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

Baynes, Jeremy; Neale, Anne; Hultgren, Torrin

doi:https://doi.org/10.5194/essd-14-2833-2022

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

Data sets

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

Model code and software

USEPA/Dasymetric-Toolbox-ArcGISPro: v1.0.0 (v1.0.0) US Environmental Protection Agency https://doi.org/10.5281/zenodo.6645816

USEPA/Dasymetric-Toolbox-OpenSource: v1.0.0 (v1.0.0) US Environmental Protection Agency https://doi.org/10.5281/zenodo.6645824

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.