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Updates backend constants to N (#1854)

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Emma Nechamkin 2022-08-23 16:19:00 -04:00 • committed by GitHub
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data/data-pipeline/data_pipeline/content/config/csv.yml
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@ -5,372 +5,372 @@ global_config:
float: 2 float: 2
loss_rate_percentage: 4 loss_rate_percentage: 4
fields: fields:
- score_name: GEOID10_TRACT - score_name: GEOID10_TRACT
label: Census tract ID label: Census tract ID
format: string format: string
- score_name: County Name - score_name: County Name
label: County Name label: County Name
format: string format: string
- score_name: State/Territory - score_name: State/Territory
label: State/Territory label: State/Territory
format: string format: string
- score_name: Percent Black or African American - score_name: Percent Black or African American
label: Percent Black or African American alone label: Percent Black or African American alone
format: float format: float
- score_name: Percent American Indian / Alaska Native - score_name: Percent American Indian / Alaska Native
label: Percent American Indian / Alaska Native label: Percent American Indian / Alaska Native
format: float format: float
- score_name: Percent Asian - score_name: Percent Asian
label: Percent Asian label: Percent Asian
format: float format: float
- score_name: Percent Native Hawaiian or Pacific - score_name: Percent Native Hawaiian or Pacific
label: Percent Native Hawaiian or Pacific label: Percent Native Hawaiian or Pacific
format: float format: float
- score_name: Percent two or more races - score_name: Percent two or more races
label: Percent two or more races label: Percent two or more races
format: float format: float
- score_name: Percent White - score_name: Percent White
label: Percent White label: Percent White
format: float format: float
- score_name: Percent Hispanic or Latino - score_name: Percent Hispanic or Latino
label: Percent Hispanic or Latino label: Percent Hispanic or Latino
format: float format: float
- score_name: Percent other races - score_name: Percent other races
label: Percent other races label: Percent other races
format: float format: float
- score_name: Percent age under 10 - score_name: Percent age under 10
label: Percent age under 10 label: Percent age under 10
format: float format: float
- score_name: Percent age 10 to 64 - score_name: Percent age 10 to 64
label: Percent age 10 to 64 label: Percent age 10 to 64
format: float format: float
- score_name: Percent age over 64 - score_name: Percent age over 64
label: Percent age over 64 label: Percent age over 64
format: float format: float
- score_name: Total threshold criteria exceeded - score_name: Total threshold criteria exceeded
label: Total threshold criteria exceeded label: Total threshold criteria exceeded
format: int64 format: int64
- score_name: Total categories exceeded - score_name: Total categories exceeded
label: Total categories exceeded label: Total categories exceeded
format: int64 format: int64
- score_name: Definition N (communities) - score_name: Definition N (communities)
label: Identified as disadvantaged without considering neighbors label: Identified as disadvantaged without considering neighbors
format: bool format: bool
- score_name: Definition N (communities) (based on adjacency index and low income alone) - score_name: Definition N (communities) (based on adjacency index and low income alone)
label: Identified as disadvantaged based on neighbors and relaxed low income threshold only label: Identified as disadvantaged based on neighbors and relaxed low income threshold only
format: bool format: bool
- score_name: Definition M community, including adjacency index tracts - score_name: Definition N community, including adjacency index tracts
label: Identified as disadvantaged label: Identified as disadvantaged
format: bool format: bool
- score_name: Definition N (communities) (average of neighbors) - score_name: Definition N (communities) (average of neighbors)
label: Share of neighbors that are identified as disadvantaged label: Share of neighbors that are identified as disadvantaged
format: percentage format: percentage
- score_name: Total population - score_name: Total population
label: Total population label: Total population
format: float format: float
- score_name: Percent of individuals below 200% Federal Poverty Line, imputed and adjusted - score_name: Percent of individuals below 200% Federal Poverty Line, imputed and adjusted
label: Adjusted percent of individuals below 200% Federal Poverty Line label: Adjusted percent of individuals below 200% Federal Poverty Line
format: float format: float
- score_name: Is low income and has a low percent of higher ed students? - score_name: Is low income (imputed and adjusted)?
label: Is low income and high percent of residents that are not higher ed students? label: Is low income?
format: bool format: bool
- score_name: Greater than or equal to the 90th percentile for expected agriculture loss rate, is low income, and has a low percent of higher ed students? - score_name: Greater than or equal to the 90th percentile for expected agriculture loss rate and is low income?
label: Greater than or equal to the 90th percentile for expected agriculture loss rate, is low income, and high percent of residents that are not higher ed students? label: Greater than or equal to the 90th percentile for expected agriculture loss rate and is low income?
format: bool format: bool
- score_name: Expected agricultural loss rate (Natural Hazards Risk Index) (percentile) - score_name: Expected agricultural loss rate (Natural Hazards Risk Index) (percentile)
label: Expected agricultural loss rate (Natural Hazards Risk Index) (percentile) label: Expected agricultural loss rate (Natural Hazards Risk Index) (percentile)
format: percentage format: percentage
- score_name: Expected agricultural loss rate (Natural Hazards Risk Index) - score_name: Expected agricultural loss rate (Natural Hazards Risk Index)
label: Expected agricultural loss rate (Natural Hazards Risk Index) label: Expected agricultural loss rate (Natural Hazards Risk Index)
format: loss_rate_percentage format: loss_rate_percentage
- score_name: Greater than or equal to the 90th percentile for expected building loss rate, is low income, and has a low percent of higher ed students? - score_name: Greater than or equal to the 90th percentile for expected building loss rate and is low income?
label: Greater than or equal to the 90th percentile for expected building loss rate, is low income, and high percent of residents that are not higher ed students? label: Greater than or equal to the 90th percentile for expected building loss rate and is low income?
format: bool format: bool
- score_name: Expected building loss rate (Natural Hazards Risk Index) (percentile) - score_name: Expected building loss rate (Natural Hazards Risk Index) (percentile)
label: Expected building loss rate (Natural Hazards Risk Index) (percentile) label: Expected building loss rate (Natural Hazards Risk Index) (percentile)
format: percentage format: percentage
- score_name: Expected building loss rate (Natural Hazards Risk Index) - score_name: Expected building loss rate (Natural Hazards Risk Index)
label: Expected building loss rate (Natural Hazards Risk Index) label: Expected building loss rate (Natural Hazards Risk Index)
format: loss_rate_percentage format: loss_rate_percentage
- score_name: Greater than or equal to the 90th percentile for expected population loss rate, is low income, and has a low percent of higher ed students? - score_name: Greater than or equal to the 90th percentile for expected population loss rate and is low income?
label: Greater than or equal to the 90th percentile for expected population loss rate, is low income, and high percent of residents that are not higher ed students? label: Greater than or equal to the 90th percentile for expected population loss rate and is low income?
format: bool format: bool
- score_name: Expected population loss rate (Natural Hazards Risk Index) (percentile) - score_name: Expected population loss rate (Natural Hazards Risk Index) (percentile)
label: Expected population loss rate (Natural Hazards Risk Index) (percentile) label: Expected population loss rate (Natural Hazards Risk Index) (percentile)
format: percentage format: percentage
- score_name: Expected population loss rate (Natural Hazards Risk Index) - score_name: Expected population loss rate (Natural Hazards Risk Index)
label: Expected population loss rate (Natural Hazards Risk Index) label: Expected population loss rate (Natural Hazards Risk Index)
format: loss_rate_percentage format: loss_rate_percentage
- score_name: Greater than or equal to the 90th percentile for energy burden, is low income, and has a low percent of higher ed students? - score_name: Greater than or equal to the 90th percentile for energy burden and is low income?
label: Greater than or equal to the 90th percentile for energy burden, is low income, and high percent of residents that are not higher ed students? label: Greater than or equal to the 90th percentile for energy burden and is low income?
format: bool format: bool
- score_name: Energy burden (percentile) - score_name: Energy burden (percentile)
label: Energy burden (percentile) label: Energy burden (percentile)
format: percentage format: percentage
- score_name: Energy burden - score_name: Energy burden
label: Energy burden label: Energy burden
format: percentage format: percentage
- score_name: Greater than or equal to the 90th percentile for PM2.5 exposure, is low income, and has a low percent of higher ed students? - score_name: Greater than or equal to the 90th percentile for PM2.5 exposure and is low income?
label: Greater than or equal to the 90th percentile for PM2.5 exposure, is low income, and high percent of residents that are not higher ed students? label: Greater than or equal to the 90th percentile for PM2.5 exposure and is low income?
format: bool format: bool
- score_name: PM2.5 in the air (percentile) - score_name: PM2.5 in the air (percentile)
label: PM2.5 in the air (percentile) label: PM2.5 in the air (percentile)
format: percentage format: percentage
- score_name: PM2.5 in the air - score_name: PM2.5 in the air
label: PM2.5 in the air label: PM2.5 in the air
format: float format: float
- score_name: Greater than or equal to the 90th percentile for diesel particulate matter, is low income, and has a low percent of higher ed students? - score_name: Greater than or equal to the 90th percentile for diesel particulate matter and is low income?
label: Greater than or equal to the 90th percentile for diesel particulate matter, is low income, and high percent of residents that are not higher ed students? label: Greater than or equal to the 90th percentile for diesel particulate matter and is low income?
format: bool format: bool
- score_name: Diesel particulate matter exposure (percentile) - score_name: Diesel particulate matter exposure (percentile)
label: Diesel particulate matter exposure (percentile) label: Diesel particulate matter exposure (percentile)
format: percentage format: percentage
- score_name: Diesel particulate matter exposure - score_name: Diesel particulate matter exposure
label: Diesel particulate matter exposure label: Diesel particulate matter exposure
format: float format: float
- score_name: Greater than or equal to the 90th percentile for traffic proximity, is low income, and has a low percent of higher ed students? - score_name: Greater than or equal to the 90th percentile for traffic proximity and is low income?
label: Greater than or equal to the 90th percentile for traffic proximity, is low income, and high percent of residents that are not higher ed students? label: Greater than or equal to the 90th percentile for traffic proximity and is low income?
format: bool format: bool
- score_name: Traffic proximity and volume (percentile) - score_name: Traffic proximity and volume (percentile)
label: Traffic proximity and volume (percentile) label: Traffic proximity and volume (percentile)
format: percentage format: percentage
- score_name: Traffic proximity and volume - score_name: Traffic proximity and volume
label: Traffic proximity and volume label: Traffic proximity and volume
format: float format: float
- score_name: Greater than or equal to the 90th percentile for housing burden, is low income, and has a low percent of higher ed students? - score_name: Greater than or equal to the 90th percentile for housing burden and is low income?
label: Greater than or equal to the 90th percentile for housing burden, is low income, and high percent of residents that are not higher ed students? label: Greater than or equal to the 90th percentile for housing burden and is low income?
format: bool format: bool
- score_name: Housing burden (percent) (percentile) - score_name: Housing burden (percent) (percentile)
label: Housing burden (percent) (percentile) label: Housing burden (percent) (percentile)
format: percentage format: percentage
- score_name: Housing burden (percent) - score_name: Housing burden (percent)
label: Housing burden (percent) label: Housing burden (percent)
format: percentage format: percentage
- score_name: Greater than or equal to the 90th percentile for lead paint, the median house value is less than 90th percentile, is low income, and has a low percent of higher ed students? - score_name: Greater than or equal to the 90th percentile for lead paint and the median house value is less than 90th percentile and is low income?
label: Greater than or equal to the 90th percentile for lead paint, the median house value is less than 90th percentile, is low income, and high percent of residents that are not higher ed students? label: Greater than or equal to the 90th percentile for lead paint, the median house value is less than 90th percentile and is low income?
format: bool format: bool
- score_name: Percent pre-1960s housing (lead paint indicator) (percentile) - score_name: Percent pre-1960s housing (lead paint indicator) (percentile)
label: Percent pre-1960s housing (lead paint indicator) (percentile) label: Percent pre-1960s housing (lead paint indicator) (percentile)
