skip to main content

SciTech ConnectSciTech Connect

Title: Locally-Adaptive, Spatially-Explicit Projection of U.S. Population for 2030 and 2050

Localized adverse events, including natural hazards, epidemiological events, and human conflict, underscore the criticality of quantifying and mapping current population. Moreover, knowing the spatial distribution of future population allows for increased preparation in the event of an emergency. 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 the projected population of the contiguous U.S. for 2030 and 2050. 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. Modelled 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 U.S. Census s projection methodology with the U.S. Census s official projection as the benchmark. Applications of our model include, but are not limited to, suitability modelling, service area planning for governmentalmore » agencies, consequence assessment, mitigation planning and implementation, and assessment of spatially vulnerable populations.« less
 [1] ;  [1] ;  [1] ;  [1] ;  [1]
  1. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Publication Date:
OSTI Identifier:
Grant/Contract Number:
Accepted Manuscript
Journal Name:
Proceedings of the National Academy of Sciences of the United States of America
Additional Journal Information:
Journal Volume: 112; Journal Issue: 5; Journal ID: ISSN 1091-6490
National Academy of Sciences
Research Org:
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
Sponsoring Org:
USDOE Office of Nuclear Energy (NE)
Country of Publication:
United States
97 MATHEMATICS AND COMPUTING population projections; population distribution; LandScan