Linked External Data Measures🔗

Area Deprivation Index (ADI)🔗

Measure Description🔗

The Neighborhood Atlas hosts the Area Deprivation Index (ADI), a scientifically validated measure of the adverse social exposome (i.e., neighborhood disadvantage) that can be used to evaluate and improve factors that impact health across populations. The ADI ranks neighborhoods by adverse social exposome in a region of interest (e.g., at the state or national level), taking into account factors related to income, education, employment, and housing quality. The 2023 version is the latest version available. This version was created using 2019-2023 ACS data.

Key Variables🔗

Variable Name	Description
adi_national_prcnt	National percentile of block group ADI score of Numeric Ranking (1-100) or Suppression Codes

Key Reference🔗

Census Return🔗

Measure Description🔗

The Census Bureau's Planning Database (PDB) contains select operational, housing, demographic, and socio-economic statistics from the Decennial Census and the American Community Survey (ACS) 5-year files. The 2023 PDB contains select 2017-2021 ACS 5-year estimates including the ACS tract level self-response rates, 2020 Census operational variables, and select Census variables typically found on the PDB.

Key Variables🔗

Variable Name	Description
censusret_return_rate_2020	Percent of occupied housing units providing a sufficient internet, paper, or phone self-response
censusret_selfresponse_rate_20192023	The self-response rate of in-ACS housing units in the tract

Key Reference🔗

Child Opportunity Index 3.0 (COI)🔗

Measure Description🔗

The Child Opportunity Index (COI) 3.0 is a composite index of neighborhood conditions and resources that, per the research evidence, matter for children's healthy development. COI 3.0 is based on 44 component indicators spanning three domains (education, health and environment, and social and economic) and 14 subdomains. The latest release of the Child Opportunity Index, COI 3.0-2023, was published in 2025 and includes data covering the years from 2012 through 2023.

Key Variables🔗

• coicoi_total{AREA}_{c5|score}
• coied_total{AREA}_{c5|score}
• coihe_total{AREA}_{c5|score}
• coise_total{AREA}_{c5|score}

Key Reference🔗

Vehicle Density (ACS)🔗

Measure Description🔗

Vehicle density was calculated using data from the 2019-2023 American Community Survey 5-year estimates for primary, secondary, and tertiary addresses at the census tract level. Vehicle density was calculated in two ways:

• As an area estimate (aggregate number of variables in a census tract per square mile of land area), and
• As a population density (aggregate number of vehicles available in a census tract per individual). Vehicle density may be associated with neighborhood residents’ levels of exposure to noxious chemicals and their vulnerability to vehicle-related injuries or fatalities

Key Variables🔗

Variable Name	Description
densveh_area_density	Aggregate Number of vehicles available per square mile of land area
densveh_pop_density	Aggregate Number of Vehicles available per individual

Key Reference🔗

Number of Jobs and Job Density (LODES)🔗

Measure Description🔗

Number of jobs and job density (number of jobs per square mile of land area) are available at the census tract level for participants’ addresses. Employment statistics are also available, broken down by race and ethnicity. Employment information was derived from the LEHD Origin-Destination Employment Statistics (LODES) dataset for the year 2019.

Key Variables🔗

• lodes_job_count
• lodes_job_density
• lodes_job{RACE-ETH}_count
• lodes_job{RACE-ETH}_density

Key Reference🔗

Minority Health Social Vulnerability Index (MHSVI)🔗

Measure Description🔗

The Minority Health Index is a data resource developed jointly by the Centers for Disease Control and Prevention (CDC) and the U.S. Department of Health and Human Services (HHS) Office of Minority Health to help identify racial and ethnic minority communities that are at greatest risk for disproportionate impacts and adverse outcomes during public health emergencies. Its purpose is to enhance existing data tools and support public health research, planning, and response efforts with a focus on health equity. The data version is 2023.

