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Adding booleans for FE to display (#1393)

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PR adds booleans for each individual threshold category for the front end to display.
This commit is contained in:
Emma Nechamkin 2022-03-29 20:17:10 -04:00 • committed by GitHub
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7 changed files with 190 additions and 74 deletions
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57
data/data-pipeline/data_pipeline/score/field_names.py
View file

@ -567,6 +567,63 @@ ISLAND_AREAS_LOW_HS_EDUCATION_FIELD = (
f"Low high school education{ISLAND_AREAS_SUFFIX}"
)
# Booleans for percentile exceeded thresholds
# TODO -- we should streamline how we make field name strings, pushed to big refactor
EXPECTED_POPULATION_LOSS_EXCEEDS_PCTILE_THRESHOLD = f"Greater than or equal to the {PERCENTILE}th percentile for expected population loss"
EXPECTED_AGRICULTURAL_LOSS_EXCEEDS_PCTILE_THRESHOLD = f"Greater than or equal to the {PERCENTILE}th percentile for expected agricultural loss"
EXPECTED_BUILDING_LOSS_EXCEEDS_PCTILE_THRESHOLD = f"Greater than or equal to the {PERCENTILE}th percentile for expected building loss"
ENERGY_BURDEN_EXCEEDS_PCTILE_THRESHOLD = (
f"Greater than or equal to the {PERCENTILE}th percentile for energy burden"
)
PM25_EXCEEDS_PCTILE_THRESHOLD = (
f"Greater than or equal to the {PERCENTILE}th percentile for pm2.5 exposure"
)
DIESEL_EXCEEDS_PCTILE_THRESHOLD = f"Greater than or equal to the {PERCENTILE}th percentile for diesel particulate matter"
TRAFFIC_PROXIMITY_PCTILE_THRESHOLD = f"Greater than or equal to the {PERCENTILE}th percentile for traffic proximity"
LEAD_PAINT_PROXY_PCTILE_THRESHOLD = (
f"Greater than or equal to the {PERCENTILE}th percentile for lead paint and"
f" the median house value is less than {MEDIAN_HOUSE_VALUE_PERCENTILE}th "
f"percentile"
)
HOUSING_BURDEN_PCTILE_THRESHOLD = (
f"Greater than or equal to the {PERCENTILE}th percentile for housing burden"
)
RMP_PCTILE_THRESHOLD = (
f"Greater than or equal to the {PERCENTILE}th percentile for RMP proximity"
)
NPL_PCTILE_THRESHOLD = f"Greater than or equal to the {PERCENTILE}th percentile for NPL (superfund sites) proximity"
TSDF_PCTILE_THRESHOLD = f"Greater than or equal to the {PERCENTILE}th percentile for proximity to hazardous waste sites"
WASTEWATER_PCTILE_THRESHOLD = f"Greater than or equal to the {PERCENTILE}th percentile for wastewater discharge"
DIABETES_PCTILE_THRESHOLD = (
f"Greater than or equal to the {PERCENTILE}th percentile for diabetes"
)
ASTHMA_PCTILE_THRESHOLD = (
f"Greater than or equal to the {PERCENTILE}th percentile for asthma"
)
HEART_DISEASE_PCTILE_THRESHOLD = (
f"Greater than or equal to the {PERCENTILE}th percentile for heart disease"
)
LOW_LIFE_EXPECTANCY_PCTILE_THRESHOLD = (
f"Greater than or equal to the {PERCENTILE}th percentile "
f"for low life expectancy"
)
UNEMPLOYMENT_PCTILE_THRESHOLD = (
f"Greater than or equal to the {PERCENTILE}th percentile for unemployment"
)
LINGUISTIC_ISOLATION_PCTILE_THRESHOLD = f"Greater than or equal to the {PERCENTILE}th percentile for households in linguistic isolation"
POVERTY_PCTILE_THRESHOLD = f"Greater than or equal to the {PERCENTILE}th percentile for households at or below 100% federal poverty level"
LOW_MEDIAN_INCOME_PCTILE_THRESHOLD = (
f"Greater than or equal to the {PERCENTILE}th percentile for low median household income as a "
f"percent of area median income"
)
ISLAND_LOW_MEDIAN_INCOME_PCTILE_THRESHOLD = (
