ed students. - # Source: Census's American Community Survey - - housing_eligibility_columns = [ - field_names.LEAD_PAINT_MEDIAN_HOUSE_VALUE_LOW_INCOME_LOW_HIGHER_ED_FIELD, - field_names.HOUSING_BURDEN_LOW_INCOME_LOW_HIGHER_ED_FIELD, - ] - - self.df[field_names.LEAD_PAINT_PROXY_PCTILE_THRESHOLD] = ( - self.df[ - field_names.LEAD_PAINT_FIELD - + field_names.PERCENTILE_FIELD_SUFFIX - ] - >= self.ENVIRONMENTAL_BURDEN_THRESHOLD - ) & ( - self.df[ - field_names.MEDIAN_HOUSE_VALUE_FIELD - + field_names.PERCENTILE_FIELD_SUFFIX - ] - <= self.MEDIAN_HOUSE_VALUE_THRESHOLD - ) - - self.df[field_names.HOUSING_BURDEN_PCTILE_THRESHOLD] = ( - self.df[ - field_names.HOUSING_BURDEN_FIELD - + field_names.PERCENTILE_FIELD_SUFFIX - ] - >= 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_LOW_HIGHER_ED_FIELD - ] = ( - self.df[field_names.LEAD_PAINT_PROXY_PCTILE_THRESHOLD] - & self.df[field_names.FPL_200_AND_COLLEGE_ATTENDANCE_SERIES] - ) - - self.df[field_names.HOUSING_BURDEN_LOW_INCOME_LOW_HIGHER_ED_FIELD] = ( - self.df[field_names.HOUSING_BURDEN_PCTILE_THRESHOLD] - & self.df[field_names.FPL_200_AND_COLLEGE_ATTENDANCE_SERIES] - ) - - 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") - - def _pollution_factor(self) -> bool: - # Proximity to Risk Management Plan sites is > X - # AND - # Low income: In Nth percentile or above for percent of block group population - # of households where household income is less than or equal to twice the federal - # poverty level and has a low percent of higher ed students. - # Source: Census's American Community Survey - - pollution_eligibility_columns = [ - field_names.RMP_LOW_INCOME_LOW_HIGHER_ED_FIELD, - field_names.SUPERFUND_LOW_INCOME_LOW_HIGHER_ED_FIELD, - field_names.HAZARDOUS_WASTE_LOW_INCOME_LOW_HIGHER_ED_FIELD, - ] - - self.df[field_names.RMP_PCTILE_THRESHOLD] = ( - self.df[field_names.RMP_FIELD + field_names.PERCENTILE_FIELD_SUFFIX] - >= self.ENVIRONMENTAL_BURDEN_THRESHOLD - ) - - self.df[field_names.NPL_PCTILE_THRESHOLD] = ( - self.df[field_names.NPL_FIELD + field_names.PERCENTILE_FIELD_SUFFIX] - >= self.ENVIRONMENTAL_BURDEN_THRESHOLD - ) - - self.df[field_names.TSDF_PCTILE_THRESHOLD] = ( - self.df[ - field_names.TSDF_FIELD + field_names.PERCENTILE_FIELD_SUFFIX - ] - >= self.ENVIRONMENTAL_BURDEN_THRESHOLD - ) - - self.df[field_names.POLLUTION_THRESHOLD_EXCEEDED] = ( - self.df[field_names.RMP_PCTILE_THRESHOLD] - | self.df[field_names.NPL_PCTILE_THRESHOLD] - ) | self.df[field_names.TSDF_PCTILE_THRESHOLD] - - # individual series-by-series - self.df[field_names.RMP_LOW_INCOME_LOW_HIGHER_ED_FIELD] = ( - self.df[field_names.RMP_PCTILE_THRESHOLD] - & self.df[field_names.FPL_200_AND_COLLEGE_ATTENDANCE_SERIES] - ) - self.df[field_names.SUPERFUND_LOW_INCOME_LOW_HIGHER_ED_FIELD] = ( - self.df[field_names.NPL_PCTILE_THRESHOLD] - & self.df[field_names.FPL_200_AND_COLLEGE_ATTENDANCE_SERIES] - ) - self.df[field_names.HAZARDOUS_WASTE_LOW_INCOME_LOW_HIGHER_ED_FIELD] = ( - self.df[field_names.TSDF_PCTILE_THRESHOLD] - & self.df[field_names.FPL_200_AND_COLLEGE_ATTENDANCE_SERIES] - ) - - self._increment_total_eligibility_exceeded( - pollution_eligibility_columns, - skip_fips=constants.DROP_FIPS_FROM_NON_WTD_THRESHOLDS, - ) - - return self.df[pollution_eligibility_columns].any(axis="columns") - - def _water_factor(self) -> bool: - # In Xth percentile or above for wastewater discharge (Source: EPA Risk-Screening Environmental Indicators (RSEI) Model) - # AND - # Low income: In Nth percentile or above for percent of block group population - # of households where household income is less than or equal to twice the federal - # poverty level and has a low percent of higher ed students - # Source: Census's American Community Survey - - self.df[field_names.WASTEWATER_PCTILE_THRESHOLD] = ( - self.df[ - field_names.WASTEWATER_FIELD - + field_names.PERCENTILE_FIELD_SUFFIX - ] - >= self.ENVIRONMENTAL_BURDEN_THRESHOLD - ) - - # Straight copy here in case we add additional water fields. - self.df[field_names.WATER_THRESHOLD_EXCEEDED] = self.df[ - field_names.WASTEWATER_PCTILE_THRESHOLD - ].copy() - - self.df[ - field_names.WASTEWATER_DISCHARGE_LOW_INCOME_LOW_HIGHER_ED_FIELD - ] = ( - self.df[field_names.WASTEWATER_PCTILE_THRESHOLD] - & self.df[field_names.FPL_200_AND_COLLEGE_ATTENDANCE_SERIES] - ) - - self._increment_total_eligibility_exceeded( - [field_names.WASTEWATER_DISCHARGE_LOW_INCOME_LOW_HIGHER_ED_FIELD], - skip_fips=constants.DROP_FIPS_FROM_NON_WTD_THRESHOLDS, - ) - - return self.df[ - field_names.WASTEWATER_DISCHARGE_LOW_INCOME_LOW_HIGHER_ED_FIELD - ] - - def _health_factor(self) -> bool: - # In Xth percentile or above for diabetes (Source: CDC Places) - # or - # In Xth percentile or above for asthma (Source: CDC Places) - # or - # In Xth percentile or above for heart disease - # or - # In Xth percentile or above for low life expectancy (Source: CDC Places) - # AND - # Low income: In Nth percentile or above for percent of block group population - # of households where household income is less than or equal to twice the federal - # poverty level and has a low percent of higher ed students - # Source: Census's American Community Survey - - health_eligibility_columns = [ - field_names.DIABETES_LOW_INCOME_LOW_HIGHER_ED_FIELD, - field_names.ASTHMA_LOW_INCOME_LOW_HIGHER_ED_FIELD, - field_names.HEART_DISEASE_LOW_INCOME_LOW_HIGHER_ED_FIELD, - field_names.LOW_LIFE_EXPECTANCY_LOW_INCOME_LOW_HIGHER_ED_FIELD, - ] - - self.df[field_names.DIABETES_PCTILE_THRESHOLD] = ( - self.df[ - field_names.DIABETES_FIELD + field_names.PERCENTILE_FIELD_SUFFIX - ] - >= self.ENVIRONMENTAL_BURDEN_THRESHOLD - ) - - self.df[field_names.ASTHMA_PCTILE_THRESHOLD] = ( - self.df[ - field_names.ASTHMA_FIELD + field_names.PERCENTILE_FIELD_SUFFIX - ] - >= self.ENVIRONMENTAL_BURDEN_THRESHOLD - ) - - self.df[field_names.HEART_DISEASE_PCTILE_THRESHOLD] = ( - self.df[ - field_names.HEART_DISEASE_FIELD - + field_names.PERCENTILE_FIELD_SUFFIX - ] - >= self.ENVIRONMENTAL_BURDEN_THRESHOLD - ) - - self.df[field_names.LOW_LIFE_EXPECTANCY_PCTILE_THRESHOLD] = ( - self.df[ - field_names.LOW_LIFE_EXPECTANCY_FIELD - + field_names.PERCENTILE_FIELD_SUFFIX - ] - >= self.ENVIRONMENTAL_BURDEN_THRESHOLD - ) - - self.df[field_names.HEALTH_THRESHOLD_EXCEEDED] = ( - ( - self.df[field_names.DIABETES_PCTILE_THRESHOLD] - | self.df[field_names.ASTHMA_PCTILE_THRESHOLD] - ) - | self.df[field_names.HEART_DISEASE_PCTILE_THRESHOLD] - ) | self.df[field_names.LOW_LIFE_EXPECTANCY_PCTILE_THRESHOLD] - - self.df[field_names.DIABETES_LOW_INCOME_LOW_HIGHER_ED_FIELD] = ( - self.df[field_names.DIABETES_PCTILE_THRESHOLD] - & self.df[field_names.FPL_200_AND_COLLEGE_ATTENDANCE_SERIES] - ) - self.df[field_names.ASTHMA_LOW_INCOME_LOW_HIGHER_ED_FIELD] = ( - self.df[field_names.ASTHMA_PCTILE_THRESHOLD] - & self.df[field_names.FPL_200_AND_COLLEGE_ATTENDANCE_SERIES] - ) - self.df[field_names.HEART_DISEASE_LOW_INCOME_LOW_HIGHER_ED_FIELD] = ( - self.df[field_names.HEART_DISEASE_PCTILE_THRESHOLD] - & self.df[field_names.FPL_200_AND_COLLEGE_ATTENDANCE_SERIES] - ) - self.df[ - field_names.LOW_LIFE_EXPECTANCY_LOW_INCOME_LOW_HIGHER_ED_FIELD - ] = ( - self.df[field_names.LOW_LIFE_EXPECTANCY_PCTILE_THRESHOLD] - & self.df[field_names.FPL_200_AND_COLLEGE_ATTENDANCE_SERIES] - ) - - self._increment_total_eligibility_exceeded( - health_eligibility_columns, - skip_fips=constants.DROP_FIPS_FROM_NON_WTD_THRESHOLDS, - ) - - return self.df[health_eligibility_columns].any(axis="columns") - - def _workforce_factor(self) -> bool: - # Where unemployment is above Xth percentile - # or - # Where median income as a percent of area median income is above Xth percentile - # or - # Where the percent of households at or below 100% of the federal poverty level - # is above Xth percentile - # or - # Where linguistic isolation is above Xth percentile - # AND - # Where the high school degree achievement rates for adults 25 years and older - # is less than Y% - # AND the higher ed attendance rates are under Z% - # (necessary to screen out university tracts) - - # Workforce criteria for states fields. - workforce_eligibility_columns = [ - field_names.UNEMPLOYMENT_LOW_HS_LOW_HIGHER_ED_FIELD, - field_names.POVERTY_LOW_HS_LOW_HIGHER_ED_FIELD, - field_names.LINGUISTIC_ISOLATION_LOW_HS_LOW_HIGHER_ED_FIELD, - field_names.LOW_MEDIAN_INCOME_LOW_HS_LOW_HIGHER_ED_FIELD, - ] - - self.df[field_names.LOW_HS_EDUCATION_LOW_HIGHER_ED_FIELD] = ( - self.df[field_names.HIGH_SCHOOL_ED_FIELD] - >= self.LACK_OF_HIGH_SCHOOL_MINIMUM_THRESHOLD - ) & ( - ( - self.df[field_names.COLLEGE_ATTENDANCE_FIELD] - <= self.MAX_COLLEGE_ATTENDANCE_THRESHOLD - ) - | ( - # If college attendance data is null for this tract, just rely on the - # poverty/AMI data - self.df[field_names.COLLEGE_ATTENDANCE_FIELD].isna() - ) - ) - - self.df[field_names.UNEMPLOYMENT_PCTILE_THRESHOLD] = ( - self.df[ - field_names.UNEMPLOYMENT_FIELD - + field_names.PERCENTILE_FIELD_SUFFIX - ] - >= self.ENVIRONMENTAL_BURDEN_THRESHOLD - ) - - self.df[field_names.LOW_MEDIAN_INCOME_PCTILE_THRESHOLD] = ( - self.df[ - field_names.LOW_MEDIAN_INCOME_AS_PERCENT_OF_AMI_FIELD - + field_names.PERCENTILE_FIELD_SUFFIX - ] - >= self.ENVIRONMENTAL_BURDEN_THRESHOLD - ) - - self.df[field_names.LINGUISTIC_ISOLATION_PCTILE_THRESHOLD] = ( - self.df[ - field_names.LINGUISTIC_ISO_FIELD - + field_names.PERCENTILE_FIELD_SUFFIX - ] - >= self.ENVIRONMENTAL_BURDEN_THRESHOLD - ) - - self.df[field_names.POVERTY_PCTILE_THRESHOLD] = ( - self.df[ - field_names.POVERTY_LESS_THAN_100_FPL_FIELD - + field_names.PERCENTILE_FIELD_SUFFIX - ] - >= self.ENVIRONMENTAL_BURDEN_THRESHOLD - ) - - self.df[field_names.LINGUISTIC_ISOLATION_LOW_HS_LOW_HIGHER_ED_FIELD] = ( - self.df[field_names.LINGUISTIC_ISOLATION_PCTILE_THRESHOLD] - & self.df[field_names.LOW_HS_EDUCATION_LOW_HIGHER_ED_FIELD] - ) - - self.df[field_names.POVERTY_LOW_HS_LOW_HIGHER_ED_FIELD] = ( - self.df[field_names.POVERTY_PCTILE_THRESHOLD] - & self.df[field_names.LOW_HS_EDUCATION_LOW_HIGHER_ED_FIELD] - ) - - self.df[field_names.LOW_MEDIAN_INCOME_LOW_HS_LOW_HIGHER_ED_FIELD] = ( - self.df[field_names.LOW_MEDIAN_INCOME_PCTILE_THRESHOLD] - & self.df[field_names.LOW_HS_EDUCATION_LOW_HIGHER_ED_FIELD] - ) - - self.df[field_names.UNEMPLOYMENT_LOW_HS_LOW_HIGHER_ED_FIELD] = ( - self.df[field_names.UNEMPLOYMENT_PCTILE_THRESHOLD] - & self.df[field_names.LOW_HS_EDUCATION_LOW_HIGHER_ED_FIELD] - ) - - workforce_combined_criteria_for_states = self.df[ - workforce_eligibility_columns - ].any(axis="columns") - - self._increment_total_eligibility_exceeded( - workforce_eligibility_columns - ) - - # Now, calculate workforce criteria for island territories. - island_areas_workforce_eligibility_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, - ] - - # First, combine unemployment. - # This will include an adjusted percentile column for the island areas - # to be used by the front end. - ( - self.df, - island_areas_unemployment_criteria_field_name, - ) = self._combine_island_areas_with_states_and_set_thresholds( - df=self.df, - column_from_island_areas=field_names.CENSUS_DECENNIAL_UNEMPLOYMENT_FIELD_2009, - column_from_decennial_census=field_names.CENSUS_UNEMPLOYMENT_FIELD_2010, - combined_column_name=field_names.COMBINED_UNEMPLOYMENT_2010, - threshold_cutoff_for_island_areas=self.ENVIRONMENTAL_BURDEN_THRESHOLD, - ) - - # TODO: Remove this, it's for checking only - assert ( - island_areas_unemployment_criteria_field_name - == field_names.ISLAND_UNEMPLOYMENT_PCTILE_THRESHOLD - ), "Error combining island columns" - - # Next, combine poverty. - # This will include an adjusted percentile column for the island areas - # to be used by the front end. - ( - self.df, - island_areas_poverty_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_100_FPL_FIELD_2009, - column_from_decennial_census=field_names.CENSUS_POVERTY_LESS_THAN_100_FPL_FIELD_2010, - combined_column_name=field_names.COMBINED_POVERTY_LESS_THAN_100_FPL_FIELD_2010, - threshold_cutoff_for_island_areas=self.ENVIRONMENTAL_BURDEN_THRESHOLD, - ) - - # TODO: Remove this, it's for checking only - assert ( - island_areas_poverty_criteria_field_name - == field_names.ISLAND_POVERTY_PCTILE_THRESHOLD - ), "Error combining island columns" - - # Also check whether low area median income is 90th percentile or higher - # within the islands. - - # Note that because the field for low median does not have to be combined, - # unlike the other fields, we do not need to create a new percentile - # column. This code should probably be refactored when (TODO) we do the big - # refactor. - self.df[field_names.ISLAND_LOW_MEDIAN_INCOME_PCTILE_THRESHOLD] = ( - self.df[ - field_names.LOW_CENSUS_DECENNIAL_AREA_MEDIAN_INCOME_PERCENT_FIELD_2009 - + field_names.PERCENTILE_FIELD_SUFFIX - ] - >= self.ENVIRONMENTAL_BURDEN_THRESHOLD - ) - - self.df[field_names.ISLAND_AREAS_LOW_HS_EDUCATION_FIELD] = ( - self.df[field_names.CENSUS_DECENNIAL_HIGH_SCHOOL_ED_FIELD_2009] - >= self.LACK_OF_HIGH_SCHOOL_MINIMUM_THRESHOLD - ) - - self.df[ - field_names.ISLAND_AREAS_UNEMPLOYMENT_LOW_HS_EDUCATION_FIELD - ] = ( - self.df[island_areas_unemployment_criteria_field_name] - & self.df[field_names.ISLAND_AREAS_LOW_HS_EDUCATION_FIELD] - ) - - self.df[field_names.ISLAND_AREAS_POVERTY_LOW_HS_EDUCATION_FIELD] = ( - self.df[island_areas_poverty_criteria_field_name] - & self.df[field_names.ISLAND_AREAS_LOW_HS_EDUCATION_FIELD] - ) - - self.df[ - field_names.ISLAND_AREAS_LOW_MEDIAN_INCOME_LOW_HS_EDUCATION_FIELD - ] = ( - self.df[field_names.ISLAND_LOW_MEDIAN_INCOME_PCTILE_THRESHOLD] - & self.df[field_names.ISLAND_AREAS_LOW_HS_EDUCATION_FIELD] - ) - - workforce_combined_criteria_for_island_areas = self.df[ - island_areas_workforce_eligibility_columns - ].any(axis="columns") - - self._increment_total_eligibility_exceeded( - island_areas_workforce_eligibility_columns - ) - - percent_of_island_tracts_highlighted = ( - 100 - * workforce_combined_criteria_for_island_areas.sum() - # Choosing a random column from island areas to calculate the denominator. - / self.df[field_names.CENSUS_DECENNIAL_UNEMPLOYMENT_FIELD_2009] - .notnull() - .sum() - ) - - logger.debug( - f"For workforce criteria in island areas, " - f"{workforce_combined_criteria_for_island_areas.sum()} (" - f"{percent_of_island_tracts_highlighted:.2f}% of tracts that have non-null data " - f"in the column) have a value of TRUE." - ) - - # Because these criteria are calculated differently for the islands, we also calculate the - # thresholds to pass to the FE slightly differently - - self.df[field_names.WORKFORCE_THRESHOLD_EXCEEDED] = ( - ## First we calculate for the non-island areas - ( - ( - self.df[field_names.POVERTY_PCTILE_THRESHOLD] - | self.df[field_names.LINGUISTIC_ISOLATION_PCTILE_THRESHOLD] - ) - | self.df[field_names.LOW_MEDIAN_INCOME_PCTILE_THRESHOLD] - ) - | self.df[field_names.UNEMPLOYMENT_PCTILE_THRESHOLD] - ) | ( - ## then we calculate just for the island areas - ( - self.df[field_names.ISLAND_UNEMPLOYMENT_PCTILE_THRESHOLD] - | self.df[field_names.ISLAND_POVERTY_PCTILE_THRESHOLD] - ) - | self.df[field_names.ISLAND_LOW_MEDIAN_INCOME_PCTILE_THRESHOLD] - ) - - # Because of the island complications, we also have to separately calculate the threshold for - # socioeconomic thresholds - self.df[field_names.WORKFORCE_SOCIO_INDICATORS_EXCEEDED] = ( - self.df[field_names.ISLAND_AREAS_LOW_HS_EDUCATION_FIELD] - | self.df[field_names.LOW_HS_EDUCATION_LOW_HIGHER_ED_FIELD] - ) - - # A tract is included if it meets either the states tract criteria or the - # island areas tract criteria. - return ( - workforce_combined_criteria_for_states - | workforce_combined_criteria_for_island_areas - ) - - def add_columns(self) -> pd.DataFrame: - logger.debug("Adding Score M") - - self.df[field_names.THRESHOLD_COUNT] = 0 - - # TODO: move this inside of - # `_create_low_income_and_low_college_attendance_threshold` - # and change the return signature of that method. - # Create a standalone field that captures the college attendance boolean - # threshold. - self.df[field_names.LOW_INCOME_THRESHOLD] = ( - self.df[ - field_names.POVERTY_LESS_THAN_200_FPL_FIELD - + field_names.PERCENTILE_FIELD_SUFFIX - ] - >= self.LOW_INCOME_THRESHOLD - ) - - # Because we are moving this variable to be in the same direction as all - # other variables, we change this to be < rather than <=. This translates - # to "80% or more of residents are not college students", rather than - # "Strictly greater than 80% of residents are not college students." - # There are two tracts that are impacted by this (that is, they have exactly) - # 20% college students -- neither of these has been a DAC under any score. - self.df[field_names.COLLEGE_ATTENDANCE_LESS_THAN_20_FIELD] = ( - self.df[field_names.COLLEGE_ATTENDANCE_FIELD] - < self.MAX_COLLEGE_ATTENDANCE_THRESHOLD - ) - - self.df[ - field_names.FPL_200_AND_COLLEGE_ATTENDANCE_SERIES - ] = self._create_low_income_and_low_college_attendance_threshold( - self.df - ) - self.df[field_names.M_CLIMATE] = self._climate_factor() - self.df[field_names.M_ENERGY] = self._energy_factor() - self.df[field_names.M_TRANSPORTATION] = self._transportation_factor() - self.df[field_names.M_HOUSING] = self._housing_factor() - self.df[field_names.M_POLLUTION] = self._pollution_factor() - self.df[field_names.M_WATER] = self._water_factor() - self.df[field_names.M_HEALTH] = self._health_factor() - self.df[field_names.M_WORKFORCE] = self._workforce_factor() - - factors = [ - field_names.M_CLIMATE, - field_names.M_ENERGY, - field_names.M_TRANSPORTATION, - field_names.M_HOUSING, - field_names.M_POLLUTION, - field_names.M_WATER, - field_names.M_HEALTH, - field_names.M_WORKFORCE, - ] - self.df[field_names.CATEGORY_COUNT] = self.df[factors].sum(axis=1) - self.df[field_names.SCORE_M_COMMUNITIES] = self.df[factors].any(axis=1) - - # Note: this is purely used for comparison tool analysis, and can be removed at a later date. - LMB. - non_workforce_factors = [ - field_names.M_CLIMATE, - field_names.M_ENERGY, - field_names.M_TRANSPORTATION, - field_names.M_HOUSING, - field_names.M_POLLUTION, - field_names.M_WATER, - field_names.M_HEALTH, - ] - self.df[field_names.M_NON_WORKFORCE] = self.df[ - non_workforce_factors - ].any(axis=1) - - self.df[ - field_names.SCORE_M + field_names.PERCENTILE_FIELD_SUFFIX - ] = self.df[field_names.SCORE_M_COMMUNITIES].astype(int) - - return self.df From ff9e7b9aa2c79ee27503df94dd650db89dc36e89 Mon Sep 17 00:00:00 2001 From: Carlos Felix <63804190+carlosfelix2@users.noreply.github.com> Date: Tue, 5 Nov 2024 11:31:54 -0500 Subject: [PATCH 02/28] Changes to allow local runs --- .github/CODEOWNERS | 1 - .github/workflows/codeql-analysis.yml | 6 +- .github/workflows/data-checks.yml | 2 +- data/data-pipeline/data_pipeline/constants.py | 7 + .../data_pipeline/etl/datasource.py | 5 +- .../data_pipeline/etl/downloader.py | 5 +- .../data_pipeline/etl/sources/census/etl.py | 1 + data/data-pipeline/data_pipeline/utils.py | 3 +- data/data-pipeline/poetry.lock | 5049 +++++++++++------ data/data-pipeline/pyproject.toml | 13 +- data/data-pipeline/tox.ini | 6 +- 11 files changed, 3231 insertions(+), 1867 deletions(-) create mode 100644 data/data-pipeline/data_pipeline/constants.py diff --git a/.github/CODEOWNERS b/.github/CODEOWNERS index eadc67c7..e69de29b 100644 --- a/.github/CODEOWNERS +++ b/.github/CODEOWNERS @@ -1 +0,0 @@ -* @vim-usds @travis-newby @sampowers-usds @mattbowen-usds diff --git a/.github/workflows/codeql-analysis.yml b/.github/workflows/codeql-analysis.yml index d7d800b3..31f44ad3 100644 --- a/.github/workflows/codeql-analysis.yml +++ b/.github/workflows/codeql-analysis.yml @@ -45,7 +45,7 @@ jobs: # Initializes the CodeQL tools for scanning. - name: Initialize CodeQL - uses: github/codeql-action/init@v1 + uses: github/codeql-action/init@v2 with: languages: ${{ matrix.language }} # If you wish to specify custom queries, you can do so here or in a config file. @@ -56,7 +56,7 @@ jobs: # Autobuild attempts to build any compiled languages (C/C++, C#, or Java). # If this step fails, then you should remove it and run the build manually (see below) - name: Autobuild - uses: github/codeql-action/autobuild@v1 + uses: github/codeql-action/autobuild@v2 # ℹ️ Command-line programs to run using the OS shell. # 📚 https://git.io/JvXDl @@ -70,4 +70,4 @@ jobs: # make release - name: Perform CodeQL Analysis - uses: github/codeql-action/analyze@v1 + uses: github/codeql-action/analyze@v2 diff --git a/.github/workflows/data-checks.yml b/.github/workflows/data-checks.yml index 280a6e2a..ccfd4bd9 100644 --- a/.github/workflows/data-checks.yml +++ b/.github/workflows/data-checks.yml @@ -16,7 +16,7 @@ jobs: strategy: matrix: # checks all of the versions allowed in pyproject.toml - python-version: [3.8, 3.9] + python-version: [3.10.15] steps: # installs Python # one execution of the tests per version listed above diff --git a/data/data-pipeline/data_pipeline/constants.py b/data/data-pipeline/data_pipeline/constants.py new file mode 100644 index 00000000..8d4b422f --- /dev/null +++ b/data/data-pipeline/data_pipeline/constants.py @@ -0,0 +1,7 @@ +import logging + +LOG_LEVEL = logging.DEBUG +"""Log level for all loggers.""" + +NO_SSL_VERIFY = True +"""Set to true to skip SSL verification when downloading files. Useful for local development.""" diff --git a/data/data-pipeline/data_pipeline/etl/datasource.py b/data/data-pipeline/data_pipeline/etl/datasource.py index 3d299207..f77656c1 100644 --- a/data/data-pipeline/data_pipeline/etl/datasource.py +++ b/data/data-pipeline/data_pipeline/etl/datasource.py @@ -22,6 +22,7 @@ from pathlib import Path from typing import List from dataclasses import dataclass from abc import ABC, abstractmethod +from data_pipeline.constants import NO_SSL_VERIFY from data_pipeline.etl.downloader import Downloader from data_pipeline.etl.sources.census_acs.etl_utils import ( @@ -65,7 +66,7 @@ class FileDataSource(DataSource): Downloader.download_file_from_url( file_url=self.source, download_file_name=self.destination, - verify=True, + verify=not NO_SSL_VERIFY, ) def __str__(self): @@ -85,7 +86,7 @@ class ZIPDataSource(DataSource): Downloader.download_zip_file_from_url( file_url=self.source, unzipped_file_path=self.destination, - verify=True, + verify=not NO_SSL_VERIFY, ) def __str__(self): diff --git a/data/data-pipeline/data_pipeline/etl/downloader.py b/data/data-pipeline/data_pipeline/etl/downloader.py index 53ea2a38..55a97f1b 100644 --- a/data/data-pipeline/data_pipeline/etl/downloader.py +++ b/data/data-pipeline/data_pipeline/etl/downloader.py @@ -6,7 +6,9 @@ import shutil from pathlib import Path from data_pipeline.config import settings +from data_pipeline.utils import get_module_logger +logger = get_module_logger(__name__) class Downloader: """A simple class to encapsulate the download capabilities of the application""" @@ -34,12 +36,13 @@ class Downloader: urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning) download_file_name.parent.mkdir(parents=True, exist_ok=True) - + logger.debug(f"Downloading {file_url}") response = requests.get( file_url, verify=verify, timeout=settings.REQUESTS_DEFAULT_TIMOUT ) if response.status_code == 200: file_contents = response.content + logger.debug("Downloaded.") else: raise Exception( f"HTTP response {response.status_code} from url {file_url}. Info: {response.content}" diff --git a/data/data-pipeline/data_pipeline/etl/sources/census/etl.py b/data/data-pipeline/data_pipeline/etl/sources/census/etl.py index 1f4b260a..e0ad704a 100644 --- a/data/data-pipeline/data_pipeline/etl/sources/census/etl.py +++ b/data/data-pipeline/data_pipeline/etl/sources/census/etl.py @@ -215,6 +215,7 @@ class CensusETL(ExtractTransformLoad): state_gdf = gpd.read_file(file_name) usa_df = usa_df.append(state_gdf) + logger.debug("Converting to CRS") usa_df = usa_df.to_crs( "+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs" ) diff --git a/data/data-pipeline/data_pipeline/utils.py b/data/data-pipeline/data_pipeline/utils.py index 31b52880..fd5ff5d7 100644 --- a/data/data-pipeline/data_pipeline/utils.py +++ b/data/data-pipeline/data_pipeline/utils.py @@ -13,6 +13,7 @@ import requests import urllib3 import yaml from data_pipeline.config import settings +from data_pipeline.constants import LOG_LEVEL from data_pipeline.content.schemas.download_schemas import CodebookConfig from data_pipeline.content.schemas.download_schemas import CSVConfig from data_pipeline.content.schemas.download_schemas import ExcelConfig @@ -48,7 +49,7 @@ def get_module_logger(module_name: str) -> logging.Logger: ) handler.setFormatter(formatter) logger.addHandler(handler) - logger.setLevel(logging.INFO) + logger.setLevel(LOG_LEVEL) logger.propagate = False # don't send log messages to the parent logger (to avoid duplicate log messages) return logger diff --git a/data/data-pipeline/poetry.lock b/data/data-pipeline/poetry.lock index 79c7f6a5..f0b51d2b 100644 --- a/data/data-pipeline/poetry.lock +++ b/data/data-pipeline/poetry.lock @@ -1,78 +1,249 @@ -# This file is automatically @generated by Poetry and should not be changed by hand. +# This file is automatically @generated by Poetry 1.8.4 and should not be changed by hand. 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b/data/data-pipeline/pyproject.toml @@ -17,7 +17,7 @@ packages = [ [tool.poetry.dependencies] CensusData = "^1.13" -certifi = "^2022.12.07" # explicit callout due to https://pyup.io/v/52365/f17/ +certifi = ">= 2024.07.04" # Due to https://data.safetycli.com/v/72083/f17 click = "8.0.4" # pinning for now per https://github.com/psf/black/issues/2964 dynaconf = "^3.1.4" geopandas = "^0.11.0" @@ -29,19 +29,20 @@ marshmallow-dataclass = "^8.5.3" marshmallow-enum = "^1.5.1" matplotlib = "^3.4.2" numpy = "^1.22.1" -pandas = "^1.2.5" +pandas = "~1.4.3" pylint = "^2.11.1" -pillow = "9.3.0" -python = "^3.8" +pillow = "9.3.0" # Newer versions break tile generation +python = "^3.10" pypandoc = "^1.6.3" PyYAML = "^6.0" requests = "^2.25.1" -tqdm = "4.62.0" +tqdm = "^4.66.3" types-requests = "^2.25.0" us = "^2.0.2" xlsxwriter = "^2.0.0" pydantic = "^1.9.0" Rtree = "^1.0.0" +fiona = "~1.8.21" [tool.poetry.dev-dependencies] black = {version = "^21.6b0", allow-prereleases = true} @@ -59,7 +60,7 @@ pandas-vet = "^0.2.2" pytest-snapshot = "^0.8.1" seaborn = "^0.11.2" papermill = "^2.3.4" -jupyterlab = "3.4.4" +jupyterlab = "^3.6.7" [build-system] build-backend = "poetry.core.masonry.api" diff --git a/data/data-pipeline/tox.ini b/data/data-pipeline/tox.ini index 558b5769..ebf462eb 100644 --- a/data/data-pipeline/tox.ini +++ b/data/data-pipeline/tox.ini @@ -1,7 +1,7 @@ [tox] # required because we use pyproject.toml isolated_build = true -envlist = py38, py39, lint, checkdeps, pytest +envlist = py310, lint, checkdeps, pytest # only checks python versions installed locally skip_missing_interpreters = true @@ -16,7 +16,9 @@ commands = black data_pipeline # checks the dependencies for security vulnerabilities and open source licenses allowlist_externals = bash commands = pip install -U wheel - safety check --ignore 51457 --ignore 44715 # known issue: https://github.com/pyupio/safety/issues/364 + # known issue: https://github.com/pyupio/safety/issues/364 + # jinja2 false positive for our use: https://data.safetycli.com/v/70612/f17 + safety check --ignore 51457 --ignore 44715 --ignore 70612 bash scripts/run-liccheck.sh [testenv:pytest] From 0da80c90d8e752eb2ab7302d285844f1ac78c098 Mon Sep 17 00:00:00 2001 From: Carlos Felix <63804190+carlosfelix2@users.noreply.github.com> Date: Wed, 13 Nov 2024 15:58:47 -0500 Subject: [PATCH 03/28] Territories in the 65th percentile of low income are added is disadvantaged --- .../data_pipeline/application.py | 171 +++++++----------- .../data_pipeline/etl/score/etl_score.py | 1 + .../data_pipeline/score/field_names.py | 7 + .../data_pipeline/score/score_narwhal.py | 51 +++++- .../tests/score/test_score_narwhal_methods.py | 53 +++++- .../data/test_mark_poverty_flag.csv | 8 + .../data/test_mark_territory_dacs.csv | 9 + data/data-pipeline/pyproject.toml | 2 +- 8 files changed, 177 insertions(+), 125 deletions(-) create mode 100644 data/data-pipeline/data_pipeline/tests/score/test_utils/data/test_mark_poverty_flag.csv create mode 100644 data/data-pipeline/data_pipeline/tests/score/test_utils/data/test_mark_territory_dacs.csv diff --git a/data/data-pipeline/data_pipeline/application.py b/data/data-pipeline/data_pipeline/application.py index cde48ae1..88c2505b 100644 --- a/data/data-pipeline/data_pipeline/application.py +++ b/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""" diff --git a/data/data-pipeline/data_pipeline/etl/score/etl_score.py b/data/data-pipeline/data_pipeline/etl/score/etl_score.py index 0314512b..887d6189 100644 --- a/data/data-pipeline/data_pipeline/etl/score/etl_score.py +++ b/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, diff --git a/data/data-pipeline/data_pipeline/score/field_names.py b/data/data-pipeline/data_pipeline/score/field_names.py index aed44c48..773a88aa 100644 --- a/data/data-pipeline/data_pipeline/score/field_names.py +++ b/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" diff --git a/data/data-pipeline/data_pipeline/score/score_narwhal.py b/data/data-pipeline/data_pipeline/score/score_narwhal.py index 88473ca5..cfdab639 100644 --- a/data/data-pipeline/data_pipeline/score/score_narwhal.py +++ b/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 diff --git a/data/data-pipeline/data_pipeline/tests/score/test_score_narwhal_methods.py b/data/data-pipeline/data_pipeline/tests/score/test_score_narwhal_methods.py index 3132c51a..a559e46c 100644 --- a/data/data-pipeline/data_pipeline/tests/score/test_score_narwhal_methods.py +++ b/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() + ) diff --git a/data/data-pipeline/data_pipeline/tests/score/test_utils/data/test_mark_poverty_flag.csv b/data/data-pipeline/data_pipeline/tests/score/test_utils/data/test_mark_poverty_flag.csv new file mode 100644 index 00000000..00ca70fd --- /dev/null +++ b/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 \ No newline at end of file diff --git a/data/data-pipeline/data_pipeline/tests/score/test_utils/data/test_mark_territory_dacs.csv b/data/data-pipeline/data_pipeline/tests/score/test_utils/data/test_mark_territory_dacs.csv new file mode 100644 index 00000000..84992e2a --- /dev/null +++ b/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 \ No newline at end of file diff --git a/data/data-pipeline/pyproject.toml b/data/data-pipeline/pyproject.toml index bbb9adc1..45403257 100644 --- a/data/data-pipeline/pyproject.toml +++ b/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 "] keywords = ["justice40", "environmental_justice", "python", "etl"] From 3e087a37a4db4910f7953ca13c28f7c9212b1e8c Mon Sep 17 00:00:00 2001 From: Carlos Felix <63804190+carlosfelix2@users.noreply.github.com> Date: Tue, 19 Nov 2024 15:01:27 -0500 Subject: [PATCH 04/28] Fix linter and dependency issues --- .../data_pipeline/application.py | 3 + .../data_pipeline/etl/downloader.py | 2 + .../etl/sources/census_acs/etl_imputations.py | 1 + .../data_pipeline/score/score_narwhal.py | 21 +- .../tests/score/test_score_narwhal_methods.py | 40 +- data/data-pipeline/poetry.lock | 1273 ++++++++--------- data/data-pipeline/pyproject.toml | 9 +- 7 files changed, 619 insertions(+), 730 deletions(-) diff --git a/data/data-pipeline/data_pipeline/application.py b/data/data-pipeline/data_pipeline/application.py index 88c2505b..66323542 100644 --- a/data/data-pipeline/data_pipeline/application.py +++ b/data/data-pipeline/data_pipeline/application.py @@ -58,6 +58,7 @@ data_source_option = click.option( help=dataset_cli_help, ) + @click.group() def cli(): """Defines a click group for the commands below""" @@ -415,6 +416,7 @@ def clear_data_source_cache(dataset: str): log_goodbye() + @cli.command( help="Generate scoring and tiles", ) @@ -441,6 +443,7 @@ def full_run(ctx, 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""" logger.info("-" * LOG_LINE_WIDTH) diff --git a/data/data-pipeline/data_pipeline/etl/downloader.py b/data/data-pipeline/data_pipeline/etl/downloader.py index 55a97f1b..fd0fec50 100644 --- a/data/data-pipeline/data_pipeline/etl/downloader.py +++ b/data/data-pipeline/data_pipeline/etl/downloader.py @@ -10,6 +10,7 @@ from data_pipeline.utils import get_module_logger logger = get_module_logger(__name__) + class Downloader: """A simple class to encapsulate the download capabilities of the application""" @@ -44,6 +45,7 @@ class Downloader: file_contents = response.content logger.debug("Downloaded.") else: + # pylint: disable-next=broad-exception-raised raise Exception( f"HTTP response {response.status_code} from url {file_url}. Info: {response.content}" ) diff --git a/data/data-pipeline/data_pipeline/etl/sources/census_acs/etl_imputations.py b/data/data-pipeline/data_pipeline/etl/sources/census_acs/etl_imputations.py index ad0514ff..4fcf0d9f 100644 --- a/data/data-pipeline/data_pipeline/etl/sources/census_acs/etl_imputations.py +++ b/data/data-pipeline/data_pipeline/etl/sources/census_acs/etl_imputations.py @@ -39,6 +39,7 @@ def _choose_best_mask( for mask in masks_in_priority_order: if any(geo_df[mask][column_to_impute].notna()): return mask + # pylint: disable-next=broad-exception-raised raise Exception("No mask found") diff --git a/data/data-pipeline/data_pipeline/score/score_narwhal.py b/data/data-pipeline/data_pipeline/score/score_narwhal.py index cfdab639..18ff8676 100644 --- a/data/data-pipeline/data_pipeline/score/score_narwhal.py +++ b/data/data-pipeline/data_pipeline/score/score_narwhal.py @@ -1014,12 +1014,12 @@ class ScoreNarwhal(Score): ) def _mark_territory_dacs(self) -> None: - """Territory tracts that are flagged as low income are Score N communities. - """ + """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], + 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], ) @@ -1049,9 +1049,14 @@ class ScoreNarwhal(Score): 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 + 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: diff --git a/data/data-pipeline/data_pipeline/tests/score/test_score_narwhal_methods.py b/data/data-pipeline/data_pipeline/tests/score/test_score_narwhal_methods.py index a559e46c..4b8fe471 100644 --- a/data/data-pipeline/data_pipeline/tests/score/test_score_narwhal_methods.py +++ b/data/data-pipeline/data_pipeline/tests/score/test_score_narwhal_methods.py @@ -2,7 +2,6 @@ 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 @@ -13,9 +12,11 @@ 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(TEST_DATA_FOLDER / "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}, ) @@ -84,7 +85,8 @@ def test_drop_all_tracts(toy_score_df): def test_mark_territory_dacs(): - test_data = pd.read_csv(TEST_DATA_FOLDER / "test_mark_territory_dacs.csv", + 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 @@ -92,18 +94,22 @@ def test_mark_territory_dacs(): 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']) + 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() + 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() + 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", + 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 @@ -111,14 +117,14 @@ def test_mark_poverty_flag(): scorer = ScoreNarwhal(test_data) scorer._mark_poverty_flag() - expected_low_income_filter = ( - test_data[field_names.GEOID_TRACT_FIELD].isin(['36087011302', '66010951100', '78010990000']) + 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() - ) + 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() - ) + assert not test_data[~expected_low_income_filter][ + field_names.FPL_200_SERIES_IMPUTED_AND_ADJUSTED + ].all() diff --git 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"^1.5.1" -matplotlib = "^3.4.2" numpy = "^1.22.1" pandas = "~1.4.3" pylint = "^2.11.1" -pillow = "9.3.0" # Newer versions break tile generation python = "^3.10" pypandoc = "^1.6.3" PyYAML = "^6.0" @@ -44,8 +42,8 @@ pydantic = "^1.9.0" Rtree = "^1.0.0" fiona = "~1.8.21" -[tool.poetry.dev-dependencies] -black = {version = "^21.6b0", allow-prereleases = true} +[tool.poetry.group.dev.dependencies] +black = "^21" flake8 = "^3.9.2" liccheck = "^0.6.2" mypy = "^0.910" @@ -126,6 +124,7 @@ authorized_licenses = [ "apache", "apache 2.0", "apache license 2.0", + "apache license, version 2.0", "apache software license", "apache software", "gnu lgpl", @@ -145,3 +144,5 @@ authorized_licenses = [ "gpl v3", "historical permission notice and disclaimer (hpnd)", ] +[tool.liccheck.authorized_packages] +ypy-websocket="0.8.4" From 109db724b1d882e04f1fe17812da4dc82b0999d2 Mon Sep 17 00:00:00 2001 From: ericiwamoto <100735505+ericiwamoto@users.noreply.github.com> Date: Tue, 19 Nov 2024 14:00:23 -0800 Subject: [PATCH 05/28] Frontend staging pipeline setup --- .github/workflows/deploy_frontend_main.yml | 85 ++++++++++++++++++++++ 1 file changed, 85 insertions(+) create mode 100644 .github/workflows/deploy_frontend_main.yml diff --git a/.github/workflows/deploy_frontend_main.yml b/.github/workflows/deploy_frontend_main.yml new file mode 100644 index 00000000..d70e9b4a --- /dev/null +++ b/.github/workflows/deploy_frontend_main.yml @@ -0,0 +1,85 @@ +name: Deploy Frontend Main +on: + push: + branches: [main] + paths: + - "client/**/*" +jobs: + build: + runs-on: ubuntu-latest + environment: Staging + defaults: + run: + working-directory: client + strategy: + matrix: + node-version: [14.x] + steps: + - uses: actions/checkout@v2 + - name: Use Node.js ${{ matrix.node-version }} + uses: actions/setup-node@v2 + with: + node-version: ${{ matrix.node-version }} + - name: Set DESTINATION_FOLDER for main + run: | + echo "DESTINATION_FOLDER=main" >> $GITHUB_ENV + - name: Install + run: npm ci + - name: Build + run: npm run build --if-present + env: + # See the client readme for more info on environment variables: + # https://github.com/usds/justice40-tool/blob/main/client/README.md + DATA_SOURCE: cdn + # TODO: Update main URL when either is back up + SITE_URL: "${{ secrets.SITE_URL }}" + MAPBOX_STYLES_READ_TOKEN: "${{ secrets.MAPBOX_STYLES_READ_TOKEN }}" + - name: Get directory contents + run: ls -la public + - name: Lint + run: npm run lint + # Disabling for now due to jsonlint - TODO: put this back + # - name: License Check + # run: npm run licenses + - name: Test + run: npm test + # - name: Check for security vulnerabilities + # run: npm audit --production + - name: Upload Artifact + uses: actions/upload-artifact@v2 + with: + name: J40Static + # Upload-artifact does not support the default working directory + # See more: https://github.com/actions/upload-artifact/issues/87 + path: ./client/public + deploy: + runs-on: ubuntu-latest + needs: build + environment: Staging + steps: + - name: Checkout source + uses: actions/checkout@v2 + - name: Download Artifacts + uses: actions/download-artifact@v2 + with: + name: J40Static + path: ./public + - name: Set DESTINATION_FOLDER for main + run: | + echo "DESTINATION_FOLDER=main" >> $GITHUB_ENV + - name: Configure AWS Credentials + uses: aws-actions/configure-aws-credentials@v1 + with: + aws-access-key-id: ${{ secrets.CLIENT_DEV_AWS_ACCESS_KEY_ID }} + aws-secret-access-key: ${{ secrets.CLIENT_DEV_AWS_SECRET_ACCESS_KEY }} + aws-region: us-east-1 + - name: Deploy to Geoplatform AWS + run: aws s3 sync ./public/ s3://${{secrets.S3_WEBSITE_BUCKET}}/justice40-tool/${{env.DESTINATION_FOLDER}} --delete + - name: Invalidate cache on AWS CDNs + uses: chetan/invalidate-cloudfront-action@master + env: + DISTRIBUTION: ${{secrets.WEB_CDN_ID}} + PATHS: "/*" + AWS_REGION: "us-east-1" + AWS_ACCESS_KEY_ID: ${{ secrets.CLIENT_DEV_AWS_ACCESS_KEY_ID }} + AWS_SECRET_ACCESS_KEY: ${{ secrets.CLIENT_DEV_AWS_SECRET_ACCESS_KEY }} \ No newline at end of file From f58f88cf1b746ba8a8db3fb40bfede86fca935af Mon Sep 17 00:00:00 2001 From: ericiwamoto <100735505+ericiwamoto@users.noreply.github.com> Date: Wed, 20 Nov 2024 08:38:39 -0800 Subject: [PATCH 06/28] update website cdn targets --- client/.env.development | 2 +- client/.env.production | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/client/.env.development b/client/.env.development index 4f232c01..0c6c369a 100644 --- a/client/.env.development +++ b/client/.env.development @@ -3,7 +3,7 @@ # Feature Tiles env variables: # The TILES_BASE_URL will be determined by the DATA_SOURCE env variable -GATSBY_CDN_TILES_BASE_URL=https://static-data-screeningtool.geoplatform.gov +GATSBY_CDN_TILES_BASE_URL=https://dig0wsohit6js.cloudfront.net GATSBY_LOCAL_TILES_BASE_URL=http://localhost:5000/data/data-pipeline GATSBY_DATA_PIPELINE_SCORE_PATH_LOCAL=data_pipeline/data/score diff --git a/client/.env.production b/client/.env.production index 540744cb..41ec89a8 100644 --- a/client/.env.production +++ b/client/.env.production @@ -3,7 +3,7 @@ # Feature Tiles env variables: # The TILES_BASE_URL will always point to the CDN -GATSBY_CDN_TILES_BASE_URL=https://static-data-screeningtool.geoplatform.gov +GATSBY_CDN_TILES_BASE_URL=https://dig0wsohit6js.cloudfront.net GATSBY_DATA_PIPELINE_TRIBAL_PATH=data-pipeline/data/tribal From 2c1fec5317baeae3bc44ecf7a145a8918bafb4cf Mon Sep 17 00:00:00 2001 From: Ryon Coleman Date: Wed, 20 Nov 2024 12:23:08 -0500 Subject: [PATCH 07/28] Add display text for island territories marked disadvantaged for low income --- .../src/components/AreaDetail/AreaDetail.tsx | 6 +++ .../PrioritizationCopy/PrioritizationCopy.tsx | 6 +++ client/src/data/constants.tsx | 15 ++++-- client/src/data/copy/explore.tsx | 50 +++++++++++-------- client/src/intl/en.json | 22 ++++---- client/src/intl/es.json | 5 +- 6 files changed, 69 insertions(+), 35 deletions(-) diff --git a/client/src/components/AreaDetail/AreaDetail.tsx b/client/src/components/AreaDetail/AreaDetail.tsx index 4e480dd8..2f3656b8 100644 --- a/client/src/components/AreaDetail/AreaDetail.tsx +++ b/client/src/components/AreaDetail/AreaDetail.tsx @@ -1146,6 +1146,12 @@ const AreaDetail = ({properties}: IAreaDetailProps) => { isAdjacencyLowIncome={ properties[constants.ADJACENCY_LOW_INCOME_EXCEEDS_THRESH] } + isIslandLowIncome={ + properties[constants.IS_FEDERAL_POVERTY_LEVEL_200] && + constants.TILES_ISLAND_AREA_FIPS_CODES.some((code) => { + return properties[constants.GEOID_PROPERTY].startsWith(code); + }) + } tribalCountAK={ properties[constants.TRIBAL_AREAS_COUNT_AK] >= 1 ? properties[constants.TRIBAL_AREAS_COUNT_AK] : diff --git a/client/src/components/PrioritizationCopy/PrioritizationCopy.tsx b/client/src/components/PrioritizationCopy/PrioritizationCopy.tsx index c2b22f6e..6769ebc3 100644 --- a/client/src/components/PrioritizationCopy/PrioritizationCopy.tsx +++ b/client/src/components/PrioritizationCopy/PrioritizationCopy.tsx @@ -9,6 +9,7 @@ interface IPrioritizationCopy { totalBurdensPrioritized: number isAdjacencyThreshMet: boolean, isAdjacencyLowIncome: boolean, + isIslandLowIncome: boolean, tribalCountAK: number | null, tribalCountUS: null, // when this signal is supported add number type percentTractTribal: number | null @@ -26,6 +27,7 @@ interface IPrioritizationCopy { * @param {number} totalBurdensPrioritized * @param {boolean} isAdjacencyThreshMet * @param {boolean} isAdjacencyLowIncome + * @param {boolean} isIslandLowIncome * @param {number | null} tribalCountAK * @param {number | null} tribalCountUS * @param {number | null} percentTractTribal @@ -36,6 +38,7 @@ const PrioritizationCopy = totalBurdensPrioritized, isAdjacencyThreshMet, isAdjacencyLowIncome, + isIslandLowIncome, tribalCountAK, tribalCountUS, percentTractTribal, @@ -48,6 +51,9 @@ const PrioritizationCopy = if (isAdjacencyThreshMet && isAdjacencyLowIncome) { prioCopyRendered = EXPLORE_COPY.PRIORITIZATION_COPY.PRIO_SURR_LI; // if 1-2 + } else if (isIslandLowIncome) { + prioCopyRendered = EXPLORE_COPY.PRIORITIZATION_COPY.PRIO_ISLAND_LI; + // if 1-3 } else if (isAdjacencyThreshMet && !isAdjacencyLowIncome) { // if 1-2-1 if ( tribalCountAK === null && tribalCountUS === null) { diff --git a/client/src/data/constants.tsx b/client/src/data/constants.tsx index cbb3b890..b60cec17 100644 --- a/client/src/data/constants.tsx +++ b/client/src/data/constants.tsx @@ -1,5 +1,7 @@ -import { LngLatBoundsLike } from "maplibre-gl"; -import { isMobile as isMobileReactDeviceDetect } from "react-device-detect"; +/* eslint quotes: [2, "double"] */ + +import {LngLatBoundsLike} from "maplibre-gl"; +import {isMobile as isMobileReactDeviceDetect} from "react-device-detect"; export const isMobile = isMobileReactDeviceDetect; @@ -77,6 +79,13 @@ export const SIDE_PANEL_STATE_VALUES = { ISLAND_AREAS: "Island Areas", }; +/** + * 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 + */ +export const TILES_ISLAND_AREA_FIPS_CODES = ["60", "66", "69", "78"]; + // Climate category export const IS_CLIMATE_FACTOR_DISADVANTAGED = "N_CLT"; export const IS_CLIMATE_EXCEED_ONE_OR_MORE_INDICATORS = "N_CLT_EOMI"; @@ -221,7 +230,7 @@ export const IS_EXCEEDS_THRESH_FOR_ISLAND_AREA_BELOW_100_POVERTY = "IA_POV_ET"; export const IS_WORKFORCE_EXCEED_BOTH_SOCIO_INDICATORS = "N_WKFC_EBSI"; -export const HIGH_SCHOOL_PROPERTY_PERCENTILE = `HSEF`; +export const HIGH_SCHOOL_PROPERTY_PERCENTILE = "HSEF"; export const IS_LOW_HS_EDUCATION_LOW_HIGHER_ED_PRIORITIZED = "LHE"; export const ISLAND_AREAS_HS_EDU_PERCENTAGE_FIELD = "IAHSEF"; export const ISLAND_AREA_LOW_HS_EDU = "IALHE"; diff --git a/client/src/data/copy/explore.tsx b/client/src/data/copy/explore.tsx index 5c59d831..af91b874 100644 --- a/client/src/data/copy/explore.tsx +++ b/client/src/data/copy/explore.tsx @@ -33,7 +33,7 @@ export const PAGE_INTRO = defineMessages({ export const PAGE_DESCRIPTION1 = ; @@ -41,7 +41,7 @@ export const PAGE_DESCRIPTION1 = ; @@ -55,8 +55,8 @@ export const LEGEND = defineMessages({ PRIORITY_DESCRIPT: { id: 'explore.map.page.legend.description.text', defaultMessage: ` - Communities identified as disadvantaged by the map are those that are marginalized, underserved, - and overburdened by pollution. These communities are at or above the thresholds in one or + Communities identified as disadvantaged by the map are those that are marginalized, underserved, + and overburdened by pollution. These communities are at or above the thresholds in one or more of eight categories of criteria. `, description: 'On the explore the map page, the description of the legend', @@ -108,7 +108,7 @@ export const MAP = defineMessages({ LOWER48_SHORT: { id: 'explore.map.page.map.territoryFocus.lower48.short', defaultMessage: '48', - description: `On the explore the map page, on the map, the abbreviated name indicating the bounds of + description: `On the explore the map page, on the map, the abbreviated name indicating the bounds of the Lower 48 states `, }, @@ -256,7 +256,7 @@ export const SIDE_PANEL_INITIAL_STATE = defineMessages({ PARA1_PART3: { id: 'explore.map.page.side.panel.info.para.1.part.3', defaultMessage: ` - , or locate yourself + , or locate yourself `, description: '`Navigate to the explore the map page. When the map is in view, click on the map. The side panel will show or locate yourself', }, @@ -291,7 +291,7 @@ export const SIDE_PANEL_INITIAL_STATE = defineMessages({ PARA2_PART2: { id: 'explore.map.page.side.panel.info.para.2.part.2', defaultMessage: ` - . Census tracts are a small unit of geography. They generally have populations + . Census tracts are a small unit of geography. They generally have populations `, description: '`Navigate to the explore the map page. When the map is in view, click on the map. The side panel will show Census tracts are a small unit of geography. They generally have populations of between 1,200 - 8,000 people.', }, @@ -305,7 +305,7 @@ export const SIDE_PANEL_INITIAL_STATE = defineMessages({ PARA3_PART1: { id: 'explore.map.page.side.panel.info.para.3.part.1', defaultMessage: ` - Communities that are disadvantaged live in tracts that experience burdens. These tracts are highlighted + Communities that are disadvantaged live in tracts that experience burdens. These tracts are highlighted `, description: '`Navigate to the explore the map page. When the map is in view, click on the map. The side panel will show Communities that are disadvantaged live in tracts that experience burdens. These tracts are highlighted on the map.', }, @@ -340,7 +340,7 @@ export const SIDE_PANEL_INITIAL_STATE = defineMessages({ PARA5_PART2: { id: 'explore.map.page.side.panel.info.para.2', defaultMessage: ` - , or cutoffs, are used to determine if communities in a tract are disadvantaged. Certain burdens use percentages + , or cutoffs, are used to determine if communities in a tract are disadvantaged. Certain burdens use percentages `, description: '`Navigate to the explore the map page. When the map is in view, click on the map. The side panel will show Thresholds, or cutoffs, are used to determine if communities in a tract are disadvantaged. Certain burdens use percentages or a simple yes/no.', }, @@ -598,7 +598,7 @@ export const SIDE_PANEL_TRIBAL_INFO = defineMessages({ LAND_AREA_NAME: { id: 'explore.map.page.side.panel.tribalInfo.landAreaName', defaultMessage: 'Land Area Name:', - description: `Navigate to the explore the map page. Click on Tribal Lands, when the map is in view, + description: `Navigate to the explore the map page. Click on Tribal Lands, when the map is in view, click on the map. The side panel will show the land area name of the feature selected`, }, }); @@ -644,6 +644,14 @@ export const PRIORITIZATION_COPY = { bold: boldFn, }} />, + PRIO_ISLAND_LI: AND is located in a U.S. Territory.'