Key Variables🔗

• ssvi_{RACE-ETH}_prcnt
• ssvi_{LANGUAGE}_prcnt
• ssvi_hosp_state_prop
• ssvi_nocomp_prcnt
• ssvi_noint_prcnt
• ssvi_pcp_state_rate
• ssvi_pharm_state_prop
• ssvi_unins_prcnt
• ssvi_urg_state_prop

Key Reference🔗

Neighborhood Socioeconomic Status and Demographics (NaNDA)🔗

Measure Description🔗

These datasets contain measures of socioeconomic and demographic characteristics by U.S. census tract for the years 2018-2022

The following were derived from the ACS 2018-2022 5-year estimates at the census tract level:

• Proportion of people who are foreign born
• Proportion of families with income greater than 75K
• Proportion of families with income greater than $100K
• Proportion 16+ civilian labor force unemployed

In addition, three factors associated with neighborhood sociodemographics and structural characteristics are included (based on Morenoff et al. (2007)):

• Neighborhood disadvantage score is characterized by high levels of poverty, unemployment, female-headed families, households receiving public assistance income, and a high proportion of African Americans in a census tract.
• Neighborhood affluence score represents a mix of characteristics associated with neighborhood affluence (concentrations of adults with a college education; with incomes>75K; and employed in managerial and professional occupations).
• Neighborhood ethnic/immigrant score represents ethnic and immigrant concentration. Higher values indicate more Hispanic and foreign born in the census tract.

Key Variables🔗

• nbhsoc_college_prop
• nbhsoc_factor{1|2|3}_tractscore
• nbhsoc_finc{75k|100k}_prop
• nbhsoc_forborn_prop
• nbhsoc_unemploy_prop

Variable Name	Description
nbhsoc*forborn_prop	Proportion of people who are foreign born
nbhsoc_college_prop	Proportion of persons with Bachelors Degree or Higher
nbhsoc_finc100k_prop	Proportion of families with income $125,000 or more; 100k no longer exist;
nbhsoc_finc75k_prop	Proportion of families with income 75,000 to 124,999; for 75 k or more, sum with pfamincge125k;
nbhsoc_unemploy_prop	Proportion of age 16+ civilian labor force unemployed;
nbhsoc_factor1_tractscore	mean of pnhblack pfhfam ppubas ppov punemp
nbhsoc_factor2_tractscore	mean of ped3* pfamincge125k pprof
nbhsoc_factor3_tractscore	mean of phispanic pfborn plimeng

Key Reference🔗

Parks (NaNDA)🔗

Measure Description🔗

Prior research has demonstrated that access to parks and greenspace can have a positive impact on many aspects of and contributors to health, including physical activity levels (Kaczynski et al., 2007), healthy aging (Finlay, 2015), and sense of well-being (Larson et al., 2016). Neighborhood parks can also contribute to sense of community (Gómez, 2015). These datasets describe the number and area of parks in each census tract or each ZIP code tabulation area (ZCTA) in the United States. Measures include the total number of parks, park area, and proportion of park area within each census tract for the years 2018-2022.

The dataset describes：

• Total number of parks per census tract (top coded at 10)
• Total number of open parks per county
• Proportion of open park land within census tract

Key Variables🔗

Variable Name	Description
parks_parks_count	Total number of open parks in the census tract
parks_parkstc10_count	Total number of open parks (top coded at 10) in census tract
parks_parks_prop	Proportion of open park land within census tract (tot_park_area / tract_area)
parks_parkscounty_count	DERIVED: groupby COUNTYFP and sum by COUNT_OPEN_PARKS

Key Reference🔗

Behavioral Health Measures (PLACES)🔗

Measure Description🔗

Measures from the PLACES dataset are available for participants’ addresses at the census tract level. The PLACES dataset is an expansion of the 500 Cities Project and is available from the Center for Disease and Control and Prevention (CDC), the Robert Wood Johnson Foundation (RWJF) and CDC Foundation. Data sources used to generate these model-based estimates are Behavioral Risk Factor Surveillance System (BRFSS) 2023 or 2022 data, Census Bureau 2020 population data, and American Community Survey 2019-2023 or 2018–2022 estimates. The 2025 release uses 2023 BRFSS data for 35 measures and 2022 BRFSS data for 5 measures (all teeth lost, dental visits, mammograms, colorectal cancer screening, and short sleep duration) that the survey collects data on every other year. Included are 24 measures for the entire United States including chronic disease-related unhealthy behaviors, health outcomes, and use of preventive services. More information about the methodology can be found at www.cdc.gov/places.

