Improving intelligent dasymetric mapping population density estimates at 30 m resolution for the conterminous United States by excluding uninhabited areas
<p>Population change impacts almost every aspect of global change from land use, to greenhouse gas emissions, to biodiversity conservation, to the spread of disease. Data on spatial patterns of population density help us understand patterns and drivers of human settlement and can help us quant...
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2022-06-01
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| Series: | Earth System Science Data |
| Online Access: | https://essd.copernicus.org/articles/14/2833/2022/essd-14-2833-2022.pdf |
| _version_ | 1818548206740439040 |
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| author | J. Baynes A. Neale T. Hultgren |
| author_facet | J. Baynes A. Neale T. Hultgren |
| author_sort | J. Baynes |
| collection | DOAJ |
| description | <p>Population change impacts almost every aspect of global
change from land use, to greenhouse gas emissions, to biodiversity
conservation, to the spread of disease. Data on spatial patterns of
population density help us understand patterns and drivers of human
settlement and can help us quantify the exposure we face to natural
disasters, pollution, and infectious disease. Human populations are
typically recorded by national or regional units that can vary in shape and
size. Using these irregularly sized units and ancillary data related to
population dynamics, we can produce high-resolution gridded estimates of
population density through intelligent dasymetric mapping (IDM). The gridded
population density provides a more detailed estimate of how the population
is distributed within larger units. Furthermore, we can refine our estimates
of population density by specifying uninhabited areas which have impacts on
the analysis of population density such as our estimates of human exposure.
In this study, we used various geospatial datasets to expand the existing
specification of uninhabited areas within the United States (US)
Environmental Protection Agency's (EPA) EnviroAtlas Dasymetric Population
Map for the conterminous United States (CONUS). When compared to the existing
definition of uninhabited areas for the EnviroAtlas dasymetric population
map, we found that IDM's population estimates for the US Census Bureau blocks
improved across all states in the CONUS. We found that IDM performed better in
states with larger urban areas than in states that are sparsely populated.
We also updated the existing EnviroAtlas Intelligent Dasymetric Mapping
toolbox and expanded its capabilities to accept uninhabited areas. The
updated 30 m population density for the CONUS is available via the EPA's
Environmental Dataset Gateway (Baynes et al., 2021,
<a href="https://doi.org/10.23719/1522948">https://doi.org/10.23719/1522948</a>) and the EPA's EnviroAtlas
(<span class="uri">https://www.epa.gov/enviroatlas</span>, last access: 15 June 2022; Pickard et al., 2015).</p> |
| first_indexed | 2024-12-12T08:16:50Z |
| format | Article |
| id | doaj.art-2a678aa2002448219fe1a605cbab4ba1 |
| institution | Directory Open Access Journal |
| issn | 1866-3508 1866-3516 |
| language | English |
| last_indexed | 2024-12-12T08:16:50Z |
| publishDate | 2022-06-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Earth System Science Data |
| spelling | doaj.art-2a678aa2002448219fe1a605cbab4ba12022-12-22T00:31:32ZengCopernicus PublicationsEarth System Science Data1866-35081866-35162022-06-01142833284910.5194/essd-14-2833-2022Improving intelligent dasymetric mapping population density estimates at 30 m resolution for the conterminous United States by excluding uninhabited areasJ. Baynes0A. Neale1T. Hultgren2Center for Public Health and Environmental Assessment, US Environmental Protection Agency, Research Triangle Park, NC 27711, USACenter for Public Health and Environmental Assessment, US Environmental Protection Agency, Research Triangle Park, NC 27711, USAEPA National Geospatial Support Team, ITS-EPA III Infrastructure Support and Application Hosting Contract, Research Triangle Park, NC 27711, USA<p>Population change impacts almost every aspect of global change from land use, to greenhouse gas emissions, to biodiversity conservation, to the spread of disease. Data on spatial patterns of population density help us understand patterns and drivers of human settlement and can help us quantify the exposure we face to natural disasters, pollution, and infectious disease. Human populations are typically recorded by national or regional units that can vary in shape and size. Using these irregularly sized units and ancillary data related to population dynamics, we can produce high-resolution gridded estimates of population density through intelligent dasymetric mapping (IDM). The gridded population density provides a more detailed estimate of how the population is distributed within larger units. Furthermore, we can refine our estimates of population density by specifying uninhabited areas which have impacts on the analysis of population density such as our estimates of human exposure. In this study, we used various geospatial datasets to expand the existing specification of uninhabited areas within the United States (US) Environmental Protection Agency's (EPA) EnviroAtlas Dasymetric Population Map for the conterminous United States (CONUS). When compared to the existing definition of uninhabited areas for the EnviroAtlas dasymetric population map, we found that IDM's population estimates for the US Census Bureau blocks improved across all states in the CONUS. We found that IDM performed better in states with larger urban areas than in states that are sparsely populated. We also updated the existing EnviroAtlas Intelligent Dasymetric Mapping toolbox and expanded its capabilities to accept uninhabited areas. The updated 30 m population density for the CONUS is available via the EPA's Environmental Dataset Gateway (Baynes et al., 2021, <a href="https://doi.org/10.23719/1522948">https://doi.org/10.23719/1522948</a>) and the EPA's EnviroAtlas (<span class="uri">https://www.epa.gov/enviroatlas</span>, last access: 15 June 2022; Pickard et al., 2015).</p>https://essd.copernicus.org/articles/14/2833/2022/essd-14-2833-2022.pdf |
| spellingShingle | J. Baynes A. Neale T. Hultgren Improving intelligent dasymetric mapping population density estimates at 30 m resolution for the conterminous United States by excluding uninhabited areas Earth System Science Data |
| title | Improving intelligent dasymetric mapping population density estimates at 30 m resolution for the conterminous United States by excluding uninhabited areas |
| title_full | Improving intelligent dasymetric mapping population density estimates at 30 m resolution for the conterminous United States by excluding uninhabited areas |
| title_fullStr | Improving intelligent dasymetric mapping population density estimates at 30 m resolution for the conterminous United States by excluding uninhabited areas |
| title_full_unstemmed | Improving intelligent dasymetric mapping population density estimates at 30 m resolution for the conterminous United States by excluding uninhabited areas |
| title_short | Improving intelligent dasymetric mapping population density estimates at 30 m resolution for the conterminous United States by excluding uninhabited areas |
| title_sort | improving intelligent dasymetric mapping population density estimates at 30 thinsp m resolution for the conterminous united states by excluding uninhabited areas |
| url | https://essd.copernicus.org/articles/14/2833/2022/essd-14-2833-2022.pdf |
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