基于疏散行为的震灾避难场所服务效率评价

科学评估避难场所的服务效率是提高城市应急水平的前提。传统对避难场所服务效率的评估多偏重于避难场所空间布局的合理性,缺少对避难者的空间布局和避难行为等避难需求的考虑,这会使评估结果造成偏差,从而容易引起资源配置的低效率。本文构建了多主体模拟模型,模拟避难者灾后对避难场所的选择、奔跑、安置等关键疏散行为过程,量化评估该地区避难场所服务效率。本文对比了两种量化评估指标在同一案例评估的差异性,一种是传统方法中空间可达性(服务半径覆盖率),一种是利用疏散行为模拟计算出的避难成功率;北京市海淀区的实证研究显示两项指标在同一案例区有巨大差异。这一分析结果显示,传统评估仅利用服务半径覆盖率这一指标来分析避难场所布局现状及规划的合理性存在不足。通过避难疏散行为的模拟发现,以下指标的使用有望辅助提高评估的真实性：①避难场所的利用效率。由于设施的利用效率不均衡,会导致避难场所超容或闲置的情况。在充分考虑避难场所的有效服务面积和服务人口的基础上,设计“人均避难面积”等反应利用效率的指标就显得十分必要。②避难标识系统的连通性。避难模拟的实验显示避难标识系统可能对避难者逃生疏散具有分流和引导作用,据此,避难场所与周边居民区的标识系统的连通性也是评价其服务效率的关键指标。     

A comparison of scenario-based hybrid bilevel and multi-objective location-allocation models for earthquake emergency shelters: a case study in the central area of Beijing,China

The accurate location and allocation of disaster emergency shelters are key components of effective urban planning and emergency management. Various models have been developed to solve the location-allocation problem, but gaps remain with regard to model realism and associated applicability. For the available location and allocation models of earthquake emergency shelters, uncertainty with respect to earthquake hazard, population exposure, rate of damage to buildings and the effects of evacuee behavior are often neglected or oversimplified. Moreover, modifying the models can be an alternative means of improving the solution quality when the optimization algorithm has difficulty coping with a complex, high-dimensional problem. This article develops a scenario-based hybrid bilevel model that addresses the concerns related to high-dimensional complexity and provides a higher degree of realism by incorporating the uncertainties of population dynamics and earthquake damage scenarios into location-allocation problems for earthquake emergency shelters. A modified particle swarm optimization algorithm combined with a simulated annealing algorithm was applied to derive solutions using the hybrid bilevel model and a conventional multi-objective model, and the solutions obtained using the two models were then compared. The novel features of the study include the hybrid bilevel model that considers the dynamic number of evacuees and its implementation for earthquake emergency shelter location and allocation. The results show that the solutions significantly differ between daytime and nighttime. When applied to the multi-objective model, the optimization algorithm is time consuming and may only find the local optima and provide suboptimal solutions in the considered scenarios with more evacuees. By contrast, the hybrid bilevel model shows more desirable performance because it significantly reduces the dimensionality of the location-allocation problem based on a two-step-to-reach approach. The proposed hybrid bilevel model is proven to be useful for optimal shelter allocation, and the presented results can be used as a reference for balancing the interests of the government and residents during the planning of shelters in Beijing.     

A multicriteria spatial decision support system for solving emergency service station location problems

Earthquakes occurring in urban areas constitute an important concern for emergency management and rescue services. Emergency service location problems may be formulated in discrete space or by restricting the potential location(s) to a specified finite set of points in continuous space. We propose a Multicriteria Spatial Decision Support System to identify shelters and emergency service locations in urban evacuation planning. The proposed system has emerged as an integration of the geographical information systems (GIS) and the multicriteria Decision-Making method of Preference Ranking Organization Method for Enrichment Evaluation IV (PROMETHEE IV). This system incorporates multiple and often conflicting criteria and decision-makers’ preferences into a spatial decision model. We consider three standard structural attributes (i.e., durability density, population density, and oldness density) in the form of spatial maps to determine the zones most vulnerable to an earthquake. The information on these spatial maps is then entered into the ArcGIS software to define the relevant scores for each point with regards to the aforementioned attributes. These scores will be used to compute the preference functions in PROMETHEE IV, whose net flow outranking for each alternative will be inputted in ArcGIS to determine the zones that are most vulnerable to an earthquake. The final scores obtained are integrated into a mathematical programming model designed to find the most suitable locations for the construction of emergency service stations. We demonstrate the applicability of the proposed method and the efficacy of the procedures and algorithms in an earthquake emergency service station planning case study in the city of Tehran.     

