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Dasymetric population mapping based on US census data and 30-m gridded estimates of impervious surface
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 d...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9422266/ https://www.ncbi.nlm.nih.gov/pubmed/36030258 http://dx.doi.org/10.1038/s41597-022-01603-z |
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author | Swanwick, Rachel H. Read, Quentin D. Guinn, Steven M. Williamson, Matthew A. Hondula, Kelly L. Elmore, Andrew J. |
author_facet | Swanwick, Rachel H. Read, Quentin D. Guinn, Steven M. Williamson, Matthew A. Hondula, Kelly L. Elmore, Andrew J. |
author_sort | Swanwick, Rachel H. |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9422266 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-94222662022-08-30 Dasymetric population mapping based on US census data and 30-m gridded estimates of impervious surface Swanwick, Rachel H. Read, Quentin D. Guinn, Steven M. Williamson, Matthew A. Hondula, Kelly L. Elmore, Andrew J. Sci Data Data Descriptor 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. Nature Publishing Group UK 2022-08-27 /pmc/articles/PMC9422266/ /pubmed/36030258 http://dx.doi.org/10.1038/s41597-022-01603-z Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Data Descriptor Swanwick, Rachel H. Read, Quentin D. Guinn, Steven M. Williamson, Matthew A. Hondula, Kelly L. Elmore, Andrew J. Dasymetric population mapping based on US census data and 30-m gridded estimates of impervious surface |
title | Dasymetric population mapping based on US census data and 30-m gridded estimates of impervious surface |
title_full | Dasymetric population mapping based on US census data and 30-m gridded estimates of impervious surface |
title_fullStr | Dasymetric population mapping based on US census data and 30-m gridded estimates of impervious surface |
title_full_unstemmed | Dasymetric population mapping based on US census data and 30-m gridded estimates of impervious surface |
title_short | Dasymetric population mapping based on US census data and 30-m gridded estimates of impervious surface |
title_sort | dasymetric population mapping based on us census data and 30-m gridded estimates of impervious surface |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9422266/ https://www.ncbi.nlm.nih.gov/pubmed/36030258 http://dx.doi.org/10.1038/s41597-022-01603-z |
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