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Issue 954: Add various data sources from Child Opportunity Index (#986)

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* Adds four fields:
    * Summer days above 90F
    * Percent low access to healthy food
    * Percent impenetrable surface areas
    * Low third grade reading proficiency

* Each of these four gets added into Definition L in various factors.

* Additionally, I add college attendance fields to the ETL for Census ACS.

* This PR also introduces the notion of "reverse percentiles", relevant to ticket #970.
This commit is contained in:
Lucas Merrill Brown 2021-12-07 11:33:49 -05:00 • committed by GitHub
parent df564658a5
commit 5a6d6d8557
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8 changed files with 357 additions and 40 deletions
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5
data/data-pipeline/data_pipeline/etl/constants.py
View file

@ -49,6 +49,11 @@ DATASET_LIST = [
"module_dir": "geocorr",
"class_name": "GeoCorrETL",
},
{
"name": "child_opportunity_index",
"module_dir": "child_opportunity_index",
"class_name": "ChildOpportunityIndex",
},
{
"name": "mapping_inequality",
"module_dir": "mapping_inequality",

61
data/data-pipeline/data_pipeline/etl/score/etl_score.py
View file

@ -1,4 +1,6 @@
import functools
from collections import namedtuple
import pandas as pd
from data_pipeline.etl.base import ExtractTransformLoad
@ -29,6 +31,7 @@ class ScoreETL(ExtractTransformLoad):
self.persistent_poverty_df: pd.DataFrame
self.census_decennial_df: pd.DataFrame
self.census_2010_df: pd.DataFrame
self.child_opportunity_index_df: pd.DataFrame
def extract(self) -> None:
logger.info("Loading data sets from disk.")
@ -162,6 +165,19 @@ class ScoreETL(ExtractTransformLoad):
low_memory=False,
)
# Load COI data
child_opportunity_index_csv = (
constants.DATA_PATH
/ "dataset"
/ "child_opportunity_index"
/ "usa.csv"
)
self.child_opportunity_index_df = pd.read_csv(
child_opportunity_index_csv,
dtype={self.GEOID_TRACT_FIELD_NAME: "string"},
low_memory=False,
)
def _join_tract_dfs(self, census_tract_dfs: list) -> pd.DataFrame:
logger.info("Joining Census Tract dataframes")
@ -255,6 +271,7 @@ class ScoreETL(ExtractTransformLoad):
self.census_acs_median_incomes_df,
self.census_decennial_df,
self.census_2010_df,
self.child_opportunity_index_df,
]
# Sanity check each data frame before merging.
@ -323,6 +340,7 @@ class ScoreETL(ExtractTransformLoad):
field_names.HIGH_SCHOOL_ED_FIELD,
field_names.UNEMPLOYMENT_FIELD,
field_names.MEDIAN_HOUSE_VALUE_FIELD,
field_names.COLLEGE_ATTENDANCE_FIELD,
field_names.EXPECTED_BUILDING_LOSS_RATE_FIELD,
field_names.EXPECTED_AGRICULTURE_LOSS_RATE_FIELD,
field_names.EXPECTED_POPULATION_LOSS_RATE_FIELD,
@ -333,6 +351,9 @@ class ScoreETL(ExtractTransformLoad):
field_names.CENSUS_POVERTY_LESS_THAN_100_FPL_FIELD_2010,
field_names.CENSUS_DECENNIAL_TOTAL_POPULATION_FIELD_2009,
field_names.CENSUS_DECENNIAL_AREA_MEDIAN_INCOME_PERCENT_FIELD_2009,
field_names.EXTREME_HEAT_FIELD,
field_names.HEALTHY_FOOD_FIELD,
field_names.IMPENETRABLE_SURFACES_FIELD,
]
non_numeric_columns = [
@ -340,7 +361,32 @@ class ScoreETL(ExtractTransformLoad):
field_names.PERSISTENT_POVERTY_FIELD,
]
columns_to_keep = non_numeric_columns + numeric_columns
# For some columns, high values are "good", so we want to reverse the percentile
# so that high values are "bad" and any scoring logic can still check if it's
# >= some threshold.
# TODO: Add more fields here.
# https://github.com/usds/justice40-tool/issues/970
ReversePercentile = namedtuple(
typename="ReversePercentile",
field_names=["field_name", "low_field_name"],
)
reverse_percentiles = [
# This dictionary follows the format:
# <field name> : <field name for low values>
# for instance, 3rd grade reading level : Low 3rd grade reading level.
# This low field will not exist yet, it is only calculated for the
# percentile.
ReversePercentile(
field_name=field_names.READING_FIELD,
low_field_name=field_names.LOW_READING_FIELD,
)
]
columns_to_keep = (
non_numeric_columns
+ numeric_columns
+ [rp.field_name for rp in reverse_percentiles]
)
df_copy = df[columns_to_keep].copy()
@ -375,6 +421,19 @@ class ScoreETL(ExtractTransformLoad):
df_copy[col] - min_value
) / (max_value - min_value)
# Create reversed percentiles for these fields
for reverse_percentile in reverse_percentiles:
# Calculate reverse percentiles
# For instance, for 3rd grade reading level (score from 0-500),
# calculate reversed percentiles and give the result the name
# `Low 3rd grade reading level (percentile)`.
df_copy[
f"{reverse_percentile.low_field_name}"
f"{field_names.PERCENTILE_FIELD_SUFFIX}"
] = df_copy[reverse_percentile.field_name].rank(
pct=True, ascending=False
)
# Special logic: create a combined population field.
# We sometimes run analytics on "population", and this makes a single field
# that is either the island area's population in 2009 or the state's