format: percentage format: percentage
- score_name: Percent pre-1960s housing (lead paint indicator) - score_name: Percent pre-1960s housing (lead paint indicator)
label: Percent pre-1960s housing (lead paint indicator) label: Percent pre-1960s housing (lead paint indicator)
format: percentage format: percentage
- score_name: Median value ($) of owner-occupied housing units (percentile) - score_name: Median value ($) of owner-occupied housing units (percentile)
label: Median value ($) of owner-occupied housing units (percentile) label: Median value ($) of owner-occupied housing units (percentile)
format: percentage format: percentage
- score_name: Median value ($) of owner-occupied housing units - score_name: Median value ($) of owner-occupied housing units
label: Median value ($) of owner-occupied housing units label: Median value ($) of owner-occupied housing units
format: float format: float
- score_name: Greater than or equal to the 90th percentile for proximity to hazardous waste facilities, is low income, and has a low percent of higher ed students? - score_name: Greater than or equal to the 90th percentile for proximity to hazardous waste facilities and is low income?
label: Greater than or equal to the 90th percentile for proximity to hazardous waste facilities, is low income, and high percent of residents that are not higher ed students? label: Greater than or equal to the 90th percentile for proximity to hazardous waste facilities and is low income?
format: bool format: bool
- score_name: Proximity to hazardous waste sites (percentile) - score_name: Proximity to hazardous waste sites (percentile)
label: Proximity to hazardous waste sites (percentile) label: Proximity to hazardous waste sites (percentile)
format: percentage format: percentage
- score_name: Proximity to hazardous waste sites - score_name: Proximity to hazardous waste sites
label: Proximity to hazardous waste sites label: Proximity to hazardous waste sites
format: float format: float
- score_name: Greater than or equal to the 90th percentile for proximity to superfund sites, is low income, and has a low percent of higher ed students? - score_name: Greater than or equal to the 90th percentile for proximity to superfund sites and is low income?
label: Greater than or equal to the 90th percentile for proximity to superfund sites, is low income, and high percent of residents that are not higher ed students? label: Greater than or equal to the 90th percentile for proximity to superfund sites and is low income?
format: bool format: bool
- score_name: Proximity to NPL sites (percentile) - score_name: Proximity to NPL sites (percentile)
label: Proximity to NPL (Superfund) sites (percentile) label: Proximity to NPL (Superfund) sites (percentile)
format: percentage format: percentage
- score_name: Proximity to NPL sites - score_name: Proximity to NPL sites
label: Proximity to NPL (Superfund) sites label: Proximity to NPL (Superfund) sites
format: float format: float
- score_name: Greater than or equal to the 90th percentile for proximity to RMP sites, is low income, and has a low percent of higher ed students? - score_name: Greater than or equal to the 90th percentile for proximity to RMP sites and is low income?
label: Greater than or equal to the 90th percentile for proximity to RMP sites, is low income, and high percent of residents that are not higher ed students? label: Greater than or equal to the 90th percentile for proximity to RMP sites and is low income?
format: bool format: bool
- score_name: Proximity to Risk Management Plan (RMP) facilities (percentile) - score_name: Proximity to Risk Management Plan (RMP) facilities (percentile)
label: Proximity to Risk Management Plan (RMP) facilities (percentile) label: Proximity to Risk Management Plan (RMP) facilities (percentile)
format: percentage format: percentage
- score_name: Proximity to Risk Management Plan (RMP) facilities - score_name: Proximity to Risk Management Plan (RMP) facilities
label: Proximity to Risk Management Plan (RMP) facilities label: Proximity to Risk Management Plan (RMP) facilities
format: float format: float
- score_name: Greater than or equal to the 90th percentile for wastewater discharge, is low income, and has a low percent of higher ed students? - score_name: Greater than or equal to the 90th percentile for wastewater discharge and is low income?
label: Greater than or equal to the 90th percentile for wastewater discharge, is low income, and high percent of residents that are not higher ed students? label: Greater than or equal to the 90th percentile for wastewater discharge and is low income?
format: bool format: bool
- score_name: Wastewater discharge (percentile) - score_name: Greater than or equal to the 90th percentile for leaky underground storage tanks and is low income?
label: Wastewater discharge (percentile) label: Greater than or equal to the 90th percentile for leaky underground storage tanks and is low income?
format: percentage format: bool
- score_name: Wastewater discharge - score_name: Wastewater discharge (percentile)
label: Wastewater discharge label: Wastewater discharge (percentile)
format: float format: percentage
- score_name: Greater than or equal to the 90th percentile for asthma, is low income, and has a low percent of higher ed students? - score_name: Leaky underground storage tanks (percentile)
label: Greater than or equal to the 90th percentile for asthma, is low income, and high percent of residents that are not higher ed students? label: Leaky underground storage tanks (percentile)
format: bool format: percentage
- score_name: Current asthma among adults aged greater than or equal to 18 years (percentile) - score_name: Wastewater discharge
label: Current asthma among adults aged greater than or equal to 18 years (percentile) label: Wastewater discharge
format: percentage format: float
- score_name: Current asthma among adults aged greater than or equal to 18 years - score_name: Leaky underground storage tanks
label: Current asthma among adults aged greater than or equal to 18 years label: Leaky underground storage tanks
format: percentage format: float
- score_name: Greater than or equal to the 90th percentile for diabetes, is low income, and has a low percent of higher ed students? - score_name: Greater than or equal to the 90th percentile for asthma and is low income?
label: Greater than or equal to the 90th percentile for diabetes, is low income, and high percent of residents that are not higher ed students? label: Greater than or equal to the 90th percentile for asthma and is low income?
format: bool format: bool
- score_name: Diagnosed diabetes among adults aged greater than or equal to 18 years (percentile) - score_name: Current asthma among adults aged greater than or equal to 18 years (percentile)
label: Diagnosed diabetes among adults aged greater than or equal to 18 years (percentile) label: Current asthma among adults aged greater than or equal to 18 years (percentile)
format: percentage format: percentage
- score_name: Diagnosed diabetes among adults aged greater than or equal to 18 years - score_name: Current asthma among adults aged greater than or equal to 18 years
label: Diagnosed diabetes among adults aged greater than or equal to 18 years label: Current asthma among adults aged greater than or equal to 18 years
format: percentage format: percentage
- score_name: Greater than or equal to the 90th percentile for heart disease, is low income, and has a low percent of higher ed students? - score_name: Greater than or equal to the 90th percentile for diabetes and is low income?
label: Greater than or equal to the 90th percentile for heart disease, is low income, and high percent of residents that are not higher ed students? label: Greater than or equal to the 90th percentile for diabetes and is low income?
format: bool format: bool
- score_name: Coronary heart disease among adults aged greater than or equal to 18 years (percentile) - score_name: Diagnosed diabetes among adults aged greater than or equal to 18 years (percentile)
label: Coronary heart disease among adults aged greater than or equal to 18 years (percentile) label: Diagnosed diabetes among adults aged greater than or equal to 18 years (percentile)
format: percentage format: percentage
- score_name: Coronary heart disease among adults aged greater than or equal to 18 years - score_name: Diagnosed diabetes among adults aged greater than or equal to 18 years
label: Coronary heart disease among adults aged greater than or equal to 18 years label: Diagnosed diabetes among adults aged greater than or equal to 18 years
format: percentage format: percentage
- score_name: Greater than or equal to the 90th percentile for low life expectancy, is low income, and has a low percent of higher ed students? - score_name: Greater than or equal to the 90th percentile for heart disease and is low income?
label: Greater than or equal to the 90th percentile for low life expectancy, is low income, and high percent of residents that are not higher ed students? label: Greater than or equal to the 90th percentile for heart disease and is low income?
format: bool format: bool
- score_name: Low life expectancy (percentile) - score_name: Coronary heart disease among adults aged greater than or equal to 18 years (percentile)
label: Low life expectancy (percentile) label: Coronary heart disease among adults aged greater than or equal to 18 years (percentile)
format: percentage format: percentage
- score_name: Life expectancy (years) - score_name: Coronary heart disease among adults aged greater than or equal to 18 years
label: Life expectancy (years) label: Coronary heart disease among adults aged greater than or equal to 18 years
format: float format: percentage
- score_name: Greater than or equal to the 90th percentile for low median household income as a percent of area median income, has low HS attainment, and has a low percent of higher ed students? - score_name: Greater than or equal to the 90th percentile for low life expectancy and is low income?
label: Greater than or equal to the 90th percentile for low median household income as a percent of area median income, has low HS attainment, and high percent of residents that are not higher ed students? label: Greater than or equal to the 90th percentile for low life expectancy and is low income?
format: bool format: bool
- score_name: Low median household income as a percent of area median income (percentile) - score_name: Low life expectancy (percentile)
label: Low median household income as a percent of area median income (percentile) label: Low life expectancy (percentile)
format: percentage format: percentage
- score_name: Median household income as a percent of area median income - score_name: Life expectancy (years)
label: Median household income as a percent of area median income label: Life expectancy (years)
format: percentage format: float
- score_name: Greater than or equal to the 90th percentile for households in linguistic isolation, has low HS attainment, and has a low percent of higher ed students? - score_name: Greater than or equal to the 90th percentile for low median household income as a percent of area median income and has low HS attainment?
label: Greater than or equal to the 90th percentile for households in linguistic isolation, has low HS attainment, and high percent of residents that are not higher ed students? label: Greater than or equal to the 90th percentile for low median household income as a percent of area median income and has low HS attainment?
format: bool format: bool
- score_name: Linguistic isolation (percent) (percentile) - score_name: Low median household income as a percent of area median income (percentile)
label: Linguistic isolation (percent) (percentile) label: Low median household income as a percent of area median income (percentile)
format: percentage format: percentage
- score_name: Linguistic isolation (percent) - score_name: Median household income as a percent of area median income
label: Linguistic isolation (percent) label: Median household income as a percent of area median income
format: percentage format: percentage
- score_name: Greater than or equal to the 90th percentile for unemployment, has low HS attainment, and has a low percent of higher ed students? - score_name: Greater than or equal to the 90th percentile for households in linguistic isolation and has low HS attainment?
label: Greater than or equal to the 90th percentile for unemployment, has low HS attainment, and high percent of residents that are not higher ed students? label: Greater than or equal to the 90th percentile for households in linguistic isolation and has low HS attainment?
format: bool format: bool
- score_name: Unemployment (percent) (percentile) - score_name: Linguistic isolation (percent) (percentile)
label: Unemployment (percent) (percentile) label: Linguistic isolation (percent) (percentile)
format: percentage format: percentage
- score_name: Unemployment (percent) - score_name: Linguistic isolation (percent)
label: Unemployment (percent) label: Linguistic isolation (percent)
format: percentage format: percentage
- score_name: Greater than or equal to the 90th percentile for households at or below 100% federal poverty level, has low HS attainment, and has a low percent of higher ed students? - score_name: Greater than or equal to the 90th percentile for unemployment and has low HS attainment?
label: Greater than or equal to the 90th percentile for households at or below 100% federal poverty level, has low HS attainment, and high percent of residents that are not higher ed students? label: Greater than or equal to the 90th percentile for unemployment and has low HS attainment?
format: bool format: bool
- score_name: Percent of individuals below 200% Federal Poverty Line (percentile) - score_name: Unemployment (percent) (percentile)
label: Percent of individuals below 200% Federal Poverty Line (percentile) label: Unemployment (percent) (percentile)
format: percentage format: percentage
- score_name: Percent of individuals below 200% Federal Poverty Line - score_name: Unemployment (percent)
label: Percent of individuals below 200% Federal Poverty Line label: Unemployment (percent)
format: percentage format: percentage
