# The Spatial Optimization of Emergency Shelters Based on an Urban-Scale Evacuation Simulation
## 7 Conclusions  
The construction of emergency shelters is of great significance to the formation of a complete network of disaster relief facilities. With the goal of day and night disaster prevention, based on the data of POIs, residential units with household attributes, building distributions, and road networks, short-term fixed shelters were taken as the object of the present study, and an evacuation simulation model and spatial optimization model were designed using the Python language. Then, the supply and demand status of the shelters was analyzed, and further suggestions were proposed regarding the spatial optimization of shelters. According to the evacuation simulation under the restriction of the shelter capacity, 680 demand points out of 2334 were not completely evacuated, of which the average number of remaining non-evacuated people at each demand point was approximately 4396, and the average evacuation time for all the evacuated people was approximately 10.3 min. Moreover, of the 888 short-term fixed shelters, only 218 did not reach their maximum capacity. Regarding the shelters that reached maximum capacity, the average time that passed before the shelter stopped receiving people was 5 min, and the shelters reached their maximum capacity after 14.6 min. In the context of short-term fixed sheltering, Haizhu had the largest scale of remaining non-evacuated people (1.11 million), and the average number of remaining non-evacuated people was the largest in Yuexiu, namely 2184. According to the results of the spatial optimization of the shelters, the number of target plots for new shelter resources in Haizhu, Yuexiu, Liwa, and Tianhe should, respectively, be increased by 406, 164, 141, and 136, with effective shelter areas of 2,621,100, 2,175,300, 812,100, and 1,344,600 m\({}^{2}\), respectively. Furthermore, the average newly added effective shelter areas of the plots for Haizhu, Yuexiu, Liwa, and Tianhe were found to be 6456, 13,264, 5759, and 9886 m\({}^{2}\), respectively. Finally, a total of 487 short-term fixed shelters and 360 temporary shelters were newly added. Recommendations for different types of shelters are helpful for the allocation of targeted emergency facilities during the construction of shelters. Based on the statistics per district, the recommended numbers of new short-term fixed shelters in Haizhu, Liwan, Tianhe, and Yuexiu are, respectively, 243, 70, 58, and 116, with average effective areas of 6169, 5577, 8707, and 12,931 m\({}^{2}\), respectively. Additionally, the recommended numbers of newly added temporary shelters in Haizhu, Liwan, Tianhe, and Yuexiu are, respectively, 163, 71, 78, and 48, with average effective areas of 2706, 2581, 4017, and 6234 m\({}^{2}\), respectively. In any case, the supply and demand of the shelters in the study area are imbalanced; in other words, the service of shelters has blind spots, which makes it impossible to provide refuge for all residents when a disaster occurs. The findings of this study act as a direct quantitative basis for the spatial optimization of various types of emergency shelters, and as a reference for the improvement of the network of urban disaster prevention facilities in future urban planning. As mentioned in the discussion, in the evacuation simulation and spatial optimization, some conditions and the model environment were simplified. In a real disaster scenario, numerous complex variables exist in the evacuation process (such as secondary disasters, interruption of roads, physical obstacles, behavior, and psychology), which are difficult to quantify in a model. In the spatial optimization of emergency shelters, the population distribution and the potential of urban land use are crucial points. Therefore, the analysis of the temporal and spatial distributions of the population based on big data and a database of the construction of urban land potential is necessary.