67
data/data-pipeline/data_pipeline/etl/sources/census_acs/etl.py
View file

@ -114,6 +114,27 @@ class CensusACSETL(ExtractTransformLoad):
)
self.HIGH_SCHOOL_ED_FIELD = "Percent individuals age 25 or over with less than high school degree"
# College attendance fields
self.COLLEGE_ATTENDANCE_TOTAL_POPULATION_ASKED = (
"B14004_001E" # Estimate!!Total
)
self.COLLEGE_ATTENDANCE_MALE_ENROLLED_PUBLIC = "B14004_003E" # Estimate!!Total!!Male!!Enrolled in public college or graduate school
self.COLLEGE_ATTENDANCE_MALE_ENROLLED_PRIVATE = "B14004_008E" # Estimate!!Total!!Male!!Enrolled in private college or graduate school
self.COLLEGE_ATTENDANCE_FEMALE_ENROLLED_PUBLIC = "B14004_019E" # Estimate!!Total!!Female!!Enrolled in public college or graduate school
self.COLLEGE_ATTENDANCE_FEMALE_ENROLLED_PRIVATE = "B14004_024E" # Estimate!!Total!!Female!!Enrolled in private college or graduate school
self.COLLEGE_ATTENDANCE_FIELDS = [
self.COLLEGE_ATTENDANCE_TOTAL_POPULATION_ASKED,
self.COLLEGE_ATTENDANCE_MALE_ENROLLED_PUBLIC,
self.COLLEGE_ATTENDANCE_MALE_ENROLLED_PRIVATE,
self.COLLEGE_ATTENDANCE_FEMALE_ENROLLED_PUBLIC,
self.COLLEGE_ATTENDANCE_FEMALE_ENROLLED_PRIVATE,
]
self.COLLEGE_ATTENDANCE_FIELD = (
"Percent enrollment in college or graduate school"
)
self.RE_FIELDS = [
"B02001_001E",
"B02001_002E",
@ -156,15 +177,30 @@ class CensusACSETL(ExtractTransformLoad):
self.STATE_GEOID_FIELD_NAME = "GEOID2"
self.COLUMNS_TO_KEEP = (
[
self.GEOID_TRACT_FIELD_NAME,
self.UNEMPLOYED_FIELD_NAME,
self.LINGUISTIC_ISOLATION_FIELD_NAME,
self.MEDIAN_INCOME_FIELD_NAME,
self.POVERTY_LESS_THAN_100_PERCENT_FPL_FIELD_NAME,
self.POVERTY_LESS_THAN_150_PERCENT_FPL_FIELD_NAME,
self.POVERTY_LESS_THAN_200_PERCENT_FPL_FIELD_NAME,
self.MEDIAN_HOUSE_VALUE_FIELD_NAME,
