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.

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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. 
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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.