基于应急疏散智能体模型模拟的城市避难所空间配置——以上海市静安区为例

城市应急避难所的空间配置一直是灾害防治和城市安全研究领域的热点问题。本文以城市居民尽快地,尽少拥挤地到达满足容纳需求的应急避难所为目标,整合遥感影像数据、高精度人口分布数据、交通路网数据和专家知识等数据,综合运用智能体模型和多准则决策方法,对城市避难所空间配置开展研究。本文设计了三类与应急疏散相关的智能体：政府智能体、避难所智能体和居民智能体,来实现应急疏散的模拟,并根据模拟结果支持应急避难所的空间选址和配置。选址方法上运用了多准则决策方法和权重敏感性分析,在选址高适宜区域内选定避难所的新建方案。以新的避难所空间布局和配置为条件,执行新一轮的应急疏散模拟过程,实现选址的循环优化,从而获得最终的避难所空间配置方案。本文以上海市静安区的应急避难所空间配置分析为案例,生成了该区域应急避难所的详细空间配置方案,该方案能帮助居民在尽少拥挤风险下尽快疏散到附近的避难所。本文提出的方法充实了中国城市避难所选址的相关理论与可操作性的技术基础,为其他地区开展避难所的配置工作提供借鉴与参考。     

Utilising volunteered geographic information to assess resident's flood evacuation shelters. Case study: Jakarta

Research on disaster response frequently uses volunteered geographic information (VGI), due to its capability to provide near real-time information during and after a disaster. It is much less commonly used in spatial planning related to disaster management. However, VGI appears to have considerable potential for use in spatial planning and offers some advantages over traditional methods. For example, VGI can capture residents' preferences in a much faster, more timely, and more comprehensive fashion than is possible with, for example, questionnaires and surveys. This research investigates the usefulness of VGI for planning flood evacuation shelters. Using Jakarta, Indonesia, as a case study, we use VGI to capture the locations of flood evacuation shelters based on residents' preferences during flood periods in 2013–2014 and 2014–2015 and compare these with the locations of official shelters. Floods frequently affect Jakarta and the city administration uses VGI in flood emergency responses. Moreover, Jakarta has been identified as having the largest number of active Twitter users among cities worldwide. Thus, Jakarta is an appropriate place to study the use of VGI for planning evacuation shelters. VGI generated by Twitter users was used to identify the shelter locations preferred by Jakarta residents, and more precisely the flood evacuees. Of 171,046 tweets using keywords relating to flood evacuation, the content of 306 tweets indicated that they had been sent from inside or near evacuation shelters. The spatial pattern showed that those tweets were sent from 215 locations, mostly located near flooded areas. The analysis further showed that 35.6% of these shelter locations preferred by residents intersected with the locations of official evacuation shelters. As a general conclusion, our study demonstrates the advantages of using VGI for spatial planning, which mainly relates to the ease of capturing community preferences over a large area.     

A modified particle swarm optimization algorithm for optimal allocation of earthquake emergency shelters

Allocation for earthquake emergency shelters is a complicated geographic optimization problem because it involves multiple sites, strict constraints, and discrete feasible domain. Huge solution space makes the problem computationally intractable. Traditional brute-force methods can obtain exact optimal solutions. However, it is not sophisticated enough to solve the complex optimization problem with reasonable time especially in high-dimensional solution space. Artificial intelligent algorithms hold the promise of improving the effectiveness of location search. This article proposes a modified particle swarm optimization (PSO) algorithm to deal with the allocation problem of earthquake emergency shelter. A new discrete PSO and the feasibility-based rule are incorporated according to the discrete solution space and strict constraints. In addition, for enhancing search capability, simulated annealing (SA) algorithm is employed to escape from local optima. The modified algorithm has been applied to the allocation of earthquake emergency shelters in the Zhuguang Block of Guangzhou City, China. The experiments have shown that the algorithm can identify the number and locations of emergency shelters. The modified PSO algorithm shows a better performance than other hybrid algorithms presented in the article, and is an effective approach for the allocation problem of earthquake emergency shelters.     

Integrating multi-agent evacuation simulation and multi-criteria evaluation for spatial allocation 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.     

