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.     

The temporal hierarchy of shelters: a hierarchical location model for earthquake-shelter planning

The problem of emergency facility location is a critical component in evacuation planning. The emergence of geographic information systems (GIS) has provided a useful operational platform to assist this issue. A previously overlooked facet is the consideration of a hierarchical structure in the placement of emergency shelters. Due to the fact that survivors' needs change over time during post-disaster evacuations, shelters have now been categorized on a temporal scale based on their functions at different evacuation phases. This article proposes a three-level hierarchical location model for optimizing the placement of earthquake shelters by taking into account this temporal variance. The article not only scrutinizes the modeling procedure but also implements the model in a planning area with many real-world details. Based on the optimization results derived from a GIS context, we have found that the quality of the earthquake response procedure is not only dependent on the placement strategy of shelters, but more importantly on the financial constraints imposed on the planning and construction of these shelters. A discussion has been proposed to balance the trade-off between budget planning and evacuation efficiency. As the first attempt to model the hierarchical configuration of emergency shelters with specific focus on evacuees' escalating sheltering demands, this article will be of great significance in helping policy makers consider both the spatial and financial aspects of the strategic placement of emergency shelters.     

An improved artificial bee colony algorithm for optimal land-use allocation

Land-use allocation is of great importance for rapid urban planning and natural resource management. This article presents an improved artificial bee colony (ABC) algorithm to solve the spatial optimization problem. The new approach consists of a heuristic information-based pseudorandom initialization (HIPI) method for initial solutions and pseudorandom search strategy based on a long-chain (LC) mechanism for neighborhood searches; together, these methods substantially improve the search efficiency and quality when handling spatial data in large areas. We evaluated the approach via a series of land-use allocation experiments and compared it with particle swarm optimization (PSO) and genetic algorithm (GA) methods. The experimental results show that the new approach outperforms the current methods in both computing efficiency and optimization quality.     

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 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.     

Integration of genetic algorithms and GIS for optimal location search

Optimal location search is frequently required in many urban applications for siting one or more facilities. However, the search may become very complex when it involves multiple sites, various constraints and multiple‐objectives. The exhaustive blind (brute‐force) search with high‐dimensional spatial data is infeasible in solving optimization problems because of a huge combinatorial solution space. Intelligent search algorithms can help to improve the performance of spatial search. This study will demonstrate that genetic algorithms can be used with Geographical Information systems (GIS) to effectively solve the spatial decision problems for optimally sitting n sites of a facility. Detailed population and transportation data from GIS are used to facilitate the calculation of fitness functions. Multiple planning objectives are also incorporated in the GA program. Experiments indicate that the proposed method has much better performance than simulated annealing and GIS neighborhood search methods. The GA method is very convenient in finding the solution with the highest utility value.     

Enhanced multi-objective optimization algorithm for renewable energy sources: optimal spatial development of wind farms

A new approach for treating multi-objective spatial optimization problems is introduced in this study, aiming at deriving the optimal spatial allocation of Wind Farms on a Greek Island (Lesvos). This work builds on the knowledge gained from numerous applications of multi-objective genetic algorithms, either for spatial planning purposes or for other engineering-related topics, by incorporating modified genetic operators and sophisticated planning criteria. Hence, a stand-alone genetic optimizer was developed that incorporates the controlled non-dominated sorting genetic algorithm-II (CNSGA-II), in which the user can model all planning criteria and constraints for every spatial entity to be allocated, and handle the genetic solver via a built-in computational framework that permits the analysis of large terrains. The presented paradigm provides interesting findings for the optimal development of renewable energy sources projects whose spatial allocation is governed by conflicting criteria and strict constraints.     

Geophysical inversion with a neighbourhood algorithm—I. Searching a parameter space

This paper presents a new derivative-free search method for finding models of acceptable data fit in a multidimensional parameter space. It falls into the same class of method as simulated annealing and genetic algorithms, which are commonly used for global optimization problems. The objective here is to find an ensemble of models that preferentially sample the good data-fitting regions of parameter space, rather than seeking a single optimal model. (A related paper deals with the quantitative appraisal of the ensemble.)
  The new search algorithm makes use of the geometrical constructs known as Voronoi cells to derive the search in parameter space. These are nearest neighbour regions defined under a suitable distance norm. The algorithm is conceptually simple, requires just two 'tuning parameters', and makes use of only the rank of a data fit criterion rather than the numerical value. In this way all difficulties associated with the scaling of a data misfit function are avoided, and any combination of data fit criteria can be used. It is also shown how Voronoi cells can be used to enhance any existing direct search algorithm, by intermittently replacing the forward modelling calculations with nearest neighbour calculations.
  The new direct search algorithm is illustrated with an application to a synthetic problem involving the inversion of receiver functions for crustal seismic structure. This is known to be a non-linear problem, where linearized inversion techniques suffer from a strong dependence on the starting solution. It is shown that the new algorithm produces a sophisticated type of 'self-adaptive' search behaviour, which to our knowledge has not been demonstrated in any previous technique of this kind.     