- score_name: Percent of individuals < 100% Federal Poverty Line (percentile) - score_name: Greater than or equal to the 90th percentile for households at or below 100% federal poverty level and has low HS attainment?
label: Percent of individuals < 100% Federal Poverty Line (percentile) label: Greater than or equal to the 90th percentile for households at or below 100% federal poverty level and has low HS attainment?
format: percentage format: bool
- score_name: Percent of individuals < 100% Federal Poverty Line - score_name: Percent of individuals below 200% Federal Poverty Line (percentile)
label: Percent of individuals < 100% Federal Poverty Line label: Percent of individuals below 200% Federal Poverty Line (percentile)
format: percentage format: percentage
- score_name: Percent individuals age 25 or over with less than high school degree (percentile) - score_name: Percent of individuals below 200% Federal Poverty Line
label: Percent individuals age 25 or over with less than high school degree (percentile) label: Percent of individuals below 200% Federal Poverty Line
format: percentage format: percentage
- score_name: Percent individuals age 25 or over with less than high school degree - score_name: Percent of individuals < 100% Federal Poverty Line (percentile)
label: Percent individuals age 25 or over with less than high school degree label: Percent of individuals < 100% Federal Poverty Line (percentile)
format: percentage format: percentage
- score_name: Unemployment (percent) in 2009 (island areas) and 2010 (states and PR) - score_name: Percent of individuals < 100% Federal Poverty Line
label: Unemployment (percent) in 2009 (island areas) and 2010 (states and PR) label: Percent of individuals < 100% Federal Poverty Line
format: percentage format: percentage
- score_name: Percentage households below 100% of federal poverty line in 2009 (island areas) and 2010 (states and PR) - score_name: Percent individuals age 25 or over with less than high school degree (percentile)
label: Percentage households below 100% of federal poverty line in 2009 (island areas) and 2010 (states and PR) label: Percent individuals age 25 or over with less than high school degree (percentile)
format: percentage format: percentage
- score_name: Greater than or equal to the 90th percentile for unemployment and has low HS education in 2009 (island areas)? - score_name: Percent individuals age 25 or over with less than high school degree
label: Greater than or equal to the 90th percentile for unemployment and has low HS education in 2009 (island areas)? label: Percent individuals age 25 or over with less than high school degree
format: bool format: percentage
- score_name: Greater than or equal to the 90th percentile for households at or below 100% federal poverty level and has low HS education in 2009 (island areas)? - score_name: Percent of population not currently enrolled in college or graduate school
label: Greater than or equal to the 90th percentile for households at or below 100% federal poverty level and has low HS education in 2009 (island areas)? label: Percent of residents who are not currently enrolled in higher ed
format: bool format: percentage
- score_name: Greater than or equal to the 90th percentile for low median household income as a percent of area median income and has low HS education in 2009 (island areas)? - score_name: Unemployment (percent) in 2009 (island areas) and 2010 (states and PR)
label: Greater than or equal to the 90th percentile for low median household income as a percent of area median income and has low HS education in 2009 (island areas)? label: Unemployment (percent) in 2009 (island areas) and 2010 (states and PR)
format: bool format: percentage
- score_name: Percent of population not currently enrolled in college or graduate school - score_name: Greater than or equal to the 90th percentile for DOT transit barriers and is low income?
label: Percent of residents who are not currently enrolled in higher ed label: Greater than or equal to the 90th percentile for DOT transit barriers and is low income?
format: percentage format: bool
- score_name: Greater than or equal to the 90th percentile for leaky underground storage tanks and is low income? - score_name: DOT Travel Barriers Score (percentile)
label: Greater than or equal to the 90th percentile for leaky underground storage tanks and is low income? label: DOT Travel Barriers Score (percentile)
format: bool format: percentage
- score_name: Greater than or equal to the 90th percentile for DOT transit barriers and is low income? - score_name: Percentage households below 100% of federal poverty line in 2009 (island areas) and 2010 (states and PR)
label: Greater than or equal to the 90th percentile for DOT transit barriers and is low income? label: Percentage households below 100% of federal poverty line in 2009 (island areas) and 2010 (states and PR)
format: bool format: percentage
- score_name: DOT Travel Barriers Score (percentile) - score_name: Greater than or equal to the 90th percentile for unemployment and has low HS education in 2009 (island areas)?
label: DOT Travel Barriers Score (percentile) label: Greater than or equal to the 90th percentile for unemployment and has low HS education in 2009 (island areas)?
format: percentage format: bool
- score_name: Leaky underground storage tanks (percentile) - score_name: Greater than or equal to the 90th percentile for households at or below 100% federal poverty level and has low HS education in 2009 (island areas)?
label: Leaky underground storage tanks (percentile) label: Greater than or equal to the 90th percentile for households at or below 100% federal poverty level and has low HS education in 2009 (island areas)?
format: percentage format: bool
- score_name: Leaky underground storage tanks - score_name: Greater than or equal to the 90th percentile for low median household income as a percent of area median income and has low HS education in 2009 (island areas)?
label: Leaky underground storage tanks label: Greater than or equal to the 90th percentile for low median household income as a percent of area median income and has low HS education in 2009 (island areas)?
format: float format: bool
- score_name: Share of properties at risk of flood in 30 years - score_name: Share of properties at risk of flood in 30 years
label: Share of properties at risk of flood in 30 years label: Share of properties at risk of flood in 30 years
format: float format: percentage
- score_name: Share of properties at risk of fire in 30 years - score_name: Share of properties at risk of fire in 30 years
label: Share of properties at risk of fire in 30 years label: Share of properties at risk of fire in 30 years
format: float format: percentage
- score_name: Greater than or equal to the 90th percentile for share of properties at risk of flood in 30 years and is low income? - score_name: Greater than or equal to the 90th percentile for share of properties at risk of flood in 30 years and is low income?
label: Greater than or equal to the 90th percentile for share of properties at risk of flood in 30 years and is low income? label: Greater than or equal to the 90th percentile for share of properties at risk of flood in 30 years and is low income?
format: bool format: bool
- score_name: Greater than or equal to the 90th percentile for share of properties at risk of fire in 30 years and is low income? - score_name: Greater than or equal to the 90th percentile for share of properties at risk of fire in 30 years and is low income?
label: Greater than or equal to the 90th percentile for share of properties at risk of fire in 30 years and is low income? label: Greater than or equal to the 90th percentile for share of properties at risk of fire in 30 years and is low income?
format: bool format: bool
- score_name: Greater than or equal to the 90th percentile for share of properties at risk of flood in 30 years - score_name: Greater than or equal to the 90th percentile for share of properties at risk of flood in 30 years
label: Greater than or equal to the 90th percentile for share of properties at risk of flood in 30 years label: Greater than or equal to the 90th percentile for share of properties at risk of flood in 30 years
format: bool format: bool
- score_name: Greater than or equal to the 90th percentile for share of properties at risk of fire in 30 years - score_name: Greater than or equal to the 90th percentile for share of properties at risk of fire in 30 years
label: Greater than or equal to the 90th percentile for share of properties at risk of fire in 30 years label: Greater than or equal to the 90th percentile for share of properties at risk of fire in 30 years
format: bool format: bool
- score_name: Greater than or equal to the 90th percentile for share of the tract's land area that is covered by impervious surface or cropland as a percent and is low income? - score_name: Greater than or equal to the 90th percentile for share of the tract's land area that is covered by impervious surface or cropland as a percent and is low income?
label: Greater than or equal to the 90th percentile for share of the tract's land area that is covered by impervious surface or cropland as a percent and is low income? label: Greater than or equal to the 90th percentile for share of the tract's land area that is covered by impervious surface or cropland as a percent and is low income?
format: bool format: bool
- score_name: Greater than or equal to the 90th percentile for share of the tract's land area that is covered by impervious surface or cropland as a percent - score_name: Greater than or equal to the 90th percentile for share of the tract's land area that is covered by impervious surface or cropland as a percent
label: Greater than or equal to the 90th percentile for share of the tract's land area that is covered by impervious surface or cropland as a percent label: Greater than or equal to the 90th percentile for share of the tract's land area that is covered by impervious surface or cropland as a percent
format: bool format: bool
- score_name: Share of the tract's land area that is covered by impervious surface or cropland as a percent - score_name: Share of the tract's land area that is covered by impervious surface or cropland as a percent
label: Share of the tract's land area that is covered by impervious surface or cropland as a percent label: Share of the tract's land area that is covered by impervious surface or cropland as a percent
format: percentage format: percentage
- score_name: Share of the tract's land area that is covered by impervious surface or cropland as a percent (percentile) - score_name: Share of the tract's land area that is covered by impervious surface or cropland as a percent (percentile)
label: Share of the tract's land area that is covered by impervious surface or cropland as a percent (percentile) label: Share of the tract's land area that is covered by impervious surface or cropland as a percent (percentile)
format: percentage format: percentage
- score_name: Share of properties at risk of flood in 30 years (percentile) - score_name: Share of properties at risk of flood in 30 years (percentile)
label: Share of properties at risk of flood in 30 years (percentile) label: Share of properties at risk of flood in 30 years (percentile)
format: percentage format: percentage
- score_name: Share of properties at risk of fire in 30 years (percentile) - score_name: Share of properties at risk of fire in 30 years (percentile)
label: Share of properties at risk of fire in 30 years (percentile) label: Share of properties at risk of fire in 30 years (percentile)
format: percentage format: percentage
- score_name: Does the tract have at least 35 acres in it? - score_name: Does the tract have at least 35 acres in it?
label: Does the tract have at least 35 acres in it? label: Does the tract have at least 35 acres in it?
format: bool format: bool
- score_name: Is there at least one Formerly Used Defense Site (FUDS) in the tract? - score_name: Is there at least one Formerly Used Defense Site (FUDS) in the tract?
label: Is there at least one Formerly Used Defense Site (FUDS) in the tract? label: Is there at least one Formerly Used Defense Site (FUDS) in the tract?
format: bool format: bool
- score_name: Is there at least one abandoned mine in this census tract? - score_name: Is there at least one abandoned mine in this census tract?
label: Is there at least one abandoned mine in this census tract? label: Is there at least one abandoned mine in this census tract?
format: bool format: bool
- score_name: There is at least one abandoned mine in this census tract and the tract is low income. - score_name: There is at least one abandoned mine in this census tract and the tract is low income.
label: There is at least one abandoned mine in this census tract and the tract is low income. label: There is at least one abandoned mine in this census tract and the tract is low income.
format: bool format: bool
- score_name: There is at least one Formerly Used Defense Site (FUDS) in the tract and the tract is low income. - score_name: There is at least one Formerly Used Defense Site (FUDS) in the tract and the tract is low income.
label: There is at least one Formerly Used Defense Site (FUDS) in the tract and the tract is low income. label: There is at least one Formerly Used Defense Site (FUDS) in the tract and the tract is low income.
format: bool format: bool
- score_name: Tract-level redlining score meets or exceeds 3.25 and is low income - score_name: Tract-level redlining score meets or exceeds 3.25 and is low income
label: Tract experienced historic underinvestment and remains low income label: Tract experienced historic underinvestment and remains low income
format: bool format: bool
- score_name: Tract-level redlining score meets or exceeds 3.25 - score_name: Tract-level redlining score meets or exceeds 3.25
label: Tract experienced historic underinvestment label: Tract experienced historic underinvestment
format: bool format: bool
- score_name: Income data has been estimated based on neighbor income - score_name: Income data has been estimated based on neighbor income
label: Income data has been estimated based on geographic neighbor income label: Income data has been estimated based on geographic neighbor income
format: bool format: bool

