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https://github.com/DOI-DO/j40-cejst-2.git
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9fb9874a15
* should be working, has unnecessary loggers * removing loggers and cleaning up * updating ejscreen tests * adding tests and responding to PR feedback * fixing broken smoke test * delete smoketest docs
663 lines
28 KiB
Python
663 lines
28 KiB
Python
from collections import namedtuple
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import os
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import pandas as pd
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import geopandas as gpd
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from data_pipeline.config import settings
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from data_pipeline.etl.base import ExtractTransformLoad
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from data_pipeline.etl.sources.census_acs.etl_utils import (
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retrieve_census_acs_data,
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)
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from data_pipeline.etl.sources.census_acs.etl_imputations import (
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calculate_income_measures,
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)
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from data_pipeline.utils import get_module_logger, unzip_file_from_url
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from data_pipeline.score import field_names
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logger = get_module_logger(__name__)
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# because now there is a requirement for the us.json, this will port from
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# AWS when a local copy does not exist.
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CENSUS_DATA_S3_URL = settings.AWS_JUSTICE40_DATASOURCES_URL + "/census.zip"
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class CensusACSETL(ExtractTransformLoad):
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NAME = "census_acs"
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ACS_YEAR = 2019
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MINIMUM_POPULATION_REQUIRED_FOR_IMPUTATION = 1
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def __init__(self):
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self.TOTAL_UNEMPLOYED_FIELD = "B23025_005E"
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self.TOTAL_IN_LABOR_FORCE = "B23025_003E"
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self.EMPLOYMENT_FIELDS = [
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self.TOTAL_UNEMPLOYED_FIELD,
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self.TOTAL_IN_LABOR_FORCE,
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]
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self.UNEMPLOYED_FIELD_NAME = "Unemployment (percent)"
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self.LINGUISTIC_ISOLATION_FIELD_NAME = "Linguistic isolation (percent)"
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self.LINGUISTIC_ISOLATION_TOTAL_FIELD_NAME = (
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"Linguistic isolation (total)"
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)
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self.LINGUISTIC_ISOLATION_FIELDS = [
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"C16002_001E", # Estimate!!Total
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"C16002_004E", # Estimate!!Total!!Spanish!!Limited English speaking household
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"C16002_007E", # Estimate!!Total!!Other Indo-European languages!!Limited English speaking household
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"C16002_010E", # Estimate!!Total!!Asian and Pacific Island languages!!Limited English speaking household
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"C16002_013E", # Estimate!!Total!!Other languages!!Limited English speaking household
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]
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self.MEDIAN_INCOME_FIELD = "B19013_001E"
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self.MEDIAN_INCOME_FIELD_NAME = (
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"Median household income in the past 12 months"
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)
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self.POVERTY_FIELDS = [
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"C17002_001E", # Estimate!!Total,
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"C17002_002E", # Estimate!!Total!!Under .50
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"C17002_003E", # Estimate!!Total!!.50 to .99
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"C17002_004E", # Estimate!!Total!!1.00 to 1.24
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"C17002_005E", # Estimate!!Total!!1.25 to 1.49
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"C17002_006E", # Estimate!!Total!!1.50 to 1.84
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"C17002_007E", # Estimate!!Total!!1.85 to 1.99
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]
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self.POVERTY_LESS_THAN_100_PERCENT_FPL_FIELD_NAME = (
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"Percent of individuals < 100% Federal Poverty Line"
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)
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self.POVERTY_LESS_THAN_150_PERCENT_FPL_FIELD_NAME = (
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"Percent of individuals < 150% Federal Poverty Line"
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)
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self.POVERTY_LESS_THAN_200_PERCENT_FPL_FIELD_NAME = (
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"Percent of individuals < 200% Federal Poverty Line"
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)
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self.IMPUTED_POVERTY_LESS_THAN_200_PERCENT_FPL_FIELD_NAME = (
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"Percent of individuals < 200% Federal Poverty Line, imputed"
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)
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self.ADJUSTED_POVERTY_LESS_THAN_200_PERCENT_FPL_FIELD_NAME = (
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"Adjusted percent of individuals < 200% Federal Poverty Line"