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.     

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 center-based modeling approach to solve the districting problem

This article deals with the districting problem arising in applications such as political districting, police patrol area delineation and sales territory design. The aim of districting is to group basic areal units into geographic districts such that some set of criteria are satisfied, with basic criteria being district balance, compactness and contiguity. This article proposes a center-based mixed-integer linear programming model to solve the districting problem. Given the central units of districts, the model optimizes weighted objectives of district balance and compactness while satisfying contiguous constraints on districts. The performance of the model was tested using three study areas with 297, 324 and 1297 areal units, respectively. Experimentation shows that, using the district centers identified by a multistart weighted K-medoids algorithm, the model instances can be solved optimally or near-optimally. Compared with local search-based algorithms, the center-based approach outperforms metaheuristics such as simulated annealing, variable neighborhood descent, iterative local search and old bachelor acceptance search in terms of solution quality.     

Identifying regions based on flexible user-defined constraints

The identification of regions is both a computational and conceptual challenge. Even with growing computational power, regionalization algorithms must rely on heuristic approaches in order to find solutions. Therefore, the constraints and evaluation criteria that define a region must be translated into an algorithm that can efficiently and effectively navigate the solution space to find the best solution. One limitation of many existing regionalization algorithms is a requirement that the number of regions be selected a priori. The recently introduced max-p algorithm does not have this requirement, and thus the number of regions is an output of, not an input to, the algorithm. In this paper, we extend the max-p algorithm to allow for greater flexibility in the constraints available to define a feasible region, placing the focus squarely on the multidimensional characteristics of the region. We also modify technical aspects of the algorithm to provide greater flexibility in its ability to search the solution space. Using synthetic spatial and attribute data, we are able to show the algorithm’s broad ability to identify regions in maps of varying complexity. We also conduct a large-scale computational experiment to identify parameter settings that result in the greatest solution accuracy under various scenarios. The rules of thumb identified from the experiment produce maps that correctly assign areas to their ‘true’ region with 94% average accuracy, with nearly 50% of the simulations reaching 100% accuracy.     

A multi-type ant colony optimization (MACO) method for optimal land use allocation in large areas

Optimizing land use allocation is a challenging task, as it involves multiple stakeholders with conflicting objectives. In addition, the solution space of the optimization grows exponentially as the size of the region and the resolution increase. This article presents a new ant colony optimization algorithm by incorporating multiple types of ants for solving complex multiple land use allocation problems. A spatial exchange mechanism is used to deal with competition between different types of land use allocation. This multi-type ant colony optimization optimal multiple land allocation (MACO-MLA) model was successfully applied to a case study in Panyu, Guangdong, China, a large region with an area of 1,454,285 cells. The proposed model took only about 25 minutes to find near-optimal solution in terms of overall suitability, compactness, and cost. Comparison indicates that MACO-MLA can yield better performances than the simulated annealing (SA) and the genetic algorithm (GA) methods. It is found that MACO-MLA has an improvement of the total utility value over SA and GA methods by 4.5% and 1.3%, respectively. The computation time of this proposed model amounts to only 2.6% and 12.3%, respectively, of that of the SA and GA methods. The experiments have demonstrated that the proposed model was an efficient and effective optimization technique for generating optimal land use patterns.     

农村学校撤并后规模约束对学校优化布局的影响——以北京延庆区为例

在城镇化和农村人口减少的背景下,中国农村地区大量学校撤并,如何优化学校布局成为研究的重点。2008年原建设部发布了农村学校的最小和最大学生规模标准,但该标准对学校布局的影响尚缺乏研究。本文在包含最大距离约束的P-中值模型中增加了学校规模约束,构建了学校布局优化模型,并以北京延庆区小学布局为例,采用分支界定算法进行了求解。结果表明：实施学校规模标准化对学校优化选址有显著的影响,在优化模型中加入学校规模约束后,有65.22%的学校位置发生了改变,呈更加分散型布局;但在乡镇尺度下考察,学校的空间格局则基本未发生变化;学校规模标准化带来的距离增长较小,平均每个学生上学距离仅增长了135 m。并根据结果进一步讨论了研究结果对学校布局优化的政策启示。     

Multi-objective spatially constrained clustering for regionalization with particle swarm optimization

