Preprints
https://doi.org/10.5194/essd-2021-277
https://doi.org/10.5194/essd-2021-277
 
15 Feb 2022
15 Feb 2022
Status: this preprint is currently under review for the journal ESSD.

Improving intelligent dasymetric mapping population density estimates at 30-meter resolution for the conterminous United States by excluding uninhabited areas

Jeremy Baynes1, Anne Neale1, and Torrin Hultgren2 Jeremy Baynes et al.
  • 1Center for Public Health and Environmental Assessment, US Environmental Protection Agency, Research Triangle Park, NC 27711, USA
  • 2EPA National Geospatial Support Team, ITS-EPA III Infrastructure Support and Application Hosting Contract, Research Triangle Park, NC 27711, USA

Abstract. 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 EPA’s EnviroAtlas Dasymetric Population Map for 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 U.S Census Bureau blocks improved across all states in CONUS. We also found that IDM performed better in states with larger urban areas than in states that are sparsely populated. Future updates of the Dasymetric Population Map might benefit from stratified (e.g., urban/exurban/rural) multi-state sampling of population density rather than state-specific sampling. We also updated the existing EnviroAtlas Intelligent Dasymetric Mapping toolbox and expanded its capabilities to accept uninhabited areas.

Jeremy Baynes et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2021-277', Anonymous Referee #1, 08 Apr 2022
  • CC1: 'Comment on essd-2021-277', Anna Dmowska, 16 Apr 2022
  • RC2: 'Comment on essd-2021-277', Anonymous Referee #2, 23 Apr 2022

Jeremy Baynes et al.

Data sets

2010 Dasymetric Population for the Conterminous United States v3 Baynes, J., Neale, A., Hultgren, T. https://doi.org/10.23719/1522948

Model code and software

USEPA / Dasymetric-Toolbox-OpenSource Khan, A., Baynes, J. https://github.com/USEPA/Dasymetric-Toolbox-OpenSource

Jeremy Baynes et al.

Viewed

Total article views: 403 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
298 89 16 403 9 9
  • HTML: 298
  • PDF: 89
  • XML: 16
  • Total: 403
  • BibTeX: 9
  • EndNote: 9
Views and downloads (calculated since 15 Feb 2022)
Cumulative views and downloads (calculated since 15 Feb 2022)

Viewed (geographical distribution)

Total article views: 355 (including HTML, PDF, and XML) Thereof 355 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 24 May 2022
Download
Short summary
Census data are typically provided in irregularly shaped spatial units. To get a more refined estimate of population density, we downscaled population counts from United States (US) Census Blocks to a 30-meter grid using intelligent dasymetric mapping. Furthermore, we improved our density estimates by using multiple spatial datasets to identify and mask uninhabited areas. Masking these uninhabited areas improved density estimates for every state in the conterminous US.