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Improving intelligent dasymetric mapping population density estimates at 30 m resolution for the conterminous United States by excluding uninhabited areas
Citation:
Baynes, J., A. Neale, AND T. Hultgren. Improving intelligent dasymetric mapping population density estimates at 30 m resolution for the conterminous United States by excluding uninhabited areas. Earth System Science Data. Copernicus Publications, Katlenburg-Lindau, Germany, 14(6):2833-2849, (2022). https://doi.org/10.5194/essd-14-2833-2022
Impact/Purpose:
Population change impacts almost every aspect of global change from land use, to greenhouse gas emissions, to biodiversity conservation, to the spread of disease. Data on spatial patterns of population density help us understand patterns and drivers of human settlement and can help us quantify the exposure we face to natural disasters, pollution, and infectious disease. Human populations are typically recorded by national or regional units that can vary in shape and size. Using these irregularly sized units and ancillary data related to population dynamics, we can produce high resolution, gridded estimates of population density through intelligent dasymetric mapping (IDM). The gridded population density provides a more detailed estimate of how the population is distributed within larger units. Furthermore, we can refine our estimates of population density by specifying uninhabited areas which have impacts on the analysis of population density such as our estimates of human exposure.
Description:
Updating code and methods to develop downscaled population density estimates at 30-m resolution for the U.S.
URLs/Downloads:
DOI: Improving intelligent dasymetric mapping population density estimates at 30 m resolution for the conterminous United States by excluding uninhabited areas