Abstract
Weeks after a disaster, crucial response and recovery decisions require information on the locations and scale of building damage. Geostatistical data integration methods estimate post-disaster damage by calibrating engineering forecasts or remote sensing-derived proxies with limited field measurements. These methods are meant to adapt to building damage and post-earthquake data sources that vary depending on location, but their performances across multiple locations have not yet been empirically evaluated. In this study, we evaluate the generalizability of data integration to various post-earthquake scenarios using damage data produced after four earthquakes: Haiti 2010, New Zealand 2011, Nepal 2015, and Italy 2016. Exhaustive surveys of true damage data were eventually collected for these events, which allowed us to evaluate the performance of data integration estimates of damage through multiple simulations representing a range of conditions of data availability after each earthquake. In all case study locations, we find that integrating forecasts or proxies of damage with field measurements results in a more accurate damage estimate than the current best practice of evaluating these input data separately. In cases when multiple damage data are not available, a map of shaking intensity can serve as the only covariate, though the addition of remote sensing-derived data can improve performance. Even when field measurements are clustered in a small area—a more realistic scenario for reconnaissance teams—damage data integration outperforms alternative damage datasets. Overall, by evaluating damage data integration across contexts and under multiple conditions, we demonstrate how integration is a reliable approach that leverages all existing damage data sources to better reflect the damage observed on the ground. We close by recommending modeling and field surveying strategies to implement damage data integration in-real-time after future earthquakes.
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Data Availability Statement
An interactive code to support the “Recommendations” section of this study is available at https://sabineloos.github.io/GDIF-Gen/Diagnostics.html, and the supporting code generated during this study is available at the following repository: https://github.com/sabineloos/GDIF-Gen (Loos 2022). Additional code and data that support the findings shared in this study are available from the corresponding author upon reasonable request. Field data used during the study were provided by a third party. Direct requests for these materials may be made to the provider as indicated in the Acknowledgments.
Acknowledgments
We thank the Ministry of Public works; the Earthquake Commission and Tonkin + Taylor; the Government of Nepal and Kathmandu Living Labs; and the European Commission and the USGS for access to the field data from Haiti, New Zealand, Nepal, and Italy, respectively. Specifically, thanks to Virginie Lacrosse, Sjoerd van Ballegooy, Sang-Ho Yun, Keiko Saito, David Wald, and Paolo Zimmaro for the access and preparation of these datasets. Thank you to Kishor Jaiswal and Nicole Paul for providing input on the development of the engineering forecasts used in this study. We also thank three anonymous reviewers who provided valuable feedback that improved this manuscript and shared code. This work was funded by the Stanford Urban Resilience Initiative; the John A. Blume Earthquake Engineering Center; the National Science Foundation Graduate Research Fellowship; and the National Research Foundation, Prime Minister’s Office, Singapore under the NRF-NRFF2018-06 award.
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Natural Hazards Review
Volume 23 • Issue 4 • November 2022
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© 2022 American Society of Civil Engineers.
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Received: Oct 6, 2021
Accepted: May 25, 2022
Published online: Aug 8, 2022
Published in print: Nov 1, 2022
Discussion open until: Jan 8, 2023
ASCE Technical Topics:
- Business management
- Case studies
- Data analysis
- Data collection
- Developing countries
- Disaster risk management
- Disasters and hazards
- Earthquakes
- Engineering fundamentals
- Forecasting
- Geohazards
- Geotechnical engineering
- Information management
- Mathematics
- Methodology (by type)
- Natural disasters
- Practice and Profession
- Research methods (by type)
- Statistics
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- Sabine Loos, David Lallemant, Feroz Khan, Jamie W. McCaughey, Robert Banick, Nama Budhathoki, Jack W. Baker, A data-driven approach to rapidly estimate recovery potential to go beyond building damage after disasters, Communications Earth & Environment, 10.1038/s43247-023-00699-4, 4, 1, (2023).