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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.
This chapter provides a detailed account of how technology, inspiration and collaboration were used to rapidly assess damage caused by the devastating January 12, 2010 Haiti earthquake. This was one of the first events where remote sensing technology (especially high spatial resolution imagery) was embraced in a truly operational sense to support post-disaster recovery planning. Sub-meter satellite imagery was available the day following the earthquake, and provided the first glimpse of the destruction caused by the earthquake. Days later, finer spatial resolution aerial imagery became available and provided even more detail on building damage. Together, these datasets allowed over 600 remote sensing experts and engineers to generate one of the most comprehensive assessments of earthquake building damage in the last decade. Furthermore, this information was shared with Haitian government in the form of a Building Damage Assessment Report in support of the Post-Disaster Needs Assessment (PDNA) and Recovery Framework. A unique crowd-sourcing initiative instigated by ImageCat in support of the World Bank’s initial response to the disaster enabled reliable and timely information on damages to be generated. The chapter describes the various phases completed by the project team, including a Phase 1 damage assessment using satellite imagery and a Phase 2 assessment using aerial imagery. We discuss the World Bank-ImageCat-Rochester Institute of Technology remote sensing team’s collection and analysis of very high spatial resolution aerial imagery over greater Port-au-Prince, which played a central role for the Phase 2 damage analysis. In addition, participation in the PDNA damage assessment with the United Nation’s UNITAR/UNOSAT unit and the European Commission’s Joint Research Centre is also discussed. The chapter concludes with a series of recommendations that are focused on better use of the technologies described in this study and a roadmap on how some of the products can be used for pre- and post-event planning for future devastating disasters.
John S. Bevington; Ronald T. Eguchi; Stuart Gill; Shubharoop Ghosh; Charles K. Huyck. A Comprehensive Analysis of Building Damage in the 2010 Haiti Earthquake Using High-Resolution Imagery and Crowdsourcing. Time-Sensitive Remote Sensing 2015, 131 -145.
AMA StyleJohn S. Bevington, Ronald T. Eguchi, Stuart Gill, Shubharoop Ghosh, Charles K. Huyck. A Comprehensive Analysis of Building Damage in the 2010 Haiti Earthquake Using High-Resolution Imagery and Crowdsourcing. Time-Sensitive Remote Sensing. 2015; ():131-145.
Chicago/Turabian StyleJohn S. Bevington; Ronald T. Eguchi; Stuart Gill; Shubharoop Ghosh; Charles K. Huyck. 2015. "A Comprehensive Analysis of Building Damage in the 2010 Haiti Earthquake Using High-Resolution Imagery and Crowdsourcing." Time-Sensitive Remote Sensing , no. : 131-145.
This chapter introduces new and emerging technologies that have proven effective in disaster management or show promise in future deployments. These technologies are discussed in the context of the four major phases of disaster management: preparedness, response, recovery and mitigation. Examples of some technologies discussed in detail include real-time hazard warning or monitoring systems; advanced loss estimation methodologies and tools; remote sensing for response and recovery; and field data collection and visualization systems, especially those that are GIS and/or GPS-based. The chapter concludes with a brief discussion of research or implementation issues, focusing specifically on the above technologies, and including issues related to real-time event monitoring; privacy protection; and information sharing and trust management.
Ronald T. Eguchi; Charles K. Huyck; Shubharoop Ghosh; Beverley J. Adams; Anneley McMillan. Utilizing New Technologies in Managing Hazards and Disasters. Geospatial Techniques in Urban Hazard and Disaster Analysis 2009, 295 -323.
AMA StyleRonald T. Eguchi, Charles K. Huyck, Shubharoop Ghosh, Beverley J. Adams, Anneley McMillan. Utilizing New Technologies in Managing Hazards and Disasters. Geospatial Techniques in Urban Hazard and Disaster Analysis. 2009; ():295-323.
Chicago/Turabian StyleRonald T. Eguchi; Charles K. Huyck; Shubharoop Ghosh; Beverley J. Adams; Anneley McMillan. 2009. "Utilizing New Technologies in Managing Hazards and Disasters." Geospatial Techniques in Urban Hazard and Disaster Analysis , no. : 295-323.