f"{LOW_CENSUS_DECENNIAL_AREA_MEDIAN_INCOME_PERCENT_FIELD_2009} 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"
# Not currently used in a factor
EXTREME_HEAT_MEDIAN_HOUSE_VALUE_LOW_INCOME_FIELD = (
f"Greater than or equal to the {PERCENTILE}th percentile for summer days above 90F and "

172
data/data-pipeline/data_pipeline/score/score_m.py
View file

@ -142,7 +142,9 @@ class ScoreM(Score):
field_names.EXPECTED_BUILDING_LOSS_RATE_LOW_INCOME_LOW_HIGHER_ED_FIELD,
]
expected_population_loss_threshold = (
self.df[
field_names.EXPECTED_POPULATION_LOSS_EXCEEDS_PCTILE_THRESHOLD
] = (
self.df[
field_names.EXPECTED_POPULATION_LOSS_RATE_FIELD
+ field_names.PERCENTILE_FIELD_SUFFIX
@ -150,7 +152,9 @@ class ScoreM(Score):
>= self.ENVIRONMENTAL_BURDEN_THRESHOLD
)
expected_agriculture_loss_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
@ -158,7 +162,7 @@ class ScoreM(Score):
>= self.ENVIRONMENTAL_BURDEN_THRESHOLD
)
expected_building_loss_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
@ -167,29 +171,39 @@ class ScoreM(Score):
)
self.df[field_names.CLIMATE_THRESHOLD_EXCEEDED] = (
expected_population_loss_threshold
| expected_agriculture_loss_threshold
| expected_building_loss_threshold
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
] = (
expected_population_loss_threshold
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
] = (
expected_agriculture_loss_threshold
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
] = (
expected_building_loss_threshold
self.df[field_names.EXPECTED_BUILDING_LOSS_EXCEEDS_PCTILE_THRESHOLD]
& self.df[field_names.FPL_200_AND_COLLEGE_ATTENDANCE_SERIES]
)
@ -213,7 +227,7 @@ class ScoreM(Score):
field_names.ENERGY_BURDEN_LOW_INCOME_LOW_HIGHER_ED_FIELD,
]
energy_burden_threshold = (
self.df[field_names.ENERGY_BURDEN_EXCEEDS_PCTILE_THRESHOLD] = (
self.df[
field_names.ENERGY_BURDEN_FIELD
+ field_names.PERCENTILE_FIELD_SUFFIX
@ -221,7 +235,7 @@ class ScoreM(Score):
>= self.ENVIRONMENTAL_BURDEN_THRESHOLD
)
pm25_threshold = (
self.df[field_names.PM25_EXCEEDS_PCTILE_THRESHOLD] = (
self.df[
field_names.PM25_FIELD + field_names.PERCENTILE_FIELD_SUFFIX
]
@ -229,16 +243,17 @@ class ScoreM(Score):
)
self.df[field_names.ENERGY_THRESHOLD_EXCEEDED] = (
energy_burden_threshold | pm25_threshold
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] = (
pm25_threshold
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] = (
energy_burden_threshold
self.df[field_names.ENERGY_BURDEN_EXCEEDS_PCTILE_THRESHOLD]
& self.df[field_names.FPL_200_AND_COLLEGE_ATTENDANCE_SERIES]
)
@ -266,14 +281,14 @@ class ScoreM(Score):
field_names.TRAFFIC_PROXIMITY_LOW_INCOME_LOW_HIGHER_ED_FIELD,
]
diesel_threshold = (
self.df[field_names.DIESEL_EXCEEDS_PCTILE_THRESHOLD] = (
self.df[
field_names.DIESEL_FIELD + field_names.PERCENTILE_FIELD_SUFFIX
]
>= self.ENVIRONMENTAL_BURDEN_THRESHOLD
)
traffic_threshold = (
self.df[field_names.TRAFFIC_PROXIMITY_PCTILE_THRESHOLD] = (
self.df[
field_names.TRAFFIC_FIELD + field_names.PERCENTILE_FIELD_SUFFIX
]
@ -281,20 +296,21 @@ class ScoreM(Score):
)
self.df[field_names.TRAFFIC_THRESHOLD_EXCEEDED] = (
traffic_threshold | diesel_threshold
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
] = (
diesel_threshold
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
] = (
traffic_threshold
self.df[field_names.TRAFFIC_PROXIMITY_PCTILE_THRESHOLD]
& self.df[field_names.FPL_200_AND_COLLEGE_ATTENDANCE_SERIES]
)
@ -325,7 +341,7 @@ class ScoreM(Score):
field_names.HOUSING_BURDEN_LOW_INCOME_LOW_HIGHER_ED_FIELD,
]