90
data/data-pipeline/data_pipeline/content/config/excel.yml
View file

@ -63,7 +63,7 @@ sheets:
- score_name: Definition N (communities) (based on adjacency index and low income alone) - score_name: Definition N (communities) (based on adjacency index and low income alone)
label: Identified as disadvantaged based on neighbors and relaxed low income threshold only label: Identified as disadvantaged based on neighbors and relaxed low income threshold only
format: bool format: bool
- score_name: Definition M community, including adjacency index tracts - score_name: Definition N community, including adjacency index tracts
label: Identified as disadvantaged label: Identified as disadvantaged
format: bool format: bool
- score_name: Definition N (communities) (average of neighbors) - score_name: Definition N (communities) (average of neighbors)
@ -75,11 +75,11 @@ sheets:
- score_name: Percent of individuals below 200% Federal Poverty Line, imputed and adjusted - score_name: Percent of individuals below 200% Federal Poverty Line, imputed and adjusted
label: Adjusted percent of individuals below 200% Federal Poverty Line label: Adjusted percent of individuals below 200% Federal Poverty Line
format: float format: float
- score_name: Is low income and has a low percent of higher ed students? - score_name: Is low income (imputed and adjusted)?
label: Is low income and high percent of residents that are not higher ed students? label: Is low income?
format: bool format: bool
- score_name: Greater than or equal to the 90th percentile for expected agriculture loss rate, is low income, and has a low percent of higher ed students? - score_name: Greater than or equal to the 90th percentile for expected agriculture loss rate and is low income?
label: Greater than or equal to the 90th percentile for expected agriculture loss rate, is low income, and high percent of residents that are not higher ed students? label: Greater than or equal to the 90th percentile for expected agriculture loss rate and is low income?
format: bool format: bool
- score_name: Expected agricultural loss rate (Natural Hazards Risk Index) (percentile) - score_name: Expected agricultural loss rate (Natural Hazards Risk Index) (percentile)
label: Expected agricultural loss rate (Natural Hazards Risk Index) (percentile) label: Expected agricultural loss rate (Natural Hazards Risk Index) (percentile)
@ -87,8 +87,8 @@ sheets:
- score_name: Expected agricultural loss rate (Natural Hazards Risk Index) - score_name: Expected agricultural loss rate (Natural Hazards Risk Index)
label: Expected agricultural loss rate (Natural Hazards Risk Index) label: Expected agricultural loss rate (Natural Hazards Risk Index)
format: loss_rate_percentage format: loss_rate_percentage
- score_name: Greater than or equal to the 90th percentile for expected building loss rate, is low income, and has a low percent of higher ed students? - score_name: Greater than or equal to the 90th percentile for expected building loss rate and is low income?
label: Greater than or equal to the 90th percentile for expected building loss rate, is low income, and high percent of residents that are not higher ed students? label: Greater than or equal to the 90th percentile for expected building loss rate and is low income?
format: bool format: bool
- score_name: Expected building loss rate (Natural Hazards Risk Index) (percentile) - score_name: Expected building loss rate (Natural Hazards Risk Index) (percentile)
label: Expected building loss rate (Natural Hazards Risk Index) (percentile) label: Expected building loss rate (Natural Hazards Risk Index) (percentile)
@ -96,8 +96,8 @@ sheets:
- score_name: Expected building loss rate (Natural Hazards Risk Index) - score_name: Expected building loss rate (Natural Hazards Risk Index)
label: Expected building loss rate (Natural Hazards Risk Index) label: Expected building loss rate (Natural Hazards Risk Index)
format: loss_rate_percentage format: loss_rate_percentage
- score_name: Greater than or equal to the 90th percentile for expected population loss rate, is low income, and has a low percent of higher ed students? - score_name: Greater than or equal to the 90th percentile for expected population loss rate and is low income?
label: Greater than or equal to the 90th percentile for expected population loss rate, is low income, and high percent of residents that are not higher ed students? label: Greater than or equal to the 90th percentile for expected population loss rate and is low income?
format: bool format: bool
- score_name: Expected population loss rate (Natural Hazards Risk Index) (percentile) - score_name: Expected population loss rate (Natural Hazards Risk Index) (percentile)
label: Expected population loss rate (Natural Hazards Risk Index) (percentile) label: Expected population loss rate (Natural Hazards Risk Index) (percentile)
@ -105,8 +105,8 @@ sheets:
- score_name: Expected population loss rate (Natural Hazards Risk Index) - score_name: Expected population loss rate (Natural Hazards Risk Index)
label: Expected population loss rate (Natural Hazards Risk Index) label: Expected population loss rate (Natural Hazards Risk Index)
format: loss_rate_percentage format: loss_rate_percentage
- score_name: Greater than or equal to the 90th percentile for energy burden, is low income, and has a low percent of higher ed students? - score_name: Greater than or equal to the 90th percentile for energy burden and is low income?
label: Greater than or equal to the 90th percentile for energy burden, is low income, and high percent of residents that are not higher ed students? label: Greater than or equal to the 90th percentile for energy burden and is low income?
format: bool format: bool
- score_name: Energy burden (percentile) - score_name: Energy burden (percentile)
label: Energy burden (percentile) label: Energy burden (percentile)
@ -114,8 +114,8 @@ sheets:
- score_name: Energy burden - score_name: Energy burden
label: Energy burden label: Energy burden
format: percentage format: percentage
- score_name: Greater than or equal to the 90th percentile for PM2.5 exposure, is low income, and has a low percent of higher ed students? - score_name: Greater than or equal to the 90th percentile for PM2.5 exposure and is low income?
label: Greater than or equal to the 90th percentile for PM2.5 exposure, is low income, and high percent of residents that are not higher ed students? label: Greater than or equal to the 90th percentile for PM2.5 exposure and is low income?
format: bool format: bool
- score_name: PM2.5 in the air (percentile) - score_name: PM2.5 in the air (percentile)
label: PM2.5 in the air (percentile) label: PM2.5 in the air (percentile)
@ -123,8 +123,8 @@ sheets:
- score_name: PM2.5 in the air - score_name: PM2.5 in the air
label: PM2.5 in the air label: PM2.5 in the air
format: float format: float
- score_name: Greater than or equal to the 90th percentile for diesel particulate matter, is low income, and has a low percent of higher ed students? - score_name: Greater than or equal to the 90th percentile for diesel particulate matter and is low income?
label: Greater than or equal to the 90th percentile for diesel particulate matter, is low income, and high percent of residents that are not higher ed students? label: Greater than or equal to the 90th percentile for diesel particulate matter and is low income?
format: bool format: bool
- score_name: Diesel particulate matter exposure (percentile) - score_name: Diesel particulate matter exposure (percentile)
label: Diesel particulate matter exposure (percentile) label: Diesel particulate matter exposure (percentile)
@ -132,8 +132,8 @@ sheets:
- score_name: Diesel particulate matter exposure - score_name: Diesel particulate matter exposure
label: Diesel particulate matter exposure label: Diesel particulate matter exposure
format: float format: float
- score_name: Greater than or equal to the 90th percentile for traffic proximity, is low income, and has a low percent of higher ed students? - score_name: Greater than or equal to the 90th percentile for traffic proximity and is low income?
label: Greater than or equal to the 90th percentile for traffic proximity, is low income, and high percent of residents that are not higher ed students? label: Greater than or equal to the 90th percentile for traffic proximity and is low income?
format: bool format: bool
- score_name: Traffic proximity and volume (percentile) - score_name: Traffic proximity and volume (percentile)
label: Traffic proximity and volume (percentile) label: Traffic proximity and volume (percentile)
@ -141,8 +141,8 @@ sheets:
- score_name: Traffic proximity and volume - score_name: Traffic proximity and volume
label: Traffic proximity and volume label: Traffic proximity and volume
format: float format: float
- score_name: Greater than or equal to the 90th percentile for housing burden, is low income, and has a low percent of higher ed students? - score_name: Greater than or equal to the 90th percentile for housing burden and is low income?
label: Greater than or equal to the 90th percentile for housing burden, is low income, and high percent of residents that are not higher ed students? label: Greater than or equal to the 90th percentile for housing burden and is low income?
format: bool format: bool
- score_name: Housing burden (percent) (percentile) - score_name: Housing burden (percent) (percentile)
label: Housing burden (percent) (percentile) label: Housing burden (percent) (percentile)
@ -150,8 +150,8 @@ sheets:
- score_name: Housing burden (percent) - score_name: Housing burden (percent)
label: Housing burden (percent) label: Housing burden (percent)
format: percentage format: percentage
- score_name: Greater than or equal to the 90th percentile for lead paint, the median house value is less than 90th percentile, is low income, and has a low percent of higher ed students? - score_name: Greater than or equal to the 90th percentile for lead paint and the median house value is less than 90th percentile and is low income?
label: Greater than or equal to the 90th percentile for lead paint, the median house value is less than 90th percentile, is low income, and high percent of residents that are not higher ed students? label: Greater than or equal to the 90th percentile for lead paint, the median house value is less than 90th percentile and is low income?
format: bool format: bool
- score_name: Percent pre-1960s housing (lead paint indicator) (percentile) - score_name: Percent pre-1960s housing (lead paint indicator) (percentile)
label: Percent pre-1960s housing (lead paint indicator) (percentile) label: Percent pre-1960s housing (lead paint indicator) (percentile)
@ -165,8 +165,8 @@ sheets:
- score_name: Median value ($) of owner-occupied housing units - score_name: Median value ($) of owner-occupied housing units
label: Median value ($) of owner-occupied housing units label: Median value ($) of owner-occupied housing units
format: float format: float
- score_name: Greater than or equal to the 90th percentile for proximity to hazardous waste facilities, is low income, and has a low percent of higher ed students? - score_name: Greater than or equal to the 90th percentile for proximity to hazardous waste facilities and is low income?
label: Greater than or equal to the 90th percentile for proximity to hazardous waste facilities, is low income, and high percent of residents that are not higher ed students? label: Greater than or equal to the 90th percentile for proximity to hazardous waste facilities and is low income?
format: bool format: bool
- score_name: Proximity to hazardous waste sites (percentile) - score_name: Proximity to hazardous waste sites (percentile)
label: Proximity to hazardous waste sites (percentile) label: Proximity to hazardous waste sites (percentile)
@ -174,8 +174,8 @@ sheets:
- score_name: Proximity to hazardous waste sites - score_name: Proximity to hazardous waste sites
label: Proximity to hazardous waste sites label: Proximity to hazardous waste sites
format: float format: float
- score_name: Greater than or equal to the 90th percentile for proximity to superfund sites, is low income, and has a low percent of higher ed students? - score_name: Greater than or equal to the 90th percentile for proximity to superfund sites and is low income?
label: Greater than or equal to the 90th percentile for proximity to superfund sites, is low income, and high percent of residents that are not higher ed students? label: Greater than or equal to the 90th percentile for proximity to superfund sites and is low income?
format: bool format: bool
- score_name: Proximity to NPL sites (percentile) - score_name: Proximity to NPL sites (percentile)
label: Proximity to NPL (Superfund) sites (percentile) label: Proximity to NPL (Superfund) sites (percentile)
@ -183,8 +183,8 @@ sheets:
- score_name: Proximity to NPL sites - score_name: Proximity to NPL sites
label: Proximity to NPL (Superfund) sites label: Proximity to NPL (Superfund) sites
format: float format: float
- score_name: Greater than or equal to the 90th percentile for proximity to RMP sites, is low income, and has a low percent of higher ed students? - score_name: Greater than or equal to the 90th percentile for proximity to RMP sites and is low income?
label: Greater than or equal to the 90th percentile for proximity to RMP sites, is low income, and high percent of residents that are not higher ed students? label: Greater than or equal to the 90th percentile for proximity to RMP sites and is low income?
format: bool format: bool
- score_name: Proximity to Risk Management Plan (RMP) facilities (percentile) - score_name: Proximity to Risk Management Plan (RMP) facilities (percentile)
label: Proximity to Risk Management Plan (RMP) facilities (percentile) label: Proximity to Risk Management Plan (RMP) facilities (percentile)
@ -192,8 +192,8 @@ sheets:
- score_name: Proximity to Risk Management Plan (RMP) facilities - score_name: Proximity to Risk Management Plan (RMP) facilities
label: Proximity to Risk Management Plan (RMP) facilities label: Proximity to Risk Management Plan (RMP) facilities
format: float format: float
- score_name: Greater than or equal to the 90th percentile for wastewater discharge, is low income, and has a low percent of higher ed students? - score_name: Greater than or equal to the 90th percentile for wastewater discharge and is low income?
label: Greater than or equal to the 90th percentile for wastewater discharge, is low income, and high percent of residents that are not higher ed students? label: Greater than or equal to the 90th percentile for wastewater discharge and is low income?
format: bool format: bool
- score_name: Greater than or equal to the 90th percentile for leaky underground storage tanks and is low income? - score_name: Greater than or equal to the 90th percentile for leaky underground storage tanks and is low income?
label: Greater than or equal to the 90th percentile for leaky underground storage tanks and is low income? label: Greater than or equal to the 90th percentile for leaky underground storage tanks and is low income?
@ -210,8 +210,8 @@ sheets:
- score_name: Leaky underground storage tanks - score_name: Leaky underground storage tanks
label: Leaky underground storage tanks label: Leaky underground storage tanks
format: float format: float
- score_name: Greater than or equal to the 90th percentile for asthma, is low income, and has a low percent of higher ed students? - score_name: Greater than or equal to the 90th percentile for asthma and is low income?
label: Greater than or equal to the 90th percentile for asthma, is low income, and high percent of residents that are not higher ed students? label: Greater than or equal to the 90th percentile for asthma and is low income?
format: bool format: bool
- score_name: Current asthma among adults aged greater than or equal to 18 years (percentile) - score_name: Current asthma among adults aged greater than or equal to 18 years (percentile)
label: Current asthma among adults aged greater than or equal to 18 years (percentile) label: Current asthma among adults aged greater than or equal to 18 years (percentile)
@ -219,8 +219,8 @@ sheets:
- score_name: Current asthma among adults aged greater than or equal to 18 years - score_name: Current asthma among adults aged greater than or equal to 18 years
label: Current asthma among adults aged greater than or equal to 18 years label: Current asthma among adults aged greater than or equal to 18 years
format: percentage format: percentage
- score_name: Greater than or equal to the 90th percentile for diabetes, is low income, and has a low percent of higher ed students? - score_name: Greater than or equal to the 90th percentile for diabetes and is low income?
label: Greater than or equal to the 90th percentile for diabetes, is low income, and high percent of residents that are not higher ed students? label: Greater than or equal to the 90th percentile for diabetes and is low income?
format: bool format: bool
- score_name: Diagnosed diabetes among adults aged greater than or equal to 18 years (percentile) - score_name: Diagnosed diabetes among adults aged greater than or equal to 18 years (percentile)
label: Diagnosed diabetes among adults aged greater than or equal to 18 years (percentile) label: Diagnosed diabetes among adults aged greater than or equal to 18 years (percentile)
@ -228,8 +228,8 @@ sheets:
- score_name: Diagnosed diabetes among adults aged greater than or equal to 18 years - score_name: Diagnosed diabetes among adults aged greater than or equal to 18 years
label: Diagnosed diabetes among adults aged greater than or equal to 18 years label: Diagnosed diabetes among adults aged greater than or equal to 18 years
format: percentage format: percentage
- score_name: Greater than or equal to the 90th percentile for heart disease, is low income, and has a low percent of higher ed students? - score_name: Greater than or equal to the 90th percentile for heart disease and is low income?
label: Greater than or equal to the 90th percentile for heart disease, is low income, and high percent of residents that are not higher ed students? label: Greater than or equal to the 90th percentile for heart disease and is low income?
format: bool format: bool
- score_name: Coronary heart disease among adults aged greater than or equal to 18 years (percentile) - score_name: Coronary heart disease among adults aged greater than or equal to 18 years (percentile)
label: Coronary heart disease among adults aged greater than or equal to 18 years (percentile) label: Coronary heart disease among adults aged greater than or equal to 18 years (percentile)
@ -237,8 +237,8 @@ sheets:
- score_name: Coronary heart disease among adults aged greater than or equal to 18 years - score_name: Coronary heart disease among adults aged greater than or equal to 18 years
label: Coronary heart disease among adults aged greater than or equal to 18 years label: Coronary heart disease among adults aged greater than or equal to 18 years
format: percentage format: percentage
- score_name: Greater than or equal to the 90th percentile for low life expectancy, is low income, and has a low percent of higher ed students? - score_name: Greater than or equal to the 90th percentile for low life expectancy and is low income?
label: Greater than or equal to the 90th percentile for low life expectancy, is low income, and high percent of residents that are not higher ed students? label: Greater than or equal to the 90th percentile for low life expectancy and is low income?
format: bool format: bool
- score_name: Low life expectancy (percentile) - score_name: Low life expectancy (percentile)
label: Low life expectancy (percentile) label: Low life expectancy (percentile)
@ -246,8 +246,8 @@ sheets:
- score_name: Life expectancy (years) - score_name: Life expectancy (years)
label: Life expectancy (years) label: Life expectancy (years)
format: float format: float
- score_name: Greater than or equal to the 90th percentile for low median household income as a percent of area median income, has low HS attainment, and has a low percent of higher ed students? - score_name: Greater than or equal to the 90th percentile for low median household income as a percent of area median income and has low HS attainment?
label: Greater than or equal to the 90th percentile for low median household income as a percent of area median income, has low HS attainment, and high percent of residents that are not higher ed students? label: Greater than or equal to the 90th percentile for low median household income as a percent of area median income and has low HS attainment?
format: bool format: bool
- score_name: Low median household income as a percent of area median income (percentile) - score_name: Low median household income as a percent of area median income (percentile)
label: Low median household income as a percent of area median income (percentile) label: Low median household income as a percent of area median income (percentile)
@ -255,8 +255,8 @@ sheets:
- score_name: Median household income as a percent of area median income - score_name: Median household income as a percent of area median income
label: Median household income as a percent of area median income label: Median household income as a percent of area median income
format: percentage format: percentage
- score_name: Greater than or equal to the 90th percentile for households in linguistic isolation, has low HS attainment, and has a low percent of higher ed students? - score_name: Greater than or equal to the 90th percentile for households in linguistic isolation and has low HS attainment?
label: Greater than or equal to the 90th percentile for households in linguistic isolation, has low HS attainment, and high percent of residents that are not higher ed students? label: Greater than or equal to the 90th percentile for households in linguistic isolation and has low HS attainment?
format: bool format: bool
- score_name: Linguistic isolation (percent) (percentile) - score_name: Linguistic isolation (percent) (percentile)
label: Linguistic isolation (percent) (percentile) label: Linguistic isolation (percent) (percentile)
@ -264,8 +264,8 @@ sheets:
- score_name: Linguistic isolation (percent) - score_name: Linguistic isolation (percent)
label: Linguistic isolation (percent) label: Linguistic isolation (percent)
format: percentage format: percentage
- score_name: Greater than or equal to the 90th percentile for unemployment, has low HS attainment, and has a low percent of higher ed students? - score_name: Greater than or equal to the 90th percentile for unemployment and has low HS attainment?
label: Greater than or equal to the 90th percentile for unemployment, has low HS attainment, and high percent of residents that are not higher ed students? label: Greater than or equal to the 90th percentile for unemployment and has low HS attainment?
format: bool format: bool
- score_name: Unemployment (percent) (percentile) - score_name: Unemployment (percent) (percentile)
label: Unemployment (percent) (percentile) label: Unemployment (percent) (percentile)
@ -273,8 +273,8 @@ sheets:
- score_name: Unemployment (percent) - score_name: Unemployment (percent)
label: Unemployment (percent) label: Unemployment (percent)
format: percentage format: percentage
- score_name: Greater than or equal to the 90th percentile for households at or below 100% federal poverty level, has low HS attainment, and has a low percent of higher ed students? - score_name: Greater than or equal to the 90th percentile for households at or below 100% federal poverty level and has low HS attainment?
label: Greater than or equal to the 90th percentile for households at or below 100% federal poverty level, has low HS attainment, and high percent of residents that are not higher ed students? label: Greater than or equal to the 90th percentile for households at or below 100% federal poverty level and has low HS attainment?
format: bool format: bool
- score_name: Percent of individuals below 200% Federal Poverty Line (percentile) - score_name: Percent of individuals below 200% Federal Poverty Line (percentile)
label: Percent of individuals below 200% Federal Poverty Line (percentile) label: Percent of individuals below 200% Federal Poverty Line (percentile)

