Abstract

Assessment of socio-environmental problems and the search for solutions often require intersecting geospatial data on environmental factors and human population densities. In the United States, Census data is the most common source for information on population. However, timely acquisition of such data at sufficient spatial resolution can be problematic, especially in cases where the analysis area spans urban-rural gradients. With this data release, we provide a 30-m resolution population estimate for the contiguous United States. The workflow dasymetrically distributes Census block level population estimates across all non-transportation impervious surfaces within each Census block. The methodology is updatable using the most recent Census data and remote sensing-based observations of impervious surface area. The dataset, known as the U.G.L.I (updatable gridded lightweight impervious) population dataset, compares favorably against other population data sources, and provides a useful balance between resolution and complexity.

Measurement(s)

Population Density

Technology Type(s)

satellite imaging

Sample Characteristic - Organism

Homo sapiens

Sample Characteristic - Environment

populated place

Sample Characteristic - Location

contiguous United States of America

Details

Title
Dasymetric population mapping based on US census data and 30-m gridded estimates of impervious surface
Author
Swanwick, Rachel H. 1 ; Read, Quentin D. 2 ; Guinn, Steven M. 3 ; Williamson, Matthew A. 4 ; Hondula, Kelly L. 5 ; Elmore, Andrew J. 6   VIAFID ORCID Logo 

1  National Socio-Environmental Synthesis Center, Annapolis, USA (GRID:grid.484514.8); University of Vermont, Rubenstein School of Environment and Natural Resources, Burlington, USA (GRID:grid.59062.38) (ISNI:0000 0004 1936 7689) 
2  National Socio-Environmental Synthesis Center, Annapolis, USA (GRID:grid.484514.8); Agricultural Research Service, United States Department of Agriculture, Raleigh, USA (GRID:grid.463419.d) (ISNI:0000 0001 0946 3608) 
3  University of Maryland Center for Environmental Science, Integration and Application Network, Annapolis, USA (GRID:grid.291951.7) (ISNI:0000 0000 8750 413X); University of Maryland Center for Environmental Science, Appalachian Laboratory, Frostburg, USA (GRID:grid.291951.7) (ISNI:0000 0000 8750 413X) 
4  Boise State University, Human-Environment Systems, Boise, USA (GRID:grid.184764.8) (ISNI:0000 0001 0670 228X) 
5  National Socio-Environmental Synthesis Center, Annapolis, USA (GRID:grid.484514.8); Arizona State University, Center for Global Discovery and Conservation Science, Tempe, USA (GRID:grid.215654.1) (ISNI:0000 0001 2151 2636) 
6  National Socio-Environmental Synthesis Center, Annapolis, USA (GRID:grid.484514.8); University of Maryland Center for Environmental Science, Integration and Application Network, Annapolis, USA (GRID:grid.291951.7) (ISNI:0000 0000 8750 413X); University of Maryland Center for Environmental Science, Appalachian Laboratory, Frostburg, USA (GRID:grid.291951.7) (ISNI:0000 0000 8750 413X) 
Publication year
2022
Publication date
2022
Publisher
Nature Publishing Group
e-ISSN
20524463
Source type
Scholarly Journal
Language of publication
English
DOI
https://doi.org/10.1038/s41597-022-01603-z
ProQuest document ID
2707270611
Copyright
© The Author(s) 2022. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.