A non-dominated sorting genetic algorithm for the location and districting planning of earthquake shelters

The improvement of emergency coping capacity is one of the most efficient measures for mitigating disaster impact. Shelter planning is an important strategy to reduce the number of casualties and injuries and facilitate disaster recovery. This study aims to address earthquake shelter location selection and the districting planning of service areas jointly. A bi-objective model is proposed to minimise the total evacuation distance and the total cost, subject to capacity and contiguity constraints. A non-dominated sorting genetic algorithm is developed to tackle the bi-objective model, which involves a multitude of decision variables. To fit the model, the chromosome structure, initialisation process and genetic operators in the algorithm are specifically designed to maintain the contiguity of the service area. And a hybrid strategy of bidirectional multi-point crossover and bidirectional single-point crossover helps promote the diversity of the solutions and accelerate the convergence. Moreover, the Pareto-optimal strategy and feasibility-based rule are combined to obtain trade-offs between objectives. The model and algorithm are validated in a case study of the earthquake shelter location and districting planning problem in Chaoyang District of Beijing, and the results confirm the effectiveness and efficiency of the method.     

Factors Affecting Evacuation Behavior: The Case of 2007 Cyclone Sidr,Bangladesh

Cyclone Sidr, a Category 4 storm, struck the southwestern coast of Bangladesh on 15 November 2007. Despite early cyclone warnings and emergency evacuation orders for coastal residents, thousands of individuals stayed in their homes. This study examines Sidr victims’ responses to cyclone warnings and evacuation orders, and explores the factors that would explain why the victims did or did not comply with the orders. Based on survey data collected from 277 Sidr survivors living in the four most severely impacted coastal districts, this study found that more than 75 percent of all respondents were aware of the cyclone warnings and evacuation orders before Sidr's landfall. Despite the efforts of the Bangladesh government, there were lapses in cyclone warnings and evacuation procedures. Field data also reveal several reasons cited by respondents for not complying with evacuation orders. Multivariate analyses of survey data show that trust in warning messages was the most important determinant in the decision to seek refuge in safer shelters, followed by distance to nearest shelter and annual level of education. Several recommendations have been made to improve cyclone warnings and the use of public shelters for similar future events.     

基于WoS和Citespace的应急避难场所知识图谱和进展分析

以近30 年Web of Science 核心数据库收录的2472 篇应急避难场所相关文献为基础，首先统计发文数量时间分布和期刊分布，然后利用Citespace可视化软件，从发文作者、机构、国家、关键词和学科方向5个方面分别进行知识图谱分析。结果表明：近30年来应急避难场所研究发文呈现由缓慢探索、稳步发展向高速发展的阶段；发文期刊主要为自然灾害类、紧急医学类、工程技术类；研究作者群较多，学术交流较为紧密；美国和中国为发文量最多的国家；路易斯安那州立大学和中国科学技术大学是主要的发文机构；从关键词发现应急避难场所目标人群主要围绕灾害救援和社会弱势群体，研究视角从单一问题和事件开始向综合系统演变，研究方法从定性的方法模型转向动力机制和信息系统开发，研究内容则注重多学科交叉和微观尺度、重视案例实证和信息共享和强调应急避难场所管理；学科方向主要为工程学和公共环境与健康两个学科。在建设韧性城市的背景下，可借鉴国际经验来构建中国应急避难场所研究体系和管理。     

基于GIS的社区居民避震疏散区划方法及应用研究

在快速城市化和地震灾害频发的背景下,灾前人群紧急疏散区划成为防灾减灾规划研究的重要课题。结合当前城市应急疏散设施布局现状,立足于社区夜间避震疏散需求,综合运用GIS空间分析技术,从应急疏散需求分布、疏散空间可达性、疏散优化归属3方面逐步构建居民避震疏散区划方法,并选择上海市内人群和建筑相对密集的陆家嘴街道为对象开展实证研究。从而为城市防震减灾规划的避难设施优化布局和应急疏散预案编制提供科学适用的技术手段。     

Sensitivity of tsunami evacuation modeling to direction and land cover assumptions