798
data/data-pipeline/data_pipeline/content/config/scratch.ipynb Normal file
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@ -0,0 +1,798 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "cf8f39b0-7735-4f7c-9178-61bbf2257951",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"\n",
"%load_ext lab_black"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "66639c20-be5e-4bf6-9b58-98338874f7cc",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'Median value ($) of owner-occupied housing units (percentile)'"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"check = pd.read_csv(\n",
" \"/Users/emmausds/j40/data_pipeline/data/score/downloadable/codebook.csv\"\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "5e525e4e-6764-4d4d-9119-b4d400ba022f",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>score_name</th>\n",
" <th>csv_field_type</th>\n",
" <th>csv_label</th>\n",
" <th>excel_label</th>\n",
" <th>calculation_notes</th>\n",
" <th>threshold_category</th>\n",
" <th>notes</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>GEOID10_TRACT</td>\n",
" <td>string</td>\n",
" <td>Census tract ID</td>\n",
" <td>Census tract ID</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>County Name</td>\n",
" <td>string</td>\n",
" <td>County Name</td>\n",
" <td>County Name</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>State/Territory</td>\n",
" <td>string</td>\n",
" <td>State/Territory</td>\n",
" <td>State/Territory</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Total threshold criteria exceeded</td>\n",
" <td>int64</td>\n",
" <td>Total threshold criteria exceeded</td>\n",
" <td>Total threshold criteria exceeded</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Lists out the total number of criteria (where ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Definition M (communities)</td>\n",
" <td>bool</td>\n",
" <td>Identified as disadvantaged</td>\n",
" <td>Identified as disadvantaged</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>True / False variable for whether a tract is a...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>77</th>\n",
" <td>Percentage households below 100% of federal po...</td>\n",
" <td>percentage</td>\n",
" <td>Percentage households below 100% of federal po...</td>\n",
" <td>Percentage households below 100% of federal po...</td>\n",
" <td>Because not all data is available for the Nati...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>78</th>\n",
" <td>Greater than or equal to the 90th percentile f...</td>\n",
" <td>bool</td>\n",
" <td>Greater than or equal to the 90th percentile f...</td>\n",
" <td>Greater than or equal to the 90th percentile f...</td>\n",
" <td>Because not all data is available for the Nati...</td>\n",
" <td>training and workforce development</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>79</th>\n",
" <td>Greater than or equal to the 90th percentile f...</td>\n",
" <td>bool</td>\n",
" <td>Greater than or equal to the 90th percentile f...</td>\n",
" <td>Greater than or equal to the 90th percentile f...</td>\n",
" <td>Because not all data is available for the Nati...</td>\n",
" <td>training and workforce development</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>80</th>\n",
" <td>Greater than or equal to the 90th percentile f...</td>\n",
" <td>bool</td>\n",
" <td>Greater than or equal to the 90th percentile f...</td>\n",
" <td>Greater than or equal to the 90th percentile f...</td>\n",
" <td>Because not all data is available for the Nati...</td>\n",
" <td>training and workforce development</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>81</th>\n",
" <td>Percent of population not currently enrolled i...</td>\n",
" <td>percentage</td>\n",
" <td>Percent of residents who are not currently enr...</td>\n",
" <td>Percent of residents who are not currently enr...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>82 rows × 7 columns</p>\n",
"</div>"
],
"text/plain": [
" score_name csv_field_type \\\n",
"0 GEOID10_TRACT string \n",
"1 County Name string \n",
"2 State/Territory string \n",
"3 Total threshold criteria exceeded int64 \n",
"4 Definition M (communities) bool \n",
".. ... ... \n",
"77 Percentage households below 100% of federal po... percentage \n",
"78 Greater than or equal to the 90th percentile f... bool \n",
"79 Greater than or equal to the 90th percentile f... bool \n",
"80 Greater than or equal to the 90th percentile f... bool \n",
"81 Percent of population not currently enrolled i... percentage \n",
"\n",
" csv_label \\\n",
"0 Census tract ID \n",
"1 County Name \n",
"2 State/Territory \n",
"3 Total threshold criteria exceeded \n",
"4 Identified as disadvantaged \n",
".. ... \n",
"77 Percentage households below 100% of federal po... \n",
"78 Greater than or equal to the 90th percentile f... \n",
"79 Greater than or equal to the 90th percentile f... \n",
"80 Greater than or equal to the 90th percentile f... \n",
"81 Percent of residents who are not currently enr... \n",
"\n",
" excel_label \\\n",
"0 Census tract ID \n",
"1 County Name \n",
"2 State/Territory \n",
"3 Total threshold criteria exceeded \n",
"4 Identified as disadvantaged \n",
".. ... \n",
"77 Percentage households below 100% of federal po... \n",
"78 Greater than or equal to the 90th percentile f... \n",
"79 Greater than or equal to the 90th percentile f... \n",
"80 Greater than or equal to the 90th percentile f... \n",
"81 Percent of residents who are not currently enr... \n",
"\n",
" calculation_notes \\\n",
"0 NaN \n",
"1 NaN \n",
"2 NaN \n",
"3 NaN \n",
"4 NaN \n",
".. ... \n",
"77 Because not all data is available for the Nati... \n",
"78 Because not all data is available for the Nati... \n",
"79 Because not all data is available for the Nati... \n",
"80 Because not all data is available for the Nati... \n",
"81 NaN \n",
"\n",
" threshold_category \\\n",
"0 NaN \n",
"1 NaN \n",
"2 NaN \n",
"3 NaN \n",
"4 NaN \n",
".. ... \n",
"77 NaN \n",
"78 training and workforce development \n",
"79 training and workforce development \n",
"80 training and workforce development \n",
"81 NaN \n",
"\n",
" notes \n",
"0 NaN \n",
"1 NaN \n",
"2 NaN \n",
"3 Lists out the total number of criteria (where ... \n",
"4 True / False variable for whether a tract is a... \n",
".. ... \n",
"77 NaN \n",
"78 NaN \n",
"79 NaN \n",
"80 NaN \n",
"81 NaN \n",
"\n",
"[82 rows x 7 columns]"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"check"
]
},
{
"cell_type": "code",
"execution_count": 28,
"id": "d86c867a-1a55-4ec0-82a6-040841406236",
"metadata": {},
"outputs": [],
"source": [
"codebook = pd.DataFrame(to_frame_dict)"
]
},
{
"cell_type": "code",
"execution_count": 62,
"id": "6215deaf-b004-4da0-a70b-bc54f636601a",
"metadata": {},
"outputs": [],
"source": [
"details_to_merge = pd.DataFrame(mapping_dictionary)"
]
},
{
"cell_type": "code",
"execution_count": 69,
"id": "ac4e65c2-09e6-4978-9440-37b3be057f65",
"metadata": {},
"outputs": [],
"source": [
"shapefile_codes = pd.read_csv(\n",
" \"/Users/emmausds/j40/data_pipeline/data/score/shapefile/columns.csv\"\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 153,
"id": "31cfd9ec-5f5f-4642-a51f-6875c2c279a4",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['Expected agricultural loss rate (Natural Hazards Risk Index) (percentile)',\n",
" 'Expected building loss rate (Natural Hazards Risk Index) (percentile)',\n",
" 'Expected population loss rate (Natural Hazards Risk Index) (percentile)',\n",
" 'Energy burden (percentile)',\n",
" 'PM2.5 in the air (percentile)',\n",
" 'Diesel particulate matter exposure (percentile)',\n",
" 'Traffic proximity and volume (percentile)',\n",
" 'Housing burden (percent) (percentile)',\n",
" 'Percent pre-1960s housing (lead paint indicator) (percentile)',\n",
" 'Median value ($) of owner-occupied housing units (percentile)',\n",
" 'Proximity to hazardous waste sites (percentile)',\n",
" 'Proximity to NPL sites (percentile)',\n",
" 'Proximity to Risk Management Plan (RMP) facilities (percentile)',\n",
" 'Wastewater discharge (percentile)',\n",
" 'Current asthma among adults aged greater than or equal to 18 years (percentile)',\n",
" 'Diagnosed diabetes among adults aged greater than or equal to 18 years (percentile)',\n",
" 'Coronary heart disease among adults aged greater than or equal to 18 years (percentile)',\n",
" 'Low life expectancy (percentile)',\n",
" 'Low median household income as a percent of area median income (percentile)',\n",
" 'Linguistic isolation (percent) (percentile)',\n",
" 'Unemployment (percent) (percentile)',\n",
" 'Percent of individuals below 200% Federal Poverty Line (percentile)',\n",
" 'Percent of individuals < 100% Federal Poverty Line (percentile)',\n",
" 'Percent individuals age 25 or over with less than high school degree (percentile)',\n",
" 'Definition M (percentile)',\n",
" 'Low median household income as a percent of territory median income in 2009 (percentile)',\n",
" 'Percentage households below 100% of federal poverty line in 2009 for island areas (percentile)',\n",
" 'Unemployment (percent) in 2009 for island areas (percentile)']"
]
},
"execution_count": 153,
"metadata": {},
"output_type": "execute_result"
}
],
"source": []
},
{
"cell_type": "code",
"execution_count": 154,
"id": "66dde4fc-48e6-4bdf-b3a6-16c766e94d8a",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" - column_name: Expected agricultural loss rate (Natural Hazards Risk Index) (percentile)\n",
" notes: all percentiles are floored (rounded down to the nearest percentile). For example, 89.7th percentile is rounded down to 89 for this field.\n",
" - column_name: Expected building loss rate (Natural Hazards Risk Index) (percentile)\n",
" notes: all percentiles are floored (rounded down to the nearest percentile). For example, 89.7th percentile is rounded down to 89 for this field.\n",
" - column_name: Expected population loss rate (Natural Hazards Risk Index) (percentile)\n",
" notes: all percentiles are floored (rounded down to the nearest percentile). For example, 89.7th percentile is rounded down to 89 for this field.\n",
" - column_name: Energy burden (percentile)\n",
" notes: all percentiles are floored (rounded down to the nearest percentile). For example, 89.7th percentile is rounded down to 89 for this field.\n",
" - column_name: PM2.5 in the air (percentile)\n",
" notes: all percentiles are floored (rounded down to the nearest percentile). For example, 89.7th percentile is rounded down to 89 for this field.\n",
" - column_name: Diesel particulate matter exposure (percentile)\n",
" notes: all percentiles are floored (rounded down to the nearest percentile). For example, 89.7th percentile is rounded down to 89 for this field.\n",
" - column_name: Traffic proximity and volume (percentile)\n",
" notes: all percentiles are floored (rounded down to the nearest percentile). For example, 89.7th percentile is rounded down to 89 for this field.\n",
" - column_name: Housing burden (percent) (percentile)\n",
" notes: all percentiles are floored (rounded down to the nearest percentile). For example, 89.7th percentile is rounded down to 89 for this field.\n",
" - column_name: Percent pre-1960s housing (lead paint indicator) (percentile)\n",
" notes: all percentiles are floored (rounded down to the nearest percentile). For example, 89.7th percentile is rounded down to 89 for this field.\n",
" - column_name: Median value ($) of owner-occupied housing units (percentile)\n",
" notes: all percentiles are floored (rounded down to the nearest percentile). For example, 89.7th percentile is rounded down to 89 for this field.\n",
" - column_name: Proximity to hazardous waste sites (percentile)\n",
" notes: all percentiles are floored (rounded down to the nearest percentile). For example, 89.7th percentile is rounded down to 89 for this field.\n",
" - column_name: Proximity to NPL sites (percentile)\n",
" notes: all percentiles are floored (rounded down to the nearest percentile). For example, 89.7th percentile is rounded down to 89 for this field.\n",
" - column_name: Proximity to Risk Management Plan (RMP) facilities (percentile)\n",
" notes: all percentiles are floored (rounded down to the nearest percentile). For example, 89.7th percentile is rounded down to 89 for this field.\n",
" - column_name: Wastewater discharge (percentile)\n",
" notes: all percentiles are floored (rounded down to the nearest percentile). For example, 89.7th percentile is rounded down to 89 for this field.\n",
" - column_name: Current asthma among adults aged greater than or equal to 18 years (percentile)\n",
" notes: all percentiles are floored (rounded down to the nearest percentile). For example, 89.7th percentile is rounded down to 89 for this field.\n",
" - column_name: Diagnosed diabetes among adults aged greater than or equal to 18 years (percentile)\n",
" notes: all percentiles are floored (rounded down to the nearest percentile). For example, 89.7th percentile is rounded down to 89 for this field.\n",
" - column_name: Coronary heart disease among adults aged greater than or equal to 18 years (percentile)\n",
" notes: all percentiles are floored (rounded down to the nearest percentile). For example, 89.7th percentile is rounded down to 89 for this field.\n",
" - column_name: Low life expectancy (percentile)\n",
" notes: (1) this percentile is reversed, meaning the lowest raw numbers become the highest percentiles, and (2) all percentiles are floored (rounded down to the nearest percentile). For example, 89.7th percentile is rounded down to 89 for this field.\n",
" - column_name: Low median household income as a percent of area median income (percentile)\n",
