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Alan T. Murray - One of the best experts on this subject based on the ideXlab platform.
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A Spatial Optimization Approach for Solving a Multi-facility Location Problem with Continuously Distributed Demand
Innovations in Urban and Regional Systems, 2020Co-Authors: Jing Yao, Alan T. MurrayAbstract:Location-related decisions are important considerations in most aspects of human activity. Facility location models are usually employed to assist decision processes concerning the siting of one or more facilities in order to best serve underlying demand that is discretely or continuously distributed across space. Of interest in this chapter, it is the perspective that demand is continuously distributed. Though surfaces defined by mathematical functions or fitted through Spatial interpolation can be employed to approximate a continuous demand representation, the results of both of these methods are subject to significant errors and uncertainties. For this reason, we introduce a generic location planning model and the continuous multi-Weber problem, in which facilities may be sited anywhere in space in order to best serve continuously distributed demand. Due to the complexity of the problem, a Spatial Optimization approach for dealing with continuous demand is proposed through the integration of Optimization techniques with geographic information system (GIS) functionality. Results from empirical applications demonstrate the effectiveness of the developed approach and highlight the importance of incorporating GIS functionality into the solution process.
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Evaluation and development of sustainable urban land use plans through Spatial Optimization
Transactions in GIS, 2019Co-Authors: Jing Yao, Alan T. Murray, Jing Wang, Xiaoxiang ZhangAbstract:Along with rapid global urbanization, cities are challenged by environmental risks and resource scarcity. Sustainable urban planning is central to address the dilemma of economic growth and ecosystem protection, where the use of land is critical. Sustainable land use patterns are Spatially explicit in nature, and can be structured and addressed using Spatial Optimization integrating GIS and mathematical models. This research discusses prominent sustainability concerns in land use planning and suggests a generalized multi‐objective Spatial Optimization model to facilitate conventional planning. The model is structured to meet land use demand while satisfying the requirements of the physical environment, society and economy. Unlike existing work relying on raster data, due to its simple data structure and ease of Spatial relationship evaluation, this research develops an approach for identifying land use solutions based on vector data that better reflects the actual shape and Spatial layout of land parcels as well as the ways land use information is managed in practice. An evolutionary algorithm is developed to find the set of efficient (Pareto) solutions given the complexity of vector‐based representations of space. The proposed approach is applied in an empirical study of Dafeng, China in order to support local urban growth and development. The results demonstrate that Spatial Optimization can be a powerful tool for deriving effective and efficient land use planning strategies. A comparison to results using a raster data approach supports the superiority of land use Optimization using vector data as part of planning practice.
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location Optimization of urban fire stations access and service coverage
Computers Environment and Urban Systems, 2019Co-Authors: Jing Yao, Xiaoxiang Zhang, Alan T. MurrayAbstract:Abstract Fire and rescue services are among the most critical public services provided by governments to protect people, property and the environment from fires and other emergencies. Efficient deployment of fire stations is essential to ensure timely response to calls for service. Given the geographic nature of such problems, Spatial Optimization approaches have long been employed in public facility location modeling along these lines. In particular, median and coverage approaches have been widely adopted to help achieve travel-cost and service-coverage goals, respectively. This paper proposes a bi-objective Spatial Optimization model that integrates coverage and median goals in the service of demand areas. Based on the properties of derived objective functions, we presented a constraint-based solution procedure to generate the Pareto frontier, enabling the identification of alternative fire station siting scenarios. The developed model is applied to an empirical study that seeks to identify the best fire station locations in Nanjing, China. The results demonstrate the value of Spatial Optimization in assisting fire station planning and rescue resource deployment, highlighting important policy implications.
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Spatial Optimization for Land-use Allocation: Accounting for Sustainability Concerns
International Regional Science Review, 2017Co-Authors: Jing Yao, Xiaoxiang Zhang, Alan T. MurrayAbstract:Land-use allocation has long been an important area of research in regional science. Land-use patterns are fundamental to the functions of the biosphere, creating interactions that have substantial impacts on the environment. The Spatial arrangement of land uses therefore has implications for activity and travel within a region. Balancing development, economic growth, social interaction, and the protection of the natural environment is at the heart of long-term sustainability. Since land-use patterns are Spatially explicit in nature, planning and management necessarily must integrate geographical information system and Spatial Optimization in meaningful ways if efficiency goals and objectives are to be achieved. This article reviews Spatial Optimization approaches that have been relied upon to support land-use planning. Characteristics of sustainable land use, particularly compactness, contiguity, and compatibility, are discussed and how Spatial Optimization techniques have addressed these characteristics are detailed. In particular, objectives and constraints in Spatial Optimization approaches are examined.
