Title: Data from: Human population growth and accessibility from cities shape rangeland condition in the American West DOI: 10.7923/earc-0518 Data, code and/or products within this dataset support the following manuscript: Manuscript Title: Human population growth and accessibility from cities shape rangeland condition in the American West Journal: Landscape and Urban Planning DOI: 10.1016/j.landurbplan.2022.104673 Description/Abstract: Compiled data utilized to run model parameters for Requena-Mullor et al. 2023. These data lead to the following conclusions: • Human population growth contributes to the decline of sagebrush-steppe rangelands. • More accessible rangelands from population centers have higher quality. • Open space preservation provides opportunities for rangeland conservation in cities. • Coordinated conservation strategies are necessary to protect rangeland ecosystems. **Data Use**: *License*: [CC-BY 4.0](https://creativecommons.org/licenses/by/4.0/) *Recommended Citation*: Requena-Mullor JM. 2023. Data from: Human population growth and accessibility from cities shape rangeland condition in the American West [Data set]. University of Idaho. https://doi.org/10.7923/earc-0518 **Funding**: US National Science Foundation Idaho EPSCoR, Award: OIA-1757324 Resource URL: https://data.nkn.uidaho.edu/dataset/data-human-population-growth-and-accessibility-cities-shape-rangeland-condition-american Creator(s): 1. Full Name: Juan M. Requena-Mullor Unique identifier: https://orcid.org/0000-0002-5120-7947 Affiliation(s): Universidad de Almería, La Cañada de San Urbano; Boise State University; University of Michigan-Ann Arbor Other Contributor(s): 1. Full Name: Jodi Brandt Unique identifier: NULL Affiliation(s): Boise State University Role: Researcher 2. Full Name: Matthew A. Williamson Unique identifier: https://orcid.org/0000-0002-2550-5828 Affiliation(s): Boise State University Role: Researcher 3. Full Name: T. Trevor Caughlin Unique identifier: https://orcid.org/0000-0001-6752-2055 Affiliation(s): Boise State University Role: Researcher Publisher: University of Idaho Publication Year: 2023 Language(s): American English Subject(s): 1. NATURAL SCIENCES 1.5 Earth and related Environmental sciences 5. SOCIAL SCIENCES 5.7 Social and economic geography Keywords/Tags: biogeography, environmental change, land use and land cover change (LULC), landscape, social-ecological change, wildfire, wildland-urban interface (WUI), human population growth, rangeland Resource Type General: Dataset Dates: NULL Date available for the public: 2023-05-01 Sizes: Format(s): csv Version: NULL Funding References: US National Science Foundation Award Number: OIA-1757324 Award Title: RII Track-1: Linking Genome to Phenome to Predict Adaptive Responses of Organisms to Changing Landscapes Award URI: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1757324 Spatial/Geographical Coverage Location: Study Area Description: western USA The study area comprises approximately 111 million hectares across 121 counties, in nine states of the western U.S., including most of the area historically covered by sagebrush. Temporal Coverage: Start Data: 1989-01-01 End Date: 2018-12-31 Granularity of the Data: NULL Contact Info: Contact Name: Juan Requena-Mullor Contact Email: juanmir@ual.es Related Content: Peer Reviewed Manuscript-Landscape and Urban Planning | https://doi.org/10.1016/j.landurbplan.2022.104673 Data/Code Files: readme.txt Requena_Mullor_etal_L&UP_dataframe.csv ASI: Community composition/structure based on fractional cover component maps with a resolution of 30m COUNTY: County cartographic boundary STATE: State cartographic boundary X: Degrees longitude in decimal degrees (-ddd.dddddd) Y: Degrees latitude in decimal degrees (dd.dddddd) YEAR: Calendar year to which data attribute values are derived. Values: 1989, 1998, 2008, 2018 HUMAN POPULATION Number of people per county MEAN ANNUAL TEMPERATURE: Temperature in ºC with a resolution of 1 km approximately ANNUAL CUMULATIVE PRECIPITATION: Precipitation in mm with a resolution of approximately 1 km SLOPE: Slope in degrees with a resolution of approximately 30 m TRAVEL TIME: Travel time in minutes with a resolution of approximately 1 km. Travel time was extracted from the “Global Accessibility Map” (Nelson 2008). This author computed accessibility using a cost-distance algorithm which computed the "cost" of travelling between two locations on a regular raster grid. Generally, this cost is measured in units of time. The cells in the raster grid (i.e., friction-surface) contain values that represent the cost required to travel across them. The friction-surface contains information on the transport network and environmental and political factors that affect travel times between locations. ELEVATION: Elevation in meters with a resolution of approximately 30 m LAND TENURE: Ownership regime. Categorical variable with four categories. Land tenure categories were assigned based on the agency responsible for managing the land designation as follows: Values: federal: ARMY, BLM, DOD, DOE, FAA, FHA, FWS, GSA, NAVY, NPS, OTHFE, USACE, USBR, USDA, USFS, VA private: PRI state-local: STA, LG tribal: BIA FIRE OCCURRENCE: Whether or not at least one wildfire has occurred in previous decades. See note below. NUMBER OF FIRES: Number of wildfires occurred in previous decades. See note below. Wildfire attributes were extracted from the Historical Fire Fataset (HFD) compiled from various federal, state, and local sources (Weber 2020). The HFD was assembled by acquiring wildfire perimeters from authoritative sources across the western US. This included the US Forest Service, Bureau of Land Management, US Geologic Survey, National Interagency Fire Center, as well as state agencies like Idaho Department of Lands and the California Department of Forestry and Fire Protection). Two fire attributes were computed: fire occurrence and the number of fires. Fire occurrence was a binary variable with 1 (fire presence) and 0 (fire absence) representing whether or not at least one wildfire has occurred in previous decades. To do that, we checked whether or not one data point fell within a fire polygon. The number of fires was calculated as the cumulative number of wildfires that occurred in previous decades, ranging from 0 to 6 fires.