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)
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self.ADJUSTED_AND_IMPUTED_POVERTY_LESS_THAN_200_PERCENT_FPL_FIELD_NAME_PRELIMINARY = (
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"Preliminary adjusted percent of individuals < 200% Federal Poverty Line,"
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+ " imputed"
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)
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self.ADJUSTED_AND_IMPUTED_POVERTY_LESS_THAN_200_PERCENT_FPL_FIELD_NAME = (
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"Adjusted percent of individuals < 200% Federal Poverty Line,"
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+ " imputed"
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)
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self.MEDIAN_HOUSE_VALUE_FIELD = "B25077_001E"
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self.MEDIAN_HOUSE_VALUE_FIELD_NAME = (
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"Median value ($) of owner-occupied housing units"
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)
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# Educational attainment figures
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self.EDUCATION_POPULATION_OVER_25 = "B15003_001E" # Estimate!!Total
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self.EDUCATION_NO_SCHOOLING = (
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"B15003_002E" # Estimate!!Total!!No schooling completed
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)
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self.EDUCATION_NURSERY = (
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"B15003_003E" # Estimate!!Total!!Nursery school
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)
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self.EDUCATION_KINDERGARTEN = (
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"B15003_004E" # Estimate!!Total!!Kindergarten
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)
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self.EDUCATION_FIRST = "B15003_005E" # Estimate!!Total!!1st grade
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self.EDUCATION_SECOND = "B15003_006E" # Estimate!!Total!!2nd grade
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self.EDUCATION_THIRD = "B15003_007E" # Estimate!!Total!!3rd grade
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self.EDUCATION_FOURTH = "B15003_008E" # Estimate!!Total!!4th grade
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self.EDUCATION_FIFTH = "B15003_009E" # Estimate!!Total!!5th grade
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self.EDUCATION_SIXTH = "B15003_010E" # Estimate!!Total!!6th grade
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self.EDUCATION_SEVENTH = "B15003_011E" # Estimate!!Total!!7th grade
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self.EDUCATION_EIGHTH = "B15003_012E" # Estimate!!Total!!8th grade
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self.EDUCATION_NINTH = "B15003_013E" # Estimate!!Total!!9th grade
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self.EDUCATION_TENTH = "B15003_014E" # Estimate!!Total!!10th grade
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self.EDUCATION_ELEVENTH = "B15003_015E" # Estimate!!Total!!11th grade
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self.EDUCATION_TWELFTH_NO_DIPLOMA = (
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"B15003_016E" # Estimate!!Total!!12th grade, no diploma
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)
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self.EDUCATIONAL_FIELDS = [
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self.EDUCATION_POPULATION_OVER_25,
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self.EDUCATION_NO_SCHOOLING,
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self.EDUCATION_NURSERY,
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self.EDUCATION_KINDERGARTEN,
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self.EDUCATION_FIRST,
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self.EDUCATION_SECOND,
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self.EDUCATION_THIRD,
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self.EDUCATION_FOURTH,
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self.EDUCATION_FIFTH,
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self.EDUCATION_SIXTH,
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self.EDUCATION_SEVENTH,
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self.EDUCATION_EIGHTH,
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self.EDUCATION_NINTH,
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self.EDUCATION_TENTH,
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self.EDUCATION_ELEVENTH,
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self.EDUCATION_TWELFTH_NO_DIPLOMA,
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]
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self.HIGH_SCHOOL_ED_RAW_COUNT_FIELD = (
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"Individuals age 25 or over with less than high school degree"
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)
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self.HIGH_SCHOOL_ED_FIELD = "Percent individuals age 25 or over with less than high school degree"
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# College attendance fields
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self.COLLEGE_ATTENDANCE_TOTAL_POPULATION_ASKED = (
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"B14004_001E" # Estimate!!Total
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)
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self.COLLEGE_ATTENDANCE_MALE_ENROLLED_PUBLIC = "B14004_003E" # Estimate!!Total!!Male!!Enrolled in public college or graduate school
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self.COLLEGE_ATTENDANCE_MALE_ENROLLED_PRIVATE = "B14004_008E" # Estimate!!Total!!Male!!Enrolled in private college or graduate school
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self.COLLEGE_ATTENDANCE_FEMALE_ENROLLED_PUBLIC = "B14004_019E" # Estimate!!Total!!Female!!Enrolled in public college or graduate school
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self.COLLEGE_ATTENDANCE_FEMALE_ENROLLED_PRIVATE = "B14004_024E" # Estimate!!Total!!Female!!Enrolled in private college or graduate school
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self.COLLEGE_ATTENDANCE_FIELDS = [
