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.
G-DIF: A geospatial data integration framework to rapidly estimate post-earthquake damage
Sabine Loos, David Lallemant, Jack Baker, Jamie McCaughey, Sang-Ho Yun, Nama Budhathoki, Feroz Khan, Ritika Singh; G-DIF: A geospatial data integration framework to rapidly estimate post-earthquake damage. Earthquake Spectra 2020;; 36 (4): 1695–1718. doi: https://doi.org/10.1177/8755293020926190
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