Earth System Science Data
Earth System Science Data
Earth System Science Data
Articles | Volume 14, issue 6
Articles | Volume 14, issue 6
https://doi.org/10.5194/essd-14-2833-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-14-2833-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 14, issue 6
Data description paper
 | 
23 Jun 2022
Data description paper |  | 23 Jun 2022

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

Jeremy Baynes, Anne Neale, and Torrin Hultgren

Viewed

Total article views: 3,320 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
2,425 798 97 3,320 72 80
  • HTML: 2,425
  • PDF: 798
  • XML: 97
  • Total: 3,320
  • BibTeX: 72
  • EndNote: 80
Views and downloads (calculated since 15 Feb 2022)
Cumulative views and downloads (calculated since 15 Feb 2022)

Viewed (geographical distribution)

Total article views: 3,320 (including HTML, PDF, and XML) Thereof 3,112 with geography defined and 208 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 28 Dec 2024
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 m 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.
Read more
Altmetrics
Final-revised paper
Preprint