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self.COLLEGE_ATTENDANCE_TOTAL_POPULATION_ASKED,
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self.COLLEGE_ATTENDANCE_MALE_ENROLLED_PUBLIC,
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self.COLLEGE_ATTENDANCE_MALE_ENROLLED_PRIVATE,
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self.COLLEGE_ATTENDANCE_FEMALE_ENROLLED_PUBLIC,
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self.COLLEGE_ATTENDANCE_FEMALE_ENROLLED_PRIVATE,
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]
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self.COLLEGE_ATTENDANCE_FIELD = (
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"Percent enrollment in college or graduate school"
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)
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self.IMPUTED_COLLEGE_ATTENDANCE_FIELD = (
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"Percent enrollment in college or graduate school, imputed"
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)
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self.COLLEGE_NON_ATTENDANCE_FIELD = "Percent of population not currently enrolled in college or graduate school"
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self.RE_FIELDS = [
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"B02001_001E",
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"B02001_002E",
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"B02001_003E",
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"B02001_004E",
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"B02001_005E",
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"B02001_006E",
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"B02001_007E",
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"B02001_008E",
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"B02001_009E",
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"B02001_010E",
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"B03002_001E",
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"B03002_003E",
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"B03003_001E",
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"B03003_003E",
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"B02001_007E", # "Some other race alone"
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]
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self.BLACK_FIELD_NAME = "Black or African American"
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self.AMERICAN_INDIAN_FIELD_NAME = "American Indian / Alaska Native"
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self.ASIAN_FIELD_NAME = "Asian"
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self.HAWAIIAN_FIELD_NAME = "Native Hawaiian or Pacific"
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self.TWO_OR_MORE_RACES_FIELD_NAME = "two or more races"
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self.NON_HISPANIC_WHITE_FIELD_NAME = "White"
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self.HISPANIC_FIELD_NAME = "Hispanic or Latino"
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# Note that `other` is lowercase because the whole field will show up in the download
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# file as "Percent other races"
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self.OTHER_RACE_FIELD_NAME = "other races"
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self.TOTAL_RACE_POPULATION_FIELD_NAME = (
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"Total population surveyed on racial data"
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)
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# Name output demographics fields.
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self.RE_OUTPUT_FIELDS = [
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self.BLACK_FIELD_NAME,
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self.AMERICAN_INDIAN_FIELD_NAME,
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self.ASIAN_FIELD_NAME,
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self.HAWAIIAN_FIELD_NAME,
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self.TWO_OR_MORE_RACES_FIELD_NAME,
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self.NON_HISPANIC_WHITE_FIELD_NAME,
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self.HISPANIC_FIELD_NAME,
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self.OTHER_RACE_FIELD_NAME,
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]
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# Note: this field does double-duty here. It's used as the total population
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# within the age questions.
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# It's also what EJScreen used as their variable for total population in the
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# census tract, so we use it similarly.
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# See p. 83 of https://www.epa.gov/sites/default/files/2021-04/documents/ejscreen_technical_document.pdf
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self.TOTAL_POPULATION_FROM_AGE_TABLE = "B01001_001E" # Estimate!!Total:
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self.AGE_INPUT_FIELDS = [
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self.TOTAL_POPULATION_FROM_AGE_TABLE,
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"B01001_003E", # Estimate!!Total:!!Male:!!Under 5 years
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"B01001_004E", # Estimate!!Total:!!Male:!!5 to 9 years
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"B01001_005E", # Estimate!!Total:!!Male:!!10 to 14 years
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"B01001_006E", # Estimate!!Total:!!Male:!!15 to 17 years
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"B01001_007E", # Estimate!!Total:!!Male:!!18 and 19 years
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"B01001_008E", # Estimate!!Total:!!Male:!!20 years
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"B01001_009E", # Estimate!!Total:!!Male:!!21 years
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"B01001_010E", # Estimate!!Total:!!Male:!!22 to 24 years
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"B01001_011E", # Estimate!!Total:!!Male:!!25 to 29 years
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"B01001_012E", # Estimate!!Total:!!Male:!!30 to 34 years
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"B01001_013E", # Estimate!!Total:!!Male:!!35 to 39 years
