Implications for Tracking SDG Indicator Metrics with Gridded Population Data

Cascade Tuholske, Andrea E. Gaughan, Alessandro Sorichetta, Alex de Sherbinin, Agathe Bucherie, Carolynne Hultquist, Forrest Stevens, Andrew Kruczkiewicz, Charles Huyck and Greg Yetman
Additional contact information
Cascade Tuholske: Center for International Earth Science Information Network, The Earth Institute, Columbia University, Palisades, NY 10964, USA
Andrea E. Gaughan: Department of Geography and Geosciences, University of Louisville, Louisville, KY 40292, USA
Alessandro Sorichetta: WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton SO17 1BJ, UK
Alex de Sherbinin: Center for International Earth Science Information Network, The Earth Institute, Columbia University, Palisades, NY 10964, USA
Agathe Bucherie: International Research Institute for Climate and Society, The Earth Institute, Columbia University, Palisades, NY 10964, USA
Carolynne Hultquist: Center for International Earth Science Information Network, The Earth Institute, Columbia University, Palisades, NY 10964, USA
Forrest Stevens: Department of Geography and Geosciences, University of Louisville, Louisville, KY 40292, USA
Andrew Kruczkiewicz: International Research Institute for Climate and Society, The Earth Institute, Columbia University, Palisades, NY 10964, USA
Charles Huyck: ImageCat, Inc., Long Beach, CA 90802, USA
Greg Yetman: Center for International Earth Science Information Network, The Earth Institute, Columbia University, Palisades, NY 10964, USA

Sustainability, 2021, vol. 13, issue 13, 1-21

Abstract: Achieving the seventeen United Nations Sustainable Development Goals (SDGs) requires accurate, consistent, and accessible population data. Yet many low- and middle-income countries lack reliable or recent census data at the sufficiently fine spatial scales needed to monitor SDG progress. While the increasing abundance of Earth observation-derived gridded population products provides analysis-ready population estimates, end users lack clear use criteria to track SDGs indicators. In fact, recent comparisons of gridded population products identify wide variation across gridded population products. Here we present three case studies to illuminate how gridded population datasets compare in measuring and monitoring SDGs to advance the “fitness for use” guidance. Our focus is on SDG 11.5, which aims to reduce the number of people impacted by disasters. We use five gridded population datasets to measure and map hazard exposure for three case studies: the 2015 earthquake in Nepal; Cyclone Idai in Mozambique, Malawi, and Zimbabwe (MMZ) in 2019; and flash flood susceptibility in Ecuador. First, we map and quantify geographic patterns of agreement/disagreement across gridded population products for Nepal, MMZ, and Ecuador, including delineating urban and rural populations estimates. Second, we quantify the populations exposed to each hazard. Across hazards and geographic contexts, there were marked differences in population estimates across the gridded population datasets. As such, it is key that researchers, practitioners, and end users utilize multiple gridded population datasets—an ensemble approach—to capture uncertainty and/or provide range estimates when using gridded population products to track SDG indicators. To this end, we made available code and globally comprehensive datasets that allows for the intercomparison of gridded population products.

Keywords: Sustainable Development Goals; hazards; Earth observations; remote sensing; demography; urbanization; gridded population (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
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