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Abstract
A rapid method for predicting earthquake impacts is proposed that uses spatial data received from non-expert individuals immediately after an earthquake. The approach addresses the issue of non-ignorable missing data caused by spatial latent random fields, which seriously affect the accuracy of predictions. A spatial model was employed to jointly model missingness and earthquake impact measurement by considering the latent spatial random fields as shared parameters. The proposed method demonstrated better prediction compared with that of existing models in both simulated and actual datasets. When applied to the real-world 2017 Sarpol-e Zahab earthquake in Iran, the model revealed concentrated impacts south and southeast of the epicenter.
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