# The Spatial Optimization of Emergency Shelters Based on an Urban-Scale Evacuation Simulation
## 6 Discussion  
Human behavior and psychology are important research directions in the field of sociology, and this is also the case in the field of disaster prevention. Numerous studies have explored evacuation decision making, evacuation groups, evacuation behavior, psychology, etc., and have achieved rich results. In a disaster, people will decide to evacuate or to take refuge in a place which is usually closely related to the individual's disaster experience and judgment of the disaster situation. For evacuated groups, such as the elderly, children, and people with limited mobility, evacuation is full of challenges: the evacuation speed will not be very fast, and the evacuation efficiency may be relatively low. After a disaster occurs, people will be evacuated outside as soon as possible. According to the disaster situation, evacuation options such as walking, taking public transportation, and driving may appear simultaneously, which will have a certain impact on the calculation of the evacuation time. Previous studies have shown that people tend to stay with friends or family members or to evacuate together when a disaster occurs, which has an impact on the organization of an evacuation. These factors were not considered in the present study, which is mainly because this study set out to explore the increase in available resources for emergency shelters from the scope of disaster prevention planning, as well as the optimization of the spatial structure of urban disaster prevention. From the perspective of realizing an evacuation simulation, complex evacuation behaviors and psychology were not considered in this study; this helped to reduce evacuation interference, and ultimately greatly reduced the evacuation complexity. However, the subject of an evacuation is people. Therefore, it is necessary to note some considerations from a sociological perspective in relation to evacuation so that more discoveries can be obtained. Based on big data, the use of machine learning, artificial intelligence, and other technical methods can enable new explorations of complex evacuation behavior and psychology. In the evacuation simulation, the plots containing the evacuees and the shelters that can accommodate the evacuees were simplified as points, meaning that the evacuation simulation became a point-to-point multi-target allocation problem. The next difficulty was to determine the corresponding relationship and path between the demand points and shelters. In the simulation, the shelter demand was located at the geometric center of the plot. When the evacuation began, people moved from this geometric center to several shelters within a certain distance nearby. One shortcoming is that the distance and time required for people to evacuate from the inside to the outside of a building were ignored, which simplified the evacuation process and led to underestimations of both the distance and time. In addition, for the selection of road exits from the demand point to the city, the nearness principle was adopted, i.e., the side that was closest to the city road was selected as the demand point to start the evacuation, and people started to evacuate along a path that was perpendicular to the city road. Furthermore, for the convenience of calculation, only one route was set up for each demand point to connect to the city road. In the simulation model design, the simulation was set to follow the established parameters for evacuation. The dynamic evacuation process was statistically set for the