Articles | Volume 14, issue 6
https://doi.org/10.5194/essd-14-2833-2022
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, Anne Neale, and Torrin Hultgren

Related authors

Configuring parallel use of custom ArcGIS toolboxes in a Linux high-performance computing environment
Jeremy Baynes, Jacob Tafrate, Donald Ebert, and Steven Lennartz
EGUsphere, https://doi.org/10.5194/egusphere-2025-1558,https://doi.org/10.5194/egusphere-2025-1558, 2025
Preprint archived
Short summary

Cited articles

Azar, D., Engstrom, R., Graesser, J., and Comenetz, J.: Generation of fine-scale population layers using multi-resolution satellite imagery and geospatial data, Remote Sens. Environ., 130, 219–232, https://doi.org/10.1016/j.rse.2012.11.022, 2013. 
Baynes, J., Neale, A., and Hultgren, T.: 2010 Dasymetric Population for the Conterminous United States v3, US Environmental Protection Agency Office of Research and Development [data set], https://doi.org/10.23719/1522948, 2021. 
Bellwood, D. R., Hoey, A. S., and Hughes, T. P.: Human activity selectively impacts the ecosystem roles of parrotfishes on coral reefs, Proc. Biol. Sci., 279, 1621–1629, https://doi.org/10.1098/rspb.2011.1906, 2012. 
Carroll, R. J., Chen, R., George, E. I., Li, T. H., Newton, H. J., Schmiediche, H., and Wang, N.: Ozone Exposure and Population Density in Harris County, Texas, J. Am. Stat. Assoc., 92, 392–404, https://doi.org/10.1080/01621459.1997.10473988, 1997. 
Cinner, J. E., Graham, N. A., Huchery, C., and Macneil, M. A.: Global effects of local human population density and distance to markets on the condition of coral reef fisheries, Conserv. Biol., 27, 453–458, https://doi.org/10.1111/j.1523-1739.2012.01933.x, 2013. 
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.
Share
Altmetrics
Final-revised paper
Preprint