} + description={`Navigate to the explore the map page. Click on tract, The side panel will show This tract is considered disadvantaged. It is an island territory that meets an adjusted low income threshold.`} + values={{ + bold: boldFn, + }} + />, PRIO_SURR_LI: AND meets an adjusted low income threshold. The adjustment does not apply to any of the categories.'} @@ -1064,7 +1072,7 @@ export const SIDE_PANEL_INDICATORS = defineMessages({ POVERTY: { id: 'explore.map.page.side.panel.indicator.poverty', defaultMessage: 'Poverty', - description: `Navigate to the explore the map page. When the map is in view, click on the map. The side panel will show Unemployment`, + description: `Navigate to the explore the map page. When the map is in view, click on the map. The side panel will show Poverty`, }, UNEMPLOY: { id: 'explore.map.page.side.panel.indicator.unemploy', @@ -1147,7 +1155,7 @@ export const SIDE_PANEL_INDICATOR_DESCRIPTION = defineMessages({ EXP_BLD_LOSS: { id: 'explore.map.page.side.panel.indicator.description.exp.bld.loss', defaultMessage: 'Economic loss to building value resulting from natural hazards each year', - description: `Navigate to the explore the map page. When the map is in view, click on the map. The side + description: `Navigate to the explore the map page. When the map is in view, click on the map. The side panel will show an indicator description of Economic loss rate to buildings resulting from natural hazards`, }, EXP_POP_LOSS: { @@ -1155,7 +1163,7 @@ export const SIDE_PANEL_INDICATOR_DESCRIPTION = defineMessages({ defaultMessage: ` Fatalities and injuries resulting from natural hazards each year `, - description: `Navigate to the explore the map page. When the map is in view, click on the map. The side panel will show an indicator description of Economic loss rate to the population in fatalities and + description: `Navigate to the explore the map page. When the map is in view, click on the map. The side panel will show an indicator description of Economic loss rate to the population in fatalities and injuries resulting from natural hazards`, }, FLOODING: { @@ -1175,17 +1183,17 @@ export const SIDE_PANEL_INDICATOR_DESCRIPTION = defineMessages({ LOW_INCOME: { id: 'explore.map.page.side.panel.indicator.description.low.income', defaultMessage: ` - People in households where income is less than or equal to twice the federal poverty level, not including students enrolled in higher ed + People in households where income is less than or equal to twice the federal poverty level, not including students enrolled in higher ed `, description: `Navigate to the explore the map page. When the map is in view, click on the map. The side panel will show an indicator description ofPeople in households where income is less than or equal to twice the federal poverty level, not including students enrolled in higher ed`, }, HIGH_ED: { id: 'explore.map.page.side.panel.indicator.description.high.ed', defaultMessage: ` - Percent of the census tract's population 15 or older not enrolled in college, university, or - graduate school + Percent of the census tract's population 15 or older not enrolled in college, university, or + graduate school `, - description: `Navigate to the explore the map page. When the map is in view, click on the map. The side panel will show an indicator description of Percent of the census tract's population 15 or older not + description: `Navigate to the explore the map page. When the map is in view, click on the map. The side panel will show an indicator description of Percent of the census tract's population 15 or older not enrolled in college, university, or graduate school`, }, @@ -1252,7 +1260,7 @@ export const SIDE_PANEL_INDICATOR_DESCRIPTION = defineMessages({ defaultMessage: ` Share of homes that are likely to have lead paint `, - description: `Navigate to the explore the map page. When the map is in view, click on the map. The side panel will show an indicator description of Share of homes that are likely to have lead paint + description: `Navigate to the explore the map page. When the map is in view, click on the map. The side panel will show an indicator description of Share of homes that are likely to have lead paint `, }, @@ -1330,14 +1338,14 @@ export const SIDE_PANEL_INDICATOR_DESCRIPTION = defineMessages({ POVERTY: { id: 'explore.map.page.side.panel.indicator.description.poverty', defaultMessage: ` - Share of people in households where income is at or below 100% of the Federal poverty level + Share of people in households where income is at or below 100% of the Federal poverty level `, description: `Navigate to the explore the map page. When the map is in view, click on the map. The side panel will show an indicator description of Share of people in households where the income is at or below 100% of the Federal poverty level`, }, UNEMPLOY: { id: 'explore.map.page.side.panel.indicator.description.unemploy', defaultMessage: 'Number of unemployed people as a part of the labor force', - description: `Navigate to the explore the map page. When the map is in view, click on the map. The side + description: `Navigate to the explore the map page. When the map is in view, click on the map. The side panel will show an indicator description of Number of unemployed people as a part of the labor force`, }, HIGH_SKL: { @@ -1398,7 +1406,7 @@ export const DOWNLOAD_DRAFT = { Download the data sources used in the CEJST (.csv, .xlxs, .pdf that describes how to use the list, and a codebook, {downloadFileSize} unzipped). Last updated: {dateUpdated}. `} description={` - Navigate to the explore the map page. Under the map, you will see a link that is placed below the + Navigate to the explore the map page. Under the map, you will see a link that is placed below the map that will download the data packet `} values={{ diff --git a/client/src/intl/en.json b/client/src/intl/en.json index 71c56013..f2a67414 100644 --- a/client/src/intl/en.json +++ b/client/src/intl/en.json @@ -741,7 +741,7 @@ }, "explore.map.page.map.territoryFocus.lower48.short": { "defaultMessage": "48", - "description": "On the explore the map page, on the map, the abbreviated name indicating the bounds of \n the Lower 48 states\n " + "description": "On the explore the map page, on the map, the abbreviated name indicating the bounds of\n the Lower 48 states\n " }, "explore.map.page.map.territoryFocus.puerto_rico.long": { "defaultMessage": "Puerto Rico", @@ -933,11 +933,11 @@ }, "explore.map.page.side.panel.indicator.description.exp.bld.loss": { "defaultMessage": "Economic loss to building value resulting from natural hazards each year", - "description": "Navigate to the explore the map page. When the map is in view, click on the map. The side \n panel will show an indicator description of Economic loss rate to buildings resulting from natural hazards" + "description": "Navigate to the explore the map page. When the map is in view, click on the map. The side\n panel will show an indicator description of Economic loss rate to buildings resulting from natural hazards" }, "explore.map.page.side.panel.indicator.description.exp.pop.loss": { "defaultMessage": "Fatalities and injuries resulting from natural hazards each year", - "description": "Navigate to the explore the map page. When the map is in view, click on the map. The side panel will show an indicator description of Economic loss rate to the population in fatalities and \n injuries resulting from natural hazards" + "description": "Navigate to the explore the map page. When the map is in view, click on the map. The side panel will show an indicator description of Economic loss rate to the population in fatalities and\n injuries resulting from natural hazards" }, "explore.map.page.side.panel.indicator.description.flooding": { "defaultMessage": "Projected risk to properties from projected floods, from tides, rain, riverine and storm surges within 30 years", @@ -953,7 +953,7 @@ }, "explore.map.page.side.panel.indicator.description.high.ed": { "defaultMessage": "Percent of the census tract's population 15 or older not enrolled in college, university, or graduate school", - "description": "Navigate to the explore the map page. When the map is in view, click on the map. The side panel will show an indicator description of Percent of the census tract's population 15 or older not \n enrolled in college, university, or graduate school" + "description": "Navigate to the explore the map page. When the map is in view, click on the map. The side panel will show an indicator description of Percent of the census tract's population 15 or older not\n enrolled in college, university, or graduate school" }, "explore.map.page.side.panel.indicator.description.high.school": { "defaultMessage": "Percent of people ages 25 years or older whose high school education is less than a high school diploma", @@ -977,7 +977,7 @@ }, "explore.map.page.side.panel.indicator.description.leadPaint": { "defaultMessage": "Share of homes that are likely to have lead paint", - "description": "Navigate to the explore the map page. When the map is in view, click on the map. The side panel will show an indicator description of Share of homes that are likely to have lead paint \n " + "description": "Navigate to the explore the map page. When the map is in view, click on the map. The side panel will show an indicator description of Share of homes that are likely to have lead paint\n " }, "explore.map.page.side.panel.indicator.description.leaky.tanks": { "defaultMessage": "Formula of the density of leaking underground storage tanks and number of all active underground storage tanks within 1500 feet of the census tract boundaries", @@ -1025,7 +1025,7 @@ }, "explore.map.page.side.panel.indicator.description.unemploy": { "defaultMessage": "Number of unemployed people as a part of the labor force", - "description": "Navigate to the explore the map page. When the map is in view, click on the map. The side \n panel will show an indicator description of Number of unemployed people as a part of the labor force" + "description": "Navigate to the explore the map page. When the map is in view, click on the map. The side\n panel will show an indicator description of Number of unemployed people as a part of the labor force" }, "explore.map.page.side.panel.indicator.description.wasteWater": { "defaultMessage": "Modeled toxic concentrations at parts of streams within 500 meters", @@ -1145,7 +1145,7 @@ }, "explore.map.page.side.panel.indicator.poverty": { "defaultMessage": "Poverty", - "description": "Navigate to the explore the map page. When the map is in view, click on the map. The side panel will show Unemployment" + "description": "Navigate to the explore the map page. When the map is in view, click on the map. The side panel will show Poverty" }, "explore.map.page.side.panel.indicator.prox.haz": { "defaultMessage": "Proximity to hazardous waste facilities", @@ -1427,6 +1427,10 @@ "defaultMessage": "The {numPoints} that are Federally Recognized Tribes in this tract are are {also} considered disadvantaged.", "description": "Navigate to the explore the map page. Click on tract, The {numPoints} that are Federally Recognized Tribes in this tract ares are {also} considered disadvantaged." }, + "explore.map.page.side.panel.prio.copy.prio.island.li": { + "defaultMessage": "This tract is considered disadvantaged because it meets the low income threshold AND is located in a U.S. Territory.", + "description": "Navigate to the explore the map page. Click on tract, The side panel will show This tract is considered disadvantaged. It is an island territory that meets an adjusted low income threshold." + }, "explore.map.page.side.panel.prio.copy.prio.n.burden": { "defaultMessage": "This tract is considered disadvantaged because it meets {burdens} burden threshold AND the associated socioeconomic threshold.", "description": "Navigate to the explore the map page. Click on tract, This tract is considered disadvantaged because it meets {burdens} burden thresholds AND the associated socioeconomic threshold." @@ -1453,7 +1457,7 @@ }, "explore.map.page.side.panel.tribalInfo.landAreaName": { "defaultMessage": "Land Area Name:", - "description": "Navigate to the explore the map page. Click on Tribal Lands, when the map is in view, \n click on the map. The side panel will show the land area name of the feature selected" + "description": "Navigate to the explore the map page. Click on Tribal Lands, when the map is in view,\n click on the map. The side panel will show the land area name of the feature selected" }, "explore.map.page.side.panel.version.title": { "defaultMessage": "Methodology version {version}", @@ -1465,7 +1469,7 @@ }, "explore.map.page.under.map.download.draft.ptag.1": { "defaultMessage": "Download the data sources used in the CEJST (.csv, .xlxs, .pdf that describes how to use the list, and a codebook, {downloadFileSize} unzipped). Last updated: {dateUpdated}.", - "description": "\n Navigate to the explore the map page. Under the map, you will see a link that is placed below the \n map that will download the data packet\n " + "description": "\n Navigate to the explore the map page. Under the map, you will see a link that is placed below the\n map that will download the data packet\n " }, "explore.map.page.under.map.note.on.territories.intro": { "defaultMessage": "U.S. territories note", diff --git a/client/src/intl/es.json b/client/src/intl/es.json index 94b5188d..5fce4c69 100644 --- a/client/src/intl/es.json +++ b/client/src/intl/es.json @@ -26,10 +26,10 @@ "about.page.title.text": "Información básica", "about.page.use.data.heading": "Cómo utilizar los datos ", "about.page.use.data.paragraph": "Los datos de la herramienta están disponibles para descargar. Estos datos se pueden usar para filtrar por estado o condado.", - "about.page.use.data.tutorial":"Descarga el tutorial de la hoja de cálculo (.pdf 5,4 MB)", + "about.page.use.data.tutorial": "Descarga el tutorial de la hoja de cálculo (.pdf 5,4 MB)", "about.page.use.map.heading": "Utilización del mapa", "about.page.use.map.para": "Amplíe y seleccione cualquier distrito censal para ver si se considera como desfavorecido.", - "about.page.use.map.tutorial":"Descarga el tutorial CEJST (.pdf 9.6 MB)", + "about.page.use.map.tutorial": "Descarga el tutorial CEJST (.pdf 9.6 MB)", "common.pages.alerts.additional_docs_available.description": "Descargue un nuevo documento de apoyo técnico y otra documentación, y envíe sus comentarios.", "common.pages.alerts.banner.beta.content": "Herramienta actualizada. La versión 1.0 de la herramienta fue publicada el {relDate}.", "common.pages.alerts.census.tract.title": "Ya hay más documentación disponible", @@ -348,6 +348,7 @@ "explore.map.page.side.panel.num.categories.exceeded": "en {numberOfDisCategories, plural, one {# categoría} otras {# categorías}}", "explore.map.page.side.panel.partial.community.of.focus": "PARCIALMENTE", "explore.map.page.side.panel.prio.copy.not.prio": "Este distrito censal no se considera desfavorecido. No cumple con ninguno de los umbrales O con al menos un umbral socioeconómico relacionado.", + "explore.map.page.side.panel.prio.copy.prio.island.li": "Este distrito censal se considera desfavorecido porque cumple con el umbral de bajos ingresos Y está ubicado en un territorio de los EE. UU.", "explore.map.page.side.panel.prio.copy.not.prio.n.burden": "Este distrito censal no se considera desfavorecido. Cumple con más de 1 umbral de carga PERO con ningún umbral socioeconómico asociado.", "explore.map.page.side.panel.prio.copy.not.prio.one.burden": "Este distrito censal no se considera desfavorecido. Cumple con 1 umbral de carga PERO con ningún umbral socioeconómico asociado.", "explore.map.page.side.panel.prio.copy.not.prio.surr.li": "Este distrito censal no se considera desfavorecido. Está rodeado de distritos censales desfavorecidos PERO no cumple con el umbral ajustado de bajos ingresos. El ajuste no corresponde a ninguna de las categorías.", From 460449f989fec081a3b8b88e61b9d3bd7833716d Mon Sep 17 00:00:00 2001 From: ericiwamoto <100735505+ericiwamoto@users.noreply.github.com> Date: Wed, 20 Nov 2024 12:16:32 -0800 Subject: [PATCH 08/28] update artifact actions version --- .github/workflows/deploy_frontend_main.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/deploy_frontend_main.yml b/.github/workflows/deploy_frontend_main.yml index d70e9b4a..35efb709 100644 --- a/.github/workflows/deploy_frontend_main.yml +++ b/.github/workflows/deploy_frontend_main.yml @@ -46,7 +46,7 @@ jobs: # - name: Check for security vulnerabilities # run: npm audit --production - name: Upload Artifact - uses: actions/upload-artifact@v2 + uses: actions/upload-artifact@v4 with: name: J40Static # Upload-artifact does not support the default working directory @@ -60,7 +60,7 @@ jobs: - name: Checkout source uses: actions/checkout@v2 - name: Download Artifacts - uses: actions/download-artifact@v2 + uses: actions/download-artifact@v4 with: name: J40Static path: ./public From 6436dfa683c6c27dcd4e5aa66cb41f14d09fa2a3 Mon Sep 17 00:00:00 2001 From: Carlos Felix <63804190+carlosfelix2@users.noreply.github.com> Date: Thu, 21 Nov 2024 10:40:51 -0500 Subject: [PATCH 09/28] Use Census Decennial 2020 data for territories --- .../data-pipeline/data_pipeline/comparator.py | 20 +- .../data_pipeline/etl/score/constants.py | 8 +- .../data_pipeline/etl/score/etl_score.py | 18 +- .../etl/sources/census_decennial/README.md | 10 +- .../etl/sources/census_decennial/constants.py | 190 ++++++ .../etl/sources/census_decennial/etl.py | 634 +++++------------- .../data_pipeline/score/field_names.py | 36 +- .../data_pipeline/score/score_narwhal.py | 12 +- .../data/extract/2020/as/60/010/census.json | 8 + .../data/extract/2020/gu/66/010/census.json | 58 ++ .../census_decennial/data/transform/usa.csv | 134 ++++ .../sources/census_decennial/test_etl.py | 152 +++++ 12 files changed, 767 insertions(+), 513 deletions(-) create mode 100644 data/data-pipeline/data_pipeline/etl/sources/census_decennial/constants.py create mode 100644 data/data-pipeline/data_pipeline/tests/sources/census_decennial/data/extract/2020/as/60/010/census.json create mode 100644 data/data-pipeline/data_pipeline/tests/sources/census_decennial/data/extract/2020/gu/66/010/census.json create mode 100644 data/data-pipeline/data_pipeline/tests/sources/census_decennial/data/transform/usa.csv create mode 100644 data/data-pipeline/data_pipeline/tests/sources/census_decennial/test_etl.py diff --git a/data/data-pipeline/data_pipeline/comparator.py b/data/data-pipeline/data_pipeline/comparator.py index 09af4e8d..6cf184b6 100644 --- a/data/data-pipeline/data_pipeline/comparator.py +++ b/data/data-pipeline/data_pipeline/comparator.py @@ -65,7 +65,9 @@ def compare_score(compare_to_version: str): # TODO: transition to downloader code when it's available production_score_url = f"https://justice40-data.s3.amazonaws.com/data-versions/{compare_to_version}/data/score/csv/full/usa.csv" - production_score_path = WORKING_PATH / "usa.csv" + production_score_path = ( + WORKING_PATH / f"prod-score-csv-full-{compare_to_version}-usa.csv" + ) log_info(f"Fetching score version {compare_to_version} from AWS") production_score_path.parent.mkdir(parents=True, exist_ok=True) @@ -200,7 +202,7 @@ def compare_score(compare_to_version: str): " The number of tracts match!\n" if len(production_disadvantaged_tracts_set) == len(local_disadvantaged_tracts_set) - else f" The difference is {abs(len(production_disadvantaged_tracts_set) - len(local_disadvantaged_tracts_set)):,} tract(s).\n" + else f" The difference is {abs(len(production_disadvantaged_tracts_set) - len(local_disadvantaged_tracts_set))} tract(s).\n" ) removed_tracts = production_disadvantaged_tracts_set.difference( @@ -209,19 +211,25 @@ def compare_score(compare_to_version: str): added_tracts = local_disadvantaged_tracts_set.difference( production_disadvantaged_tracts_set ) + removed_tracts_str = ", ".join(list(removed_tracts)) + added_tracts_str = ", ".join(list(added_tracts)) log_info( - f"There are {len(removed_tracts):,} tract(s) marked as disadvantaged in the prod score that are not disadvantaged in the local score." + f"There are {len(removed_tracts):,} tract(s) marked as disadvantaged in the prod " + "score that are not disadvantaged in the local score. Those tracts are:" ) + log_info(removed_tracts_str) log_info( - f"There are {len(added_tracts):,} tract(s) marked as disadvantaged in the local score that are not disadvantaged in the prod score." + f"There are {len(added_tracts):,} tract(s) marked as disadvantaged in the local " + "score that are not disadvantaged in the prod score. Those tracts are:" ) + log_info(added_tracts_str) summary += ( f"* There are {len(removed_tracts):,} tract(s) marked as disadvantaged in the production score that are not disadvantaged in the locally" - " generated score (i.e. disadvantaged tracts that were removed by the new score)." + f" generated score (i.e. disadvantaged tracts that were removed by the new score). Those tracts are:\n{removed_tracts_str}\n" f" There are {len(added_tracts):,} tract(s) marked as disadvantaged in the locally generated score that are not disadvantaged in the" - " production score (i.e. disadvantaged tracts that were added by the new score).\n" + f" production score (i.e. disadvantaged tracts that were added by the new score). Those tracts are:\n{added_tracts_str}\n\n" ) try: diff --git a/data/data-pipeline/data_pipeline/etl/score/constants.py b/data/data-pipeline/data_pipeline/etl/score/constants.py index ea63f854..4adf7e32 100644 --- a/data/data-pipeline/data_pipeline/etl/score/constants.py +++ b/data/data-pipeline/data_pipeline/etl/score/constants.py @@ -315,18 +315,18 @@ TILES_SCORE_COLUMNS = { field_names.ISLAND_AREAS_POVERTY_LOW_HS_EDUCATION_FIELD: "IAPLHSE", field_names.ISLAND_AREAS_LOW_MEDIAN_INCOME_LOW_HS_EDUCATION_FIELD: "IALMILHSE", # Percentiles for Island areas' workforce columns - field_names.LOW_CENSUS_DECENNIAL_AREA_MEDIAN_INCOME_PERCENT_FIELD_2009 + field_names.LOW_CENSUS_DECENNIAL_AREA_MEDIAN_INCOME_PERCENT_FIELD_2019 + field_names.PERCENTILE_FIELD_SUFFIX: "IALMILHSE_PFS", - field_names.CENSUS_DECENNIAL_POVERTY_LESS_THAN_100_FPL_FIELD_2009 + field_names.CENSUS_DECENNIAL_POVERTY_LESS_THAN_100_FPL_FIELD_2019 + field_names.ISLAND_AREAS_PERCENTILE_ADJUSTMENT_FIELD + field_names.PERCENTILE_FIELD_SUFFIX: "IAPLHSE_PFS", - field_names.CENSUS_DECENNIAL_UNEMPLOYMENT_FIELD_2009 + field_names.CENSUS_DECENNIAL_UNEMPLOYMENT_FIELD_2019 + field_names.ISLAND_AREAS_PERCENTILE_ADJUSTMENT_FIELD + field_names.PERCENTILE_FIELD_SUFFIX: "IAULHSE_PFS", field_names.LOW_HS_EDUCATION_FIELD: "LHE", field_names.ISLAND_AREAS_LOW_HS_EDUCATION_FIELD: "IALHE", # Percentage of HS Degree completion for Islands - field_names.CENSUS_DECENNIAL_HIGH_SCHOOL_ED_FIELD_2009: "IAHSEF", + field_names.CENSUS_DECENNIAL_HIGH_SCHOOL_ED_FIELD_2019: "IAHSEF", # Booleans for the front end about the types of thresholds exceeded field_names.CLIMATE_THRESHOLD_EXCEEDED: "N_CLT_EOMI", field_names.ENERGY_THRESHOLD_EXCEEDED: "N_ENY_EOMI", diff --git a/data/data-pipeline/data_pipeline/etl/score/etl_score.py b/data/data-pipeline/data_pipeline/etl/score/etl_score.py index 887d6189..afd8ad9a 100644 --- a/data/data-pipeline/data_pipeline/etl/score/etl_score.py +++ b/data/data-pipeline/data_pipeline/etl/score/etl_score.py @@ -166,7 +166,7 @@ class ScoreETL(ExtractTransformLoad): census_decennial_csv = ( constants.DATA_PATH / "dataset" - / "census_decennial_2010" + / "census_decennial_2020" / "usa.csv" ) self.census_decennial_df = pd.read_csv( @@ -470,13 +470,13 @@ class ScoreETL(ExtractTransformLoad): field_names.EXPECTED_BUILDING_LOSS_RATE_FIELD, field_names.EXPECTED_AGRICULTURE_LOSS_RATE_FIELD, 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_DECENNIAL_HIGH_SCHOOL_ED_FIELD_2019, + field_names.CENSUS_DECENNIAL_POVERTY_LESS_THAN_100_FPL_FIELD_2019, + field_names.CENSUS_DECENNIAL_POVERTY_LESS_THAN_200_FPL_FIELD_2019, + field_names.CENSUS_DECENNIAL_UNEMPLOYMENT_FIELD_2019, 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.CENSUS_DECENNIAL_TOTAL_POPULATION_FIELD_2019, field_names.UST_FIELD, field_names.DOT_TRAVEL_BURDEN_FIELD, field_names.FUTURE_FLOOD_RISK_FIELD, @@ -542,8 +542,8 @@ class ScoreETL(ExtractTransformLoad): low_field_name=field_names.LOW_LIFE_EXPECTANCY_FIELD, ), ReversePercentile( - field_name=field_names.CENSUS_DECENNIAL_AREA_MEDIAN_INCOME_PERCENT_FIELD_2009, - low_field_name=field_names.LOW_CENSUS_DECENNIAL_AREA_MEDIAN_INCOME_PERCENT_FIELD_2009, + field_name=field_names.CENSUS_DECENNIAL_AREA_MEDIAN_INCOME_PERCENT_FIELD_2019, + low_field_name=field_names.LOW_CENSUS_DECENNIAL_AREA_MEDIAN_INCOME_PERCENT_FIELD_2019, ), ] @@ -658,7 +658,7 @@ class ScoreETL(ExtractTransformLoad): df_copy[field_names.COMBINED_CENSUS_TOTAL_POPULATION_2010] = df_copy[ [ field_names.TOTAL_POP_FIELD, - field_names.CENSUS_DECENNIAL_TOTAL_POPULATION_FIELD_2009, + field_names.CENSUS_DECENNIAL_TOTAL_POPULATION_FIELD_2019, ] ].mean(axis=1, skipna=True) diff --git a/data/data-pipeline/data_pipeline/etl/sources/census_decennial/README.md b/data/data-pipeline/data_pipeline/etl/sources/census_decennial/README.md index e043f232..3f9b3c16 100644 --- a/data/data-pipeline/data_pipeline/etl/sources/census_decennial/README.md +++ b/data/data-pipeline/data_pipeline/etl/sources/census_decennial/README.md @@ -1,10 +1,10 @@ # Decennial Census of Island Areas -Adding a new ETL folder for Decennial Census of Island Areas since the Island areas aren't included in ACS. +Decennial Census of Island Areas since the Island areas aren't included in ACS. There's the American Samoa Summary File, the Guam Summary File, the Commonwealth of the Northern Mariana Islands Summary File, and the US Virgin Islands Summary File: -1. https://api.census.gov/data/2010/dec/as.html -1. https://api.census.gov/data/2010/dec/gu.html -1. https://api.census.gov/data/2010/dec/mp.html -1. https://api.census.gov/data/2010/dec/vi.html +1. https://api.census.gov/data/2020/dec/dhcas.html +1. https://api.census.gov/data/2020/dec/dhcgu.html +1. https://api.census.gov/data/2020/dec/dhcmp.html +1. https://api.census.gov/data/2020/dec/dhcvi.html diff --git a/data/data-pipeline/data_pipeline/etl/sources/census_decennial/constants.py b/data/data-pipeline/data_pipeline/etl/sources/census_decennial/constants.py new file mode 100644 index 00000000..891279c3 --- /dev/null +++ b/data/data-pipeline/data_pipeline/etl/sources/census_decennial/constants.py @@ -0,0 +1,190 @@ +from enum import Enum +from data_pipeline.score import field_names + + +class DEC_FIELD_NAMES(str, Enum): + """Field/column names for the decennial data""" + + MALE_HIGH_SCHOOL_ED = "Total male high school graduates 25 and over" + FEMALE_HIGH_SCHOOL_ED = "Total female high school graduates 25 and over" + IMPUTED_COLLEGE_ATTENDANCE = "Percent enrollment in college, graduate or professional school, imputed" + TOTAL_RACE_POPULATION = "Total population surveyed on racial data" + BLACK = "Black or African American" + AMERICAN_INDIAN = "American Indian / Alaska Native" + ASIAN = "Asian" + HAWAIIAN = "Native Hawaiian or Pacific" + TWO_OR_MORE_RACES = "two or more races" + NON_HISPANIC_WHITE = "White" + HISPANIC = "Hispanic or Latino" + OTHER_RACE = "other races" + HOUSEHOLD_POVERTY_LEVEL_UNDER_0_5 = ( + "Household poverty level Under 0.50 IN 2019" + ) + HOUSEHOLD_POVERTY_LEVEL_UNDER_0_74 = ( + "Household poverty level Under 0.74 IN 2019" + ) + HOUSEHOLD_POVERTY_LEVEL_UNDER_0_99 = ( + "Household poverty level Under 0.99 IN 2019" + ) + HOUSEHOLD_POVERTY_LEVEL_OVER_2_0 = ( + "Household poverty level Over 2.0 IN 2019" + ) + TOTAL_HOUSEHOLD_POVERTY_LEVEL = "Total Household poverty level IN 2019" + TERRITORY_MEDIAN_INCOME = "Territory Median Income" + EMPLOYMENT_MALE_UNEMPLOYED = "Total males not in labor force" + EMPLOYMENT_FEMALE_UNEMPLOYED = "Total females not in labor force" + EMPLOYMENT_MALE_IN_LABOR_FORCE = "Total males in labor force" + EMPLOYMENT_FEMALE_IN_LABOR_FORCE = "Total females in labor force" + COLLEGE_ATTENDANCE_TOTAL_ENROLLED = "Total asked enrolled in college or graduate school (excludes military housing)" + COLLEGE_NON_ATTENDANCE = "Percent of population not currently enrolled in college, graduate or professional school" + COLLEGE_ATTENDANCE_MALE_ENROLLED = "Males enrolled in college or graduate school (excludes military housing)" + COLLEGE_ATTENDANCE_FEMALE_ENROLLED = "Females enrolled in college or graduate school (excludes military housing)" + COLLEGE_ATTENDANCE_POPULATION = ( + "Population enrolled in college, graduate or professional school" + ) + COLLEGE_ATTENDANCE_PERCENT = ( + "Percent enrollment in college, graduate or professional school" + ) + COLLEGE_NON_ATTENDANCE_PERCENT = "Percent of population not currently enrolled in college, graduate or professional school" + + def __str__(self) -> str: + """This method removes the need to use the value attribute from the Enums""" + return str.__str__(self) + + +__FIELD_NAME_COMMON_XWALK = { + "P1_001N": field_names.CENSUS_DECENNIAL_TOTAL_POPULATION_FIELD_2019, + "PBG19_005N": DEC_FIELD_NAMES.MALE_HIGH_SCHOOL_ED, + "PBG19_012N": DEC_FIELD_NAMES.FEMALE_HIGH_SCHOOL_ED, + "PCT31_001N": DEC_FIELD_NAMES.COLLEGE_ATTENDANCE_TOTAL_ENROLLED, + "PBG32_003N": DEC_FIELD_NAMES.EMPLOYMENT_MALE_IN_LABOR_FORCE, + "PBG32_007N": DEC_FIELD_NAMES.EMPLOYMENT_MALE_UNEMPLOYED, + "PBG32_010N": DEC_FIELD_NAMES.EMPLOYMENT_FEMALE_IN_LABOR_FORCE, + "PBG32_014N": DEC_FIELD_NAMES.EMPLOYMENT_FEMALE_UNEMPLOYED, + "PCT34_003N": DEC_FIELD_NAMES.COLLEGE_ATTENDANCE_MALE_ENROLLED, + "PCT34_016N": DEC_FIELD_NAMES.COLLEGE_ATTENDANCE_FEMALE_ENROLLED, + "PBG43_001N": field_names.CENSUS_DECENNIAL_MEDIAN_INCOME_2019, + "PBG74_001N": DEC_FIELD_NAMES.TOTAL_HOUSEHOLD_POVERTY_LEVEL, + "PBG74_002N": DEC_FIELD_NAMES.HOUSEHOLD_POVERTY_LEVEL_UNDER_0_5, + "PBG74_003N": DEC_FIELD_NAMES.HOUSEHOLD_POVERTY_LEVEL_UNDER_0_74, + "PBG74_004N": DEC_FIELD_NAMES.HOUSEHOLD_POVERTY_LEVEL_UNDER_0_99, + "PBG74_010N": DEC_FIELD_NAMES.HOUSEHOLD_POVERTY_LEVEL_OVER_2_0, +} +""" +Census variable to text column name mapping. For details on Census variables see: +https://api.census.gov/data/2020/dec/dhcas/variables.html +https://api.census.gov/data/2020/dec/dhcgu/variables.html +https://api.census.gov/data/2020/dec/dhcmp/variables.html +https://api.census.gov/data/2020/dec/dhcvi/variables.html +""" + +# Note that the 2010 census for island areas does not break out +# hispanic and non-hispanic white, so this is slightly different from +# our other demographic data +__FIELD_NAME_AS_XWALK = { + "PCT9_001N": DEC_FIELD_NAMES.TOTAL_RACE_POPULATION, + "PCT9_003N": DEC_FIELD_NAMES.HAWAIIAN, + "PCT9_079N": DEC_FIELD_NAMES.ASIAN, + "PCT9_130N": DEC_FIELD_NAMES.NON_HISPANIC_WHITE, + "PCT9_155N": DEC_FIELD_NAMES.BLACK, + "PCT9_180N": DEC_FIELD_NAMES.AMERICAN_INDIAN, + "PCT9_205N": DEC_FIELD_NAMES.OTHER_RACE, + "PCT9_230N": DEC_FIELD_NAMES.TWO_OR_MORE_RACES, + "P5_002N": DEC_FIELD_NAMES.HISPANIC, +} +"""American Samoa specific race fields.""" + +__FIELD_NAME_VI_XWALK = { + "PCT7_001N": DEC_FIELD_NAMES.TOTAL_RACE_POPULATION, + "PCT7_003N": DEC_FIELD_NAMES.BLACK, + "PCT7_205N": DEC_FIELD_NAMES.ASIAN, + "PCT7_230N": DEC_FIELD_NAMES.AMERICAN_INDIAN, + "PCT7_255N": DEC_FIELD_NAMES.HAWAIIAN, + "PCT7_280N": DEC_FIELD_NAMES.OTHER_RACE, + "PCT7_305N": DEC_FIELD_NAMES.TWO_OR_MORE_RACES, + "P5_021N": DEC_FIELD_NAMES.NON_HISPANIC_WHITE, + "PCT6_003N": DEC_FIELD_NAMES.HISPANIC, +} +"""US Virgin Islands specific race fields.""" + +__FIELD_NAME_GU_XWALK = { + "PCT10_001N": DEC_FIELD_NAMES.TOTAL_RACE_POPULATION, + "PCT10_003N": DEC_FIELD_NAMES.HAWAIIAN, + "PCT10_204N": DEC_FIELD_NAMES.ASIAN, + "PCT10_330N": DEC_FIELD_NAMES.BLACK, + "PCT10_355N": DEC_FIELD_NAMES.AMERICAN_INDIAN, + "PCT10_380N": DEC_FIELD_NAMES.OTHER_RACE, + "PCT10_405N": DEC_FIELD_NAMES.TWO_OR_MORE_RACES, + "P5_026N": DEC_FIELD_NAMES.NON_HISPANIC_WHITE, + "PCT9_003N": DEC_FIELD_NAMES.HISPANIC, +} +"""Guam specific race fields.""" + +__FIELD_NAME_MP_XWALK = { + "PCT9_001N": DEC_FIELD_NAMES.TOTAL_RACE_POPULATION, + "PCT9_003N": DEC_FIELD_NAMES.ASIAN, + "PCT9_129N": DEC_FIELD_NAMES.HAWAIIAN, + "PCT9_330N": DEC_FIELD_NAMES.BLACK, + "PCT9_355N": DEC_FIELD_NAMES.AMERICAN_INDIAN, + "PCT9_380N": DEC_FIELD_NAMES.OTHER_RACE, + "PCT9_405N": DEC_FIELD_NAMES.TWO_OR_MORE_RACES, + "P5_002N": DEC_FIELD_NAMES.HISPANIC, + "P5_024N": DEC_FIELD_NAMES.NON_HISPANIC_WHITE, +} +"""Northern Mariana Islands specific race fields.""" + +OUTPUT_RACE_FIELDS = [ + DEC_FIELD_NAMES.BLACK, + DEC_FIELD_NAMES.AMERICAN_INDIAN, + DEC_FIELD_NAMES.ASIAN, + DEC_FIELD_NAMES.HAWAIIAN, + DEC_FIELD_NAMES.TWO_OR_MORE_RACES, + DEC_FIELD_NAMES.NON_HISPANIC_WHITE, + DEC_FIELD_NAMES.HISPANIC, + DEC_FIELD_NAMES.OTHER_RACE, +] +"""Race fields to output in the results.""" + +DEC_TERRITORY_PARAMS = [ + { + "state_abbreviation": "as", + "fips": "60", + # https://www2.census.gov/geo/docs/reference/codes2020/cou/st60_as_cou2020.txt + "county_fips": ["010", "020", "030", "040", "050"], + "xwalk": __FIELD_NAME_COMMON_XWALK | __FIELD_NAME_AS_XWALK, + # Note: we hardcode the median income for each territory in this dict, + # because that data is hard to programmatically access. + # https://www.ruralhealthinfo.org/states/american-samoa + "median_income": 26352, + }, + { + "state_abbreviation": "gu", + "fips": "66", + # https://www2.census.gov/geo/docs/reference/codes2020/cou/st66_gu_cou2020.txt + "county_fips": ["010"], + "xwalk": __FIELD_NAME_COMMON_XWALK | __FIELD_NAME_GU_XWALK, + # https://www.ruralhealthinfo.org/states/guam + # https://data.census.gov/table/DECENNIALDPGU2020.DP3?g=040XX00US66&d=DECIA%20Guam%20Demographic%20Profile + "median_income": 58289, + }, + { + "state_abbreviation": "mp", + "fips": "69", + # https://www2.census.gov/geo/docs/reference/codes2020/cou/st69_mp_cou2020.txt + "county_fips": ["085", "100", "110", "120"], + "xwalk": __FIELD_NAME_COMMON_XWALK | __FIELD_NAME_MP_XWALK, + # https://www.ruralhealthinfo.org/states/northern-mariana + # https://data.census.gov/table/DECENNIALDPMP2020.DP3?d=DECIA%20Commonwealth%20of%20the%20Northern%20Mariana%20Islands%20Demographic%20Profile + "median_income": 31362, + }, + { + "state_abbreviation": "vi", + "fips": "78", + # https://www2.census.gov/geo/docs/reference/codes2020/cou/st78_vi_cou2020.txt + "county_fips": ["010", "020", "030"], + "xwalk": __FIELD_NAME_COMMON_XWALK | __FIELD_NAME_VI_XWALK, + # https://www.ruralhealthinfo.org/states/us-virgin-islands + "median_income": 40408, + }, +] +"""List of territories to process.""" diff --git a/data/data-pipeline/data_pipeline/etl/sources/census_decennial/etl.py b/data/data-pipeline/data_pipeline/etl/sources/census_decennial/etl.py index 4fe26249..32a58ffd 100644 --- a/data/data-pipeline/data_pipeline/etl/sources/census_decennial/etl.py +++ b/data/data-pipeline/data_pipeline/etl/sources/census_decennial/etl.py @@ -1,14 +1,19 @@ -import json -from typing import List import os - import numpy as np import pandas as pd +import json +from typing import List +from pathlib import Path +from data_pipeline.etl.sources.census_decennial.constants import ( + DEC_TERRITORY_PARAMS, + DEC_FIELD_NAMES, + OUTPUT_RACE_FIELDS, +) from data_pipeline.etl.base import ExtractTransformLoad -from data_pipeline.score import field_names -from data_pipeline.utils import get_module_logger from data_pipeline.etl.datasource import DataSource from data_pipeline.etl.datasource import FileDataSource +from data_pipeline.score import field_names +from data_pipeline.utils import get_module_logger pd.options.mode.chained_assignment = "raise" @@ -16,514 +21,209 @@ logger = get_module_logger(__name__) class CensusDecennialETL(ExtractTransformLoad): - def __init__(self): - self.DECENNIAL_YEAR = 2010 - self.OUTPUT_PATH = ( - self.DATA_PATH - / "dataset" - / f"census_decennial_{self.DECENNIAL_YEAR}" - ) + DECENNIAL_YEAR = 2020 + OUTPUT_PATH = ( + ExtractTransformLoad.DATA_PATH + / "dataset" + / f"census_decennial_{DECENNIAL_YEAR}" + ) - # Income Fields - # AS, GU, and MP all share the same variable names, but VI is different - # https://api.census.gov/data/2010/dec/as.html - # https://api.census.gov/data/2010/dec/gu/variables.html - # https://api.census.gov/data/2010/dec/mp/variables.html - # https://api.census.gov/data/2010/dec/vi/variables.html - - # Total population field is the same in all island areas - self.TOTAL_POP_FIELD = self.TOTAL_POP_VI_FIELD = "P001001" - self.TOTAL_POP_FIELD_NAME = "Total population in 2009" - - self.MEDIAN_INCOME_FIELD = "PBG049001" - self.MEDIAN_INCOME_VI_FIELD = "PBG047001" - self.MEDIAN_INCOME_FIELD_NAME = "Median household income in 2009 ($)" - self.AREA_MEDIAN_INCOME_FIELD_NAME = ( - "Median household income as a percent of " - "territory median income in 2009" + def __get_api_url( + self, + state_abbreviation: str, + name_list: List[str], + fips: str, + county: str, + ) -> str: + url = ( + f"https://api.census.gov/data/{self.DECENNIAL_YEAR}/dec/dhc{state_abbreviation}?get=NAME,{name_list}" + + f"&for=tract:*&in=state:{fips}%20county:{county}" ) - - self.TERRITORY_MEDIAN_INCOME_FIELD = "Territory Median Income" - - self.TOTAL_HOUSEHOLD_RATIO_INCOME_TO_POVERTY_LEVEL_FIELD = "PBG083001" - self.TOTAL_HOUSEHOLD_RATIO_INCOME_TO_POVERTY_LEVEL_VI_FIELD = ( - "PBG077001" - ) - self.TOTAL_HOUSEHOLD_RATIO_INCOME_TO_POVERTY_LEVEL_FIELD_NAME = ( - "TOTAL; RATIO OF INCOME TO POVERTY LEVEL IN 2009" - ) - - self.HOUSEHOLD_OVER_200_PERC_POVERTY_LEVEL_FIELD = "PBG083010" - self.HOUSEHOLD_OVER_200_PERC_POVERTY_LEVEL_VI_FIELD = "PBG077010" - self.HOUSEHOLD_OVER_200_PERC_POVERTY_LEVEL_FIELD_NAME = ( - "Total!!2.00 and over; RATIO OF INCOME TO POVERTY LEVEL IN 2009" - ) - - self.PERCENTAGE_HOUSEHOLDS_BELOW_200_PERC_POVERTY_LEVEL_FIELD_NAME = ( - "Percentage households below 200% of federal poverty line in 2009" - ) - - # We will combine three fields to get households < 100% FPL. - self.HOUSEHOLD_UNDER_100_PERC_POVERTY_LEVEL_FIELD_PART_ONE = ( - "PBG083002" # Total!!Under .50 - ) - self.HOUSEHOLD_UNDER_100_PERC_POVERTY_LEVEL_FIELD_PART_TWO = ( - "PBG083003" # Total!!.50 to .74 - ) - self.HOUSEHOLD_UNDER_100_PERC_POVERTY_LEVEL_FIELD_PART_THREE = ( - "PBG083004" # Total!!.75 to .99 - ) - - # Same fields, for Virgin Islands. - self.HOUSEHOLD_UNDER_100_PERC_POVERTY_LEVEL_FIELD_VI_PART_ONE = ( - "PBG077002" # Total!!Under .50 - ) - self.HOUSEHOLD_UNDER_100_PERC_POVERTY_LEVEL_FIELD_VI_PART_TWO = ( - "PBG077003" # Total!!.50 to .74 - ) - self.HOUSEHOLD_UNDER_100_PERC_POVERTY_LEVEL_FIELD_VI_PART_THREE = ( - "PBG077004" # Total!!.75 to .99 - ) - - self.HOUSEHOLD_OVER_200_PERC_POVERTY_LEVEL_FIELD = "PBG083010" - self.HOUSEHOLD_OVER_200_PERC_POVERTY_LEVEL_VI_FIELD = "PBG077010" - self.HOUSEHOLD_OVER_200_PERC_POVERTY_LEVEL_FIELD_NAME = ( - "Total!!2.00 and over; RATIO OF INCOME TO POVERTY LEVEL IN 2009" - ) - - self.PERCENTAGE_HOUSEHOLDS_BELOW_100_PERC_POVERTY_LEVEL_FIELD_NAME = ( - "Percentage households below 100% of federal poverty line in 2009" - ) - - # High School Education Fields - self.TOTAL_POPULATION_FIELD = "PBG026001" - self.TOTAL_POPULATION_VI_FIELD = "PCT032001" - self.TOTAL_POPULATION_FIELD_NAME = "Total; SEX BY EDUCATIONAL ATTAINMENT FOR THE POPULATION 25 YEARS AND OVER" - - self.MALE_HIGH_SCHOOL_ED_FIELD = "PBG026005" - self.MALE_HIGH_SCHOOL_ED_VI_FIELD = "PCT032011" - self.MALE_HIGH_SCHOOL_ED_FIELD_NAME = ( - "Total!!Male!!High school graduate, GED, or alternative; " - "SEX BY EDUCATIONAL ATTAINMENT FOR THE POPULATION 25 YEARS AND OVER" - ) - - self.FEMALE_HIGH_SCHOOL_ED_FIELD = "PBG026012" - self.FEMALE_HIGH_SCHOOL_ED_VI_FIELD = "PCT032028" - self.FEMALE_HIGH_SCHOOL_ED_FIELD_NAME = ( - "Total!!Female!!High school graduate, GED, or alternative; " - "SEX BY EDUCATIONAL ATTAINMENT FOR THE POPULATION 25 YEARS AND OVER" - ) - - self.PERCENTAGE_HIGH_SCHOOL_ED_FIELD_NAME = "Percent individuals age 25 or over with less than high school degree in 2009" - - # Employment fields - self.EMPLOYMENT_MALE_IN_LABOR_FORCE_FIELD = ( - "PBG038003" # Total!!Male!!In labor force - ) - self.EMPLOYMENT_MALE_UNEMPLOYED_FIELD = ( - "PBG038007" # Total!!Male!!In labor force!!Civilian!!Unemployed - ) - self.EMPLOYMENT_FEMALE_IN_LABOR_FORCE_FIELD = ( - "PBG038010" # Total!!Female!!In labor force - ) - self.EMPLOYMENT_FEMALE_UNEMPLOYED_FIELD = ( - "PBG038014" # Total!!Female!!In labor force!!Civilian!!Unemployed - ) - - # Same fields, Virgin Islands. - self.EMPLOYMENT_MALE_IN_LABOR_FORCE_VI_FIELD = ( - "PBG036003" # Total!!Male!!In labor force - ) - self.EMPLOYMENT_MALE_UNEMPLOYED_VI_FIELD = ( - "PBG036007" # Total!!Male!!In labor force!!Civilian!!Unemployed - ) - self.EMPLOYMENT_FEMALE_IN_LABOR_FORCE_VI_FIELD = ( - "PBG036010" # Total!!Female!!In labor force - ) - self.EMPLOYMENT_FEMALE_UNEMPLOYED_VI_FIELD = ( - "PBG036014" # Total!!Female!!In labor force!!Civilian!!Unemployed - ) - - self.UNEMPLOYMENT_FIELD_NAME = ( - field_names.CENSUS_DECENNIAL_UNEMPLOYMENT_FIELD_2009 - ) - - # Race/Ethnicity fields - self.TOTAL_RACE_POPULATION_FIELD = "PCT086001" # Total - self.ASIAN_FIELD = "PCT086002" # Total!!Asian - self.BLACK_FIELD = "PCT086003" # Total!!Black or African American - self.HAWAIIAN_FIELD = ( - "PCT086004" # Total!!Native Hawaiian and Other Pacific Islander - ) - # Note that the 2010 census for island araeas does not break out - # hispanic and non-hispanic white, so this is slightly different from - # our other demographic data - self.NON_HISPANIC_WHITE_FIELD = "PCT086005" # Total!!White - self.HISPANIC_FIELD = "PCT086006" # Total!!Hispanic or Latino - self.OTHER_RACE_FIELD = "PCT086007" # Total!!Other Ethnic Origin or Ra - - self.TOTAL_RACE_POPULATION_VI_FIELD = "P003001" # Total - self.BLACK_VI_FIELD = ( - "P003003" # Total!!One race!!Black or African American alone - ) - self.AMERICAN_INDIAN_VI_FIELD = "P003005" # Total!!One race!!American Indian and Alaska Native alone - self.ASIAN_VI_FIELD = "P003006" # Total!!One race!!Asian alone - self.HAWAIIAN_VI_FIELD = "P003007" # Total!!One race!!Native Hawaiian and Other Pacific Islander alone - self.TWO_OR_MORE_RACES_VI_FIELD = "P003009" # Total!!Two or More Races - self.NON_HISPANIC_WHITE_VI_FIELD = ( - "P005006" # Total!!Not Hispanic or Latino!!One race!!White alone - ) - self.HISPANIC_VI_FIELD = "P005002" # Total!!Hispanic or Latino - self.OTHER_RACE_VI_FIELD = ( - "P003008" # Total!!One race!!Some Other Race alone - ) - self.TOTAL_RACE_POPULATION_VI_FIELD = "P003001" # Total - - self.TOTAL_RACE_POPULATION_FIELD_NAME = ( - "Total population surveyed on racial data" - ) - self.BLACK_FIELD_NAME = "Black or African American" - self.AMERICAN_INDIAN_FIELD_NAME = "American Indian / Alaska Native" - self.ASIAN_FIELD_NAME = "Asian" - self.HAWAIIAN_FIELD_NAME = "Native Hawaiian or Pacific" - self.TWO_OR_MORE_RACES_FIELD_NAME = "two or more races" - self.NON_HISPANIC_WHITE_FIELD_NAME = "White" - self.HISPANIC_FIELD_NAME = "Hispanic or Latino" - # Note that `other` is lowercase because the whole field will show up in the download - # file as "Percent other races" - self.OTHER_RACE_FIELD_NAME = "other races" - - # Name output demographics fields. - self.RE_OUTPUT_FIELDS = [ - self.BLACK_FIELD_NAME, - self.AMERICAN_INDIAN_FIELD_NAME, - self.ASIAN_FIELD_NAME, - self.HAWAIIAN_FIELD_NAME, - self.TWO_OR_MORE_RACES_FIELD_NAME, - self.NON_HISPANIC_WHITE_FIELD_NAME, - self.HISPANIC_FIELD_NAME, - self.OTHER_RACE_FIELD_NAME, - ] - - var_list = [ - self.MEDIAN_INCOME_FIELD, - self.TOTAL_HOUSEHOLD_RATIO_INCOME_TO_POVERTY_LEVEL_FIELD, - self.HOUSEHOLD_OVER_200_PERC_POVERTY_LEVEL_FIELD, - self.TOTAL_POPULATION_FIELD, - self.MALE_HIGH_SCHOOL_ED_FIELD, - self.FEMALE_HIGH_SCHOOL_ED_FIELD, - self.HOUSEHOLD_UNDER_100_PERC_POVERTY_LEVEL_FIELD_PART_ONE, - self.HOUSEHOLD_UNDER_100_PERC_POVERTY_LEVEL_FIELD_PART_TWO, - self.HOUSEHOLD_UNDER_100_PERC_POVERTY_LEVEL_FIELD_PART_THREE, - self.EMPLOYMENT_MALE_IN_LABOR_FORCE_FIELD, - self.EMPLOYMENT_MALE_UNEMPLOYED_FIELD, - self.EMPLOYMENT_FEMALE_IN_LABOR_FORCE_FIELD, - self.EMPLOYMENT_FEMALE_UNEMPLOYED_FIELD, - self.TOTAL_POP_FIELD, - self.TOTAL_RACE_POPULATION_FIELD, - self.ASIAN_FIELD, - self.BLACK_FIELD, - self.HAWAIIAN_FIELD, - self.NON_HISPANIC_WHITE_FIELD, - self.HISPANIC_FIELD, - self.OTHER_RACE_FIELD, - ] - var_list = ",".join(var_list) - - var_list_vi = [ - self.MEDIAN_INCOME_VI_FIELD, - self.TOTAL_HOUSEHOLD_RATIO_INCOME_TO_POVERTY_LEVEL_VI_FIELD, - self.HOUSEHOLD_OVER_200_PERC_POVERTY_LEVEL_VI_FIELD, - self.TOTAL_POPULATION_VI_FIELD, - self.MALE_HIGH_SCHOOL_ED_VI_FIELD, - self.FEMALE_HIGH_SCHOOL_ED_VI_FIELD, - self.HOUSEHOLD_UNDER_100_PERC_POVERTY_LEVEL_FIELD_VI_PART_ONE, - self.HOUSEHOLD_UNDER_100_PERC_POVERTY_LEVEL_FIELD_VI_PART_TWO, - self.HOUSEHOLD_UNDER_100_PERC_POVERTY_LEVEL_FIELD_VI_PART_THREE, - self.EMPLOYMENT_MALE_IN_LABOR_FORCE_VI_FIELD, - self.EMPLOYMENT_MALE_UNEMPLOYED_VI_FIELD, - self.EMPLOYMENT_FEMALE_IN_LABOR_FORCE_VI_FIELD, - self.EMPLOYMENT_FEMALE_UNEMPLOYED_VI_FIELD, - self.TOTAL_POP_VI_FIELD, - self.BLACK_VI_FIELD, - self.AMERICAN_INDIAN_VI_FIELD, - self.ASIAN_VI_FIELD, - self.HAWAIIAN_VI_FIELD, - self.TWO_OR_MORE_RACES_VI_FIELD, - self.NON_HISPANIC_WHITE_VI_FIELD, - self.HISPANIC_VI_FIELD, - self.OTHER_RACE_VI_FIELD, - self.TOTAL_RACE_POPULATION_VI_FIELD, - ] - var_list_vi = ",".join(var_list_vi) - - self.FIELD_NAME_XWALK = { - self.MEDIAN_INCOME_FIELD: self.MEDIAN_INCOME_FIELD_NAME, - self.MEDIAN_INCOME_VI_FIELD: self.MEDIAN_INCOME_FIELD_NAME, - self.TOTAL_HOUSEHOLD_RATIO_INCOME_TO_POVERTY_LEVEL_FIELD: self.TOTAL_HOUSEHOLD_RATIO_INCOME_TO_POVERTY_LEVEL_FIELD_NAME, - self.TOTAL_HOUSEHOLD_RATIO_INCOME_TO_POVERTY_LEVEL_VI_FIELD: self.TOTAL_HOUSEHOLD_RATIO_INCOME_TO_POVERTY_LEVEL_FIELD_NAME, - self.HOUSEHOLD_OVER_200_PERC_POVERTY_LEVEL_FIELD: self.HOUSEHOLD_OVER_200_PERC_POVERTY_LEVEL_FIELD_NAME, - self.HOUSEHOLD_OVER_200_PERC_POVERTY_LEVEL_VI_FIELD: self.HOUSEHOLD_OVER_200_PERC_POVERTY_LEVEL_FIELD_NAME, - self.TOTAL_POPULATION_FIELD: self.TOTAL_POPULATION_FIELD_NAME, - self.TOTAL_POPULATION_VI_FIELD: self.TOTAL_POPULATION_FIELD_NAME, - self.MALE_HIGH_SCHOOL_ED_FIELD: self.MALE_HIGH_SCHOOL_ED_FIELD_NAME, - self.MALE_HIGH_SCHOOL_ED_VI_FIELD: self.MALE_HIGH_SCHOOL_ED_FIELD_NAME, - self.FEMALE_HIGH_SCHOOL_ED_FIELD: self.FEMALE_HIGH_SCHOOL_ED_FIELD_NAME, - self.FEMALE_HIGH_SCHOOL_ED_VI_FIELD: self.FEMALE_HIGH_SCHOOL_ED_FIELD_NAME, - self.HOUSEHOLD_UNDER_100_PERC_POVERTY_LEVEL_FIELD_PART_ONE: self.HOUSEHOLD_UNDER_100_PERC_POVERTY_LEVEL_FIELD_PART_ONE, - self.HOUSEHOLD_UNDER_100_PERC_POVERTY_LEVEL_FIELD_VI_PART_ONE: self.HOUSEHOLD_UNDER_100_PERC_POVERTY_LEVEL_FIELD_PART_ONE, - self.HOUSEHOLD_UNDER_100_PERC_POVERTY_LEVEL_FIELD_PART_TWO: self.HOUSEHOLD_UNDER_100_PERC_POVERTY_LEVEL_FIELD_PART_TWO, - self.HOUSEHOLD_UNDER_100_PERC_POVERTY_LEVEL_FIELD_VI_PART_TWO: self.HOUSEHOLD_UNDER_100_PERC_POVERTY_LEVEL_FIELD_PART_TWO, - self.HOUSEHOLD_UNDER_100_PERC_POVERTY_LEVEL_FIELD_PART_THREE: self.HOUSEHOLD_UNDER_100_PERC_POVERTY_LEVEL_FIELD_PART_THREE, - self.HOUSEHOLD_UNDER_100_PERC_POVERTY_LEVEL_FIELD_VI_PART_THREE: self.HOUSEHOLD_UNDER_100_PERC_POVERTY_LEVEL_FIELD_PART_THREE, - self.EMPLOYMENT_MALE_IN_LABOR_FORCE_VI_FIELD: self.EMPLOYMENT_MALE_IN_LABOR_FORCE_FIELD, - self.EMPLOYMENT_MALE_UNEMPLOYED_VI_FIELD: self.EMPLOYMENT_MALE_UNEMPLOYED_FIELD, - self.EMPLOYMENT_FEMALE_IN_LABOR_FORCE_VI_FIELD: self.EMPLOYMENT_FEMALE_IN_LABOR_FORCE_FIELD, - self.EMPLOYMENT_FEMALE_UNEMPLOYED_VI_FIELD: self.EMPLOYMENT_FEMALE_UNEMPLOYED_FIELD, - self.EMPLOYMENT_MALE_IN_LABOR_FORCE_FIELD: self.EMPLOYMENT_MALE_IN_LABOR_FORCE_FIELD, - self.EMPLOYMENT_MALE_UNEMPLOYED_FIELD: self.EMPLOYMENT_MALE_UNEMPLOYED_FIELD, - self.EMPLOYMENT_FEMALE_IN_LABOR_FORCE_FIELD: self.EMPLOYMENT_FEMALE_IN_LABOR_FORCE_FIELD, - self.EMPLOYMENT_FEMALE_UNEMPLOYED_FIELD: self.EMPLOYMENT_FEMALE_UNEMPLOYED_FIELD, - self.TOTAL_RACE_POPULATION_FIELD: self.TOTAL_RACE_POPULATION_FIELD_NAME, - self.TOTAL_RACE_POPULATION_VI_FIELD: self.TOTAL_RACE_POPULATION_FIELD_NAME, - # Note there is no American Indian data for AS/GU/MI - self.AMERICAN_INDIAN_VI_FIELD: self.AMERICAN_INDIAN_FIELD_NAME, - self.ASIAN_FIELD: self.ASIAN_FIELD_NAME, - self.ASIAN_VI_FIELD: self.ASIAN_FIELD_NAME, - self.BLACK_FIELD: self.BLACK_FIELD_NAME, - self.BLACK_VI_FIELD: self.BLACK_FIELD_NAME, - self.HAWAIIAN_FIELD: self.HAWAIIAN_FIELD_NAME, - self.HAWAIIAN_VI_FIELD: self.HAWAIIAN_FIELD_NAME, - self.TWO_OR_MORE_RACES_VI_FIELD: self.TWO_OR_MORE_RACES_FIELD_NAME, - self.NON_HISPANIC_WHITE_FIELD: self.NON_HISPANIC_WHITE_FIELD_NAME, - self.NON_HISPANIC_WHITE_VI_FIELD: self.NON_HISPANIC_WHITE_FIELD_NAME, - self.HISPANIC_FIELD: self.HISPANIC_FIELD_NAME, - self.HISPANIC_VI_FIELD: self.HISPANIC_FIELD_NAME, - self.OTHER_RACE_FIELD: self.OTHER_RACE_FIELD_NAME, - self.OTHER_RACE_VI_FIELD: self.OTHER_RACE_FIELD_NAME, - } - - # To do: Ask Census Slack Group about whether you need to hardcode the county fips - # https://uscensusbureau.slack.com/archives/C6DGLC05B/p1635218909012600 - self.ISLAND_TERRITORIES = [ - { - "state_abbreviation": "as", - "fips": "60", - "county_fips": ["010", "020", "030", "040", "050"], - "var_list": var_list, - # Note: we hardcode the median income for each territory in this dict, - # because that data is hard to programmatically access. - self.TERRITORY_MEDIAN_INCOME_FIELD: 23892, - }, - { - "state_abbreviation": "gu", - "fips": "66", - "county_fips": ["010"], - "var_list": var_list, - self.TERRITORY_MEDIAN_INCOME_FIELD: 48274, - }, - { - "state_abbreviation": "mp", - "fips": "69", - "county_fips": ["085", "100", "110", "120"], - "var_list": var_list, - self.TERRITORY_MEDIAN_INCOME_FIELD: 19958, - }, - { - "state_abbreviation": "vi", - "fips": "78", - "county_fips": ["010", "020", "030"], - "var_list": var_list_vi, - self.TERRITORY_MEDIAN_INCOME_FIELD: 37254, - }, - ] - - self.API_URL = ( - "https://api.census.gov/data/{}/dec/{}?get=NAME,{}" - + "&for=tract:*&in=state:{}%20county:{}" - ) - census_api_key = os.environ.get("CENSUS_API_KEY") if census_api_key: - self.API_URL = self.API_URL + f"&key={census_api_key}" + url += f"&key={census_api_key}" + return url - self.final_race_fields: List[str] = [] + def __get_destination_path( + self, + state_abbreviation: str, + fips: str, + county: str, + test_path: Path = None, + ) -> str: + root_path = test_path or self.get_sources_path() + return ( + root_path + / str(self.DECENNIAL_YEAR) + / state_abbreviation + / fips + / county + / "census.json" + ) - self.df: pd.DataFrame - self.df_vi: pd.DataFrame - self.df_all: pd.DataFrame - - def get_data_sources(self) -> [DataSource]: + def __init__(self): + self.df_all = pd.DataFrame() + self.final_race_fields = [] + def get_data_sources(self) -> List[DataSource]: sources = [] - - for island in self.ISLAND_TERRITORIES: + for island in DEC_TERRITORY_PARAMS: for county in island["county_fips"]: - - api_url = self.API_URL.format( - self.DECENNIAL_YEAR, + api_url = self.