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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.
Cascade Tuholske; Andrea Gaughan; Alessandro Sorichetta; Alex de Sherbinin; Agathe Bucherie; Carolynne Hultquist; Forrest Stevens; Andrew Kruczkiewicz; Charles Huyck; Greg Yetman. Implications for Tracking SDG Indicator Metrics with Gridded Population Data. Sustainability 2021, 13, 7329 .
AMA StyleCascade Tuholske, Andrea Gaughan, Alessandro Sorichetta, Alex de Sherbinin, Agathe Bucherie, Carolynne Hultquist, Forrest Stevens, Andrew Kruczkiewicz, Charles Huyck, Greg Yetman. Implications for Tracking SDG Indicator Metrics with Gridded Population Data. Sustainability. 2021; 13 (13):7329.
Chicago/Turabian StyleCascade Tuholske; Andrea Gaughan; Alessandro Sorichetta; Alex de Sherbinin; Agathe Bucherie; Carolynne Hultquist; Forrest Stevens; Andrew Kruczkiewicz; Charles Huyck; Greg Yetman. 2021. "Implications for Tracking SDG Indicator Metrics with Gridded Population Data." Sustainability 13, no. 13: 7329.
Low- and middle-income country cities face unprecedented urbanization and growth in slums. Gridded population data (e.g., ~100 × 100 m) derived from demographic and spatial data are a promising source of population estimates, but face limitations in slums due to the dynamic nature of this population as well as modelling assumptions. In this study, we compared field-referenced boundaries and population counts from Slum Dwellers International in Lagos (Nigeria), Port Harcourt (Nigeria), and Nairobi (Kenya) with nine gridded population datasets to assess their statistical accuracy in slums. We found that all gridded population estimates vastly underestimated population in slums (RMSE: 4958 to 14,422, Bias: −2853 to −7638), with the most accurate dataset (HRSL) estimating just 39 per cent of slum residents. Using a modelled map of all slums in Lagos to compare gridded population datasets in terms of SDG 11.1.1 (percent of population living in deprived areas), all gridded population datasets estimated this indicator at just 1–3 per cent compared to 56 per cent using UN-Habitat’s approach. We outline steps that might improve that accuracy of each gridded population dataset in deprived urban areas. While gridded population estimates are not yet sufficiently accurate to estimate SDG 11.1.1, we are optimistic that some could be used in the future following updates to their modelling approaches.
Dana Thomson; Andrea Gaughan; Forrest Stevens; Gregory Yetman; Peter Elias; Robert Chen. Evaluating the Accuracy of Gridded Population Estimates in Slums: A Case Study in Nigeria and Kenya. Urban Science 2021, 5, 48 .
AMA StyleDana Thomson, Andrea Gaughan, Forrest Stevens, Gregory Yetman, Peter Elias, Robert Chen. Evaluating the Accuracy of Gridded Population Estimates in Slums: A Case Study in Nigeria and Kenya. Urban Science. 2021; 5 (2):48.
Chicago/Turabian StyleDana Thomson; Andrea Gaughan; Forrest Stevens; Gregory Yetman; Peter Elias; Robert Chen. 2021. "Evaluating the Accuracy of Gridded Population Estimates in Slums: A Case Study in Nigeria and Kenya." Urban Science 5, no. 2: 48.
The spatial distribution of humans on the earth is critical knowledge that informs many disciplines and is available in a spatially explicit manner through gridded population techniques. While many approaches exist to produce specialized gridded population maps, little has been done to explore how remotely sensed, built-area datasets might be used to dasymetrically constrain these estimates. This study presents the effectiveness of three different high-resolution built area datasets for producing gridded population estimates through the dasymetric disaggregation of census counts in Haiti, Malawi, Madagascar, Nepal, Rwanda, and Thailand. Modeling techniques include a binary dasymetric redistribution, a random forest with a dasymetric component, and a hybrid of the previous two. The relative merits of these approaches and the data are discussed with regards to studying human populations and related spatially explicit phenomena. Results showed that the accuracy of random forest and hybrid models was comparable in five of six countries.
Fennis J. Reed; Andrea E. Gaughan; Forrest R. Stevens; Greg Yetman; Alessandro Sorichetta; Andrew J. Tatem. Gridded Population Maps Informed by Different Built Settlement Products. Data 2018, 3, 33 .
AMA StyleFennis J. Reed, Andrea E. Gaughan, Forrest R. Stevens, Greg Yetman, Alessandro Sorichetta, Andrew J. Tatem. Gridded Population Maps Informed by Different Built Settlement Products. Data. 2018; 3 (3):33.
Chicago/Turabian StyleFennis J. Reed; Andrea E. Gaughan; Forrest R. Stevens; Greg Yetman; Alessandro Sorichetta; Andrew J. Tatem. 2018. "Gridded Population Maps Informed by Different Built Settlement Products." Data 3, no. 3: 33.
The use of Global Positioning Systems (GPS) and Geographical Information Systems (GIS) in disease surveys and reporting is becoming increasingly routine, enabling a better understanding of spatial epidemiology and the improvement of surveillance and control strategies. In turn, the greater availability of spatially referenced epidemiological data is driving the rapid expansion of disease mapping and spatial modeling methods, which are becoming increasingly detailed and sophisticated, with rigorous handling of uncertainties. This expansion has, however, not been matched by advancements in the development of spatial datasets of human population distribution that accompany disease maps or spatial models.
Andrew J Tatem; Susana Adamo; Nita Bharti; Clara R Burgert; Marcia Castro; Audrey Dorélien; Gunter Fink; Catherine Linard; Mendelsohn John; Livia Montana; Mark R Montgomery; Andrew Nelson; Abdisalan M Noor; Deepa Pindolia; Greg Yetman; Deborah Balk. Mapping populations at risk: improving spatial demographic data for infectious disease modeling and metric derivation. Population Health Metrics 2012, 10, 8 -8.
AMA StyleAndrew J Tatem, Susana Adamo, Nita Bharti, Clara R Burgert, Marcia Castro, Audrey Dorélien, Gunter Fink, Catherine Linard, Mendelsohn John, Livia Montana, Mark R Montgomery, Andrew Nelson, Abdisalan M Noor, Deepa Pindolia, Greg Yetman, Deborah Balk. Mapping populations at risk: improving spatial demographic data for infectious disease modeling and metric derivation. Population Health Metrics. 2012; 10 (1):8-8.
Chicago/Turabian StyleAndrew J Tatem; Susana Adamo; Nita Bharti; Clara R Burgert; Marcia Castro; Audrey Dorélien; Gunter Fink; Catherine Linard; Mendelsohn John; Livia Montana; Mark R Montgomery; Andrew Nelson; Abdisalan M Noor; Deepa Pindolia; Greg Yetman; Deborah Balk. 2012. "Mapping populations at risk: improving spatial demographic data for infectious disease modeling and metric derivation." Population Health Metrics 10, no. 1: 8-8.