self.HIGH_SCHOOL_ED_FIELD,
self.COLLEGE_ATTENDANCE_FIELD,
]
+ self.RE_OUTPUT_FIELDS
+ [self.PERCENT_PREFIX + field for field in self.RE_OUTPUT_FIELDS]
)
self.df: pd.DataFrame
def extract(self) -> None:
# Define the variables to retrieve
variables = (
[
# Income field
self.MEDIAN_INCOME_FIELD,
# House value
self.MEDIAN_HOUSE_VALUE_FIELD,
]
+ self.EMPLOYMENT_FIELDS
@ -172,6 +208,7 @@ class CensusACSETL(ExtractTransformLoad):
+ self.POVERTY_FIELDS
+ self.EDUCATIONAL_FIELDS
+ self.RE_FIELDS
+ self.COLLEGE_ATTENDANCE_FIELDS
)
self.df = retrieve_census_acs_data(
@ -308,6 +345,14 @@ class CensusACSETL(ExtractTransformLoad):
df["B03003_003E"] / df["B03003_001E"]
)
# Calculate college attendance:
df[self.COLLEGE_ATTENDANCE_FIELD] = (
df[self.COLLEGE_ATTENDANCE_MALE_ENROLLED_PUBLIC]
+ df[self.COLLEGE_ATTENDANCE_MALE_ENROLLED_PRIVATE]
+ df[self.COLLEGE_ATTENDANCE_FEMALE_ENROLLED_PUBLIC]
+ df[self.COLLEGE_ATTENDANCE_FEMALE_ENROLLED_PRIVATE]
) / df[self.COLLEGE_ATTENDANCE_TOTAL_POPULATION_ASKED]
# Save results to self.
self.df = df
@ -317,23 +362,7 @@ class CensusACSETL(ExtractTransformLoad):
# mkdir census
self.OUTPUT_PATH.mkdir(parents=True, exist_ok=True)
columns_to_include = (
[
self.GEOID_TRACT_FIELD_NAME,
self.UNEMPLOYED_FIELD_NAME,
self.LINGUISTIC_ISOLATION_FIELD_NAME,
self.MEDIAN_INCOME_FIELD_NAME,
self.POVERTY_LESS_THAN_100_PERCENT_FPL_FIELD_NAME,
self.POVERTY_LESS_THAN_150_PERCENT_FPL_FIELD_NAME,
self.POVERTY_LESS_THAN_200_PERCENT_FPL_FIELD_NAME,
self.MEDIAN_HOUSE_VALUE_FIELD_NAME,
self.HIGH_SCHOOL_ED_FIELD,
]
+ self.RE_OUTPUT_FIELDS
+ [self.PERCENT_PREFIX + field for field in self.RE_OUTPUT_FIELDS]
)
self.df[columns_to_include].to_csv(
self.df[self.COLUMNS_TO_KEEP].to_csv(
path_or_buf=self.OUTPUT_PATH / "usa.csv", index=False
)

