WorldPop Global, gridded population estimates and associated covariate layers for the years 2000-2020

Authors: Forrest R. Stevens*, University of Louisville, Gregory Yetman, Columbia University, Jane Mills, Columbia University, Andrea E. Gaughan, University of Louisville, Alessandro Sorichetta, University of Southampton, Catherine Linard, Université Libre de Bruxelles , Maksym Bondarenko, University of Southampton, Alessandra Carioloi, University of Southampton, Sophie Hanspal, University of Southampton, Theo Hilber, University of Southampton, Graeme Hornby, University of Southampton, William H. M. James, University of Southampton, David Kerr, University of Southampton, Christopher Lloyd, University of Southampton, Jeremiah Nieves, University of Southampton, Kristine Nielsen, University of Southampton, Carla Pezzulo, University of Southampton, Linda Pistolesi, Columbia University, Natalia Tejedor-Garavito, University of Southampton, Nikolaos Vesnikos, University of Southampton, Adelle Wigley, University of Southampton, Andrew J. Tatem, University of Southampton
Topics: Population Geography, Spatial Analysis & Modeling, Remote Sensing
Keywords: demographics, population mapping, urbanization, dasymetric mapping, Random Forest
Session Type: Paper
Day: 4/6/2019
Start / End Time: 5:00 PM / 6:40 PM
Room: Tyler, Marriott, Mezzanine Level
Presentation File: No File Uploaded

End users of gridded population data are often working across applications of hazard risk and mitigation management, health and disease modelling, and economic-, environmental-, and sustainability-related research. But to understand how human population changes through time, and incorporate these estimates across such applications at fine spatial scales, especially between countries where census data is inconsistently collected, many challenges must be met. We present here the WorldPop Global data collection which includes population surfaces for total populations as well as breakdowns by five-year age groups and sex, at annual time-steps between 2000 and 2020, with a spatial resolution of 3 arc seconds. Seamless, global covariate layers are implemented using consistent analytical methods, and are accompanied by metadata outlining inputs and quality assessments. The unique, high-resolution and value open data source is created to meet stakeholder needs, and several application areas will be highlighted. The population maps are discussed in the context of globally available data and how such data are used for better monitoring, planning, and decision making regarding development, conservation and land management.

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