lead_paint_median_home_value_threshold = (
self.df[field_names.LEAD_PAINT_PROXY_PCTILE_THRESHOLD] = (
self.df[
field_names.LEAD_PAINT_FIELD
+ field_names.PERCENTILE_FIELD_SUFFIX
@ -339,7 +355,7 @@ class ScoreM(Score):
<= self.MEDIAN_HOUSE_VALUE_THRESHOLD
)
housing_burden_threshold = (
self.df[field_names.HOUSING_BURDEN_PCTILE_THRESHOLD] = (
self.df[
field_names.HOUSING_BURDEN_FIELD
+ field_names.PERCENTILE_FIELD_SUFFIX
@ -348,19 +364,20 @@ class ScoreM(Score):
)
self.df[field_names.HOUSING_THREHSOLD_EXCEEDED] = (
lead_paint_median_home_value_threshold | housing_burden_threshold
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
] = (
lead_paint_median_home_value_threshold
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] = (
housing_burden_threshold
self.df[field_names.HOUSING_BURDEN_PCTILE_THRESHOLD]
& self.df[field_names.FPL_200_AND_COLLEGE_ATTENDANCE_SERIES]
)
@ -385,17 +402,17 @@ class ScoreM(Score):
field_names.HAZARDOUS_WASTE_LOW_INCOME_LOW_HIGHER_ED_FIELD,
]
rmp_sites_threshold = (
self.df[field_names.RMP_PCTILE_THRESHOLD] = (
self.df[field_names.RMP_FIELD + field_names.PERCENTILE_FIELD_SUFFIX]
>= self.ENVIRONMENTAL_BURDEN_THRESHOLD
)
npl_sites_threshold = (
self.df[field_names.NPL_PCTILE_THRESHOLD] = (
self.df[field_names.NPL_FIELD + field_names.PERCENTILE_FIELD_SUFFIX]
>= self.ENVIRONMENTAL_BURDEN_THRESHOLD
)
tsdf_sites_threshold = (
self.df[field_names.TSDF_PCTILE_THRESHOLD] = (
self.df[
field_names.TSDF_FIELD + field_names.PERCENTILE_FIELD_SUFFIX
]
@ -403,20 +420,21 @@ class ScoreM(Score):
)
self.df[field_names.POLLUTION_THRESHOLD_EXCEEDED] = (
rmp_sites_threshold | npl_sites_threshold
) | tsdf_sites_threshold
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] = (
rmp_sites_threshold
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] = (
npl_sites_threshold
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] = (
tsdf_sites_threshold
self.df[field_names.TSDF_PCTILE_THRESHOLD]
& self.df[field_names.FPL_200_AND_COLLEGE_ATTENDANCE_SERIES]
)
@ -435,7 +453,7 @@ class ScoreM(Score):
# poverty level and has a low percent of higher ed students
# Source: Census's American Community Survey
self.df[field_names.WATER_THRESHOLD_EXCEEDED] = (
self.df[field_names.WASTEWATER_PCTILE_THRESHOLD] = (
self.df[
field_names.WASTEWATER_FIELD
+ field_names.PERCENTILE_FIELD_SUFFIX
@ -443,10 +461,15 @@ class ScoreM(Score):
>= 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.WATER_THRESHOLD_EXCEEDED]
self.df[field_names.WASTEWATER_PCTILE_THRESHOLD]
& self.df[field_names.FPL_200_AND_COLLEGE_ATTENDANCE_SERIES]
)
@ -480,21 +503,21 @@ class ScoreM(Score):
field_names.LOW_LIFE_EXPECTANCY_LOW_INCOME_LOW_HIGHER_ED_FIELD,
]
diabetes_threshold = (
self.df[field_names.DIABETES_PCTILE_THRESHOLD] = (
self.df[
field_names.DIABETES_FIELD + field_names.PERCENTILE_FIELD_SUFFIX
]
>= self.ENVIRONMENTAL_BURDEN_THRESHOLD
)
asthma_threshold = (
self.df[field_names.ASTHMA_PCTILE_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_PCTILE_THRESHOLD] = (
self.df[
field_names.HEART_DISEASE_FIELD
+ field_names.PERCENTILE_FIELD_SUFFIX
@ -502,7 +525,7 @@ class ScoreM(Score):
>= self.ENVIRONMENTAL_BURDEN_THRESHOLD
)
low_life_expectancy_threshold = (
self.df[field_names.LOW_LIFE_EXPECTANCY_PCTILE_THRESHOLD] = (
self.df[
field_names.LOW_LIFE_EXPECTANCY_FIELD
+ field_names.PERCENTILE_FIELD_SUFFIX
@ -511,25 +534,29 @@ class ScoreM(Score):
)
self.df[field_names.HEALTH_THRESHOLD_EXCEEDED] = (
(diabetes_threshold | asthma_threshold) | heart_disease_threshold
) | low_life_expectancy_threshold
(