Key Variables🔗

• places_*

Key Reference🔗

Religious/Civic Organizations (NaNDa)🔗

Measure Description🔗

This dataset contains measures of the number and density of select types of civic, social, and religious organizations per United States Census Tract for the year 2021.

• Total count of religious organizations
• Number of all religious organizations per 1000 people
• Total count of civic/social organizations
• Number of all civic/social organizations per 1000 people

Key Variables🔗

• relcivcivsoc{count|prop}
• relcivcivsoccounty{count|prop}
• relcivrelorg{count|prop}
• relcivrelorgcounty{count|prop}

Variable Name	Description
relciv_relorg_count	Total counts: Religious Organizations
relciv_relorg_prop	Religious Organizations per 1000 people
relciv_relorgcounty_count	groupby(county) and sum(count_religiousorgs)
relciv_relorgcounty_prop	groupby(county) and sum(den_religiousorgs)
relciv_civsoc_count	Total counts: Civic and Social Associations
relciv_civsoc_prop	Civic and Social Associations per 1000 people
relciv_civsoccounty_count	groupby(county) and sum(count_civsoc)
relciv_civsoccounty_prop	groupby(county) and sum(den_civsoc)

Key Reference🔗

Rent and Mortgage Statistics (ACS)🔗

Measure Description🔗

In order to approximate cost of living that may be associated with housing, the following variables from the 2019-2023 American Community Survey (ACS) 5-year estimates have been linked to participants’ addresses at the census tract level:

• Percent home ownership
• Percent of households living with rent burden (rent is at least 30% of income),
• Percent of households living with severe rent burden (rent is at least 50% of income)
• Median monthly gross rent
• Median monthly mortgage payments.

Key Variables🔗

Variable Name	Description
rentmort_ownership_prcnt	Residential history derived - Percent Homeownership;ACS2019-2023; census tract; Occupied housing units(owner occupied) / Total tenure occupied housing units
rentmort_homevalue_med	Residential history derived - Median house value; ACS2019-2023; census tract
rentmort_rentburden_prcnt	Residential history derived - Percent rent burden(30% or more of income); ACS2019-2023; census tract
rentmort_rentburdensev_prcnt	Residential history derived - Percent severe rent burden (50% or more of income); ACS2019-2023; census tract
rentmort_rent_med	Residential history derived - Median rent per month; ACS2019-2023; census tract

Key Reference🔗

From https://www.census.gov/data/developers/data-sets/acs-5year.html

Building Density (EPA)🔗

Measure Description🔗

This data is part of the Smart Location Database for the year 2021, which is a nationwide geographic data resource for measuring location efficiency. It measures Gross residential density (housing units per acre) on unprotected land.

Key Variables🔗

Variable Name	Description
densbld_density	Gross residential density (HU/acre) on unprotected land

Key Reference🔗

Walkability (EPA)🔗

Measure Description🔗

The National Walkability Index is a nationwide geographic data resource that ranks block groups according to their relative walkability. The national dataset for the year 2021 includes walkability scores for all block groups as well as the underlying attributes that are used to rank the block groups.

Key Variables🔗

Variable Name	Description
walk_idx	Walkability index comprised of weighted sum of the ranked values of [D2a_EpHHm] (D2A_Ranked), [D2b_E8MixA] (D2B_Ranked), [D3b] (D3B_Ranked) and [D4a] (D4A_Ranked)

Key Reference🔗

Social Mobility (Opportunity Atlas)🔗

Measure Description🔗

The Opportunity Atlas uses anonymous data based on 20 million Americans followed from childhood to their mid-30s to provide outcomes for adults who grew up in each census tract. Percentile household incomes correspond to outcomes in adulthood of people who grew up in each census tract and were born between 1978 and 1983.

