Geographical Disparities of Community Disaster Resilience in the United States

Authors: Lei Zou*, Texas A&M University, Nina Lam, Louisiana State University, Yi Qiang, University of South Florida, Heng Cai, Texas A&M University, Volodymyr Mihunov, Louisiana State University
Topics: Coupled Human and Natural Systems, Geographic Information Science and Systems, Hazards, Risks, and Disasters
Keywords: disaster resilience, resilience inference measurement (RIM) model, natural hazards, geographical disparities
Session Type: Virtual Paper
Day: 4/7/2021
Start / End Time: 11:10 AM / 12:25 PM
Room: Virtual 41
Presentation File: No File Uploaded

As the disaster frequency increased dramatically during the past few decades, measuring and understanding communities’ resilience capacity is urgently essential for building actionable plans to reduce potential impacts from future hazards. Despite extensive discussions, assessing community disaster resilience and its driving factors is still challenging due to the inconsistent definition, unclear mechanisms, and the lack of validation. Consequently, there is a need for a theoretically sound and empirically validated framework to measure disaster resilience, driving forces, and geographical disparities. This study developed an Improved Resilience Inference Measurement (IRIM) model to evaluate community disaster resilience in the United States at the county-level. First, we measured the empirical disaster resilience using the K-means analysis based on normalized indicators representing three dimensions: hazard threat, damage, and recovery. Second, the underlying relationships between socioeconomic-environmental characteristics and empirical resilience levels were examined by discriminant analysis to estimate communities’ inherent resilience scores. Third, we simulated the geographical disparities of community resilience in the United States in 2000, 2010, and 2020. The IRIM model's result reflects communities’ ability to adapt, re-organize, and retain functions through multiple hazards. The discriminant functions derived can estimate other study areas’ resilience levels using the same set of variables. The identified mechanisms between resilience capacities and driving forces could guide policymakers on where and how investments in intervention strategies may make a difference in resilience improvement.

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