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evaluating polygon overlay to support Spatial Optimization coverage modeling
Geographical Analysis, 2014Co-Authors: Ran Wei, Alan T. MurrayAbstract:Minimizing costs and maximizing coverage are important goals in many planning contexts. These goals often necessitate an abstraction of a continuous demand region, resulting in potential errors when applying traditional coverage models. To reduce coverage errors caused by Spatial abstraction, a number of Spatial representation schemes have been proposed and applied. A new representation scheme using polygon overlay recently received much attention because potentially it can eliminate representation errors in coverage modeling. However, this overlay-based approach is computationally challenging in terms of both the generation of demand units and the complexity of the resulting coverage model. This article investigates the operational and computational challenges of polygon overlay for delineating continuous demand in coverage models, an issue that has yet to be fully explored. We present a theoretical evaluation of the computational complexity associated with representation using polygon overlay in coverage modeling. Evaluations of two study regions provide empirical support for the computational complexity analysis. The analysis results provide insight regarding expected problem size and computational requirements if polygon overlay is relied upon to delineate demand unit boundaries in coverage modeling. La minimizacion de costos y la maximizacion de la cobertura espacial son objetivos importantes en muchos contextos de planificacion. Estas metas a menudo requieren una abstraccion de una region continua de demanda , dando lugar a posibles errores en la aplicacion de modelos de cobertura tradicionales. Para reducir los errores de cobertura provocadas por la abstraccion, la comunidad academica ha propuesto y aplicado una serie de esquemas de representacion espacial. Recientemente un nuevo esquema de representacion que utiliza la superposicion de poligonos ha recibido mucha atencion porque potencialmente puede eliminar los errores de representacion en el modelado de la cobertura. Sin embargo, este enfoque es computacionalmente dificil, tanto en terminos de la generacion de unidades de demanda, como en la complejidad del modelo de cobertura resultante. Este articulo investiga los retos operacionales y de computo de la superposicion de poligonos para delinear la region continua de demanda en los modelos de cobertura, un problema que aun no se ha explorado a fondo. Se presenta una evaluacion teorica de la complejidad computacional asociada a la representacion mediante superposicion de poligonos en el modelado de cobertura espacial. Se presentan evaluaciones de dos regiones de estudio como apoyo empirico para el analisis de la complejidad computacional. Los resultados del analisis proporcionan informacion sobre el tamano del problema esperado y los requerimientos computacionales en los casos en que el metodo de superposicion de poligonos es usado para delinear limites de la region de demanda para el modelado de cobertura espacial 最小成本和最大区域覆盖是许多规划情境研究中的重要目标。实现这些目标通常需要对连续需求区域进行抽象，而这又会导致在应用传统覆盖模型时出现潜在误差。为减小由空间抽象引起的覆盖误差，已提出了一系列空间表达方案并得到应用。一种新型的利用多边形覆盖的表达方案，因其或可消除覆盖建模过程的表达误差，近来得到较多的关注。然而，这种基于覆盖的方法在需求单元生成与覆盖模型结果复杂性等方面面临着计算挑战。本文提出了一种在覆盖建模中采用多边形叠加表征的计算复杂度的理论评估方法。两个研究区域的评估为计算复杂度分析提供了经验支撑，如果在覆盖建模中多边形叠加依赖于描述需求单元边界时，该分析结果有助于深入考察预期问题规模大小及计算需求。
Indrajeet Chaubey - One of the best experts on this subject based on the ideXlab platform.
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Spatial Optimization of six conservation practices using swat in tile drained agricultural watersheds
Journal of The American Water Resources Association, 2015Co-Authors: Margaret Kalcic, Jane Frankenberger, Indrajeet ChaubeyAbstract:Targeting of agricultural conservation practices to the most effective locations in a watershed can promote wise use of conservation funds to protect surface waters from agricultural nonpoint source pollution. A Spatial Optimization procedure using the Soil and Water Assessment Tool was used to target six widely used conservation practices, namely no-tillage, cereal rye cover crops (CC), filter strips (FS), grassed waterways (GW), created wetlands, and restored prairie habitats, in two west-central Indiana watersheds. These watersheds were small, fairly flat, extensively agricultural, and heavily subsurface tile-drained. The targeting approach was also used to evaluate the model's representation of conservation practices in cost and water quality improvement, defined as export of total nitrogen, total phosphorus, and sediment from cropped fields. FS, GW, and habitats were the most effective at improving water quality, while CC and wetlands made the greatest water quality improvement in lands with multiple existing conservation practices. Spatial Optimization resulted in similar cost-environmental benefit tradeoff curves for each watershed, with the greatest possible water quality improvement being a reduction in total pollutant loads by approximately 60%, with nitrogen reduced by 20-30%, phosphorus by 70%, and sediment by 80-90%.