__get_api_url( island["state_abbreviation"], - island["var_list"], + ",".join(island["xwalk"].keys()), island["fips"], county, ) - sources.append( FileDataSource( - source=api_url, - destination=self.get_sources_path() - / str(self.DECENNIAL_YEAR) - / island["state_abbreviation"] - / island["fips"] - / county - / "census.json", + api_url, + self.__get_destination_path( + island["state_abbreviation"], island["fips"], county + ), ) ) - return sources - def extract(self, use_cached_data_sources: bool = False) -> None: - - super().extract( - use_cached_data_sources - ) # download and extract data sources - - dfs = [] - dfs_vi = [] - for island in self.ISLAND_TERRITORIES: - logger.debug( - f"Downloading data for state/territory {island['state_abbreviation']}" - ) - for county in island["county_fips"]: - + def extract( + self, + use_cached_data_sources: bool = False, + test_territory_params=None, + test_path: Path = None, + ) -> None: + super().extract(use_cached_data_sources) + for territory in test_territory_params or DEC_TERRITORY_PARAMS: + for county in territory["county_fips"]: + abbr = territory["state_abbreviation"] + file_path = self.__get_destination_path( + abbr, territory["fips"], county, test_path=test_path + ) try: - filepath = ( - self.get_sources_path() - / str(self.DECENNIAL_YEAR) - / island["state_abbreviation"] - / island["fips"] - / county - / "census.json" - ) - df = json.load(filepath.open()) - except ValueError as e: + json_data = json.load(file_path.open()) + except (FileNotFoundError, ValueError) as e: logger.error( f"Could not load content in census decennial ETL because {e}." ) + raise + df = pd.DataFrame(json_data[1:], columns=json_data[0]) + # Rename the columns to their common names + df.rename(columns=territory["xwalk"], inplace=True) - # First row is the header - df = pd.DataFrame(df[1:], columns=df[0]) + # Convert columns to numeric where applicable + for column in df.columns: + if column not in ["state", "county", "NAME", "tract"]: + df[column] = pd.to_numeric(df[column], errors="ignore") - for col in island["var_list"].split(","): - # Converting appropriate variables to numeric. - # Also replacing 0s with NaNs - df[col] = pd.to_numeric(df[col]) + # Add the territory median income + df.loc[ + df[field_names.CENSUS_DECENNIAL_TOTAL_POPULATION_FIELD_2019] + > 0, + DEC_FIELD_NAMES.TERRITORY_MEDIAN_INCOME, + ] = territory["median_income"] + self.df_all = pd.concat([self.df_all, df]) - # TO-DO: CHECK THIS. I think it makes sense to replace 0 with NaN - # because for our variables of interest (e.g. Median Household Income, - # it doesn't make sense for that to be 0.) - # Likely, it's actually missing but can't find a cite for that in the docs - df[col] = df[col].replace(0, np.nan) - - if island["state_abbreviation"] == "vi": - dfs_vi.append(df) - else: - dfs.append(df) - - self.df = pd.concat(dfs) - self.df_vi = pd.concat(dfs_vi) + def _merge_tracts_2010_compatibility(self): + """Merges tract 69120950200 to match 2010 tracts""" + # MP 69/120 69120950200 = 69120950201, 69120950202 + # Tract has been split, but 69120950202 has no data, so we just make 69120950200 = 69120950201 + self.df_all = self.df_all.drop( + self.df_all[ + self.df_all[field_names.GEOID_TRACT_FIELD] == "69120950202" + ].index + ) + self.df_all.loc[ + self.df_all[field_names.GEOID_TRACT_FIELD] == "69120950201", + field_names.GEOID_TRACT_FIELD, + ] = "69120950200" def transform(self) -> None: - # Rename All Fields - self.df.rename(columns=self.FIELD_NAME_XWALK, inplace=True) - self.df_vi.rename(columns=self.FIELD_NAME_XWALK, inplace=True) + # Creating Geo ID (Census Block Group) Field Name + self.df_all[field_names.GEOID_TRACT_FIELD] = ( + self.df_all["state"] + self.df_all["county"] + self.df_all["tract"] + ) - # Combine the dfs after renaming - self.df_all = pd.concat([self.df, self.df_vi]) + # Combine the two MP 2020 tracts that were split from one 2010 tract + self._merge_tracts_2010_compatibility() - # Rename total population: - self.df_all[self.TOTAL_POP_FIELD_NAME] = self.df_all[ - self.TOTAL_POP_FIELD + # Replace invalid numeric values with NaN + numeric_columns = self.df_all.select_dtypes(include="number").columns + for num_column in numeric_columns: + self.df_all.loc[self.df_all[num_column] < -999, num_column] = np.nan + + # Percentage of households below 100% FPL + self.df_all[ + field_names.CENSUS_DECENNIAL_POVERTY_LESS_THAN_100_FPL_FIELD_2019 + ] = ( + self.df_all[DEC_FIELD_NAMES.HOUSEHOLD_POVERTY_LEVEL_UNDER_0_5] + + self.df_all[DEC_FIELD_NAMES.HOUSEHOLD_POVERTY_LEVEL_UNDER_0_74] + + self.df_all[DEC_FIELD_NAMES.HOUSEHOLD_POVERTY_LEVEL_UNDER_0_99] + ) / self.df_all[ + DEC_FIELD_NAMES.TOTAL_HOUSEHOLD_POVERTY_LEVEL ] # Percentage of households below 200% which is - # [PBG083001 (total) - PBG083010 (num households over 200%)] / PBG083001 (total) self.df_all[ - self.PERCENTAGE_HOUSEHOLDS_BELOW_200_PERC_POVERTY_LEVEL_FIELD_NAME + field_names.CENSUS_DECENNIAL_POVERTY_LESS_THAN_200_FPL_FIELD_2019 ] = ( - self.df_all[ - self.TOTAL_HOUSEHOLD_RATIO_INCOME_TO_POVERTY_LEVEL_FIELD_NAME - ] - - self.df_all[self.HOUSEHOLD_OVER_200_PERC_POVERTY_LEVEL_FIELD_NAME] + self.df_all[DEC_FIELD_NAMES.TOTAL_HOUSEHOLD_POVERTY_LEVEL] + - self.df_all[DEC_FIELD_NAMES.HOUSEHOLD_POVERTY_LEVEL_OVER_2_0] ) / self.df_all[ - self.TOTAL_HOUSEHOLD_RATIO_INCOME_TO_POVERTY_LEVEL_FIELD_NAME - ] - - # Percentage of households below 100% FPL - # which we get by adding `Total!!Under .50`, `Total!!.50 to .74`, ` Total!!.75 to .99`, - # and then dividing by PBG083001 (total) - self.df_all[ - self.PERCENTAGE_HOUSEHOLDS_BELOW_100_PERC_POVERTY_LEVEL_FIELD_NAME - ] = ( - self.df_all[ - self.HOUSEHOLD_UNDER_100_PERC_POVERTY_LEVEL_FIELD_PART_ONE - ] - + self.df_all[ - self.HOUSEHOLD_UNDER_100_PERC_POVERTY_LEVEL_FIELD_PART_TWO - ] - + self.df_all[ - self.HOUSEHOLD_UNDER_100_PERC_POVERTY_LEVEL_FIELD_PART_THREE - ] - ) / self.df_all[ - self.TOTAL_HOUSEHOLD_RATIO_INCOME_TO_POVERTY_LEVEL_FIELD_NAME + DEC_FIELD_NAMES.TOTAL_HOUSEHOLD_POVERTY_LEVEL ] # Percentage High School Achievement is # Percentage = (Male + Female) / (Total) - self.df_all[self.PERCENTAGE_HIGH_SCHOOL_ED_FIELD_NAME] = ( - self.df_all[self.MALE_HIGH_SCHOOL_ED_FIELD_NAME] - + self.df_all[self.FEMALE_HIGH_SCHOOL_ED_FIELD_NAME] - ) / self.df_all[self.TOTAL_POPULATION_FIELD_NAME] + self.df_all[field_names.CENSUS_DECENNIAL_HIGH_SCHOOL_ED_FIELD_2019] = ( + self.df_all[DEC_FIELD_NAMES.MALE_HIGH_SCHOOL_ED] + + self.df_all[DEC_FIELD_NAMES.FEMALE_HIGH_SCHOOL_ED] + ) / self.df_all[ + field_names.CENSUS_DECENNIAL_TOTAL_POPULATION_FIELD_2019 + ] # Calculate employment. - self.df_all[self.UNEMPLOYMENT_FIELD_NAME] = ( - self.df_all[self.EMPLOYMENT_MALE_UNEMPLOYED_FIELD] - + self.df_all[self.EMPLOYMENT_FEMALE_UNEMPLOYED_FIELD] + self.df_all[field_names.CENSUS_DECENNIAL_UNEMPLOYMENT_FIELD_2019] = ( + self.df_all[DEC_FIELD_NAMES.EMPLOYMENT_MALE_UNEMPLOYED] + + self.df_all[DEC_FIELD_NAMES.EMPLOYMENT_FEMALE_UNEMPLOYED] ) / ( - self.df_all[self.EMPLOYMENT_MALE_IN_LABOR_FORCE_FIELD] - + self.df_all[self.EMPLOYMENT_FEMALE_IN_LABOR_FORCE_FIELD] + self.df_all[DEC_FIELD_NAMES.EMPLOYMENT_MALE_IN_LABOR_FORCE] + + self.df_all[DEC_FIELD_NAMES.EMPLOYMENT_FEMALE_IN_LABOR_FORCE] ) # Calculate area median income - median_income_df = pd.DataFrame(self.ISLAND_TERRITORIES) - median_income_df = median_income_df[ - ["fips", self.TERRITORY_MEDIAN_INCOME_FIELD] - ] - self.df_all = self.df_all.merge( - right=median_income_df, left_on="state", right_on="fips", how="left" - ) - self.df_all[self.AREA_MEDIAN_INCOME_FIELD_NAME] = ( - self.df_all[self.MEDIAN_INCOME_FIELD_NAME] - / self.df_all[self.TERRITORY_MEDIAN_INCOME_FIELD] + self.df_all[ + field_names.CENSUS_DECENNIAL_AREA_MEDIAN_INCOME_PERCENT_FIELD_2019 + ] = ( + self.df_all[field_names.CENSUS_DECENNIAL_MEDIAN_INCOME_2019] + / self.df_all[DEC_FIELD_NAMES.TERRITORY_MEDIAN_INCOME] ) - # Creating Geo ID (Census Block Group) Field Name - self.df_all[self.GEOID_TRACT_FIELD_NAME] = ( - self.df_all["state"] + self.df_all["county"] + self.df_all["tract"] + # Calculate college attendance + self.df_all[DEC_FIELD_NAMES.COLLEGE_ATTENDANCE_POPULATION] = ( + self.df_all[DEC_FIELD_NAMES.COLLEGE_ATTENDANCE_MALE_ENROLLED] + + self.df_all[DEC_FIELD_NAMES.COLLEGE_ATTENDANCE_FEMALE_ENROLLED] + ) + self.df_all[DEC_FIELD_NAMES.COLLEGE_ATTENDANCE_PERCENT] = ( + self.df_all[DEC_FIELD_NAMES.COLLEGE_ATTENDANCE_MALE_ENROLLED] + + self.df_all[DEC_FIELD_NAMES.COLLEGE_ATTENDANCE_FEMALE_ENROLLED] + ) / self.df_all[DEC_FIELD_NAMES.COLLEGE_ATTENDANCE_TOTAL_ENROLLED] + self.df_all[DEC_FIELD_NAMES.COLLEGE_NON_ATTENDANCE_PERCENT] = ( + 1 - self.df_all[DEC_FIELD_NAMES.COLLEGE_ATTENDANCE_PERCENT] ) # Calculate stats by race - for race_field_name in self.RE_OUTPUT_FIELDS: + for race_field_name in OUTPUT_RACE_FIELDS: output_field_name = ( field_names.PERCENT_PREFIX + race_field_name - + field_names.ISLAND_AREA_BACKFILL_SUFFIX + # 2010 vs 2020 WARNING + # We must keep the old 2009 date to make it compatible with all the other 2010 data + + f" in {field_names.DEC_DATA_YEAR}" ) - self.final_race_fields.append(output_field_name) self.df_all[output_field_name] = ( self.df_all[race_field_name] - / self.df_all[self.TOTAL_RACE_POPULATION_FIELD_NAME] + / self.df_all[DEC_FIELD_NAMES.TOTAL_RACE_POPULATION] ) + self.final_race_fields.append(output_field_name) # Reporting Missing Values for col in self.df_all.columns: @@ -533,21 +233,21 @@ class CensusDecennialETL(ExtractTransformLoad): ) def load(self) -> None: - # mkdir census self.OUTPUT_PATH.mkdir(parents=True, exist_ok=True) - columns_to_include = [ - self.GEOID_TRACT_FIELD_NAME, - self.TOTAL_POP_FIELD_NAME, - self.MEDIAN_INCOME_FIELD_NAME, - self.TERRITORY_MEDIAN_INCOME_FIELD, - self.AREA_MEDIAN_INCOME_FIELD_NAME, - self.PERCENTAGE_HOUSEHOLDS_BELOW_100_PERC_POVERTY_LEVEL_FIELD_NAME, - self.PERCENTAGE_HOUSEHOLDS_BELOW_200_PERC_POVERTY_LEVEL_FIELD_NAME, - self.PERCENTAGE_HIGH_SCHOOL_ED_FIELD_NAME, - self.UNEMPLOYMENT_FIELD_NAME, + field_names.GEOID_TRACT_FIELD, + field_names.CENSUS_DECENNIAL_TOTAL_POPULATION_FIELD_2019, + field_names.CENSUS_DECENNIAL_MEDIAN_INCOME_2019, + DEC_FIELD_NAMES.TERRITORY_MEDIAN_INCOME, + field_names.CENSUS_DECENNIAL_AREA_MEDIAN_INCOME_PERCENT_FIELD_2019, + field_names.CENSUS_DECENNIAL_POVERTY_LESS_THAN_100_FPL_FIELD_2019, + field_names.CENSUS_DECENNIAL_POVERTY_LESS_THAN_200_FPL_FIELD_2019, + field_names.CENSUS_DECENNIAL_UNEMPLOYMENT_FIELD_2019, + field_names.CENSUS_DECENNIAL_HIGH_SCHOOL_ED_FIELD_2019, + DEC_FIELD_NAMES.COLLEGE_ATTENDANCE_PERCENT, + DEC_FIELD_NAMES.COLLEGE_NON_ATTENDANCE, + DEC_FIELD_NAMES.COLLEGE_ATTENDANCE_POPULATION, ] + self.final_race_fields - self.df_all[columns_to_include].to_csv( path_or_buf=self.OUTPUT_PATH / "usa.csv", index=False ) diff --git a/data/data-pipeline/data_pipeline/score/field_names.py b/data/data-pipeline/data_pipeline/score/field_names.py index 773a88aa..a2dbadbf 100644 --- a/data/data-pipeline/data_pipeline/score/field_names.py +++ b/data/data-pipeline/data_pipeline/score/field_names.py @@ -182,22 +182,26 @@ AGGREGATION_POPULATION_FIELD = "Population Characteristics" UNDER_5_FIELD = "Individuals under 5 years old" OVER_64_FIELD = "Individuals over 64 years old" -# Fields from 2010 decennial census (generally only loaded for the territories) -CENSUS_DECENNIAL_MEDIAN_INCOME_2009 = "Median household income in 2009 ($)" -CENSUS_DECENNIAL_POVERTY_LESS_THAN_100_FPL_FIELD_2009 = ( - "Percentage households below 100% of federal poverty line in 2009" +# Fields from 2020 decennial census (generally only loaded for the territories) +# 2010 vs 2020 WARNING +# We must keep the old 2009 date to make it compatible with all the other 2010 data +DEC_DATA_YEAR = "2009" +CENSUS_DECENNIAL_MEDIAN_INCOME_2019 = ( + f"Median household income in {DEC_DATA_YEAR} ($)" ) -CENSUS_DECENNIAL_POVERTY_LESS_THAN_200_FPL_FIELD_2009 = ( - "Percentage households below 200% of federal poverty line in 2009" +CENSUS_DECENNIAL_POVERTY_LESS_THAN_100_FPL_FIELD_2019 = f"Percentage households below 100% of federal poverty line in {DEC_DATA_YEAR}" +CENSUS_DECENNIAL_POVERTY_LESS_THAN_200_FPL_FIELD_2019 = f"Percentage households below 200% of federal poverty line in {DEC_DATA_YEAR}" +CENSUS_DECENNIAL_HIGH_SCHOOL_ED_FIELD_2019 = f"Percent individuals age 25 or over with less than high school degree in {DEC_DATA_YEAR}" +CENSUS_DECENNIAL_UNEMPLOYMENT_FIELD_2019 = ( + f"Unemployment (percent) in {DEC_DATA_YEAR}" ) -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" -CENSUS_DECENNIAL_AREA_MEDIAN_INCOME_PERCENT_FIELD_2009 = ( - "Median household income as a percent of territory median income in 2009" +CENSUS_DECENNIAL_TOTAL_POPULATION_FIELD_2019 = ( + f"Total population in {DEC_DATA_YEAR}" ) -LOW_CENSUS_DECENNIAL_AREA_MEDIAN_INCOME_PERCENT_FIELD_2009 = "Low median household income as a percent of territory median income in 2009" -# Fields from 2010 ACS (loaded for comparison with the territories) +CENSUS_DECENNIAL_AREA_MEDIAN_INCOME_PERCENT_FIELD_2019 = f"Median household income as a percent of territory median income in {DEC_DATA_YEAR}" +LOW_CENSUS_DECENNIAL_AREA_MEDIAN_INCOME_PERCENT_FIELD_2019 = f"Low median household income as a percent of territory median income in {DEC_DATA_YEAR}" + +# # Fields from 2010 ACS (loaded for comparison with the territories) CENSUS_UNEMPLOYMENT_FIELD_2010 = "Unemployment (percent) in 2010" CENSUS_POVERTY_LESS_THAN_100_FPL_FIELD_2010 = ( "Percent of individuals less than 100% Federal Poverty Line in 2010" @@ -698,11 +702,11 @@ LOW_MEDIAN_INCOME_PCTILE_THRESHOLD = ( f"percent of area median income" ) ISLAND_LOW_MEDIAN_INCOME_PCTILE_THRESHOLD = ( - f"{LOW_CENSUS_DECENNIAL_AREA_MEDIAN_INCOME_PERCENT_FIELD_2009} exceeds " + f"{LOW_CENSUS_DECENNIAL_AREA_MEDIAN_INCOME_PERCENT_FIELD_2019} exceeds " f"{PERCENTILE}th percentile" ) -ISLAND_UNEMPLOYMENT_PCTILE_THRESHOLD = f"{CENSUS_DECENNIAL_UNEMPLOYMENT_FIELD_2009} exceeds {PERCENTILE}th percentile" -ISLAND_POVERTY_PCTILE_THRESHOLD = f"{CENSUS_DECENNIAL_POVERTY_LESS_THAN_100_FPL_FIELD_2009} exceeds {PERCENTILE}th percentile" +ISLAND_UNEMPLOYMENT_PCTILE_THRESHOLD = f"{CENSUS_DECENNIAL_UNEMPLOYMENT_FIELD_2019} exceeds {PERCENTILE}th percentile" +ISLAND_POVERTY_PCTILE_THRESHOLD = f"{CENSUS_DECENNIAL_POVERTY_LESS_THAN_100_FPL_FIELD_2019} exceeds {PERCENTILE}th percentile" # Not currently used in a factor EXTREME_HEAT_MEDIAN_HOUSE_VALUE_LOW_INCOME_FIELD = ( diff --git a/data/data-pipeline/data_pipeline/score/score_narwhal.py b/data/data-pipeline/data_pipeline/score/score_narwhal.py index 18ff8676..a4960a08 100644 --- a/data/data-pipeline/data_pipeline/score/score_narwhal.py +++ b/data/data-pipeline/data_pipeline/score/score_narwhal.py @@ -807,7 +807,7 @@ class ScoreNarwhal(Score): island_areas_unemployment_criteria_field_name, ) = self._combine_island_areas_with_states_and_set_thresholds( df=self.df, - column_from_island_areas=field_names.CENSUS_DECENNIAL_UNEMPLOYMENT_FIELD_2009, + column_from_island_areas=field_names.CENSUS_DECENNIAL_UNEMPLOYMENT_FIELD_2019, column_from_decennial_census=field_names.CENSUS_UNEMPLOYMENT_FIELD_2010, combined_column_name=field_names.COMBINED_UNEMPLOYMENT_2010, threshold_cutoff_for_island_areas=self.ENVIRONMENTAL_BURDEN_THRESHOLD, @@ -827,7 +827,7 @@ class ScoreNarwhal(Score): island_areas_poverty_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_100_FPL_FIELD_2009, + column_from_island_areas=field_names.CENSUS_DECENNIAL_POVERTY_LESS_THAN_100_FPL_FIELD_2019, column_from_decennial_census=field_names.CENSUS_POVERTY_LESS_THAN_100_FPL_FIELD_2010, combined_column_name=field_names.COMBINED_POVERTY_LESS_THAN_100_FPL_FIELD_2010, threshold_cutoff_for_island_areas=self.ENVIRONMENTAL_BURDEN_THRESHOLD, @@ -848,14 +848,14 @@ class ScoreNarwhal(Score): # refactor. self.df[field_names.ISLAND_LOW_MEDIAN_INCOME_PCTILE_THRESHOLD] = ( self.df[ - field_names.LOW_CENSUS_DECENNIAL_AREA_MEDIAN_INCOME_PERCENT_FIELD_2009 + field_names.LOW_CENSUS_DECENNIAL_AREA_MEDIAN_INCOME_PERCENT_FIELD_2019 + field_names.PERCENTILE_FIELD_SUFFIX ] >= self.ENVIRONMENTAL_BURDEN_THRESHOLD ) self.df[field_names.ISLAND_AREAS_LOW_HS_EDUCATION_FIELD] = ( - self.df[field_names.CENSUS_DECENNIAL_HIGH_SCHOOL_ED_FIELD_2009] + self.df[field_names.CENSUS_DECENNIAL_HIGH_SCHOOL_ED_FIELD_2019] >= self.LACK_OF_HIGH_SCHOOL_MINIMUM_THRESHOLD ) @@ -890,7 +890,7 @@ class ScoreNarwhal(Score): 100 * workforce_combined_criteria_for_island_areas.sum() # Choosing a random column from island areas to calculate the denominator. - / self.df[field_names.CENSUS_DECENNIAL_UNEMPLOYMENT_FIELD_2009] + / self.df[field_names.CENSUS_DECENNIAL_UNEMPLOYMENT_FIELD_2019] .notnull() .sum() ) @@ -1044,7 +1044,7 @@ class ScoreNarwhal(Score): 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_island_areas=field_names.CENSUS_DECENNIAL_POVERTY_LESS_THAN_200_FPL_FIELD_2019, 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, diff --git a/data/data-pipeline/data_pipeline/tests/sources/census_decennial/data/extract/2020/as/60/010/census.json b/data/data-pipeline/data_pipeline/tests/sources/census_decennial/data/extract/2020/as/60/010/census.json new file mode 100644 index 00000000..429c3d37 --- /dev/null +++ b/data/data-pipeline/data_pipeline/tests/sources/census_decennial/data/extract/2020/as/60/010/census.json @@ -0,0 +1,8 @@ +[["NAME","P1_001N","PBG19_005N","PBG19_012N","PCT31_001N","PBG32_003N","PBG32_007N","PBG32_010N","PBG32_014N","PCT34_003N","PCT34_016N","PBG43_001N","PBG74_001N","PBG74_002N","PBG74_003N","PBG74_004N","PBG74_010N","PCT9_001N","PCT9_003N","PCT9_079N","PCT9_130N","PCT9_155N","PCT9_180N","PCT9_205N","PCT9_230N","P5_002N","state","county","tract"], +["Census Tract 9505, Eastern District, American Samoa","2623","305","308","2044","443","42","369","44","175","164","26000","2151","538","338","329","352","2623","2021","342","77","0","0","99","84","107","60","010","950500"], +["Census Tract 9503, Eastern District, American Samoa","2415","325","345","2293","418","35","379","53","173","174","33631","2414","448","332","320","565","2415","2259","39","6","0","0","1","110","14","60","010","950300"], +["Census Tract 9501, Eastern District, American 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Guam","0","0","0","0","0","0","0","0","0","0","-666666666","0","0","0","0","0","0","0","0","0","0","0","0","0","0","66","010","980300"], +["Census Tract 9900, Guam, Guam","0","0","0","0","0","0","0","0","0","0","-666666666","0","0","0","0","0","0","0","0","0","0","0","0","0","0","66","010","990000"]] \ No newline at end of file diff --git a/data/data-pipeline/data_pipeline/tests/sources/census_decennial/data/transform/usa.csv b/data/data-pipeline/data_pipeline/tests/sources/census_decennial/data/transform/usa.csv new file mode 100644 index 00000000..3addabba --- /dev/null +++ b/data/data-pipeline/data_pipeline/tests/sources/census_decennial/data/transform/usa.csv @@ -0,0 +1,134 @@ +,NAME,Total population in 2009,Total male high school graduates 25 and over,Total female high school graduates 25 and over,Total asked enrolled in college or graduate school (excludes military housing),Total males in labor force,Total males not in labor force,Total females in labor force,Total females not in labor force,Males enrolled in college or graduate school (excludes military housing),Females enrolled in college or graduate school (excludes military housing),Median household income in 2009 ($),Total Household poverty level IN 2019,Household poverty level Under 0.50 IN 2019,Household poverty level Under 0.74 IN 2019,Household poverty level Under 0.99 IN 2019,Household poverty level Over 2.0 IN 2019,Total population surveyed on racial data,Native Hawaiian or Pacific,Asian,White,Black or African American,American Indian / Alaska Native,other races,two or more races,Hispanic or Latino,state,county,tract,Territory Median Income +0,"Census Tract 9505, Eastern District, American Samoa",2623.0,305.0,308.0,2044.0,443.0,42.0,369.0,44.0,175.0,164.0,26000.0,2151.0,538.0,338.0,329.0,352.0,2623.0,2021.0,342.0,77.0,0.0,0.0,99.0,84.0,107.0,60,010,950500,26352.0 +1,"Census Tract 9503, Eastern District, American Samoa",2415.0,325.0,345.0,2293.0,418.0,35.0,379.0,53.0,173.0,174.0,33631.0,2414.0,448.0,332.0,320.0,565.0,2415.0,2259.0,39.0,6.0,0.0,0.0,1.0,110.0,14.0,60,010,950300,26352.0 +2,"Census Tract 9501, Eastern District, American Samoa",1487.0,248.0,214.0,1404.0,269.0,38.0,254.0,39.0,103.0,106.0,24219.0,1485.0,448.0,243.0,146.0,200.0,1487.0,1449.0,3.0,1.0,1.0,0.0,1.0,32.0,6.0,60,010,950100,26352.0 +3,"Census Tract 9502, Eastern District, American Samoa",1158.0,164.0,143.0,1096.0,221.0,23.0,163.0,18.0,84.0,75.0,28333.0,1148.0,236.0,172.0,175.0,335.0,1158.0,1092.0,11.0,7.0,0.0,0.0,1.0,47.0,5.0,60,010,950200,26352.0 +4,"Census Tract 9506, Eastern District, American Samoa",3218.0,452.0,458.0,3031.0,645.0,57.0,573.0,56.0,216.0,242.0,26970.0,3188.0,774.0,462.0,439.0,666.0,3218.0,2781.0,167.0,9.0,1.0,0.0,5.0,255.0,40.0,60,010,950600,26352.0 +5,"Census Tract 9507, Eastern District, American Samoa",2727.0,381.0,364.0,2489.0,569.0,60.0,456.0,48.0,208.0,183.0,29083.0,2612.0,593.0,458.0,315.0,494.0,2727.0,2366.0,217.0,29.0,8.0,2.0,10.0,95.0,38.0,60,010,950700,26352.0 +6,"Census Tract 9509, Eastern District, American Samoa",3431.0,512.0,476.0,3239.0,718.0,76.0,682.0,89.0,238.0,244.0,30643.0,3412.0,724.0,510.0,573.0,623.0,3431.0,2988.0,332.0,28.0,0.0,0.0,2.0,81.0,27.0,60,010,950900,26352.0 +0,"Census Tract 9518, Manu'a District, American Samoa",832.0,169.0,119.0,784.0,213.0,23.0,148.0,36.0,63.0,63.0,26818.0,831.0,167.0,155.0,181.0,116.0,832.0,800.0,0.0,2.0,0.0,0.0,0.0,30.0,3.0,60,020,951800,26352.0 +0,"Census Tract 9519, Rose Island, American Samoa",0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,-666666666.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,60,030,951900, +0,"Census Tract 9520, Swains Island, American Samoa",0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,-666666666.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,60,040,952000, +0,"Census Tract 9510, Western District, American Samoa",3593.0,484.0,488.0,3386.0,738.0,34.0,559.0,25.0,272.0,225.0,24688.0,3572.0,1032.0,693.0,578.0,399.0,3593.0,3002.0,473.0,10.0,0.0,0.0,5.0,103.0,22.0,60,050,951000,26352.0 +1,"Census Tract 9511, Western District, American Samoa",6252.0,1007.0,961.0,5818.0,1162.0,80.0,842.0,72.0,477.0,422.0,24227.0,6201.0,2026.0,1206.0,876.0,725.0,6252.0,5765.0,212.0,50.0,1.0,0.0,1.0,223.0,40.0,60,050,951100,26352.0 +2,"Census Tract 9512.01, Western District, American Samoa",3406.0,411.0,386.0,2896.0,625.0,33.0,483.0,43.0,239.0,206.0,29107.0,3061.0,723.0,517.0,451.0,408.0,3406.0,3030.0,230.0,12.0,0.0,2.0,4.0,128.0,11.0,60,050,951201,26352.0 +3,"Census Tract 9512.02, Western District, American Samoa",4582.0,586.0,616.0,4322.0,906.0,59.0,814.0,64.0,319.0,320.0,31714.0,4535.0,784.0,687.0,577.0,1184.0,4582.0,3903.0,355.0,50.0,3.0,6.0,3.0,262.0,31.0,60,050,951202,26352.0 +4,"Census Tract 9512.03, Western District, American Samoa",4994.0,605.0,648.0,4721.0,964.0,89.0,896.0,106.0,330.0,340.0,32196.0,4948.0,1019.0,724.0,626.0,1199.0,4994.0,4308.0,264.0,61.0,3.0,3.0,4.0,351.0,33.0,60,050,951203,26352.0 +5,"Census Tract 9513, Western District, American Samoa",3010.0,443.0,419.0,2844.0,632.0,115.0,542.0,107.0,240.0,220.0,27330.0,2994.0,791.0,488.0,384.0,593.0,3010.0,2833.0,97.0,10.0,1.0,0.0,1.0,68.0,11.0,60,050,951300,26352.0 +6,"Census Tract 9515, Western District, American Samoa",1689.0,270.0,235.0,1588.0,355.0,77.0,307.0,96.0,139.0,110.0,28611.0,1674.0,484.0,184.0,217.0,246.0,1689.0,1593.0,14.0,2.0,1.0,0.0,0.0,79.0,2.0,60,050,951500,26352.0 +7,"Census Tract 9516, Western District, American Samoa",4293.0,629.0,565.0,4077.0,1075.0,306.0,937.0,351.0,311.0,322.0,28958.0,4280.0,990.0,726.0,621.0,889.0,4293.0,3900.0,122.0,20.0,5.0,0.0,2.0,244.0,16.0,60,050,951600,26352.0 +0,"Census Tract 9501, Guam, Guam",1347.0,7.0,6.0,77.0,25.0,1.0,14.0,0.0,2.0,29.0,40000.0,80.0,6.0,5.0,12.0,32.0,1347.0,13.0,128.0,764.0,159.0,11.0,63.0,111.0,221.0,66,010,950100,58289.0 +1,"Census Tract 9502, Guam, Guam",626.0,9.0,9.0,87.0,23.0,2.0,19.0,2.0,3.0,30.0,43750.0,102.0,6.0,0.0,10.0,44.0,626.0,29.0,91.0,296.0,56.0,3.0,32.0,86.0,81.0,66,010,950200,58289.0 +2,"Census Tract 9503, Guam, Guam",629.0,4.0,1.0,6.0,1.0,0.0,1.0,0.0,0.0,1.0,53750.0,6.0,0.0,0.0,0.0,0.0,629.0,3.0,620.0,5.0,0.0,0.0,0.0,1.0,0.0,66,010,950300,58289.0 +3,"Census Tract 9504.01, Guam, Guam",5809.0,669.0,581.0,5273.0,1447.0,150.0,1191.0,141.0,151.0,2096.0,53309.0,5688.0,455.0,322.0,376.0,2836.0,5809.0,1592.0,3775.0,54.0,14.0,9.0,3.0,359.0,70.0,66,010,950401,58289.0 +4,"Census Tract 9504.02, Guam, Guam",6606.0,735.0,668.0,5972.0,1581.0,168.0,1234.0,157.0,145.0,2198.0,50980.0,6579.0,834.0,472.0,531.0,2759.0,6606.0,2722.0,3135.0,122.0,32.0,0.0,13.0,574.0,101.0,66,010,950402,58289.0 +5,"Census Tract 9505.01, Guam, Guam",1834.0,211.0,188.0,1638.0,398.0,45.0,295.0,48.0,35.0,588.0,42054.0,1803.0,309.0,166.0,171.0,646.0,1834.0,1060.0,560.0,23.0,2.0,0.0,1.0,186.0,39.0,66,010,950501,58289.0 +6,"Census Tract 9505.02, Guam, Guam",5343.0,660.0,577.0,4952.0,1387.0,115.0,1052.0,86.0,160.0,1932.0,55833.0,5285.0,532.0,206.0,297.0,2911.0,5343.0,1833.0,2813.0,183.0,46.0,2.0,36.0,421.0,110.0,66,010,950502,58289.0 +7,"Census Tract 9507.01, Guam, Guam",5213.0,549.0,535.0,4849.0,1327.0,75.0,1088.0,86.0,162.0,1927.0,66023.0,5187.0,316.0,190.0,224.0,3283.0,5213.0,1279.0,2776.0,448.0,64.0,12.0,50.0,556.0,134.0,66,010,950701,58289.0 +8,"Census Tract 9507.02, Guam, Guam",4020.0,485.0,449.0,3640.0,930.0,89.0,742.0,61.0,102.0,1338.0,56406.0,3978.0,513.0,294.0,235.0,1894.0,4020.0,2093.0,1426.0,61.0,17.0,3.0,2.0,408.0,44.0,66,010,950702,58289.0 +9,"Census Tract 9508.01, Guam, Guam",3921.0,503.0,470.0,3607.0,932.0,127.0,836.0,124.0,66.0,1365.0,51528.0,3898.0,545.0,217.0,210.0,1804.0,3921.0,2058.0,1539.0,36.0,8.0,2.0,4.0,273.0,36.0,66,010,950801,58289.0 +10,"Census Tract 9508.02, Guam, Guam",4110.0,483.0,550.0,3846.0,894.0,102.0,746.0,111.0,97.0,1580.0,35372.0,4099.0,691.0,292.0,375.0,1531.0,4110.0,2055.0,1629.0,51.0,3.0,2.0,14.0,352.0,56.0,66,010,950802,58289.0 +11,"Census Tract 9509, Guam, Guam",4653.0,531.0,506.0,4404.0,1149.0,94.0,1014.0,94.0,98.0,1837.0,53221.0,4638.0,386.0,196.0,284.0,2672.0,4653.0,1048.0,3251.0,40.0,2.0,5.0,16.0,291.0,57.0,66,010,950900,58289.0 +12,"Census Tract 9510, Guam, Guam",3449.0,441.0,400.0,3240.0,821.0,74.0,739.0,57.0,105.0,1277.0,51806.0,3400.0,325.0,175.0,219.0,1743.0,3449.0,1137.0,1993.0,37.0,4.0,3.0,3.0,271.0,50.0,66,010,951000,58289.0 +13,"Census Tract 9511, Guam, Guam",6498.0,775.0,751.0,6108.0,1745.0,98.0,1498.0,134.0,216.0,2433.0,55673.0,6472.0,517.0,279.0,356.0,3630.0,6498.0,1974.0,3791.0,155.0,26.0,9.0,23.0,511.0,118.0,66,010,951100,58289.0 +14,"Census Tract 9516, Guam, Guam",142.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,142.0,111.0,16.0,3.0,4.0,0.0,1.0,7.0,3.0,66,010,951600,58289.0 +15,"Census Tract 9517, Guam, Guam",2239.0,193.0,198.0,1910.0,523.0,24.0,442.0,40.0,57.0,756.0,78333.0,2014.0,138.0,46.0,97.0,1387.0,2239.0,779.0,993.0,149.0,21.0,4.0,22.0,269.0,57.0,66,010,951700,58289.0 +16,"Census Tract 9518, Guam, Guam",99.0,18.0,13.0,83.0,27.0,1.0,18.0,3.0,1.0,33.0,50000.0,92.0,2.0,5.0,3.0,36.0,99.0,80.0,14.0,0.0,0.0,0.0,0.0,5.0,0.0,66,010,951800,58289.0 +17,"Census Tract 9519.01, Guam, Guam",4081.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,4081.0,797.0,2733.0,197.0,34.0,5.0,24.0,287.0,47.0,66,010,951901,58289.0 +18,"Census Tract 9519.02, Guam, Guam",3484.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,3484.0,575.0,2057.0,456.0,52.0,6.0,48.0,262.0,108.0,66,010,951902,58289.0 +19,"Census Tract 9522, Guam, Guam",3273.0,322.0,333.0,3102.0,900.0,84.0,749.0,105.0,58.0,1282.0,49355.0,3253.0,267.0,161.0,185.0,1805.0,3273.0,912.0,1845.0,125.0,26.0,1.0,13.0,349.0,47.0,66,010,952200,58289.0 +20,"Census Tract 9523, Guam, Guam",2829.0,250.0,289.0,2639.0,776.0,67.0,684.0,71.0,54.0,1080.0,59083.0,2811.0,233.0,141.0,128.0,1763.0,2829.0,848.0,1355.0,242.0,29.0,11.0,27.0,307.0,68.0,66,010,952300,58289.0 +21,"Census Tract 9524, Guam, Guam",1637.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,1637.0,614.0,737.0,72.0,16.0,0.0,18.0,176.0,46.0,66,010,952400,58289.0 +22,"Census Tract 9527, Guam, Guam",4468.0,537.0,542.0,3973.0,1016.0,78.0,874.0,68.0,117.0,1550.0,67578.0,4302.0,367.0,183.0,208.0,2623.0,4468.0,2929.0,823.0,253.0,19.0,0.0,11.0,423.0,136.0,66,010,952700,58289.0 +23,"Census Tract 9528, Guam, Guam",,,,,,,,,,,,,,,,,,,,,,,,,,66,010,952800, +24,"Census Tract 9529, Guam, Guam",5145.0,599.0,604.0,4121.0,1096.0,96.0,971.0,99.0,132.0,1631.0,62031.0,4430.0,349.0,125.0,215.0,2754.0,5145.0,3508.0,779.0,234.0,27.0,9.0,41.0,540.0,133.0,66,010,952900,58289.0 +25,"Census Tract 9530, Guam, Guam",3388.0,359.0,419.0,3023.0,775.0,85.0,742.0,100.0,112.0,1222.0,49605.0,3251.0,360.0,178.0,189.0,1777.0,3388.0,1990.0,747.0,157.0,16.0,0.0,22.0,453.0,98.0,66,010,953000,58289.0 +26,"Census Tract 9531.01, Guam, Guam",3966.0,530.0,500.0,3720.0,1029.0,110.0,889.0,75.0,111.0,1435.0,76413.0,3947.0,250.0,133.0,202.0,2659.0,3966.0,2585.0,551.0,236.0,15.0,1.0,20.0,544.0,171.0,66,010,953101,58289.0 +27,"Census Tract 9531.02, Guam, Guam",3098.0,362.0,380.0,2885.0,776.0,50.0,637.0,80.0,107.0,1078.0,67841.0,3074.0,196.0,114.0,161.0,1948.0,3098.0,1891.0,609.0,122.0,16.0,1.0,13.0,445.0,116.0,66,010,953102,58289.0 +28,"Census Tract 9532, Guam, Guam",2611.0,351.0,356.0,2410.0,614.0,72.0,577.0,77.0,48.0,960.0,67813.0,2597.0,286.0,80.0,137.0,1612.0,2611.0,1725.0,343.0,118.0,28.0,3.0,5.0,385.0,105.0,66,010,953200,58289.0 +29,"Census Tract 9533, Guam, Guam",3808.0,461.0,449.0,3478.0,902.0,85.0,769.0,94.0,78.0,1352.0,47419.0,3764.0,499.0,256.0,243.0,1889.0,3808.0,2268.0,910.0,137.0,18.0,0.0,11.0,452.0,99.0,66,010,953300,58289.0 +30,"Census Tract 9534, Guam, Guam",943.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,943.0,609.0,120.0,61.0,10.0,0.0,9.0,129.0,38.0,66,010,953400,58289.0 +31,"Census Tract 9535, Guam, Guam",,,,,,,,,,,,,,,,,,,,,,,,,,66,010,953500, +32,"Census Tract 9536, Guam, Guam",3616.0,418.0,408.0,3349.0,925.0,87.0,773.0,78.0,103.0,1295.0,71711.0,3574.0,278.0,171.0,104.0,2372.0,3616.0,2403.0,427.0,144.0,16.0,5.0,16.0,596.0,278.0,66,010,953600,58289.0 +33,"Census Tract 9539, Guam, Guam",4192.0,558.0,522.0,3831.0,946.0,97.0,789.0,74.0,85.0,1456.0,62386.0,4174.0,560.0,256.0,232.0,2215.0,4192.0,3101.0,328.0,176.0,3.0,5.0,39.0,532.0,137.0,66,010,953900,58289.0 +34,"Census Tract 9540, Guam, Guam",2106.0,259.0,234.0,1961.0,527.0,29.0,446.0,39.0,61.0,732.0,83889.0,2102.0,111.0,58.0,88.0,1447.0,2106.0,1283.0,277.0,207.0,11.0,1.0,16.0,308.0,82.0,66,010,954000,58289.0 +35,"Census Tract 9543, Guam, Guam",1532.0,153.0,172.0,1405.0,426.0,41.0,325.0,39.0,42.0,534.0,77125.0,1526.0,131.0,63.0,58.0,993.0,1532.0,932.0,160.0,160.0,10.0,3.0,14.0,237.0,61.0,66,010,954300,58289.0 +36,"Census Tract 9544, Guam, Guam",53.0,7.0,4.0,48.0,14.0,4.0,10.0,2.0,0.0,19.0,92500.0,50.0,8.0,0.0,1.0,32.0,53.0,39.0,4.0,1.0,0.0,0.0,0.0,9.0,0.0,66,010,954400,58289.0 +37,"Census Tract 9545, Guam, Guam",2483.0,9.0,7.0,69.0,20.0,1.0,14.0,3.0,3.0,21.0,40000.0,83.0,12.0,15.0,3.0,28.0,2483.0,71.0,203.0,1444.0,257.0,35.0,98.0,219.0,324.0,66,010,954500,58289.0 +38,"Census Tract 9547, Guam, Guam",2089.0,287.0,255.0,1910.0,497.0,71.0,407.0,48.0,52.0,729.0,54931.0,2061.0,238.0,108.0,95.0,1129.0,2089.0,1259.0,485.0,111.0,30.0,1.0,3.0,197.0,53.0,66,010,954700,58289.0 +39,"Census Tract 9548, Guam, Guam",2426.0,288.0,273.0,2183.0,472.0,99.0,444.0,83.0,31.0,833.0,47969.0,2416.0,504.0,186.0,173.0,979.0,2426.0,1638.0,489.0,69.0,5.0,0.0,2.0,217.0,68.0,66,010,954800,58289.0 +40,"Census Tract 9551, Guam, Guam",3550.0,514.0,473.0,3285.0,873.0,78.0,677.0,80.0,69.0,1200.0,80909.0,3518.0,265.0,165.0,162.0,2364.0,3550.0,2554.0,168.0,340.0,28.0,5.0,25.0,409.0,175.0,66,010,955100,58289.0 +41,"Census Tract 9552, Guam, Guam",2317.0,367.0,322.0,2116.0,529.0,56.0,416.0,39.0,31.0,799.0,67813.0,2295.0,252.0,105.0,149.0,1358.0,2317.0,2016.0,68.0,59.0,7.0,0.0,3.0,163.0,90.0,66,010,955200,58289.0 +42,"Census Tract 9553, Guam, Guam",1604.0,230.0,235.0,1481.0,305.0,50.0,264.0,28.0,25.0,582.0,51667.0,1600.0,267.0,82.0,84.0,726.0,1604.0,1362.0,43.0,65.0,4.0,0.0,1.0,129.0,50.0,66,010,955300,58289.0 +43,"Census Tract 9554, Guam, Guam",647.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,647.0,565.0,10.0,16.0,0.0,2.0,3.0,51.0,24.0,66,010,955400,58289.0 +44,"Census Tract 9556, Guam, Guam",1008.0,125.0,118.0,940.0,267.0,11.0,209.0,11.0,22.0,355.0,81250.0,1001.0,63.0,36.0,48.0,640.0,1008.0,683.0,168.0,37.0,8.0,1.0,4.0,106.0,24.0,66,010,955600,58289.0 +45,"Census Tract 9557, Guam, Guam",4940.0,625.0,550.0,4642.0,1263.0,132.0,1059.0,105.0,139.0,1803.0,63352.0,4901.0,456.0,235.0,198.0,2988.0,4940.0,2251.0,2010.0,146.0,22.0,2.0,8.0,497.0,95.0,66,010,955700,58289.0 +46,"Census Tract 9558, Guam, Guam",7804.0,920.0,801.0,7224.0,1979.0,153.0,1695.0,167.0,267.0,2723.0,66349.0,7738.0,475.0,326.0,410.0,4635.0,7804.0,2631.0,3870.0,393.0,71.0,16.0,48.0,759.0,179.0,66,010,955800,58289.0 +47,"Census Tract 9559, Guam, Guam",3185.0,322.0,324.0,3023.0,1011.0,65.0,740.0,66.0,99.0,1230.0,64833.0,3150.0,237.0,90.0,119.0,2201.0,3185.0,650.0,1461.0,562.0,50.0,28.0,39.0,389.0,139.0,66,010,955900,58289.0 +48,"Census Tract 9560, Guam, Guam",2572.0,260.0,285.0,2256.0,536.0,46.0,481.0,77.0,42.0,855.0,59375.0,2524.0,337.0,208.0,179.0,1227.0,2572.0,1693.0,419.0,159.0,5.0,3.0,20.0,259.0,60.0,66,010,956000,58289.0 +49,"Census Tract 9561, Guam, Guam",2011.0,218.0,236.0,1846.0,528.0,50.0,425.0,36.0,53.0,721.0,77500.0,1981.0,150.0,98.0,84.0,1301.0,2011.0,1306.0,239.0,169.0,12.0,1.0,16.0,263.0,69.0,66,010,956100,58289.0 +50,"Census Tract 9562, Guam, Guam",3945.0,469.0,420.0,3270.0,797.0,69.0,747.0,68.0,66.0,1255.0,77455.0,3488.0,220.0,71.0,122.0,2406.0,3945.0,1911.0,833.0,643.0,20.0,2.0,35.0,471.0,158.0,66,010,956200,58289.0 +51,"Census Tract 9563, Guam, Guam",2385.0,278.0,250.0,2173.0,592.0,54.0,429.0,64.0,58.0,822.0,57031.0,2346.0,242.0,140.0,121.0,1133.0,2385.0,1316.0,748.0,112.0,7.0,2.0,14.0,181.0,51.0,66,010,956300,58289.0 +52,"Census Tract 9801, Guam, Guam",42.0,7.0,9.0,41.0,12.0,0.0,11.0,2.0,1.0,17.0,60000.0,42.0,6.0,0.0,3.0,28.0,42.0,26.0,7.0,0.0,0.0,0.0,0.0,9.0,0.0,66,010,980100,58289.0 +53,"Census Tract 9802, Guam, Guam",0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,-666666666.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,66,010,980200, +54,"Census Tract 9803, Guam, Guam",0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,-666666666.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,66,010,980300, +55,"Census Tract 9804, Guam, Guam",0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,-666666666.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,66,010,980400, +56,"Census Tract 9900, Guam, Guam",0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,-666666666.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,66,010,990000, +0,"Census Tract 9501, Northern Islands Municipality, Commonwealth of the Northern Mariana Islands",7.0,2.0,0.0,7.0,5.0,0.0,0.0,0.0,2.0,0.0,13125.0,7.0,2.0,0.0,1.0,0.0,7.0,7.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,69,085,950100,31362.0 +0,"Census Tract 9501, Rota Municipality, Commonwealth of the Northern Mariana Islands",1893.0,347.0,252.0,1810.0,514.0,96.0,374.0,75.0,109.0,119.0,31289.0,1891.0,357.0,175.0,157.0,548.0,1893.0,1212.0,557.0,27.0,1.0,0.0,3.0,92.0,17.0,69,100,950100,31362.0 +1,"Census Tract 9900, Rota Municipality, Commonwealth of the Northern Mariana Islands",0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,-666666666.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,69,100,990000, +0,"Census Tract 1, Saipan Municipality, Commonwealth of the Northern Mariana Islands",1206.0,161.0,143.0,1164.0,342.0,40.0,231.0,37.0,112.0,65.0,42000.0,1203.0,130.0,86.0,121.0,427.0,1206.0,613.0,413.0,41.0,1.0,1.0,1.0,136.0,10.0,69,110,000100,31362.0 +1,"Census Tract 2, Saipan Municipality, Commonwealth of the Northern Mariana Islands",1399.0,193.0,148.0,1340.0,328.0,39.0,229.0,39.0,91.0,86.0,31250.0,1394.0,280.0,162.0,189.0,362.0,1399.0,817.0,454.0,36.0,0.0,1.0,3.0,88.0,6.0,69,110,000200,31362.0 +2,"Census Tract 3, Saipan Municipality, Commonwealth of the Northern Mariana Islands",1616.0,189.0,158.0,1551.0,478.0,53.0,365.0,62.0,93.0,117.0,48281.0,1602.0,147.0,115.0,114.0,754.0,1616.0,497.0,856.0,103.0,3.0,1.0,8.0,148.0,33.0,69,110,000300,31362.0 +3,"Census Tract 4, Saipan Municipality, Commonwealth of the Northern Mariana Islands",3096.0,477.0,364.0,2993.0,980.0,123.0,651.0,92.0,140.0,122.0,24056.0,3087.0,423.0,325.0,508.0,622.0,3096.0,457.0,2462.0,47.0,5.0,2.0,8.0,114.0,15.0,69,110,000400,31362.0 +4,"Census Tract 5, Saipan Municipality, Commonwealth of the Northern Mariana Islands",2448.0,311.0,269.0,2367.0,645.0,79.0,442.0,61.0,180.0,154.0,30431.0,2440.0,345.0,243.0,307.0,749.0,2448.0,799.0,1393.0,67.0,3.0,0.0,7.0,177.0,41.0,69,110,000500,31362.0 +5,"Census Tract 6, Saipan Municipality, Commonwealth of the Northern Mariana Islands",2739.0,337.0,240.0,2633.0,830.0,77.0,562.0,67.0,209.0,170.0,31250.0,2728.0,448.0,181.0,381.0,826.0,2739.0,801.0,1736.0,44.0,2.0,2.0,1.0,153.0,28.0,69,110,000600,31362.0 +6,"Census Tract 7, Saipan Municipality, Commonwealth of the Northern Mariana Islands",3071.0,508.0,308.0,2937.0,949.0,67.0,599.0,61.0,171.0,176.0,26417.0,3066.0,504.0,377.0,406.0,701.0,3071.0,973.0,1919.0,33.0,2.0,0.0,3.0,140.0,17.0,69,110,000700,31362.0 +7,"Census Tract 8, Saipan Municipality, Commonwealth of the Northern Mariana Islands",1840.0,224.0,183.0,1771.0,494.0,77.0,315.0,53.0,133.0,104.0,23693.0,1657.0,308.0,200.0,224.0,289.0,1840.0,576.0,1131.0,39.0,1.0,0.0,4.0,87.0,5.0,69,110,000800,31362.0 +8,"Census Tract 9, Saipan Municipality, Commonwealth of the Northern Mariana Islands",2967.0,438.0,295.0,2870.0,838.0,138.0,520.0,108.0,183.0,172.0,23365.0,2957.0,651.0,395.0,384.0,506.0,2967.0,947.0,1871.0,15.0,2.0,0.0,0.0,132.0,18.0,69,110,000900,31362.0 +9,"Census Tract 10, Saipan Municipality, Commonwealth of the Northern Mariana Islands",2566.0,335.0,232.0,2446.0,733.0,89.0,487.0,73.0,153.0,156.0,32500.0,2564.0,372.0,255.0,344.0,797.0,2566.0,906.0,1365.0,32.0,6.0,2.0,4.0,249.0,26.0,69,110,001000,31362.0 +10,"Census Tract 11, Saipan Municipality, Commonwealth of the Northern Mariana Islands",1597.0,248.0,178.0,1536.0,471.0,48.0,294.0,50.0,111.0,84.0,23015.0,1594.0,375.0,169.0,187.0,341.0,1597.0,392.0,1099.0,19.0,3.0,0.0,1.0,83.0,14.0,69,110,001100,31362.0 +11,"Census Tract 12, Saipan Municipality, Commonwealth of the Northern Mariana Islands",1900.0,321.0,214.0,1835.0,535.0,100.0,327.0,76.0,130.0,101.0,21250.0,1895.0,455.0,207.0,268.0,373.0,1900.0,645.0,1158.0,15.0,0.0,0.0,0.0,82.0,2.0,69,110,001200,31362.0 +12,"Census Tract 13, Saipan Municipality, Commonwealth of the Northern Mariana Islands",3236.0,434.0,331.0,3119.0,738.0,117.0,521.0,105.0,234.0,229.0,27850.0,3225.0,777.0,351.0,379.0,746.0,3236.0,1702.0,1264.0,23.0,0.0,0.0,5.0,242.0,44.0,69,110,001300,31362.0 +13,"Census Tract 14, Saipan Municipality, Commonwealth of the Northern Mariana Islands",4266.0,565.0,478.0,4081.0,1007.0,132.0,753.0,129.0,311.0,261.0,36086.0,4263.0,674.0,426.0,487.0,1265.0,4266.0,2577.0,1282.0,46.0,8.0,0.0,2.0,350.0,86.0,69,110,001400,31362.0 +14,"Census Tract 15, Saipan Municipality, Commonwealth of the Northern Mariana Islands",3306.0,458.0,356.0,3172.0,909.0,88.0,689.0,89.0,194.0,196.0,37550.0,3296.0,456.0,280.0,289.0,1196.0,3306.0,1506.0,1358.0,136.0,12.0,1.0,4.0,282.0,82.0,69,110,001500,31362.0 +15,"Census Tract 16, Saipan Municipality, Commonwealth of the Northern Mariana Islands",4027.0,569.0,496.0,3816.0,952.0,139.0,748.0,141.0,326.0,315.0,43073.0,4022.0,653.0,309.0,310.0,1425.0,4027.0,3049.0,469.0,36.0,3.0,0.0,3.0,460.0,30.0,69,110,001600,31362.0 +16,"Census Tract 17, Saipan Municipality, Commonwealth of the Northern Mariana Islands",2105.0,264.0,232.0,2020.0,516.0,55.0,417.0,53.0,126.0,107.0,54554.0,2102.0,226.0,103.0,154.0,1115.0,2105.0,1156.0,497.0,204.0,11.0,2.0,5.0,225.0,55.0,69,110,001700,31362.0 +17,"Census Tract 9900, Saipan Municipality, Commonwealth of the Northern Mariana Islands",0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,-666666666.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,69,110,990000, +0,"Census Tract 9501.01, Tinian Municipality, Commonwealth of the Northern Mariana Islands",1250.0,230.0,149.0,1207.0,402.0,52.0,262.0,33.0,62.0,54.0,31771.0,1249.0,245.0,124.0,114.0,346.0,1250.0,535.0,589.0,9.0,0.0,0.0,3.0,113.0,21.0,69,120,950101,31362.0 +1,"Census Tract 9501.02, Tinian Municipality, Commonwealth of the Northern Mariana Islands",0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,-666666666.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,69,120,950102, +2,"Census Tract 9502.01, Tinian Municipality, Commonwealth of the Northern Mariana Islands",794.0,119.0,95.0,763.0,220.0,28.0,171.0,27.0,38.0,51.0,41875.0,793.0,98.0,73.0,70.0,329.0,794.0,498.0,181.0,13.0,2.0,0.0,0.0,100.0,4.0,69,120,950201,31362.0 +3,"Census Tract 9502.02, Tinian Municipality, Commonwealth of the Northern Mariana Islands",0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,-666666666.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,69,120,950202, +4,"Census Tract 9900, Tinian Municipality, Commonwealth of the Northern Mariana Islands",0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,-666666666.