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"B01001_014E", # Estimate!!Total:!!Male:!!40 to 44 years
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"B01001_015E", # Estimate!!Total:!!Male:!!45 to 49 years
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"B01001_016E", # Estimate!!Total:!!Male:!!50 to 54 years
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"B01001_017E", # Estimate!!Total:!!Male:!!55 to 59 years
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"B01001_018E", # Estimate!!Total:!!Male:!!60 and 61 years
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"B01001_019E", # Estimate!!Total:!!Male:!!62 to 64 years
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"B01001_020E", # Estimate!!Total:!!Male:!!65 and 66 years
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"B01001_021E", # Estimate!!Total:!!Male:!!67 to 69 years
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"B01001_022E", # Estimate!!Total:!!Male:!!70 to 74 years
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"B01001_023E", # Estimate!!Total:!!Male:!!75 to 79 years
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"B01001_024E", # Estimate!!Total:!!Male:!!80 to 84 years
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"B01001_025E", # Estimate!!Total:!!Male:!!85 years and over
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"B01001_027E", # Estimate!!Total:!!Female:!!Under 5 years
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"B01001_028E", # Estimate!!Total:!!Female:!!5 to 9 years
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"B01001_029E", # Estimate!!Total:!!Female:!!10 to 14 years
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"B01001_030E", # Estimate!!Total:!!Female:!!15 to 17 years
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"B01001_031E", # Estimate!!Total:!!Female:!!18 and 19 years
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"B01001_032E", # Estimate!!Total:!!Female:!!20 years
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"B01001_033E", # Estimate!!Total:!!Female:!!21 years
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"B01001_034E", # Estimate!!Total:!!Female:!!22 to 24 years
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"B01001_035E", # Estimate!!Total:!!Female:!!25 to 29 years
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"B01001_036E", # Estimate!!Total:!!Female:!!30 to 34 years
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"B01001_037E", # Estimate!!Total:!!Female:!!35 to 39 years
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"B01001_038E", # Estimate!!Total:!!Female:!!40 to 44 years
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"B01001_039E", # Estimate!!Total:!!Female:!!45 to 49 years
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"B01001_040E", # Estimate!!Total:!!Female:!!50 to 54 years
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"B01001_041E", # Estimate!!Total:!!Female:!!55 to 59 years
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"B01001_042E", # Estimate!!Total:!!Female:!!60 and 61 years
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"B01001_043E", # Estimate!!Total:!!Female:!!62 to 64 years
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"B01001_044E", # Estimate!!Total:!!Female:!!65 and 66 years
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"B01001_045E", # Estimate!!Total:!!Female:!!67 to 69 years
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"B01001_046E", # Estimate!!Total:!!Female:!!70 to 74 years
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"B01001_047E", # Estimate!!Total:!!Female:!!75 to 79 years
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"B01001_048E", # Estimate!!Total:!!Female:!!80 to 84 years
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"B01001_049E", # Estimate!!Total:!!Female:!!85 years and over
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]
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self.AGE_OUTPUT_FIELDS = [
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field_names.PERCENT_AGE_UNDER_10,
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field_names.PERCENT_AGE_10_TO_64,
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field_names.PERCENT_AGE_OVER_64,
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]
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self.STATE_GEOID_FIELD_NAME = "GEOID2"
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self.COLUMNS_TO_KEEP = (
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[
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self.GEOID_TRACT_FIELD_NAME,
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field_names.TOTAL_POP_FIELD,
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self.UNEMPLOYED_FIELD_NAME,
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self.LINGUISTIC_ISOLATION_FIELD_NAME,
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self.MEDIAN_INCOME_FIELD_NAME,
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self.POVERTY_LESS_THAN_100_PERCENT_FPL_FIELD_NAME,
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self.POVERTY_LESS_THAN_150_PERCENT_FPL_FIELD_NAME,
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self.IMPUTED_POVERTY_LESS_THAN_200_PERCENT_FPL_FIELD_NAME,
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self.MEDIAN_HOUSE_VALUE_FIELD_NAME,
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self.HIGH_SCHOOL_ED_FIELD,
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self.COLLEGE_ATTENDANCE_FIELD,
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self.COLLEGE_NON_ATTENDANCE_FIELD,
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self.IMPUTED_COLLEGE_ATTENDANCE_FIELD,
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field_names.IMPUTED_INCOME_FLAG_FIELD_NAME,
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]
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+ self.RE_OUTPUT_FIELDS
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+ [
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field_names.PERCENT_PREFIX + field
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for field in self.RE_OUTPUT_FIELDS
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]
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+ self.AGE_OUTPUT_FIELDS
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+ [
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field_names.POVERTY_LESS_THAN_200_FPL_FIELD,
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field_names.POVERTY_LESS_THAN_200_FPL_IMPUTED_FIELD,
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]