Although anisotropic least-cost-distance (LCD) modeling is becoming a common tool for estimating pedestrian-evacuation travel times out of tsunami hazard zones, there has been insufficient attention paid to understanding model sensitivity behind the estimates. To support tsunami risk-reduction planning, we explore two aspects of LCD modeling as it applies to pedestrian evacuations and use the coastal community of Seward, Alaska, as our case study. First, we explore the sensitivity of modeling to the direction of movement by comparing standard safety-to-hazard evacuation times to hazard-to-safety evacuation times for a sample of 3985 points in Seward's tsunami-hazard zone. Safety-to-hazard evacuation times slightly overestimated hazard-to-safety evacuation times but the strong relationship to the hazard-to-safety evacuation times, slightly conservative bias, and shorter processing times of the safety-to-hazard approach make it the preferred approach. Second, we explore how variations in land cover speed conservation values (SCVs) influence model performance using a Monte Carlo approach with one thousand sets of land cover SCVs. The LCD model was relatively robust to changes in land cover SCVs with the magnitude of local model sensitivity greatest in areas with higher evacuation times or with wetland or shore land cover types, where model results may slightly underestimate travel times. This study demonstrates that emergency managers should be concerned not only with populations in locations with evacuation times greater than wave arrival times, but also with populations with evacuation times lower than but close to expected wave arrival times, particularly if they are required to cross wetlands or beaches.     

"Homed" in Arizona: The Architecture of Emergency Shelters

Although much has been written about the abstract spaces of homelessness, relatively less has been documented on how the architecture of homeless shelters intersects with the homeless experience. Emergency shelters are the first places where homeless families begin their journey toward becoming "homed." Studies suggest that there is a middle-class bias in the design of emergency homeless shelters, but how do its spaces contribute to the route out of homelessness? Through narratives of homeless families, and observations of the places they occupy in an emergency shelter in Arizona, this article illustrates how they engage with the shelter's architecture to construct notions of home and homelessness. This study suggests that while homeless families try to achieve the psychological qualities of a "home" in the shelter, the materiality of the places in the shelter, where these qualities are acquired or negotiated, become important symbolic markers to becoming "homed."     

A Review of “India and South Asia: Economic Developments in the Age of Globalization”

Any analysis of health service problems facing the world today must consider that these problems exist in a geographic context. This fact has led to an increased need for accurate and current information to support emergency planning and decision making. In this article we combine geographic information systems (GIS) and neural networks for performing health emergency assessments and generating hazard maps that show areas that are potentially at high risk for emergencies. Through the use of neural networks we predict the location of future emergency events. On these events we use a kernel density estimator to create maps of areas that have a high risk for future emergencies. As a result, emergency services will know in advance where there is a high possibility of an emergency event occurring and can formulate a response, thus improving incident management and health planning. For example, the service can locate ambulances in places near the expected emergency cases, minimizing response time. The approach was tested in stroke-event analysis in the city of Athens, Greece.     

A parallel algorithm for coverage optimization on multi-core architectures

Location siting is an important part of service provision, with much potential to impact operational efficiency, safety, security, system reliability, etc. A class of location models seeks to optimize coverage of demand for service that is continuously distributed across space. Decision-making and planning contexts include police/fire resource allocation for a community, siting cellular towers to support cell phone signal transmission, locating emergency warning sirens to alert the public of severe weather and other related dangers, and many others as well. When facilities can be sited anywhere in continuous space to provide coverage to an entire region, this is a very computationally challenging problem to solve because potential demand for service is everywhere and there are an infinite number of potential facility sites to consider. This article develops a new parallel solution approach for this location coverage optimization problem through an iterative bounding scheme on multi-core architectures. The developed approach is applied to site emergency warning sirens in Dublin, Ohio, and fire stations in Elk Grove, California. Results demonstrate the effectiveness and efficiency of the proposed approach, enabling real-time analysis and planning. This work illustrates that the integration of cyberinfrastructure can significantly improve computational efficiency in solving challenging spatial optimization problems, fitting the themes of this special issue: cyberinfrastructure, GIS, and spatial optimization.     

