Interactions between humans,diseases,and the environment take place across a range of temporal and spatial scales,making accurate,contemporary data on human population distributions critical for a variety of disciplin...Interactions between humans,diseases,and the environment take place across a range of temporal and spatial scales,making accurate,contemporary data on human population distributions critical for a variety of disciplines.Methods for disaggregating census data to finer-scale,gridded population density estimates continue to be refined as computational power increases and more detailed census,input,and validation datasets become available.However,the availability of spatially detailed census data still varies widely by country.In this study,we develop quantitative guidelines for choosing regionally-parameterized census count disaggregation models over country-specific models.We examine underlying methodological considerations for improving gridded population datasets for countries with coarser scale census data by investigating regional versus country-specific models used to estimate density surfaces for redistributing census counts.Consideration is given to the spatial resolution of input census data using examples from East Africa and Southeast Asia.Results suggest that for many countries more accurate population maps can be produced by using regionally-parameterized models where more spatially refined data exists than that which is available for the focal country.This study highlights the advancement of statistical toolsets and considerations for underlying data used in generating widely used gridded population data.展开更多
基金This work was supported by the RAPIDD program of the Science and Technology Directorate,Department of Homeland Security,and the Fogarty International Center,National Institutes of HealthNIH/NIAID[grant number U19AI089674]and the Bill and Melinda Gates Foundation[grant number OPP1106427],[grant number 1032350].CL is supported by the Fonds National de la Recherche Scientifique(F.R.S./FNRS),Brussels,Belgium.This work forms part of the outputs of the WorldPop Project(www.worldpop.org.uk)and Flowminder Foundation(www.flowminder.org).
文摘Interactions between humans,diseases,and the environment take place across a range of temporal and spatial scales,making accurate,contemporary data on human population distributions critical for a variety of disciplines.Methods for disaggregating census data to finer-scale,gridded population density estimates continue to be refined as computational power increases and more detailed census,input,and validation datasets become available.However,the availability of spatially detailed census data still varies widely by country.In this study,we develop quantitative guidelines for choosing regionally-parameterized census count disaggregation models over country-specific models.We examine underlying methodological considerations for improving gridded population datasets for countries with coarser scale census data by investigating regional versus country-specific models used to estimate density surfaces for redistributing census counts.Consideration is given to the spatial resolution of input census data using examples from East Africa and Southeast Asia.Results suggest that for many countries more accurate population maps can be produced by using regionally-parameterized models where more spatially refined data exists than that which is available for the focal country.This study highlights the advancement of statistical toolsets and considerations for underlying data used in generating widely used gridded population data.