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)
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self.df: pd.DataFrame
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# pylint: disable=too-many-arguments
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def _merge_geojson(
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self,
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df: pd.DataFrame,
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usa_geo_df: gpd.GeoDataFrame,
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geoid_field: str = "GEOID10",
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geometry_field: str = "geometry",
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state_code_field: str = "STATEFP10",
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county_code_field: str = "COUNTYFP10",
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) -> gpd.GeoDataFrame:
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usa_geo_df[geoid_field] = (
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usa_geo_df[geoid_field].astype(str).str.zfill(11)
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)
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return gpd.GeoDataFrame(
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df.merge(
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usa_geo_df[
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[
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geoid_field,
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geometry_field,
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state_code_field,
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county_code_field,
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]
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],
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left_on=[self.GEOID_TRACT_FIELD_NAME],
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right_on=[geoid_field],
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)
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)
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def extract(self) -> None:
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# Define the variables to retrieve
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variables = (
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[
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self.MEDIAN_INCOME_FIELD,
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self.MEDIAN_HOUSE_VALUE_FIELD,
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]
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+ self.EMPLOYMENT_FIELDS
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+ self.LINGUISTIC_ISOLATION_FIELDS
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+ self.POVERTY_FIELDS
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+ self.EDUCATIONAL_FIELDS
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+ self.RE_FIELDS
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+ self.COLLEGE_ATTENDANCE_FIELDS
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+ self.AGE_INPUT_FIELDS
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)
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self.df = retrieve_census_acs_data(
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acs_year=self.ACS_YEAR,
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variables=variables,
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tract_output_field_name=self.GEOID_TRACT_FIELD_NAME,
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data_path_for_fips_codes=self.DATA_PATH,
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)
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def transform(self) -> None:
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logger.info("Starting Census ACS Transform")
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df = self.df
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# Here we join the geometry of the US to the dataframe so that we can impute
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# The income of neighbors. first this looks locally; if there's no local
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# geojson file for all of the US, this will read it off of S3
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logger.info("Reading in geojson for the country")
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if not os.path.exists(
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self.DATA_PATH / "census" / "geojson" / "us.json"
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):
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logger.info("Fetching Census data from AWS S3")
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unzip_file_from_url(
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CENSUS_DATA_S3_URL,
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self.DATA_PATH / "tmp",
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self.DATA_PATH,
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)
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geo_df = gpd.read_file(
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self.DATA_PATH / "census" / "geojson" / "us.json",
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)
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df = self._merge_geojson(
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df=df,
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usa_geo_df=geo_df,
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)
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# Rename some fields.
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df = df.rename(
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columns={
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self.MEDIAN_HOUSE_VALUE_FIELD: self.MEDIAN_HOUSE_VALUE_FIELD_NAME,
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self.MEDIAN_INCOME_FIELD: self.MEDIAN_INCOME_FIELD_NAME,
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self.TOTAL_POPULATION_FROM_AGE_TABLE: field_names.TOTAL_POP_FIELD,
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},
|
|
errors="raise",
|
|
)
|
|
|
|
# Handle null values for various fields, which are `-666666666`.
|
|
for field in [
|
|
self.MEDIAN_INCOME_FIELD_NAME,
|
|
self.MEDIAN_HOUSE_VALUE_FIELD_NAME,
|
|
]:
|
|
missing_value_count = sum(df[field] == -666666666)
|
|
logger.info(
|
|
f"There are {missing_value_count} ({int(100*missing_value_count/df[field].count())}%) values of "
|
|
+ f"`{field}` being marked as null values."