0
data/data-pipeline/data_pipeline/etl/sources/child_opportunity_index/README.md Normal file
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0
data/data-pipeline/data_pipeline/etl/sources/child_opportunity_index/__init__.py Normal file
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120
data/data-pipeline/data_pipeline/etl/sources/child_opportunity_index/etl.py Normal file
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@ -0,0 +1,120 @@
from pathlib import Path
import pandas as pd
from data_pipeline.etl.base import ExtractTransformLoad
from data_pipeline.score import field_names
from data_pipeline.utils import get_module_logger, unzip_file_from_url
logger = get_module_logger(__name__)
class ChildOpportunityIndex(ExtractTransformLoad):
"""ETL Child Opportunity Index data.
COI compiles a number of useful data sets. In the future, we could pull these
data sets in directly from their original creators.
Data dictionary available when you download zip from `self.COI_FILE_URL`.
Data source overview: https://data.diversitydatakids.org/dataset/coi20-child-opportunity-index-2-0-database.
Full technical documents: https://www.diversitydatakids.org/sites/default/files/2020-02/ddk_coi2.0_technical_documentation_20200212.pdf.
Github repo: https://github.com/diversitydatakids/COI/
"""
def __init__(self):
self.COI_FILE_URL = (
"https://data.diversitydatakids.org/datastore/zip/f16fff12-b1e5-4f60-85d3-"
"3a0ededa30a0?format=csv"
)
self.OUTPUT_PATH: Path = (
self.DATA_PATH / "dataset" / "child_opportunity_index"
)
self.TRACT_INPUT_COLUMN_NAME = "geoid"
self.EXTREME_HEAT_INPUT_FIELD = "HE_HEAT"
self.HEALTHY_FOOD_INPUT_FIELD = "HE_FOOD"
self.IMPENETRABLE_SURFACES_INPUT_FIELD = "HE_GREEN"
self.READING_INPUT_FIELD = "ED_READING"
# Constants for output
self.COLUMNS_TO_KEEP = [
self.GEOID_TRACT_FIELD_NAME,
field_names.EXTREME_HEAT_FIELD,
field_names.HEALTHY_FOOD_FIELD,
field_names.IMPENETRABLE_SURFACES_FIELD,
field_names.READING_FIELD,
]
self.raw_df: pd.DataFrame
self.output_df: pd.DataFrame
def extract(self) -> None:
logger.info("Starting 51MB data download.")
unzip_file_from_url(
file_url=self.COI_FILE_URL,
download_path=self.TMP_PATH,
unzipped_file_path=self.TMP_PATH / "child_opportunity_index",
)
self.raw_df = pd.read_csv(
filepath_or_buffer=self.TMP_PATH
/ "child_opportunity_index"
/ "raw.csv",
# The following need to remain as strings for all of their digits, not get
# converted to numbers.
dtype={
self.TRACT_INPUT_COLUMN_NAME: "string",
},
low_memory=False,
)
def transform(self) -> None:
logger.info("Starting transforms.")
output_df = self.raw_df.rename(
columns={
self.TRACT_INPUT_COLUMN_NAME: self.GEOID_TRACT_FIELD_NAME,
self.EXTREME_HEAT_INPUT_FIELD: field_names.EXTREME_HEAT_FIELD,
self.HEALTHY_FOOD_INPUT_FIELD: field_names.HEALTHY_FOOD_FIELD,
self.IMPENETRABLE_SURFACES_INPUT_FIELD: field_names.IMPENETRABLE_SURFACES_FIELD,
self.READING_INPUT_FIELD: field_names.READING_FIELD,
}
)
# Sanity check the tract field.
if len(output_df[self.GEOID_TRACT_FIELD_NAME].str.len().unique()) != 1:
raise ValueError("Wrong tract length.")
# COI has two rows per tract: one for 2010 and one for 2015.
output_df = output_df[output_df["year"] == 2015]
# Convert percents from 0-100 to 0-1 to standardize with our other fields.
percent_fields_to_convert = [
field_names.HEALTHY_FOOD_FIELD,
field_names.IMPENETRABLE_SURFACES_FIELD,
]
for percent_field_to_convert in percent_fields_to_convert:
output_df[percent_field_to_convert] = (
output_df[percent_field_to_convert] / 100
)
self.output_df = output_df
def validate(self) -> None:
logger.info("Validating data.")
pass
def load(self) -> None:
logger.info("Saving CSV")
self.OUTPUT_PATH.mkdir(parents=True, exist_ok=True)
self.output_df[self.COLUMNS_TO_KEEP].to_csv(
path_or_buf=self.OUTPUT_PATH / "usa.csv", index=False
)