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] = (
diabetes_threshold
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] = (
asthma_threshold
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] = (
heart_disease_threshold
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
] = (
low_life_expectancy_threshold
self.df[field_names.LOW_LIFE_EXPECTANCY_PCTILE_THRESHOLD]
& self.df[field_names.FPL_200_AND_COLLEGE_ATTENDANCE_SERIES]
)
@ -578,7 +605,7 @@ class ScoreM(Score):
)
)
unemployment_threshold = (
self.df[field_names.UNEMPLOYMENT_PCTILE_THRESHOLD] = (
self.df[
field_names.UNEMPLOYMENT_FIELD
+ field_names.PERCENTILE_FIELD_SUFFIX
@ -586,7 +613,7 @@ class ScoreM(Score):
>= self.ENVIRONMENTAL_BURDEN_THRESHOLD
)
low_median_income_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
@ -594,7 +621,7 @@ class ScoreM(Score):
>= self.ENVIRONMENTAL_BURDEN_THRESHOLD
)
linguistic_isolation_threshold = (
self.df[field_names.LINGUISTIC_ISOLATION_PCTILE_THRESHOLD] = (
self.df[
field_names.LINGUISTIC_ISO_FIELD
+ field_names.PERCENTILE_FIELD_SUFFIX
@ -602,7 +629,7 @@ class ScoreM(Score):
>= self.ENVIRONMENTAL_BURDEN_THRESHOLD
)
poverty_threshold = (
self.df[field_names.POVERTY_PCTILE_THRESHOLD] = (
self.df[
field_names.POVERTY_LESS_THAN_100_FPL_FIELD
+ field_names.PERCENTILE_FIELD_SUFFIX
@ -611,22 +638,22 @@ class ScoreM(Score):
)
self.df[field_names.LINGUISTIC_ISOLATION_LOW_HS_LOW_HIGHER_ED_FIELD] = (
linguistic_isolation_threshold
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] = (
poverty_threshold
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] = (
low_median_income_threshold
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] = (
unemployment_threshold
self.df[field_names.UNEMPLOYMENT_PCTILE_THRESHOLD]
& self.df[field_names.LOW_HS_EDUCATION_LOW_HIGHER_ED_FIELD]
)
@ -659,6 +686,12 @@ class ScoreM(Score):
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.
@ -673,6 +706,12 @@ class ScoreM(Score):
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.
@ -680,13 +719,7 @@ class ScoreM(Score):
# 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.
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.ISLAND_LOW_MEDIAN_INCOME_PCTILE_THRESHOLD] = (
self.df[
field_names.LOW_CENSUS_DECENNIAL_AREA_MEDIAN_INCOME_PERCENT_FIELD_2009
+ field_names.PERCENTILE_FIELD_SUFFIX
@ -714,9 +747,7 @@ class ScoreM(Score):
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_LOW_MEDIAN_INCOME_PCTILE_THRESHOLD]
& self.df[field_names.ISLAND_AREAS_LOW_HS_EDUCATION_FIELD]
)
@ -750,19 +781,20 @@ class ScoreM(Score):
self.df[field_names.WORKFORCE_THRESHOLD_EXCEEDED] = (
## First we calculate for the non-island areas
(
(poverty_threshold | linguistic_isolation_threshold)
| low_median_income_threshold
(
self.df[field_names.POVERTY_PCTILE_THRESHOLD]
| self.df[field_names.LINGUISTIC_ISOLATION_PCTILE_THRESHOLD]
)
| self.df[field_names.LOW_MEDIAN_INCOME_PCTILE_THRESHOLD]
)
| unemployment_threshold
| self.df[field_names.UNEMPLOYMENT_PCTILE_THRESHOLD]
) | (
## then we calculate just for the island areas
(
self.df[island_areas_unemployment_criteria_field_name]
| self.df[island_areas_poverty_criteria_field_name]
self.df[field_names.ISLAND_UNEMPLOYMENT_PCTILE_THRESHOLD]
| self.df[field_names.ISLAND_POVERTY_PCTILE_THRESHOLD]
)
| self.df[
island_areas_low_median_income_as_a_percent_of_ami_criteria_field_name
]
| self.df[field_names.ISLAND_LOW_MEDIAN_INCOME_PCTILE_THRESHOLD]
)
# Because of the island complications, we also have to separately calculate the threshold for