This article introduces a framework for the multi-criteria satisfaction assessment of the spatial distribution of urban emergency shelters. A GIS-based analytic hierarchy process approach was utilized to conduct the assessment based on selected criteria layers for daytime and nighttime scenarios, respectively. The layers were generated from high-precision land use data based on high-resolution aerial images and census data. Considering the uncertainty in criteria weighting, a spatial sensitivity analysis was undertaken for deriving more accurate results. The feasibility of the framework was tested on a case study in Jing’an District, Shanghai, China. The assessment results show that both at nighttime and during daytime, even if all potentially available shelters are open, the demand in large areas can only be marginally satisfied or not satisfied, especially in the northern, eastern, and central parts of Jing’an District. The quantitative analysis of the satisfaction conditions of the buildings or land parcels and the affected people, especially children and the elderly, shows a low satisfaction level of shelter services in these areas. The satisfaction assessment of emergency shelters can help government decision makers find low satisfaction areas of sheltering services and support further location-allocation optimization of urban emergency shelters.

The timely and secure evacuation of residents to nearby urban emergency shelters is of great importance during unexpected disaster events. However, evacuation and allocation of shelters are seldom examined as a whole, even though they are usually closely related tasks in disaster management. To conduct better spatial allocation of emergency shelters in cities, this study proposes a new method which integrates techniques of multi-agent system and multi-criteria evaluation for spatial allocation of urban emergency shelters. Compared with the traditional emergency shelter allocation methods, the proposed method highlights the importance of dynamic emergency evacuation simulations for spatial allocation suitability analysis. Three kinds of agents involved in evacuation and sheltering procedures are designed: government agents, shelter agents, and resident agents. Emergency evacuations are simulated based on the interactions of these agents to find potential problems, for example, time-consuming evacuation processes and road congestion. A case study in Jing’an District, Shanghai, China was conducted to demonstrate the feasibility of the proposed method. After three rounds of simulation and optimization, new shelters were spatially allocated and a detailed recommended plan of shelters and related facilities was generated. The optimized spatial allocation of shelters may help local residents to be evacuated more quickly and securely.

The timely and secure evacuation of an urban residential community is crucial to residents’ safety when emergency events happen. This is different to evacuation of office spaces or schools, emergency evacuation in residential communities must consider the pre-evacuation time. The importance of estimating evacuation time components has been recognized for approximately 40 years. However, pre-evacuation time is rarely discussed in previous community-scale emergency evacuation studies. This paper proposes a new method that estimates the pre-evacuation time, which makes the evacuation simulation in urban residential communities more realistic. This method integrates the residents’ pre-evacuation behavior data obtained by surveys to explore the influencing factors of pre-evacuation time and builds a predictive model to forecast pre-evacuation times based on the Random Forest algorithm. A sensitivity analysis is also conducted to find the critical parameters in evacuation simulations. The results of evacuation simulations in different scenarios can be compared to identify potential evacuation problems. A case study in Luoshanqicun Community, Pudong New District, Shanghai, China, was conducted to demonstrate the feasibility of the proposed method. The simulation results showed that the pre-evacuation times have significant impacts on the simulation procedure, including the total evacuation time, the congestion time and the congestion degree. This study can help to gain a deeper understanding of residents’ behaviors under emergencies and improve emergency managements of urban communities.

Timely and secure evacuation of residents in communities is of great importance during unexpected disasters or emergency events. This study proposes a framework of evacuation simulation for optimizing emergency management in urban residential communities. Compared to traditional methods, the advantage of our framework lies in three aspects: (1) The method highlights easy-crowded areas in both indoor and outdoor evacuations. (2) Family behaviors are considered and implemented in evacuations. (3) Detailed measures on management optimization are spatially mapped based on a multi-level analysis and the comparison of evacuation simulation results in different scenarios. A case study in Changhongfang residential community, Xuhui District, Shanghai, China, was conducted to demonstrate the method feasibility. Simulation results have exposed potential evacuation problems in the community. A series of detailed recommended measures have been generated. These measures can help to create better emergency management for the community.

Supply–demand analysis is an important part of the planning of urban emergency shelters. Using Pudong New Area, Shanghai, China as an example, this study estimated daytime and nighttime population of the study area based on fine-scale land use data, census data, statistical yearbook information, and Tencent user-density big data. An exponential function-based, probability density estimation method was used to analyze the spatial supply of and demand for shelters under an earthquake scenario. The results show that even if all potential available shelters are considered, they still cannot satisfy the demand of the existing population for evacuation and sheltering, especially in the northern region of Pudong, under both the daytime and the nighttime scenarios. The proposed method can reveal the spatiotemporal imbalance between shelter supply and demand. We also conducted a preliminary location selection analysis of shelters based on the supply–demand analysis results. The location selection results demonstrate the advantage of the proposed method. It can be applied to identify the areas where the supply of shelters is seriously inadequate, and provide effective decision support for the planning of urban emergency shelters.