59
data/data-pipeline/data_pipeline/score/field_names.py
View file

@ -63,6 +63,8 @@ MEDIAN_INCOME_AS_PERCENT_OF_AMI_FIELD = "Median household income (% of AMI)"
PERSISTENT_POVERTY_FIELD = "Persistent Poverty Census Tract"
AMI_FIELD = "Area Median Income (State or metropolitan)"
COLLEGE_ATTENDANCE_FIELD = "Percent enrollment in college or graduate school"
# Climate
FEMA_RISK_FIELD = "FEMA Risk Index Expected Annual Loss Score"
EXPECTED_BUILDING_LOSS_RATE_FIELD = (
@ -206,30 +208,63 @@ HOLC_GRADE_D_TRACT_50_PERCENT_FIELD: str = "Tract is >50% HOLC Grade D"
HOLC_GRADE_D_TRACT_75_PERCENT_FIELD: str = "Tract is >75% HOLC Grade D"
# Child Opportunity Index data
# Summer days with maximum temperature above 90F.
EXTREME_HEAT_FIELD = "Summer days above 90F"
# Percentage households without a car located further than a half-mile from the
# nearest supermarket.
HEALTHY_FOOD_FIELD = "Percent low access to healthy food"
# Percentage impenetrable surface areas such as rooftops, roads or parking lots.
IMPENETRABLE_SURFACES_FIELD = "Percent impenetrable surface areas"
# Percentage third graders scoring proficient on standardized reading tests,
# converted to NAEP scale score points.
READING_FIELD = "Third grade reading proficiency"
LOW_READING_FIELD = "Low third grade reading proficiency"
# Names for individual factors being exceeded
# Climate Change
EXPECTED_POPULATION_LOSS_RATE_LOW_INCOME_FIELD = f"At or above the {PERCENTILE}th percentile for expected population loss rate and is low income"
EXPECTED_AGRICULTURE_LOSS_RATE_LOW_INCOME_FIELD = f"At or above the {PERCENTILE}th percentile for expected agriculture loss rate and is low income"
EXPECTED_BUILDING_LOSS_RATE_LOW_INCOME_FIELD = f"At or above the {PERCENTILE}th percentile for expected building loss rate and is low income"
EXTREME_HEAT_MEDIAN_HOUSE_VALUE_LOW_INCOME_FIELD = (
f"At or above the {PERCENTILE}th percentile for summer days above 90F and "
f"the median house value is less than {MEDIAN_HOUSE_VALUE_PERCENTILE}th "
f"percentile and is low income"
)
# Clean energy and efficiency
PM25_EXPOSURE_LOW_INCOME_FIELD = f"At or above the {PERCENTILE}th percentile for PM2.5 exposure and is low income"
ENERGY_BURDEN_LOW_INCOME_FIELD = f"At or above the {PERCENTILE}th percentile for energy burden and is low income"
# Clean transportation
DIESEL_PARTICULATE_MATTER_LOW_INCOME_FIELD = f"At or above the {PERCENTILE}th percentile for diesel particulate matter and is low income"
TRAFFIC_PROXIMITY_LOW_INCOME_FIELD = f"At or above the {PERCENTILE}th percentile for traffic proximity and is low income"
# Affordable and Sustainable Housing
LEAD_PAINT_MEDIAN_HOME_VALUE_LOW_INCOME_FIELD = (
