# The Spatial Optimization of Emergency Shelters Based on an Urban-Scale Evacuation Simulation
## 4 Materials and Methods  
### Overview of the Study Area and Data  
The study area includes the districts of Liwan, Yuexiu, Tianhe, and Haizhu, which are located at the center of downtown Guangzhou, China. According to the data released by the Guangzhou Statistics Bureau ([[http://ij.gz.gov.cn/](http://ij.gz.gov.cn/)]([http://ij.gz.gov.cn/](http://ij.gz.gov.cn/)) accessed on 28 August 2021), the total land area of the four districts is 279.63 km\({}^{2}\) (accounting for 3.76% of the whole city), and the study area has a high economic density, the GDP of which accounts for 47.39% of the GDP of all 11 districts in Guangzhou. Yuexiu and Liwan are traditional old towns with small areas of 33.8 and 59.1 km\({}^{2}\), respectively, whereas Tianhe and Haizhu are newly built, and, respectively, cover areas of 96.33 and 90.4 km\({}^{2}\). According to data from the Guangzhou Municipal Bureau of Statistics, at the end of 2020, the population of Guangzhou was 15,305,900, of which the study area accounts for 37.47%. To facilitate the construction of models to analyze the supply, demand, and spatial optimization of emergency shelters, plots with evacuees and plots with available resources for shelters were, respectively, converted into points of demand and shelter, and the core study scope was narrowed to the more mature built-up areas in the four districts, with a total area of approximately 177.92 km\({}^{2}\) (Figure 4). The demand points contained information about the number of evacuees, whereas the shelter points contained information about the effective evacuation area, which is necessary for the simulation.  
Figure 3: The principle of shelter spatial optimization.  
In addition to basic geospatial and statistical data, POI data, residential unit data with household attributes, building distribution data, and road network data were used in this study. The POI data were divided into 14 categories and 524 subcategories (including catering services, scenic spots, enterprises, shopping services, transportation facilities, finance and insurance, education and culture, business housing, living services, sports and leisure, healthcare, government agencies, accommodation services, and public facilities), which cover virtually all kinds of facilities that citizens can access. POI data and official employment demographic data were used to predict the shelter demand; this means that, because the urban population distribution must be analyzed via economic activities, the industry classification of the two data types must be consistent. Therefore, it was necessary to screen and reclassify the POI data according to the official statistical caliber, and this process was conducted in GIS. The residential unit data included data on residential quarters, apartments, and dormitories. The building distribution data covered the spatial distribution of all buildings in the four districts. The road network data were divided into railways, highways, high-speed guide roads, national roads, provincial roads, county roads, township roads, urban first-class roads, urban second-class roads, other roads, and pedestrian roads. There exist both single lines and double lines. According to the needs of the study, the road network was treated as a single-line network, and was simplified to a certain extent. The attributes of each type of data are presented in Table 1.