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,69,120,990000, +0,"Census Tract 9704, St. Croix Island, United States Virgin Islands",3983.0,666.0,538.0,3362.0,923.0,47.0,881.0,33.0,33.0,82.0,56402.0,3971.0,318.0,71.0,145.0,2742.0,3983.0,0.0,39.0,595.0,2676.0,28.0,430.0,183.0,940.0,78,010,970400,40408.0 +1,"Census Tract 9701, St. Croix Island, United States Virgin Islands",1832.0,197.0,170.0,1583.0,475.0,23.0,410.0,23.0,1.0,78.0,75625.0,1767.0,69.0,31.0,48.0,1411.0,1832.0,0.0,28.0,986.0,534.0,20.0,149.0,94.0,267.0,78,010,970100,40408.0 +2,"Census Tract 9702, St. Croix Island, United States Virgin Islands",2446.0,369.0,361.0,1947.0,536.0,41.0,501.0,27.0,32.0,39.0,33400.0,2366.0,438.0,90.0,147.0,1154.0,2446.0,0.0,18.0,299.0,1816.0,14.0,181.0,102.0,695.0,78,010,970200,40408.0 +3,"Census Tract 9703, St. Croix Island, United States Virgin Islands",3025.0,410.0,459.0,2384.0,638.0,45.0,623.0,50.0,3.0,44.0,34444.0,3002.0,603.0,174.0,219.0,1440.0,3025.0,0.0,17.0,368.0,2081.0,8.0,318.0,215.0,907.0,78,010,970300,40408.0 +4,"Census Tract 9705, St. Croix Island, United States Virgin Islands",4814.0,567.0,541.0,3195.0,903.0,84.0,815.0,73.0,20.0,51.0,44196.0,3779.0,371.0,145.0,219.0,2251.0,4814.0,44.0,63.0,530.0,2999.0,31.0,581.0,448.0,1405.0,78,010,970500,40408.0 +5,"Census Tract 9706, St. Croix Island, United States Virgin Islands",2074.0,236.0,195.0,1773.0,570.0,31.0,500.0,27.0,5.0,70.0,62750.0,2074.0,111.0,45.0,86.0,1516.0,2074.0,0.0,59.0,531.0,1089.0,13.0,193.0,170.0,344.0,78,010,970600,40408.0 +6,"Census Tract 9707, St. Croix Island, United States Virgin Islands",1909.0,280.0,269.0,1651.0,435.0,31.0,400.0,27.0,8.0,34.0,40809.0,1896.0,147.0,80.0,109.0,1179.0,1909.0,0.0,5.0,208.0,1393.0,13.0,184.0,105.0,410.0,78,010,970700,40408.0 +7,"Census Tract 9708, St. Croix Island, United States Virgin Islands",3157.0,383.0,397.0,2405.0,585.0,62.0,595.0,91.0,19.0,15.0,29375.0,3086.0,648.0,214.0,244.0,1270.0,3157.0,0.0,5.0,59.0,2181.0,19.0,630.0,230.0,1331.0,78,010,970800,40408.0 +8,"Census Tract 9709, St. Croix Island, United States Virgin Islands",914.0,77.0,151.0,665.0,169.0,18.0,231.0,36.0,6.0,9.0,30385.0,911.0,227.0,48.0,50.0,363.0,914.0,0.0,1.0,40.0,779.0,1.0,67.0,22.0,135.0,78,010,970900,40408.0 +9,"Census Tract 9710, St. Croix Island, United States Virgin Islands",2041.0,270.0,259.0,1690.0,430.0,44.0,404.0,42.0,25.0,33.0,34550.0,2032.0,340.0,128.0,121.0,1018.0,2041.0,0.0,6.0,278.0,1499.0,11.0,118.0,115.0,270.0,78,010,971000,40408.0 +10,"Census Tract 9711, St. Croix Island, United States Virgin Islands",3079.0,399.0,433.0,2317.0,527.0,99.0,627.0,103.0,26.0,35.0,24036.0,3011.0,743.0,265.0,223.0,1087.0,3079.0,0.0,6.0,171.0,2569.0,5.0,203.0,117.0,683.0,78,010,971100,40408.0 +11,"Census Tract 9712, St. Croix Island, United States Virgin Islands",3372.0,546.0,506.0,2835.0,712.0,67.0,622.0,44.0,11.0,31.0,34250.0,3366.0,445.0,176.0,200.0,1800.0,3372.0,0.0,21.0,123.0,2708.0,6.0,266.0,215.0,644.0,78,010,971200,40408.0 +12,"Census Tract 9713, St. Croix Island, United States Virgin Islands",2678.0,361.0,344.0,2191.0,582.0,63.0,536.0,53.0,25.0,33.0,37132.0,2674.0,306.0,145.0,186.0,1397.0,2678.0,0.0,21.0,161.0,2113.0,15.0,231.0,134.0,585.0,78,010,971300,40408.0 +13,"Census Tract 9714, St. Croix Island, United States Virgin Islands",2550.0,297.0,359.0,1759.0,402.0,20.0,467.0,33.0,9.0,14.0,28641.0,2337.0,570.0,110.0,190.0,996.0,2550.0,0.0,6.0,80.0,2067.0,7.0,261.0,98.0,563.0,78,010,971400,40408.0 +14,"Census Tract 9715, St. Croix Island, United States Virgin Islands",3130.0,450.0,436.0,2573.0,707.0,26.0,754.0,36.0,14.0,35.0,45292.0,3124.0,255.0,106.0,205.0,1887.0,3130.0,0.0,20.0,129.0,2603.0,11.0,190.0,171.0,557.0,78,010,971500,40408.0 +15,"Census Tract 9900, St. Croix Island, United States Virgin Islands",0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,-666666666.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,78,010,990000, +0,"Census Tract 9501, St. John Island, United States Virgin Islands",1233.0,164.0,130.0,1058.0,322.0,34.0,329.0,32.0,8.0,35.0,52143.0,1228.0,97.0,36.0,63.0,799.0,1233.0,0.0,7.0,570.0,532.0,6.0,37.0,75.0,88.0,78,020,950100,40408.0 +1,"Census Tract 9502, St. John Island, United States Virgin Islands",2648.0,496.0,401.0,2305.0,853.0,72.0,700.0,99.0,25.0,47.0,49100.0,2623.0,301.0,98.0,134.0,1617.0,2648.0,1.0,13.0,777.0,1420.0,19.0,158.0,248.0,444.0,78,020,950200,40408.0 +2,"Census Tract 9900, St. John Island, United States Virgin Islands",0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,-666666666.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,78,020,990000, +0,"Census Tract 9601, St. Thomas Island, United States Virgin Islands",3329.0,485.0,449.0,2761.0,856.0,108.0,861.0,118.0,37.0,39.0,40234.0,3313.0,339.0,160.0,200.0,1840.0,3329.0,0.0,11.0,409.0,2403.0,7.0,90.0,402.0,542.0,78,030,960100,40408.0 +1,"Census Tract 9602, St. Thomas Island, United States Virgin Islands",3762.0,556.0,652.0,3101.0,894.0,116.0,871.0,90.0,84.0,22.0,37628.0,3722.0,401.0,175.0,209.0,1959.0,3762.0,0.0,13.0,41.0,3427.0,1.0,62.0,213.0,287.0,78,030,960200,40408.0 +2,"Census Tract 9603, St. Thomas Island, United States Virgin Islands",3117.0,516.0,560.0,2708.0,692.0,61.0,766.0,65.0,56.0,45.0,41746.0,3099.0,253.0,160.0,159.0,1926.0,3117.0,0.0,1.0,19.0,2838.0,1.0,59.0,199.0,248.0,78,030,960300,40408.0 +3,"Census Tract 9604, St. Thomas Island, United States Virgin Islands",3872.0,494.0,547.0,3303.0,992.0,52.0,1068.0,65.0,14.0,99.0,50602.0,3868.0,284.0,100.0,176.0,2548.0,3872.0,3.0,98.0,1000.0,2369.0,24.0,106.0,239.0,333.0,78,030,960400,40408.0 +4,"Census Tract 9605, St. Thomas Island, United States Virgin Islands",5001.0,676.0,580.0,4375.0,1340.0,90.0,1435.0,102.0,14.0,138.0,61962.0,4990.0,253.0,103.0,180.0,3793.0,5001.0,2.0,51.0,1715.0,2608.0,27.0,135.0,430.0,414.0,78,030,960500,40408.0 +5,"Census Tract 9606, St. Thomas Island, United States Virgin Islands",3114.0,402.0,423.0,2538.0,743.0,59.0,741.0,79.0,30.0,53.0,39393.0,3081.0,286.0,145.0,212.0,1658.0,3114.0,0.0,71.0,332.0,2220.0,13.0,158.0,302.0,561.0,78,030,960600,40408.0 +6,"Census Tract 9607, St. Thomas Island, United States Virgin Islands",2900.0,486.0,379.0,2489.0,783.0,77.0,688.0,64.0,23.0,51.0,44375.0,2897.0,273.0,135.0,147.0,1797.0,2900.0,0.0,15.0,488.0,2088.0,10.0,63.0,224.0,263.0,78,030,960700,40408.0 +7,"Census Tract 9608, St. Thomas Island, United States Virgin Islands",3161.0,430.0,428.0,2399.0,687.0,66.0,702.0,88.0,41.0,37.0,41375.0,2837.0,306.0,101.0,207.0,1578.0,3161.0,1.0,33.0,302.0,2351.0,11.0,173.0,278.0,602.0,78,030,960800,40408.0 +8,"Census Tract 9609, St. Thomas Island, United States Virgin Islands",3721.0,449.0,501.0,3049.0,930.0,107.0,826.0,119.0,57.0,66.0,38444.0,3700.0,478.0,239.0,241.0,1959.0,3721.0,0.0,82.0,437.0,2662.0,24.0,90.0,419.0,490.0,78,030,960900,40408.0 +9,"Census Tract 9610, St. Thomas Island, United States Virgin Islands",4386.0,681.0,620.0,3582.0,1095.0,135.0,976.0,156.0,60.0,29.0,31429.0,4276.0,559.0,301.0,317.0,1861.0,4386.0,0.0,85.0,217.0,3437.0,11.0,123.0,491.0,1050.0,78,030,961000,40408.0 +10,"Census Tract 9611, St. Thomas Island, United States Virgin Islands",3780.0,521.0,647.0,3144.0,833.0,114.0,866.0,117.0,24.0,24.0,29135.0,3754.0,511.0,272.0,278.0,1592.0,3780.0,0.0,92.0,84.0,3033.0,5.0,137.0,420.0,623.0,78,030,961100,40408.0 +11,"Census Tract 9612, St. Thomas Island, United States Virgin Islands",2118.0,261.0,280.0,1608.0,436.0,53.0,432.0,69.0,59.0,13.0,28523.0,1982.0,354.0,153.0,131.0,751.0,2118.0,0.0,23.0,87.0,1688.0,10.0,85.0,210.0,394.0,78,030,961200,40408.0 +12,"Census Tract 9900, St. Thomas Island, United States Virgin Islands",0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,-666666666.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,78,030,990000, diff --git a/data/data-pipeline/data_pipeline/tests/sources/census_decennial/test_etl.py b/data/data-pipeline/data_pipeline/tests/sources/census_decennial/test_etl.py new file mode 100644 index 00000000..4f489674 --- /dev/null +++ b/data/data-pipeline/data_pipeline/tests/sources/census_decennial/test_etl.py @@ -0,0 +1,152 @@ +import pytest +import pandas as pd +from pathlib import Path +from data_pipeline.etl.sources.census_decennial.constants import ( + DEC_TERRITORY_PARAMS, + DEC_FIELD_NAMES, + OUTPUT_RACE_FIELDS, +) +from data_pipeline.etl.sources.census_decennial.etl import CensusDecennialETL +from data_pipeline.score import field_names + + +@pytest.fixture +def territory_params_fixture(): + return [ + { + "state_abbreviation": "as", + "fips": "60", + "county_fips": ["010"], + "xwalk": DEC_TERRITORY_PARAMS[0]["xwalk"], + "median_income": 26352, + }, + { + "state_abbreviation": "gu", + "fips": "66", + "county_fips": ["010"], + "xwalk": DEC_TERRITORY_PARAMS[1]["xwalk"], + "median_income": 58289, + }, + ] + + +@pytest.fixture +def extract_path_fixture(): + return Path(__file__).parents[0] / "data/extract" + + +@pytest.fixture +def transform_path_fixture(): + return Path(__file__).parents[0] / "data/transform" + + +@pytest.fixture +def transformed_data_fixture(transform_path_fixture): + """Load the test data and call the ETL transform""" + dec = CensusDecennialETL() + dec.df_all = pd.read_csv( + transform_path_fixture / "usa.csv", + # Make sure these columns are string as expected of the original + dtype={"state": "object", "county": "object", "tract": "object"}, + ) + dec.transform() + return dec.df_all + + +############### +# Extract tests +############### +def test_no_files_found(territory_params_fixture): + """Test the ETL raises an exception if the file is not found""" + dec = CensusDecennialETL() + with pytest.raises(FileNotFoundError): + dec.extract( + use_cached_data_sources=True, + test_territory_params=territory_params_fixture, + test_path=Path("/path_does_not_exist"), + ) + + +def test_load_data(extract_path_fixture, territory_params_fixture): + """Test the ETL loads and translates the data""" + dec = CensusDecennialETL() + dec.extract( + use_cached_data_sources=True, + test_territory_params=territory_params_fixture, + test_path=extract_path_fixture, + ) + df = dec.df_all + assert len(df) == 64 + assert len(df.columns) == 30 + + # Columns should not have any census variable names + census_vars = list(DEC_TERRITORY_PARAMS[0]["xwalk"].keys()) + list( + DEC_TERRITORY_PARAMS[1]["xwalk"].keys() + ) + for var in census_vars: + assert var not in df.columns + + # Median income is added for entries with population + assert ( + df.loc[ + df[field_names.CENSUS_DECENNIAL_TOTAL_POPULATION_FIELD_2019] > 0, + DEC_FIELD_NAMES.TERRITORY_MEDIAN_INCOME, + ] + > 0 + ).all() + assert not ( + df.loc[ + df[field_names.CENSUS_DECENNIAL_TOTAL_POPULATION_FIELD_2019] == 0, + DEC_FIELD_NAMES.TERRITORY_MEDIAN_INCOME, + ] + > 0 + ).any() + + +############### +# Transform tests +############### +def test_geo_tract_generation(transformed_data_fixture): + result = transformed_data_fixture + assert field_names.GEOID_TRACT_FIELD in result.columns + assert result[field_names.GEOID_TRACT_FIELD].notnull().all() + + # Grab one GEO ID and test it + assert ( + result[field_names.GEOID_TRACT_FIELD][0] + == result["state"][0] + result["county"][0] + result["tract"][0] + ) + + +def test_merge_tracts(transformed_data_fixture): + result = transformed_data_fixture + # 69120950200 exists, but the tract split does now + assert ( + result.loc[result[field_names.GEOID_TRACT_FIELD] == "69120950200"] + .any() + .any() + ) + assert ( + not result.loc[result[field_names.GEOID_TRACT_FIELD] == "69120950201"] + .any() + .any() + ) + assert ( + not result.loc[result[field_names.GEOID_TRACT_FIELD] == "69120950202"] + .any() + .any() + ) + + +def test_remove_invalid_values(transformed_data_fixture): + numeric_df = transformed_data_fixture.select_dtypes(include="number") + assert not (numeric_df < -999).any().any() + + +def test_race_fields(transformed_data_fixture): + for race_field_name in OUTPUT_RACE_FIELDS: + assert race_field_name in transformed_data_fixture.columns + assert any( + col.startswith(field_names.PERCENT_PREFIX + race_field_name) + for col in transformed_data_fixture.columns + ) From cce91fb47bdf5f4fc807c2a4147bba93aabc2a25 Mon Sep 17 00:00:00 2001 From: Carlos Felix <63804190+carlosfelix2@users.noreply.github.com> Date: Thu, 21 Nov 2024 15:08:15 -0500 Subject: [PATCH 10/28] Add decennial 2020 territory imputations --- .github/workflows/data-checks.yml | 3 - .../data_pipeline/etl/score/etl_score.py | 1 + .../etl/sources/census_acs/etl.py | 27 ++-- .../etl/sources/census_decennial/constants.py | 103 +++++++------ .../etl/sources/census_decennial/etl.py | 81 +++++++++- .../data_pipeline/score/field_names.py | 3 + .../data_pipeline/score/score_narwhal.py | 2 +- .../data/test_mark_poverty_flag.csv | 2 +- .../census-us-territory-geojson.json | 138 ++++++++++++++++++ .../sources/census_decennial/test_etl.py | 135 +++++++++++++++-- 10 files changed, 420 insertions(+), 75 deletions(-) create mode 100644 data/data-pipeline/data_pipeline/tests/sources/census_decennial/data/imputation/census-us-territory-geojson.json diff --git a/.github/workflows/data-checks.yml b/.github/workflows/data-checks.yml index ccfd4bd9..be9cbeea 100644 --- a/.github/workflows/data-checks.yml +++ b/.github/workflows/data-checks.yml @@ -2,9 +2,6 @@ name: Data Checks on: pull_request: - branches: - - main - - "**/release/**" paths: - "data/**" jobs: diff --git a/data/data-pipeline/data_pipeline/etl/score/etl_score.py b/data/data-pipeline/data_pipeline/etl/score/etl_score.py index afd8ad9a..78e10a53 100644 --- a/data/data-pipeline/data_pipeline/etl/score/etl_score.py +++ b/data/data-pipeline/data_pipeline/etl/score/etl_score.py @@ -473,6 +473,7 @@ class ScoreETL(ExtractTransformLoad): field_names.CENSUS_DECENNIAL_HIGH_SCHOOL_ED_FIELD_2019, field_names.CENSUS_DECENNIAL_POVERTY_LESS_THAN_100_FPL_FIELD_2019, field_names.CENSUS_DECENNIAL_POVERTY_LESS_THAN_200_FPL_FIELD_2019, + field_names.CENSUS_DECENNIAL_ADJUSTED_POVERTY_LESS_THAN_200_FPL_FIELD_2019, field_names.CENSUS_DECENNIAL_UNEMPLOYMENT_FIELD_2019, field_names.CENSUS_UNEMPLOYMENT_FIELD_2010, field_names.CENSUS_POVERTY_LESS_THAN_100_FPL_FIELD_2010, diff --git a/data/data-pipeline/data_pipeline/etl/sources/census_acs/etl.py b/data/data-pipeline/data_pipeline/etl/sources/census_acs/etl.py index d67a2bc3..5a894a3c 100644 --- a/data/data-pipeline/data_pipeline/etl/sources/census_acs/etl.py +++ b/data/data-pipeline/data_pipeline/etl/sources/census_acs/etl.py @@ -25,6 +25,9 @@ class CensusACSETL(ExtractTransformLoad): NAME = "census_acs" ACS_YEAR = 2019 MINIMUM_POPULATION_REQUIRED_FOR_IMPUTATION = 1 + ImputeVariables = namedtuple( + "ImputeVariables", ["raw_field_name", "imputed_field_name"] + ) def __init__(self): @@ -284,7 +287,7 @@ class CensusACSETL(ExtractTransformLoad): self.COLUMNS_TO_KEEP = ( [ - self.GEOID_TRACT_FIELD_NAME, + field_names.GEOID_TRACT_FIELD, field_names.TOTAL_POP_FIELD, self.UNEMPLOYED_FIELD_NAME, self.LINGUISTIC_ISOLATION_FIELD_NAME, @@ -335,15 +338,15 @@ class CensusACSETL(ExtractTransformLoad): destination=self.census_acs_source, acs_year=self.ACS_YEAR, variables=variables, - tract_output_field_name=self.GEOID_TRACT_FIELD_NAME, + tract_output_field_name=field_names.GEOID_TRACT_FIELD, data_path_for_fips_codes=self.DATA_PATH, acs_type="acs5", ) ] # pylint: disable=too-many-arguments - def _merge_geojson( - self, + @staticmethod + def merge_geojson( df: pd.DataFrame, usa_geo_df: gpd.GeoDataFrame, geoid_field: str = "GEOID10", @@ -364,7 +367,7 @@ class CensusACSETL(ExtractTransformLoad): county_code_field, ] ], - left_on=[self.GEOID_TRACT_FIELD_NAME], + left_on=[field_names.GEOID_TRACT_FIELD], right_on=[geoid_field], ) ) @@ -377,7 +380,7 @@ class CensusACSETL(ExtractTransformLoad): self.df = pd.read_csv( self.census_acs_source, - dtype={self.GEOID_TRACT_FIELD_NAME: "string"}, + dtype={field_names.GEOID_TRACT_FIELD: "string"}, ) def transform(self) -> None: @@ -401,7 +404,7 @@ class CensusACSETL(ExtractTransformLoad): self.DATA_PATH / "census" / "geojson" / "us.json", ) - df = self._merge_geojson( + df = CensusACSETL.merge_geojson( df=df, usa_geo_df=geo_df, ) @@ -608,23 +611,19 @@ class CensusACSETL(ExtractTransformLoad): # we impute income for both income measures ## TODO: Convert to pydantic for clarity logger.debug("Imputing income information") - ImputeVariables = namedtuple( - "ImputeVariables", ["raw_field_name", "imputed_field_name"] - ) - df = calculate_income_measures( impute_var_named_tup_list=[ - ImputeVariables( + CensusACSETL.ImputeVariables( raw_field_name=self.POVERTY_LESS_THAN_200_PERCENT_FPL_FIELD_NAME, imputed_field_name=self.IMPUTED_POVERTY_LESS_THAN_200_PERCENT_FPL_FIELD_NAME, ), - ImputeVariables( + CensusACSETL.ImputeVariables( raw_field_name=self.COLLEGE_ATTENDANCE_FIELD, imputed_field_name=self.IMPUTED_COLLEGE_ATTENDANCE_FIELD, ), ], geo_df=df, - geoid_field=self.GEOID_TRACT_FIELD_NAME, + geoid_field=field_names.GEOID_TRACT_FIELD, minimum_population_required_for_imputation=self.MINIMUM_POPULATION_REQUIRED_FOR_IMPUTATION, ) diff --git a/data/data-pipeline/data_pipeline/etl/sources/census_decennial/constants.py b/data/data-pipeline/data_pipeline/etl/sources/census_decennial/constants.py index 891279c3..9cf52bc4 100644 --- a/data/data-pipeline/data_pipeline/etl/sources/census_decennial/constants.py +++ b/data/data-pipeline/data_pipeline/etl/sources/census_decennial/constants.py @@ -1,4 +1,5 @@ from enum import Enum +from types import MappingProxyType from data_pipeline.score import field_names @@ -29,6 +30,7 @@ class DEC_FIELD_NAMES(str, Enum): HOUSEHOLD_POVERTY_LEVEL_OVER_2_0 = ( "Household poverty level Over 2.0 IN 2019" ) + IMPUTED_PERCENTAGE_HOUSEHOLDS_BELOW_200_PERC_POVERTY_LEVEL = f"{field_names.CENSUS_DECENNIAL_POVERTY_LESS_THAN_200_FPL_FIELD_2019}, imputed" TOTAL_HOUSEHOLD_POVERTY_LEVEL = "Total Household poverty level IN 2019" TERRITORY_MEDIAN_INCOME = "Territory Median Income" EMPLOYMENT_MALE_UNEMPLOYED = "Total males not in labor force" @@ -45,6 +47,9 @@ class DEC_FIELD_NAMES(str, Enum): COLLEGE_ATTENDANCE_PERCENT = ( "Percent enrollment in college, graduate or professional school" ) + IMPUTED_COLLEGE_ATTENDANCE_PERCENT = ( + f"{COLLEGE_ATTENDANCE_PERCENT}, imputed" + ) COLLEGE_NON_ATTENDANCE_PERCENT = "Percent of population not currently enrolled in college, graduate or professional school" def __str__(self) -> str: @@ -146,45 +151,61 @@ OUTPUT_RACE_FIELDS = [ """Race fields to output in the results.""" DEC_TERRITORY_PARAMS = [ - { - "state_abbreviation": "as", - "fips": "60", - # https://www2.census.gov/geo/docs/reference/codes2020/cou/st60_as_cou2020.txt - "county_fips": ["010", "020", "030", "040", "050"], - "xwalk": __FIELD_NAME_COMMON_XWALK | __FIELD_NAME_AS_XWALK, - # Note: we hardcode the median income for each territory in this dict, - # because that data is hard to programmatically access. - # https://www.ruralhealthinfo.org/states/american-samoa - "median_income": 26352, - }, - { - "state_abbreviation": "gu", - "fips": "66", - # https://www2.census.gov/geo/docs/reference/codes2020/cou/st66_gu_cou2020.txt - "county_fips": ["010"], - "xwalk": __FIELD_NAME_COMMON_XWALK | __FIELD_NAME_GU_XWALK, - # https://www.ruralhealthinfo.org/states/guam - # https://data.census.gov/table/DECENNIALDPGU2020.DP3?g=040XX00US66&d=DECIA%20Guam%20Demographic%20Profile - "median_income": 58289, - }, - { - "state_abbreviation": "mp", - "fips": "69", - # https://www2.census.gov/geo/docs/reference/codes2020/cou/st69_mp_cou2020.txt - "county_fips": ["085", "100", "110", "120"], - "xwalk": __FIELD_NAME_COMMON_XWALK | __FIELD_NAME_MP_XWALK, - # https://www.ruralhealthinfo.org/states/northern-mariana - # https://data.census.gov/table/DECENNIALDPMP2020.DP3?d=DECIA%20Commonwealth%20of%20the%20Northern%20Mariana%20Islands%20Demographic%20Profile - "median_income": 31362, - }, - { - "state_abbreviation": "vi", - "fips": "78", - # https://www2.census.gov/geo/docs/reference/codes2020/cou/st78_vi_cou2020.txt - "county_fips": ["010", "020", "030"], - "xwalk": __FIELD_NAME_COMMON_XWALK | __FIELD_NAME_VI_XWALK, - # https://www.ruralhealthinfo.org/states/us-virgin-islands - "median_income": 40408, - }, + MappingProxyType( + { + "state_abbreviation": "as", + "fips": "60", + # https://www2.census.gov/geo/docs/reference/codes2020/cou/st60_as_cou2020.txt + "county_fips": ("010", "020", "030", "040", "050"), + "xwalk": MappingProxyType( + __FIELD_NAME_COMMON_XWALK | __FIELD_NAME_AS_XWALK + ), + # Note: we hardcode the median income for each territory in this dict, + # because that data is hard to programmatically access. + # https://www.ruralhealthinfo.org/states/american-samoa + "median_income": 26352, + } + ), + MappingProxyType( + { + "state_abbreviation": "gu", + "fips": "66", + # https://www2.census.gov/geo/docs/reference/codes2020/cou/st66_gu_cou2020.txt + "county_fips": ("010",), + "xwalk": MappingProxyType( + __FIELD_NAME_COMMON_XWALK | __FIELD_NAME_GU_XWALK + ), + # https://www.ruralhealthinfo.org/states/guam + # https://data.census.gov/table/DECENNIALDPGU2020.DP3?g=040XX00US66&d=DECIA%20Guam%20Demographic%20Profile + "median_income": 58289, + } + ), + MappingProxyType( + { + "state_abbreviation": "mp", + "fips": "69", + # https://www2.census.gov/geo/docs/reference/codes2020/cou/st69_mp_cou2020.txt + "county_fips": ("085", "100", "110", "120"), + "xwalk": MappingProxyType( + __FIELD_NAME_COMMON_XWALK | __FIELD_NAME_MP_XWALK + ), + # https://www.ruralhealthinfo.org/states/northern-mariana + # https://data.census.gov/table/DECENNIALDPMP2020.DP3?d=DECIA%20Commonwealth%20of%20the%20Northern%20Mariana%20Islands%20Demographic%20Profile + "median_income": 31362, + } + ), + MappingProxyType( + { + "state_abbreviation": "vi", + "fips": "78", + # https://www2.census.gov/geo/docs/reference/codes2020/cou/st78_vi_cou2020.txt + "county_fips": ("010", "020", "030"), + "xwalk": MappingProxyType( + __FIELD_NAME_COMMON_XWALK | __FIELD_NAME_VI_XWALK + ), + # https://www.ruralhealthinfo.org/states/us-virgin-islands + "median_income": 40408, + } + ), ] -"""List of territories to process.""" +"""Read-only list of territories to process.""" diff --git a/data/data-pipeline/data_pipeline/etl/sources/census_decennial/etl.py b/data/data-pipeline/data_pipeline/etl/sources/census_decennial/etl.py index 32a58ffd..6794b45d 100644 --- a/data/data-pipeline/data_pipeline/etl/sources/census_decennial/etl.py +++ b/data/data-pipeline/data_pipeline/etl/sources/census_decennial/etl.py @@ -1,6 +1,7 @@ import os import numpy as np import pandas as pd +import geopandas as gpd import json from typing import List from pathlib import Path @@ -14,6 +15,10 @@ from data_pipeline.etl.datasource import DataSource from data_pipeline.etl.datasource import FileDataSource from data_pipeline.score import field_names from data_pipeline.utils import get_module_logger +from data_pipeline.etl.sources.census_acs.etl import CensusACSETL +from data_pipeline.etl.sources.census_acs.etl_imputations import ( + calculate_income_measures, +) pd.options.mode.chained_assignment = "raise" @@ -27,6 +32,9 @@ class CensusDecennialETL(ExtractTransformLoad): / "dataset" / f"census_decennial_{DECENNIAL_YEAR}" ) + CENSUS_GEOJSON_PATH = ( + ExtractTransformLoad.DATA_PATH / "census" / "geojson" / "us.json" + ) def __get_api_url( self, @@ -136,7 +144,73 @@ class CensusDecennialETL(ExtractTransformLoad): field_names.GEOID_TRACT_FIELD, ] = "69120950200" - def transform(self) -> None: + def _impute_income(self, geojson_path: Path): + """Impute income for both income measures.""" + # Merges Census geojson to imput values from. + logger.debug(f"Reading GeoJSON from {geojson_path}") + geo_df = gpd.read_file(geojson_path) + self.df_all = CensusACSETL.merge_geojson( + df=self.df_all, + usa_geo_df=geo_df, + ) + + logger.debug("Imputing income information") + impute_var_named_tup_list = [ + CensusACSETL.ImputeVariables( + raw_field_name=field_names.CENSUS_DECENNIAL_POVERTY_LESS_THAN_200_FPL_FIELD_2019, + imputed_field_name=DEC_FIELD_NAMES.IMPUTED_PERCENTAGE_HOUSEHOLDS_BELOW_200_PERC_POVERTY_LEVEL, + ), + ] + self.df_all = calculate_income_measures( + impute_var_named_tup_list=impute_var_named_tup_list, + geo_df=self.df_all, + geoid_field=self.GEOID_TRACT_FIELD_NAME, + population_field=field_names.CENSUS_DECENNIAL_TOTAL_POPULATION_FIELD_2019, + ) + + logger.debug("Calculating with imputed values") + self.df_all[ + field_names.CENSUS_DECENNIAL_ADJUSTED_POVERTY_LESS_THAN_200_FPL_FIELD_2019 + ] = ( + self.df_all[ + field_names.CENSUS_DECENNIAL_POVERTY_LESS_THAN_200_FPL_FIELD_2019 + ].fillna( + self.df_all[ + DEC_FIELD_NAMES.IMPUTED_PERCENTAGE_HOUSEHOLDS_BELOW_200_PERC_POVERTY_LEVEL + ] + ) + # Use clip to ensure that the values are not negative + ).clip( + lower=0 + ) + + # All values should have a value at this point for tracts with >0 population + assert ( + self.df_all[ + self.df_all[ + field_names.CENSUS_DECENNIAL_TOTAL_POPULATION_FIELD_2019 + ] + >= 1 + ][ + field_names.CENSUS_DECENNIAL_ADJUSTED_POVERTY_LESS_THAN_200_FPL_FIELD_2019 + ] + .isna() + .sum() + == 0 + ), "Error: not all values were filled with imputations..." + + # We generate a boolean that is TRUE when there is an imputed income but not a baseline income, and FALSE otherwise. + # This allows us to see which tracts have an imputed income. + self.df_all[field_names.ISLAND_AREAS_IMPUTED_INCOME_FLAG_FIELD] = ( + self.df_all[ + field_names.CENSUS_DECENNIAL_ADJUSTED_POVERTY_LESS_THAN_200_FPL_FIELD_2019 + ].notna() + & self.df_all[ + field_names.CENSUS_DECENNIAL_POVERTY_LESS_THAN_200_FPL_FIELD_2019 + ].isna() + ) + + def transform(self, geojson_path: Path = CENSUS_GEOJSON_PATH) -> None: # Creating Geo ID (Census Block Group) Field Name self.df_all[field_names.GEOID_TRACT_FIELD] = ( self.df_all["state"] + self.df_all["county"] + self.df_all["tract"] @@ -232,6 +306,8 @@ class CensusDecennialETL(ExtractTransformLoad): f"There are {missing_value_count} missing values in the field {col} out of a total of {self.df_all.shape[0]} rows" ) + self._impute_income(geojson_path) + def load(self) -> None: self.OUTPUT_PATH.mkdir(parents=True, exist_ok=True) columns_to_include = [ @@ -242,11 +318,14 @@ class CensusDecennialETL(ExtractTransformLoad): field_names.CENSUS_DECENNIAL_AREA_MEDIAN_INCOME_PERCENT_FIELD_2019, field_names.CENSUS_DECENNIAL_POVERTY_LESS_THAN_100_FPL_FIELD_2019, field_names.CENSUS_DECENNIAL_POVERTY_LESS_THAN_200_FPL_FIELD_2019, + DEC_FIELD_NAMES.IMPUTED_PERCENTAGE_HOUSEHOLDS_BELOW_200_PERC_POVERTY_LEVEL, + field_names.CENSUS_DECENNIAL_ADJUSTED_POVERTY_LESS_THAN_200_FPL_FIELD_2019, field_names.CENSUS_DECENNIAL_UNEMPLOYMENT_FIELD_2019, field_names.CENSUS_DECENNIAL_HIGH_SCHOOL_ED_FIELD_2019, DEC_FIELD_NAMES.COLLEGE_ATTENDANCE_PERCENT, DEC_FIELD_NAMES.COLLEGE_NON_ATTENDANCE, DEC_FIELD_NAMES.COLLEGE_ATTENDANCE_POPULATION, + field_names.ISLAND_AREAS_IMPUTED_INCOME_FLAG_FIELD, ] + self.final_race_fields self.df_all[columns_to_include].to_csv( path_or_buf=self.OUTPUT_PATH / "usa.csv", index=False diff --git a/data/data-pipeline/data_pipeline/score/field_names.py b/data/data-pipeline/data_pipeline/score/field_names.py index a2dbadbf..de69d24b 100644 --- a/data/data-pipeline/data_pipeline/score/field_names.py +++ b/data/data-pipeline/data_pipeline/score/field_names.py @@ -191,6 +191,7 @@ CENSUS_DECENNIAL_MEDIAN_INCOME_2019 = ( ) CENSUS_DECENNIAL_POVERTY_LESS_THAN_100_FPL_FIELD_2019 = f"Percentage households below 100% of federal poverty line in {DEC_DATA_YEAR}" CENSUS_DECENNIAL_POVERTY_LESS_THAN_200_FPL_FIELD_2019 = f"Percentage households below 200% of federal poverty line in {DEC_DATA_YEAR}" +CENSUS_DECENNIAL_ADJUSTED_POVERTY_LESS_THAN_200_FPL_FIELD_2019 = f"{CENSUS_DECENNIAL_POVERTY_LESS_THAN_200_FPL_FIELD_2019}, adjusted and imputed" CENSUS_DECENNIAL_HIGH_SCHOOL_ED_FIELD_2019 = f"Percent individuals age 25 or over with less than high school degree in {DEC_DATA_YEAR}" CENSUS_DECENNIAL_UNEMPLOYMENT_FIELD_2019 = ( f"Unemployment (percent) in {DEC_DATA_YEAR}" @@ -707,6 +708,8 @@ ISLAND_LOW_MEDIAN_INCOME_PCTILE_THRESHOLD = ( ) ISLAND_UNEMPLOYMENT_PCTILE_THRESHOLD = f"{CENSUS_DECENNIAL_UNEMPLOYMENT_FIELD_2019} exceeds {PERCENTILE}th percentile" ISLAND_POVERTY_PCTILE_THRESHOLD = f"{CENSUS_DECENNIAL_POVERTY_LESS_THAN_100_FPL_FIELD_2019} exceeds {PERCENTILE}th percentile" +# Low Income Island Areas +ISLAND_AREAS_IMPUTED_INCOME_FLAG_FIELD = f"Income data has been estimated based on neighbor income{ISLAND_AREAS_SUFFIX}" # Not currently used in a factor EXTREME_HEAT_MEDIAN_HOUSE_VALUE_LOW_INCOME_FIELD = ( diff --git a/data/data-pipeline/data_pipeline/score/score_narwhal.py b/data/data-pipeline/data_pipeline/score/score_narwhal.py index a4960a08..bb921231 100644 --- a/data/data-pipeline/data_pipeline/score/score_narwhal.py +++ b/data/data-pipeline/data_pipeline/score/score_narwhal.py @@ -1044,7 +1044,7 @@ class ScoreNarwhal(Score): 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_2019, + column_from_island_areas=field_names.CENSUS_DECENNIAL_ADJUSTED_POVERTY_LESS_THAN_200_FPL_FIELD_2019, 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, diff --git a/data/data-pipeline/data_pipeline/tests/score/test_utils/data/test_mark_poverty_flag.csv b/data/data-pipeline/data_pipeline/tests/score/test_utils/data/test_mark_poverty_flag.csv index 00ca70fd..92110932 100644 --- a/data/data-pipeline/data_pipeline/tests/score/test_utils/data/test_mark_poverty_flag.csv +++ b/data/data-pipeline/data_pipeline/tests/score/test_utils/data/test_mark_poverty_flag.csv @@ -1,4 +1,4 @@ -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)? +GEOID10_TRACT,"Percentage households below 200% of federal poverty line in 2009, adjusted and imputed","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 diff --git a/data/data-pipeline/data_pipeline/tests/sources/census_decennial/data/imputation/census-us-territory-geojson.json b/data/data-pipeline/data_pipeline/tests/sources/census_decennial/data/imputation/census-us-territory-geojson.json new file mode 100644 index 00000000..5f3e791c --- /dev/null +++ b/data/data-pipeline/data_pipeline/tests/sources/census_decennial/data/imputation/census-us-territory-geojson.json @@ -0,0 +1,138 @@ +{ +"type": "FeatureCollection", +"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:OGC:1.3:CRS84" } }, +"features": [ +{ "type": "Feature", "properties": { "STATEFP10": "69", "COUNTYFP10": "085", "TRACTCE10": "950100", "GEOID10": "69085950100", "NAME10": "9501", "NAMELSAD10": "Census Tract 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13.512946 ], [ 144.845792, 13.512679 ] ] ] } } +] +} diff --git a/data/data-pipeline/data_pipeline/tests/sources/census_decennial/test_etl.py b/data/data-pipeline/data_pipeline/tests/sources/census_decennial/test_etl.py index 4f489674..feb3f8d9 100644 --- a/data/data-pipeline/data_pipeline/tests/sources/census_decennial/test_etl.py +++ b/data/data-pipeline/data_pipeline/tests/sources/census_decennial/test_etl.py @@ -10,6 +10,11 @@ from data_pipeline.etl.sources.census_decennial.etl import CensusDecennialETL from data_pipeline.score import field_names +def _check_fields_exist(df: pd.DataFrame, field_names: list): + for field in field_names: + assert field in df.columns + + @pytest.fixture def territory_params_fixture(): return [ @@ -31,25 +36,39 @@ def territory_params_fixture(): @pytest.fixture -def extract_path_fixture(): +def extract_path_fixture() -> Path: return Path(__file__).parents[0] / "data/extract" @pytest.fixture -def transform_path_fixture(): +def transform_path_fixture() -> Path: return Path(__file__).parents[0] / "data/transform" @pytest.fixture -def transformed_data_fixture(transform_path_fixture): - """Load the test data and call the ETL transform""" - dec = CensusDecennialETL() - dec.df_all = pd.read_csv( +def imputed_path_fixture() -> Path: + return Path(__file__).parents[0] / "data/imputation" + + +@pytest.fixture +def extracted_data_fixture( + transform_path_fixture: pd.DataFrame, +) -> pd.DataFrame: + return pd.read_csv( transform_path_fixture / "usa.csv", # Make sure these columns are string as expected of the original dtype={"state": "object", "county": "object", "tract": "object"}, ) - dec.transform() + + +@pytest.fixture +def transformed_data_fixture( + extracted_data_fixture: pd.DataFrame, imputed_path_fixture: Path +) -> pd.DataFrame: + """Load the test data and call the ETL transform""" + dec = CensusDecennialETL() + dec.df_all = extracted_data_fixture + dec.transform(imputed_path_fixture / "census-us-territory-geojson.json") return dec.df_all @@ -67,7 +86,7 @@ def test_no_files_found(territory_params_fixture): ) -def test_load_data(extract_path_fixture, territory_params_fixture): +def test_load_data(extract_path_fixture: Path, territory_params_fixture): """Test the ETL loads and translates the data""" dec = CensusDecennialETL() dec.extract( @@ -103,10 +122,10 @@ def test_load_data(extract_path_fixture, territory_params_fixture): ).any() -############### +################# # Transform tests -############### -def test_geo_tract_generation(transformed_data_fixture): +################# +def test_geo_tract_generation(transformed_data_fixture: pd.DataFrame): result = transformed_data_fixture assert field_names.GEOID_TRACT_FIELD in result.columns assert result[field_names.GEOID_TRACT_FIELD].notnull().all() @@ -118,7 +137,7 @@ def test_geo_tract_generation(transformed_data_fixture): ) -def test_merge_tracts(transformed_data_fixture): +def test_merge_tracts(transformed_data_fixture: pd.DataFrame): result = transformed_data_fixture # 69120950200 exists, but the tract split does now assert ( @@ -138,15 +157,103 @@ def test_merge_tracts(transformed_data_fixture): ) -def test_remove_invalid_values(transformed_data_fixture): +def test_remove_invalid_values(transformed_data_fixture: pd.DataFrame): numeric_df = transformed_data_fixture.select_dtypes(include="number") assert not (numeric_df < -999).any().any() -def test_race_fields(transformed_data_fixture): +def test_race_fields(transformed_data_fixture: pd.DataFrame): for race_field_name in OUTPUT_RACE_FIELDS: assert race_field_name in transformed_data_fixture.columns assert any( col.startswith(field_names.PERCENT_PREFIX + race_field_name) for col in transformed_data_fixture.columns ) + + +def test_transformation_fields(transformed_data_fixture: pd.DataFrame): + _check_fields_exist( + transformed_data_fixture, + [ + field_names.CENSUS_DECENNIAL_POVERTY_LESS_THAN_100_FPL_FIELD_2019, + field_names.CENSUS_DECENNIAL_POVERTY_LESS_THAN_200_FPL_FIELD_2019, + field_names.CENSUS_DECENNIAL_HIGH_SCHOOL_ED_FIELD_2019, + field_names.CENSUS_DECENNIAL_UNEMPLOYMENT_FIELD_2019, + field_names.CENSUS_DECENNIAL_AREA_MEDIAN_INCOME_PERCENT_FIELD_2019, + DEC_FIELD_NAMES.COLLEGE_ATTENDANCE_POPULATION, + DEC_FIELD_NAMES.COLLEGE_ATTENDANCE_PERCENT, + ], + ) + + +################## +# Imputation tests +################## +def test_merge_geojson(transformed_data_fixture: pd.DataFrame): + _check_fields_exist(transformed_data_fixture, ["STATEFP10", "COUNTYFP10"]) + + +def test_imputation_added(transformed_data_fixture: pd.DataFrame): + assert ( + DEC_FIELD_NAMES.IMPUTED_PERCENTAGE_HOUSEHOLDS_BELOW_200_PERC_POVERTY_LEVEL + in transformed_data_fixture.columns + ) + + # All rows with population > 0 need to have an value (real or imputed) + df_has_pop = transformed_data_fixture[ + transformed_data_fixture[ + field_names.CENSUS_DECENNIAL_TOTAL_POPULATION_FIELD_2019 + ] + > 0 + ] + assert ( + df_has_pop[ + DEC_FIELD_NAMES.IMPUTED_PERCENTAGE_HOUSEHOLDS_BELOW_200_PERC_POVERTY_LEVEL + ] + .notnull() + .all() + ) + + # The imputed value equals the real value when available + df_has_real_data = transformed_data_fixture[ + transformed_data_fixture[ + field_names.CENSUS_DECENNIAL_POVERTY_LESS_THAN_200_FPL_FIELD_2019 + ].notnull() + ] + assert ( + df_has_real_data[ + DEC_FIELD_NAMES.IMPUTED_PERCENTAGE_HOUSEHOLDS_BELOW_200_PERC_POVERTY_LEVEL + ] + == df_has_real_data[ + field_names.CENSUS_DECENNIAL_POVERTY_LESS_THAN_200_FPL_FIELD_2019 + ] + ).all() + + # The imputed value exists when no real value exists + df_missing_data = transformed_data_fixture[ + transformed_data_fixture[ + field_names.CENSUS_DECENNIAL_POVERTY_LESS_THAN_200_FPL_FIELD_2019 + ].isnull() + ] + assert ( + df_missing_data[ + df_missing_data[ + DEC_FIELD_NAMES.IMPUTED_PERCENTAGE_HOUSEHOLDS_BELOW_200_PERC_POVERTY_LEVEL + ].notnull() + ][ + DEC_FIELD_NAMES.IMPUTED_PERCENTAGE_HOUSEHOLDS_BELOW_200_PERC_POVERTY_LEVEL + ] + .notnull() + .all() + ) + + # Test the imputation flag is set + df_missing_no_pop = df_missing_data[ + df_missing_data[ + field_names.CENSUS_DECENNIAL_TOTAL_POPULATION_FIELD_2019 + ] + > 0 + ] + assert df_missing_no_pop[ + field_names.ISLAND_AREAS_IMPUTED_INCOME_FLAG_FIELD + ].all() From 999c1f6deeef22b0eb637e5e7fb1268d4cad67c3 Mon Sep 17 00:00:00 2001 From: ericiwamoto <100735505+ericiwamoto@users.noreply.github.com> Date: Fri, 22 Nov 2024 08:39:39 -0800 Subject: [PATCH 11/28] Staging Data Pipeline --- .github/workflows/deploy_backend_main.yml | 126 ++++++++++++++++++++++ 1 file changed, 126 insertions(+) create mode 100644 .github/workflows/deploy_backend_main.yml diff --git a/.github/workflows/deploy_backend_main.yml b/.github/workflows/deploy_backend_main.yml new file mode 100644 index 00000000..fb928dc3 --- /dev/null +++ b/.github/workflows/deploy_backend_main.yml @@ -0,0 +1,126 @@ +name: Deploy Backend Main +on: + workflow_dispatch: + inputs: + score_version: + description: "Which version of the score are you generating?" + required: true + default: '2.0' + type: choice + options: + - beta + - 1.0 + - 2.0 + - test + +env: + CENSUS_API_KEY: ${{ secrets.CENSUS_API_KEY }} + J40_VERSION_LABEL_STRING: ${{ inputs.score_version }} + +jobs: + generate-score-tiles: + runs-on: ubuntu-latest + defaults: + run: + working-directory: data/data-pipeline + strategy: + matrix: + python-version: [3.10] + steps: + - name: Checkout source + uses: actions/checkout@v2 + - name: Print variables to help debug + uses: hmarr/debug-action@v2 + - name: Set up Python ${{ matrix.python-version }} + uses: actions/setup-python@v2 + with: + python-version: ${{ matrix.python-version }} + - name: Load cached Poetry installation + id: cached-poetry-dependencies + uses: actions/cache@v4 + with: + path: ~/.cache/pypoetry/virtualenvs + key: env-${{ runner.os }}-${{ matrix.python-version }}-${{ hashFiles('**/poetry.lock') }}-${{ hashFiles('.github/workflows/deploy_backend_main.yml') }} + - name: Install poetry + uses: snok/install-poetry@v1 + - name: Print Poetry settings + run: poetry show -v + - name: Install dependencies + run: poetry add s4cmd && poetry install + if: steps.cached-poetry-dependencies.outputs.cache-hit != 'true' + - name: Configure AWS Credentials + uses: aws-actions/configure-aws-credentials@v1 + with: + aws-access-key-id: ${{ secrets.DATA_DEV_AWS_ACCESS_KEY_ID }} + aws-secret-access-key: ${{ secrets.DATA_DEV_AWS_SECRET_ACCESS_KEY }} + aws-region: us-east-1 + - name: Install GDAL/ogr2ogr + run: | + sudo apt-get update + sudo apt-get -y install gdal-bin + ogrinfo --version + - name: Generate Score + run: | + poetry run python3 data_pipeline/application.py score-full-run + - name: Generate Score Post + run: | + poetry run python3 data_pipeline/application.py generate-score-post -s aws + - name: Confirm we generated the version of the score we think we did + if: ${{ env.J40_VERSION_LABEL_STRING == '1.0' || env.J40_VERSION_LABEL_STRING == 'test' }} + run: | + grep "Identified as disadvantaged due to tribal overlap" data_pipeline/data/score/downloadable/* > /dev/null + - name: Confirm we generated the version of the score we think we did + if: ${{ env.J40_VERSION_LABEL_STRING == '2.0' || env.J40_VERSION_LABEL_STRING == 'beta' }} + run: | + grep -v "Identified as disadvantaged due to tribal overlap" data_pipeline/data/score/downloadable/* > /dev/null + - name: Generate Score Geo + run: | + poetry run python3 data_pipeline/application.py geo-score + - name: Run smoketest for 1.0 + if: ${{ env.J40_VERSION_LABEL_STRING == '1.0' || env.J40_VERSION_LABEL_STRING == 'test' }} + run: | + poetry run pytest data_pipeline/ -m smoketest + - name: Deploy Score to Geoplatform AWS + run: | + poetry run s4cmd put ./data_pipeline/data/score/csv/ s3://${{secrets.S3_DATA_BUCKET}}/data-versions/${{env.J40_VERSION_LABEL_STRING}}/data/score/csv --recursive --force + poetry run s4cmd put ./data_pipeline/files/ s3://${{secrets.S3_DATA_BUCKET}}/data-versions/${{env.J40_VERSION_LABEL_STRING}}/data/score/downloadable --recursive --force + poetry run s4cmd put ./data_pipeline/data/score/downloadable/ s3://${{secrets.S3_DATA_BUCKET}}/data-versions/${{env.J40_VERSION_LABEL_STRING}}/data/score/downloadable --recursive --force + - name: Confirm we generated the version of the score we think we did + if: ${{ env.J40_VERSION_LABEL_STRING == '1.0' || env.J40_VERSION_LABEL_STRING == 'test' }} + run: | + curl "${{secrets.DATA_URL}}/data-versions/1.0/data/score/downloadable/1.0-shapefile-codebook.zip" -s -f -I -o /dev/null && \ + curl "${{secrets.DATA_URL}}/data-versions/1.0/data/score/downloadable/1.0-communities.xlsx" -s -f -I -o /dev/null && \ + curl "${{secrets.DATA_URL}}/data-versions/1.0/data/score/downloadable/1.0-communities.csv" -s -f -I -o /dev/null && \ + curl "${{secrets.DATA_URL}}/data-versions/1.0/data/score/downloadable/1.0-shapefile-codebook.zip" -s -f -I -o /dev/null && \ + curl "${{secrets.DATA_URL}}/data-versions/1.0/data/score/downloadable/cejst-technical-support-document.pdf" -s -f -I -o /dev/null && \ + curl "${{secrets.DATA_URL}}/data-versions/1.0/data/score/downloadable/draft-communities-list.pdf" -s -f -I -o /dev/null + - name: Confirm we generated the version of the score we think we did + if: ${{ env.J40_VERSION_LABEL_STRING == '2.0' || env.J40_VERSION_LABEL_STRING == 'beta' }} + run: | + curl "${{secrets.DATA_URL}}/data-versions/2.0/data/score/downloadable/2.0-data-documentation.zip" -s -f -I -o /dev/null && \ + curl "${{secrets.DATA_URL}}/data-versions/2.0/data/score/downloadable/2.0-shapefile-codebook.zip" -s -f -I -o /dev/null + - name: Set timezone for tippecanoe + uses: szenius/set-timezone@v1.0 + with: + timezoneLinux: "America/Los_Angeles" + - name: Get tippecanoe + run: | + sudo apt-get install -y software-properties-common libsqlite3-dev zlib1g-dev + sudo apt-add-repository -y ppa:git-core/ppa + sudo mkdir -p /tmp/tippecanoe-src + sudo git clone https://github.com/mapbox/tippecanoe.git /tmp/tippecanoe-src + - name: Make tippecanoe + working-directory: /tmp/tippecanoe-src + run: | + sudo /usr/bin/bash -c make + mkdir -p /usr/local/bin + cp tippecanoe /usr/local/bin/tippecanoe + tippecanoe -v + - name: Generate Tiles + run: | + poetry run python3 data_pipeline/application.py generate-map-tiles + - name: Deploy Map to Geoplatform AWS + run: | + poetry run s4cmd put ./data_pipeline/data/score/geojson/ s3://${{secrets.S3_DATA_BUCKET}}/data-versions/${{env.J40_VERSION_LABEL_STRING}}/data/score/geojson --recursive --force --num-threads=250 + poetry run s4cmd put ./data_pipeline/data/score/shapefile/ s3://${{secrets.S3_DATA_BUCKET}}/data-versions/${{env.J40_VERSION_LABEL_STRING}}/data/score/shapefile --recursive --force + poetry run s4cmd put ./data_pipeline/data/score/tiles/ s3://${{secrets.S3_DATA_BUCKET}}/data-versions/${{env.J40_VERSION_LABEL_STRING}}/data/score/tiles --recursive --force --num-threads=250 From 1f5df1195448b088b689fefea74c516c93a2abf3 Mon Sep 17 00:00:00 2001 From: ericiwamoto <100735505+ericiwamoto@users.noreply.github.com> Date: Mon, 25 Nov 2024 08:17:15 -0800 Subject: [PATCH 12/28] Fix data pipeline first-run errors --- .github/workflows/deploy_backend_main.yml | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/.github/workflows/deploy_backend_main.yml b/.github/workflows/deploy_backend_main.yml index fb928dc3..e2f0245a 100644 --- a/.github/workflows/deploy_backend_main.yml +++ b/.github/workflows/deploy_backend_main.yml @@ -25,14 +25,14 @@ jobs: working-directory: data/data-pipeline strategy: matrix: - python-version: [3.10] + python-version: ['3.10'] steps: - name: Checkout source - uses: actions/checkout@v2 + uses: actions/checkout@v4 - name: Print variables to help debug - uses: hmarr/debug-action@v2 + uses: hmarr/debug-action@v3 - name: Set up Python ${{ matrix.python-version }} - uses: actions/setup-python@v2 + uses: actions/setup-python@v5 with: python-version: ${{ matrix.python-version }} - name: Load cached Poetry installation From 3654aac8190a0e3e0e3196e66484131e6ffe288e Mon Sep 17 00:00:00 2001 From: ericiwamoto <100735505+ericiwamoto@users.noreply.github.com> Date: Mon, 25 Nov 2024 12:56:50 -0800 Subject: [PATCH 13/28] More data pipeline fixes --- .github/workflows/deploy_backend_main.yml | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/.github/workflows/deploy_backend_main.yml b/.github/workflows/deploy_backend_main.yml index e2f0245a..a3077ca2 100644 --- a/.github/workflows/deploy_backend_main.yml +++ b/.github/workflows/deploy_backend_main.yml @@ -26,6 +26,7 @@ jobs: strategy: matrix: python-version: ['3.10'] + environment: Staging steps: - name: Checkout source uses: actions/checkout@v4 @@ -59,6 +60,9 @@ jobs: sudo apt-get update sudo apt-get -y install gdal-bin ogrinfo --version + - name: Get Census Data + run: | + poetry run python3 data_pipeline/application.py pull-census-data -s aws - name: Generate Score run: | poetry run python3 data_pipeline/application.py score-full-run From 313f4f7c8fceace299cbd0d7ac9af65990f33998 Mon Sep 17 00:00:00 2001 From: ericiwamoto <100735505+ericiwamoto@users.noreply.github.com> Date: Tue, 26 Nov 2024 09:36:42 -0800 Subject: [PATCH 14/28] Null frontend change to trigger workflow --- client/README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/client/README.md b/client/README.md index 48b8d7e3..da2079b3 100644 --- a/client/README.md +++ b/client/README.md @@ -214,6 +214,7 @@ When developing, to use a flag: 6. Set breakpoints in VS code! + ## Package Versions The following attemps to explain why certain packages versions have been chosen and what their current limitations are From 35f1cffbb451704b8c3e8de3e96f9d41c9d4fc8d Mon Sep 17 00:00:00 2001 From: Carlos Felix <63804190+carlosfelix2@users.noreply.github.com> Date: Tue, 26 Nov 2024 13:51:00 -0500 Subject: [PATCH 15/28] Comparison tool can use local files instead of downloading production score --- .../data-pipeline/data_pipeline/comparator.py | 96 +++++++++++-------- 1 file changed, 56 insertions(+), 40 deletions(-) diff --git a/data/data-pipeline/data_pipeline/comparator.py b/data/data-pipeline/data_pipeline/comparator.py index 6cf184b6..6aa85ffd 100644 --- a/data/data-pipeline/data_pipeline/comparator.py +++ b/data/data-pipeline/data_pipeline/comparator.py @@ -2,6 +2,7 @@ import sys import click import difflib import pandas as pd +from pathlib import Path from data_pipeline.etl.score import constants from data_pipeline.utils import get_module_logger, download_file_from_url @@ -16,6 +17,22 @@ pd.set_option("display.width", 10000) pd.set_option("display.colheader_justify", "left") +def _read_from_file(file_path: Path): + """Read a CSV file into a Dataframe.""" + if not file_path.is_file(): + logger.error( + f"- No score file exists at {file_path}. " + "Please generate the score and try again." + ) + sys.exit(1) + return pd.read_csv( + file_path, + index_col="GEOID10_TRACT", + dtype={"GEOID10_TRACT": str}, + low_memory=False, + ).sort_index() + + @click.group() def cli(): """ @@ -33,8 +50,22 @@ def cli(): default="1.0", required=False, type=str, + help="Set the production score version to compare to", ) -def compare_score(compare_to_version: str): +@click.option( + "-f", + "--compare_to_file", + type=click.Path(exists=True, dir_okay=False, path_type=Path), + help="Compare to the specified score CSV file instead of downloading from production", +) +@click.option( + "-l", + "--local_score_file", + type=click.Path(exists=True, dir_okay=False, path_type=Path), + default=constants.DATA_SCORE_CSV_FULL_FILE_PATH, + help="Compare to the specified score CSV file instead of downloading from production", +) +def compare_score(compare_to_version: str, compare_to_file: str, local_score_file: str): """Compares the score in the production environment to the locally generated score. The algorithm is pretty simple: @@ -56,39 +87,25 @@ def compare_score(compare_to_version: str): log_title("Compare Score", "Compare production score to local score") - locally_generated_score_path = constants.DATA_SCORE_CSV_FULL_FILE_PATH - if not locally_generated_score_path.is_file(): - logger.error( - f"- No score file exists at {locally_generated_score_path}. Please generate the score and try again." + if compare_to_file: + log_info(f"Comparing to file {compare_to_file}...") + production_score_path = compare_to_file + else: + # TODO: transition to downloader code when it's available + production_score_url = f"https://justice40-data.s3.amazonaws.com/data-versions/{compare_to_version}/data/score/csv/full/usa.csv" + production_score_path = WORKING_PATH / "usa.csv" + + log_info(f"Fetching score version {compare_to_version} from AWS") + production_score_path.parent.mkdir(parents=True, exist_ok=True) + download_file_from_url( + file_url=production_score_url, + download_file_name=production_score_path, ) - sys.exit(1) - # TODO: transition to downloader code when it's available - production_score_url = f"https://justice40-data.s3.amazonaws.com/data-versions/{compare_to_version}/data/score/csv/full/usa.csv" - production_score_path = ( - WORKING_PATH / f"prod-score-csv-full-{compare_to_version}-usa.csv" - ) - - log_info(f"Fetching score version {compare_to_version} from AWS") - production_score_path.parent.mkdir(parents=True, exist_ok=True) - download_file_from_url( - file_url=production_score_url, download_file_name=production_score_path - ) - - log_info("Loading files into pandas for comparisons") - - local_score_df = pd.read_csv( - locally_generated_score_path, - index_col="GEOID10_TRACT", - dtype={"GEOID10_TRACT": str}, - low_memory=False, - ).sort_index() - production_score_df = pd.read_csv( - production_score_path, - index_col="GEOID10_TRACT", - dtype={"GEOID10_TRACT": str}, - low_memory=False, - ).sort_index() + log_info(f"Loading local score from {local_score_file}") + local_score_df = _read_from_file(local_score_file) + log_info(f"Loading production score from {production_score_path}") + production_score_df = _read_from_file(production_score_path) # Because of variations in Python versions and machine-level calculations, some of # our numbers can be really close but not the same. That throws off our comparisons. @@ -241,17 +258,16 @@ def compare_score(compare_to_version: str): summary += "* I compared all values across all census tracts." summary += f" There are {len(comparison_results_df.index):,} tracts with at least one difference." summary += " Please examine the logs or run the score comparison locally to view them all.\n" + log_info( f"There are {len(comparison_results_df.index)} rows with any differences." ) - - log_info("Those differences are:") - log_info("\n" + str(comparison_results_df)) - - comparison_path = WORKING_PATH / "deltas.csv" - comparison_results_df.to_csv(path_or_buf=comparison_path) - - log_info(f"Wrote comparison results to {comparison_path}") + if len(comparison_results_df.index) > 0: + log_info("Those differences are:") + log_info("\n" + str(comparison_results_df)) + comparison_path = WORKING_PATH / "deltas.csv" + comparison_results_df.to_csv(path_or_buf=comparison_path) + log_info(f"Wrote comparison results to {comparison_path}") except ValueError as e: summary += "* I could not run a full comparison. This is likely because there are column or index (census tract) differences." From dd6af869d782c6108a574486128d8b5330db8304 Mon Sep 17 00:00:00 2001 From: Carlos Felix <63804190+carlosfelix2@users.noreply.github.com> Date: Tue, 26 Nov 2024 13:51:22 -0500 Subject: [PATCH 16/28] Removed old score files and updated some dev dependencies --- data/data-pipeline/.flake8 | 10 +- data/data-pipeline/poetry.lock | 428 ++++++++++++++---------------- data/data-pipeline/pyproject.toml | 10 +- 3 files changed, 211 insertions(+), 237 deletions(-) diff --git a/data/data-pipeline/.flake8 b/data/data-pipeline/.flake8 index f217b57d..c9ae846f 100644 --- a/data/data-pipeline/.flake8 +++ b/data/data-pipeline/.flake8 @@ -1,9 +1,11 @@ [flake8] +# E266, to many leading '#' for block comment +# W503, line break before binary operator +# TODO: Uncomment and fix the issues here that we care about, see +# https://github.com/usds/justice40-tool/issues/1123 ignore = - E266, # to many leading '#' for block comment - W503, # line break before binary operator - # TODO: Uncomment and fix the issues here that we care about, see - # https://github.com/usds/justice40-tool/issues/1123 + E266, + W503, PD002, PD003, PD004, diff --git a/data/data-pipeline/poetry.lock b/data/data-pipeline/poetry.lock index 5df8dbec..51b54284 100644 --- a/data/data-pipeline/poetry.lock +++ b/data/data-pipeline/poetry.lock @@ -24,87 +24,87 @@ files = [ [[package]] name = "aiohttp" -version = "3.11.2" +version = "3.11.7" description = "Async http client/server framework (asyncio)" optional = false python-versions = ">=3.9" files = [ - {file = "aiohttp-3.11.2-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:783741f534c14957fbe657d62a34b947ec06db23d45a2fd4a8aeb73d9c84d7e6"}, - {file = "aiohttp-3.11.2-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:435f7a08d8aa42371a94e7c141205a9cb092ba551084b5e0c57492e6673601a3"}, - {file = "aiohttp-3.11.2-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:c681f34e2814bc6e1eef49752b338061b94a42c92734d0be9513447d3f83718c"}, - {file = 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= "^0.2.2" -pytest-snapshot = "^0.8.1" +pytest-snapshot = "^0.9.0" seaborn = "^0.11.2" papermill = "^2.3.4" jupyterlab = "^3.6.7" From 179660944cb150168d8fa54d68d597d552b3d4df Mon Sep 17 00:00:00 2001 From: ericiwamoto <100735505+ericiwamoto@users.noreply.github.com> Date: Wed, 27 Nov 2024 08:26:47 -0800 Subject: [PATCH 17/28] Update frontend test snapshots --- .gitignore | 3 +++ client/package.json | 1 + .../MapLegend/tests/__snapshots__/mapLegend.test.tsx.snap | 4 ++-- .../__snapshots__/SidePanelInfo.test.tsx.snap | 8 ++++---- .../components/__snapshots__/mapInfoPanel.test.tsx.snap | 8 ++++---- 5 files changed, 14 insertions(+), 10 deletions(-) diff --git a/.gitignore b/.gitignore index 36c2ca61..07d1cb50 100644 --- a/.gitignore +++ b/.gitignore @@ -150,3 +150,6 @@ node_modules .python-version .DS_Store temp_dir + +# asdf tooling +.tool-versions diff --git a/client/package.json b/client/package.json index fefac8fd..0bd78a81 100644 --- a/client/package.json +++ b/client/package.json @@ -68,6 +68,7 @@ "husky": "^7.0.4", "identity-obj-proxy": "^3.0.0", "jest": "^27.5.1", + "jest-environment-jsdom": "^27.5.1", "license-checker": "^25.0.1", "prettier": "^2.6.0", "react-test-renderer": "^17.0.2", diff --git a/client/src/components/MapLegend/tests/__snapshots__/mapLegend.test.tsx.snap b/client/src/components/MapLegend/tests/__snapshots__/mapLegend.test.tsx.snap index f772b0dd..a464fdc3 100644 --- a/client/src/components/MapLegend/tests/__snapshots__/mapLegend.test.tsx.snap +++ b/client/src/components/MapLegend/tests/__snapshots__/mapLegend.test.tsx.snap @@ -18,8 +18,8 @@ exports[`rendering of the MapLegend checks if snapshots have changed 1`] = ` class="secondary" > - Communities identified as disadvantaged by the map are those that are marginalized, underserved, - and overburdened by pollution. These communities are at or above the thresholds in one or + Communities identified as disadvantaged by the map are those that are marginalized, underserved, + and overburdened by pollution. These communities are at or above the thresholds in one or more of eight categories of criteria.