|
|
)
|
|
df[field] = df[field].replace(to_replace=-666666666, value=None)
|
|
|
|
# Calculate percent unemployment.
|
|
# TODO: remove small-sample data that should be `None` instead of a high-variance fraction.
|
|
df[self.UNEMPLOYED_FIELD_NAME] = (
|
|
df[self.TOTAL_UNEMPLOYED_FIELD] / df[self.TOTAL_IN_LABOR_FORCE]
|
|
)
|
|
|
|
# Calculate linguistic isolation.
|
|
individual_limited_english_fields = [
|
|
"C16002_004E",
|
|
"C16002_007E",
|
|
"C16002_010E",
|
|
"C16002_013E",
|
|
]
|
|
|
|
df[self.LINGUISTIC_ISOLATION_TOTAL_FIELD_NAME] = df[
|
|
individual_limited_english_fields
|
|
].sum(axis=1, skipna=True)
|
|
df[self.LINGUISTIC_ISOLATION_FIELD_NAME] = (
|
|
df[self.LINGUISTIC_ISOLATION_TOTAL_FIELD_NAME].astype(float)
|
|
/ df["C16002_001E"]
|
|
)
|
|
|
|
# Calculate percent at different poverty thresholds
|
|
df[self.POVERTY_LESS_THAN_100_PERCENT_FPL_FIELD_NAME] = (
|
|
df["C17002_002E"] + df["C17002_003E"]
|
|
) / df["C17002_001E"]
|
|
|
|
df[self.POVERTY_LESS_THAN_150_PERCENT_FPL_FIELD_NAME] = (
|
|
df["C17002_002E"]
|
|
+ df["C17002_003E"]
|
|
+ df["C17002_004E"]
|
|
+ df["C17002_005E"]
|
|
) / df["C17002_001E"]
|
|
|
|
df[self.POVERTY_LESS_THAN_200_PERCENT_FPL_FIELD_NAME] = (
|
|
df["C17002_002E"]
|
|
+ df["C17002_003E"]
|
|
+ df["C17002_004E"]
|
|
+ df["C17002_005E"]
|
|
+ df["C17002_006E"]
|
|
+ df["C17002_007E"]
|
|
) / df["C17002_001E"]
|
|
|
|
# Calculate educational attainment
|
|
educational_numerator_fields = [
|
|
self.EDUCATION_NO_SCHOOLING,
|
|
self.EDUCATION_NURSERY,
|
|
self.EDUCATION_KINDERGARTEN,
|
|
self.EDUCATION_FIRST,
|
|
self.EDUCATION_SECOND,
|
|
self.EDUCATION_THIRD,
|
|
self.EDUCATION_FOURTH,
|
|
self.EDUCATION_FIFTH,
|
|
self.EDUCATION_SIXTH,
|
|
self.EDUCATION_SEVENTH,
|
|
self.EDUCATION_EIGHTH,
|
|
self.EDUCATION_NINTH,
|
|
self.EDUCATION_TENTH,
|
|
self.EDUCATION_ELEVENTH,
|
|
self.EDUCATION_TWELFTH_NO_DIPLOMA,
|
|
]
|
|
|
|
df[self.HIGH_SCHOOL_ED_RAW_COUNT_FIELD] = df[
|
|
educational_numerator_fields
|
|
].sum(axis=1)
|
|
df[self.HIGH_SCHOOL_ED_FIELD] = (
|
|
df[self.HIGH_SCHOOL_ED_RAW_COUNT_FIELD]
|
|
/ df[self.EDUCATION_POPULATION_OVER_25]
|
|
)
|
|
|
|
# Calculate some demographic information.
|
|
df = df.rename(
|
|
columns={
|
|
"B02001_003E": self.BLACK_FIELD_NAME,
|
|
"B02001_004E": self.AMERICAN_INDIAN_FIELD_NAME,
|
|
"B02001_005E": self.ASIAN_FIELD_NAME,
|
|
"B02001_006E": self.HAWAIIAN_FIELD_NAME,
|
|
"B02001_008E": self.TWO_OR_MORE_RACES_FIELD_NAME,
|
|
"B03002_003E": self.NON_HISPANIC_WHITE_FIELD_NAME,
|
|
"B03003_003E": self.HISPANIC_FIELD_NAME,
|
|
"B02001_007E": self.OTHER_RACE_FIELD_NAME,
|
|
"B02001_001E": self.TOTAL_RACE_POPULATION_FIELD_NAME,
|
|
},
|
|
errors="raise",
|
|
)
|
|
|
|
for race_field_name in self.RE_OUTPUT_FIELDS:
|
|
df[field_names.PERCENT_PREFIX + race_field_name] = (
|
|
df[race_field_name] / df[self.TOTAL_RACE_POPULATION_FIELD_NAME]
|
|
)
