G-DIF: A geospatial data integration framework to rapidly estimate post-earthquake damage. Issue 4 (November 2020)

• Record Type: Journal Article
• Authors: Loos, Sabine; Lallemant, David; Baker, Jack; McCaughey, Jamie; Yun, Sang-Ho; Budhathoki, Nama; Khan, Feroz; Singh, Ritika
• Is Part Of: Earthquake spectra. Volume 36:Issue 4(2020)
• Page Start: 1695
• Page End: 1718
• Publication Date: 2020-11
• Subjects: Post-earthquake damage -- geostatistics -- data fusion -- remote sensing -- rapid loss model -- damage map; Earthquake engineering -- Periodicals; Earthquake engineering; Periodicals; 624.1762
• DOI: 10.1177/8755293020926190
• Languages: English
• ISSNs: 8755-2930

Abstract

While unprecedented amounts of building damage data are now produced after earthquakes, stakeholders do not have a systematic method to synthesize and evaluate damage information, thus leaving many datasets unused. We propose a Geospatial Data Integration Framework (G-DIF) that employs regression kriging to combine a sparse sample of accurate field surveys with spatially exhaustive, though uncertain, damage data from forecasts or remote sensing. The framework can be implemented after an earthquake to produce a spatially distributed estimate of damage and, importantly, its uncertainty. An example application with real data collected after the 2015 Nepal earthquake illustrates how regression kriging can combine a diversity of datasets—and downweight uninformative sources—reflecting its ability to accommodate context-specific variations in data type and quality. Through a sensitivity analysis on the number of field surveys, we demonstrate that with only a few surveys, this method can provide more accurate results than a standard engineering forecast.