diff --git a/client/src/components/SidePanelInfo/__snapshots__/SidePanelInfo.test.tsx.snap b/client/src/components/SidePanelInfo/__snapshots__/SidePanelInfo.test.tsx.snap index 332c2f33..15688eec 100644 --- a/client/src/components/SidePanelInfo/__snapshots__/SidePanelInfo.test.tsx.snap +++ b/client/src/components/SidePanelInfo/__snapshots__/SidePanelInfo.test.tsx.snap @@ -34,7 +34,7 @@ exports[`rendering of the component expects the render to match snapshot 1`] = ` tabindex="0" /> - , or locate yourself + , or locate yourself @@ -70,7 +70,7 @@ exports[`rendering of the component expects the render to match snapshot 1`] = ` tabindex= - . Census tracts are a small unit of geography. They generally have populations + . Census tracts are a small unit of geography. They generally have populations @@ -87,7 +87,7 @@ exports[`rendering of the component expects the render to match snapshot 1`] = ` tabindex= - Communities that are disadvantaged live in tracts that experience burdens. These tracts are highlighted + Communities that are disadvantaged live in tracts that experience burdens. These tracts are highlighted @@ -131,7 +131,7 @@ exports[`rendering of the component expects the render to match snapshot 1`] = ` tabindex= - , or cutoffs, are used to determine if communities in a tract are disadvantaged. Certain burdens use percentages + , or cutoffs, are used to determine if communities in a tract are disadvantaged. Certain burdens use percentages diff --git a/client/src/components/__snapshots__/mapInfoPanel.test.tsx.snap b/client/src/components/__snapshots__/mapInfoPanel.test.tsx.snap index a619ebb2..4c6cf268 100644 --- a/client/src/components/__snapshots__/mapInfoPanel.test.tsx.snap +++ b/client/src/components/__snapshots__/mapInfoPanel.test.tsx.snap @@ -39,7 +39,7 @@ exports[`simulate app starting up, no click on map should match the snapshot of tabindex= - , or locate yourself + , or locate yourself @@ -75,7 +75,7 @@ exports[`simulate app starting up, no click on map should match the snapshot of tabindex= - . Census tracts are a small unit of geography. They generally have populations + . Census tracts are a small unit of geography. They generally have populations @@ -92,7 +92,7 @@ exports[`simulate app starting up, no click on map should match the snapshot of tabindex= - Communities that are disadvantaged live in tracts that experience burdens. These tracts are highlighted + Communities that are disadvantaged live in tracts that experience burdens. These tracts are highlighted @@ -136,7 +136,7 @@ exports[`simulate app starting up, no click on map should match the snapshot of tabindex= - , or cutoffs, are used to determine if communities in a tract are disadvantaged. Certain burdens use percentages + , or cutoffs, are used to determine if communities in a tract are disadvantaged. Certain burdens use percentages From 3b5e040f3a27a900f6b0950ccb837b24c8901bb5 Mon Sep 17 00:00:00 2001 From: ericiwamoto <100735505+ericiwamoto@users.noreply.github.com> Date: Wed, 27 Nov 2024 08:51:53 -0800 Subject: [PATCH 18/28] Refactor markdown check workflow --- .github/workflows/markdown-link-check.yml | 15 +++++++-------- 1 file changed, 7 insertions(+), 8 deletions(-) diff --git a/.github/workflows/markdown-link-check.yml b/.github/workflows/markdown-link-check.yml index 780dd59c..9db37edb 100644 --- a/.github/workflows/markdown-link-check.yml +++ b/.github/workflows/markdown-link-check.yml @@ -1,15 +1,14 @@ -name: Check Markdown links +name: Check Markdown Links on: pull_request: - # The branches below must be a subset of the branches above branches: [ main ] - # Run only if the pull request changes a markdown file: - # https://docs.github.com/en/actions/learn-github-actions/workflow-syntax-for-github-actions#onpushpull_requestpaths paths: - '**.md' jobs: - markdown-link-check: - runs-on: ubuntu-latest + check-links: + name: runner / linkspector + runs-on: ubuntu-22.04 steps: - - uses: actions/checkout@v2 - - uses: gaurav-nelson/github-action-markdown-link-check@v1 + - uses: actions/checkout@v4 + - name: Run linkspector + uses: umbrelladocs/action-linkspector@v1 \ No newline at end of file From f8bd1acf26f9b70329705a9897758755db8cbec1 Mon Sep 17 00:00:00 2001 From: ericiwamoto <100735505+ericiwamoto@users.noreply.github.com> Date: Wed, 27 Nov 2024 09:07:17 -0800 Subject: [PATCH 19/28] Even more data pipeline fixes --- .github/workflows/deploy_backend_main.yml | 8 ++++---- .../data_pipeline/files/readme-version-2.0.md | 0 2 files changed, 4 insertions(+), 4 deletions(-) create mode 100644 data/data-pipeline/data_pipeline/files/readme-version-2.0.md diff --git a/.github/workflows/deploy_backend_main.yml b/.github/workflows/deploy_backend_main.yml index a3077ca2..7cc0684a 100644 --- a/.github/workflows/deploy_backend_main.yml +++ b/.github/workflows/deploy_backend_main.yml @@ -50,7 +50,7 @@ jobs: run: poetry add s4cmd && poetry install if: steps.cached-poetry-dependencies.outputs.cache-hit != 'true' - name: Configure AWS Credentials - uses: aws-actions/configure-aws-credentials@v1 + uses: aws-actions/configure-aws-credentials@v4 with: aws-access-key-id: ${{ secrets.DATA_DEV_AWS_ACCESS_KEY_ID }} aws-secret-access-key: ${{ secrets.DATA_DEV_AWS_SECRET_ACCESS_KEY }} @@ -89,7 +89,7 @@ jobs: poetry run s4cmd put ./data_pipeline/data/score/csv/ s3://${{secrets.S3_DATA_BUCKET}}/data-versions/${{env.J40_VERSION_LABEL_STRING}}/data/score/csv --recursive --force poetry run s4cmd put ./data_pipeline/files/ s3://${{secrets.S3_DATA_BUCKET}}/data-versions/${{env.J40_VERSION_LABEL_STRING}}/data/score/downloadable --recursive --force poetry run s4cmd put ./data_pipeline/data/score/downloadable/ s3://${{secrets.S3_DATA_BUCKET}}/data-versions/${{env.J40_VERSION_LABEL_STRING}}/data/score/downloadable --recursive --force - - name: Confirm we generated the version of the score we think we did + - name: Deploy 1.0 score post if: ${{ env.J40_VERSION_LABEL_STRING == '1.0' || env.J40_VERSION_LABEL_STRING == 'test' }} run: | curl Date: Mon, 2 Dec 2024 09:03:23 -0800 Subject: [PATCH 20/28] Multiple workflow fixes --- .github/workflows/deploy_backend_main.yml | 50 +++++++++++----------- .github/workflows/deploy_frontend_main.yml | 6 +-- .github/workflows/markdown-link-check.yml | 4 +- 3 files changed, 32 insertions(+), 28 deletions(-) diff --git a/.github/workflows/deploy_backend_main.yml b/.github/workflows/deploy_backend_main.yml index 7cc0684a..1a420687 100644 --- a/.github/workflows/deploy_backend_main.yml +++ b/.github/workflows/deploy_backend_main.yml @@ -1,21 +1,12 @@ name: Deploy Backend Main on: - workflow_dispatch: - inputs: - score_version: - description: "Which version of the score are you generating?" - required: true - default: '2.0' - type: choice - options: - - beta - - 1.0 - - 2.0 - - test - + push: + branches: [main] + paths: + - "data/**" env: CENSUS_API_KEY: ${{ secrets.CENSUS_API_KEY }} - J40_VERSION_LABEL_STRING: ${{ inputs.score_version }} + J40_VERSION_LABEL_STRING: ${{ vars.SCORE_VERSION }} jobs: generate-score-tiles: @@ -81,28 +72,31 @@ jobs: run: | poetry run python3 data_pipeline/application.py geo-score - name: Run smoketest for 1.0 - if: ${{ env.J40_VERSION_LABEL_STRING == '1.0' || env.J40_VERSION_LABEL_STRING == 'test' }} + if: ${{ env.J40_VERSION_LABEL_STRING == '1.0' }} run: | poetry run pytest data_pipeline/ -m smoketest - name: Deploy Score to Geoplatform AWS run: | - poetry run s4cmd put ./data_pipeline/data/score/csv/ s3://${{secrets.S3_DATA_BUCKET}}/data-versions/${{env.J40_VERSION_LABEL_STRING}}/data/score/csv --recursive --force - poetry run s4cmd put ./data_pipeline/files/ s3://${{secrets.S3_DATA_BUCKET}}/data-versions/${{env.J40_VERSION_LABEL_STRING}}/data/score/downloadable --recursive --force - poetry run s4cmd put ./data_pipeline/data/score/downloadable/ s3://${{secrets.S3_DATA_BUCKET}}/data-versions/${{env.J40_VERSION_LABEL_STRING}}/data/score/downloadable --recursive --force + poetry run s4cmd put ./data_pipeline/data/score/csv/ s3://${{secrets.S3_DATA_BUCKET}}/data-versions/${{env.J40_VERSION_LABEL_STRING}}/data/score/csv --sync-check --recursive --force + poetry run s4cmd put ./data_pipeline/files/ s3://${{secrets.S3_DATA_BUCKET}}/data-versions/${{env.J40_VERSION_LABEL_STRING}}/data/score/downloadable --sync-check --recursive --force + poetry run s4cmd put ./data_pipeline/data/score/downloadable/ s3://${{secrets.S3_DATA_BUCKET}}/data-versions/${{env.J40_VERSION_LABEL_STRING}}/data/score/downloadable --sync-check --recursive --force - name: Deploy 1.0 score post - if: ${{ env.J40_VERSION_LABEL_STRING == '1.0' || env.J40_VERSION_LABEL_STRING == 'test' }} + if: ${{ env.J40_VERSION_LABEL_STRING == '1.0' }} run: | curl "${{secrets.DATA_URL}}/data-versions/1.0/data/score/downloadable/1.0-shapefile-codebook.zip" -s -f -I -o /dev/null && \ curl "${{secrets.DATA_URL}}/data-versions/1.0/data/score/downloadable/1.0-communities.xlsx" -s -f -I -o /dev/null && \ curl "${{secrets.DATA_URL}}/data-versions/1.0/data/score/downloadable/1.0-communities.csv" -s -f -I -o /dev/null && \ - curl "${{secrets.DATA_URL}}/data-versions/1.0/data/score/downloadable/1.0-shapefile-codebook.zip" -s -f -I -o /dev/null && \ curl "${{secrets.DATA_URL}}/data-versions/1.0/data/score/downloadable/cejst-technical-support-document.pdf" -s -f -I -o /dev/null && \ curl "${{secrets.DATA_URL}}/data-versions/1.0/data/score/downloadable/draft-communities-list.pdf" -s -f -I -o /dev/null - name: Deploy 2.0 score post - if: ${{ env.J40_VERSION_LABEL_STRING == '2.0' || env.J40_VERSION_LABEL_STRING == 'beta' }} + if: ${{ env.J40_VERSION_LABEL_STRING == '2.0' }} run: | curl "${{secrets.DATA_URL}}/data-versions/2.0/data/score/downloadable/2.0-data-documentation.zip" -s -f -I -o /dev/null && \ curl "${{secrets.DATA_URL}}/data-versions/2.0/data/score/downloadable/2.0-shapefile-codebook.zip" -s -f -I -o /dev/null + curl "${{secrets.DATA_URL}}/data-versions/2.0/data/score/downloadable/2.0-communities.xlsx" -s -f -I -o /dev/null && \ + curl "${{secrets.DATA_URL}}/data-versions/2.0/data/score/downloadable/2.0-communities.csv" -s -f -I -o /dev/null && \ + curl "${{secrets.DATA_URL}}/data-versions/2.0/data/score/downloadable/cejst-technical-support-document.pdf" -s -f -I -o /dev/null && \ + curl "${{secrets.DATA_URL}}/data-versions/2.0/data/score/downloadable/draft-communities-list.pdf" -s -f -I -o /dev/null - name: Set timezone for tippecanoe uses: szenius/set-timezone@v2.0 with: @@ -125,6 +119,14 @@ jobs: poetry run python3 data_pipeline/application.py generate-map-tiles - name: Deploy Map to Geoplatform AWS run: | - poetry run s4cmd put ./data_pipeline/data/score/geojson/ s3://${{secrets.S3_DATA_BUCKET}}/data-versions/${{env.J40_VERSION_LABEL_STRING}}/data/score/geojson --recursive --force --num-threads=250 - poetry run s4cmd put ./data_pipeline/data/score/shapefile/ s3://${{secrets.S3_DATA_BUCKET}}/data-versions/${{env.J40_VERSION_LABEL_STRING}}/data/score/shapefile --recursive --force - poetry run s4cmd put ./data_pipeline/data/score/tiles/ s3://${{secrets.S3_DATA_BUCKET}}/data-versions/${{env.J40_VERSION_LABEL_STRING}}/data/score/tiles --recursive --force --num-threads=250 + poetry run s4cmd put ./data_pipeline/data/score/geojson/ s3://${{secrets.S3_DATA_BUCKET}}/data-versions/${{env.J40_VERSION_LABEL_STRING}}/data/score/geojson --sync-check --recursive --force --delete-removed --num-threads=250 + poetry run s4cmd put ./data_pipeline/data/score/shapefile/ s3://${{secrets.S3_DATA_BUCKET}}/data-versions/${{env.J40_VERSION_LABEL_STRING}}/data/score/shapefile --sync-check --recursive --force --delete-removed + poetry run s4cmd put ./data_pipeline/data/score/tiles/ s3://${{secrets.S3_DATA_BUCKET}}/data-versions/${{env.J40_VERSION_LABEL_STRING}}/data/score/tiles --sync-check --recursive --force --delete-removed --num-threads=250 + - name: Invalidate cache on AWS CDN + uses: chetan/invalidate-cloudfront-action@master + env: + DISTRIBUTION: ${{secrets.DATA_CDN_ID}} + PATHS: "/*" + AWS_REGION: "us-east-1" + AWS_ACCESS_KEY_ID: ${{ secrets.CLIENT_DEV_AWS_ACCESS_KEY_ID }} + AWS_SECRET_ACCESS_KEY: ${{ secrets.CLIENT_DEV_AWS_SECRET_ACCESS_KEY }} diff --git a/.github/workflows/deploy_frontend_main.yml b/.github/workflows/deploy_frontend_main.yml index 35efb709..662edeaa 100644 --- a/.github/workflows/deploy_frontend_main.yml +++ b/.github/workflows/deploy_frontend_main.yml @@ -15,7 +15,7 @@ jobs: matrix: node-version: [14.x] steps: - - uses: actions/checkout@v2 + - uses: actions/checkout@v4 - name: Use Node.js ${{ matrix.node-version }} uses: actions/setup-node@v2 with: @@ -58,7 +58,7 @@ jobs: environment: Staging steps: - name: Checkout source - uses: actions/checkout@v2 + uses: actions/checkout@v4 - name: Download Artifacts uses: actions/download-artifact@v4 with: @@ -68,7 +68,7 @@ jobs: run: | echo "DESTINATION_FOLDER=main" >> $GITHUB_ENV - name: Configure AWS Credentials - uses: aws-actions/configure-aws-credentials@v1 + uses: aws-actions/configure-aws-credentials@v4 with: aws-access-key-id: ${{ secrets.CLIENT_DEV_AWS_ACCESS_KEY_ID }} aws-secret-access-key: ${{ secrets.CLIENT_DEV_AWS_SECRET_ACCESS_KEY }} diff --git a/.github/workflows/markdown-link-check.yml b/.github/workflows/markdown-link-check.yml index 9db37edb..dfe4d024 100644 --- a/.github/workflows/markdown-link-check.yml +++ b/.github/workflows/markdown-link-check.yml @@ -11,4 +11,6 @@ jobs: steps: - uses: actions/checkout@v4 - name: Run linkspector - uses: umbrelladocs/action-linkspector@v1 \ No newline at end of file + uses: umbrelladocs/action-linkspector@v1 + with: + fail_on_error: true \ No newline at end of file From a983a42b55a47cd95d64a1b180f54b46caaca151 Mon Sep 17 00:00:00 2001 From: Carlos Felix <63804190+carlosfelix2@users.noreply.github.com> Date: Mon, 2 Dec 2024 12:55:34 -0500 Subject: [PATCH 21/28] Fixed Decennial 2020 tract removal bug --- .../data_pipeline/etl/sources/census_decennial/etl.py | 2 +- .../data_pipeline/tests/sources/census_decennial/test_etl.py | 1 + 2 files changed, 2 insertions(+), 1 deletion(-) diff --git a/data/data-pipeline/data_pipeline/etl/sources/census_decennial/etl.py b/data/data-pipeline/data_pipeline/etl/sources/census_decennial/etl.py index 6794b45d..8d32ec98 100644 --- a/data/data-pipeline/data_pipeline/etl/sources/census_decennial/etl.py +++ b/data/data-pipeline/data_pipeline/etl/sources/census_decennial/etl.py @@ -128,7 +128,7 @@ class CensusDecennialETL(ExtractTransformLoad): > 0, DEC_FIELD_NAMES.TERRITORY_MEDIAN_INCOME, ] = territory["median_income"] - self.df_all = pd.concat([self.df_all, df]) + self.df_all = pd.concat([self.df_all, df], ignore_index=True) def _merge_tracts_2010_compatibility(self): """Merges tract 69120950200 to match 2010 tracts""" diff --git a/data/data-pipeline/data_pipeline/tests/sources/census_decennial/test_etl.py b/data/data-pipeline/data_pipeline/tests/sources/census_decennial/test_etl.py index feb3f8d9..0471733b 100644 --- a/data/data-pipeline/data_pipeline/tests/sources/census_decennial/test_etl.py +++ b/data/data-pipeline/data_pipeline/tests/sources/census_decennial/test_etl.py @@ -97,6 +97,7 @@ def test_load_data(extract_path_fixture: Path, territory_params_fixture): df = dec.df_all assert len(df) == 64 assert len(df.columns) == 30 + assert df.index.is_unique # Columns should not have any census variable names census_vars = list(DEC_TERRITORY_PARAMS[0]["xwalk"].keys()) + list( From 0ad64588ea509af11bcd65de00a67c8bd292013e Mon Sep 17 00:00:00 2001 From: Carlos Felix <63804190+carlosfelix2@users.noreply.github.com> Date: Mon, 2 Dec 2024 15:14:02 -0500 Subject: [PATCH 22/28] Comparator generate delta even with column differences --- .../data-pipeline/data_pipeline/comparator.py | 221 ++++++++++-------- 1 file changed, 121 insertions(+), 100 deletions(-) diff --git a/data/data-pipeline/data_pipeline/comparator.py b/data/data-pipeline/data_pipeline/comparator.py index 6aa85ffd..eed14b4f 100644 --- a/data/data-pipeline/data_pipeline/comparator.py +++ b/data/data-pipeline/data_pipeline/comparator.py @@ -1,6 +1,5 @@ import sys import click -import difflib import pandas as pd from pathlib import Path @@ -16,6 +15,26 @@ pd.set_option("display.max_rows", None) pd.set_option("display.width", 10000) pd.set_option("display.colheader_justify", "left") +result_text = [] + + +def _add_text(text: str): + """Add a line to the output result. + + Args: + line (str): a line to add + """ + result_text.append(text) + + +def _get_result_doc() -> str: + """Gets the document with results. + + Returns: + str: the results document as text + """ + return "".join(result_text) + def _read_from_file(file_path: Path): """Read a CSV file into a Dataframe.""" @@ -65,7 +84,9 @@ def cli(): default=constants.DATA_SCORE_CSV_FULL_FILE_PATH, help="Compare to the specified score CSV file instead of downloading from production", ) -def compare_score(compare_to_version: str, compare_to_file: str, local_score_file: str): +def compare_score( + compare_to_version: str, compare_to_file: str, local_score_file: str +): """Compares the score in the production environment to the locally generated score. The algorithm is pretty simple: @@ -81,10 +102,6 @@ def compare_score(compare_to_version: str, compare_to_file: str, local_score_fil FLOAT_ROUNDING_PLACES = 2 WORKING_PATH = constants.TMP_PATH / "Comparator" / "Score" - summary = "# Score Comparison Summary\n" - summary += f"Hi! I'm the Score Comparator. I compared the score in production (version {compare_to_version}) to the" - summary += " locally calculated score. Here are the results.\n" - log_title("Compare Score", "Compare production score to local score") if compare_to_file: @@ -118,59 +135,73 @@ def compare_score(compare_to_version: str, compare_to_file: str, local_score_fil production_score_df_columns = sorted( production_score_df.columns.array.tolist() ) - - log_info("Comparing columns (production vs local). Differences are: ") - summary += "\n## Columns\n" - summary += "I compared the columns. Here's what I found.\n" - - col_diff = difflib.unified_diff( - production_score_df_columns, local_score_df_columns + extra_cols_in_local = set(local_score_df_columns) - set( + production_score_df_columns + ) + extra_cols_in_prod = set(production_score_df_columns) - set( + local_score_df_columns ) - col_diff_res = "" - for d in col_diff: - col_diff_res += str(d) + "\n" - if len(col_diff_res) == 0: - log_info("None. Columns are the same") - summary += "* There are no differences in the column names.\n" + _add_text("# Score Comparison Summary\n") + _add_text( + f"Hi! I'm the Score Comparator. I compared the score in production (version {compare_to_version}) to the" + " locally calculated score. Here are the results:\n\n" + ) + + ##################### + # Compare the columns + ##################### + log_info("Comparing columns (production vs local)") + _add_text("## Columns\n") + if len(extra_cols_in_local) == 0 and len(extra_cols_in_prod) == 0: + _add_text("* There are no differences in the column names.\n") else: - log_info("There are differences. The diff is:") - log_info(col_diff_res) - summary += f"* There are differences in the column names. Here's a diff:\n{col_diff_res}\n" + _add_text( + f"* There are {len(extra_cols_in_local)} columns that were added as compared to the production score." + ) + if len(extra_cols_in_local) > 0: + _add_text(f" Those colums are:\n{extra_cols_in_local}") + _add_text( + f"\n* There are {len(extra_cols_in_prod)} columns that were removed as compared to the production score." + ) + if len(extra_cols_in_prod) > 0: + _add_text(f" Those colums are:\n{extra_cols_in_prod}") + #################### + # Compare the scores + #################### log_info("Comparing dataframe contents (production vs local)") - summary += "\n## Scores\n" - summary += "I compared the scores, too. Here's what I found.\n" + _add_text("\n\n## Scores\n") production_row_count = len(production_score_df.index) local_row_count = len(local_score_df.index) - summary += f"* The production score has {production_row_count:,} census tracts, and the freshly calculated score has {local_row_count:,}." - summary += ( - " They match!\n" - if production_row_count == local_row_count - else " They don't match.\n" + _add_text( + f"* The production score has {production_row_count:,} census tracts, and the freshly calculated score has {local_row_count:,}." ) + if production_row_count == local_row_count: + _add_text(" They match!\n") + else: + _add_text(" They don't match. The differences are:\n") + _add_text( + " * New tracts added to the local score are:\n" + f"{local_score_df.index.difference(production_score_df.index).to_list()}" + "\n * Tracts removed from the local score are:\n" + f"{production_score_df.index.difference(local_score_df.index).to_list()}" + "\n" + ) production_total_population = production_score_df["Total population"].sum() local_total_population = local_score_df["Total population"].sum() - log_info( - f"The total population in all census tracts in production is {production_total_population:,}." + _add_text( + f"* The total population in all census tracts in the production score is {production_total_population:,}. " + f"The total population in all census tracts locally is {local_total_population:,}. " ) - log_info( - f"The total population in all census tracts locally is {local_total_population:,}." - ) - log_info( - f"The difference in population is {abs(production_total_population - local_total_population):,}." - ) - - summary += f"* The total population in all census tracts in the production score is {production_total_population:,}." - summary += f" The total population in all census tracts locally is {local_total_population:,}." - summary += ( - " They match!\n" + _add_text( + "They match!\n" if production_total_population == local_total_population - else f" The difference is {abs(production_total_population - local_total_population):,}.\n" + else f"The difference is {abs(production_total_population - local_total_population):,}.\n" ) production_disadvantaged_tracts_df = production_score_df.query( @@ -196,30 +227,18 @@ def compare_score(compare_to_version: str, compare_to_file: str, local_score_fil / local_total_population ) - log_info( - f"There are {len(production_disadvantaged_tracts_set):,} disadvantaged tracts in the production score." + _add_text( + f"* There are {len(production_disadvantaged_tracts_set):,} disadvantaged tracts in the production score representing" + f" {production_pct_of_population_represented:.1%} of the total population, and {len(local_disadvantaged_tracts_set):,}" ) - log_info( - f"This represents {production_pct_of_population_represented:.1%} of the total population." + _add_text( + f" in the locally generated score representing {local_pct_of_population_represented:.1%} of the total population." ) - log_info( - f"There are {len(local_disadvantaged_tracts_set):,} in the locally generated score." - ) - log_info( - f"This represents {local_pct_of_population_represented:.1%} of the total population." - ) - log_info( - f"The difference is {abs(len(production_disadvantaged_tracts_set) - len(local_disadvantaged_tracts_set)):,} tract(s)." - ) - - summary += f"* There are {len(production_disadvantaged_tracts_set):,} disadvantaged tracts in the production score representing" - summary += f" {production_pct_of_population_represented:.1%} of the total population, and {len(local_disadvantaged_tracts_set):,}" - summary += f" in the locally generated score representing {local_pct_of_population_represented:.1%} of the total population." - summary += ( - " The number of tracts match!\n" + _add_text( + " The number of tracts match!\n " if len(production_disadvantaged_tracts_set) == len(local_disadvantaged_tracts_set) - else f" The difference is {abs(len(production_disadvantaged_tracts_set) - len(local_disadvantaged_tracts_set))} tract(s).\n" + else f" The difference is {abs(len(production_disadvantaged_tracts_set) - len(local_disadvantaged_tracts_set))} tract(s).\n " ) removed_tracts = production_disadvantaged_tracts_set.difference( @@ -228,58 +247,60 @@ def compare_score(compare_to_version: str, compare_to_file: str, local_score_fil added_tracts = local_disadvantaged_tracts_set.difference( production_disadvantaged_tracts_set ) - removed_tracts_str = ", ".join(list(removed_tracts)) - added_tracts_str = ", ".join(list(added_tracts)) - - log_info( - f"There are {len(removed_tracts):,} tract(s) marked as disadvantaged in the prod " - "score that are not disadvantaged in the local score. Those tracts are:" - ) - log_info(removed_tracts_str) - log_info( - f"There are {len(added_tracts):,} tract(s) marked as disadvantaged in the local " - "score that are not disadvantaged in the prod score. Those tracts are:" - ) - log_info(added_tracts_str) - - summary += ( + _add_text( f"* There are {len(removed_tracts):,} tract(s) marked as disadvantaged in the production score that are not disadvantaged in the locally" - f" generated score (i.e. disadvantaged tracts that were removed by the new score). Those tracts are:\n{removed_tracts_str}\n" - f" There are {len(added_tracts):,} tract(s) marked as disadvantaged in the locally generated score that are not disadvantaged in the" - f" production score (i.e. disadvantaged tracts that were added by the new score). Those tracts are:\n{added_tracts_str}\n\n" + f" generated score (i.e. disadvantaged tracts that were removed by the new score). " ) + if len(removed_tracts) > 0: + _add_text(f"Those tracts are:\n{removed_tracts}\n") + _add_text( + f"* There are {len(added_tracts):,} tract(s) marked as disadvantaged in the locally generated score that are not disadvantaged in the" + f" production score (i.e. disadvantaged tracts that were added by the new score). " + ) + if len(added_tracts) > 0: + _add_text(f"Those tracts are:\n{added_tracts}\n") + + ################ + # Create a delta + ################ + _add_text("\n## Delta\n") + # First we make the columns on two dataframes to be the same to be able to compare + trimmed_prod_df = production_score_df.drop(extra_cols_in_prod, axis=1) + trimmed_local_df = local_score_df.drop(extra_cols_in_local, axis=1) try: - comparison_results_df = production_score_df.compare( - local_score_df, align_axis=1, keep_shape=False, keep_equal=False + comparison_results_df = trimmed_prod_df.compare( + trimmed_local_df, align_axis=1, keep_shape=False, keep_equal=False ).rename({"self": "Production", "other": "Local"}, axis=1, level=1) - summary += "* I compared all values across all census tracts." - summary += f" There are {len(comparison_results_df.index):,} tracts with at least one difference." - summary += " Please examine the logs or run the score comparison locally to view them all.\n" - - log_info( - f"There are {len(comparison_results_df.index)} rows with any differences." + _add_text( + "* I compared all values across all census tracts. Note this ignores any columns that have been added or removed." + f" There are {len(comparison_results_df.index):,} tracts with at least one difference.\n" ) - if len(comparison_results_df.index) > 0: - log_info("Those differences are:") - log_info("\n" + str(comparison_results_df)) - comparison_path = WORKING_PATH / "deltas.csv" - comparison_results_df.to_csv(path_or_buf=comparison_path) - log_info(f"Wrote comparison results to {comparison_path}") + + comparison_path = WORKING_PATH / "deltas.csv" + comparison_results_df.to_csv(path_or_buf=comparison_path) + + _add_text(f"* Wrote comparison results to {comparison_path}") except ValueError as e: - summary += "* I could not run a full comparison. This is likely because there are column or index (census tract) differences." - summary += " Please examine the logs or run the score comparison locally to find out more.\n" - log_info( - f"Encountered an exception while performing the comparison: {repr(e)}" + _add_text( + "* I could not run a full comparison. This is likely because there are column or index (census tract) differences." + " Please examine the logs or run the score comparison locally to find out more.\n" + ) + _add_text( + f"Encountered an exception while performing the comparison: {repr(e)}\n" ) + result_doc = _get_result_doc() + print(result_doc) + + # Write the report summary_path = WORKING_PATH / "comparison-summary.md" with open(summary_path, "w", encoding="utf-8") as f: - f.write(summary) + f.write(result_doc) log_info(f"Wrote comparison summary to {summary_path}") log_goodbye() From 84c1a3aaf4cf8fd369cab260adb734e37cdd8134 Mon Sep 17 00:00:00 2001 From: alene Date: Tue, 3 Dec 2024 10:46:45 -0500 Subject: [PATCH 23/28] Donut Hole DAC Qualification - Solution for Water Boundaries --- .../ipython/donut_hole_testing_2024.ipynb | 5773 +++++++++++++++++ .../data_pipeline/score/field_names.py | 3 + .../data_pipeline/score/utils.py | 40 + 3 files changed, 5816 insertions(+) create mode 100644 data/data-pipeline/data_pipeline/ipython/donut_hole_testing_2024.ipynb diff --git a/data/data-pipeline/data_pipeline/ipython/donut_hole_testing_2024.ipynb b/data/data-pipeline/data_pipeline/ipython/donut_hole_testing_2024.ipynb new file mode 100644 index 00000000..ba196d42 --- /dev/null +++ b/data/data-pipeline/data_pipeline/ipython/donut_hole_testing_2024.ipynb @@ -0,0 +1,5773 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "75d2705f", + "metadata": {}, + "source": [ + "# EXPLORATION OF DONUT HOLE WATER BOUNDARY ISSUE" + ] + }, + { + "cell_type": "markdown", + "id": "dea57328", + "metadata": {}, + "source": [ + "## Imports, constants, and data load" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "7ac0d5d9", + "metadata": {}, + "outputs": [], + "source": [ + "import geopandas as gpd\n", + "import pandas as pd\n", + "import numpy as np\n", + "import os\n", + "import sys\n", + "\n", + "module_path = os.path.abspath(os.path.join(\"../..\"))\n", + "if module_path not in sys.path:\n", + " sys.path.append(module_path)\n", + "\n", + "from data_pipeline.config import settings\n", + "from data_pipeline.etl.sources.geo_utils import (\n", + " add_tracts_for_geometries,\n", + " get_tract_geojson,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "16b9ffd5", + "metadata": {}, + "outputs": [], + "source": [ + "# pull out necessary definitions from field_names.py\n", + "ORIGINAL_TRACT = \"ORIGINAL_TRACT\"\n", + "SCORE_N_COMMUNITIES = \"Definition N (communities)\"\n", + "GEOID_TRACT_FIELD = \"GEOID10_TRACT\"\n", + "ADJACENT_MEAN_SUFFIX = \" (based on adjacency index and low income alone)\"\n", + "ADJACENCY_INDEX_SUFFIX = \" (average of neighbors)\"\n", + "CAM_ID = '48061012305'" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "b3c256f9", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-11-19 19:29:38,131 [ data_pipeline.etl.sources.geo_utils] DEBUG Loading tract geometry data from census ETL\n" + ] + }, + { + "data": { + "text/plain": [ + "Index(['STATEFP10', 'COUNTYFP10', 'TRACTCE10', 'GEOID10_TRACT', 'NAME10',\n", + " 'NAMELSAD10', 'MTFCC10', 'FUNCSTAT10', 'ALAND10', 'AWATER10',\n", + " 'INTPTLAT10', 'INTPTLON10', 'geometry'],\n", + " dtype='object')" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# load Census data\n", + "tract_data = get_tract_geojson()\n", + "tract_data.columns" + ] + }, + { + "cell_type": "markdown", + "id": "f829c206", + "metadata": {}, + "source": [ + "## Look at example tract in Cameron, TX" + ] + }, + { + "cell_type": "markdown", + "id": "f6d133ca", + "metadata": {}, + "source": [ + "Tract no. 48061012305 in Cameron, TX is an example of a tract that displays the donut hole water boundary issue: all the tracts that it borders are classified as disadvantaged, and it meets the poverty threshold, yet it is being incorrectly categorized as non-disadvantaged due to its water boundary. Here we pull all adjacent tracts, compare to what we see on the map, and then look for solutions." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "106425b6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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STATEFP10COUNTYFP10TRACTCE10ORIGINAL_TRACTNAME10NAMELSAD10MTFCC10FUNCSTAT10ALAND10AWATER10INTPTLAT10INTPTLON10geometryDefinition N (communities)
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" + ], + "text/plain": [ + " STATEFP10 COUNTYFP10 TRACTCE10 ORIGINAL_TRACT NAME10 NAMELSAD10 \\\n", + "0 48 061 012305 48061012305 123.05 Census Tract 123.05 \n", + "\n", + " MTFCC10 FUNCSTAT10 ALAND10 AWATER10 INTPTLAT10 INTPTLON10 \\\n", + "0 G5020 S 25920881 324996211 +26.2732070 -097.2763703 \n", + "\n", + " geometry \\\n", + "0 POLYGON ((-97.22490 26.41153, -97.22436 26.411... \n", + "\n", + " Definition N (communities) \n", + "0 False " + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "## look at census data for Cameron\n", + "cam_only_df = pd.DataFrame({GEOID_TRACT_FIELD:[CAM_ID], SCORE_N_COMMUNITIES:[False]})\n", + "\n", + "cam_only_df: gpd.GeoDataFrame = tract_data.merge(\n", + " cam_only_df, on=GEOID_TRACT_FIELD\n", + " )\n", + "cam_only_df = cam_only_df.rename(columns={GEOID_TRACT_FIELD: ORIGINAL_TRACT})\n", + "cam_only_df" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "87cdbbaf", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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04806101230548061012305123.05Census Tract 123.05G5020S25920881324996211...01010048061010100101Census Tract 101G5020S365458278113041841+26.2713946-097.4414470
04806101230548061012305123.05Census Tract 123.05G5020S25920881324996211...950700484899507009507Census Tract 9507G5020S1025415174377000048+26.5154317-097.5790835
04806101230548061012305123.05Census Tract 123.05G5020S25920881324996211...990000484899900009900Census Tract 9900G5020S0121414926+26.5064260-097.2240134
04806101230548061012305123.05Census Tract 123.05G5020S25920881324996211...01270048061012700127Census Tract 127G5020S12962279967725054+25.9786218-097.2580863
04806101230548061012305123.05Census Tract 123.05G5020S25920881324996211...01230448061012304123.04Census Tract 123.04G5020S57612996286773+26.0631407-097.2184868
04806101230548061012305123.05Census Tract 123.05G5020S25920881324996211...01230148061012301123.01Census Tract 123.01G5020S137622651119430273+26.1581170-097.3180348
04806101230548061012305123.05Census Tract 123.05G5020S25920881324996211...990000480619900009900Census Tract 9900G5020S0268272237+26.1902408-097.1473235
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" + ], + "text/plain": [ + " STATEFP10_left COUNTYFP10_left TRACTCE10_left ORIGINAL_TRACT NAME10_left \\\n", + "0 48 061 012305 48061012305 123.05 \n", + "0 48 061 012305 48061012305 123.05 \n", + "0 48 061 012305 48061012305 123.05 \n", + "0 48 061 012305 48061012305 123.05 \n", + "0 48 061 012305 48061012305 123.05 \n", + "0 48 061 012305 48061012305 123.05 \n", + "0 48 061 012305 48061012305 123.05 \n", + "\n", + " NAMELSAD10_left MTFCC10_left FUNCSTAT10_left ALAND10_left \\\n", + "0 Census Tract 123.05 G5020 S 25920881 \n", + "0 Census Tract 123.05 G5020 S 25920881 \n", + "0 Census Tract 123.05 G5020 S 25920881 \n", + "0 Census Tract 123.05 G5020 S 25920881 \n", + "0 Census Tract 123.05 G5020 S 25920881 \n", + "0 Census Tract 123.05 G5020 S 25920881 \n", + "0 Census Tract 123.05 G5020 S 25920881 \n", + "\n", + " AWATER10_left ... TRACTCE10_right GEOID10_TRACT NAME10_right \\\n", + "0 324996211 ... 010100 48061010100 101 \n", + "0 324996211 ... 950700 48489950700 9507 \n", + "0 324996211 ... 990000 48489990000 9900 \n", + "0 324996211 ... 012700 48061012700 127 \n", + "0 324996211 ... 012304 48061012304 123.04 \n", + "0 324996211 ... 012301 48061012301 123.01 \n", + "0 324996211 ... 990000 48061990000 9900 \n", + "\n", + " NAMELSAD10_right MTFCC10_right FUNCSTAT10_right ALAND10_right \\\n", + "0 Census Tract 101 G5020 S 365458278 \n", + "0 Census Tract 9507 G5020 S 1025415174 \n", + "0 Census Tract 9900 G5020 S 0 \n", + "0 Census Tract 127 G5020 S 129622799 \n", + "0 Census Tract 123.04 G5020 S 5761299 \n", + "0 Census Tract 123.01 G5020 S 137622651 \n", + "0 Census Tract 9900 G5020 S 0 \n", + "\n", + " AWATER10_right INTPTLAT10_right INTPTLON10_right \n", + "0 113041841 +26.2713946 -097.4414470 \n", + "0 377000048 +26.5154317 -097.5790835 \n", + "0 121414926 +26.5064260 -097.2240134 \n", + "0 67725054 +25.9786218 -097.2580863 \n", + "0 6286773 +26.0631407 -097.2184868 \n", + "0 119430273 +26.1581170 -097.3180348 \n", + "0 268272237 +26.1902408 -097.1473235 \n", + "\n", + "[7 rows x 27 columns]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# get all the tracts that are adjacent to cameron\n", + "cam_adjacent_tracts: gpd.GeoDataFrame = cam_only_df.sjoin(\n", + " tract_data, predicate=\"touches\"\n", + " )\n", + "cam_adjacent_tracts" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "af3aa581", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['STATEFP10_left', 'COUNTYFP10_left', 'TRACTCE10_left', 'ORIGINAL_TRACT',\n", + " 'NAME10_left', 'NAMELSAD10_left', 'MTFCC10_left', 'FUNCSTAT10_left',\n", + " 'ALAND10_left', 'AWATER10_left', 'INTPTLAT10_left', 'INTPTLON10_left',\n", + " 'geometry', 'Definition N (communities)', 'index_right',\n", + " 'STATEFP10_right', 'COUNTYFP10_right', 'TRACTCE10_right',\n", + " 'GEOID10_TRACT', 'NAME10_right', 'NAMELSAD10_right', 'MTFCC10_right',\n", + " 'FUNCSTAT10_right', 'ALAND10_right', 'AWATER10_right',\n", + " 'INTPTLAT10_right', 'INTPTLON10_right'],\n", + " dtype='object')" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cam_adjacent_tracts.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "85559ed4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " ORIGINAL_TRACT GEOID10_TRACT AWATER10_left ALAND10_left AWATER10_right \\\n", + "0 48061012305 48061010100 324996211 25920881 113041841 \n", + "0 48061012305 48489950700 324996211 25920881 377000048 \n", + "0 48061012305 48489990000 324996211 25920881 121414926 \n", + "0 48061012305 48061012700 324996211 25920881 67725054 \n", + "0 48061012305 48061012304 324996211 25920881 6286773 \n", + "0 48061012305 48061012301 324996211 25920881 119430273 \n", + "0 48061012305 48061990000 324996211 25920881 268272237 \n", + "\n", + " ALAND10_right \n", + "0 365458278 \n", + "0 1025415174 \n", + "0 0 \n", + "0 129622799 \n", + "0 5761299 \n", + "0 137622651 \n", + "0 0 " + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cam_adjacent_tracts[['ORIGINAL_TRACT', 'GEOID10_TRACT', \n", + " 'AWATER10_left', 'ALAND10_left', 'AWATER10_right', 'ALAND10_right']]" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "60d0801f", + "metadata": {}, + "outputs": [], + "source": [ + "# # compare to what I see on the map for Cameron:\n", + "\n", + "# 48489950700 Willacy County\n", + "# 48061010100 Cameron County (contains Laguna Atascosa)\n", + "# 48061012301 Cameron County (contains KPIL)\n", + "# 48061012304 Cameron County (contains Port Isabel)\n", + "# 48061012700 Cameron County (contains Boca Chica Village)\n", + "\n", + "# # what about 48489990000 and 48061990000?\n", + "# both have ALAND10 = 0\n", + "# and both tract IDs end in 9990000" + ] + }, + { + "cell_type": "markdown", + "id": "5201e422", + "metadata": {}, + "source": [ + "Proposed solution: remove tracts where ALAND10=10 (ie water-only tracts)" + ] + }, + { + "cell_type": "markdown", + "id": "43dc09e8", + "metadata": {}, + "source": [ + "## Compare water-only tracts to tracts with land area" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "4045bb4f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "367" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# How many of these water-only tracts exist?\n", + "len(tract_data[tract_data.ALAND10==0])" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "37fdf29b", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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STATEFP10COUNTYFP10TRACTCE10GEOID10_TRACTNAME10NAMELSAD10MTFCC10FUNCSTAT10ALAND10AWATER10INTPTLAT10INTPTLON10geometrywater_only
80836063990000360639900009900Census Tract 9900G5020S01550634668+43.4569085-078.7926530POLYGON ((-78.46554 43.37337, -78.47053 43.372...True
1015360850089003608500890089Census Tract 89G5020S0100710+40.6469814-074.1024106POLYGON ((-74.10504 40.64808, -74.10025 40.648...True
114936085990100360859901009901Census Tract 9901G5020S080255151+40.5255512-074.1085829POLYGON ((-74.25482 40.49390, -74.25308 40.491...True
121936013990000360139900009900Census Tract 9900G5020S01015062614+42.5006582-079.5141161POLYGON ((-79.24982 42.53745, -79.24982 42.537...True
122636075990000360759900009900Census Tract 9900G5020S0724224875+43.5955454-076.4442549POLYGON ((-76.55001 43.45923, -76.55064 43.458...True
.............................................
