title: Locally adaptive, spatially explicit projection of US population for 2030 and 2050.
abstract: Localized adverse events, including natural hazards, epidemiological events, and human conflict, underscore the criticality of quantifying and mapping current population. Building on the spatial interpolation technique previously developed for high-resolution population distribution data (LandScan Global and LandScan USA), we have constructed an empirically informed spatial distribution of projected population of the contiguous United States for 2030 and 2050, depicting one of many possible population futures. Whereas most current large-scale, spatially explicit population projections typically rely on a population gravity model to determine areas of future growth, our projection model departs from these by accounting for multiple components that affect population distribution. Modeled variables, which included land cover, slope, distances to larger cities, and a moving average of current population, were locally adaptive and geographically varying. The resulting weighted surface was used to determine which areas had the greatest likelihood for future population change. Population projections of county level numbers were developed using a modified version of the US Census's projection methodology, with the US Census's official projection as the benchmark. Applications of our model include incorporating multiple various scenario-driven events to produce a range of spatially explicit population futures for suitability modeling, service area planning for governmental agencies, consequence assessment, mitigation planning and implementation, and assessment of spatially vulnerable populations.
authors.affiliation: Geographic Information Science and Technology Group, Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831 mckeejj@ornl.gov.
authors.forename: Jacob J
authors.initials: JJ
authors.lastname: McKee
authors.affiliation: Geographic Information Science and Technology Group, Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831.
authors.forename: Amy N
authors.initials: AN
authors.lastname: Rose
authors.affiliation: Geographic Information Science and Technology Group, Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831.
authors.forename: Edward A
authors.initials: EA
authors.lastname: Bright
authors.affiliation: Geographic Information Science and Technology Group, Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831.
authors.forename: Timmy
authors.initials: T
authors.lastname: Huynh
authors.affiliation: Geographic Information Science and Technology Group, Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831.
authors.forename: Budhendra L
authors.initials: BL
authors.lastname: Bhaduri
mesh_terms: D005544:Forecasting; D006801:Humans; D008962:Models, Theoretical; D011158:Population Growth; D014481:United States
keywords: LandScan; high-resolution population; population distribution; population projections