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A pixel level evaluation of five multitemporal global gridded population datasets: a case study in Sweden, 1990-2015
Lund University, Department of Human Geography, Sölvegatan 10, Lund, 223 62, Sweden.
Lund University, Department of Human Geography, Sölvegatan 10, Lund, 223 62, Sweden.
Department of Analysis and Coordination, Region Halland, Halmstad, Sweden; Stockholm University, Department of Human Geography, Stockholm, Sweden .
University West, Department of Engineering Science, Division of Mathematics, Computer and Surveying Engineering.ORCID iD: 0000-0003-4559-6453
2020 (English)In: Population and environment, ISSN 0199-0039, E-ISSN 1573-7810, Vol. 42, p. 255-277Article in journal (Refereed) Published
Abstract [en]

Human activity is a major driver of change and has contributed to many of the challenges we face today. Detailed information about human population distribution is fundamental and use of freely available, high-resolution, gridded datasets on global population as a source of such information is increasing. However, there is little research to guide users in dataset choice. This study evaluates five of the most commonly used global gridded population datasets against a high-resolution Swedish population dataset on a pixel level. We show that datasets which employ more complex modeling techniques exhibit lower errors overall but no one dataset performs best under all situations. Furthermore, differences exist in how unpopulated areas are identified and changes in algorithms over time affect accuracy. Our results provide guidance in navigating the differences between the most commonly used gridded population datasets and will help researchers and policy makers identify the most suitable datasets under varying conditions. © 2020, The Author(s).

Place, publisher, year, edition, pages
2020. Vol. 42, p. 255-277
Keywords [en]
Global population, datasets
National Category
Physical Geography
Identifiers
URN: urn:nbn:se:hv:diva-15812DOI: 10.1007/s11111-020-00360-8ISI: 000565150200002Scopus ID: 2-s2.0-85090130289OAI: oai:DiVA.org:hv-15812DiVA, id: diva2:1466913
Available from: 2020-09-14 Created: 2020-09-14 Last updated: 2020-11-05Bibliographically approved

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Ernstson, Ulf

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CiteExportLink to record
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  • apa
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