# The Spatial Optimization of Emergency Shelters Based on an Urban-Scale Evacuation Simulation
## 6 Discussion
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Index Recommendation** & **Haizhu** & **Liwan** & **Tianhe** & **Yuexiu** \\ \hline Increased EA of temporary shelters & 1,098,521 & 363,926 & 546,288 & 1,022,414 \\ Increased EA of fixed shelters & 2,504,614 & 786,294 & 1,184,124 & 2,120,760 \\ Recommended EA increase of the shelters & 2,621,070 & 812,084 & 1,344,550 & 2,175,331 \\ Recommended number of fixed shelters & 243 & 70 & 58 & 116 \\ Maximum EA of a single fixed shelter & 52,238 & 55,020 & 56,576 & 66,828 \\ Average EA of fixed shelters & 6169 & 5577 & 8707 & 12,931 \\ Recommended number of temporary shelters & 163 & 71 & 78 & 48 \\ Maximum EA of a single temporary shelter & 46,889 & 24,256 & 31,183 & 38,349 \\ Average EA of temporary shelters & 2706 & 2581 & 4017 & 6234 \\ \hline \hline \end{tabular} _Notes:_ EA is the effective area (m\({}^{2}\)).  
\end{table} 
Table 7: Index recommendations for the spatial optimization of emergency shelters. entire simulation, which affected the evacuation time and the calculation of the point-to-point evacuation personnel scale to a certain extent. Because this study was based on an urban-scale evacuation simulation, it is difficult to ensure high accuracy. Moreover, from the perspective of the calculation and data basis, it is impossible to realize an evacuation simulation of individuals under the condition of free path selection, although this could be explored in a smaller-scale area. The urban road network is a system full of uncertainties when disasters occur. In an earthquake scenario, buildings and other structures may collapse, which will affect (or even interrupt) road traffic. The movement of the crowd on the evacuation route may be congested, thereby affecting the evacuation speed. After the path is accidentally blocked, people may also take refuge in other nearby shelters. In this case, the entire evacuation plan will undergo continuous dynamic changes, resulting in changes to the scale and routes of evacuees from demand points to shelters. The original data for urban roads were double lines without the road width information, and were set as single lines in the simulation. Therefore, the sidewalk width was averaged according to the road level, which affected the evacuation capacity of the road to a certain extent. Even when a disaster occurs, urban traffic must maintain normal operation, meaning that the signal lights will delay the evacuation process and the evacuation time may consequently be underestimated. For an evacuation simulation in this type of complex environment, a large amount of data is required, and urban-scale modeling is difficult to achieve; however, the development of big data application technology provides support in solving these problems. In the spatial optimization of emergency shelters, a basic consideration is that the existing land plots with available resources for shelters will no longer consider the allocation of the remaining non-evacuated people from the incomplete evacuated demand points (regardless of site expansion factors), and such plots can no longer provide new facilities or be used as targets for the optimization of emergency shelters. More realistically, it is necessary to investigate the potential of the current urban land, find plots that can be changed in terms of their function, and mark them as potential target plots for spatial optimization to participate in the model calculation. In addition, in the actual urban renewal and development process, the potential of plots will change. Therefore, if certain conditions are met, a dynamic up-to-date database of urban buildings and land should be established to provide data references for the optimization of shelters. The basic principle of spatial optimization is to realize the redistribution of non-evacuated people.