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.