Overall data structure description:

File	Spatial Layer	Source(s)	file type
ESA_WorldCover.zip	cropland, forest, lakes, oceans	ESA WorldCover, HydroLAKES, Natural Earth	raster
Census_data.zip	bachelor_degree, graduate_degree, income, white_race(non-minority)	USA and CAN census data	vector
GHL.zip	BUILT_C (2018), BUILT_S (2018 (10m), 2015 (100m)), BUILT_V (2020), BUILT_POP (1975 & 2020), SMOD (1975, 2000, 2020, 2025)	Global Human Settlement	raster
Climate.zip	Degrees under 0 (DD), Summer Precipitation (MSP), relative humidity (RH), Atmospheric temperature (TD) for years 2019-2023 and a mean across those 5 years.	Climate NA	raster
Biomes_and_Ecoregions.zip	Biomes and Ecoregions across all three levels	US Environmental Protection Agency	vector
SLOPE.zip	elevation, slope, terrain_roughness (TRIriley)	Shuttle Radar Topography Mission (SRTM)	raster
roads_lines.zip	OSM roads (all types as in the OSM manuscript) and rivers	OpenStreetMap	vector
NDVI.zip	NDVI estimated from Landsat images	Landsat 8/9	raster
envelopes_all_cities.zip	catalogue of cities included, names, codes and locations		vector
IUCN.zip	species richness, species presence, and rarity-weighted species-richness	IUCN	raster, vector

Layer specific information:

File	Spatial Layer	Source file	Source webpage	File type	Resolution	Projection (EPSG)	script	countries
ESA_WorldCover.zip	cropland	ESA WorldCover	DATA | WORLDCOVER	raster	0.0083	4326		All
ESA_WorldCover.zip	forest	ESA WorldCover	DATA | WORLDCOVER	raster	0.0083	4326		All
ESA_WorldCover.zip	lakes	HydroLAKES	HydroLAKES	vector	NA	4326		All
ESA_WorldCover.zip	oceans	Natural Earth	Natural Earth - Free vector and raster map data at 1:10m, 1:50m, and 1:110m scales	vector	NA	4326		All
Census_data.zip	Number of bachelor degrees	USA and CAN census data	https://walker-data.com/tidycensus/; https://mountainmath.github.io/cancensus/index.html	vector	NA	4269		USA and Canada
Census_data.zip	Number of graduate degrees	USA census data	https://walker-data.com/tidycensus/; https://mountainmath.github.io/cancensus/index.html	vector	NA	4269		USA
Census_data.zip	Median Income	USA and CAN census data	https://walker-data.com/tidycensus/; https://mountainmath.github.io/cancensus/index.html	vector	NA	4269		USA and Canada
Census_data.zip	Number of non-minority individuals (white)	USA and CAN census data	https://walker-data.com/tidycensus/; https://mountainmath.github.io/cancensus/index.html	vector	NA	4269		USA and Canada
GHL.zip	Building components 2018	GHS_BUILT_C_MSZ_E2018_GLOBE_R2023A_54009_10_V1_0	Global Human Settlement - Visualisations of the GHSL datasets - European Commission	raster	10	9001		All
GHL.zip	Built Surface Area

This dataset was created for a master's thesis in geography at the University of Utah. County-level RSEI scores and county demographics were combined for a spatiotemporal regression analysis. Counties in the contiguous United States and years 2011–2021 are included.

Risk-Screening Environmental Indicators. Annual, county-level RSEI risk scores for surface water were obtained from the EPA RSEI model through the EasyRSEI Dashboard. RSEI scores are defined as the relative potential for health risks, given the estimated exposure calculated by the EPA. A binary risk indicator was created, signifying whether the county had any risk in a given year.

American Community Survey 5-year estimates. County-level population characteristics were collected as five-year estimates from the American Community Survey, with annual estimates for the survey end year applying to each year from 2011–2021. We used the R tidycensus package to collect variables and geometry from the US Census Bureau API. Variables included total population (scaled by 10,000), percent racial composition (American Indian or Alaska Native, or AIAN; Asian; Black or African American; Native Hawaiian or Other Pacific Islander, or NHPI; Some Other Race; Two or More Races; and White), and percent ethnic composition (Hispanic or Latinx). Each census-defined racial category was collected as non-Hispanic or Latinx, such that each group was mutually exclusive.