" notes: (1) this percentile is reversed, meaning the lowest raw numbers become the highest percentiles, and (2) all percentiles are floored (rounded down to the nearest percentile). For example, 89.7th percentile is rounded down to 89 for this field.\n",
" - column_name: Linguistic isolation (percent) (percentile)\n",
" notes: all percentiles are floored (rounded down to the nearest percentile). For example, 89.7th percentile is rounded down to 89 for this field.\n",
" - column_name: Unemployment (percent) (percentile)\n",
" notes: all percentiles are floored (rounded down to the nearest percentile). For example, 89.7th percentile is rounded down to 89 for this field.\n",
" - column_name: Percent of individuals below 200% Federal Poverty Line (percentile)\n",
" notes: all percentiles are floored (rounded down to the nearest percentile). For example, 89.7th percentile is rounded down to 89 for this field.\n",
" - column_name: Percent of individuals < 100% Federal Poverty Line (percentile)\n",
" notes: all percentiles are floored (rounded down to the nearest percentile). For example, 89.7th percentile is rounded down to 89 for this field.\n",
" - column_name: Percent individuals age 25 or over with less than high school degree (percentile)\n",
" notes: all percentiles are floored (rounded down to the nearest percentile). For example, 89.7th percentile is rounded down to 89 for this field.\n",
" - column_name: Definition M (percentile)\n",
" notes: all percentiles are floored (rounded down to the nearest percentile). For example, 89.7th percentile is rounded down to 89 for this field.\n",
" - column_name: Low median household income as a percent of territory median income in 2009 (percentile)\n",
" notes: (1) this percentile is reversed, meaning the lowest raw numbers become the highest percentiles, and (2) all percentiles are floored (rounded down to the nearest percentile). For example, 89.7th percentile is rounded down to 89 for this field.\n",
" - column_name: Percentage households below 100% of federal poverty line in 2009 for island areas (percentile)\n",
" notes: all percentiles are floored (rounded down to the nearest percentile). For example, 89.7th percentile is rounded down to 89 for this field.\n",
" - column_name: Unemployment (percent) in 2009 for island areas (percentile)\n",
" notes: all percentiles are floored (rounded down to the nearest percentile). For example, 89.7th percentile is rounded down to 89 for this field.\n"
]
}
],
"source": [
"for col in [col for col in download_codebook.index.to_list() if \"(percentile)\" in col]:\n",
" print(f\" - column_name: {col}\")\n",
" if \"Low\" not in col:\n",
" print(\n",
" f\" notes: all percentiles are floored (rounded down to the nearest percentile). For example, 89.7th percentile is rounded down to 89 for this field.\"\n",
" )\n",
" else:\n",
" print(\n",
" f\" notes: (1) this percentile is reversed, meaning the lowest raw numbers become the highest percentiles, and (2) all percentiles are floored (rounded down to the nearest percentile). For example, 89.7th percentile is rounded down to 89 for this field.\"\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 143,
"id": "5c08708e-4ebf-4cfe-8efb-7ee6c7930427",
"metadata": {},
"outputs": [
{
"data": {
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"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>excel_label</th>\n",
" <th>format</th>\n",
" <th>shapefile_column</th>\n",
" <th>notes</th>\n",
" <th>category</th>\n",
" </tr>\n",
" <tr>\n",
" <th>score_name</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>GEOID10_TRACT</th>\n",
" <td>Census tract ID</td>\n",
" <td>string</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>County Name</th>\n",
" <td>County Name</td>\n",
" <td>string</td>\n",
" <td>CF</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>State/Territory</th>\n",
" <td>State/Territory</td>\n",
" <td>string</td>\n",
" <td>SF</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Total threshold criteria exceeded</th>\n",
" <td>Total threshold criteria exceeded</td>\n",
" <td>int64</td>\n",
" <td>TC</td>\n",
" <td>Lists out the total number of criteria (where ...</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Definition M (communities)</th>\n",
" <td>Identified as disadvantaged</td>\n",
" <td>bool</td>\n",
" <td>SM_C</td>\n",
" <td>True / False variable for whether a tract is a...</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Unemployment (percent) in 2009 (island areas) and 2010 (states and PR)</th>\n",
" <td>Unemployment (percent) in 2009 (island areas) ...</td>\n",
" <td>percentage</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Percentage households below 100% of federal poverty line in 2009 (island areas) and 2010 (states and PR)</th>\n",
" <td>Percentage households below 100% of federal po...</td>\n",
" <td>percentage</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Greater than or equal to the 90th percentile for unemployment and has low HS education in 2009 (island areas)?</th>\n",
" <td>Greater than or equal to the 90th percentile f...</td>\n",
" <td>bool</td>\n",
" <td>IAULHSE</td>\n",
" <td>island area information comes from the dicenni...</td>\n",
" <td>training and workforce development</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Greater than or equal to the 90th percentile for households at or below 100% federal poverty level and has low HS education in 2009 (island areas)?</th>\n",
" <td>Greater than or equal to the 90th percentile f...</td>\n",
" <td>bool</td>\n",
" <td>IAPLHSE</td>\n",
" <td>island area information comes from the dicenni...</td>\n",
" <td>training and workforce development</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Greater than or equal to the 90th percentile for low median household income as a percent of area median income and has low HS education in 2009 (island areas)?</th>\n",
" <td>Greater than or equal to the 90th percentile f...</td>\n",
" <td>bool</td>\n",
" <td>IALMILHSE</td>\n",
" <td>island area information comes from the dicenni...</td>\n",
" <td>training and workforce development</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>82 rows × 5 columns</p>\n",
"</div>"
],
"text/plain": [
" excel_label \\\n",
"score_name \n",
"GEOID10_TRACT Census tract ID \n",
"County Name County Name \n",
"State/Territory State/Territory \n",
"Total threshold criteria exceeded Total threshold criteria exceeded \n",
"Definition M (communities) Identified as disadvantaged \n",
"... ... \n",
"Unemployment (percent) in 2009 (island areas) a... Unemployment (percent) in 2009 (island areas) ... \n",
"Percentage households below 100% of federal pov... Percentage households below 100% of federal po... \n",
"Greater than or equal to the 90th percentile fo... Greater than or equal to the 90th percentile f... \n",
"Greater than or equal to the 90th percentile fo... Greater than or equal to the 90th percentile f... \n",
"Greater than or equal to the 90th percentile fo... Greater than or equal to the 90th percentile f... \n",
"\n",
" format \\\n",
"score_name \n",
"GEOID10_TRACT string \n",
"County Name string \n",
"State/Territory string \n",
"Total threshold criteria exceeded int64 \n",
"Definition M (communities) bool \n",
"... ... \n",
"Unemployment (percent) in 2009 (island areas) a... percentage \n",
"Percentage households below 100% of federal pov... percentage \n",
"Greater than or equal to the 90th percentile fo... bool \n",
"Greater than or equal to the 90th percentile fo... bool \n",
"Greater than or equal to the 90th percentile fo... bool \n",
"\n",
" shapefile_column \\\n",
"score_name \n",
"GEOID10_TRACT NaN \n",
"County Name CF \n",
"State/Territory SF \n",
"Total threshold criteria exceeded TC \n",
"Definition M (communities) SM_C \n",
"... ... \n",
"Unemployment (percent) in 2009 (island areas) a... NaN \n",
"Percentage households below 100% of federal pov... NaN \n",
"Greater than or equal to the 90th percentile fo... IAULHSE \n",
"Greater than or equal to the 90th percentile fo... IAPLHSE \n",
"Greater than or equal to the 90th percentile fo... IALMILHSE \n",
"\n",
" notes \\\n",
"score_name \n",
"GEOID10_TRACT NaN \n",
"County Name NaN \n",
"State/Territory NaN \n",
"Total threshold criteria exceeded Lists out the total number of criteria (where ... \n",
"Definition M (communities) True / False variable for whether a tract is a... \n",
"... ... \n",
"Unemployment (percent) in 2009 (island areas) a... NaN \n",
"Percentage households below 100% of federal pov... NaN \n",
"Greater than or equal to the 90th percentile fo... island area information comes from the dicenni... \n",
"Greater than or equal to the 90th percentile fo... island area information comes from the dicenni... \n",
"Greater than or equal to the 90th percentile fo... island area information comes from the dicenni... \n",
"\n",
" category \n",
"score_name \n",
"GEOID10_TRACT NaN \n",
"County Name NaN \n",
"State/Territory NaN \n",
"Total threshold criteria exceeded NaN \n",
"Definition M (communities) NaN \n",
"... ... \n",
"Unemployment (percent) in 2009 (island areas) a... NaN \n",
"Percentage households below 100% of federal pov... NaN \n",
"Greater than or equal to the 90th percentile fo... training and workforce development \n",
"Greater than or equal to the 90th percentile fo... training and workforce development \n",
"Greater than or equal to the 90th percentile fo... training and workforce development \n",
"\n",
"[82 rows x 5 columns]"
]
},
"execution_count": 143,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"download_codebook.dropna(subset=[\"format\"]).reset_index()[\"score_name\"]"
]
},
{
"cell_type": "code",
"execution_count": 137,
"id": "7139ce5d-db5e-49dd-8bb3-122c7b73b395",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>excel_label</th>\n",
" <th>format</th>\n",
" <th>shapefile_column</th>\n",
" <th>notes</th>\n",
" <th>category</th>\n",
" </tr>\n",
" <tr>\n",
" <th>score_name</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"Empty DataFrame\n",
"Columns: [excel_label, format, shapefile_column, notes, category]\n",
"Index: []"
]
},
"execution_count": 137,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"download_codebook.loc[\n",
" sum([download_codebook[col] == \"percentile\" for col in [\"format\"]]) > 0\n",
"]"
]
},
{
"cell_type": "code",
"execution_count": 134,
"id": "e31ef01c-b225-48f0-bdf5-1efb8d4ed95c",
"metadata": {},
"outputs": [
{
"ename": "ValueError",
"evalue": "Cannot index with multidimensional key",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"Input \u001b[0;32mIn [134]\u001b[0m, in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mdownload_codebook\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mloc\u001b[49m\u001b[43m[\u001b[49m\u001b[43mdownload_codebook\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfilter\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlike\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mformat\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m==\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mpercentile\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\n",
"File \u001b[0;32m/usr/local/lib/python3.9/site-packages/pandas/core/indexing.py:931\u001b[0m, in \u001b[0;36m_LocationIndexer.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 928\u001b[0m axis \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxis \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;241m0\u001b[39m\n\u001b[1;32m 930\u001b[0m maybe_callable \u001b[38;5;241m=\u001b[39m com\u001b[38;5;241m.\u001b[39mapply_if_callable(key, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj)\n\u001b[0;32m--> 931\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_getitem_axis\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmaybe_callable\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m/usr/local/lib/python3.9/site-packages/pandas/core/indexing.py:1151\u001b[0m, in \u001b[0;36m_LocIndexer._getitem_axis\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m 1148\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28misinstance\u001b[39m(key, \u001b[38;5;28mtuple\u001b[39m) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(labels, MultiIndex)):\n\u001b[1;32m 1150\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(key, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mndim\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;129;01mand\u001b[39;00m key\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m-> 1151\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCannot index with multidimensional key\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 1153\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getitem_iterable(key, axis\u001b[38;5;241m=\u001b[39maxis)\n\u001b[1;32m 1155\u001b[0m \u001b[38;5;66;03m# nested tuple slicing\u001b[39;00m\n",
"\u001b[0;31mValueError\u001b[0m: Cannot index with multidimensional key"
]
}
],
"source": [
"download_codebook.loc[download_codebook.filter(like=\"format\") == \"percentile\"]"
]
},
{
"cell_type": "code",
"execution_count": 131,
"id": "73268de4-3378-4ac7-bf85-f483a78c3966",
"metadata": {},
"outputs": [],
"source": [
"download_codebook = pd.concat(\n",
" [\n",
" codebook.set_index(\"score_name\"),\n",
" shapefile_codes.rename(\n",
" columns={\"meaning\": \"shapefile_column\", \"column\": \"score_name\"}\n",
" ).set_index(\"score_name\"),\n",
" details_to_merge.set_index(\"score_name\"),\n",
" ],\n",
" axis=1,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6321ed42-aee6-40fc-8bf8-2a4ce4276eca",
"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": "3.9.10"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