LEAD_PAINT_MEDIAN_HOUSE_VALUE_LOW_INCOME_FIELD = (
f"At or above the {PERCENTILE}th percentile for lead paint and"
" the median house value is less than {MEDIAN_HOUSE_VALUE_PERCENTILE}th percentile and is low income"
f" the median house value is less than {MEDIAN_HOUSE_VALUE_PERCENTILE}th "
f"percentile and is low income"
)
HOUSING_BURDEN_LOW_INCOME_FIELD = f"At or above the {PERCENTILE}th percentile for housing burden and is low income"
IMPENETRABLE_SURFACES_LOW_INCOME_FIELD = (
f"At or above the {PERCENTILE}th percentile for impenetrable surfaces and is low "
f"income"
)
# Remediation and Reduction of Legacy Pollution
RMP_LOW_INCOME_FIELD = f"At or above the {PERCENTILE}th percentile for proximity to RMP sites and is low income"
SUPERFUND_LOW_INCOME_FIELD = f"At or above the {PERCENTILE}th percentile for proximity to superfund sites and is low income"
HAZARDOUS_WASTE_LOW_INCOME_FIELD = f"At or above the {PERCENTILE}th percentile for proximity to hazardous waste facilities and is low income"
# Critical Clean Water and Waste Infrastructure
WASTEWATER_DISCHARGE_LOW_INCOME_FIELD = f"At or above the {PERCENTILE}th percentile for wastewater discharge and is low income"
# Health Burden
# Health Burdens
DIABETES_LOW_INCOME_FIELD = (
f"At or above the {PERCENTILE}th percentile for diabetes and is low income"
)
@ -240,25 +275,35 @@ HEART_DISEASE_LOW_INCOME_FIELD = f"At or above the {PERCENTILE}th percentile for
LIFE_EXPECTANCY_LOW_INCOME_FIELD = f"At or above the {PERCENTILE}th percentile for life expectancy and is low income"
HEALTHY_FOOD_LOW_INCOME_FIELD = (
f"At or above the {PERCENTILE}th percentile for low "
f"access to healthy food and is low income"
)
# Workforce
UNEMPLOYMENT_LOW_HS_EDUCATION_FIELD = (
f"At or above the {PERCENTILE}th percentile for unemployment"
" and low HS education"
" and has low HS education"
)
LINGUISTIC_ISOLATION_LOW_HS_EDUCATION_FIELD = (
f"At or above the {PERCENTILE}th percentile for households in linguistic isolation"
" and low HS education"
" and has low HS education"
)
POVERTY_LOW_HS_EDUCATION_FIELD = (
f"At or above the {PERCENTILE}th percentile for households at or below 100% federal poverty level"
" and low HS education"
" and has low HS education"
)
LOW_READING_LOW_HS_EDUCATION_FIELD = (
f"At or above the {PERCENTILE}th percentile for low 3rd grade reading proficiency"
" and has low HS education"
)
MEDIAN_INCOME_LOW_HS_EDUCATION_FIELD = (
f"At or below the {PERCENTILE}th percentile for median income"
" and low HS education"
" and has low HS education"
)
THRESHOLD_COUNT = "Total threshold criteria exceeded"

