# The Spatial Optimization of Emergency Shelters Based on an Urban-Scale Evacuation Simulation
## 2 Literature Review
### Spatial Optimization of Emergency Shelters  
The analysis of the supply and demand of shelters is helpful for finding areas where there are gaps in post-disaster sheltering services, and for carrying out the targeting of the spatial layout and the optimization of shelters in the process of urban planning and renewal [12]. In the current studies on spatial optimization, focus is largely placed on the construction of various shelter location and allocation models, which usually include considerations such as minimizing the evacuation time or distance, the construction cost or the number of shelters, and the risk during the evacuation, and maximizing the capacity of the demand points and shelters within the service radius [33; 34; 35; 36]. [PERSON] [37] proposed an active decision-making tool that combines the experimental evaluation method with interval type-2 fuzzy sets, which is used to determine the need for a location to set up shelters or make evacuation plans in disaster emergency management. [PERSON] and [PERSON] [5] used a hybrid multi-objective decision-making model based on the analytic hierarchy process (AHP), fuzzy set theory, and the goal-planning method, and converted all the goals into a single goal to allocate and redistribute the location of the refuge area. This method also considers demand, capacity, utilization, and budget constraints, and can achieve a compromise solution for multiple goals in the decision-making process. [PERSON] et al. [38] proposed a linear mixed-integer programming model that takes evacuation demand as an uncertain parameter. To solve the uncertainty problem, a robust possibility planning method was adopted to provide relief services (transportation of the wounded, supply of relief goods, etc.) to locate the transmission points and shelters. Existing optimization algorithms are usually used to solve the location problem in small areas. In the case of complex and high-dimensional problems related to large areas, the current algorithms may require excessive amounts of time and computing power, and it is difficult to find the global optimal solution [39; 40]. Some studies have proposed methods for spatial optimization from other perspectives, such as cost-effectiveness, accessibility, and service coverage [20; 41; 42]. To evaluate the feasibility of the construction of shelters in areas with hurricane hazards, [PERSON] et al. [43] developed and tested a cost-effectiveness model with the consideration of occurrence probability, historical death and injury rates, economic incentives, local construction, and maintenance costs. [PERSON] et al. [44] integrated and compared the location set coverage problem with the maximum coverage location problem to maximize the shelter for the affected population, and combined cost and benefit factors to provide a feasible way for developing cities with insufficient budgets to make decisions. In summary, carrying out an urban-scale evacuation simulation and use it as a quantitative basis to continuously optimize the spatial distribution of emergency shelters is a substantial research challenge. It was found that shelter demand analysis is mainly based on demographic data, which are characterized by disadvantages such as poor timeliness and large spatial statistical units, meaning that these data cannot meet the evacuation needs in diurnal disaster scenarios. In addition, the distribution of emergency shelters in many regions is uneven (leading to widespread service gaps), and the supply and demand of shelters are imbalanced. Although some studies have considered related issues and achieved certain results, continuous exploration is still required. In general, the current research on evacuation is primarily focused on evacuation plans, decision-making methods, transportation methods, influencing factors, and route selection. In terms of scale, focus is largely placed on traffic evacuation and movement simulations of specific urban areas (central areas, industrial areas, etc.) and individual buildings.