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added indoor plumbing to score housing burden

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This commit is contained in:
Emma Nechamkin 2022-08-03 17:44:24 -04:00
parent 8c7519063a
commit 15450cf91f
3 changed files with 41 additions and 18 deletions
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6
data/data-pipeline/data_pipeline/etl/score/etl_score.py
View file

@ -366,6 +366,7 @@ class ScoreETL(ExtractTransformLoad):
numeric_columns = [
field_names.HOUSING_BURDEN_FIELD,
field_names.NO_KITCHEN_OR_INDOOR_PLUMBING_FIELD,
field_names.TOTAL_POP_FIELD,
field_names.MEDIAN_INCOME_AS_PERCENT_OF_STATE_FIELD,
field_names.ASTHMA_FIELD,
@ -416,7 +417,6 @@ class ScoreETL(ExtractTransformLoad):
field_names.HEALTHY_FOOD_FIELD,
field_names.IMPENETRABLE_SURFACES_FIELD,
field_names.UST_FIELD,
# We have to pass this boolean here in order to include it in ag value loss percentiles.
field_names.AGRICULTURAL_VALUE_BOOL_FIELD,
field_names.POVERTY_LESS_THAN_200_FPL_IMPUTED_FIELD,
]
@ -473,9 +473,7 @@ class ScoreETL(ExtractTransformLoad):
set(numeric_columns)
), "You have a double-entered column in the numeric columns list"
df_copy[numeric_columns] = df_copy[numeric_columns].apply(
pd.to_numeric
)
df_copy[numeric_columns] = df_copy[numeric_columns].apply(pd.to_numeric)
# Convert all columns to numeric and do math
# Note that we have a few special conditions here, that we handle explicitly.

13
data/data-pipeline/data_pipeline/score/field_names.py
View file

@ -176,6 +176,10 @@ LOW_INCOME_THRESHOLD = "Exceeds FPL200 threshold"
# Housing
HOUSING_BURDEN_FIELD = "Housing burden (percent)"
NO_KITCHEN_OR_INDOOR_PLUMBING_FIELD = (
"Share of homes with no kitchen or indoor plumbing (percent)"
)
HT_INDEX_FIELD = (
"Housing + Transportation Costs % Income for the Regional Typical Household"
)
@ -419,6 +423,10 @@ LEAD_PAINT_MEDIAN_HOUSE_VALUE_LOW_INCOME_FIELD = (
f"percentile and is low income?"
)
HOUSING_BURDEN_LOW_INCOME_FIELD = f"Greater than or equal to the {PERCENTILE}th percentile for housing burden and is low income?"
NO_KITCHEN_OR_INDOOR_PLUMBING_LOW_INCOME_FIELD = (
f"Greater than or equal to the {PERCENTILE}th percentile for "
+ "share of homes with no kitchen or indoor plumbing and is low income?"
)
# Remediation and Reduction of Legacy Pollution
RMP_LOW_INCOME_FIELD = f"Greater than or equal to the {PERCENTILE}th percentile for proximity to RMP sites and is low income?"
@ -624,6 +632,11 @@ LEAD_PAINT_PROXY_PCTILE_THRESHOLD = (
HOUSING_BURDEN_PCTILE_THRESHOLD = (
f"Greater than or equal to the {PERCENTILE}th percentile for housing burden"
)
NO_KITCHEN_OR_INDOOR_PLUMBING_PCTILE_THRESHOLD = (
f"Greater than or equal to the {PERCENTILE}th percentile for share "
"of homes without indoor plumbing or a kitchen"
)
RMP_PCTILE_THRESHOLD = (
f"Greater than or equal to the {PERCENTILE}th percentile for RMP proximity"
)

40
data/data-pipeline/data_pipeline/score/score_narwhal.py
View file

@ -315,9 +315,10 @@ class ScoreNarwhal(Score):
field_names.LEAD_PAINT_MEDIAN_HOUSE_VALUE_LOW_INCOME_FIELD,
field_names.HOUSING_BURDEN_LOW_INCOME_FIELD,
field_names.HISTORIC_REDLINING_SCORE_EXCEEDED_LOW_INCOME_FIELD,
field_names.NO_KITCHEN_OR_INDOOR_PLUMBING_LOW_INCOME_FIELD,
]
# # design question -- should read in scalar with threshold here instead?
# Historic disinvestment
self.df[
field_names.HISTORIC_REDLINING_SCORE_EXCEEDED_LOW_INCOME_FIELD
] = (
@ -325,6 +326,18 @@ class ScoreNarwhal(Score):
& self.df[field_names.FPL_200_SERIES_IMPUTED_AND_ADJUSTED]
)
# Kitchen / plumbing
self.df[field_names.NO_KITCHEN_OR_INDOOR_PLUMBING_PCTILE_THRESHOLD] = (
self.df[field_names.NO_KITCHEN_OR_INDOOR_PLUMBING_FIELD]
>= self.ENVIRONMENTAL_BURDEN_THRESHOLD
)
self.df[field_names.NO_KITCHEN_OR_INDOOR_PLUMBING_LOW_INCOME_FIELD] = (
self.df[field_names.NO_KITCHEN_OR_INDOOR_PLUMBING_PCTILE_THRESHOLD]
& self.df[field_names.FPL_200_SERIES_IMPUTED_AND_ADJUSTED]
)
# Lead paint
self.df[field_names.LEAD_PAINT_PROXY_PCTILE_THRESHOLD] = (
self.df[
field_names.LEAD_PAINT_FIELD
@ -339,6 +352,12 @@ class ScoreNarwhal(Score):
<= self.MEDIAN_HOUSE_VALUE_THRESHOLD
)
self.df[field_names.LEAD_PAINT_MEDIAN_HOUSE_VALUE_LOW_INCOME_FIELD] = (
self.df[field_names.LEAD_PAINT_PROXY_PCTILE_THRESHOLD]
& self.df[field_names.FPL_200_SERIES_IMPUTED_AND_ADJUSTED]
)
# Housing burden
self.df[field_names.HOUSING_BURDEN_PCTILE_THRESHOLD] = (
self.df[
field_names.HOUSING_BURDEN_FIELD
@ -346,29 +365,22 @@ class ScoreNarwhal(Score):
]
>= 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_FIELD] = (
self.df[field_names.LEAD_PAINT_PROXY_PCTILE_THRESHOLD]
& self.df[field_names.FPL_200_SERIES_IMPUTED_AND_ADJUSTED]
)
self.df[field_names.HOUSING_BURDEN_LOW_INCOME_FIELD] = (
self.df[field_names.HOUSING_BURDEN_PCTILE_THRESHOLD]
& self.df[field_names.FPL_200_SERIES_IMPUTED_AND_ADJUSTED]
)
# any of the burdens
self.df[field_names.HOUSING_THREHSOLD_EXCEEDED] = self.df[
housing_eligibility_columns
].any(axis="columns")
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")
return self.df[field_names.HOUSING_THREHSOLD_EXCEEDED]
def _pollution_factor(self) -> bool:
# Proximity to Risk Management Plan sites is > X