Integrating GIS Community Resilience Assessment: Multidisaster Perspective

Community resilience is the fundamental capacity of a community to cope with a crisis or disruption and mitigate the adverse effects of a disaster. Identifying and quantifying community resilience before a disaster occurs is increasingly becoming a prerequisite for managers to make informed decisions and take action. Assessing community resilience for multiple disasters is more complex and dynamic than for a single disaster. This paper proposes a multidisaster community resilience assessment method that comprises three indexes: the topology and seismic performance–based physical resilience index (TSP-PRI), the medical care–based health resilience index (MC-HRI), and the capital and population–based socioeconomic resilience index (CP-SRI). These indexes are computed using accurate and objective data on building information, road information, and government statistical yearbooks. GIS offer rich geospatial analysis functions for network infrastructure systems that involve geographic references. A plug-in has been developed in ArcGIS Pro to link these data to geospatial modeling, which can automatically calculate the TSP-PRI, MC-HRI, and CP-SRI. This decision-making tool can be used to systematically and visually examine the disaster characteristics and topological attributes of communities before and after the occurrence of disasters. Furthermore, the k-means clustering algorithm was applied to classify the types and characteristics of these three indexes to prioritize investments for different communities. A case study of community waterlogging and earthquakes in Nanjing, China, is presented to show the feasibility and effectiveness of the proposed approach.Practical ApplicationsAssessing community resilience prior to a disaster is crucial for informed decision-making and effective action by managers. This study presents a thorough assessment method that includes three indexes for assessing the resilience of various communities in multidisaster scenarios. To facilitate the assessment process, a plug-in has been developed in ArcGIS Pro, enabling automated computation of these indexes using reliable and objective data. A case study was conducted in 11 districts of Nanjing, China, examining the flooding and earthquake disasters. The districts were then grouped into clusters with similar resilience characteristics, utilizing the k-means clustering algorithm. This facilitated the prioritization of investments in different communities. The proposed method offers a comprehensive and quantitative framework that helps managers to measure and compare community resilience across districts and disasters. Furthermore, the method has the potential to be generalized and applied to other communities or countries, providing a valuable framework for resilience assessment in diverse settings.