The variable corresponding to the 25th percentile is typically the main index of upward social mobility for each census tract, as it captures the mean income rank in adulthood for children who grew up in low-income families in this census tract. See the US Census Bureau website for more details.

Key Variables🔗

• socmobkfrpp{count|mean|se}
• socmob_kfrpp_p{1|25|50|75|100}_prcntile

Note: All variables are residential history–derived Opportunity Atlas measures at the census tract level. Child outcomes reflect mean earnings in 2014–2015 for ages 31–37.

Variable Name	Description
socmob_kfrpp_mean	Mean outcome for all children
socmob_kfrpp_count	Number of children
socmob_kfrpp_p1_prcntile	Mean household income rank for children with parents at the 1st percentile
socmob_kfrpp_p25_prcntile	Mean household income rank for children with parents at the 25th percentile (“upward mobility” per Chetty et al.)
socmob_kfrpp_p50_prcntile	Mean household income rank for children with parents at the 50th percentile
socmob_kfrpp_p75_prcntile	Mean household income rank for children with parents at the 75th percentile
socmob_kfrpp_p100_prcntile	Mean household income rank for children with parents at the 100th percentile
socmob_kfrpp_se	Estimated standard error (all children)

Key Reference🔗

Social Service (NaNDa)🔗

Measure Description🔗

Measure includes senior centers, youth centers, food banks, job training programs, and day care centers(per census tract and per county) for the year 2021:

• Total count of social services
• Number of all social services per 1000 people

Key Variables🔗

Variable Name	Description
socsrv_socsrv_count	Total counts: Total Individual and Family Services
socsrv_socsrv_prop	Total Individual and Family Services per 1000 people
socsrv_socsrvcounty_count	groupby(county) and sum(count_indivfamilyservices)
socsrv_socsrvcounty_prop	groupby(county) and sum(den_indivfamilyservices)

Key Reference🔗

Social Vulnerability Index (SVI)🔗

Measure Description🔗

Every community must prepare for and respond to hazardous events, whether a natural disaster like a tornado or a disease outbreak, or an anthropogenic event such as a harmful chemical spill. The degree to which a community exhibits certain social conditions, including high poverty, low percentage of vehicle access, or crowded households, among others, may affect that community’s ability to prevent human suffering and financial loss in the event of a disaster. These factors describe a community’s social vulnerability.

ATSDR’s Geospatial Research, Analysis, & Services Program (GRASP) created the Centers for Disease Control and Prevention and Agency for Toxic Substances and Disease Registry Social Vulnerability Index (hereafter, CDC/ATSDR SVI or SVI) to help public health officials and emergency response planners identify and map the communities that will most likely need support before, during, and after a hazardous event.

SVI data for the year 2022 indicates the relative vulnerability of every U.S. census tract. Census tracts are subdivisions of counties for which the Census collects statistical data. SVI ranks the tracts on 16 social factors, such as unemployment, racial and ethnic minority status, and disability status. Then, SVI further groups the factors into four related themes. Thus, each tract receives a ranking for each Census variable and for each of the four themes as well as an overall ranking.

Key Variables🔗

• svi_*

Key Reference🔗

Urban/Rural Area (Census)🔗

Measure Description🔗

The Census Bureau’s urban-rural classification is a delineation of geographic areas, identifying both individual urban areas and the rural area of the nation. The Census Bureau’s urban areas represent densely developed territory, and encompass residential, commercial, and other non-residential urban land uses. The Census Bureau delineates urban areas after each decennial census by applying specified criteria to decennial census and other data. Rural encompasses all population, housing, and territory not included within an urban area.

For the 2020 Census, an urban area will comprise a densely settled core of census blocks that meet minimum housing unit density and/or population density requirements. This includes adjacent territory containing non-residential urban land uses. To qualify as an urban area, the territory identified according to criteria must encompass at least 2,000 housing units or have a population of at least 5,000.