Regionalization is an important part of the spatial analysis process, and the solution should be contiguity-constrained in each region. In general, several objectives need to be optimized in practical regionalization, such as the homogeneity of regions and the heterogeneity among regions. Therefore, multi-objective techniques are more suitable for solving regionalization problems. In this paper, we design a multi-objective particle swarm optimization algorithm for solving regionalization problems. Towards this goal, a novel particle representation for regionalization is proposed, which can be expressed in continuous space and has flexible constraints on the number of regions. In the process of optimization, a contiguous-region method is designed that satisfies the constraints and improves the efficiency. The decision solution is selected in the Pareto set based on a trade-off between the objective functions, and the number of regions can be automatically determined. The proposed method outperforms six regionalization algorithms in terms of both the number and the quality of the solutions.     

An agent-based model for optimal land allocation (AgentLA) with a contiguity constraint

Spatial optimization is complex because it usually involves numerous spatial factors and constraints. The optimization becomes more challenging if a large set of spatial data with fine resolutions are used. This article presents an agent-based model for optimal land allocation (AgentLA) by maximizing the total amount of land-use suitability and the compactness of patterns. The essence of the optimization is based on the collective efforts of agents for formulating the optimal patterns. A local and global search strategy is proposed to inform the agents to select the sites properly. Three sets of hypothetical data were first used to verify the optimization effects. AgentLA was then applied to the solution of the actual land allocation optimization problems in Panyu city in the Pearl River Delta. The study has demonstrated that the proposed method has better performance than the simulated annealing method for solving complex spatial optimization problems. Experiments also indicate that the proposed model can produce patterns that are very close to the global optimums.     

Optimal rural land use allocation in central China: Linking the effect of spatiotemporal patterns and policy interventions

Rural land use development is experiencing a transition stage of socioeconomic and land use development in China. Historic land use transition process and policy interventions have key influence on the applicability of land use allocation solutions in future land use management. Strategic land use allocation is therefore required to possess a good adjustment capability to the transition process. Although heuristic optimization methods have been promising to solve land use allocation problems, most of them ignored the spatially explicit effect of historic land use transition and policies. To help resolve this issue, this study aims to optimize future land use pattern in the context of rural land use development. We took Yunmeng County, one of the typical major grain producing and rapidly urbanizing areas in central China, as a case study and solved the sustainable land use allocation problem by using an improved heuristic optimization model. The model was constructed based on the integration of a spatial discrete particle swarm optimization and cellular automata-Markov simulation approach. The spatiotemporal land use patterns and policy interventions were represented by the CA-Markov as in spatially explicit transition rules, and then incorporated into the discrete PSO for optimal land use solutions. We examined the influence of the joint effect of spatiotemporal land use patterns and policy interventions on the land use allocation outcome. Our results demonstrate the robustness and potential of the proposed model, and, more importantly, indicate the significance of incorporating the spatiotemporal land use patterns and policy interventions into rural land use allocation.     

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。     

EMERGENCY SHELTER NETWORKS IN AN URBAN AREA: SERVING THE HOMELESS IN METROPOLITAN TORONTO

A framework for analyzing the structure of urban emergency shelter networks is proposed. The shelter and service network consists of clients, shelters, support services, and a political context shaped by state policies and community attitudes. The example of Metropolitan Toronto shows that, while it might be true that there is a need for more permanent housing, there also exists a real need for emergency shelters. A problem exists for certain groups who might seek temporary shelter in suburban areas because of the unequal spatial distribution of shelters and support services between the inner City of Toronto and suburban municipalities. The recent suburbanization of some shelters has been dominated by shelters for women and children. Patterns of repeat usage of the shelter network suggest that certain client groups might not have access to all the support services they need. The conclusions argue that urban shelter networks offer an opportunity for geographers to consider more closely the links between housing, policy, and political ideologies.     

基于微粒群优化算法的空间优化决策

空间优化决策是GIS应用中复杂而又常见的问题。由于涉及到大量的组合,使用穷举法等方法难以找到最优的解决方案,因此需要运用新的理论方法来解决这类问题。微粒群优化算法是近年来新兴的一种优化技术,与GIS相结合可解决空间优化决策问题。首先,对微粒群优化算法和空间优化决策问题作了简单介绍;然后,基于人口密度、最短距离约束条件下,通过GIS技术,对微粒群优化算法用于空间优化决策的方法、实施过程作了详细阐述;接着,用4×4方格单元对PSO方法的正确性、有效性进行了验证;最后,以广州市芳村区为例,对该方法进行实例验证。通过实验,证明微粒群优化算法具有较好的收敛速度、较高的结果精度,是解决空间优化决策问题的一种有效方法。