7337923015990000230159900009900Census Tract 9900G5020S0375299033+43.8029690-069.4652520POLYGON ((-69.68695 43.81613, -69.68695 43.815...True
7339923031990100230319901009901Census Tract 9901G5020S0567248819+43.1483531-070.4998364POLYGON ((-70.22209 43.46688, -70.22432 43.464...True
7350923023990000230239900009900Census Tract 9900G5020S0161372101+43.7084463-069.7721513POLYGON ((-69.69756 43.81594, -69.69755 43.813...True
7352723013990000230139900009900Census Tract 9900G5020S01636919697+43.9538894-068.9327255POLYGON ((-68.73563 44.11917, -68.73179 44.117...True
7362923005990000230059900009900Census Tract 9900G5020S0361952519+43.6058367-070.0776892POLYGON ((-70.07870 43.66881, -70.06517 43.673...True
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367 rows × 14 columns

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" + ], + "text/plain": [ + " STATEFP10 COUNTYFP10 TRACTCE10 GEOID10_TRACT NAME10 NAMELSAD10 \\\n", + "808 36 063 990000 36063990000 9900 Census Tract 9900 \n", + "1015 36 085 008900 36085008900 89 Census Tract 89 \n", + "1149 36 085 990100 36085990100 9901 Census Tract 9901 \n", + "1219 36 013 990000 36013990000 9900 Census Tract 9900 \n", + "1226 36 075 990000 36075990000 9900 Census Tract 9900 \n", + "... ... ... ... ... ... ... \n", + "73379 23 015 990000 23015990000 9900 Census Tract 9900 \n", + "73399 23 031 990100 23031990100 9901 Census Tract 9901 \n", + "73509 23 023 990000 23023990000 9900 Census Tract 9900 \n", + "73527 23 013 990000 23013990000 9900 Census Tract 9900 \n", + "73629 23 005 990000 23005990000 9900 Census Tract 9900 \n", + "\n", + " MTFCC10 FUNCSTAT10 ALAND10 AWATER10 INTPTLAT10 INTPTLON10 \\\n", + "808 G5020 S 0 1550634668 +43.4569085 -078.7926530 \n", + "1015 G5020 S 0 100710 +40.6469814 -074.1024106 \n", + "1149 G5020 S 0 80255151 +40.5255512 -074.1085829 \n", + "1219 G5020 S 0 1015062614 +42.5006582 -079.5141161 \n", + "1226 G5020 S 0 724224875 +43.5955454 -076.4442549 \n", + "... ... ... ... ... ... ... \n", + "73379 G5020 S 0 375299033 +43.8029690 -069.4652520 \n", + "73399 G5020 S 0 567248819 +43.1483531 -070.4998364 \n", + "73509 G5020 S 0 161372101 +43.7084463 -069.7721513 \n", + "73527 G5020 S 0 1636919697 +43.9538894 -068.9327255 \n", + "73629 G5020 S 0 361952519 +43.6058367 -070.0776892 \n", + "\n", + " geometry water_only \n", + "808 POLYGON ((-78.46554 43.37337, -78.47053 43.372... True \n", + "1015 POLYGON ((-74.10504 40.64808, -74.10025 40.648... True \n", + "1149 POLYGON ((-74.25482 40.49390, -74.25308 40.491... True \n", + "1219 POLYGON ((-79.24982 42.53745, -79.24982 42.537... True \n", + "1226 POLYGON ((-76.55001 43.45923, -76.55064 43.458... True \n", + "... ... ... \n", + "73379 POLYGON ((-69.68695 43.81613, -69.68695 43.815... True \n", + "73399 POLYGON ((-70.22209 43.46688, -70.22432 43.464... True \n", + "73509 POLYGON ((-69.69756 43.81594, -69.69755 43.813... True \n", + "73527 POLYGON ((-68.73563 44.11917, -68.73179 44.117... True \n", + "73629 POLYGON ((-70.07870 43.66881, -70.06517 43.673... True \n", + "\n", + "[367 rows x 14 columns]" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# make a copy of the tract data\n", + "tract_explore = tract_data.copy()\n", + "\n", + "# add bool for whether tract has land area\n", + "tract_explore['water_only'] = tract_explore.ALAND10==0\n", + "\n", + "# preview water tracts\n", + "tract_explore[tract_explore.water_only]" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "d0c30aef", + "metadata": {}, + "outputs": [], + "source": [ + "# confirm we can get the tract code stored in TRACTCE10 by taking last 6 chars of GEOID10_TRACT\n", + "# (because input df may not contain the isolated tract code column)\n", + "tract_explore['tract_code'] = tract_explore.GEOID10_TRACT.apply(lambda x: x[-6:])\n", + "assert all(tract_explore.tract_code==tract_explore.TRACTCE10)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "5436fdd6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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GEOID10_TRACTTRACTCE10tract_codetract_code_num
020071958100958100958100958100
120175965600965600965600965600
220175965700965700965700965700
32004302030002030002030020300
42004302020002020002020020200
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" + ], + "text/plain": [ + " GEOID10_TRACT TRACTCE10 tract_code tract_code_num\n", + "0 20071958100 958100 958100 958100\n", + "1 20175965600 965600 965600 965600\n", + "2 20175965700 965700 965700 965700\n", + "3 20043020300 020300 020300 20300\n", + "4 20043020200 020200 020200 20200" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# convert tract codes to ints to check ranges\n", + "tract_explore['tract_code_num'] = tract_explore['tract_code'].apply(lambda x: int(x))\n", + "tract_explore[['GEOID10_TRACT', 'TRACTCE10', 'tract_code', 'tract_code_num']].head()" + ] + }, + { + "cell_type": "markdown", + "id": "a2e1814b", + "metadata": {}, + "source": [ + "Per Census documentation:\n", + "\n", + "- 000100 to 989900—Basic number range for census tracts\n", + "- 990000 to 990099—Basic number for census tracts in water areas\n", + "- 990100 to 998900—Basic number range for census tracts\n", + "\n", + "source: https://www2.census.gov/geo/pdfs/maps-data/data/tiger/tgrshp2017/TGRSHP2017_TechDoc_Ch3.pdf (page 3-26)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "cf09b1b6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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GEOID10_TRACTTRACTCE10tract_codetract_code_numtract_in_water_range
020071958100958100958100958100False
120175965600965600965600965600False
220175965700965700965700965700False
32004302030002030002030020300False
42004302020002020002020020200False
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" + ], + "text/plain": [ + " GEOID10_TRACT TRACTCE10 tract_code tract_code_num tract_in_water_range\n", + "0 20071958100 958100 958100 958100 False\n", + "1 20175965600 965600 965600 965600 False\n", + "2 20175965700 965700 965700 965700 False\n", + "3 20043020300 020300 020300 20300 False\n", + "4 20043020200 020200 020200 20200 False" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def in_water_range(x):\n", + " if x >= 990000 and x <= 990099:\n", + " return True\n", + " return False\n", + "\n", + "tract_explore['tract_in_water_range'] = tract_explore['tract_code_num'].apply(in_water_range)\n", + "tract_explore[['GEOID10_TRACT', 'TRACTCE10', 'tract_code', 'tract_code_num', 'tract_in_water_range']].head()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "1c0fab10", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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GEOID10_TRACTTRACTCE10tract_codetract_code_numtract_in_water_range
80836063990000990000990000990000True
1015360850089000089000089008900False
114936085990100990100990100990100False
121936013990000990000990000990000True
122636075990000990000990000990000True
..................
7337923015990000990000990000990000True
7339923031990100990100990100990100False
7350923023990000990000990000990000True
7352723013990000990000990000990000True
7362923005990000990000990000990000True
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367 rows × 5 columns

\n", + "
" + ], + "text/plain": [ + " GEOID10_TRACT TRACTCE10 tract_code tract_code_num tract_in_water_range\n", + "808 36063990000 990000 990000 990000 True\n", + "1015 36085008900 008900 008900 8900 False\n", + "1149 36085990100 990100 990100 990100 False\n", + "1219 36013990000 990000 990000 990000 True\n", + "1226 36075990000 990000 990000 990000 True\n", + "... ... ... ... ... ...\n", + "73379 23015990000 990000 990000 990000 True\n", + "73399 23031990100 990100 990100 990100 False\n", + "73509 23023990000 990000 990000 990000 True\n", + "73527 23013990000 990000 990000 990000 True\n", + "73629 23005990000 990000 990000 990000 True\n", + "\n", + "[367 rows x 5 columns]" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# look at water-only tracts\n", + "tract_explore[tract_explore.water_only]\\\n", + " [['GEOID10_TRACT', 'TRACTCE10', 'tract_code', 'tract_code_num', 'tract_in_water_range']]" + ] + }, + { + "cell_type": "markdown", + "id": "4ba84ff5", + "metadata": {}, + "source": [ + "Some of the tracts with ALAND10=0 have tract numbers not in the water-only range." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "320bd044", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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GEOID10_TRACTtract_in_water_range
countsummean
water_only
False7376700.000000
True3672270.618529
\n", + "
" + ], + "text/plain": [ + " GEOID10_TRACT tract_in_water_range \n", + " count sum mean\n", + "water_only \n", + "False 73767 0 0.000000\n", + "True 367 227 0.618529" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tract_explore.groupby('water_only').agg({'GEOID10_TRACT': 'count', \n", + " 'tract_in_water_range': ['sum', 'mean']})" + ] + }, + { + "cell_type": "markdown", + "id": "612c72b8", + "metadata": {}, + "source": [ + "There are no tracts with land area that have tract codes in the water-only range. But about 38% of tracts with 0 land area have normal tract codes outside the water range." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "e0d5fb77", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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STATEFP10COUNTYFP10TRACTCE10GEOID10_TRACTNAME10NAMELSAD10MTFCC10FUNCSTAT10ALAND10AWATER10INTPTLAT10INTPTLON10geometrywater_onlytract_codetract_code_numtract_in_water_range
1015360850089003608500890089Census Tract 89G5020S0100710+40.6469814-074.1024106POLYGON ((-74.10504 40.64808, -74.10025 40.648...True0089008900False
114936085990100360859901009901Census Tract 9901G5020S080255151+40.5255512-074.1085829POLYGON ((-74.25482 40.49390, -74.25308 40.491...True990100990100False
284036047990100360479901009901Census Tract 9901G5020S017793514+40.5649933-074.0148865POLYGON ((-74.00620 40.59437, -74.00619 40.594...True990100990100False
309536059990100360599901009901Census Tract 9901G5020S013672168+40.8790568-073.7020503POLYGON ((-73.76050 40.84184, -73.76102 40.841...True990100990100False
309636059990200360599902009902Census Tract 9902G5020S027086403+40.9078457-073.6526612POLYGON ((-73.65151 40.88524, -73.66481 40.875...True990200990200False
......................................................
6894615009990200150099902009902Census Tract 9902G5020S01643042883+20.6128423-156.5486800POLYGON ((-156.70541 20.82632, -156.70658 20.8...True990200990200False
6894915009991200150099912009912Census Tract 9912G5020S0482573725+20.9476085-156.9795251POLYGON ((-157.11121 20.87500, -157.11725 20.8...True991200991200False
7033939093990200390939902009902Census Tract 9902G5020S01094173093+41.6347746-082.1526215POLYGON ((-82.16712 41.47534, -82.16943 41.474...True990200990200False
7097939043990100390439901009901Census Tract 9901G5020S0853315204+41.5559059-082.5241364POLYGON ((-82.51199 41.39475, -82.51472 41.395...True990100990100False
7339923031990100230319901009901Census Tract 9901G5020S0567248819+43.1483531-070.4998364POLYGON ((-70.22209 43.46688, -70.22432 43.464...True990100990100False
\n", + "

140 rows × 17 columns

\n", + "
" + ], + "text/plain": [ + " STATEFP10 COUNTYFP10 TRACTCE10 GEOID10_TRACT NAME10 NAMELSAD10 \\\n", + "1015 36 085 008900 36085008900 89 Census Tract 89 \n", + "1149 36 085 990100 36085990100 9901 Census Tract 9901 \n", + "2840 36 047 990100 36047990100 9901 Census Tract 9901 \n", + "3095 36 059 990100 36059990100 9901 Census Tract 9901 \n", + "3096 36 059 990200 36059990200 9902 Census Tract 9902 \n", + "... ... ... ... ... ... ... \n", + "68946 15 009 990200 15009990200 9902 Census Tract 9902 \n", + "68949 15 009 991200 15009991200 9912 Census Tract 9912 \n", + "70339 39 093 990200 39093990200 9902 Census Tract 9902 \n", + "70979 39 043 990100 39043990100 9901 Census Tract 9901 \n", + "73399 23 031 990100 23031990100 9901 Census Tract 9901 \n", + "\n", + " MTFCC10 FUNCSTAT10 ALAND10 AWATER10 INTPTLAT10 INTPTLON10 \\\n", + "1015 G5020 S 0 100710 +40.6469814 -074.1024106 \n", + "1149 G5020 S 0 80255151 +40.5255512 -074.1085829 \n", + "2840 G5020 S 0 17793514 +40.5649933 -074.0148865 \n", + "3095 G5020 S 0 13672168 +40.8790568 -073.7020503 \n", + "3096 G5020 S 0 27086403 +40.9078457 -073.6526612 \n", + "... ... ... ... ... ... ... \n", + "68946 G5020 S 0 1643042883 +20.6128423 -156.5486800 \n", + "68949 G5020 S 0 482573725 +20.9476085 -156.9795251 \n", + "70339 G5020 S 0 1094173093 +41.6347746 -082.1526215 \n", + "70979 G5020 S 0 853315204 +41.5559059 -082.5241364 \n", + "73399 G5020 S 0 567248819 +43.1483531 -070.4998364 \n", + "\n", + " geometry water_only \\\n", + "1015 POLYGON ((-74.10504 40.64808, -74.10025 40.648... True \n", + "1149 POLYGON ((-74.25482 40.49390, -74.25308 40.491... True \n", + "2840 POLYGON ((-74.00620 40.59437, -74.00619 40.594... True \n", + "3095 POLYGON ((-73.76050 40.84184, -73.76102 40.841... True \n", + "3096 POLYGON ((-73.65151 40.88524, -73.66481 40.875... True \n", + "... ... ... \n", + "68946 POLYGON ((-156.70541 20.82632, -156.70658 20.8... True \n", + "68949 POLYGON ((-157.11121 20.87500, -157.11725 20.8... True \n", + "70339 POLYGON ((-82.16712 41.47534, -82.16943 41.474... True \n", + "70979 POLYGON ((-82.51199 41.39475, -82.51472 41.395... True \n", + "73399 POLYGON ((-70.22209 43.46688, -70.22432 43.464... True \n", + "\n", + " tract_code tract_code_num tract_in_water_range \n", + "1015 008900 8900 False \n", + "1149 990100 990100 False \n", + "2840 990100 990100 False \n", + "3095 990100 990100 False \n", + "3096 990200 990200 False \n", + "... ... ... ... \n", + "68946 990200 990200 False \n", + "68949 991200 991200 False \n", + "70339 990200 990200 False \n", + "70979 990100 990100 False \n", + "73399 990100 990100 False \n", + "\n", + "[140 rows x 17 columns]" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# view the exceptions (no land area, but tract code not in water range)\n", + "water_exceptions = tract_explore[(tract_explore.water_only==True) & (tract_explore.tract_in_water_range==False)]\n", + "water_exceptions" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "b36fdc0d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "36063990000, 36013990000, 36075990000, 36055990000, 36045990001, 36029990000, 36073990000, 41007990000, 41039990000, 41019990000, 01003990000, 01097990000, 17031990000, 17097990000, 10001990000, 10005990000, 06083990000, 06053990000, 06061990000, 06015990000, 06079990000, 06013990000, 06017990000, 06001990000, 51710990000, 26157990000, 26011990000, 26001990000, 26141990000, 26019990000, 26069990000, 26053990000, 26147990000, 26153990000, 26063990000, 26159990000, 26033990000, 26005990000, 26103990000, 26061990000, 26003990000, 26095990000, 26041990000, 26031990000, 26009990000, 26139990000, 26047990000, 26109990000, 26007990000, 26021990000, 26089990000, 26017990000, 26115990000, 26151990000, 26055990000, 26127990000, 26097990000, 26121990000, 26105990000, 26101990000, 26029990000, 66010990000, 27031990000, 69100990000, 69110990000, 69120990000, 28045990000, 28059990000, 28047990000, 53031990000, 53061990002, 53027990000, 12009990000, 12017990000, 12037990000, 12011990000, 12087990000, 12081990000, 12093990000, 12029990000, 12031990000, 12071990000, 12065990000, 12033990000, 12045990000, 12086990000, 12129990000, 12103990000, 12057990000, 12115990000, 12101990000, 12123990000, 12085990000, 12051990000, 12005990000, 12035990000, 12053990000, 12043990000, 12131990000, 12099990000, 12075990000, 12015990000, 12061990000, 12095990000, 12021990000, 12113990000, 12089990000, 12111990000, 12127990000, 32031990000, 32005990000, 32510990000, 24029990000, 24017990000, 24047990000, 24041990000, 24037990000, 24035990000, 24019990000, 24003990000, 48039990000, 48489990000, 48261990000, 48355990000, 48273990000, 48321990000, 48167990000, 48061990000, 48071990000, 48007990000, 48245990000, 48057990000, 09001990000, 09009990000, 25001990000, 25023990003, 25005990000, 25007990000, 25019990000, 72059990001, 72027990000, 72005990000, 72091990025, 72037990000, 72095990000, 72145990000, 72147990000, 72111990000, 72097990000, 72031990000, 72115990000, 72133990000, 72089990001, 72011990000, 72137990000, 72065990016, 72071990000, 72103990013, 72087990000, 72151990000, 72003990000, 72017990000, 72075990001, 72023990000, 72143990000, 72055990000, 72051990021, 72109990000, 72127990000, 33015990000, 44005990000, 13039990000, 13127990000, 13051990000, 13179990000, 13191990000, 34001990000, 34029990000, 34033990000, 34011990000, 34025990000, 22071990000, 22087990000, 22109990000, 22045990000, 22023990000, 22101990000, 22113990000, 22057990000, 22075990000, 22103990000, 22051990000, 18091990000, 18089990000, 18127990000, 55031990000, 55061990000, 55051990000, 55083990000, 55059990000, 55101990000, 55007990000, 55075990000, 55003990000, 55029990000, 55079990000, 55071990000, 55117990000, 55089990000, 42049990000, 15003990001, 15001990000, 15005990000, 15009990000, 78010990000, 78020990000, 78030990000, 39007990000, 39085990000, 39095990000, 39035990000, 23029990000, 23009990000, 23015990000, 23023990000, 23013990000, 23005990000\n" + ] + } + ], + "source": [ + "# print list of \"normal\" water-only IDs to put into compare tool\n", + "water_normals = tract_explore[(tract_explore.water_only==True) & (tract_explore.tract_in_water_range==True)]\n", + "print(', '.join(water_normals.GEOID10_TRACT.values))" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "e9380f21", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "36063990000, 36085008900, 36085990100, 36013990000, 36075990000, 36055990000, 36045990001, 36047990100, 36059990100, 36059990200, 36059990301, 36059990302, 36059990400, 36081990100, 36029990000, 36073990000, 36103990100, 36117990100, 36011990200, 41007990000, 41057990100, 41039990000, 41041990100, 41015990101, 41011990101, 41019990000, 01003990000, 01097990000, 17031990000, 17097990000, 37031990100, 37031990200, 37053990100, 37019990100, 37055990100, 37055990200, 37129990100, 37095990100, 37095990200, 37141990100, 37137990100, 37133990100, 10001990000, 10005990000, 10003990100, 06083990000, 06053990000, 06061990000, 06059990100, 06097990100, 06045990100, 06073990100, 06041990100, 06015990000, 06081990100, 06079990000, 06013990000, 06111990100, 06017990000, 06001990000, 06075990100, 06087990100, 06023990100, 06037990200, 06037990300, 06037990100, 51103990100, 51115990100, 51650990100, 51131990100, 51735990100, 51810990100, 51133990100, 51001990100, 51001990200, 51119990100, 51199990100, 51710990000, 26157990000, 26011990000, 26163990100, 26163990200, 26001990000, 26141990000, 26011990100, 26019990000, 26069990000, 26053990000, 26147990000, 26153990000, 26063990000, 26159990000, 26033990000, 26033990100, 26131990100, 26005990000, 26103990000, 26099990100, 26061990000, 26061990100, 26003990000, 26095990000, 26041990000, 26031990000, 26009990000, 26083990100, 26139990000, 26041990100, 26047990000, 26109990000, 26007990000, 26021990000, 26089990000, 26017990000, 26115990000, 26151990000, 26013990100, 26055990000, 26127990000, 26097990000, 26121990000, 26105990000, 26101990000, 26029990000, 66010990000, 27137990100, 27031990000, 27075990100, 69100990000, 69110990000, 69120990000, 28045990000, 28059990000, 28047990000, 53061990100, 53035990100, 53057990100, 53009990100, 53067990100, 53049990100, 53031990000, 53033990100, 53061990002, 53029992201, 53027990000, 53055990100, 12009990000, 12017990000, 12037990000, 12037990100, 12011990000, 12087990000, 12081990000, 12093990000, 12029990000, 12031990000, 12071990000, 12065990000, 12033990000, 12045990000, 12103990100, 12086990000, 12129990000, 12103990000, 12057990000, 12057990100, 12115990000, 12101990000, 12109990200, 12109990100, 12123990000, 12085990000, 12085990100, 12051990000, 12005990000, 12035990000, 12053990000, 12043990000, 12131990000, 12099990000, 12075990000, 12015990000, 12099990100, 12061990000, 12095990000, 12021990000, 12113990000, 12089990000, 12111990000, 12127990000, 12091990200, 12091990100, 45013990100, 45053990100, 45019990100, 45029990100, 45043990100, 45051990100, 32031990000, 32031990100, 32005990000, 32510990000, 24029990000, 24017990000, 24047990000, 24041990000, 24039990100, 24037990000, 24035990200, 24035990100, 24035990000, 24019990000, 24003990000, 24009990100, 48039990000, 48489990000, 48261990000, 48355990000, 48273990000, 48321990000, 48167990000, 48061990000, 48071990000, 48007990000, 48245990000, 48057990000, 09001990000, 09011990100, 09007990100, 09009990000, 25001990000, 25023990003, 25005990000, 25025990101, 25009990100, 25007990000, 25019990000, 72049990501, 72059990001, 72027990000, 72033990201, 72053990103, 72113993000, 72015991500, 72005990000, 72091990025, 72013992900, 72037990000, 72095990000, 72145990000, 72117990400, 72147990000, 72111990000, 72097990000, 72079991100, 72031990000, 72115990000, 72133990000, 72089990001, 72011990000, 72123992800, 72137990000, 72065990016, 72071990000, 72103990013, 72087990000, 72069991800, 72151990000, 72003990000, 72057992600, 72017990000, 72075990001, 72119992700, 72023990000, 72143990000, 72055990000, 72051990021, 72109990000, 72127990000, 33015990000, 44009990200, 44009990100, 44005990000, 13039990000, 13127990000, 13051990000, 13179990000, 13191990000, 34001990000, 34029990000, 34033990000, 34011990000, 34009990100, 34025990000, 22071990000, 22087990000, 22109990000, 22045990000, 22023990000, 22101990000, 22113990000, 22057990000, 22051990100, 22075990000, 22103990000, 22051990000, 18091990000, 18089990000, 18127990000, 55031990000, 55061990000, 55051990000, 55083990000, 55059990000, 55101990000, 55007990000, 55025991703, 55025991702, 55075990000, 55003990000, 55029990000, 55079990000, 55071990000, 55117990000, 55089990000, 42049990000, 15003990001, 15001991700, 15001990400, 15001990000, 15001990300, 15001991100, 15001991200, 15001991500, 15001991300, 15001991400, 15001991600, 15001991000, 15001990100, 15001990500, 15001990600, 15001990900, 15001990800, 15001990700, 15005990000, 15007990100, 15007990300, 15007990200, 15009990200, 15009991200, 15009990000, 78010990000, 78020990000, 78030990000, 39007990000, 39085990000, 39093990200, 39043990100, 39095990000, 39035990000, 23029990000, 23009990000, 23015990000, 23031990100, 23023990000, 23013990000, 23005990000\n" + ] + } + ], + "source": [ + "# print list of ALAND10=0 IDs to put into compare tool\n", + "print(', '.join(tract_explore[tract_explore.water_only==True].GEOID10_TRACT.values))" + ] + }, + { + "cell_type": "markdown", + "id": "8d82e594", + "metadata": {}, + "source": [ + "Tract comparison tool shoes that 21 of the 140 \"water exception\" tracts are in Hawaii. California next highest with 13. Puerto Rico has 12. (No other territories have \"water exception\" tracts.)" + ] + }, + { + "cell_type": "markdown", + "id": "74544236", + "metadata": {}, + "source": [ + "## Simulate calculate_tract_adjacency_scores()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "3703ffb1", + "metadata": {}, + "outputs": [], + "source": [ + "def calculate_tract_adjacency_scores(\n", + " df: pd.DataFrame, score_column: str, tract_data\n", + ") -> pd.DataFrame:\n", + " \"\"\"Calculate the mean score of each tract in df based on its neighbors\n", + "\n", + " Args:\n", + " df (pandas.DataFrame): A dataframe with at least the following columns:\n", + " * field_names.GEOID_TRACT_FIELD\n", + " * score_column\n", + "\n", + " score_column (str): The name of the column that contains the scores\n", + " to average\n", + " \n", + " tract_data (GeoDataFrame): tract data normally loaded in first line of \n", + " function: tract_data = get_tract_geojson()\n", + " Returns :\n", + " tuple containing final returned df in actual function, as well as intermediates:\n", + " - returned_donut_bools (pandas.DataFrame): A dataframe with two columns:\n", + " * field_names.GEOID_TRACT_FIELD\n", + " * {score_column}_ADJACENT_MEAN, which is the average of score_column for\n", + " each tract that touches the tract identified\n", + " in field_names.GEOID_TRACT_FIELD\n", + " NB: this is the df that gets returned in the actual function\n", + " - df (pandas.DataFrame): input df after merging with Census data\n", + " - adjacent_tracts (pandas.DataFrame): adjacency df\n", + " \"\"\"\n", + "\n", + " df: gpd.GeoDataFrame = tract_data.merge(\n", + " df, on=GEOID_TRACT_FIELD\n", + " )\n", + " df = df.rename(columns={GEOID_TRACT_FIELD: ORIGINAL_TRACT})\n", + "\n", + "\n", + " adjacent_tracts: gpd.GeoDataFrame = df.sjoin(\n", + " tract_data, predicate=\"touches\"\n", + " )\n", + "\n", + "\n", + " returned_donut_bools = (\n", + " adjacent_tracts.groupby(GEOID_TRACT_FIELD)[[score_column]]\n", + " .mean()\n", + " .reset_index()\n", + " .rename(\n", + " columns={\n", + " score_column: f\"{score_column}{ADJACENCY_INDEX_SUFFIX}\",\n", + " }\n", + " )\n", + " )\n", + " \n", + " return (returned_donut_bools, df, adjacent_tracts)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "d4f50253", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['48061010100',\n", + " '48489950700',\n", + " '48489990000',\n", + " '48061012700',\n", + " '48061012304',\n", + " '48061012301',\n", + " '48061990000']" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# save tract ids that are adjacent to cameron so we can simulate \n", + "# passing them into calculate_tract_adjacency_scores() via df arg\n", + "adjacent_track_id_list_cam = list(cam_adjacent_tracts.GEOID10_TRACT.unique())\n", + "adjacent_track_id_list_cam" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "4bac089c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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GEOID10_TRACTDefinition N (communities)
048061012305False
148061010100True
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348489990000False
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\n", + "
" + ], + "text/plain": [ + " GEOID10_TRACT Definition N (communities)\n", + "0 48061012305 False\n", + "1 48061010100 True\n", + "2 48489950700 True\n", + "3 48489990000 False\n", + "4 48061012700 True\n", + "5 48061012304 True\n", + "6 48061012301 True\n", + "7 48061990000 False" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# df is passed into the function, must contain the score column we're looking at\n", + "# (in this case, SCORE_N_COMMUNITIES)\n", + "# here we pass in our water boundary issue example in Cameron TX pluss all its adjacencies.\n", + "# initialize disadvantaged bool as FALSE for our example, and TRUE for others.\n", + "df_cam_plus = pd.DataFrame({GEOID_TRACT_FIELD:[CAM_ID]+adjacent_track_id_list_cam, \n", + " SCORE_N_COMMUNITIES:[False]+([True]*len(adjacent_track_id_list_cam))})\n", + "\n", + "# set disadvantaged bool to False for water tracts\n", + "df_cam_plus.loc[df_cam_plus[GEOID_TRACT_FIELD].isin(['48489990000', '48061990000']), SCORE_N_COMMUNITIES]=False\n", + "\n", + "df_cam_plus" + ] + }, + { + "cell_type": "markdown", + "id": "ad9570b0", + "metadata": {}, + "source": [ + "### Simulate v1 run of of calculate_tract_adjacency_scores()" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "74826fc9", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "returned_donut_bools__v1, df_census__v1, adjacent_tracts__v1 = calculate_tract_adjacency_scores(\n", + " df=df_cam_plus, score_column=SCORE_N_COMMUNITIES, tract_data=tract_data\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "8e69447f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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STATEFP10_leftCOUNTYFP10_leftTRACTCE10_leftORIGINAL_TRACTNAME10_leftNAMELSAD10_leftMTFCC10_leftFUNCSTAT10_leftALAND10_leftAWATER10_left...TRACTCE10_rightGEOID10_TRACTNAME10_rightNAMELSAD10_rightMTFCC10_rightFUNCSTAT10_rightALAND10_rightAWATER10_rightINTPTLAT10_rightINTPTLON10_right
048489990000484899900009900Census Tract 9900G5020S0121414926...01230548061012305123.05Census Tract 123.05G5020S25920881324996211+26.2732070-097.2763703
148489950700484899507009507Census Tract 9507G5020S1025415174377000048...01230548061012305123.05Census Tract 123.05G5020S25920881324996211+26.2732070-097.2763703
248061990000480619900009900Census Tract 9900G5020S0268272237...01230548061012305123.05Census Tract 123.05G5020S25920881324996211+26.2732070-097.2763703
44806101230448061012304123.04Census Tract 123.04G5020S57612996286773...01230548061012305123.05Census Tract 123.05G5020S25920881324996211+26.2732070-097.2763703
54806101010048061010100101Census Tract 101G5020S365458278113041841...01230548061012305123.05Census Tract 123.05G5020S25920881324996211+26.2732070-097.2763703
64806101270048061012700127Census Tract 127G5020S12962279967725054...01230548061012305123.05Census Tract 123.05G5020S25920881324996211+26.2732070-097.2763703
74806101230148061012301123.01Census Tract 123.01G5020S137622651119430273...01230548061012305123.05Census Tract 123.05G5020S25920881324996211+26.2732070-097.2763703
048489990000484899900009900Census Tract 9900G5020S0121414926...950700484899507009507Census Tract 9507G5020S1025415174377000048+26.5154317-097.5790835
34806101230548061012305123.05Census Tract 123.05G5020S25920881324996211...950700484899507009507Census Tract 9507G5020S1025415174377000048+26.5154317-097.5790835
54806101010048061010100101Census Tract 101G5020S365458278113041841...950700484899507009507Census Tract 9507G5020S1025415174377000048+26.5154317-097.5790835
048489990000484899900009900Census Tract 9900G5020S0121414926...990000482619900009900Census Tract 9900G5020S0394659578+26.9389899-097.3234546
148489950700484899507009507Census Tract 9507G5020S1025415174377000048...990000482619900009900Census Tract 9900G5020S0394659578+26.9389899-097.3234546
048489990000484899900009900Census Tract 9900G5020S0121414926...950100482619501009501Census Tract 9501G5020S3777053964867877948+26.9241932-097.6694694
148489950700484899507009507Census Tract 9507G5020S1025415174377000048...950100482619501009501Census Tract 9501G5020S3777053964867877948+26.9241932-097.6694694
048489990000484899900009900Census Tract 9900G5020S0121414926...990000480619900009900Census Tract 9900G5020S0268272237+26.1902408-097.1473235
34806101230548061012305123.05Census Tract 123.05G5020S25920881324996211...990000480619900009900Census Tract 9900G5020S0268272237+26.1902408-097.1473235
64806101270048061012700127Census Tract 127G5020S12962279967725054...990000480619900009900Census Tract 9900G5020S0268272237+26.1902408-097.1473235
148489950700484899507009507Census Tract 9507G5020S1025415174377000048...01020148061010201102.01Census Tract 102.01G5020S138787038773532+26.2865300-097.6765589
54806101010048061010100101Census Tract 101G5020S365458278113041841...01020148061010201102.01Census Tract 102.01G5020S138787038773532+26.2865300-097.6765589
148489950700484899507009507Census Tract 9507G5020S1025415174377000048...01010048061010100101Census Tract 101G5020S365458278113041841+26.2713946-097.4414470
34806101230548061012305123.05Census Tract 123.05G5020S25920881324996211...01010048061010100101Census Tract 101G5020S365458278113041841+26.2713946-097.4414470
74806101230148061012301123.01Census Tract 123.01G5020S137622651119430273...01010048061010100101Census Tract 101G5020S365458278113041841+26.2713946-097.4414470
148489950700484899507009507Census Tract 9507G5020S1025415174377000048...950600484899506009506Census Tract 9506G5020S142823217867417+26.3413027-097.6980863
54806101010048061010100101Census Tract 101G5020S365458278113041841...950600484899506009506Census Tract 9506G5020S142823217867417+26.3413027-097.6980863
148489950700484899507009507Census Tract 9507G5020S1025415174377000048...950500484899505009505Census Tract 9505G5020S1986630411916760+26.4046271-097.7275193
148489950700484899507009507Census Tract 9507G5020S1025415174377000048...950400484899504009504Census Tract 9504G5020S95793488440761+26.4857271-097.7274534
148489950700484899507009507Census Tract 9507G5020S1025415174377000048...990000484899900009900Census Tract 9900G5020S0121414926+26.5064260-097.2240134
248061990000480619900009900Census Tract 9900G5020S0268272237...990000484899900009900Census Tract 9900G5020S0121414926+26.5064260-097.2240134
34806101230548061012305123.05Census Tract 123.05G5020S25920881324996211...990000484899900009900Census Tract 9900G5020S0121414926+26.5064260-097.2240134
148489950700484899507009507Census Tract 9507G5020S1025415174377000048...02430248215024302243.02Census Tract 243.02G5020S15223338310002679+26.4228640-097.9710985
148489950700484899507009507Census Tract 9507G5020S1025415174377000048...950300484899503009503Census Tract 9503G5020S6683375575864+26.4851686-097.8236397
148489950700484899507009507Census Tract 9507G5020S1025415174377000048...02430148215024301243.01Census Tract 243.01G5020S1426811393527958+26.6175624-098.1948738
248061990000480619900009900Census Tract 9900G5020S0268272237...01270048061012700127Census Tract 127G5020S12962279967725054+25.9786218-097.2580863
34806101230548061012305123.05Census Tract 123.05G5020S25920881324996211...01270048061012700127Census Tract 127G5020S12962279967725054+25.9786218-097.2580863
44806101230448061012304123.04Census Tract 123.04G5020S57612996286773...01270048061012700127Census Tract 127G5020S12962279967725054+25.9786218-097.2580863
34806101230548061012305123.05Census Tract 123.05G5020S25920881324996211...01230448061012304123.04Census Tract 123.04G5020S57612996286773+26.0631407-097.2184868
64806101270048061012700127Census Tract 127G5020S12962279967725054...01230448061012304123.04Census Tract 123.04G5020S57612996286773+26.0631407-097.2184868
74806101230148061012301123.01Census Tract 123.01G5020S137622651119430273...01230448061012304123.04Census Tract 123.04G5020S57612996286773+26.0631407-097.2184868
34806101230548061012305123.05Census Tract 123.05G5020S25920881324996211...01230148061012301123.01Census Tract 123.01G5020S137622651119430273+26.1581170-097.3180348
44806101230448061012304123.04Census Tract 123.04G5020S57612996286773...01230148061012301123.01Census Tract 123.01G5020S137622651119430273+26.1581170-097.3180348
54806101010048061010100101Census Tract 101G5020S365458278113041841...01230148061012301123.01Census Tract 123.01G5020S137622651119430273+26.1581170-097.3180348
44806101230448061012304123.04Census Tract 123.04G5020S57612996286773...01420048061014200142Census Tract 142G5020S22948544652743725+26.0321232-097.3592760
64806101270048061012700127Census Tract 127G5020S12962279967725054...01420048061014200142Census Tract 142G5020S22948544652743725+26.0321232-097.3592760
74806101230148061012301123.01Census Tract 123.01G5020S137622651119430273...01420048061014200142Census Tract 142G5020S22948544652743725+26.0321232-097.3592760
54806101010048061010100101Census Tract 101G5020S365458278113041841...01080048061010800108Census Tract 108G5020S24214554372874+26.2018913-097.6365382
54806101010048061010100101Census Tract 101G5020S365458278113041841...01020348061010203102.03Census Tract 102.03G5020S36355923529676+26.2392001-097.7004164
54806101010048061010100101Census Tract 101G5020S365458278113041841...01220048061012200122Census Tract 122G5020S1810870879690866+26.1431884-097.4883500
74806101230148061012301123.01Census Tract 123.01G5020S137622651119430273...01220048061012200122Census Tract 122G5020S1810870879690866+26.1431884-097.4883500
54806101010048061010100101Census Tract 101G5020S365458278113041841...01140048061011400114Census Tract 114G5020S525950691716432+26.1576649-097.6030070
64806101270048061012700127Census Tract 127G5020S12962279967725054...01410048061014100141Census Tract 141G5020S587505421786306+25.8820153-097.4007135
64806101270048061012700127Census Tract 127G5020S12962279967725054...01320748061013207132.07Census Tract 132.07G5020S3884436409892+25.9278897-097.4159201
64806101270048061012700127Census Tract 127G5020S12962279967725054...01320348061013203132.03Census Tract 132.03G5020S11039573034+25.9335830-097.4379216
64806101270048061012700127Census Tract 127G5020S12962279967725054...01260748061012607126.07Census Tract 126.07G5020S3230089151343+25.9479994-097.4340712
\n", + "

53 rows × 27 columns

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" + ], + "text/plain": [ + " STATEFP10_left COUNTYFP10_left TRACTCE10_left ORIGINAL_TRACT NAME10_left \\\n", + "0 48 489 990000 48489990000 9900 \n", + "1 48 489 950700 48489950700 9507 \n", + "2 48 061 990000 48061990000 9900 \n", + "4 48 061 012304 48061012304 123.04 \n", + "5 48 061 010100 48061010100 101 \n", + "6 48 061 012700 48061012700 127 \n", + "7 48 061 012301 48061012301 123.01 \n", + "0 48 489 990000 48489990000 9900 \n", + "3 48 061 012305 48061012305 123.05 \n", + "5 48 061 010100 48061010100 101 \n", + "0 48 489 990000 48489990000 9900 \n", + "1 48 489 950700 48489950700 9507 \n", + "0 48 489 990000 48489990000 9900 \n", + "1 48 489 950700 48489950700 9507 \n", + "0 48 489 990000 48489990000 9900 \n", + "3 48 061 012305 48061012305 123.05 \n", + "6 48 061 012700 48061012700 127 \n", + "1 48 489 950700 48489950700 9507 \n", + "5 48 061 010100 48061010100 101 \n", + "1 48 489 950700 48489950700 9507 \n", + "3 48 061 012305 48061012305 123.05 \n", + "7 48 061 012301 48061012301 123.01 \n", + "1 48 489 950700 48489950700 9507 \n", + "5 48 061 010100 48061010100 101 \n", + "1 48 489 950700 48489950700 9507 \n", + "1 48 489 950700 48489950700 9507 \n", + "1 48 489 950700 48489950700 9507 \n", + "2 48 061 990000 48061990000 9900 \n", + "3 48 061 012305 48061012305 123.05 \n", + "1 48 489 950700 48489950700 9507 \n", + "1 48 489 950700 48489950700 9507 \n", + "1 48 489 950700 48489950700 9507 \n", + "2 48 061 990000 48061990000 9900 \n", + "3 48 061 012305 48061012305 123.05 \n", + "4 48 061 012304 48061012304 123.04 \n", + "3 48 061 012305 48061012305 123.05 \n", + "6 48 061 012700 48061012700 127 \n", + "7 48 061 012301 48061012301 123.01 \n", + "3 48 061 012305 48061012305 123.05 \n", + "4 48 061 012304 48061012304 123.04 \n", + "5 48 061 010100 48061010100 101 \n", + "4 48 061 012304 48061012304 123.04 \n", + "6 48 061 012700 48061012700 127 \n", + "7 48 061 012301 48061012301 123.01 \n", + "5 48 061 010100 48061010100 101 \n", + "5 48 061 010100 48061010100 101 \n", + "5 48 061 010100 48061010100 101 \n", + "7 48 061 012301 48061012301 123.01 \n", + "5 48 061 010100 48061010100 101 \n", + "6 48 061 012700 48061012700 127 \n", + "6 48 061 012700 48061012700 127 \n", + "6 48 061 012700 48061012700 127 \n", + "6 48 061 012700 48061012700 127 \n", + "\n", + " NAMELSAD10_left MTFCC10_left FUNCSTAT10_left ALAND10_left \\\n", + "0 Census Tract 9900 G5020 S 0 \n", + "1 Census Tract 9507 G5020 S 1025415174 \n", + "2 Census Tract 9900 G5020 S 0 \n", + "4 Census Tract 123.04 G5020 S 5761299 \n", + "5 Census Tract 101 G5020 S 365458278 \n", + "6 Census Tract 127 G5020 S 129622799 \n", + "7 Census Tract 123.01 G5020 S 137622651 \n", + "0 Census Tract 9900 G5020 S 0 \n", + "3 Census Tract 123.05 G5020 S 25920881 \n", + "5 Census Tract 101 G5020 S 365458278 \n", + "0 Census Tract 9900 G5020 S 0 \n", + "1 Census Tract 9507 G5020 S 1025415174 \n", + "0 Census Tract 9900 G5020 S 0 \n", + "1 Census Tract 9507 G5020 S 1025415174 \n", + "0 Census Tract 9900 G5020 S 0 \n", + "3 Census Tract 123.05 G5020 S 25920881 \n", + "6 Census Tract 127 G5020 S 129622799 \n", + "1 Census Tract 9507 G5020 S 1025415174 \n", + "5 Census Tract 101 G5020 S 365458278 \n", + "1 Census Tract 9507 G5020 S 1025415174 \n", + "3 Census Tract 123.05 G5020 S 25920881 \n", + "7 Census Tract 123.01 G5020 S 137622651 \n", + "1 Census Tract 9507 G5020 S 1025415174 \n", + "5 Census Tract 101 G5020 S 365458278 \n", + "1 Census Tract 9507 G5020 S 1025415174 \n", + "1 Census Tract 9507 G5020 S 1025415174 \n", + "1 Census Tract 9507 G5020 S 1025415174 \n", + "2 Census Tract 9900 G5020 S 0 \n", + "3 Census Tract 123.05 G5020 S 25920881 \n", + "1 Census Tract 9507 G5020 S 1025415174 \n", + "1 Census Tract 9507 G5020 S 1025415174 \n", + "1 Census Tract 9507 G5020 S 1025415174 \n", + "2 Census Tract 9900 G5020 S 0 \n", + "3 Census Tract 123.05 G5020 S 25920881 \n", + "4 Census Tract 123.04 G5020 S 5761299 \n", + "3 Census Tract 123.05 G5020 S 25920881 \n", + "6 Census Tract 127 G5020 S 129622799 \n", + "7 Census Tract 123.01 G5020 S 137622651 \n", + "3 Census Tract 123.05 G5020 S 25920881 \n", + "4 Census Tract 123.04 G5020 S 5761299 \n", + "5 Census Tract 101 G5020 S 365458278 \n", + "4 Census Tract 123.04 G5020 S 5761299 \n", + "6 Census Tract 127 G5020 S 129622799 \n", + "7 Census Tract 123.01 G5020 S 137622651 \n", + "5 Census Tract 101 G5020 S 365458278 \n", + "5 Census Tract 101 G5020 S 365458278 \n", + "5 Census Tract 101 G5020 S 365458278 \n", + "7 Census Tract 123.01 G5020 S 137622651 \n", + "5 Census Tract 101 G5020 S 365458278 \n", + "6 Census Tract 127 G5020 S 129622799 \n", + "6 Census Tract 127 G5020 S 129622799 \n", + "6 Census Tract 127 G5020 S 129622799 \n", + "6 Census Tract 127 G5020 S 129622799 \n", + "\n", + " AWATER10_left ... 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010100 48061010100 101 \n", + "3 324996211 ... 010100 48061010100 101 \n", + "7 119430273 ... 010100 48061010100 101 \n", + "1 377000048 ... 950600 48489950600 9506 \n", + "5 113041841 ... 950600 48489950600 9506 \n", + "1 377000048 ... 950500 48489950500 9505 \n", + "1 377000048 ... 950400 48489950400 9504 \n", + "1 377000048 ... 990000 48489990000 9900 \n", + "2 268272237 ... 990000 48489990000 9900 \n", + "3 324996211 ... 990000 48489990000 9900 \n", + "1 377000048 ... 024302 48215024302 243.02 \n", + "1 377000048 ... 950300 48489950300 9503 \n", + "1 377000048 ... 024301 48215024301 243.01 \n", + "2 268272237 ... 012700 48061012700 127 \n", + "3 324996211 ... 012700 48061012700 127 \n", + "4 6286773 ... 012700 48061012700 127 \n", + "3 324996211 ... 012304 48061012304 123.04 \n", + "6 67725054 ... 012304 48061012304 123.04 \n", + "7 119430273 ... 012304 48061012304 123.04 \n", + "3 324996211 ... 012301 48061012301 123.01 \n", + "4 6286773 ... 012301 48061012301 123.01 \n", + "5 113041841 ... 012301 48061012301 123.01 \n", + "4 6286773 ... 014200 48061014200 142 \n", + "6 67725054 ... 014200 48061014200 142 \n", + "7 119430273 ... 014200 48061014200 142 \n", + "5 113041841 ... 010800 48061010800 108 \n", + "5 113041841 ... 010203 48061010203 102.03 \n", + "5 113041841 ... 012200 48061012200 122 \n", + "7 119430273 ... 012200 48061012200 122 \n", + "5 113041841 ... 011400 48061011400 114 \n", + "6 67725054 ... 014100 48061014100 141 \n", + "6 67725054 ... 013207 48061013207 132.07 \n", + "6 67725054 ... 013203 48061013203 132.03 \n", + "6 67725054 ... 012607 48061012607 126.07 \n", + "\n", + " NAMELSAD10_right MTFCC10_right FUNCSTAT10_right ALAND10_right \\\n", + "0 Census Tract 123.05 G5020 S 25920881 \n", + "1 Census Tract 123.05 G5020 S 25920881 \n", + "2 Census Tract 123.05 G5020 S 25920881 \n", + "4 Census Tract 123.05 G5020 S 25920881 \n", + "5 Census Tract 123.05 G5020 S 25920881 \n", + "6 Census Tract 123.05 G5020 S 25920881 \n", + "7 Census Tract 123.05 G5020 S 25920881 \n", + "0 Census Tract 9507 G5020 S 1025415174 \n", + "3 Census Tract 9507 G5020 S 1025415174 \n", + "5 Census Tract 9507 G5020 S 1025415174 \n", + "0 Census Tract 9900 G5020 S 0 \n", + "1 Census Tract 9900 G5020 S 0 \n", + "0 Census Tract 9501 G5020 S 3777053964 \n", + "1 Census Tract 9501 G5020 S 3777053964 \n", + "0 Census Tract 9900 G5020 S 0 \n", + "3 Census Tract 9900 G5020 S 0 \n", + "6 Census Tract 9900 G5020 S 0 \n", + "1 Census Tract 102.01 G5020 S 138787038 \n", + "5 Census Tract 102.01 G5020 S 138787038 \n", + "1 Census Tract 101 G5020 S 365458278 \n", + "3 Census Tract 101 G5020 S 365458278 \n", + "7 Census Tract 101 G5020 S 365458278 \n", + "1 Census Tract 9506 G5020 S 142823217 \n", + "5 Census Tract 9506 G5020 S 142823217 \n", + "1 Census Tract 9505 G5020 S 198663041 \n", + "1 Census Tract 9504 G5020 S 95793488 \n", + "1 Census Tract 9900 G5020 S 0 \n", + "2 Census Tract 9900 G5020 S 0 \n", + "3 Census Tract 9900 G5020 S 0 \n", + "1 Census Tract 243.02 G5020 S 152233383 \n", + "1 Census Tract 9503 G5020 S 66833755 \n", + "1 Census Tract 243.01 G5020 S 1426811393 \n", + "2 Census Tract 127 G5020 S 129622799 \n", + "3 Census Tract 127 G5020 S 129622799 \n", + "4 Census Tract 127 G5020 S 129622799 \n", + "3 Census Tract 123.04 G5020 S 5761299 \n", + "6 Census Tract 123.04 G5020 S 5761299 \n", + "7 Census Tract 123.04 G5020 S 5761299 \n", + "3 Census Tract 123.01 G5020 S 137622651 \n", + "4 Census Tract 123.01 G5020 S 137622651 \n", + "5 Census Tract 123.01 G5020 S 137622651 \n", + "4 Census Tract 142 G5020 S 229485446 \n", + "6 Census Tract 142 G5020 S 229485446 \n", + "7 Census Tract 142 G5020 S 229485446 \n", + "5 Census Tract 108 G5020 S 24214554 \n", + "5 Census Tract 102.03 G5020 S 36355923 \n", + "5 Census Tract 122 G5020 S 181087087 \n", + "7 Census Tract 122 G5020 S 181087087 \n", + "5 Census Tract 114 G5020 S 52595069 \n", + "6 Census Tract 141 G5020 S 58750542 \n", + "6 Census Tract 132.07 G5020 S 3884436 \n", + "6 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GEOID10_TRACTDefinition N (communities) (average of neighbors)
