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Global spatio-temporally harmonised datasets for producing high-resolution gridded population distribution datasets

Big Earth Data 2019年第2期3卷 108-139页

作者： Christopher T.Lloyd Heather Chamberlain David Kerr Greg Yetman Linda Pistolesi Forrest R.Stevens Andrea E.Gaughan Jeremiah J.Nieves Graeme Hornby Kytt MacManus Parmanand Sinha Maksym Bondarenko Alessandro Sorichetta Andrew J.Tatem WorldPop School of Geography and Environmental ScienceUniversity of SouthamptonSouthamptonUK Flowminder Foundation StockholmSweden Center for International Earth Science Information Network(CIESIN) Columbia UniversityPalisadesNYUSA Department of Geography and Geosciences University of LouisvilleLouisvilleKYUSA GeoData University of SouthamptonSouthamptonUK

Multi-temporal,globally consistent,high-resolution human population datasets provide consistent and comparable population distributions in support of mapping sub-national heterogeneities in health,wealth,and resource access,and monitoring change in these over time.The production of more reliable and spatially detailed population datasets is increasingly necessary due to the importance of improving metrics at sub-national and multitemporal scales.This is in support of measurement and monitoring of UN Sustainable Development Goals and related agendas.In response to these agendas,a method has been developed to assemble and harmonise a unique,open access,archive of geospatial datasets.Datasets are provided as global,annual time series,where pertinent at the timescale of population analyses and where data is available,for use in the construction of population distribution layers.The archive includes sub-national census-based population estimates,matched to a geospatial layer denoting administrative unit boundaries,and a number of co-registered gridded geospatial factors that correlate strongly with population presence and density.Here,we describe these harmonised datasets and their limitations,along with the production workflow.Further,we demonstrate applications of the archive by producing multi-temporal gridded population outputs for Africa and using these to derive health and development metrics.The geospatial archive is available at https://***/10.5258/SOTON/WP00650.

关键词： Human population subnational global spatial dataset multi-temporal

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