Implications for Tracking SDG Indicator Metrics with Gridded Population Data
Abstract
:1. Introduction
“By 2030, significantly reduce the number of deaths and the number of people affected and substantially decrease the direct economic losses relative to global gross domestic product caused by disasters, including water-related disasters, with a focus on protecting the poor and people in vulnerable situations.”
2. Materials and Methods
2.1. Dataset Descriptions
2.1.1. Population Data
2.1.2. Hazards Impacts & Data
2015 Nepal Earthquake
2019 Cyclone Idai
Ecuador Flash Flood Susceptibility
Urban/Rural Data
2.2. Raster Processing & Analysis
3. Results
3.1. Pixel-Level Comparisons
3.1.1. Cyclone Idai Exposure in MMZ
3.1.2. Flash Flood Susceptibility in Ecuador
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Dataset | Producer | EO Data | Population | Constrained | Model Description | Citation |
---|---|---|---|---|---|---|
GPW-15: Gridded Population of the World v4.11, 2015 | CIESIN, Columbia University | None | Residential | No | Equal allocation of population to cells within admin. units | [32] |
GHS-15: Global Human Settlement Layer-POP, 2015 | European Commission, Joint Research Centre (JRC) | Landsat | Residential | Yes | Binary dasymetric, proportional allocation to built-up areas extracted primarily from 30 m Landsat imagery | [31] |
WP-16: WorldPop Global, Unconstrained, 2016 | WorldPop, Univ. of Southampton | Landsat, DMSP-OLS, VIIRS, MODIS, MERIS | Residential | No | Random Forest model with 24 covariates and weighted dasymetric redistribution | [34,37] |
LS-15: LandScan, 2015 | Oak Ridge National Laboratory | Landsat, MODIS, DMSP-OLS | Ambient (24-h average) | No | Multivariable dasymetric model with 4 covariate types and weighted redistribution | [33,38] |
WPE-16: ESRI World Population Estimate, 2016 | Esri Inc. | Landsat | Residential | No | Dasymetric algorithm with 16 covariate weighting data sets | [30] |
Geography | Urban Pop | Rural Pop | Total Pop | Pct Urban | Urban Max | Rural Max | Urban Pixels | Rural Pixels | Uninhabited Pixels |
---|---|---|---|---|---|---|---|---|---|
Nepal 3990 level 3 units | |||||||||
WPE-16 | 2.66 | 30.71 | 33.37 | 7.97% | 45,982 | 25,237 | 275 | 118,437 | 76,844 (39%) |
GHS-15 | 3.3 | 25.16 | 28.46 | 11.6% | 46,472 | 117,462 | 271 | 104,208 | 91,077 (47%) |
GPW-15 | 2.88 | 27.84 | 30.72 | 9.38% | 32,592 | 28,114 | 275 | 175,048 | 20,233 (10%) |
LS-15 | 2.85 | 28.69 | 31.54 | 9.04% | 57,668 | 44,892 | 275 | 145,639 | 49,642 (25%) |
WP-16 | 3.64 | 28.6 | 32.24 | 11.29% | 48,358 | 46,939 | 275 | 167,188 | 28,093 (14%) |
UN-15 | 5.32 | 23.34 | 28.66 | 18.56% | |||||
MMZ—Mozambique 413 level 3 units, Malawi 12,647 level 3 units, Zimbabwe 92 level 2 units | |||||||||
WPE-16 | 6.84 | 59.52 | 66.36 | 10.31% | 26,168 | 17,138 | 1695 | 232,593 | 1,356,048 (85%) |
GHS-15 | 8.34 | 52.13 | 60.47 | 13.79% | 81,852 | 156,171 | 1810 | 176,110 | 1,412,416 (89%) |
GPW-15 | 3.46 | 51.9 | 55.37 | 6.25% | 26,555 | 17,190 | 2011 | 1,467,545 | 120,780 (8%) |
LS-15 | 6.51 | 50.93 | 57.45 | 11.33% | 61,126 | 40,592 | 1976 | 1,370,043 | 218,317 (14%) |
WP-16 | 5.01 | 51.81 | 56.82 | 8.82% | 26,995 | 25,233 | 2009 | 1,409,324 | 179,003 (11%) |
UN-15 | 17.61 | 43.75 | 61.36 | 28.7% | |||||
Ecuador 1047 level 3 units | |||||||||
WPE-16 | 7.97 | 10.28 | 18.26 | 43.65% | 18,108 | 14,867 | 1457 | 41,897 | 247,071 (85%) |
GHS-15 | 8.19 | 7.86 | 16.05 | 51.03% | 31,851 | 43,017 | 1510 | 41,755 | 247,160 (85%) |
GPW-15 | 2.06 | 13.83 | 15.89 | 12.96% | 4172 | 4172 | 1664 | 235,809 | 52,952 (18%) |
LS-15 | 7.58 | 8.24 | 15.82 | 47.91% | 44,304 | 31,740 | 1645 | 192,868 | 95,912 (33%) |
WP-16 | 5.35 | 10.87 | 16.23 | 32.96% | 8782 | 8428 | 1663 | 224,407 | 64,355 (22%) |
UN-15 | 10.24 | 5.91 | 16.14 | 63.44% |
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Tuholske, C.; Gaughan, A.E.; Sorichetta, A.; de Sherbinin, A.; Bucherie, A.; Hultquist, C.; Stevens, F.; Kruczkiewicz, A.; Huyck, C.; Yetman, G. Implications for Tracking SDG Indicator Metrics with Gridded Population Data. Sustainability 2021, 13, 7329. https://doi.org/10.3390/su13137329
Tuholske C, Gaughan AE, Sorichetta A, de Sherbinin A, Bucherie A, Hultquist C, Stevens F, Kruczkiewicz A, Huyck C, Yetman G. Implications for Tracking SDG Indicator Metrics with Gridded Population Data. Sustainability. 2021; 13(13):7329. https://doi.org/10.3390/su13137329
Chicago/Turabian StyleTuholske, Cascade, Andrea E. Gaughan, Alessandro Sorichetta, Alex de Sherbinin, Agathe Bucherie, Carolynne Hultquist, Forrest Stevens, Andrew Kruczkiewicz, Charles Huyck, and Greg Yetman. 2021. "Implications for Tracking SDG Indicator Metrics with Gridded Population Data" Sustainability 13, no. 13: 7329. https://doi.org/10.3390/su13137329