地震灾害预测和应急模拟系统的设计与应用——以永安市城市应急系统为例

城市防震减灾是地震潜在频发区提高自然灾害防治能力建设的重要方面。本文结合福建省永安市城市防震减灾信息管理系统的建设,探讨设计防震减灾数值模型,分析模型集成的关键技术,构建了地震风险评估、建筑物易损性评价、生命与财产损失估算、救援与救灾管理调度等模型和基于GIS开发的震害预测和应急模拟系统。该系统在福建省永安市的实际应用表明,通过对地震灾害预测结果的分析,可加强城市抗震中的薄弱环节,为灾区政府应急响应和制订对策提供决策支持辅助信息,从而显著提高城市防震减灾的综合能力。     

Earthquake‐induced landslide hazard monitoring and assessment using SOM and PROMETHEE techniques: A case study at the Chiufenershan area in Central Taiwan

Monitoring and assessment of landslide hazard is an important task for decision making and policy planning in the landslide area. Massive landslides, caused by the catastrophic Chi‐Chi earthquake in 1999, occurred in Central Taiwan, especially at Chiufenershan area in Nantou county. This study proposed two useful indicators coupled with the Self‐organizing map (SOM) neural network and the Preference Ranking Organization METHod for Enrichment Evaluations (PROMETHEE) technique to quickly extract accurate post‐quake landslides from multi‐temporal Système Probatoire de l'Observation de la Terre (SPOT) images. A GIS‐based system was developed to simplify and integrate the procedures such as image pre‐processing, the SOM training, the PROMETHEE calculation, landslide extraction and accuracy assessment. The evaluated result shows that the landslide area soon after the earthquake is 209.50 ha (Kappa coefficient 96.88%). Over seven years of vegetation recovery, the denudation area has declined to 112.64 ha (Kappa coefficient 90.64%). Most earthquake‐induced landslides could be restored by natural vegetation succession. The developed system is a useful decision‐making tool for landslide area planning.     

Integration of linear programming and GIS for land-use modelling

Abstract

Geographical Information Systems (GIS) are becoming basic tools for a wide variety of earth science and land-use applications. This article presents linear programming (LP) as a promising tool for spatial modelling within a GIS. Although LP is not properly a spatial technique, it may be used to optimize spatial distributions or to guide the integration of variables. An example of the use of LP in land-use planning is described, with minimizing rural unemployment as the main goal. Technical, financial and ecological constraints are established to show the influence of several limitations on achieving the optimal solution. LP makes it possible to achieve optimal land-use, where the objective is maximized and the constraints respected. LP can also be used to simulate different planning scenarios, by modifying both the objective function coefficients and the constraints. The integration of LP and GIS is presented in two phases: (i) acquisition of attribute data for the LP model, and (ii) modelling and mapping the results.     

中国地震灾害宏观人口脆弱性评估

地震灾害脆弱性不但是国家和地区中长期规划的基础,也是地震应急时的重要决策依据,因此地震灾害脆弱性评估是当前研究的热点和重点。本文首先介绍了地震灾害人口脆弱性定义,指出了地震灾害人口脆弱性的影响因子和影响模式;然后通过分析传统地震灾害人口脆弱性的宏观和微观评价方法和模型,利用人口总量、儿童人口比例、老年人口比例、人口密度和人均GDP 5 个指标建立了地震灾害宏观人口脆弱性综合评价模型,并对全国各县市区的人口脆弱性进行了评估。结果表明：中国地震灾害人口脆弱性区域差异明显,东部地区人口脆弱性明显高于西部;人口总量是脆弱性的主要因素,宏观人口脆弱性综合评估模型对地震灾害人口脆弱性的修正作用显著。     

A new assessment model for evacuation vulnerability in urban areas

In the high-speed urbanization process of China, the urban population has been increasing significantly, leading to a high-density aggregation of population. However, the sharp increase in population density has not produced commensurate improvements in the road networks. On the contrary, the population increase induced a serious evacuation vulnerability, which cities experience during various hazards and catastrophic events. Therefore, research on evacuation vulnerability is important to urban planning. To assess the evacuation vulnerability, the optimal and worst scenarios should be considered because all possible evacuation plans occur between these extremes. However, most previous evacuation vulnerability studies are based on the worst-case scenario, only providing an upper bound of a potential evacuation assessment. To provide a more comprehensive theoretical basis for decision-makers to understand the consequences caused by all possible evacuations, this paper proposes an optimal evacuation vulnerability assessment model that provides the lower bound on potential evacuation difficulties. The model is solved by a stepwise spreading algorithm based on Graph Theory. Subsequently, to evaluate the effectiveness of the model, the study adopts the model to assess the evacuation capability of different road network topologies. A comparison with previous research was performed. The model was demonstrated in an application to the South Luogu Alley of Beijing, China. The significance of this paper is that the combination of our model with previous research may provide a more complete theoretical basis for an evacuation vulnerability assessment.