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" + ], + "text/plain": [ + " GEOID10_TRACT Definition N (communities) (average of neighbors)\n", + "8 48061012305 0.714286" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# check result for our example tract\n", + "returned_donut_bools__v1[returned_donut_bools__v1.GEOID10_TRACT==CAM_ID]" + ] + }, + { + "cell_type": "markdown", + "id": "936d0ebc", + "metadata": {}, + "source": [ + "Demonstrates that because 2 of the 7 adjacent tracts have meaningless water-only non-disadvantaged status, the average of neighbors is less than 1. This is why our example tract isn't being classified as disadvantaged in v1." + ] + }, + { + "cell_type": "markdown", + "id": "a02b6194", + "metadata": {}, + "source": [ + "### Simulate proposed v2 implementations of calculate_tract_adjacency_scores()" + ] + }, + { + "cell_type": "markdown", + "id": "5688387a", + "metadata": {}, + "source": [ + "Testing three ways to do this: filtering tract_data, filtering df, and filtering both. For the first two ways, we'll test both land area and tract ID range methods." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "72963a39", + "metadata": {}, + "outputs": [], + "source": [ + "def view_results(returned_donut_bools, post_merge_df, adjacent_tracts):\n", + " # function to print what we need to see after each experiment\n", + " \n", + " print(f'Length of input df after Census merge: {len(post_merge_df)}') \n", + " print(f'That is {len(df_census__v1) - len(post_merge_df)} less than in v1')\n", + " \n", + " print(f'\\nLength of adjacency frame: {len(adjacent_tracts)}')\n", + " print(f'That is {len(adjacent_tracts__v1) - len(adjacent_tracts)} less than in v1')\n", + " \n", + " n_dupes = len(adjacent_tracts[['ORIGINAL_TRACT', 'GEOID10_TRACT']])\\\n", + " - len(adjacent_tracts[['ORIGINAL_TRACT', 'GEOID10_TRACT']].drop_duplicates())\n", + " if n_dupes>0:\n", + " print(\"ALERT: duplicates present in adjacency frame!\")\n", + "\n", + " \n", + " print(f'\\nLength of returned frame with donut bools: {len(returned_donut_bools)}')\n", + " if len(returned_donut_bools__v1) == len(returned_donut_bools):\n", + " print('Returning same number of final bools as in v1')\n", + " else:\n", + " print(f'ALERT: returning {len(returned_donut_bools__v1) - len(returned_donut_bools)} less than in v1')\n", + "\n", + " cam_return = returned_donut_bools[returned_donut_bools.GEOID10_TRACT==CAM_ID]\n", + " display(cam_return)\n", + "\n", + " if cam_return['Definition N (communities) (average of neighbors)'].values[0]==1:\n", + " print('SUCCESSFULLY RE-ASSIGNING CAMERON, TX EXAMPLE TO DISADVANTAGED STATUS')\n", + " else:\n", + " print('ALERT: CAMERON, TX EXAMPLE IS STILL BEING MARKED NON-DISADVANTAGED')" + ] + }, + { + "cell_type": "markdown", + "id": "c1712378", + "metadata": {}, + "source": [ + "#### Implementation 1a: Filter water-only tracts out of tract_data using ALAND10" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "9e0401e1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Length of input df after Census merge: 6\n", + "That is 2 less than in v1\n", + "\n", + "Length of adjacency frame: 40\n", + "That is 13 less than in v1\n", + "\n", + "Length of returned frame with donut bools: 23\n", + "ALERT: returning 3 less than in v1\n" + ] + }, + { + "data": { + "text/html": [ + "
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GEOID10_TRACTDefinition N (communities) (average of neighbors)
8480610123051.0
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" + ], + "text/plain": [ + " GEOID10_TRACT Definition N (communities) (average of neighbors)\n", + "8 48061012305 1.0" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SUCCESSFULLY RE-ASSIGNING CAMERON, TX EXAMPLE TO DISADVANTAGED STATUS\n" + ] + } + ], + "source": [ + "tract_data_land_area_only = tract_data.copy()\n", + "tract_data_land_area_only = tract_data_land_area_only[tract_data_land_area_only.ALAND10>0]\n", + "\n", + "returned_donut_bools__filter_tracts_aland10,\\\n", + "df_census__filter_tracts_aland10,\\\n", + "adjacent_tracts__filter_tracts_aland10 =calculate_tract_adjacency_scores(\n", + " df=df_cam_plus, \n", + " score_column=SCORE_N_COMMUNITIES, \n", + " tract_data=tract_data_land_area_only\n", + ")\n", + "\n", + "view_results(returned_donut_bools=returned_donut_bools__filter_tracts_aland10, \n", + " post_merge_df=df_census__filter_tracts_aland10, \n", + " adjacent_tracts=adjacent_tracts__filter_tracts_aland10)" + ] + }, + { + "cell_type": "markdown", + "id": "f277cc9a", + "metadata": {}, + "source": [ + "#### Implementation 1b: Filter water-only tracts out of df using tract range" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "0ea48370", + "metadata": {}, + "outputs": [], + "source": [ + "def full_geo_id_to_water_range_bool(x:str):\n", + " num_x = int(x[-6:])\n", + " return(in_water_range(num_x))" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "a2ef8844", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Length of input df after Census merge: 6\n", + "That is 2 less than in v1\n", + "\n", + "Length of adjacency frame: 40\n", + "That is 13 less than in v1\n", + "\n", + "Length of returned frame with donut bools: 23\n", + "ALERT: returning 3 less than in v1\n" + ] + }, + { + "data": { + "text/html": [ + "
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GEOID10_TRACTDefinition N (communities) (average of neighbors)
8480610123051.0
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" + ], + "text/plain": [ + " GEOID10_TRACT Definition N (communities) (average of neighbors)\n", + "8 48061012305 1.0" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SUCCESSFULLY RE-ASSIGNING CAMERON, TX EXAMPLE TO DISADVANTAGED STATUS\n" + ] + } + ], + "source": [ + "tract_data_non_water_range_only = tract_data.copy()\n", + "tract_data_non_water_range_only = tract_data_non_water_range_only[tract_data_non_water_range_only.GEOID10_TRACT\\\n", + " .apply(full_geo_id_to_water_range_bool)\\\n", + " ==False]\n", + "\n", + "returned_donut_bools__filter_tracts_id_range,\\\n", + "df_census__filter_tracts_id_range,\\\n", + "adjacent_tracts__filter_tracts_id_range =calculate_tract_adjacency_scores(\n", + " df=df_cam_plus, \n", + " score_column=SCORE_N_COMMUNITIES, \n", + " tract_data=tract_data_non_water_range_only\n", + ")\n", + "\n", + "view_results(returned_donut_bools=returned_donut_bools__filter_tracts_id_range, \n", + " post_merge_df=df_census__filter_tracts_id_range, \n", + " adjacent_tracts=adjacent_tracts__filter_tracts_id_range)" + ] + }, + { + "cell_type": "markdown", + "id": "1fcee2e2", + "metadata": {}, + "source": [ + "#### Implementation 2a: Filter water-only tracts out of df using ALAND10" + ] + }, + { + "cell_type": "markdown", + "id": "477a886c", + "metadata": {}, + "source": [ + "Note: we can't filter the input df based on ALAND10 using the standard calculate_tract_adjacency_scores() function, because it doesn't have the ALAND10 column until after it is merged with tract data. Need to write a new test function for this method." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "9ad212d3", + "metadata": {}, + "outputs": [], + "source": [ + "def calculate_tract_adjacency_scores__filter_df_aland10(\n", + " df: pd.DataFrame, score_column: str, tract_data\n", + ") -> pd.DataFrame:\n", + " \"\"\"Calculate the mean score of each tract in df based on its neighbors\n", + "\n", + " Args:\n", + " df (pandas.DataFrame): A dataframe with at least the following columns:\n", + " * field_names.GEOID_TRACT_FIELD\n", + " * score_column\n", + "\n", + " score_column (str): The name of the column that contains the scores\n", + " to average\n", + " \n", + " tract_data (GeoDataFrame): tract data normally loaded in first line of \n", + " function: tract_data = get_tract_geojson()\n", + " Returns :\n", + " tuple containing final returned df in actual function, as well as intermediates:\n", + " - returned_donut_bools (pandas.DataFrame): A dataframe with two columns:\n", + " * field_names.GEOID_TRACT_FIELD\n", + " * {score_column}_ADJACENT_MEAN, which is the average of score_column for\n", + " each tract that touches the tract identified\n", + " in field_names.GEOID_TRACT_FIELD\n", + " NB: this is the df that gets returned in the actual function\n", + " - df (pandas.DataFrame): input df after merging with Census data\n", + " - adjacent_tracts (pandas.DataFrame): adjacency df\n", + " \"\"\"\n", + "\n", + " df: gpd.GeoDataFrame = tract_data.merge(\n", + " df, on=GEOID_TRACT_FIELD\n", + " )\n", + " df = df.rename(columns={GEOID_TRACT_FIELD: ORIGINAL_TRACT})\n", + " \n", + " \n", + " # remove water areas from input frame\n", + " df = df[df['ALAND10']>0]\n", + "\n", + "\n", + " adjacent_tracts: gpd.GeoDataFrame = df.sjoin(\n", + " tract_data, predicate=\"touches\"\n", + " )\n", + "\n", + "\n", + " returned_donut_bools = (\n", + " adjacent_tracts.groupby(GEOID_TRACT_FIELD)[[score_column]]\n", + " .mean()\n", + " .reset_index()\n", + " .rename(\n", + " columns={\n", + " score_column: f\"{score_column}{ADJACENCY_INDEX_SUFFIX}\",\n", + " }\n", + " )\n", + " )\n", + " \n", + " return (returned_donut_bools, df, adjacent_tracts)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "ff9d13cd", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Length of input df after Census merge: 6\n", + "That is 2 less than in v1\n", + "\n", + "Length of adjacency frame: 45\n", + "That is 8 less than in v1\n", + "\n", + "Length of returned frame with donut bools: 26\n", + "Returning same number of final bools as in v1\n" + ] + }, + { + "data": { + "text/html": [ + "
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GEOID10_TRACTDefinition N (communities) (average of neighbors)
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" + ], + "text/plain": [ + " GEOID10_TRACT Definition N (communities) (average of neighbors)\n", + "8 48061012305 1.0" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SUCCESSFULLY RE-ASSIGNING CAMERON, TX EXAMPLE TO DISADVANTAGED STATUS\n" + ] + } + ], + "source": [ + "returned_donut_bools__filter_df_aland10,\\\n", + "df_census__filter_df_aland10,\\\n", + "adjacent_tracts__filter_df_aland10 = calculate_tract_adjacency_scores__filter_df_aland10(\n", + " df=df_cam_plus, \n", + " score_column=SCORE_N_COMMUNITIES, \n", + " tract_data=tract_data\n", + ")\n", + "\n", + "view_results(returned_donut_bools=returned_donut_bools__filter_df_aland10, \n", + " post_merge_df=df_census__filter_df_aland10, \n", + " adjacent_tracts=adjacent_tracts__filter_df_aland10)" + ] + }, + { + "cell_type": "markdown", + "id": "c7401f01", + "metadata": {}, + "source": [ + "#### Implementation 2b: Filter water-only tracts out of df using tract range" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "3cb027ab", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Length of input df after Census merge: 6\n", + "That is 2 less than in v1\n", + "\n", + "Length of adjacency frame: 45\n", + "That is 8 less than in v1\n", + "\n", + "Length of returned frame with donut bools: 26\n", + "Returning same number of final bools as in v1\n" + ] + }, + { + "data": { + "text/html": [ + "
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GEOID10_TRACTDefinition N (communities) (average of neighbors)
8480610123051.0
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" + ], + "text/plain": [ + " GEOID10_TRACT Definition N (communities) (average of neighbors)\n", + "8 48061012305 1.0" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SUCCESSFULLY RE-ASSIGNING CAMERON, TX EXAMPLE TO DISADVANTAGED STATUS\n" + ] + } + ], + "source": [ + "df_cam_plus_no_water = df_cam_plus.copy()\n", + "df_cam_plus_no_water = df_cam_plus_no_water[df_cam_plus_no_water.GEOID10_TRACT\\\n", + " .apply(full_geo_id_to_water_range_bool)\\\n", + " ==False]\n", + "\n", + "returned_donut_bools__filter_df_id_range,\\\n", + "df_census__filter_df_id_range,\\\n", + "adjacent_tracts__filter_df_id_range = calculate_tract_adjacency_scores(\n", + " df=df_cam_plus_no_water, \n", + " score_column=SCORE_N_COMMUNITIES, \n", + " tract_data=tract_data\n", + ")\n", + "\n", + "view_results(returned_donut_bools=returned_donut_bools__filter_df_id_range, \n", + " post_merge_df=df_census__filter_df_id_range, \n", + " adjacent_tracts=adjacent_tracts__filter_df_id_range)" + ] + }, + { + "cell_type": "markdown", + "id": "c0f94a03", + "metadata": {}, + "source": [ + "#### Implementation 3: Filter water-only tracts out of both tract_data and df" + ] + }, + { + "cell_type": "markdown", + "id": "e96c981f", + "metadata": {}, + "source": [ + "Note: test id method for simplicity" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "8e385e22", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Length of input df after Census merge: 6\n", + "That is 2 less than in v1\n", + "\n", + "Length of adjacency frame: 40\n", + "That is 13 less than in v1\n", + "\n", + "Length of returned frame with donut bools: 23\n", + "ALERT: returning 3 less than in v1\n" + ] + }, + { + "data": { + "text/html": [ + "
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GEOID10_TRACTDefinition N (communities) (average of neighbors)
8480610123051.0
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" + ], + "text/plain": [ + " GEOID10_TRACT Definition N (communities) (average of neighbors)\n", + "8 48061012305 1.0" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SUCCESSFULLY RE-ASSIGNING CAMERON, TX EXAMPLE TO DISADVANTAGED STATUS\n" + ] + } + ], + "source": [ + "returned_donut_bools__filter_both,\\\n", + "df_census__filter_both,\\\n", + "adjacent_tracts__filter_both =calculate_tract_adjacency_scores(\n", + " df=df_cam_plus_no_water, \n", + " score_column=SCORE_N_COMMUNITIES, \n", + " tract_data=tract_data_non_water_range_only\n", + ")\n", + "\n", + "view_results(returned_donut_bools=returned_donut_bools__filter_both, \n", + " post_merge_df=df_census__filter_both, \n", + " adjacent_tracts=adjacent_tracts__filter_both)" + ] + }, + { + "cell_type": "markdown", + "id": "dde21850", + "metadata": {}, + "source": [ + "#### Summary of results: All solutions successfully re-assigned our example tract to disadvantaged status. Only implementations 2a & 2b (filtering on the dataframe) also preserved the number of tracts that the function returns" + ] + }, + { + "cell_type": "markdown", + "id": "04d07bdb", + "metadata": {}, + "source": [ + "## Compare full scoring runs from different methods" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "aa462404", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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GEOID10_TRACTdac_adj_avg_proddac_adj_avg_localis_donut_hole_prodis_donut_hole_localfinal_dac_prodfinal_dac_localclass_change
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370121010302020.831.0FalseTrueFalseTrueTrue
376121010303030.831.0FalseTrueFalseTrueTrue
419121270811020.751.0FalseTrueFalseTrueTrue
806260690002000.861.0FalseTrueFalseTrueTrue
812260830001000.331.0FalseTrueFalseTrueTrue
912280470014000.881.0FalseTrueFalseTrueTrue
1138390070006020.751.0FalseTrueFalseTrueTrue
1654410419503030.751.0FalseTrueFalseTrueTrue
1739480610123050.711.0FalseTrueFalseTrueTrue
1766510010906000.831.0FalseTrueFalseTrueTrue
1780511319302000.671.0FalseTrueFalseTrueTrue
1944551010001000.751.0FalseTrueFalseTrueTrue
2012720133010000.711.0FalseTrueFalseTrueTrue
2037720371601000.831.0FalseTrueFalseTrueTrue
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" + ], + "text/plain": [ + " GEOID10_TRACT dac_adj_avg_prod dac_adj_avg_local is_donut_hole_prod \\\n", + "332 12087971001 0.67 1.0 False \n", + "370 12101030202 0.83 1.0 False \n", + "376 12101030303 0.83 1.0 False \n", + "419 12127081102 0.75 1.0 False \n", + "806 26069000200 0.86 1.0 False \n", + "812 26083000100 0.33 1.0 False \n", + "912 28047001400 0.88 1.0 False \n", + "1138 39007000602 0.75 1.0 False \n", + "1654 41041950303 0.75 1.0 False \n", + "1739 48061012305 0.71 1.0 False \n", + "1766 51001090600 0.83 1.0 False \n", + "1780 51131930200 0.67 1.0 False \n", + "1944 55101000100 0.75 1.0 False \n", + "2012 72013301000 0.71 1.0 False \n", + "2037 72037160100 0.83 1.0 False \n", + "\n", + " is_donut_hole_local final_dac_prod final_dac_local class_change \n", + "332 True False True True \n", + "370 True False True True \n", + "376 True False True True \n", + "419 True False True True \n", + "806 True False True True \n", + "812 True False True True \n", + "912 True False True True \n", + "1138 True False True True \n", + "1654 True False True True \n", + "1739 True False True True \n", + "1766 True False True True \n", + "1780 True False True True \n", + "1944 True False True True \n", + "2012 True False True True \n", + "2037 True False True True " + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# read in CSV with deltas from implementation 1a \n", + "# (water areas filtered from tract_data via ALAND10)\n", + "deltas__tract_area = pd.read_csv('../data/tmp/Comparator/Score/deltas__tract_area.csv')\n", + "\n", + "# rename key columns\n", + "deltas__tract_area.rename(columns = {'Unnamed: 0': 'GEOID10_TRACT',\n", + " 'Definition N community, including adjacency index tracts': 'final_dac_prod',\n", + " 'Definition N community, including adjacency index tracts.1': 'final_dac_local',\n", + " 'Definition N (communities) (average of neighbors)': 'dac_adj_avg_prod',\n", + " 'Definition N (communities) (average of neighbors).1': 'dac_adj_avg_local',\n", + " 'Is the tract surrounded by disadvantaged communities?': 'is_donut_hole_prod',\n", + " 'Is the tract surrounded by disadvantaged communities?.1': 'is_donut_hole_local'\n", + " },\n", + " inplace=True) \n", + "\n", + "# drop first two rows, which old column information\n", + "deltas__tract_area.drop(index=[0,1], inplace=True)\n", + "\n", + "# create bool to store whether final DAC designation was updated\n", + "deltas__tract_area['class_change'] = deltas__tract_area.final_dac_prod!=deltas__tract_area.final_dac_local\n", + "\n", + "# set class change bool to false where both designations are null\n", + "deltas__tract_area.loc[((deltas__tract_area.final_dac_prod.isna()==True)\\\n", + " & (deltas__tract_area.final_dac_local.isna()==True)), \n", + " 'class_change'] = False\n", + "\n", + "# view tracts that had their status updated with this method\n", + "deltas__tract_area[deltas__tract_area.class_change][['GEOID10_TRACT', \n", + " 'dac_adj_avg_prod', 'dac_adj_avg_local',\n", + " 'is_donut_hole_prod', 'is_donut_hole_local',\n", + " 'final_dac_prod', 'final_dac_local',\n", + " 'class_change']]" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "3dc9500a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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GEOID10_TRACTdac_adj_avg_proddac_adj_avg_localis_donut_hole_prodis_donut_hole_localfinal_dac_prodfinal_dac_localclass_change
312120879710010.671.0FalseTrueFalseTrueTrue
346121010302020.831.0FalseTrueFalseTrueTrue
352121010303030.831.0FalseTrueFalseTrueTrue
388121270811020.751.0FalseTrueFalseTrueTrue
741260690002000.861.0FalseTrueFalseTrueTrue
747260830001000.331.0FalseTrueFalseTrueTrue
836280470014000.881.0FalseTrueFalseTrueTrue
1044390070006020.751.0FalseTrueFalseTrueTrue
1559410419503030.751.0FalseTrueFalseTrueTrue
1643480610123050.711.0FalseTrueFalseTrueTrue
1668510010906000.831.0FalseTrueFalseTrueTrue
1679511319302000.671.0FalseTrueFalseTrueTrue
1828551010001000.751.0FalseTrueFalseTrueTrue
1894720133010000.711.0FalseTrueFalseTrueTrue
1919720371601000.831.0FalseTrueFalseTrueTrue
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" + ], + "text/plain": [ + " GEOID10_TRACT dac_adj_avg_prod dac_adj_avg_local is_donut_hole_prod \\\n", + "312 12087971001 0.67 1.0 False \n", + "346 12101030202 0.83 1.0 False \n", + "352 12101030303 0.83 1.0 False \n", + "388 12127081102 0.75 1.0 False \n", + "741 26069000200 0.86 1.0 False \n", + "747 26083000100 0.33 1.0 False \n", + "836 28047001400 0.88 1.0 False \n", + "1044 39007000602 0.75 1.0 False \n", + "1559 41041950303 0.75 1.0 False \n", + "1643 48061012305 0.71 1.0 False \n", + "1668 51001090600 0.83 1.0 False \n", + "1679 51131930200 0.67 1.0 False \n", + "1828 55101000100 0.75 1.0 False \n", + "1894 72013301000 0.71 1.0 False \n", + "1919 72037160100 0.83 1.0 False \n", + "\n", + " is_donut_hole_local final_dac_prod final_dac_local class_change \n", + "312 True False True True \n", + "346 True False True True \n", + "352 True False True True \n", + "388 True False True True \n", + "741 True False True True \n", + "747 True False True True \n", + "836 True False True True \n", + "1044 True False True True \n", + "1559 True False True True \n", + "1643 True False True True \n", + "1668 True False True True \n", + "1679 True False True True \n", + "1828 True False True True \n", + "1894 True False True True \n", + "1919 True False True True " + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# read in CSV with deltas from implementation 2a \n", + "# (water areas filtered from input df via ALAND10)\n", + "deltas__df_area = pd.read_csv('../data/tmp/Comparator/Score/deltas__df_area.csv')\n", + "\n", + "# rename key columns\n", + "deltas__df_area.rename(columns = {'Unnamed: 0': 'GEOID10_TRACT',\n", + " 'Definition N community, including adjacency index tracts': 'final_dac_prod',\n", + " 'Definition N community, including adjacency index tracts.1': 'final_dac_local',\n", + " 'Definition N (communities) (average of neighbors)': 'dac_adj_avg_prod',\n", + " 'Definition N (communities) (average of neighbors).1': 'dac_adj_avg_local',\n", + " 'Is the tract surrounded by disadvantaged communities?': 'is_donut_hole_prod',\n", + " 'Is the tract surrounded by disadvantaged communities?.1': 'is_donut_hole_local'\n", + " },\n", + " inplace=True) \n", + "\n", + "# drop first two rows, which old column information\n", + "deltas__df_area.drop(index=[0,1], inplace=True)\n", + "\n", + "# create bool to store whether final DAC designation was updated\n", + "deltas__df_area['class_change'] = deltas__df_area.final_dac_prod!=deltas__df_area.final_dac_local\n", + "\n", + "# set class change bool to false where both designations are null\n", + "deltas__df_area.loc[((deltas__df_area.final_dac_prod.isna()==True)\\\n", + " & (deltas__df_area.final_dac_local.isna()==True)), \n", + " 'class_change'] = False\n", + "\n", + "# view tracts that had their status updated with this method\n", + "deltas__df_area[deltas__df_area.class_change][['GEOID10_TRACT', \n", + " 'dac_adj_avg_prod', 'dac_adj_avg_local',\n", + " 'is_donut_hole_prod', 'is_donut_hole_local',\n", + " 'final_dac_prod', 'final_dac_local',\n", + " 'class_change']]" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "5187abcb", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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GEOID10_TRACTdac_adj_avg_proddac_adj_avg_localis_donut_hole_prodis_donut_hole_localfinal_dac_prodfinal_dac_localclass_change
228120879710010.671.0FalseTrueFalseTrueTrue
261121010302020.831.0FalseTrueFalseTrueTrue
267121010303030.831.0FalseTrueFalseTrueTrue
292121270811020.751.0FalseTrueFalseTrueTrue
570260690002000.861.0FalseTrueFalseTrueTrue
648280470014000.881.0FalseTrueFalseTrueTrue
755390070006020.751.0FalseTrueFalseTrueTrue
1281480610123050.711.0FalseTrueFalseTrueTrue
1388551010001000.751.0FalseTrueFalseTrueTrue
1470720371601000.831.0FalseTrueFalseTrueTrue
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" + ], + "text/plain": [ + " GEOID10_TRACT dac_adj_avg_prod dac_adj_avg_local is_donut_hole_prod \\\n", + "228 12087971001 0.67 1.0 False \n", + "261 12101030202 0.83 1.0 False \n", + "267 12101030303 0.83 1.0 False \n", + "292 12127081102 0.75 1.0 False \n", + "570 26069000200 0.86 1.0 False \n", + "648 28047001400 0.88 1.0 False \n", + "755 39007000602 0.75 1.0 False \n", + "1281 48061012305 0.71 1.0 False \n", + "1388 55101000100 0.75 1.0 False \n", + "1470 72037160100 0.83 1.0 False \n", + "\n", + " is_donut_hole_local final_dac_prod final_dac_local class_change \n", + "228 True False True True \n", + "261 True False True True \n", + "267 True False True True \n", + "292 True False True True \n", + "570 True False True True \n", + "648 True False True True \n", + "755 True False True True \n", + "1281 True False True True \n", + "1388 True False True True \n", + "1470 True False True True " + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# read in CSV with deltas from implementation 2b \n", + "# (water areas filtered from input df via IDs)\n", + "deltas__df_id = pd.read_csv('../data/tmp/Comparator/Score/deltas__df_id.csv')\n", + "\n", + "# rename key columns\n", + "deltas__df_id.rename(columns = {'Unnamed: 0': 'GEOID10_TRACT',\n", + " 'Definition N community, including adjacency index tracts': 'final_dac_prod',\n", + " 'Definition N community, including adjacency index tracts.1': 'final_dac_local',\n", + " 'Definition N (communities) (average of neighbors)': 'dac_adj_avg_prod',\n", + " 'Definition N (communities) (average of neighbors).1': 'dac_adj_avg_local',\n", + " 'Is the tract surrounded by disadvantaged communities?': 'is_donut_hole_prod',\n", + " 'Is the tract surrounded by disadvantaged communities?.1': 'is_donut_hole_local'\n", + " },\n", + " inplace=True) \n", + "\n", + "# drop first two rows, which old column information\n", + "deltas__df_id.drop(index=[0,1], inplace=True)\n", + "\n", + "# create bool to store whether final DAC designation was updated\n", + "deltas__df_id['class_change'] = deltas__df_id.final_dac_prod!=deltas__df_id.final_dac_local\n", + "\n", + "# set class change bool to false where both designations are null\n", + "deltas__df_id.loc[((deltas__df_id.final_dac_prod.isna()==True)\\\n", + " & (deltas__df_id.final_dac_local.isna()==True)), \n", + " 'class_change'] = False\n", + "\n", + "# view tracts that had their status updated with this method\n", + "deltas__df_id[deltas__df_id.class_change][['GEOID10_TRACT', \n", + " 'dac_adj_avg_prod', 'dac_adj_avg_local',\n", + " 'is_donut_hole_prod', 'is_donut_hole_local',\n", + " 'final_dac_prod', 'final_dac_local',\n", + " 'class_change']]" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "6d1c7b0e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "12087971001, 12101030202, 12101030303, 12127081102, 26069000200, 26083000100, 28047001400, 39007000602, 41041950303, 48061012305, 51001090600, 51131930200, 55101000100, 72013301000, 72037160100\n" + ] + } + ], + "source": [ + "# print list of tract IDs where status was updated by method 1a\n", + "updated_ids__tract_area = deltas__tract_area[deltas__tract_area.class_change]['GEOID10_TRACT'].values\n", + "print(', '.join(updated_ids__tract_area))" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "a1ea1384", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "12087971001, 12101030202, 12101030303, 12127081102, 26069000200, 26083000100, 28047001400, 39007000602, 41041950303, 48061012305, 51001090600, 51131930200, 55101000100, 72013301000, 72037160100\n" + ] + } + ], + "source": [ + "# print list of tract IDs where status was updated by method 2a\n", + "updated_ids__df_area = deltas__df_area[deltas__df_area.class_change]['GEOID10_TRACT'].values\n", + "print(', '.join(updated_ids__df_area))" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "f6516263", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "12087971001, 12101030202, 12101030303, 12127081102, 26069000200, 28047001400, 39007000602, 48061012305, 55101000100, 72037160100\n" + ] + } + ], + "source": [ + "# print list of tract IDs where status was updated by method 2b\n", + "updated_ids__df_id = deltas__df_id[deltas__df_id.class_change]['GEOID10_TRACT'].values\n", + "print(', '.join(updated_ids__df_id))" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "f540133f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "15" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# how many tracts updated by method 1a?\n", + "len(updated_ids__tract_area)" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "ffb41759", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "15" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# how many tracts updated by method 2a?\n", + "len(updated_ids__df_area)" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "4e5099ff", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "10" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# how many tracts updated by method 2b?\n", + "len(updated_ids__df_id)" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "8534563c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "15" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# how many tracts are changed by both 1a and 2a?\n", + "len(set(updated_ids__tract_area) & set(updated_ids__df_area))" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "b8270c9a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "10" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# how many tracts are changed by all 3?\n", + "len(set(updated_ids__tract_area) & set(updated_ids__df_id))" + ] + }, + { + "cell_type": "markdown", + "id": "6494e4c3", + "metadata": {}, + "source": [ + "Implementations 1a & 2a result in the same designations. They makes all the same changes as implementation 2b, plus 5 more." + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "8c80b1d6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'26083000100', '41041950303', '51001090600', '51131930200', '72013301000'}" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# view the tracts that are changed with 1a/2a but not with 2b\n", + "\n", + "set(updated_ids__tract_area) - set(updated_ids__df_id)" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "bb4a3c4b", + "metadata": {}, + "outputs": [], + "source": [ + "### VA:\n", + "# 51001090600\n", + "# https://screeningtool.geoplatform.gov/en/#9.26/37.7027/-75.8891\n", + "# 51131930200\n", + "# https://screeningtool.geoplatform.gov/en/#9.84/37.3186/-75.9043\n", + "\n", + "### MI: \n", + "# 26083000100\n", + "# https://screeningtool.geoplatform.gov/en/#8.87/47.3424/-88.1965\n", + "\n", + "### OR:\n", + "# 41041950303\n", + "# https://screeningtool.geoplatform.gov/en/#11.94/45.015/-123.98189\n", + "\n", + "### PR:\n", + "# 72013301000\n", + "# https://screeningtool.geoplatform.gov/en/#13.45/18.47558/-66.75585" + ] + }, + { + "cell_type": "markdown", + "id": "9c3e7297", + "metadata": {}, + "source": [ + "These all look like they should be re-classified. Indicates we should not go with 2b." + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "67bbb703", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'12087971001',\n", + " '12101030202',\n", + " '12101030303',\n", + " '12127081102',\n", + " '26069000200',\n", + " '28047001400',\n", + " '39007000602',\n", + " '48061012305',\n", + " '55101000100',\n", + " '72037160100'}" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# check the rest of the changed tracts\n", + "set(updated_ids__tract_area) & set(updated_ids__df_id)" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "id": "b51cfa18", + "metadata": {}, + "outputs": [], + "source": [ + "### FL:\n", + "# 12087971001\n", + "# https://screeningtool.geoplatform.gov/en/#12.3/24.73756/-81.0061\n", + "# 12101030202\n", + "# https://screeningtool.geoplatform.gov/en/#13.28/28.34631/-82.71162\n", + "# 12101030303\n", + "# https://screeningtool.geoplatform.gov/en/#12.8/28.22897/-82.75138\n", + "# 12127081102\n", + "# https://screeningtool.geoplatform.gov/en/#13.54/29.24789/-81.02269\n", + "\n", + "### MI:\n", + "# 26069000200\n", + "# https://screeningtool.geoplatform.gov/en/#9.71/44.3623/-83.5055\n", + "\n", + "### MS:\n", + "# 28047001400\n", + "# https://screeningtool.geoplatform.gov/en/#11.54/30.3705/-89.0511\n", + "\n", + "### OH:\n", + "# 39007000602\n", + "# https://screeningtool.geoplatform.gov/en/#12.33/41.89305/-80.82678\n", + "\n", + "### TX:\n", + "# 48061012305\n", + "# https://screeningtool.geoplatform.gov/en/#9.76/26.2291/-97.2455\n", + "\n", + "### WI:\n", + "# 55101000100\n", + "# https://screeningtool.geoplatform.gov/en/#13.44/42.7299/-87.77935\n", + "\n", + "### PR:\n", + "# 72037160100\n", + "# https://screeningtool.geoplatform.gov/en/#11.59/18.2405/-65.6222" + ] + }, + { + "cell_type": "markdown", + "id": "3410f026", + "metadata": {}, + "source": [ + "These all look like legitimate donut holes." + ] + }, + { + "cell_type": "markdown", + "id": "327894d4", + "metadata": {}, + "source": [ + "#### Final recommendation: Use implementation 2a (filter the input df using ALAND10). This will allow us to update the status of all 15 of the above Census tracts, and will also let the function continue to return rows for the water tracts (as we currently do in prod)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4455847a", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/data/data-pipeline/data_pipeline/score/field_names.py b/data/data-pipeline/data_pipeline/score/field_names.py index de69d24b..f03dce3a 100644 --- a/data/data-pipeline/data_pipeline/score/field_names.py +++ b/data/data-pipeline/data_pipeline/score/field_names.py @@ -10,6 +10,9 @@ GEOID_TRACT_FIELD = "GEOID10_TRACT" STATE_FIELD = "State/Territory" COUNTY_FIELD = "County Name" +# Census per-tract land area field name +LAND_AREA_FIELD = "ALAND10" + # Definition Narwhal fields SCORE_N_COMMUNITIES = "Definition N (communities)" N_CLIMATE = "Climate Factor (Definition N)" diff --git a/data/data-pipeline/data_pipeline/score/utils.py b/data/data-pipeline/data_pipeline/score/utils.py index 5450faf6..092bc646 100644 --- a/data/data-pipeline/data_pipeline/score/utils.py +++ b/data/data-pipeline/data_pipeline/score/utils.py @@ -11,6 +11,42 @@ from data_pipeline.utils import get_module_logger logger = get_module_logger(__name__) +def in_water_range(x: int): + """Check if a tract ID is within the water area ID range + + Input must be integer representation of the 6-char census tract code + (equivalent to last 6 characters of full geo ID) + + Returns TRUE if ID is in the water area range. + Returns FALSE if ID is NOT in the water area range. + + NB: Not currently in use; artifact of attempt to filter + water areas using Census tract ID rangers. + """ + if x >= 990000 and x <= 990099: + return True + return False + + +def full_geo_id_to_water_range_bool(x: str): + """Check if a geo ID is within the water area ID range. + + First, convert full ID string to int of last six digits, + Then check if this int is in the correct range. + + Input should be the 11 character Census tract identifier, + the last 6 characters of which are the census tract code. + + Returns TRUE if ID is in the water area range. + Returns FALSE if ID is NOT in the water area range. + + NB: Not currently in use; artifact of attempt to filter + water areas using Census tract ID rangers. + """ + num_x = int(x[-6:]) + return in_water_range(num_x) + + def calculate_tract_adjacency_scores( df: pd.DataFrame, score_column: str ) -> pd.DataFrame: @@ -33,11 +69,15 @@ def calculate_tract_adjacency_scores( ORIGINAL_TRACT = "ORIGINAL_TRACT" logger.debug("Calculating tract adjacency scores") tract_data = get_tract_geojson() + df: gpd.GeoDataFrame = tract_data.merge( df, on=field_names.GEOID_TRACT_FIELD ) df = df.rename(columns={field_names.GEOID_TRACT_FIELD: ORIGINAL_TRACT}) + # remove water areas from input frame + df = df[df[field_names.LAND_AREA_FIELD] > 0] + logger.debug("Perfoming spatial join to find all adjacent tracts") adjacent_tracts: gpd.GeoDataFrame = df.sjoin( tract_data, predicate="touches" From 77e09964416bc4f4508c85c60d225c5b6f07ff62 Mon Sep 17 00:00:00 2001 From: ericiwamoto <100735505+ericiwamoto@users.noreply.github.com> Date: Tue, 3 Dec 2024 08:06:44 -0800 Subject: [PATCH 24/28] Change frontent variable references from v1.0 to v2.0 --- client/.env.development | 24 +-- client/.env.production | 24 +-- .../MapTractLayers/MapTractLayers.tsx | 4 +- .../ReleaseUpdate/ReleaseUpdate.tsx | 188 +++++++++--------- client/src/data/copy/about.tsx | 14 +- client/src/data/copy/common.tsx | 6 +- client/src/data/copy/downloads.tsx | 48 ++--- client/src/data/copy/previousVer.tsx | 4 +- .../tests/__snapshots__/about.test.tsx.snap | 6 +- .../freqAskedQuestions.test.tsx.snap | 2 +- 10 files changed, 160 insertions(+), 160 deletions(-) diff --git a/client/.env.development b/client/.env.development index 0c6c369a..a9594395 100644 --- a/client/.env.development +++ b/client/.env.development @@ -10,7 +10,7 @@ GATSBY_DATA_PIPELINE_SCORE_PATH_LOCAL=data_pipeline/data/score GATSBY_DATA_PIPELINE_TRIBAL_PATH=data-pipeline/data/tribal GATSBY_BETA_SCORE_PATH = data-versions/beta/data/score -GATSBY_1_0_SCORE_PATH = data-versions/1.0/data/score +GATSBY_2_0_SCORE_PATH = data-versions/2.0/data/score GATSBY_FILE_DL_PATH_BETA_COMMUNITIES_LIST_XLS=downloadable/beta-communities.xlsx GATSBY_FILE_DL_PATH_BETA_COMMUNITIES_LIST_CSV=downloadable/beta-communities.csv @@ -19,17 +19,17 @@ GATSBY_FILE_DL_PATH_BETA_DATA_DOC=downloadable/beta-data-documentation.zip GATSBY_FILE_DL_PATH_BETA_TRAINING_SLIDES_PPT=downloadable/technical-training-slides.pptx -GATSBY_FILE_DL_PATH_1_0_COMMUNITIES_LIST_XLS=downloadable/1.0-communities.xlsx -GATSBY_FILE_DL_PATH_1_0_COMMUNITIES_LIST_CSV=downloadable/1.0-communities.csv -GATSBY_FILE_DL_PATH_1_0_COMMUNITIES_LIST_PDF=downloadable/1.0-communities-list.pdf -GATSBY_FILE_DL_PATH_1_0_COMMUNITIES_LIST_ES_PDF=downloadable/1.0-communities-list-es.pdf -GATSBY_FILE_DL_PATH_1_0_SHAPE_FILE_ZIP=downloadable/1.0-shapefile-codebook.zip -GATSBY_FILE_DL_PATH_1_0_INSTRUCT_PDF=downloadable/CEQ-CEJST-Instructions.pdf -GATSBY_FILE_DL_PATH_1_0_INSTRUCT_ES_PDF=downloadable/CEQ-CEJST-Instructions-es.pdf -GATSBY_FILE_DL_PATH_1_0_COMP_CHART_PDF=downloadable/total-comparison-chart.pdf -GATSBY_FILE_DL_PATH_1_0_TSD_PDF=downloadable/1.0-cejst-technical-support-document.pdf -GATSBY_FILE_DL_PATH_1_0_TOOL_COMP_PDF=downloadable/1.0-climate-and-environmental-burden-tool-comparison.pdf -GATSBY_FILE_DL_PATH_1_0_M_23_09_SIGNED_PDF=downloadable/M-23-09_Signed_CEQ_CPO_es.pdf +GATSBY_FILE_DL_PATH_2_0_COMMUNITIES_LIST_XLS=downloadable/2.0-communities.xlsx +GATSBY_FILE_DL_PATH_2_0_COMMUNITIES_LIST_CSV=downloadable/2.0-communities.csv +GATSBY_FILE_DL_PATH_2_0_COMMUNITIES_LIST_PDF=downloadable/2.0-communities-list.pdf +GATSBY_FILE_DL_PATH_2_0_COMMUNITIES_LIST_ES_PDF=downloadable/2.0-communities-list-es.pdf +GATSBY_FILE_DL_PATH_2_0_SHAPE_FILE_ZIP=downloadable/2.0-shapefile-codebook.zip +GATSBY_FILE_DL_PATH_2_0_INSTRUCT_PDF=downloadable/CEQ-CEJST-Instructions.pdf +GATSBY_FILE_DL_PATH_2_0_INSTRUCT_ES_PDF=downloadable/CEQ-CEJST-Instructions-es.pdf +GATSBY_FILE_DL_PATH_2_0_COMP_CHART_PDF=downloadable/total-comparison-chart.pdf +GATSBY_FILE_DL_PATH_2_0_TSD_PDF=downloadable/2.0-cejst-technical-support-document.pdf +GATSBY_FILE_DL_PATH_2_0_TOOL_COMP_PDF=downloadable/2.0-climate-and-environmental-burden-tool-comparison.pdf +GATSBY_FILE_DL_PATH_2_0_M_23_09_SIGNED_PDF=downloadable/M-23-09_Signed_CEQ_CPO_es.pdf GATSBY_FILE_DL_PATH_TSD_ES_PDF=downloadable/cejst-technical-support-document-es.pdf GATSBY_FILE_DL_PATH_HOW_TO_COMMUNITIES_PDF=downloadable/draft-communities-list.pdf diff --git a/client/.env.production b/client/.env.production index 41ec89a8..a925c4a2 100644 --- a/client/.env.production +++ b/client/.env.production @@ -8,7 +8,7 @@ GATSBY_CDN_TILES_BASE_URL=https://dig0wsohit6js.cloudfront.net GATSBY_DATA_PIPELINE_TRIBAL_PATH=data-pipeline/data/tribal GATSBY_BETA_SCORE_PATH = data-versions/beta/data/score -GATSBY_1_0_SCORE_PATH = data-versions/1.0/data/score +GATSBY_2_0_SCORE_PATH = data-versions/2.0/data/score GATSBY_FILE_DL_PATH_BETA_COMMUNITIES_LIST_XLS=downloadable/beta-communities.xlsx GATSBY_FILE_DL_PATH_BETA_COMMUNITIES_LIST_CSV=downloadable/beta-communities.csv @@ -16,17 +16,17 @@ GATSBY_FILE_DL_PATH_BETA_SHAPE_FILE_ZIP=downloadable/beta-shapefile-codebook.zip GATSBY_FILE_DL_PATH_BETA_DATA_DOC=downloadable/beta-data-documentation.zip GATSBY_FILE_DL_PATH_BETA_TRAINING_SLIDES_PPT=downloadable/technical-training-slides.pptx -GATSBY_FILE_DL_PATH_1_0_COMMUNITIES_LIST_XLS=downloadable/1.0-communities.xlsx -GATSBY_FILE_DL_PATH_1_0_COMMUNITIES_LIST_CSV=downloadable/1.0-communities.csv -GATSBY_FILE_DL_PATH_1_0_COMMUNITIES_LIST_PDF=downloadable/1.0-communities-list.pdf -GATSBY_FILE_DL_PATH_1_0_COMMUNITIES_LIST_ES_PDF=downloadable/1.0-communities-list-es.pdf -GATSBY_FILE_DL_PATH_1_0_SHAPE_FILE_ZIP=downloadable/1.0-shapefile-codebook.zip -GATSBY_FILE_DL_PATH_1_0_INSTRUCT_PDF=downloadable/CEQ-CEJST-Instructions.pdf -GATSBY_FILE_DL_PATH_1_0_INSTRUCT_ES_PDF=downloadable/CEQ-CEJST-Instructions-es.pdf -GATSBY_FILE_DL_PATH_1_0_COMP_CHART_PDF=downloadable/total-comparison-chart.pdf -GATSBY_FILE_DL_PATH_1_0_TSD_PDF=downloadable/1.0-cejst-technical-support-document.pdf -GATSBY_FILE_DL_PATH_1_0_TOOL_COMP_PDF=downloadable/1.0-climate-and-environmental-burden-tool-comparison.pdf -GATSBY_FILE_DL_PATH_1_0_M_23_09_SIGNED_PDF=downloadable/M-23-09_Signed_CEQ_CPO_es.pdf +GATSBY_FILE_DL_PATH_2_0_COMMUNITIES_LIST_XLS=downloadable/2.0-communities.xlsx +GATSBY_FILE_DL_PATH_2_0_COMMUNITIES_LIST_CSV=downloadable/2.0-communities.csv +GATSBY_FILE_DL_PATH_2_0_COMMUNITIES_LIST_PDF=downloadable/2.0-communities-list.pdf +GATSBY_FILE_DL_PATH_2_0_COMMUNITIES_LIST_ES_PDF=downloadable/2.0-communities-list-es.pdf +GATSBY_FILE_DL_PATH_2_0_SHAPE_FILE_ZIP=downloadable/2.0-shapefile-codebook.zip +GATSBY_FILE_DL_PATH_2_0_INSTRUCT_PDF=downloadable/CEQ-CEJST-Instructions.pdf +GATSBY_FILE_DL_PATH_2_0_INSTRUCT_ES_PDF=downloadable/CEQ-CEJST-Instructions-es.pdf +GATSBY_FILE_DL_PATH_2_0_COMP_CHART_PDF=downloadable/total-comparison-chart.pdf +GATSBY_FILE_DL_PATH_2_0_TSD_PDF=downloadable/2.0-cejst-technical-support-document.pdf +GATSBY_FILE_DL_PATH_2_0_TOOL_COMP_PDF=downloadable/2.0-climate-and-environmental-burden-tool-comparison.pdf +GATSBY_FILE_DL_PATH_2_0_M_23_09_SIGNED_PDF=downloadable/M-23-09_Signed_CEQ_CPO_es.pdf GATSBY_FILE_DL_PATH_TSD_ES_PDF=downloadable/cejst-technical-support-document-es.pdf GATSBY_FILE_DL_PATH_HOW_TO_COMMUNITIES_PDF=downloadable/draft-communities-list.pdf diff --git a/client/src/components/MapTractLayers/MapTractLayers.tsx b/client/src/components/MapTractLayers/MapTractLayers.tsx index 7970d4d1..c36295e8 100644 --- a/client/src/components/MapTractLayers/MapTractLayers.tsx +++ b/client/src/components/MapTractLayers/MapTractLayers.tsx @@ -24,7 +24,7 @@ interface IMapTractLayers { export const featureURLForTilesetName = (tilesetName: string): string => { const flags = useFlags(); - const pipelineStagingBaseURL = `https://justice40-data.s3.amazonaws.com/data-pipeline-staging`; + const pipelineStagingBaseURL = process.env.GATSBY_CDN_TILES_BASE_URL +`/data-pipeline-staging`; const XYZ_SUFFIX = '{z}/{x}/{y}.pbf'; if ('stage_hash' in flags) { @@ -44,7 +44,7 @@ export const featureURLForTilesetName = (tilesetName: string): string => { const featureTilePath = process.env.DATA_SOURCE === 'local' ? process.env.GATSBY_DATA_PIPELINE_SCORE_PATH_LOCAL : - process.env.GATSBY_1_0_SCORE_PATH; + process.env.GATSBY_2_0_SCORE_PATH; return [ featureTileBaseURL, diff --git a/client/src/components/ReleaseUpdate/ReleaseUpdate.tsx b/client/src/components/ReleaseUpdate/ReleaseUpdate.tsx index 57b4f3e4..fc509e81 100644 --- a/client/src/components/ReleaseUpdate/ReleaseUpdate.tsx +++ b/client/src/components/ReleaseUpdate/ReleaseUpdate.tsx @@ -77,195 +77,195 @@ const ReleaseUpdate = ({}: IReleaseUpdateProps) => {
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          • {DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B6_1_2_2_2}
            • +
          • {DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B6_1_2_2_3}
            • +
          • {DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B6_1_2_2_4}
        */}
      -
    • {DOWNLOAD_COPY.RELEASE_1_0.SECTION1_B6_2}
      • +
    • {DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B6_2}
        • -
      • {DOWNLOAD_COPY.RELEASE_1_0.SECTION1_B6_2_1}
        • - {/*
      • {DOWNLOAD_COPY.RELEASE_1_0.SECTION1_B6_1_2}
        • +
      • {DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B6_2_1}
        • + {/*
      • {DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B6_1_2}
          • -
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            • -
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            • +
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          -
        • {DOWNLOAD_COPY.RELEASE_1_0.SECTION1_B6_1_2_2}
          • +
        • {DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B6_1_2_2}
            • -
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            • +
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            • +
          • {DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B6_1_2_2_4}
        */}
      - {/*
    • {DOWNLOAD_COPY.RELEASE_1_0.SECTION1_B6_3}
      • + {/*
    • {DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B6_3}
        • -
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        • +
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            • -
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            • +
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          -
        • {DOWNLOAD_COPY.RELEASE_1_0.SECTION1_B6_1_2_2}
          • +
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            • -
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            • -
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            • +
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            • +
          • {DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B6_1_2_2_4}
      -
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      • +
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        • -
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        • +
      • {DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B6_1_2}
          • -
        • {DOWNLOAD_COPY.RELEASE_1_0.SECTION1_B6_1_2_1}
          • +
        • {DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B6_1_2_1}
            • -
          • {DOWNLOAD_COPY.RELEASE_1_0.SECTION1_B6_1_2_1_1}
            • +
          • {DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B6_1_2_1_1}
          -
        • {DOWNLOAD_COPY.RELEASE_1_0.SECTION1_B6_1_2_2}
          • +
        • {DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B6_1_2_2}
            • -
          • {DOWNLOAD_COPY.RELEASE_1_0.SECTION1_B6_1_2_2_4}
            • +
          • {DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B6_1_2_2_4}
      */} -
    • {DOWNLOAD_COPY.RELEASE_1_0.SECTION1_B6_5}
      • +
    • {DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B6_5}
        • -
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        • -
      • {DOWNLOAD_COPY.RELEASE_1_0.SECTION1_B6_5_2}
        • +
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          • -
        • {DOWNLOAD_COPY.RELEASE_1_0.SECTION1_B6_5_2_1}
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          • -
        • {DOWNLOAD_COPY.RELEASE_1_0.SECTION1_B6_1_2_2_1}
          • -
        • {DOWNLOAD_COPY.RELEASE_1_0.SECTION1_B6_1_2_2_2}
          • +
