G-DIF: A geospatial data integration framework to rapidly estimate post-earthquake damage
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Date
2020-11-01
Publication Type
Journal Article
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yes
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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.
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published
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Journal / series
Volume
36 (4)
Pages / Article No.
1695 - 1718
Publisher
SAGE
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Software
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Subject
Post-earthquake damage; geostatistics; data fusion; remote sensing; rapid loss model; damage map
Organisational unit
09576 - Bresch, David Niklaus / Bresch, David Niklaus