# The Spatial Optimization of Emergency Shelters Based on an Urban-Scale Evacuation Simulation
## 1 Introduction  
In the 20 years between 2000 and 2019, (the international disasters database) EM-DAT recorded 7348 disaster events, which claimed a total of approximately 1.2 million lives and affected more than 4.03 billion people. On average, there were 367 disaster events each year, the majority of which were floods and storms [1]. In 2020, 416 natural disaster events worldwide caused approximately 8100 fatalities and USD 210 billion in losses, as several countries, including the U.S. and China, battled hurricanes, floods, and wildfires [2]. In the process of disaster relief, emergency shelters play an important role in post-disaster rescue and reducing casualties [3]. Therefore, the construction of a complete network of refuge facilities will help to improve disaster prevention capabilities. In planning practice, the geographic information system (GIS) has become the most widely used technology platform in refuge site selection [4]. Numerous studies have yielded rich results, which can be summarized into the following three main categories: shelter location selection and allocation [5; 6; 7], sheltering behavior and psychology from the perspective of sociology [8], and planning tools [9; 10]. In any case, what scholars are most inter9 ested in are the spatiallayout and optimization of emergency shelters, which are directly related to whether the city can quickly provide nearby refuge services to evacuees after a disaster [11]. In the current practices of shelter planning and construction in China, the general spatial layout method is based on the existing available resources in the city; according to the effective area that the resources can cover and considering a balanced layout, the service area of shelters is divided by a certain service radius [12]. The spatial layout method has a certain degree of operability in the master plan of shelters, because the master planning level is mainly used for the layout of the central shelter and other mid- or long-term shelters. A crucial problem is that shelter planning based on the selection and layout of the existing resources in the city causes shelter planning to lag behind urban planning, and it is unclear which plots or areas should increase their shelter resources in the future [13]. The scale is mostly based on qualitative (or semi-quantitative) analysis guidance, which cannot provide effective feedback on the land use layout in urban planning, meaning that the shelters can never provide reasonable coverage for the surrounding residents. It is a significant and practical challenge to optimize the spatial layout of emergency shelters on a sufficient basis. This study aims to address the challenge of how to optimize the spatial distribution of emergency shelters by an urban-scale evacuation simulation. Based on (point of interest) POI data, the scale and distribution of shelter demand were analyzed, the supply and demand status of emergency shelters was explored via evacuation simulations, and a spatial optimization strategy for shelters is provided. The remainder of this paper is organized as follows. The next section provides a review of relevant literature regarding spatial distribution of emergency shelter demand, emergency evacuation simulation, and spatial optimization of emergency shelters. Section 3 describes the theoretical framework, including the overall research framework, evacuation simulation using network analysis in GIS and Python, and spatial optimization of shelters based on the redistribution of remaining non-evacuated evacuees. Section 4 presents the study area, the types of data, and the construction of evacuation simulation model. Section 5 provides an analysis of the status of evacuees and refugees after evacuation, along with the scale and type of shelter optimization. Section 6 discusses the deficiencies and limitations of this study. Finally, conclusions and directions for future work are presented in Section 7.