Gray Measure and Spatial Distribution Exploration of Local Emergency Resilience on Compound Disasters

Abstract

The complexity and uncertainty of compound disasters highlight the significance of local emergency resilience. This paper puts forward a framework, including the Projection Pursuit Model based on Real-coded Accelerating Genetic Algorithm and the Moran’s Index (Moran’s I), to measure the local emergency resilience and analyze its spatial distribution. An empirical test is conducted with the case of Hubei Province, China. The results show that: (1) the measurement indices related to infrastructure, material reserves, and resource allocation have a larger weight, while those related to personnel and their practice have a smaller weight. (2) The measurement value of local emergency resilience of sub-provincial regions in Hubei Province is vital in the eastern and weak in the western, and there are apparent east-west segmentation and north-south aggregation characteristics. (3) Although the sub-provincial regions do not show significant spatial correlation, the eastern regions centered on Wuhan are negatively correlated, and the western regions are positively correlated. Furthermore, this study provides theories and methods for local emergency resilience evaluation and spatial correlation exploration, and it has specific guidance recommendations for optimizing local emergency management resource allocation and improving local emergency resilience.