Articles | Volume 14, issue 6
Earth Syst. Sci. Data, 14, 2833–2849, 2022
https://doi.org/10.5194/essd-14-2833-2022
Earth Syst. Sci. Data, 14, 2833–2849, 2022
https://doi.org/10.5194/essd-14-2833-2022
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 et al.

Download

Interactive discussion

Status: closed

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
    • AC1: 'Reply on RC1', Jeremy Baynes, 27 May 2022
  • CC1: 'Comment on essd-2021-277', Anna Dmowska, 16 Apr 2022
    • AC3: 'Reply on CC1', Jeremy Baynes, 27 May 2022
  • RC2: 'Comment on essd-2021-277', Anonymous Referee #2, 23 Apr 2022
    • AC2: 'Reply on RC2', Jeremy Baynes, 27 May 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Jeremy Baynes on behalf of the Authors (27 May 2022)  Author's response    Author's tracked changes    Manuscript
ED: Publish as is (30 May 2022) by David Carlson
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.