Information fusion to estimate resilience of dense urban neighborhoods

Anthony Palladino; Elisa J. Bienenstock; Bradley M. West; Jake R. Nelson; Tony H. Grubesic

doi:10.1117/12.2519304

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7 May 2019 Information fusion to estimate resilience of dense urban neighborhoods

Anthony Palladino, Elisa J. Bienenstock, Bradley M. West, Jake R. Nelson, Tony H. Grubesic

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Proceedings Volume 11018, Signal Processing, Sensor/Information Fusion, and Target Recognition XXVIII; 110180K (2019) https://doi.org/10.1117/12.2519304
Event: SPIE Defense + Commercial Sensing, 2019, Baltimore, Maryland, United States

Abstract

Diverse sociocultural influences in rapidly growing dense urban areas may induce strain on civil services and reduce the resilience of those areas to exogenous and endogenous shocks. We present a novel approach with foundations in computer and social sciences, to estimate the resilience of dense urban areas at finer spatiotemporal scales compared to the state-ofthe-art. We fuse multi-modal data sources to estimate resilience indicators from social science theory and leverage a structured ontology for factor combinations to enhance explainability. Estimates of destabilizing areas can improve the decision-making capabilities of civil governments by identifying critical areas needing increased social services.

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Anthony Palladino, Elisa J. Bienenstock, Bradley M. West, Jake R. Nelson, and Tony H. Grubesic "Information fusion to estimate resilience of dense urban neighborhoods", Proc. SPIE 11018, Signal Processing, Sensor/Information Fusion, and Target Recognition XXVIII, 110180K (7 May 2019); https://doi.org/10.1117/12.2519304

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