|
|
|
|
# First value is the `age bucket`, and the second value is a list of all fields
|
|
# that will be summed in the calculations of the total population in that age
|
|
# bucket.
|
|
age_bucket_and_its_sum_columns = [
|
|
(
|
|
field_names.PERCENT_AGE_UNDER_10,
|
|
[
|
|
"B01001_003E", # Estimate!!Total:!!Male:!!Under 5 years
|
|
"B01001_004E", # Estimate!!Total:!!Male:!!5 to 9 years
|
|
"B01001_027E", # Estimate!!Total:!!Female:!!Under 5 years
|
|
"B01001_028E", # Estimate!!Total:!!Female:!!5 to 9 years
|
|
],
|
|
),
|
|
(
|
|
field_names.PERCENT_AGE_10_TO_64,
|
|
[
|
|
"B01001_005E", # Estimate!!Total:!!Male:!!10 to 14 years
|
|
"B01001_006E", # Estimate!!Total:!!Male:!!15 to 17 years
|
|
"B01001_007E", # Estimate!!Total:!!Male:!!18 and 19 years
|
|
"B01001_008E", # Estimate!!Total:!!Male:!!20 years
|
|
"B01001_009E", # Estimate!!Total:!!Male:!!21 years
|
|
"B01001_010E", # Estimate!!Total:!!Male:!!22 to 24 years
|
|
"B01001_011E", # Estimate!!Total:!!Male:!!25 to 29 years
|
|
"B01001_012E", # Estimate!!Total:!!Male:!!30 to 34 years
|
|
"B01001_013E", # Estimate!!Total:!!Male:!!35 to 39 years
|
|
"B01001_014E", # Estimate!!Total:!!Male:!!40 to 44 years
|
|
"B01001_015E", # Estimate!!Total:!!Male:!!45 to 49 years
|
|
"B01001_016E", # Estimate!!Total:!!Male:!!50 to 54 years
|
|
"B01001_017E", # Estimate!!Total:!!Male:!!55 to 59 years
|
|
"B01001_018E", # Estimate!!Total:!!Male:!!60 and 61 years
|
|
"B01001_019E", # Estimate!!Total:!!Male:!!62 to 64 years
|
|
"B01001_029E", # Estimate!!Total:!!Female:!!10 to 14 years
|
|
"B01001_030E", # Estimate!!Total:!!Female:!!15 to 17 years
|
|
"B01001_031E", # Estimate!!Total:!!Female:!!18 and 19 years
|
|
"B01001_032E", # Estimate!!Total:!!Female:!!20 years
|
|
"B01001_033E", # Estimate!!Total:!!Female:!!21 years
|
|
"B01001_034E", # Estimate!!Total:!!Female:!!22 to 24 years
|
|
"B01001_035E", # Estimate!!Total:!!Female:!!25 to 29 years
|
|
"B01001_036E", # Estimate!!Total:!!Female:!!30 to 34 years
|
|
"B01001_037E", # Estimate!!Total:!!Female:!!35 to 39 years
|
|
"B01001_038E", # Estimate!!Total:!!Female:!!40 to 44 years
|
|
"B01001_039E", # Estimate!!Total:!!Female:!!45 to 49 years
|
|
"B01001_040E", # Estimate!!Total:!!Female:!!50 to 54 years
|
|
"B01001_041E", # Estimate!!Total:!!Female:!!55 to 59 years
|
|
"B01001_042E", # Estimate!!Total:!!Female:!!60 and 61 years
|
|
"B01001_043E", # Estimate!!Total:!!Female:!!62 to 64 years
|
|
],
|
|
),
|
|
(
|
|
field_names.PERCENT_AGE_OVER_64,
|
|
[
|
|
"B01001_020E", # Estimate!!Total:!!Male:!!65 and 66 years
|
|
"B01001_021E", # Estimate!!Total:!!Male:!!67 to 69 years
|
|
"B01001_022E", # Estimate!!Total:!!Male:!!70 to 74 years
|
|
"B01001_023E", # Estimate!!Total:!!Male:!!75 to 79 years
|
|
"B01001_024E", # Estimate!!Total:!!Male:!!80 to 84 years
|
|
"B01001_025E", # Estimate!!Total:!!Male:!!85 years and over
|
|
"B01001_044E", # Estimate!!Total:!!Female:!!65 and 66 years
|
|
"B01001_045E", # Estimate!!Total:!!Female:!!67 to 69 years
|
|
"B01001_046E", # Estimate!!Total:!!Female:!!70 to 74 years
|
|
"B01001_047E", # Estimate!!Total:!!Female:!!75 to 79 years
|
|
"B01001_048E", # Estimate!!Total:!!Female:!!80 to 84 years
|
|
"B01001_049E", # Estimate!!Total:!!Female:!!85 years and over
|
|
],
|
|
),
|
|
]