Key Variables🔗

Variable Name	Description
urban_urbanclassification	Urban/Rural Classification 1=urban; 0 not urban; urban cluster label no longer exist;

Key Reference🔗

U.S. Census Bureau. (2024, December 16). Urban and rural. U.S. Census Bureau. from https://www.census.gov/programs-surveys/geography/guidance/geo-areas/urban-rural.html

Satellite-based Particulate Measures🔗

Measure Description🔗

The Annual Mean PM2.5 Components (EC, NH4, NO3, OC, SO4) 50m Urban and 1km Non-Urban Area Grids for Contiguous U.S., 2019, v1 data set contains annual predictions of the chemical concentrations at a hyper resolution (50m x 50m grid cells) in urban areas and at a high resolution (1km x 1km grid cells) in non-urban areas for the years 2019. Particulate matter with an aerodynamic diameter less than 2.5 microgram per cubic meter (PM2.5) increases mortality and morbidity. The Coordinate Reference System (CRS) for predictions is the World Geodetic System 1984 (WGS84) and the Units for the PM2.5 Components are microgram per cubic meter.

Key Variables🔗

Variable Name	Description
particulat_ec_mean_yb0	annual average of Elemental Carbon in mcg/m^3
particulat_nh4_mean_yb0	annual average of Ammonium in mcg/m^3
particulat_no3_mean_yb0	annual average of Nitrate in mcg/m^3
particulat_oc_mean_yb0	annual average of Organic Carbon in mcg/m^3
particulat_so4_mean_yb0	annual average of Sulfate in mcg/m^3

Key Reference🔗

Population Density (EPA)🔗

Measure Description🔗

The spatial distribution of population density in 2020 in 1km resolution, United States

Key Variables🔗

Variable Name	Description
denspop_density	UN adjusted Population density (people per square mile) in 2020

Key Reference🔗

Measure of Land Cover and Tree Canopy (NLCD)🔗

Measure Description🔗

The USGS Land Cover program has combined the tried-and-true methodologies from premier land cover projects, National Land Cover Database (NLCD) and Land Change Monitoring, Assessment, and Projection (LCMAP), together with modern innovations in geospatial deep learning technologies to create the next generation of land cover and land change information. These land cover science product algorithms harness the remotely sensed Landsat data record to provide state-of-the-art land surface change information needed by scientists, resource managers, and decision-makers. Annual NLCD uses a modernized, integrated approach to map, monitor, synthesize, and understand the complexities of land use, cover, and condition change. Included in this release is the Annual NLCD, Collection 1.1, for the Conterminous U.S. for 2024. Questions about the Annual NLCD product suite can be directed to the Annual NLCD mapping team at USGS EROS, Sioux Falls, SD (605) 594-6151 or custserv@usgs.gov. See included spatial metadata for more details.

Key Variables🔗

Variable Name	Description
nlcd	National Land Cover Database (NLCD)
tcc	Tree Canopy Cover (TCC)

Key Reference🔗

Soil Contamination Measures🔗

Lithium Concentrations in Groundwater Sources of Drinking Water🔗

Measure Description🔗

This data release contains data used to develop models and maps that estimate the occurrence of lithium in groundwater used as drinking water throughout the conterminous United States. The measure is lithium concentration in soil C horizon milligrams per kilogram.

Key Variables🔗

Variable Name	Description
gw_li	Lithium concentration in soil C horizon (milligrams per kilogram)

Key Reference🔗

Grid-level Soil Pollution Measures by Toxic Metals🔗

Measure Description🔗

A compilation of a comprehensive global dataset on soil contamination by arsenic, cadmium, cobalt, chromium, copper, nickel and lead, sourced from investigations encompassing sampling locations across various climate zones, geological formations, and land usage patterns. The data measures the probability of exceedance for different toxic metals under Human Health and Ecological Thresholds (HHET) or the Agricultural Thresholds (AT) for each grid.

Key Variables🔗

• soilpoll_at_{As|Cd|Co|Cr|Cu|Ni|Pb}
• soilpoll_hhet_{As|Cd|Co|Cr|Cu|Ni|Pb}

Key Reference🔗