Locally-Adaptive, Spatially-Explicit Projection of U.S. 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. 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 »
- Authors:
-
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
- Publication Date:
- Research Org.:
- Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
- Sponsoring Org.:
- USDOE Office of Nuclear Energy (NE)
- OSTI Identifier:
- 1185456
- Grant/Contract Number:
- AC05-00OR22725
- Resource Type:
- Journal Article: Accepted Manuscript
- Journal Name:
- Proceedings of the National Academy of Sciences of the United States of America (Online)
- Additional Journal Information:
- Journal Volume: 112; Journal Issue: 5; Journal ID: ISSN 1091-6490
- Publisher:
- National Academy of Sciences
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 97 MATHEMATICS AND COMPUTING; population projections; population distribution; LandScan
Citation Formats
McKee, Jacob J., Rose, Amy N., Bright, Eddie A., Huynh, Timmy N., and Bhaduri, Budhendra L. Locally-Adaptive, Spatially-Explicit Projection of U.S. Population for 2030 and 2050. United States: N. p., 2015.
Web. doi:10.1073/pnas.1405713112.
McKee, Jacob J., Rose, Amy N., Bright, Eddie A., Huynh, Timmy N., & Bhaduri, Budhendra L. Locally-Adaptive, Spatially-Explicit Projection of U.S. Population for 2030 and 2050. United States. https://doi.org/10.1073/pnas.1405713112
McKee, Jacob J., Rose, Amy N., Bright, Eddie A., Huynh, Timmy N., and Bhaduri, Budhendra L. 2015.
"Locally-Adaptive, Spatially-Explicit Projection of U.S. Population for 2030 and 2050". United States. https://doi.org/10.1073/pnas.1405713112. https://www.osti.gov/servlets/purl/1185456.
@article{osti_1185456,
title = {Locally-Adaptive, Spatially-Explicit Projection of U.S. Population for 2030 and 2050},
author = {McKee, Jacob J. and Rose, Amy N. and Bright, Eddie A. and Huynh, Timmy N. and Bhaduri, Budhendra L.},
abstractNote = {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 governmental agencies, consequence assessment, mitigation planning and implementation, and assessment of spatially vulnerable populations.},
doi = {10.1073/pnas.1405713112},
url = {https://www.osti.gov/biblio/1185456},
journal = {Proceedings of the National Academy of Sciences of the United States of America (Online)},
issn = {1091-6490},
number = 5,
volume = 112,
place = {United States},
year = {Tue Feb 03 00:00:00 EST 2015},
month = {Tue Feb 03 00:00:00 EST 2015}
}
Web of Science
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