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

[摘要] Population change impacts almost every aspect of globalchange from land use, to greenhouse gas emissions, to biodiversityconservation, to the spread of disease. Data on spatial patterns ofpopulation density help us understand patterns and drivers of humansettlement and can help us quantify the exposure we face to naturaldisasters, pollution, and infectious disease. Human populations aretypically recorded by national or regional units that can vary in shape andsize. Using these irregularly sized units and ancillary data related topopulation dynamics, we can produce high-resolution gridded estimates ofpopulation density through intelligent dasymetric mapping (IDM). The griddedpopulation density provides a more detailed estimate of how the populationis distributed within larger units. Furthermore, we can refine our estimatesof population density by specifying uninhabited areas which have impacts onthe analysis of population density such as our estimates of human exposure.In this study, we used various geospatial datasets to expand the existingspecification of uninhabited areas within the United States (US)Environmental Protection Agency's (EPA) EnviroAtlas Dasymetric PopulationMap for the conterminous United States (CONUS). When compared to the existingdefinition of uninhabited areas for the EnviroAtlas dasymetric populationmap, we found that IDM's population estimates for the US Census Bureau blocksimproved across all states in the CONUS. We found that IDM performed better instates with larger urban areas than in states that are sparsely populated.We also updated the existing EnviroAtlas Intelligent Dasymetric Mappingtoolbox and expanded its capabilities to accept uninhabited areas. Theupdated 30 m population density for the CONUS is available via the EPA'sEnvironmental Dataset Gateway (Baynes et al., 2021, https://doi.org/10.23719/1522948 ) and the EPA's EnviroAtlas( https://www.epa.gov/enviroatlas , last access: 15 June 2022; Pickard et al., 2015).

[学科分类] 眼科学