AbstractWeeks 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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