|
|
|
|
# For each age bucket, sum the relevant columns and calculate the total
|
|
# percentage.
|
|
for age_bucket, sum_columns in age_bucket_and_its_sum_columns:
|
|
df[age_bucket] = (
|
|
df[sum_columns].sum(axis=1) / df[field_names.TOTAL_POP_FIELD]
|
|
)
|
|
|
|
# Calculate college attendance and adjust low income
|
|
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]
|
|
|
|
df[self.COLLEGE_NON_ATTENDANCE_FIELD] = (
|
|
1 - df[self.COLLEGE_ATTENDANCE_FIELD]
|
|
)
|
|
|
|
# we impute income for both income measures
|
|
## TODO: Convert to pydantic for clarity
|
|
logger.info("Imputing income information")
|
|
ImputeVariables = namedtuple(
|
|
"ImputeVariables", ["raw_field_name", "imputed_field_name"]
|
|
)
|
|
|
|
df = calculate_income_measures(
|
|
impute_var_named_tup_list=[
|
|
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(
|
|
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,
|
|
minimum_population_required_for_imputation=self.MINIMUM_POPULATION_REQUIRED_FOR_IMPUTATION,
|
|
)
|
|
|
|
logger.info("Calculating with imputed values")
|
|
|
|
df[
|
|
self.ADJUSTED_AND_IMPUTED_POVERTY_LESS_THAN_200_PERCENT_FPL_FIELD_NAME
|
|
] = (
|
|
df[self.POVERTY_LESS_THAN_200_PERCENT_FPL_FIELD_NAME].fillna(
|
|
df[self.IMPUTED_POVERTY_LESS_THAN_200_PERCENT_FPL_FIELD_NAME]
|
|
)
|
|
- df[self.COLLEGE_ATTENDANCE_FIELD].fillna(
|
|
df[self.IMPUTED_COLLEGE_ATTENDANCE_FIELD]
|
|
)
|
|
# Use clip to ensure that the values are not negative if college attendance
|
|
# is very high
|
|
).clip(
|
|
lower=0
|
|
)
|
|
|
|
# All values should have a value at this point
|
|
assert (
|
|
# For tracts with >0 population
|
|
df[
|
|
df[field_names.TOTAL_POP_FIELD]
|
|
>= self.MINIMUM_POPULATION_REQUIRED_FOR_IMPUTATION
|
|
][
|
|
# Then the imputed field should have no nulls
|
|
self.ADJUSTED_AND_IMPUTED_POVERTY_LESS_THAN_200_PERCENT_FPL_FIELD_NAME
|
|
]
|
|
.isna()
|
|
.sum()
|
|
== 0
|
|
), "Error: not all values were filled..."
|
|
|
|
logger.info("Renaming columns...")
|
|
df = df.rename(
|
|
columns={
|
|
self.ADJUSTED_AND_IMPUTED_POVERTY_LESS_THAN_200_PERCENT_FPL_FIELD_NAME: field_names.POVERTY_LESS_THAN_200_FPL_IMPUTED_FIELD,
|
|
self.POVERTY_LESS_THAN_200_PERCENT_FPL_FIELD_NAME: field_names.POVERTY_LESS_THAN_200_FPL_FIELD,
|
|
}
|
|
)
|
|
|
|
# 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.
|
|
df[field_names.IMPUTED_INCOME_FLAG_FIELD_NAME] = (
|
|
df[field_names.POVERTY_LESS_THAN_200_FPL_IMPUTED_FIELD].notna()
|
|
& df[field_names.POVERTY_LESS_THAN_200_FPL_FIELD].isna()
|
|
)
|
|
|
|
# Save results to self.
|
|
self.output_df = df
|