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Spatial Optimization of Six Conservation Practices Using Swat in Tile‐Drained Agricultural Watersheds
JAWRA Journal of the American Water Resources Association, 2015Co-Authors: Margaret Kalcic, Jane Frankenberger, Indrajeet ChaubeyAbstract:Targeting of agricultural conservation practices to the most effective locations in a watershed can promote wise use of conservation funds to protect surface waters from agricultural nonpoint source pollution. A Spatial Optimization procedure using the Soil and Water Assessment Tool was used to target six widely used conservation practices, namely no-tillage, cereal rye cover crops (CC), filter strips (FS), grassed waterways (GW), created wetlands, and restored prairie habitats, in two west-central Indiana watersheds. These watersheds were small, fairly flat, extensively agricultural, and heavily subsurface tile-drained. The targeting approach was also used to evaluate the model's representation of conservation practices in cost and water quality improvement, defined as export of total nitrogen, total phosphorus, and sediment from cropped fields. FS, GW, and habitats were the most effective at improving water quality, while CC and wetlands made the greatest water quality improvement in lands with multiple existing conservation practices. Spatial Optimization resulted in similar cost-environmental benefit tradeoff curves for each watershed, with the greatest possible water quality improvement being a reduction in total pollutant loads by approximately 60%, with nitrogen reduced by 20-30%, phosphorus by 70%, and sediment by 80-90%.
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A computationally efficient approach for watershed scale Spatial Optimization
Environmental Modelling & Software, 2015Co-Authors: Raj Cibin, Indrajeet ChaubeyAbstract:A multi-level Spatial Optimization (MLSOPT) approach is developed for solving complex watershed scale Optimization problems. The method works at two levels: a watershed is divided into small sub-watersheds and optimum solutions for each sub-watershed are identified individually. Subsequently sub-watershed optimum solutions are used for watershed scale Optimization. The approach is tested with complex Spatial Optimization case studies designed to maximize crop residue (corn stover) harvest with minimum environmental impacts in a 2000?km2 watershed. Results from case studies indicated that the MLSOPT approach is robust in convergence and computationally efficient compared to the traditional single-level Optimization frameworks. The MLSOPT was 20 times computationally efficient in solving source area based Optimization problem while it was 3 times computationally efficient for watershed outlet based Optimization problem compared to a corresponding single-level Optimizations. The MLSOPT Optimization approach can be used in solving complex watershed scale Spatial Optimization problems effectively. Display Omitted A novel Spatial Optimization approach (MLSOPT) is developed for complex Spatial Optimization.MLSOPT reduced Optimization complexity with multiple Optimization levels.Performance of MLSOPT with single level Optimization test cases was evaluated.MLSOPT was robust in convergence very efficient in solving Spatial Optimization problems.
R. Laplana - One of the best experts on this subject based on the ideXlab platform.
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Comparison of two Spatial Optimization techniques: a framework to solve multiobjective land use distribution problems
Environmental Management, 2009Co-Authors: B.c. Meyer, Jean-marie Lescot, R. LaplanaAbstract:Two Spatial Optimization approaches, developed from the opposing perspectives of ecological economics and landscape planning and aimed at the definition of new distributions of farming systems and of land use elements, are compared and integrated into a general framework.
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Comparison of two Spatial Optimization techniques: a framework to solve multiobjective land use distribution problems.
Environmental management, 2008Co-Authors: B.c. Meyer, Jean-marie Lescot, R. LaplanaAbstract:Two Spatial Optimization approaches, developed from the opposing perspectives of ecological economics and landscape planning and aimed at the definition of new distributions of farming systems and of land use elements, are compared and integrated into a general framework. The first approach, applied to a small river catchment in southwestern France, uses SWAT (Soil and Water Assessment Tool) and a weighted goal programming model in combination with a geographical information system (GIS) for the determination of optimal farming system patterns, based on selected objective functions to minimize deviations from the goals of reducing nitrogen and maintaining income. The second approach, demonstrated in a suburban landscape near Leipzig, Germany, defines a GIS-based predictive habitat model for the search of unfragmented regions suitable for hare populations (Lepus europaeus), followed by compromise Optimization with the aim of planning a new habitat structure distribution for the hare. The multifunctional problem is solved by the integration of the three landscape functions (“production of cereals,” “resistance to soil erosion by water,” and “landscape water retention”). Through the comparison, we propose a framework for the definition of optimal land use patterns based on Optimization techniques. The framework includes the main aspects to solve land use distribution problems with the aim of finding the optimal or best land use decisions. It integrates indicators, goals of Spatial developments and stakeholders, including weighting, and model tools for the prediction of objective functions and risk assessments. Methodological limits of the uncertainty of data and model outcomes are stressed. The framework clarifies the use of Optimization techniques in Spatial planning.