        • {DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B6_5_2_1}
          • +
        • {DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B6_5_2_2}
          • +
        • {DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B6_5_2_3}
          • +
        • {DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B6_5_2_4}
          • +
        • {DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B6_1_2_2_1}
          • +
        • {DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B6_1_2_2_2}
    - {/*
  • {DOWNLOAD_COPY.RELEASE_1_0.SECTION1_B7}
    • + {/*
  • {DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B7}
      • -
    • {DOWNLOAD_COPY.RELEASE_1_0.SECTION1_B6_1_2_1}
      • +
    • {DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B6_1_2_1}
        • -
      • {DOWNLOAD_COPY.RELEASE_1_0.SECTION1_B7_1_1}
        • +
      • {DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B7_1_1}
      -
    • {DOWNLOAD_COPY.RELEASE_1_0.SECTION1_B6_1_2_2}
      • +
    • {DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B6_1_2_2}
        • -
      • {DOWNLOAD_COPY.RELEASE_1_0.SECTION1_B7_2_1}
        • -
      • {DOWNLOAD_COPY.RELEASE_1_0.SECTION1_B7_2_2}
        • -
      • {DOWNLOAD_COPY.RELEASE_1_0.SECTION1_B7_2_3}
        • +
      • {DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B7_2_1}
        • +
      • {DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B7_2_2}
        • +
      • {DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B7_2_3}
      -
    • {DOWNLOAD_COPY.RELEASE_1_0.SECTION1_B7_3}
      • +
    • {DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B7_3}
        • -
      • {DOWNLOAD_COPY.RELEASE_1_0.SECTION1_B7_3_1}
        • +
      • {DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B7_3_1}
    */} -
  • {DOWNLOAD_COPY.RELEASE_1_0.SECTION1_B8}
    • +
  • {DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B8}
      • -
    • {DOWNLOAD_COPY.RELEASE_1_0.SECTION1_B8_1}
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      • -
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    • {DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B8_1}
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    • {DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B8_4}
    -
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    • {DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B9_4}
    -
  • {DOWNLOAD_COPY.RELEASE_1_0.SECTION1_B10}
    • +
  • {DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B10}
- {DOWNLOAD_COPY.RELEASE_1_0.SECTION2} + {DOWNLOAD_COPY.RELEASE_2_0.SECTION2}
    -
  • {DOWNLOAD_COPY.RELEASE_1_0.SECTION2_P1}
    • -
  • {DOWNLOAD_COPY.RELEASE_1_0.SECTION2_P2}
    • +
  • {DOWNLOAD_COPY.RELEASE_2_0.SECTION2_P1}
    • +
  • {DOWNLOAD_COPY.RELEASE_2_0.SECTION2_P2}
- {DOWNLOAD_COPY.RELEASE_1_0.FOOTER} + {DOWNLOAD_COPY.RELEASE_2_0.FOOTER}
diff --git a/client/src/data/copy/about.tsx b/client/src/data/copy/about.tsx index 4326ef4b..78a15480 100644 --- a/client/src/data/copy/about.tsx +++ b/client/src/data/copy/about.tsx @@ -12,16 +12,16 @@ export const FAQS_LINK = 'https://www.whitehouse.gov/wp-content/uploads/2022/02/ export const FED_RECOGNIZED_INDIAN_ENTITIES = `https://www.federalregister.gov/documents/2022/01/28/2022-01789/indian-entities-recognized-by-and-eligible-to-receive-services-from-the-united-states-bureau-of`; export const EJSCREEN = 'https://www.epa.gov/ejscreen/how-does-epa-use-ejscreen'; -export const CEJST_INSTRUCT = `https://static-data-screeningtool.geoplatform.gov/data-versions/1.0/data/score/downloadable/CEQ-CEJST-Instructions.pdf`; -export const CEJST_INSTRUCT_ES = `https://static-data-screeningtool.geoplatform.gov/data-versions/1.0/data/score/downloadable/CEQ-CEJST-Instructions-es.pdf`; +export const CEJST_INSTRUCT = process.env.GATSBY_CDN_TILES_BASE_URL +`/data-versions/2.0/data/score/downloadable/CEQ-CEJST-Instructions.pdf`; +export const CEJST_INSTRUCT_ES = process.env.GATSBY_CDN_TILES_BASE_URL +`/data-versions/2.0/data/score/downloadable/CEQ-CEJST-Instructions-es.pdf`; export const CEJST_MEMO = `https://www.whitehouse.gov/wp-content/uploads/2023/01/M-23-09_Signed_CEQ_CPO.pdf`; -export const CEJST_MEMO_ES = `https://static-data-screeningtool.geoplatform.gov/data-versions/1.0/data/score/downloadable/M-23-09_Signed_CEQ_CPO_es.pdf`; +export const CEJST_MEMO_ES = process.env.GATSBY_CDN_TILES_BASE_URL +`/data-versions/2.0/data/score/downloadable/M-23-09_Signed_CEQ_CPO_es.pdf`; -export const USE_MAP_TUTORIAL_LINK = `https://static-data-screeningtool.geoplatform.gov/data-versions/1.0/data/score/downloadable/Using-the-CEJST-Tutorial.pdf`; -export const USE_MAP_TUTORIAL_LINK_ES = `https://static-data-screeningtool.geoplatform.gov/data-versions/1.0/data/score/downloadable/Using-the-CEJST-Tutorial-es.pdf`; -export const USE_DATA_TUTORIAL_LINK = `https://static-data-screeningtool.geoplatform.gov/data-versions/1.0/data/score/downloadable/Using-the-CEJST-Spreadsheet-Tutorial.pdf`; -export const USE_DATA_TUTORIAL_LINK_ES = `https://static-data-screeningtool.geoplatform.gov/data-versions/1.0/data/score/downloadable/Using-the-CEJST-Spreadsheet-Tutorial-es.pdf`; +export const USE_MAP_TUTORIAL_LINK = process.env.GATSBY_CDN_TILES_BASE_URL +`/data-versions/2.0/data/score/downloadable/Using-the-CEJST-Tutorial.pdf`; +export const USE_MAP_TUTORIAL_LINK_ES = process.env.GATSBY_CDN_TILES_BASE_URL +`/data-versions/2.0/data/score/downloadable/Using-the-CEJST-Tutorial-es.pdf`; +export const USE_DATA_TUTORIAL_LINK = process.env.GATSBY_CDN_TILES_BASE_URL +`/data-versions/2.0/data/score/downloadable/Using-the-CEJST-Spreadsheet-Tutorial.pdf`; +export const USE_DATA_TUTORIAL_LINK_ES = process.env.GATSBY_CDN_TILES_BASE_URL +`/data-versions/2.0/data/score/downloadable/Using-the-CEJST-Spreadsheet-Tutorial-es.pdf`; export const GITHUB_LINK = 'https://github.com/usds/justice40-tool'; export const GITHUB_LINK_ES = 'https://github.com/usds/justice40-tool/blob/main/README-es.md'; diff --git a/client/src/data/copy/common.tsx b/client/src/data/copy/common.tsx index 1433615f..f9248517 100644 --- a/client/src/data/copy/common.tsx +++ b/client/src/data/copy/common.tsx @@ -28,7 +28,7 @@ export const linkFn = (to:string | IDefineMessage, isInternal:boolean, isOpenNew export const FEEDBACK_EMAIL = 'Screeningtool-Support@omb.eop.gov'; -export const METH_1_0_RELEASE_DATE = new Date(2022, 10, 22, 11, 59, 59); // Nov 22 2022 +export const METH_2_0_RELEASE_DATE = new Date(2022, 10, 22, 11, 59, 59); // Nov 22 2022 export const METH_BETA_RELEASE_DATE = new Date(2022, 1, 18, 11, 59, 59); // Feb 18 2022 @@ -40,7 +40,7 @@ export const BETA_BANNER_CONTENT = { return [ process.env.GATSBY_CDN_TILES_BASE_URL, - (isBeta ? process.env.GATSBY_BETA_SCORE_PATH : process.env.GATSBY_1_0_SCORE_PATH), + (isBeta ? process.env.GATSBY_BETA_SCORE_PATH : process.env.GATSBY_2_0_SCORE_PATH), filePath, ].join('/'); }; @@ -41,58 +41,58 @@ export const DOWNLOAD_FILES = { NARWAL: { COMMUNITIES_LIST_XLS: { SIZE: 35.6, // MB - URL: getDownloadFileUrl(process.env.GATSBY_FILE_DL_PATH_1_0_COMMUNITIES_LIST_XLS, false), - LAST_UPDATED: COMMON_COPY.METH_1_0_RELEASE_DATE, + URL: getDownloadFileUrl(process.env.GATSBY_FILE_DL_PATH_2_0_COMMUNITIES_LIST_XLS, false), + LAST_UPDATED: COMMON_COPY.METH_2_0_RELEASE_DATE, }, COMMUNITIES_LIST_CSV: { SIZE: 42, // MB - URL: getDownloadFileUrl(process.env.GATSBY_FILE_DL_PATH_1_0_COMMUNITIES_LIST_CSV, false), - LAST_UPDATED: COMMON_COPY.METH_1_0_RELEASE_DATE, + URL: getDownloadFileUrl(process.env.GATSBY_FILE_DL_PATH_2_0_COMMUNITIES_LIST_CSV, false), + LAST_UPDATED: COMMON_COPY.METH_2_0_RELEASE_DATE, }, SHAPE_FILE: { SIZE: 356.8, // MB - URL: getDownloadFileUrl(process.env.GATSBY_FILE_DL_PATH_1_0_SHAPE_FILE_ZIP, false), - LAST_UPDATED: COMMON_COPY.METH_1_0_RELEASE_DATE, + URL: getDownloadFileUrl(process.env.GATSBY_FILE_DL_PATH_2_0_SHAPE_FILE_ZIP, false), + LAST_UPDATED: COMMON_COPY.METH_2_0_RELEASE_DATE, }, TSD: { SIZE: 4.4, // MB - URL: getDownloadFileUrl(process.env.GATSBY_FILE_DL_PATH_1_0_TSD_PDF, false), - LAST_UPDATED: COMMON_COPY.METH_1_0_RELEASE_DATE, + URL: getDownloadFileUrl(process.env.GATSBY_FILE_DL_PATH_2_0_TSD_PDF, false), + LAST_UPDATED: COMMON_COPY.METH_2_0_RELEASE_DATE, }, TSD_ES: { SIZE: 4.8, // MB URL: getDownloadFileUrl(process.env.GATSBY_FILE_DL_PATH_TSD_ES_PDF, false), - LAST_UPDATED: COMMON_COPY.METH_1_0_RELEASE_DATE, + LAST_UPDATED: COMMON_COPY.METH_2_0_RELEASE_DATE, }, HOW_TO_COMMUNITIES: { SIZE: 687.9, // KB - URL: getDownloadFileUrl(process.env.GATSBY_FILE_DL_PATH_1_0_COMMUNITIES_LIST_PDF, false), - LAST_UPDATED: COMMON_COPY.METH_1_0_RELEASE_DATE, + URL: getDownloadFileUrl(process.env.GATSBY_FILE_DL_PATH_2_0_COMMUNITIES_LIST_PDF, false), + LAST_UPDATED: COMMON_COPY.METH_2_0_RELEASE_DATE, }, HOW_TO_COMMUNITIES_ES: { SIZE: 761.8, // KB - URL: getDownloadFileUrl(process.env.GATSBY_FILE_DL_PATH_1_0_COMMUNITIES_LIST_ES_PDF, false), - LAST_UPDATED: COMMON_COPY.METH_1_0_RELEASE_DATE, + URL: getDownloadFileUrl(process.env.GATSBY_FILE_DL_PATH_2_0_COMMUNITIES_LIST_ES_PDF, false), + LAST_UPDATED: COMMON_COPY.METH_2_0_RELEASE_DATE, }, INSTRUCTIONS: { SIZE: 228.4, // KB // Todo: Update when actual file is uploaded - URL: getDownloadFileUrl(process.env.GATSBY_FILE_DL_PATH_1_0_INSTRUCT_PDF, false), - LAST_UPDATED: COMMON_COPY.METH_1_0_RELEASE_DATE, + URL: getDownloadFileUrl(process.env.GATSBY_FILE_DL_PATH_2_0_INSTRUCT_PDF, false), + LAST_UPDATED: COMMON_COPY.METH_2_0_RELEASE_DATE, }, INSTRUCTIONS_ES: { SIZE: 173.6, // KB // Todo: Update when actual file is uploaded - URL: getDownloadFileUrl(process.env.GATSBY_FILE_DL_PATH_1_0_INSTRUCT_ES_PDF, false), - LAST_UPDATED: COMMON_COPY.METH_1_0_RELEASE_DATE, + URL: getDownloadFileUrl(process.env.GATSBY_FILE_DL_PATH_2_0_INSTRUCT_ES_PDF, false), + LAST_UPDATED: COMMON_COPY.METH_2_0_RELEASE_DATE, }, COMP_CHART: { SIZE: 33.1, // KB - URL: getDownloadFileUrl(process.env.GATSBY_FILE_DL_PATH_1_0_TOOL_COMP_PDF, false), - LAST_UPDATED: COMMON_COPY.METH_1_0_RELEASE_DATE, + URL: getDownloadFileUrl(process.env.GATSBY_FILE_DL_PATH_2_0_TOOL_COMP_PDF, false), + LAST_UPDATED: COMMON_COPY.METH_2_0_RELEASE_DATE, }, M_23_09_ES: { SIZE: 120.5, // KB - URL: getDownloadFileUrl(process.env.GATSBY_FILE_DL_PATH_1_0_M_23_09_SIGNED_PDF, false), - LAST_UPDATED: COMMON_COPY.METH_1_0_RELEASE_DATE, + URL: getDownloadFileUrl(process.env.GATSBY_FILE_DL_PATH_2_0_M_23_09_SIGNED_PDF, false), + LAST_UPDATED: COMMON_COPY.METH_2_0_RELEASE_DATE, }, }, BETA: { @@ -138,7 +138,7 @@ export const getDownloadIconAltTag = () => defineMessages({ }, }); -export const RELEASE_1_0 = { +export const RELEASE_2_0 = { UPDATE_1: , releaseDate: @@ -614,7 +614,7 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis @@ -681,7 +681,7 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis diff --git a/client/src/pages/tests/__snapshots__/freqAskedQuestions.test.tsx.snap b/client/src/pages/tests/__snapshots__/freqAskedQuestions.test.tsx.snap index a337eaa5..d3d90ca8 100644 --- a/client/src/pages/tests/__snapshots__/freqAskedQuestions.test.tsx.snap +++ b/client/src/pages/tests/__snapshots__/freqAskedQuestions.test.tsx.snap @@ -490,7 +490,7 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis From e0bb33211af189e7f9ac0fb5b4ec55beb092f8e5 Mon Sep 17 00:00:00 2001 From: Carlos Felix <63804190+carlosfelix2@users.noreply.github.com> Date: Wed, 4 Dec 2024 10:00:14 -0500 Subject: [PATCH 25/28] Added grandfathering of v1.0 DACS --- .../data-pipeline/data_pipeline/comparator.py | 39 +- .../content/static_data/README.md | 5 + .../static_data/v1.0-score-results-usa.csv | 74135 ++++++++++++++++ .../data_pipeline/etl/score/constants.py | 2 + .../data_pipeline/etl/score/etl_score.py | 19 + .../data_pipeline/etl/score/tests/conftest.py | 10 + .../etl/score/tests/snapshots/README.md | 23 + .../snapshots/create_tile_data_expected.pkl | Bin 0 -> 6657 bytes .../create_tile_score_data_input.pkl | Bin 0 -> 28696 bytes .../etl/score/tests/test_score_post.py | 6 +- .../data_pipeline/score/field_names.py | 5 + .../data_pipeline/score/score_narwhal.py | 15 + .../tests/score/test_score_narwhal_methods.py | 27 + 13 files changed, 74271 insertions(+), 15 deletions(-) create mode 100644 data/data-pipeline/data_pipeline/content/static_data/README.md create mode 100644 data/data-pipeline/data_pipeline/content/static_data/v1.0-score-results-usa.csv create mode 100644 data/data-pipeline/data_pipeline/etl/score/tests/snapshots/README.md create mode 100644 data/data-pipeline/data_pipeline/etl/score/tests/snapshots/create_tile_data_expected.pkl create mode 100644 data/data-pipeline/data_pipeline/etl/score/tests/snapshots/create_tile_score_data_input.pkl diff --git a/data/data-pipeline/data_pipeline/comparator.py b/data/data-pipeline/data_pipeline/comparator.py index eed14b4f..860be7bb 100644 --- a/data/data-pipeline/data_pipeline/comparator.py +++ b/data/data-pipeline/data_pipeline/comparator.py @@ -6,6 +6,7 @@ from pathlib import Path from data_pipeline.etl.score import constants from data_pipeline.utils import get_module_logger, download_file_from_url from data_pipeline.application import log_title, log_info, log_goodbye +from data_pipeline.score import field_names logger = get_module_logger(__name__) @@ -176,6 +177,7 @@ def compare_score( production_row_count = len(production_score_df.index) local_row_count = len(local_score_df.index) + # Tract comparison _add_text( f"* The production score has {production_row_count:,} census tracts, and the freshly calculated score has {local_row_count:,}." ) @@ -191,8 +193,11 @@ def compare_score( "\n" ) - production_total_population = production_score_df["Total population"].sum() - local_total_population = local_score_df["Total population"].sum() + # Population comparison + production_total_population = production_score_df[ + field_names.TOTAL_POP_FIELD + ].sum() + local_total_population = local_score_df[field_names.TOTAL_POP_FIELD].sum() _add_text( f"* The total population in all census tracts in the production score is {production_total_population:,}. " @@ -204,12 +209,9 @@ def compare_score( else f"The difference is {abs(production_total_population - local_total_population):,}.\n" ) - production_disadvantaged_tracts_df = production_score_df.query( - "`Definition N community, including adjacency index tracts` == True" - ) - local_disadvantaged_tracts_df = local_score_df.query( - "`Definition N community, including adjacency index tracts` == True" - ) + dacs_query = f"`{field_names.FINAL_SCORE_N_BOOLEAN}` == True" + production_disadvantaged_tracts_df = production_score_df.query(dacs_query) + local_disadvantaged_tracts_df = local_score_df.query(dacs_query) production_disadvantaged_tracts_set = set( production_disadvantaged_tracts_df.index.array @@ -219,14 +221,15 @@ def compare_score( ) production_pct_of_population_represented = ( - production_disadvantaged_tracts_df["Total population"].sum() + production_disadvantaged_tracts_df[field_names.TOTAL_POP_FIELD].sum() / production_total_population ) local_pct_of_population_represented = ( - local_disadvantaged_tracts_df["Total population"].sum() + local_disadvantaged_tracts_df[field_names.TOTAL_POP_FIELD].sum() / local_total_population ) + # DACS comparison _add_text( f"* There are {len(production_disadvantaged_tracts_set):,} disadvantaged tracts in the production score representing" f" {production_pct_of_population_represented:.1%} of the total population, and {len(local_disadvantaged_tracts_set):,}" @@ -252,15 +255,27 @@ def compare_score( f" generated score (i.e. disadvantaged tracts that were removed by the new score). " ) if len(removed_tracts) > 0: - _add_text(f"Those tracts are:\n{removed_tracts}\n") + _add_text(f"Those tracts are:\n{removed_tracts}") _add_text( - f"* There are {len(added_tracts):,} tract(s) marked as disadvantaged in the locally generated score that are not disadvantaged in the" + f"\n* There are {len(added_tracts):,} tract(s) marked as disadvantaged in the locally generated score that are not disadvantaged in the" f" production score (i.e. disadvantaged tracts that were added by the new score). " ) if len(added_tracts) > 0: _add_text(f"Those tracts are:\n{added_tracts}\n") + # Grandfathered tracts from v1.0 + grandfathered_tracts = local_score_df.loc[ + local_score_df[field_names.GRANDFATHERED_N_COMMUNITIES_V1_0] + ].index + if len(grandfathered_tracts) > 0: + _add_text( + f"* This includes {len(grandfathered_tracts)} grandfathered tract(s) from v1.0 scoring. They are:\n" + f"{grandfathered_tracts.to_list()}\n" + ) + else: + _add_text("* There are NO grandfathered tracts from v1.0 scoring.\n") + ################ # Create a delta ################ diff --git a/data/data-pipeline/data_pipeline/content/static_data/README.md b/data/data-pipeline/data_pipeline/content/static_data/README.md new file mode 100644 index 00000000..840e2f78 --- /dev/null +++ b/data/data-pipeline/data_pipeline/content/static_data/README.md @@ -0,0 +1,5 @@ +Static data is used in the computation of the score as stated. + +# v1.0-score-results-usa.csv +This is the v1.0 score results used for grandfathering computations. 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+++ b/data/data-pipeline/data_pipeline/etl/score/constants.py @@ -9,6 +9,7 @@ from data_pipeline.score import field_names # Base Paths DATA_PATH = Path(settings.APP_ROOT) / "data" +STATIC_DATA_PATH = Path(settings.APP_ROOT) / "content" / "static_data" TMP_PATH = DATA_PATH / "tmp" FILES_PATH = Path(settings.APP_ROOT) / "files" @@ -275,6 +276,7 @@ TILES_SCORE_COLUMNS = { # temporarily update this so that it's the Narwhal score that gets visualized on the map # The NEW final score value INCLUDES the adjacency index. field_names.FINAL_SCORE_N_BOOLEAN: "SN_C", + field_names.FINAL_SCORE_N_BOOLEAN_V1_0: "SN_C_V10", field_names.IS_TRIBAL_DAC: "SN_T", field_names.DIABETES_LOW_INCOME_FIELD: "DLI", field_names.ASTHMA_LOW_INCOME_FIELD: "ALI", diff --git a/data/data-pipeline/data_pipeline/etl/score/etl_score.py b/data/data-pipeline/data_pipeline/etl/score/etl_score.py index 78e10a53..afa03ae9 100644 --- a/data/data-pipeline/data_pipeline/etl/score/etl_score.py +++ b/data/data-pipeline/data_pipeline/etl/score/etl_score.py @@ -54,6 +54,7 @@ class ScoreETL(ExtractTransformLoad): self.eamlis_df: pd.DataFrame self.fuds_df: pd.DataFrame self.tribal_overlap_df: pd.DataFrame + self.v1_0_score_results_df: pd.DataFrame self.ISLAND_DEMOGRAPHIC_BACKFILL_FIELDS: List[str] = [] @@ -205,6 +206,22 @@ class ScoreETL(ExtractTransformLoad): header=None, ) + # Load v1.0 score results for grandfathering purposes + score_v1_0_csv = ( + constants.STATIC_DATA_PATH / "v1.0-score-results-usa.csv" + ) + self.v1_0_score_results_df = pd.read_csv( + score_v1_0_csv, + dtype={self.GEOID_TRACT_FIELD_NAME: "string"}, + low_memory=False, + ) + self.v1_0_score_results_df.rename( + columns={ + field_names.FINAL_SCORE_N_BOOLEAN: field_names.FINAL_SCORE_N_BOOLEAN_V1_0, + }, + inplace=True, + ) + def _join_tract_dfs(self, census_tract_dfs: list) -> pd.DataFrame: logger.debug("Joining Census Tract dataframes") @@ -364,6 +381,7 @@ class ScoreETL(ExtractTransformLoad): self.eamlis_df, self.fuds_df, self.tribal_overlap_df, + self.v1_0_score_results_df, ] # Sanity check each data frame before merging. @@ -514,6 +532,7 @@ class ScoreETL(ExtractTransformLoad): field_names.ELIGIBLE_FUDS_BINARY_FIELD_NAME, field_names.HISTORIC_REDLINING_SCORE_EXCEEDED, field_names.IS_TRIBAL_DAC, + field_names.FINAL_SCORE_N_BOOLEAN_V1_0, ] # For some columns, high values are "good", so we want to reverse the percentile diff --git a/data/data-pipeline/data_pipeline/etl/score/tests/conftest.py b/data/data-pipeline/data_pipeline/etl/score/tests/conftest.py index 8353869d..ab746a27 100644 --- a/data/data-pipeline/data_pipeline/etl/score/tests/conftest.py +++ b/data/data-pipeline/data_pipeline/etl/score/tests/conftest.py @@ -129,6 +129,16 @@ def tile_data_expected(): return pd.read_pickle(pytest.SNAPSHOT_DIR / "tile_data_expected.pkl") +@pytest.fixture() +def create_tile_score_data_input(): + return pd.read_pickle(pytest.SNAPSHOT_DIR / "create_tile_score_data_input.pkl") + + +@pytest.fixture() +def create_tile_data_expected(): + return pd.read_pickle(pytest.SNAPSHOT_DIR / "create_tile_data_expected.pkl") + + @pytest.fixture() def downloadable_data_expected(): return pd.read_pickle( diff --git a/data/data-pipeline/data_pipeline/etl/score/tests/snapshots/README.md b/data/data-pipeline/data_pipeline/etl/score/tests/snapshots/README.md new file mode 100644 index 00000000..01ae8488 --- /dev/null +++ b/data/data-pipeline/data_pipeline/etl/score/tests/snapshots/README.md @@ -0,0 +1,23 @@ +These files are used as inputs to unit tests. Some notes in their creation is below. + +### create_tile_data_expected.pkl +1. Set a breakpoint in the `test_create_tile_data` method in `data_pipeline/etl/score/tests/test_score_post.py` +after the call to `_create_tile_data` and debug the test. +2. Extract a subset of the `output_tiles_df_actual` dataframe. Do not extract the whole score as the file +will be too big and the test will run slow. Also, you need to extract the same tracts that are in +the `create_tile_score_data_input.pkl` input data. For example, use the following command once the breakpoint is reached +to extract a few rows at the top and bottom of the score. This will some capture states and territories. +```python +import pandas as pd +pd.concat([output_tiles_df_actual.head(3), output_tiles_df_actual.tail(3)], ignore_index=True).to_pickle('data_pipeline/etl/score/tests/snapshots/create_tile_data_expected.pkl') +``` + +### create_tile_score_data_input.pkl +1. Set a breakpoint in the transform method in `data_pipeline/etl/score/etl_score_post.py` before the call to +`_create_tile_data` and run the post scoring. +2. Extract a subset of the `output_score_county_state_merged_df` dataframe. Do not extract the whole score as the file +will be too big and the test will run slow. For example, use the following command once the breakpoint is reached +to extract a few rows at the top and bottom of the score. This will some capture states and territories. +```python +pd.concat([output_score_county_state_merged_df.head(3), output_score_county_state_merged_df.tail(3)], ignore_index=True).to_pickle('data_pipeline/etl/score/tests/snapshots/create_tile_score_data_input.pkl') +``` \ No newline at end of file diff --git a/data/data-pipeline/data_pipeline/etl/score/tests/snapshots/create_tile_data_expected.pkl b/data/data-pipeline/data_pipeline/etl/score/tests/snapshots/create_tile_data_expected.pkl new file mode 100644 index 0000000000000000000000000000000000000000..3257e33c17b61cf489876893d14e893da23c9f03 GIT binary patch literal 6657 zcmdTJOKcm*mHO~w$*xn|X_F##+)E+#AW(5*$v}(^DUu>Js})UBRs%TLE~S;05=FA) z5{~?VTcA!YU;!7%G(dX@0wmYEXc3@31wn$MXwd-f!Eg^n>%?j8_>=UKz_<3zyqP6; z*DJNh^04o{nfKnj-@F~p{xJHpFQ1O^pYzq0(O5A$6U)uEIkDO{Hcjg6$r!egG?rqM9g%yx%5d(-vi^2R*U^fi$Dnh??JwQ6U= zm7&glz1wKjmN)9AUS&eS?Hu$l8|}8S1xlaM_14xaJ$5aVVl!!DdBa>`V&CXD3#BGH zcDvSC)2TCB<`@?N=DBO97x!sDp(SeVmvb3=t7XC*ch-q_>}DJMp-!x|Wn0Y#?Nps7 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zX20v>M!7G=-46g(A6plA|CbnektLoptm5d5|FJMPtT4@hpX9dZ?{(M)r@;!m$!}q~ z5rM4=#q1jF-ry8pEZ3n~Fy-L`q4>}YEM%rX2tnUkB`PG1{tJH7e=zN+k{SPJCwEn^ z61C@At`JT#!zKY7ZBR0MN!8^_xbH2vwq+FdHwIRNsG5C>zSUHh_Zz(2;wrard^s89 z`xegls`gTRB2|qL#yl_qs+cs`>iTNvs@v0}Qi;G+6m2_f#fe9`2x-^E3RN`@TyqJ9 z9$2Bpep>-IvsW`K*$AhBa59v56xHUo z5BtPum+_&k2Bh6wsLAD<;mnwO?RM_F2~3E5oC1JgYoQzcC9wsXlrA3dz>budr6v6; z8B9rT`%jE>6B#0-c#L9L;-Cg-PGnUi;oKYk!7_YEM?S`a*^HK)cj=f1K4d1IorBFz zIDO`Mq{OXxKr6K>TCM{#1drmZfb~L74luyR9xxH>flMak!?6@N#GVXsV{IM_62WKY zJP>*G2!ns$%Xy3?)Xk3JloT9O2ARU(0Wa92)o?Uh{LTsZfiUR{NZ>#lK8f8>I=P-; z*1}Cw@upX{d`L;)TS%|ta&xWRWWBU_#NS22*EZqm8IC#dBVH57Y==x-0{Je9aCA%;Bfk_}bu8S_E}n{DFfHfg)xOqle$)gCCEB zpX$S*8~JDZa^Rpniyy$(b?|jy^#kATFdcrxE+-Gpyo(=Ovj*rPTaMOYfnRt7wa None: + """Territory tracts that are flagged as DACS in the V1.0 score are also marked.""" + self.df[field_names.GRANDFATHERED_N_COMMUNITIES_V1_0] = np.where( + self.df[field_names.FINAL_SCORE_N_BOOLEAN_V1_0] + & ~self.df[field_names.FINAL_SCORE_N_BOOLEAN], + True, + False, + ) + self.df[field_names.FINAL_SCORE_N_BOOLEAN] = np.where( + self.df[field_names.FINAL_SCORE_N_BOOLEAN_V1_0], + True, + self.df[field_names.FINAL_SCORE_N_BOOLEAN], + ) + 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 @@ -1111,6 +1125,7 @@ class ScoreNarwhal(Score): ] = self.df[field_names.SCORE_N_COMMUNITIES].astype(int) self._mark_donut_hole_tracts() + self._mark_grandfathered_dacs() self.df[ field_names.PERCENT_OF_TRACT_IS_DAC ] = self._get_percent_of_tract_that_is_dac() diff --git a/data/data-pipeline/data_pipeline/tests/score/test_score_narwhal_methods.py b/data/data-pipeline/data_pipeline/tests/score/test_score_narwhal_methods.py index 4b8fe471..943efeb0 100644 --- a/data/data-pipeline/data_pipeline/tests/score/test_score_narwhal_methods.py +++ b/data/data-pipeline/data_pipeline/tests/score/test_score_narwhal_methods.py @@ -128,3 +128,30 @@ def test_mark_poverty_flag(): assert not test_data[~expected_low_income_filter][ field_names.FPL_200_SERIES_IMPUTED_AND_ADJUSTED ].all() + + +def test_mark_grandfathered_dacs(): + data = { + field_names.GEOID_TRACT_FIELD: [ + "78010971500", + "78010970500", + "66010954400", + "66010953400", + ], + field_names.FINAL_SCORE_N_BOOLEAN_V1_0: [False, False, True, True], + field_names.FINAL_SCORE_N_BOOLEAN: [False, True, False, True], + } + test_df = pd.DataFrame(data) + scorer = ScoreNarwhal(test_df) + scorer._mark_grandfathered_dacs() + result = scorer.df + assert field_names.GRANDFATHERED_N_COMMUNITIES_V1_0 in result.columns + assert not result[field_names.GRANDFATHERED_N_COMMUNITIES_V1_0][0] + assert not result[field_names.GRANDFATHERED_N_COMMUNITIES_V1_0][1] + assert result[field_names.GRANDFATHERED_N_COMMUNITIES_V1_0][2] + assert not result[field_names.GRANDFATHERED_N_COMMUNITIES_V1_0][3] + + assert not result[field_names.FINAL_SCORE_N_BOOLEAN][0] + assert result[field_names.FINAL_SCORE_N_BOOLEAN][1] + assert result[field_names.FINAL_SCORE_N_BOOLEAN][2] + assert result[field_names.FINAL_SCORE_N_BOOLEAN][3] From 4130c46aee94b3a320935e68a3dc715afe90bfa9 Mon Sep 17 00:00:00 2001 From: Carlos Felix <63804190+carlosfelix2@users.noreply.github.com> Date: Wed, 4 Dec 2024 14:24:25 -0500 Subject: [PATCH 26/28] Fix bug in the score generation due to column clash with use of v1.0 score --- data/data-pipeline/data_pipeline/etl/score/etl_score.py | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) diff --git a/data/data-pipeline/data_pipeline/etl/score/etl_score.py b/data/data-pipeline/data_pipeline/etl/score/etl_score.py index afa03ae9..6dc13fe0 100644 --- a/data/data-pipeline/data_pipeline/etl/score/etl_score.py +++ b/data/data-pipeline/data_pipeline/etl/score/etl_score.py @@ -215,11 +215,14 @@ class ScoreETL(ExtractTransformLoad): dtype={self.GEOID_TRACT_FIELD_NAME: "string"}, low_memory=False, ) - self.v1_0_score_results_df.rename( + # Only keep the columns we need and rename them as they will clash + # with the new score DF. + self.v1_0_score_results_df = self.v1_0_score_results_df[ + [field_names.GEOID_TRACT_FIELD, field_names.FINAL_SCORE_N_BOOLEAN] + ].rename( columns={ field_names.FINAL_SCORE_N_BOOLEAN: field_names.FINAL_SCORE_N_BOOLEAN_V1_0, - }, - inplace=True, + } ) def _join_tract_dfs(self, census_tract_dfs: list) -> pd.DataFrame: From cf4e35acceec6a92ee047dce4bf5efae8b4aed3a Mon Sep 17 00:00:00 2001 From: Carlos Felix <63804190+carlosfelix2@users.noreply.github.com> Date: Wed, 4 Dec 2024 14:36:46 -0500 Subject: [PATCH 27/28] Allow for Census Tract search in UI --- client/package-lock.json | 261 +++++++++--------- client/package.json | 1 + client/src/components/J40Map.tsx | 7 +- client/src/components/MapSearch/MapSearch.tsx | 126 +++++++-- .../__snapshots__/MapSearch.test.tsx.snap | 2 +- .../MapTractLayers/MapTractLayers.tsx | 9 +- client/src/data/constants.tsx | 10 + client/src/data/copy/explore.tsx | 2 +- client/src/intl/en.json | 2 +- client/src/intl/es.json | 2 +- .../data_pipeline/etl/score/constants.py | 5 + .../data_pipeline/etl/score/etl_score_post.py | 45 +++ .../data_pipeline/etl/score/tests/conftest.py | 11 + .../score/tests/sample_data/census_60.geojson | 25 ++ .../etl/score/tests/test_score_post.py | 16 ++ 15 files changed, 362 insertions(+), 162 deletions(-) create mode 100644 data/data-pipeline/data_pipeline/etl/score/tests/sample_data/census_60.geojson diff --git a/client/package-lock.json b/client/package-lock.json index 72420b06..98143395 100644 --- a/client/package-lock.json +++ b/client/package-lock.json @@ -3024,7 +3024,7 @@ "@types/configstore": { "version": "2.1.1", "resolved": "https://registry.npmjs.org/@types/configstore/-/configstore-2.1.1.tgz", - "integrity": "sha1-zR6FU2M60xhcPy8jns/10mQ+krY=", + "integrity": "sha512-YY+hm3afkDHeSM2rsFXxeZtu0garnusBWNG1+7MknmDWQHqcH2w21/xOU9arJUi8ch4qyFklidANLCu3ihhVwQ==", "dev": true }, "@types/cookie": { @@ -3451,7 +3451,7 @@ "@types/tmp": { "version": "0.0.33", "resolved": "https://registry.npmjs.org/@types/tmp/-/tmp-0.0.33.tgz", - "integrity": "sha1-EHPEvIJHVK49EM+riKsCN7qWTk0=", + "integrity": "sha512-gVC1InwyVrO326wbBZw+AO3u2vRXz/iRWq9jYhpG4W8LXyIgDv3ZmcLQ5Q4Gs+gFMyqx+viFoFT+l3p61QFCmQ==", "dev": true }, "@types/unist": { @@ -4082,7 +4082,7 @@ "any-promise": { "version": "1.3.0", "resolved": "https://registry.npmjs.org/any-promise/-/any-promise-1.3.0.tgz", - "integrity": "sha1-q8av7tzqUugJzcA3au0845Y10X8=", + "integrity": "sha512-7UvmKalWRt1wgjL1RrGxoSJW/0QZFIegpeGvZG9kjp8vrRu55XTHbwnqq2GpXm9uLbcuhxm3IqX9OB4MZR1b2A==", "dev": true }, "anymatch": { @@ -4098,7 +4098,7 @@ "append-field": { "version": "1.0.0", "resolved": "https://registry.npmjs.org/append-field/-/append-field-1.0.0.tgz", - "integrity": "sha1-HjRA6RXwsSA9I3SOeO3XubW0PlY=", + "integrity": "sha512-klpgFSWLW1ZEs8svjfb7g4qWY0YS5imI82dTg+QahUvJ8YqAY0P10Uk8tTyh9ZGuYEZEMaeJYCF5BFuX552hsw==", "dev": true }, "application-config-path": { @@ 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"path-platform": "~0.11.15" @@ -16073,7 +16078,7 @@ "path-platform": { "version": "0.11.15", "resolved": "https://registry.npmjs.org/path-platform/-/path-platform-0.11.15.tgz", - "integrity": "sha1-6GQhf3TDaFDwhSt43Hv31KVyG/I=", + "integrity": "sha512-Y30dB6rab1A/nfEKsZxmr01nUotHX0c/ZiIAsCTatEe1CmS5Pm5He7fZ195bPT7RdquoaL8lLxFCMQi/bS7IJg==", "dev": true }, "path-to-regexp": { @@ -16124,19 +16129,19 @@ "pend": { "version": "1.2.0", "resolved": "https://registry.npmjs.org/pend/-/pend-1.2.0.tgz", - "integrity": "sha1-elfrVQpng/kRUzH89GY9XI4AelA=", + "integrity": "sha512-F3asv42UuXchdzt+xXqfW1OGlVBe+mxa2mqI0pg5yAHZPvFmY3Y6drSf/GQ1A86WgWEN9Kzh/WrgKa6iGcHXLg==", "dev": true }, "performance-now": { "version": "2.1.0", "resolved": "https://registry.npmjs.org/performance-now/-/performance-now-2.1.0.tgz", - "integrity": "sha1-Ywn04OX6kT7BxpMHrjZLSzd8nns=", + "integrity": "sha512-7EAHlyLHI56VEIdK57uwHdHKIaAGbnXPiw0yWbarQZOKaKpvUIgW0jWRVLiatnM+XXlSwsanIBH/hzGMJulMow==", "dev": true }, 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"requires": { "mute-stream": "~0.0.4" @@ -17383,7 +17388,7 @@ "read-only-stream": { "version": "2.0.0", "resolved": "https://registry.npmjs.org/read-only-stream/-/read-only-stream-2.0.0.tgz", - "integrity": "sha1-JyT9aoET1zdkrCiNQ4YnDB2/F/A=", + "integrity": "sha512-3ALe0bjBVZtkdWKIcThYpQCLbBMd/+Tbh2CDSrAIDO3UsZ4Xs+tnyjv2MjCOMMgBG+AsUOeuP1cgtY1INISc8w==", "dev": true, "requires": { "readable-stream": "^2.0.2" @@ -18008,7 +18013,7 @@ "remove-trailing-separator": { "version": "1.1.0", "resolved": "https://registry.npmjs.org/remove-trailing-separator/-/remove-trailing-separator-1.1.0.tgz", - "integrity": "sha1-wkvOKig62tW8P1jg1IJJuSN52O8=", + "integrity": "sha512-/hS+Y0u3aOfIETiaiirUFwDBDzmXPvO+jAfKTitUngIPzdKc6Z0LoFjM/CK5PL4C+eKwHohlHAb6H0VFfmmUsw==", "dev": true }, "renderkid": { @@ -18033,7 +18038,7 @@ "strip-ansi": { "version": "3.0.1", "resolved": "https://registry.npmjs.org/strip-ansi/-/strip-ansi-3.0.1.tgz", - "integrity": "sha1-ajhfuIU9lS1f8F0Oiq+UJ43GPc8=", + "integrity": 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"resolved": "https://registry.npmjs.org/stack-trace/-/stack-trace-0.0.10.tgz", - "integrity": "sha1-VHxws0fo0ytOEI6hoqFZ5f3eGcA=", + "integrity": "sha512-KGzahc7puUKkzyMt+IqAep+TVNbKP+k2Lmwhub39m1AsTSkaDutx56aDCo+HLDzf/D26BIHTJWNiTG1KAJiQCg==", "dev": true }, "stack-utils": { @@ -19452,7 +19457,7 @@ "stream-combiner2": { "version": "1.1.1", "resolved": "https://registry.npmjs.org/stream-combiner2/-/stream-combiner2-1.1.1.tgz", - "integrity": "sha1-+02KFCDqNidk4hrUeAOXvry0HL4=", + "integrity": "sha512-3PnJbYgS56AeWgtKF5jtJRT6uFJe56Z0Hc5Ngg/6sI6rIt8iiMBTa9cvdyFfpMQjaVHr8dusbNeFGIIonxOvKw==", "dev": true, "requires": { "duplexer2": "~0.1.0", @@ -19508,7 +19513,7 @@ "string-argv": { "version": "0.0.2", "resolved": "https://registry.npmjs.org/string-argv/-/string-argv-0.0.2.tgz", - "integrity": "sha1-2sMECGkMIfPDYwo/86BYd73L1zY=", + "integrity": "sha512-p6/Mqq0utTQWUeGMi/m0uBtlLZEwXSY3+mXzeRRqw7fz5ezUb28Wr0R99NlfbWaMmL/jCyT9be4jpn7Yz8IO8w==", "dev": true }, "string-env-interpolation": { @@ -19643,7 +19648,7 @@ "strip-eof": { "version": "1.0.0", "resolved": "https://registry.npmjs.org/strip-eof/-/strip-eof-1.0.0.tgz", - "integrity": "sha1-u0P/VZim6wXYm1n80SnJgzE2Br8=", + "integrity": "sha512-7FCwGGmx8mD5xQd3RPUvnSpUXHM3BWuzjtpD4TXsfcZ9EL4azvVVUscFYwD9nx8Kh+uCBC00XBtAykoMHwTh8Q==", "dev": true }, "strip-final-newline": { @@ -19735,7 +19740,7 @@ "subarg": { "version": "1.0.0", "resolved": "https://registry.npmjs.org/subarg/-/subarg-1.0.0.tgz", - "integrity": "sha1-9izxdYHplrSPyWVpn1TAauJouNI=", + "integrity": "sha512-RIrIdRY0X1xojthNcVtgT9sjpOGagEUKpZdgBUi054OEPFo282yg+zE+t1Rj3+RqKq2xStL7uUHhY+AjbC4BXg==", "dev": true, "requires": { "minimist": "^1.1.0" @@ -20027,7 +20032,7 @@ "text-table": { "version": "0.2.0", "resolved": "https://registry.npmjs.org/text-table/-/text-table-0.2.0.tgz", - "integrity": "sha1-f17oI66AUgfACvLfSoTsP8+lcLQ=", + "integrity": "sha512-N+8UisAXDGk8PFXP4HAzVR9nbfmVJ3zYLAWiTIoqC5v5isinhr+r5uaO8+7r3BMfuNIufIsA7RdpVgacC2cSpw==", "dev": true }, "thenify": { @@ -20042,7 +20047,7 @@ "thenify-all": { "version": "1.6.0", "resolved": "https://registry.npmjs.org/thenify-all/-/thenify-all-1.6.0.tgz", - "integrity": "sha1-GhkY1ALY/D+Y+/I02wvMjMEOlyY=", + "integrity": "sha512-RNxQH/qI8/t3thXJDwcstUO4zeqo64+Uy/+sNVRBx4Xn2OX+OZ9oP+iJnNFqplFra2ZUVeKCSa2oVWi3T4uVmA==", "dev": true, "requires": { "thenify": ">= 3.1.0 < 4" @@ -20084,7 +20089,7 @@ "timers-browserify": { "version": "1.4.2", "resolved": "https://registry.npmjs.org/timers-browserify/-/timers-browserify-1.4.2.tgz", - "integrity": "sha1-ycWLV1voQHN1y14kYtrO50NZ9B0=", + "integrity": "sha512-PIxwAupJZiYU4JmVZYwXp9FKsHMXb5h0ZEFyuXTAn8WLHOlcij+FEcbrvDsom1o5dr1YggEtFbECvGCW2sT53Q==", "dev": true, "requires": { "process": "~0.11.0" @@ -20114,7 +20119,7 @@ "title-case": { "version": "2.1.1", "resolved": "https://registry.npmjs.org/title-case/-/title-case-2.1.1.tgz", - "integrity": "sha1-PhJyFtpY0rxb7PE3q5Ha46fNj6o=", + "integrity": "sha512-EkJoZ2O3zdCz3zJsYCsxyq2OC5hrxR9mfdd5I+w8h/tmFfeOxJ+vvkxsKxdmN0WtS9zLdHEgfgVOiMVgv+Po4Q==", "dev": true, "requires": { "no-case": "^2.2.0", @@ -20124,7 +20129,7 @@ "lower-case": { "version": "1.1.4", "resolved": "https://registry.npmjs.org/lower-case/-/lower-case-1.1.4.tgz", - "integrity": "sha1-miyr0bno4K6ZOkv31YdcOcQujqw=", + "integrity": "sha512-2Fgx1Ycm599x+WGpIYwJOvsjmXFzTSc34IwDWALRA/8AopUKAVPwfJ+h5+f85BCp0PWmmJcWzEpxOpoXycMpdA==", "dev": true }, "no-case": { @@ -20438,7 +20443,7 @@ "tweetnacl": { "version": "0.14.5", "resolved": "https://registry.npmjs.org/tweetnacl/-/tweetnacl-0.14.5.tgz", - "integrity": "sha1-WuaBd/GS1EViadEIr6k/+HQ/T2Q=", + "integrity": "sha512-KXXFFdAbFXY4geFIwoyNK+f5Z1b7swfXABfL7HXCmoIWMKU3dmS26672A4EeQtDzLKy7SXmfBu51JolvEKwtGA==", "dev": true }, "type": { @@ -20480,7 +20485,7 @@ "type-of": { "version": "2.0.1", "resolved": "https://registry.npmjs.org/type-of/-/type-of-2.0.1.tgz", - "integrity": "sha1-5yoXQYllaOn2KDeNgW1pEvfyOXI=", + "integrity": 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"sha1-GrKLVW4Zg2Oowab35vogE3/mrtk=", + "integrity": "sha512-3pKJwH184Xo/lnH6oyP1q2pMd7HcypqqmRs91/6/i2CGtWwIKGCkOOMTm/zXbgTEWHw1uNpNi/igc3ePOYHb6w==", "dev": true, "requires": { "remove-trailing-separator": "^1.0.1" @@ -20755,7 +20760,7 @@ "unpipe": { "version": "1.0.0", "resolved": "https://registry.npmjs.org/unpipe/-/unpipe-1.0.0.tgz", - "integrity": "sha1-sr9O6FFKrmFltIF4KdIbLvSZBOw=", + "integrity": "sha512-pjy2bYhSsufwWlKwPc+l3cN7+wuJlK6uz0YdJEOlQDbl6jo/YlPi4mb8agUkVC8BF7V8NuzeyPNqRksA3hztKQ==", "dev": true }, "unset-value": { @@ -20868,7 +20873,7 @@ "upper-case": { "version": "1.1.3", "resolved": "https://registry.npmjs.org/upper-case/-/upper-case-1.1.3.tgz", - "integrity": "sha1-9rRQHC7EzdJrp4vnIilh3ndiFZg=", + "integrity": "sha512-WRbjgmYzgXkCV7zNVpy5YgrHgbBv126rMALQQMrmzOVC4GM2waQ9x7xtm8VU+1yF2kWyPzI9zbZ48n4vSxwfSA==", "dev": true }, "uri-js": { @@ -20945,7 +20950,7 @@ "url-parse-lax": { "version": "3.0.0", "resolved": 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"resolved": "https://registry.npmjs.org/utila/-/utila-0.4.0.tgz", - "integrity": "sha1-ihagXURWV6Oupe7MWxKk+lN5dyw=", + "integrity": "sha512-Z0DbgELS9/L/75wZbro8xAnT50pBVFQZ+hUEueGDU5FN51YSCYM+jdxsfCiHjwNP/4LCDD0i/graKpeBnOXKRA==", "dev": true }, "utils-merge": { "version": "1.0.1", "resolved": "https://registry.npmjs.org/utils-merge/-/utils-merge-1.0.1.tgz", - "integrity": "sha1-n5VxD1CiZ5R7LMwSR0HBAoQn5xM=", + "integrity": "sha512-pMZTvIkT1d+TFGvDOqodOclx0QWkkgi6Tdoa8gC8ffGAAqz9pzPTZWAybbsHHoED/ztMtkv/VoYTYyShUn81hA==", "dev": true }, "uuid": { @@ -21096,7 +21101,7 @@ "vary": { "version": "1.1.2", "resolved": "https://registry.npmjs.org/vary/-/vary-1.1.2.tgz", - "integrity": "sha1-IpnwLG3tMNSllhsLn3RSShj2NPw=", + "integrity": "sha512-BNGbWLfd0eUPabhkXUVm0j8uuvREyTh5ovRa/dyow/BqAbZJyC+5fU+IzQOzmAKzYqYRAISoRhdQr3eIZ/PXqg==", "dev": true }, "verror": { @@ -21873,7 +21878,7 @@ "yauzl": { "version": "2.10.0", "resolved": "https://registry.npmjs.org/yauzl/-/yauzl-2.10.0.tgz", - "integrity": "sha1-x+sXyT4RLLEIb6bY5R+wZnt5pfk=", + "integrity": "sha512-p4a9I6X6nu6IhoGmBqAcbJy1mlC4j27vEPZX9F4L4/vZT3Lyq1VkFHw/V/PUcB9Buo+DG3iHkT0x3Qya58zc3g==", "dev": true, "requires": { "buffer-crc32": "~0.2.3", diff --git a/client/package.json b/client/package.json index 0bd78a81..aebfd24f 100644 --- a/client/package.json +++ b/client/package.json @@ -85,6 +85,7 @@ "gatsby-plugin-env-variables": "^2.2.0", "gatsby-plugin-robots-txt": "^1.7.0", "gatsby-plugin-sitemap": "^4.10.0", + "js-search": "^2.0.1", "mapbox-gl": "^1.13.2", "maplibre-gl": "^1.14.0", "query-string": "^7.1.3", diff --git a/client/src/components/J40Map.tsx b/client/src/components/J40Map.tsx index 675428f3..adc249b9 100644 --- a/client/src/components/J40Map.tsx +++ b/client/src/components/J40Map.tsx @@ -130,7 +130,7 @@ const J40Map = ({location}: IJ40Interface) => { const onClick = (event: MapEvent | React.MouseEvent) => { // Stop all propagation / bubbling / capturing event.preventDefault(); - event.stopPropagation(); + (event as React.MouseEvent).stopPropagation?.(); // Check if the click is for territories. Given the territories component's design, it can be // guaranteed that each territory control will have an id. We use this ID to determine @@ -167,8 +167,9 @@ const J40Map = ({location}: IJ40Interface) => { default: break; } - } else { - // This else clause will fire when the ID is null or empty. This is the case where the map is clicked + } else if (event.target && (event.target as HTMLElement).nodeName == 'DIV' ) { + // This else clause will fire when the user clicks on the map and will ignore other controls + // such as the search box and buttons. // @ts-ignore const feature = event.features && event.features[0]; diff --git a/client/src/components/MapSearch/MapSearch.tsx b/client/src/components/MapSearch/MapSearch.tsx index 4fc4e808..681332ea 100644 --- a/client/src/components/MapSearch/MapSearch.tsx +++ b/client/src/components/MapSearch/MapSearch.tsx @@ -4,6 +4,8 @@ import {LngLatBoundsLike} from 'maplibre-gl'; import {useIntl} from 'gatsby-plugin-intl'; import {Search} from '@trussworks/react-uswds'; import {useWindowSize} from 'react-use'; +import * as JsSearch from 'js-search'; +import * as constants from '../../data/constants'; import MapSearchMessage from '../MapSearchMessage'; @@ -14,6 +16,16 @@ interface IMapSearch { goToPlace(bounds: LngLatBoundsLike):void; } +interface ISearchResult { + addresstype: string; + lat: string; + lon: string; + boundingbox: string[]; + type: string; + // eslint-disable-next-line camelcase + place_rank: number; +} + const MapSearch = ({goToPlace}:IMapSearch) => { // State to hold if the search results are empty or not: const [isSearchResultsNull, setIsSearchResultsNull] = useState(false); @@ -30,44 +42,118 @@ const MapSearch = ({goToPlace}:IMapSearch) => { */ const {width, height} = useWindowSize(); const [placeholderText, setPlaceholderText]= useState(EXPLORE_COPY.MAP.SEARCH_PLACEHOLDER); + const [tractSearch, setTractSearch] = useState(null); + + /** + * Gets the tract search data and loads in the state. + */ + const getTractSearchData = async () => { + const searchDataUrl = `${constants.TILE_BASE_URL}/${constants.MAP_TRACT_SEARCH_PATH}`; + fetch(searchDataUrl) + .then((response) => { + if (response.ok) { + return response.json(); + } else { + throw new Error(`${response.statusText} error with status code of ${response.status}`); + } + }) + .then((data) => { + // We use JsSearch to make it easy to load and quick to search. + const search = new JsSearch.Search('GEOID10'); + search.indexStrategy = new JsSearch.ExactWordIndexStrategy(); + search.addIndex('GEOID10'); + search.addDocuments(data); + setTractSearch(search); + }) + .catch((error) => + console.error('Unable to read search tract table:', error)); + }; useEffect( () => { width > height ? setPlaceholderText(EXPLORE_COPY.MAP.SEARCH_PLACEHOLDER): setPlaceholderText(EXPLORE_COPY.MAP.SEARCH_PLACEHOLDER_MOBILE); }, [width]); + useEffect(()=>{ + getTractSearchData(); + }, []); + + /** + * Searchs for a given Census tract ID. + * @param {string} tract the 11 digit tract ID as a string + * @return {Array} an array of one search result, or null if no result found + */ + const searchForTract = (tract: string): [ISearchResult] | [] => { + // We create a bounding box just to get the tract in the view box. + // The size is not important. + const BOUNDING_BOX_SIZE_DD = 0.2; + if (tractSearch) { + // Convert 10 digit tracts to 11. + const searchTerm = tract.length == 10 ? '0' + tract : tract; + const result = tractSearch.search(searchTerm); + if (result.length > 0) { + const lat = Number(result[0].INTPTLAT10); + const lon = Number(result[0].INTPTLON10); + return [{ + addresstype: 'tract', + boundingbox: [ + (lat - (BOUNDING_BOX_SIZE_DD / 2)).toString(), + (lat + (BOUNDING_BOX_SIZE_DD / 2)).toString(), + (lon - (BOUNDING_BOX_SIZE_DD / 2)).toString(), + (lon + (BOUNDING_BOX_SIZE_DD / 2)).toString(), + ], + lat: result[0].INTPTLAT10, + lon: result[0].INTPTLON10, + type: 'tract', + place_rank: 1, + }]; + } + } + return []; + }; + /* onSearchHandler will 1. extract the search term from the input field - 2. fetch data from the API and return the results as JSON and results to US only - 3. if data is valid, destructure the boundingBox values from the search results - 4. pan the map to that location + 2. Determine if the search term is a Census Tract or not. + 3. If it is a Census Tract, it will search the tract table for a bounding box. + 4. If it is NOT a Census Tract, it will fetch data from the API and return the + results as JSON and results to US only. If data is valid, destructure the + boundingBox values from the search results. + 4. Pan the map to that location */ const onSearchHandler = async (event: React.FormEvent) => { event.preventDefault(); + event.stopPropagation(); const searchTerm = (event.currentTarget.elements.namedItem('search') as HTMLInputElement).value; + let searchResults = null; - const searchResults = await fetch( - `https://nominatim.openstreetmap.org/search?q=${searchTerm}&format=json&countrycodes=us`, - { - mode: 'cors', - }) - .then((response) => { - if (!response.ok) { - throw new Error('Network response was not OK'); - } - return response.json(); - }) - .catch((error) => { - console.error('There has been a problem with your fetch operation:', error); - }); - + // If the search term a Census tract + const isTract = /^\d{10,11}$/.test(searchTerm); + if (isTract) { + setIsSearchResultsNull(false); + searchResults = searchForTract(searchTerm); + } else { + searchResults = await fetch( + `https://nominatim.openstreetmap.org/search?q=${searchTerm}&format=json&countrycodes=us`, + { + mode: 'cors', + }) + .then((response) => { + if (!response.ok) { + throw new Error('Network response was not OK'); + } + return response.json(); + }) + .catch((error) => { + console.error('There has been a problem with your fetch operation:', error); + }); + console.log('Nominatum search results: ', searchResults); + } // If results are valid, set isSearchResultsNull to false and pan map to location: if (searchResults && searchResults.length > 0) { setIsSearchResultsNull(false); - console.log('Nominatum search results: ', searchResults); - const [latMin, latMax, longMin, longMax] = searchResults[0].boundingbox; goToPlace([[Number(longMin), Number(latMin)], [Number(longMax), Number(latMax)]]); } else { diff --git a/client/src/components/MapSearch/__snapshots__/MapSearch.test.tsx.snap b/client/src/components/MapSearch/__snapshots__/MapSearch.test.tsx.snap index 9d1c383c..74a2597d 100644 --- a/client/src/components/MapSearch/__snapshots__/MapSearch.test.tsx.snap +++ b/client/src/components/MapSearch/__snapshots__/MapSearch.test.tsx.snap @@ -23,7 +23,7 @@ exports[`rendering of the MapSearch checks if component renders 1`] = ` data-testid="textInput" id="search-field" name="search" - placeholder="Search for an address, city, state or ZIP" + placeholder="Search for an address, city, state, ZIP or Census Tract" type="search" />