Relationships Between Evacuation Population Size, Earthquake Emergency Shelter Capacity, and Evacuation Time

Abstract

Determining the location of earthquake emergency shelters and the allocation of affected population to them are key issues that face shelter planning and emergency management. To solve this emergency shelter location-allocation problem, evacuation time and the construction cost of shelters-both influenced by the evacuation population size and its spatial distribution-are two important considerations. In this article, a mathematical model with two objectives-to minimize total weighted evacuation time (TWET) and total shelter area (TSA)-is allied with a modified particle swarm optimization algorithm to address the problem. The relationships between evacuation population size, evacuation time, and total shelter area are examined using Jinzhan Town in Chaoyang District of Beijing, China, as a case study. The results show that TWET has a power function relationship with TSA under different population size scenarios, and a linear function applies between evacuation population and TWET under different TSAs. The joint relationships of TSA, TWET, and population size show that TWET increases with population increase and TSA decrease, and compared with TSA, population influences TWET more strongly. Given a reliable projection of population change and spatial planning of a study area, this method can be useful for government decision making on the location of earthquake emergency shelters and on the allocation of evacuees to those shelters.

A GIS-Based System for Spatial-Temporal Availability Evaluation of the Open Spaces Used as Emergency Shelters: The Case of Victoria, British Columbia, Canada

“…The post-earthquake recovery process is divided into four stages: immediate relief (hour), immediate sheltering (day), temporary housing (week), and permanent housing (month) [7]. Service time decides the types of shelters and their supporting facilities, and some countries provide standards for building such shelters [8,9]. The emergency shelter service period is from hours to days and does not require permanent buildings.…”

Instantaneous Networking Service Availability for Disaster Recovery

“…It is observed that residents in the disaster situation tend to gather at the designated places such as shelters or evacuation centers [12]. Zhao et al [13] examined the relationship between evacuation population size, emergency shelter capacity, and evacuation time. The proposed model was considered to facilitate decision-making on the position of emergency shelters and the allocation of evacuees to the shelters.…”

“…First, it is observed that the majority of communication destinations of a user are in the nearby area [11]. Second, residents in the disaster situation tend to gather at the designated safe places such as shelters or evacuation centers [12,13]. Hence, even in the absence of conventional global communication services, the prompt and local cloud services are expected to be effective in quickly increasing the availability of networking services [14].…”

Site Selection Models in Natural Disaster Shelters: A Review

“…Hu et al [52] developed an earthquake shelter location-allocation model, with the objectives of minimizing the total distance from communities to shelters and the total cost, and solved it using a genetic algorithm. Zhao et al [53,54] introduced an earthquake shelter allocation model, with the objectives of minimizing the total weighted evacuation time and the total area of shelters, and solved it using a modified PSO algorithm combined with the SA algorithm. Zhou et al [55] proposed a multiobjective urban shelter location planning model, which comprehensively considered the principles of fairness and efficiency in the location selection by integrating the maximum coverage model and the p-median model.…”