B.c. Meyer - One of the best experts on this subject based on the ideXlab platform.
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Comparison of two Spatial Optimization techniques: a framework to solve multiobjective land use distribution problems
Environmental Management, 2009Co-Authors: B.c. Meyer, Jean-marie Lescot, R. LaplanaAbstract:Two Spatial Optimization approaches, developed from the opposing perspectives of ecological economics and landscape planning and aimed at the definition of new distributions of farming systems and of land use elements, are compared and integrated into a general framework.
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Comparison of two Spatial Optimization techniques: a framework to solve multiobjective land use distribution problems.
Environmental management, 2008Co-Authors: B.c. Meyer, Jean-marie Lescot, R. LaplanaAbstract:Two Spatial Optimization approaches, developed from the opposing perspectives of ecological economics and landscape planning and aimed at the definition of new distributions of farming systems and of land use elements, are compared and integrated into a general framework. The first approach, applied to a small river catchment in southwestern France, uses SWAT (Soil and Water Assessment Tool) and a weighted goal programming model in combination with a geographical information system (GIS) for the determination of optimal farming system patterns, based on selected objective functions to minimize deviations from the goals of reducing nitrogen and maintaining income. The second approach, demonstrated in a suburban landscape near Leipzig, Germany, defines a GIS-based predictive habitat model for the search of unfragmented regions suitable for hare populations (Lepus europaeus), followed by compromise Optimization with the aim of planning a new habitat structure distribution for the hare. The multifunctional problem is solved by the integration of the three landscape functions (“production of cereals,” “resistance to soil erosion by water,” and “landscape water retention”). Through the comparison, we propose a framework for the definition of optimal land use patterns based on Optimization techniques. The framework includes the main aspects to solve land use distribution problems with the aim of finding the optimal or best land use decisions. It integrates indicators, goals of Spatial developments and stakeholders, including weighting, and model tools for the prediction of objective functions and risk assessments. Methodological limits of the uncertainty of data and model outcomes are stressed. The framework clarifies the use of Optimization techniques in Spatial planning.
Kamyoung Kim - One of the best experts on this subject based on the ideXlab platform.
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A Spatial Optimization Approach for Simultaneously Districting Precincts and Locating Polling Places
ISPRS International Journal of Geo-Information, 2020Co-Authors: Kamyoung KimAbstract:Voting is the most basic form of political participation. The agencies that are responsible for voting must delineate precincts and designate a polling place for each precinct. This Spatial decision-making requires a strategic approach for several reasons. First, changes in the location of polling places induce transportation and search costs from the perspective of voters. Second, improving accessibility to polling places can increase turnout. Third, differences in the population sizes of precincts may produce biased voting results. Spatial Optimization approaches can be a strategic method for delimiting precincts and siting polling places. The purpose of this paper is to develop a Spatial Optimization model, namely, the capacitated double p-median problem with preference (CDPMP-P), which simultaneously delimits boundaries of precincts and selects potential facilities in terms of mixed integer programming (MIP). The CDPMP-P explicitly includes realistic requirements, such as population balance, the Spatial continuity of precincts, the preferences of potential facilities where polling places can be installed, and the possibility of allocating multiple polling places in one facility.
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Spatial Optimization for regionalization problems with Spatial interaction: a heuristic approach
International Journal of Geographical Information Science, 2015Co-Authors: Kamyoung Kim, Denis J. Dean, Hyun Kim, Yongwan ChunAbstract:Spatial Optimization techniques are commonly used for regionalization problems, often represented as p-regions problems. Although various Spatial Optimization approaches have been proposed for finding exact solutions to p-regions problems, these approaches are not practical when applied to large-size problems. Alternatively, various heuristics provide effective ways to find near-optimal solutions for p-regions problem. However, most heuristic approaches are specifically designed for particular geographic settings. This paper proposes a new heuristic approach named Automated Zoning Procedure-Center Interchange AZP-CI to solve the p-functional regions problem PFRP, which constructs regions by combining small areas that share common characteristics with predefined functional centers and have tight connections among themselves through Spatial interaction. The AZP-CI consists of two subprocesses. First, the dissolving/splitting process enhances diversification and thereby produces an extensive exploration of the solution space. Second, the standard AZP locally improves the objective value. The AZP-CI was tested using randomly simulated datasets and two empirical datasets with different sizes. These evaluations indicate that AZP-CI outperforms two established heuristic algorithms: the AZP and simulated annealing, in terms of both solution quality and consistency of producing reliable solutions regardless of initial conditions. It is also noted that AZP-CI, as a general heuristic method, can be easily extended to other regionalization problems. Furthermore, the AZP-CI could be a more scalable algorithm to solve computational intensive Spatial Optimization problems when it is combined with cyberinfrastructure.