85
data/data-pipeline/data_pipeline/score/score_l.py
View file

@ -177,6 +177,8 @@ class ScoreL(Score):
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,
field_names.EXTREME_HEAT_MEDIAN_HOUSE_VALUE_LOW_INCOME_FIELD,
field_names.IMPENETRABLE_SURFACES_LOW_INCOME_FIELD,
]
expected_population_loss_threshold = (
@ -203,6 +205,28 @@ class ScoreL(Score):
>= self.ENVIRONMENTAL_BURDEN_THRESHOLD
)
extreme_heat_median_home_value_threshold = (
self.df[
field_names.EXTREME_HEAT_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
)
impenetrable_surfaces_threshold = (
self.df[
field_names.IMPENETRABLE_SURFACES_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]
@ -218,6 +242,18 @@ class ScoreL(Score):
& self.df[field_names.FPL_200_SERIES]
)
self.df[
field_names.EXTREME_HEAT_MEDIAN_HOUSE_VALUE_LOW_INCOME_FIELD
] = (
extreme_heat_median_home_value_threshold
& self.df[field_names.FPL_200_SERIES]
)
self.df[field_names.IMPENETRABLE_SURFACES_LOW_INCOME_FIELD] = (
impenetrable_surfaces_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")
@ -320,11 +356,11 @@ class ScoreL(Score):
# poverty level. Source: Census's American Community Survey]
housing_eligibility_columns = [
field_names.LEAD_PAINT_MEDIAN_HOME_VALUE_LOW_INCOME_FIELD,
field_names.LEAD_PAINT_MEDIAN_HOUSE_VALUE_LOW_INCOME_FIELD,
field_names.HOUSING_BURDEN_LOW_INCOME_FIELD,
]
lead_paint_median_house_hold_threshold = (
lead_paint_median_home_value_threshold = (
self.df[
field_names.LEAD_PAINT_FIELD
+ field_names.PERCENTILE_FIELD_SUFFIX
@ -347,8 +383,8 @@ class ScoreL(Score):
)
# series by series indicators
self.df[field_names.LEAD_PAINT_MEDIAN_HOME_VALUE_LOW_INCOME_FIELD] = (
lead_paint_median_house_hold_threshold
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]
)
@ -449,6 +485,7 @@ class ScoreL(Score):
field_names.DIABETES_LOW_INCOME_FIELD,
field_names.ASTHMA_LOW_INCOME_FIELD,
field_names.HEART_DISEASE_LOW_INCOME_FIELD,
field_names.HEALTHY_FOOD_LOW_INCOME_FIELD,
field_names.LIFE_EXPECTANCY_LOW_INCOME_FIELD,
]
@ -474,6 +511,14 @@ class ScoreL(Score):
>= self.ENVIRONMENTAL_BURDEN_THRESHOLD
)
healthy_food_threshold = (
self.df[
field_names.HEALTHY_FOOD_FIELD
+ field_names.PERCENTILE_FIELD_SUFFIX
]
>= self.ENVIRONMENTAL_BURDEN_THRESHOLD
)
life_expectancy_threshold = (
self.df[
field_names.LIFE_EXPECTANCY_FIELD
@ -496,6 +541,9 @@ class ScoreL(Score):
self.df[field_names.LIFE_EXPECTANCY_LOW_INCOME_FIELD] = (
life_expectancy_threshold & self.df[field_names.FPL_200_SERIES]
)
self.df[field_names.HEALTHY_FOOD_LOW_INCOME_FIELD] = (
healthy_food_threshold & self.df[field_names.FPL_200_SERIES]
)
self._increment_total_eligibility_exceeded(health_eligibility_columns)
@ -513,6 +561,15 @@ class ScoreL(Score):
# Where the high school degree achievement rates for adults 25 years and older is less than 95%
# (necessary to screen out university block groups)
# 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.MEDIAN_INCOME_LOW_HS_EDUCATION_FIELD,
field_names.LOW_READING_LOW_HS_EDUCATION_FIELD,
]
high_scool_achievement_rate_threshold = (
self.df[field_names.HIGH_SCHOOL_ED_FIELD]
>= self.LACK_OF_HIGH_SCHOOL_MINIMUM_THRESHOLD
@ -552,6 +609,14 @@ class ScoreL(Score):
>= self.ENVIRONMENTAL_BURDEN_THRESHOLD
)
low_reading_threshold = (
self.df[
field_names.LOW_READING_FIELD
+ field_names.PERCENTILE_FIELD_SUFFIX
]
>= self.ENVIRONMENTAL_BURDEN_THRESHOLD
)
self.df[field_names.LINGUISTIC_ISOLATION_LOW_HS_EDUCATION_FIELD] = (
linguistic_isolation_threshold
& high_scool_achievement_rate_threshold
@ -569,15 +634,9 @@ class ScoreL(Score):
unemployment_threshold & high_scool_achievement_rate_threshold
)
# Workforce criteria for states fields that create indicator columns
# for each tract in order to indicate whether they met any of the four
# criteria. We will used this create individual indicator columns.
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.MEDIAN_INCOME_LOW_HS_EDUCATION_FIELD,
]
self.df[field_names.LOW_READING_LOW_HS_EDUCATION_FIELD] = (
low_reading_threshold & high_scool_achievement_rate_threshold
)
workforce_combined_criteria_for_states = self.df[
workforce_eligibility_columns