90
data/data-pipeline/data_pipeline/etl/score/constants.py
View file

@ -198,42 +198,42 @@ TILES_SCORE_COLUMNS = {
field_names.WASTEWATER_FIELD field_names.WASTEWATER_FIELD
+ field_names.PERCENTILE_FIELD_SUFFIX: "WF_PFS", + field_names.PERCENTILE_FIELD_SUFFIX: "WF_PFS",
field_names.UST_FIELD + field_names.PERCENTILE_FIELD_SUFFIX: "UST_PFS", field_names.UST_FIELD + field_names.PERCENTILE_FIELD_SUFFIX: "UST_PFS",
field_names.M_WATER: "M_WTR", field_names.N_WATER: "N_WTR",
field_names.M_WORKFORCE: "M_WKFC", field_names.N_WORKFORCE: "N_WKFC",
field_names.M_CLIMATE: "M_CLT", field_names.N_CLIMATE: "N_CLT",
field_names.M_ENERGY: "M_ENY", field_names.N_ENERGY: "N_ENY",
field_names.M_TRANSPORTATION: "M_TRN", field_names.N_TRANSPORTATION: "N_TRN",
field_names.M_HOUSING: "M_HSG", field_names.N_HOUSING: "N_HSG",
field_names.M_POLLUTION: "M_PLN", field_names.N_POLLUTION: "N_PLN",
field_names.M_HEALTH: "M_HLTH", field_names.N_HEALTH: "N_HLTH",
# temporarily update this so that it's the Narwhal score that gets visualized on the map # 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. # The NEW final score value INCLUDES the adjacency index.
field_names.FINAL_SCORE_N_BOOLEAN: "SM_C", field_names.FINAL_SCORE_N_BOOLEAN: "SN_C",
field_names.SCORE_N_COMMUNITIES field_names.SCORE_N_COMMUNITIES
+ field_names.ADJACENT_MEAN_SUFFIX: "SM_DON", + field_names.ADJACENT_MEAN_SUFFIX: "SN_DON",
field_names.SCORE_N_COMMUNITIES: "SM_NO_DON", field_names.SCORE_N_COMMUNITIES: "SN_NO_DON",
field_names.EXPECTED_POPULATION_LOSS_RATE_LOW_INCOME_LOW_HIGHER_ED_FIELD: "EPLRLI", field_names.EXPECTED_POPULATION_LOSS_RATE_LOW_INCOME_FIELD: "EPLRLI",
field_names.EXPECTED_AGRICULTURE_LOSS_RATE_LOW_INCOME_LOW_HIGHER_ED_FIELD: "EALRLI", field_names.EXPECTED_AGRICULTURE_LOSS_RATE_LOW_INCOME_FIELD: "EALRLI",
field_names.EXPECTED_BUILDING_LOSS_RATE_LOW_INCOME_LOW_HIGHER_ED_FIELD: "EBLRLI", field_names.EXPECTED_BUILDING_LOSS_RATE_LOW_INCOME_FIELD: "EBLRLI",
field_names.PM25_EXPOSURE_LOW_INCOME_LOW_HIGHER_ED_FIELD: "PM25LI", field_names.PM25_EXPOSURE_LOW_INCOME_FIELD: "PM25LI",
field_names.ENERGY_BURDEN_LOW_INCOME_LOW_HIGHER_ED_FIELD: "EBLI", field_names.ENERGY_BURDEN_LOW_INCOME_FIELD: "EBLI",
field_names.DIESEL_PARTICULATE_MATTER_LOW_INCOME_LOW_HIGHER_ED_FIELD: "DPMLI", field_names.DIESEL_PARTICULATE_MATTER_LOW_INCOME_FIELD: "DPMLI",
field_names.TRAFFIC_PROXIMITY_LOW_INCOME_LOW_HIGHER_ED_FIELD: "TPLI", field_names.TRAFFIC_PROXIMITY_LOW_INCOME_FIELD: "TPLI",
field_names.LEAD_PAINT_MEDIAN_HOUSE_VALUE_LOW_INCOME_LOW_HIGHER_ED_FIELD: "LPMHVLI", field_names.LEAD_PAINT_MEDIAN_HOUSE_VALUE_LOW_INCOME_FIELD: "LPMHVLI",
field_names.HOUSING_BURDEN_LOW_INCOME_LOW_HIGHER_ED_FIELD: "HBLI", field_names.HOUSING_BURDEN_LOW_INCOME_FIELD: "HBLI",
field_names.RMP_LOW_INCOME_LOW_HIGHER_ED_FIELD: "RMPLI", field_names.RMP_LOW_INCOME_FIELD: "RMPLI",
field_names.SUPERFUND_LOW_INCOME_LOW_HIGHER_ED_FIELD: "SFLI", field_names.SUPERFUND_LOW_INCOME_FIELD: "SFLI",
field_names.HAZARDOUS_WASTE_LOW_INCOME_LOW_HIGHER_ED_FIELD: "HWLI", field_names.HAZARDOUS_WASTE_LOW_INCOME_FIELD: "HWLI",
field_names.WASTEWATER_DISCHARGE_LOW_INCOME_LOW_HIGHER_ED_FIELD: "WDLI", field_names.WASTEWATER_DISCHARGE_LOW_INCOME_FIELD: "WDLI",
field_names.UST_LOW_INCOME_FIELD: "USTLI", field_names.UST_LOW_INCOME_FIELD: "USTLI",
field_names.DIABETES_LOW_INCOME_LOW_HIGHER_ED_FIELD: "DLI", field_names.DIABETES_LOW_INCOME_FIELD: "DLI",
field_names.ASTHMA_LOW_INCOME_LOW_HIGHER_ED_FIELD: "ALI", field_names.ASTHMA_LOW_INCOME_FIELD: "ALI",
field_names.HEART_DISEASE_LOW_INCOME_LOW_HIGHER_ED_FIELD: "HDLI", field_names.HEART_DISEASE_LOW_INCOME_FIELD: "HDLI",
field_names.LOW_LIFE_EXPECTANCY_LOW_INCOME_LOW_HIGHER_ED_FIELD: "LLELI", field_names.LOW_LIFE_EXPECTANCY_LOW_INCOME_FIELD: "LLELI",
field_names.LINGUISTIC_ISOLATION_LOW_HS_LOW_HIGHER_ED_FIELD: "LILHSE", field_names.LINGUISTIC_ISOLATION_LOW_HS_EDUCATION_FIELD: "LILHSE",
field_names.POVERTY_LOW_HS_LOW_HIGHER_ED_FIELD: "PLHSE", field_names.POVERTY_LOW_HS_EDUCATION_FIELD: "PLHSE",
field_names.LOW_MEDIAN_INCOME_LOW_HS_LOW_HIGHER_ED_FIELD: "LMILHSE", field_names.LOW_MEDIAN_INCOME_LOW_HS_EDUCATION_FIELD: "LMILHSE",
field_names.UNEMPLOYMENT_LOW_HS_LOW_HIGHER_ED_FIELD: "ULHSE", field_names.UNEMPLOYMENT_LOW_HS_EDUCATION_FIELD: "ULHSE",
# new booleans only for the environmental factors # new booleans only for the environmental factors
field_names.EXPECTED_POPULATION_LOSS_EXCEEDS_PCTILE_THRESHOLD: "EPL_ET", field_names.EXPECTED_POPULATION_LOSS_EXCEEDS_PCTILE_THRESHOLD: "EPL_ET",
field_names.EXPECTED_AGRICULTURAL_LOSS_EXCEEDS_PCTILE_THRESHOLD: "EAL_ET", field_names.EXPECTED_AGRICULTURAL_LOSS_EXCEEDS_PCTILE_THRESHOLD: "EAL_ET",
@ -276,28 +276,24 @@ TILES_SCORE_COLUMNS = {
field_names.CENSUS_DECENNIAL_UNEMPLOYMENT_FIELD_2009 field_names.CENSUS_DECENNIAL_UNEMPLOYMENT_FIELD_2009
+ field_names.ISLAND_AREAS_PERCENTILE_ADJUSTMENT_FIELD + field_names.ISLAND_AREAS_PERCENTILE_ADJUSTMENT_FIELD
+ field_names.PERCENTILE_FIELD_SUFFIX: "IAULHSE_PFS", + field_names.PERCENTILE_FIELD_SUFFIX: "IAULHSE_PFS",
field_names.LOW_HS_EDUCATION_LOW_HIGHER_ED_FIELD: "LHE", field_names.LOW_HS_EDUCATION_FIELD: "LHE",
field_names.ISLAND_AREAS_LOW_HS_EDUCATION_FIELD: "IALHE", field_names.ISLAND_AREAS_LOW_HS_EDUCATION_FIELD: "IALHE",
# Percentage of HS Degree completion for Islands # 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_2009: "IAHSEF",
field_names.COLLEGE_ATTENDANCE_FIELD: "CA",
field_names.COLLEGE_NON_ATTENDANCE_FIELD: "NCA",
# This is logically equivalent to "non-college greater than 80%"
field_names.COLLEGE_ATTENDANCE_LESS_THAN_20_FIELD: "CA_LT20",
# Booleans for the front end about the types of thresholds exceeded # Booleans for the front end about the types of thresholds exceeded
field_names.CLIMATE_THRESHOLD_EXCEEDED: "M_CLT_EOMI", field_names.CLIMATE_THRESHOLD_EXCEEDED: "N_CLT_EOMI",
field_names.ENERGY_THRESHOLD_EXCEEDED: "M_ENY_EOMI", field_names.ENERGY_THRESHOLD_EXCEEDED: "N_ENY_EOMI",
field_names.TRAFFIC_THRESHOLD_EXCEEDED: "M_TRN_EOMI", field_names.TRAFFIC_THRESHOLD_EXCEEDED: "N_TRN_EOMI",
field_names.HOUSING_THREHSOLD_EXCEEDED: "M_HSG_EOMI", field_names.HOUSING_THREHSOLD_EXCEEDED: "N_HSG_EOMI",
field_names.POLLUTION_THRESHOLD_EXCEEDED: "M_PLN_EOMI", field_names.POLLUTION_THRESHOLD_EXCEEDED: "N_PLN_EOMI",
field_names.WATER_THRESHOLD_EXCEEDED: "M_WTR_EOMI", field_names.WATER_THRESHOLD_EXCEEDED: "N_WTR_EOMI",
field_names.HEALTH_THRESHOLD_EXCEEDED: "M_HLTH_EOMI", field_names.HEALTH_THRESHOLD_EXCEEDED: "N_HLTH_EOMI",
field_names.WORKFORCE_THRESHOLD_EXCEEDED: "M_WKFC_EOMI", field_names.WORKFORCE_THRESHOLD_EXCEEDED: "N_WKFC_EOMI",
# These are the booleans for socioeconomic indicators # These are the booleans for socioeconomic indicators
## this measures low income boolean ## this measures low income boolean
field_names.FPL_200_SERIES_IMPUTED_AND_ADJUSTED: "FPL200S", field_names.FPL_200_SERIES_IMPUTED_AND_ADJUSTED: "FPL200S",
## Low high school for t&wd ## Low high school for t&wd
field_names.WORKFORCE_SOCIO_INDICATORS_EXCEEDED: "M_WKFC_EBSI", field_names.WORKFORCE_SOCIO_INDICATORS_EXCEEDED: "N_WKFC_EBSI",
field_names.DOT_BURDEN_PCTILE_THRESHOLD: "TD_ET", field_names.DOT_BURDEN_PCTILE_THRESHOLD: "TD_ET",
field_names.DOT_TRAVEL_BURDEN_FIELD field_names.DOT_TRAVEL_BURDEN_FIELD
+ field_names.PERCENTILE_FIELD_SUFFIX: "TD_PFS", + field_names.PERCENTILE_FIELD_SUFFIX: "TD_PFS",
@ -377,8 +373,6 @@ TILES_SCORE_FLOAT_COLUMNS = [
# Island areas HS degree attainment rate # Island areas HS degree attainment rate
field_names.CENSUS_DECENNIAL_HIGH_SCHOOL_ED_FIELD_2009, field_names.CENSUS_DECENNIAL_HIGH_SCHOOL_ED_FIELD_2009,
field_names.WASTEWATER_FIELD + field_names.PERCENTILE_FIELD_SUFFIX, field_names.WASTEWATER_FIELD + field_names.PERCENTILE_FIELD_SUFFIX,
field_names.COLLEGE_NON_ATTENDANCE_FIELD,
field_names.COLLEGE_ATTENDANCE_FIELD,
field_names.DOT_TRAVEL_BURDEN_FIELD + field_names.PERCENTILE_FIELD_SUFFIX, field_names.DOT_TRAVEL_BURDEN_FIELD + field_names.PERCENTILE_FIELD_SUFFIX,
field_names.FUTURE_FLOOD_RISK_FIELD + field_names.PERCENTILE_FIELD_SUFFIX, field_names.FUTURE_FLOOD_RISK_FIELD + field_names.PERCENTILE_FIELD_SUFFIX,
field_names.FUTURE_WILDFIRE_RISK_FIELD field_names.FUTURE_WILDFIRE_RISK_FIELD

13
data/data-pipeline/data_pipeline/etl/score/etl_score.py
View file

@ -403,6 +403,7 @@ class ScoreETL(ExtractTransformLoad):
df[field_names.MEDIAN_INCOME_FIELD] / df[field_names.AMI_FIELD] df[field_names.MEDIAN_INCOME_FIELD] / df[field_names.AMI_FIELD]
) )
# Donut columns get added later
numeric_columns = [ numeric_columns = [
field_names.HOUSING_BURDEN_FIELD, field_names.HOUSING_BURDEN_FIELD,
field_names.NO_KITCHEN_OR_INDOOR_PLUMBING_FIELD, field_names.NO_KITCHEN_OR_INDOOR_PLUMBING_FIELD,
@ -477,12 +478,15 @@ class ScoreETL(ExtractTransformLoad):
non_numeric_columns = [ non_numeric_columns = [
self.GEOID_TRACT_FIELD_NAME, self.GEOID_TRACT_FIELD_NAME,
field_names.PERSISTENT_POVERTY_FIELD, field_names.PERSISTENT_POVERTY_FIELD,
field_names.HISTORIC_REDLINING_SCORE_EXCEEDED,
field_names.TRACT_ELIGIBLE_FOR_NONNATURAL_THRESHOLD, field_names.TRACT_ELIGIBLE_FOR_NONNATURAL_THRESHOLD,
field_names.AGRICULTURAL_VALUE_BOOL_FIELD, field_names.AGRICULTURAL_VALUE_BOOL_FIELD,
field_names.ELIGIBLE_FUDS_BINARY_FIELD_NAME, ]
boolean_columns = [
field_names.AML_BOOLEAN, field_names.AML_BOOLEAN,
field_names.IMPUTED_INCOME_FLAG_FIELD_NAME, field_names.IMPUTED_INCOME_FLAG_FIELD_NAME,
field_names.ELIGIBLE_FUDS_BINARY_FIELD_NAME,
field_names.HISTORIC_REDLINING_SCORE_EXCEEDED,
] ]
# For some columns, high values are "good", so we want to reverse the percentile # For some columns, high values are "good", so we want to reverse the percentile
@ -523,6 +527,7 @@ class ScoreETL(ExtractTransformLoad):
non_numeric_columns non_numeric_columns
+ numeric_columns + numeric_columns
+ [rp.field_name for rp in reverse_percentiles] + [rp.field_name for rp in reverse_percentiles]
+ boolean_columns
) )
df_copy = df[columns_to_keep].copy() df_copy = df[columns_to_keep].copy()
@ -533,6 +538,10 @@ class ScoreETL(ExtractTransformLoad):
df_copy[numeric_columns] = df_copy[numeric_columns].apply(pd.to_numeric) df_copy[numeric_columns] = df_copy[numeric_columns].apply(pd.to_numeric)
# coerce all booleans to bools
for col in boolean_columns:
df_copy[col] = df_copy[col].astype(bool)
# Convert all columns to numeric and do math # Convert all columns to numeric and do math
# Note that we have a few special conditions here and we handle them explicitly. # Note that we have a few special conditions here and we handle them explicitly.
# For *Linguistic Isolation*, we do NOT want to include Puerto Rico in the percentile # For *Linguistic Isolation*, we do NOT want to include Puerto Rico in the percentile

2
data/data-pipeline/data_pipeline/etl/score/etl_score_geo.py
View file

@ -53,7 +53,7 @@ class GeoScoreETL(ExtractTransformLoad):
self.TARGET_SCORE_SHORT_FIELD = constants.TILES_SCORE_COLUMNS[ self.TARGET_SCORE_SHORT_FIELD = constants.TILES_SCORE_COLUMNS[
field_names.SCORE_N field_names.SCORE_N
] ]
self.TARGET_SCORE_RENAME_TO = "M_SCORE" self.TARGET_SCORE_RENAME_TO = "SCORE"
# Import the shortened name for tract ("GTF") that's used on the tiles. # Import the shortened name for tract ("GTF") that's used on the tiles.
self.TRACT_SHORT_FIELD = constants.TILES_SCORE_COLUMNS[ self.TRACT_SHORT_FIELD = constants.TILES_SCORE_COLUMNS[

6
data/data-pipeline/data_pipeline/etl/score/tests/sample_data/score_data_initial.csv
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data/data-pipeline/data_pipeline/ipython/TractArea.ipynb
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@ -1,418 +0,0 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": 54,
"id": "df048f08",
"metadata": {},
"outputs": [],
"source": [
"import geopandas as gpd\n",
"import pathlib"
]
},
{
"cell_type": "code",
"execution_count": 55,
"id": "62366f7d",
"metadata": {},
"outputs": [],
"source": [
"lowJson = pathlib.Path() / 'usa-low.json'\n",
"assert lowJson.exists()\n",
"highJson = pathlib.Path() / 'usa-high.json'\n",
"assert highJson.exists()"
]
},
{
"cell_type": "code",
"execution_count": 50,
"id": "4077ed78",
"metadata": {},
"outputs": [],
"source": [
"gdf = gpd.read_file(highJson)"
]
},
{
"cell_type": "code",
"execution_count": 56,
"id": "d4abfc64",
"metadata": {},
"outputs": [],
"source": [
"gdf['area'] = gdf.apply(lambda row : gpd.GeoSeries(row['geometry']).area, axis = 1)"
]
},
{
"cell_type": "markdown",
"id": "5077d9ef",
"metadata": {},
"source": [
"Add `zlfc` = *zoom level full containment*, This field will indicate the maximum zoom level the user can go up to while still keeping the entire tract in view. Below, we sample a few tracts to get an idea of the relationship between zoom level and area"
]
},
{
"cell_type": "code",
"execution_count": 89,
"id": "a1234574",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>GEOID10</th>\n",
" <th>SF</th>\n",
" <th>CF</th>\n",
" <th>area</th>\n",
" <th>zlfc</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>9846</th>\n",
" <td>02185000200</td>\n",
" <td>Alaska</td>\n",
" <td>North Slope Borough</td>\n",
" <td>53.323702</td>\n",
" <td>4.45</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9937</th>\n",
" <td>02290000100</td>\n",
" <td>Alaska</td>\n",
" <td>Yukon-Koyukuk Census Area</td>\n",
" <td>21.653154</td>\n",
" <td>5.50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9857</th>\n",
" <td>02188000100</td>\n",
" <td>Alaska</td>\n",
" <td>Northwest Arctic Borough</td>\n",
" <td>21.188159</td>\n",
" <td>5.50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9935</th>\n",
" <td>02290000200</td>\n",
" <td>Alaska</td>\n",
" <td>Yukon-Koyukuk Census Area</td>\n",
" <td>20.744770</td>\n",
" <td>5.38</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9934</th>\n",
" <td>02290000300</td>\n",
" <td>Alaska</td>\n",
" <td>Yukon-Koyukuk Census Area</td>\n",
" <td>17.140826</td>\n",
" <td>0.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9936</th>\n",
" <td>02290000400</td>\n",
" <td>Alaska</td>\n",
" <td>Yukon-Koyukuk Census Area</td>\n",
" <td>14.687448</td>\n",
" <td>5.77</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9893</th>\n",
" <td>02180000100</td>\n",
" <td>Alaska</td>\n",
" <td>Nome Census Area</td>\n",
" <td>13.377817</td>\n",
" <td>0.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9847</th>\n",
" <td>02164000100</td>\n",
" <td>Alaska</td>\n",
" <td>Lake and Peninsula Borough</td>\n",
" <td>13.061644</td>\n",
" <td>5.33</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9918</th>\n",
" <td>02261000100</td>\n",
" <td>Alaska</td>\n",
" <td>Valdez-Cordova Census Area</td>\n",
" <td>11.118835</td>\n",
" <td>0.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9945</th>\n",
" <td>02050000100</td>\n",
" <td>Alaska</td>\n",
" <td>Bethel Census Area</td>\n",
" <td>10.951888</td>\n",
" <td>0.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9841</th>\n",
" <td>02270000100</td>\n",
" <td>Alaska</td>\n",
" <td>Wade Hampton Census Area</td>\n",
" <td>8.771806</td>\n",
" <td>0.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9839</th>\n",
" <td>02240000100</td>\n",
" <td>Alaska</td>\n",
" <td>Southeast Fairbanks Census Area</td>\n",
" <td>8.613690</td>\n",
" <td>0.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9843</th>\n",
" <td>02070000100</td>\n",
" <td>Alaska</td>\n",
" <td>Dillingham Census Area</td>\n",
" <td>8.575307</td>\n",
" <td>0.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9947</th>\n",
" <td>02050000300</td>\n",
" <td>Alaska</td>\n",
" <td>Bethel Census Area</td>\n",
" <td>8.408040</td>\n",
" <td>0.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9899</th>\n",
" <td>02170000101</td>\n",
" <td>Alaska</td>\n",
" <td>Matanuska-Susitna Borough</td>\n",
" <td>6.480444</td>\n",
" <td>0.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9944</th>\n",
" <td>02068000100</td>\n",
" <td>Alaska</td>\n",
" <td>Denali Borough</td>\n",
" <td>5.997236</td>\n",
" <td>0.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9836</th>\n",
" <td>02013000100</td>\n",
" <td>Alaska</td>\n",
" <td>Aleutians East Borough</td>\n",
" <td>5.487726</td>\n",
" <td>0.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9921</th>\n",
" <td>02122000100</td>\n",
" <td>Alaska</td>\n",
" <td>Kenai Peninsula Borough</td>\n",
" <td>4.831831</td>\n",
" <td>6.10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9851</th>\n",
" <td>02150000100</td>\n",
" <td>Alaska</td>\n",
" <td>Kodiak Island Borough</td>\n",
" <td>4.664009</td>\n",
" <td>0.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9850</th>\n",
" <td>02105000300</td>\n",
" <td>Alaska</td>\n",
" <td>Hoonah-Angoon Census Area</td>\n",
" <td>4.305716</td>\n",
" <td>0.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9838</th>\n",
" <td>02016000100</td>\n",
" <td>Alaska</td>\n",
" <td>Aleutians West Census Area</td>\n",
" <td>4.053520</td>\n",
" <td>0.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9917</th>\n",
" <td>02282000100</td>\n",
" <td>Alaska</td>\n",
" <td>Yakutat City and Borough</td>\n",
" <td>3.926182</td>\n",
" <td>0.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9920</th>\n",
" <td>02261000300</td>\n",
" <td>Alaska</td>\n",
" <td>Valdez-Cordova Census Area</td>\n",
" <td>3.285482</td>\n",
" <td>0.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9840</th>\n",
" <td>02240000400</td>\n",
" <td>Alaska</td>\n",
" <td>Southeast Fairbanks Census Area</td>\n",
" <td>3.233961</td>\n",
" <td>0.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9919</th>\n",
" <td>02261000200</td>\n",
" <td>Alaska</td>\n",
" <td>Valdez-Cordova Census Area</td>\n",
" <td>3.156317</td>\n",
" <td>0.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10354</th>\n",
" <td>41045970900</td>\n",
" <td>Oregon</td>\n",
" <td>Malheur County</td>\n",
" <td>2.731719</td>\n",
" <td>0.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9888</th>\n",
" <td>02198000100</td>\n",
" <td>Alaska</td>\n",
" <td>Prince of Wales-Hyder Census Area</td>\n",
" <td>2.606286</td>\n",
" <td>0.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10212</th>\n",
" <td>41025960200</td>\n",
" <td>Oregon</td>\n",
" <td>Harney County</td>\n",
" <td>2.568943</td>\n",
" <td>7.08</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9844</th>\n",
" <td>02185000300</td>\n",
" <td>Alaska</td>\n",
" <td>North Slope Borough</td>\n",
" <td>2.463165</td>\n",
" <td>0.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9858</th>\n",
" <td>02130000100</td>\n",
" <td>Alaska</td>\n",
" <td>Ketchikan Gateway Borough</td>\n",
" <td>2.440051</td>\n",
" <td>0.00</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" GEOID10 SF CF area zlfc\n",
"9846 02185000200 Alaska North Slope Borough 53.323702 4.45\n",
"9937 02290000100 Alaska Yukon-Koyukuk Census Area 21.653154 5.50\n",
"9857 02188000100 Alaska Northwest Arctic Borough 21.188159 5.50\n",
"9935 02290000200 Alaska Yukon-Koyukuk Census Area 20.744770 5.38\n",
"9934 02290000300 Alaska Yukon-Koyukuk Census Area 17.140826 0.00\n",
"9936 02290000400 Alaska Yukon-Koyukuk Census Area 14.687448 5.77\n",
"9893 02180000100 Alaska Nome Census Area 13.377817 0.00\n",
"9847 02164000100 Alaska Lake and Peninsula Borough 13.061644 5.33\n",
"9918 02261000100 Alaska Valdez-Cordova Census Area 11.118835 0.00\n",
"9945 02050000100 Alaska Bethel Census Area 10.951888 0.00\n",
"9841 02270000100 Alaska Wade Hampton Census Area 8.771806 0.00\n",
"9839 02240000100 Alaska Southeast Fairbanks Census Area 8.613690 0.00\n",
"9843 02070000100 Alaska Dillingham Census Area 8.575307 0.00\n",
"9947 02050000300 Alaska Bethel Census Area 8.408040 0.00\n",
"9899 02170000101 Alaska Matanuska-Susitna Borough 6.480444 0.00\n",
"9944 02068000100 Alaska Denali Borough 5.997236 0.00\n",
"9836 02013000100 Alaska Aleutians East Borough 5.487726 0.00\n",
"9921 02122000100 Alaska Kenai Peninsula Borough 4.831831 6.10\n",
"9851 02150000100 Alaska Kodiak Island Borough 4.664009 0.00\n",
"9850 02105000300 Alaska Hoonah-Angoon Census Area 4.305716 0.00\n",
"9838 02016000100 Alaska Aleutians West Census Area 4.053520 0.00\n",
"9917 02282000100 Alaska Yakutat City and Borough 3.926182 0.00\n",
"9920 02261000300 Alaska Valdez-Cordova Census Area 3.285482 0.00\n",
"9840 02240000400 Alaska Southeast Fairbanks Census Area 3.233961 0.00\n",
"9919 02261000200 Alaska Valdez-Cordova Census Area 3.156317 0.00\n",
"10354 41045970900 Oregon Malheur County 2.731719 0.00\n",
"9888 02198000100 Alaska Prince of Wales-Hyder Census Area 2.606286 0.00\n",
"10212 41025960200 Oregon Harney County 2.568943 7.08\n",
"9844 02185000300 Alaska North Slope Borough 2.463165 0.00\n",
"9858 02130000100 Alaska Ketchikan Gateway Borough 2.440051 0.00"
]
},
"execution_count": 89,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gdf['zlfc'] = 0\n",
"gdf.at[9846, 'zlfc'] = 4.45\n",
"gdf.at[10212, 'zlfc'] = 7.08\n",
"gdf.at[9937, 'zlfc'] = 5.5\n",
"gdf.at[9857, 'zlfc'] = 5.5\n",
"gdf.at[9935, 'zlfc'] = 5.38\n",
"gdf.at[9936, 'zlfc'] = 5.77\n",
"gdf.at[9921, 'zlfc'] = 6.1\n",
"gdf.at[9847, 'zlfc'] = 5.33\n",
"gdf_short = gdf[[\"GEOID10\", \"SF\", \"CF\", \"area\", \"zlfc\"]]\n",
"gdf_short_sorted = gdf_short.sort_values(by='area', ascending=False);\n",
"gdf_short_sorted.head(30)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5930de0e",
"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": "3.9.13"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

2
data/data-pipeline/data_pipeline/ipython/compare_two_score_files_for_differences.ipynb
View file

@ -150,7 +150,7 @@
"name": "python", "name": "python",
"nbconvert_exporter": "python", "nbconvert_exporter": "python",
"pygments_lexer": "ipython3", "pygments_lexer": "ipython3",
"version": "3.9.6" "version": "3.9.10"
} }
}, },
"nbformat": 4, "nbformat": 4,

28
data/data-pipeline/data_pipeline/score/field_names.py
View file

@ -9,23 +9,6 @@ GEOID_TRACT_FIELD = "GEOID10_TRACT"
STATE_FIELD = "State/Territory" STATE_FIELD = "State/Territory"
COUNTY_FIELD = "County Name" COUNTY_FIELD = "County Name"
# Score file field names
# Definition M fields
SCORE_M = "Definition M"
FINAL_SCORE_N_BOOLEAN = (
"Definition M community, including adjacency index tracts"
)
SCORE_M_COMMUNITIES = "Definition M (communities)"
M_CLIMATE = "Climate Factor (Definition M)"
M_ENERGY = "Energy Factor (Definition M)"
M_TRANSPORTATION = "Transportation Factor (Definition M)"
M_HOUSING = "Housing Factor (Definition M)"
M_POLLUTION = "Pollution Factor (Definition M)"
M_WATER = "Water Factor (Definition M)"
M_HEALTH = "Health Factor (Definition M)"
M_WORKFORCE = "Workforce Factor (Definition M)"
M_NON_WORKFORCE = "Any Non-Workforce Factor (Definition M)"
# Definition Narwhal fields # Definition Narwhal fields
SCORE_N = "Definition N (communities)" SCORE_N = "Definition N (communities)"
SCORE_N_COMMUNITIES = "Definition N (communities)" SCORE_N_COMMUNITIES = "Definition N (communities)"
@ -38,6 +21,9 @@ N_WATER = "Water Factor (Definition N)"
N_HEALTH = "Health Factor (Definition N)" N_HEALTH = "Health Factor (Definition N)"
N_WORKFORCE = "Workforce Factor (Definition N)" N_WORKFORCE = "Workforce Factor (Definition N)"
N_NON_WORKFORCE = "Any Non-Workforce Factor (Definition N)" N_NON_WORKFORCE = "Any Non-Workforce Factor (Definition N)"
FINAL_SCORE_N_BOOLEAN = (
"Definition N community, including adjacency index tracts"
)
PERCENTILE = 90 PERCENTILE = 90
MEDIAN_HOUSE_VALUE_PERCENTILE = 90 MEDIAN_HOUSE_VALUE_PERCENTILE = 90
@ -545,22 +531,22 @@ LOW_LIFE_EXPECTANCY_LOW_INCOME_LOW_HIGHER_ED_FIELD = (
# Workforce # Workforce
UNEMPLOYMENT_LOW_HS_EDUCATION_FIELD = ( UNEMPLOYMENT_LOW_HS_EDUCATION_FIELD = (
f"Greater than or equal to the {PERCENTILE}th percentile for unemployment" f"Greater than or equal to the {PERCENTILE}th percentile for unemployment"
" and has low HS education?" " and has low HS attainment?"
) )
LINGUISTIC_ISOLATION_LOW_HS_EDUCATION_FIELD = ( LINGUISTIC_ISOLATION_LOW_HS_EDUCATION_FIELD = (
f"Greater than or equal to the {PERCENTILE}th percentile for households in linguistic isolation" f"Greater than or equal to the {PERCENTILE}th percentile for households in linguistic isolation"
" and has low HS education?" " and has low HS attainment?"
) )
POVERTY_LOW_HS_EDUCATION_FIELD = ( POVERTY_LOW_HS_EDUCATION_FIELD = (
f"Greater than or equal to the {PERCENTILE}th percentile for households at or below 100% federal poverty level" f"Greater than or equal to the {PERCENTILE}th percentile for households at or below 100% federal poverty level"
" and has low HS education?" " and has low HS attainment?"
) )
LOW_MEDIAN_INCOME_LOW_HS_EDUCATION_FIELD = ( LOW_MEDIAN_INCOME_LOW_HS_EDUCATION_FIELD = (
f"Greater than or equal to the {PERCENTILE}th percentile for low median household income as a " f"Greater than or equal to the {PERCENTILE}th percentile for low median household income as a "
f"percent of area median income and has low HS education?" f"percent of area median income and has low HS attainment?"
) )
# Score M Workforce Variables # Score M Workforce Variables

3
data/data-pipeline/data_pipeline/score/score_runner.py
View file

@ -1,5 +1,4 @@
import pandas as pd import pandas as pd
from data_pipeline.score.score_m import ScoreM
from data_pipeline.score.score_narwhal import ScoreNarwhal from data_pipeline.score.score_narwhal import ScoreNarwhal
from data_pipeline.utils import get_module_logger from data_pipeline.utils import get_module_logger
@ -13,8 +12,6 @@ class ScoreRunner:
self.df = df self.df = df
def calculate_scores(self) -> pd.DataFrame: def calculate_scores(self) -> pd.DataFrame:
# Index scores
self.df = ScoreM(df=self.df).add_columns()
self.df = ScoreNarwhal(df=self.df).add_columns() self.df = ScoreNarwhal(df=self.df).add_columns()
return self.df return self.df