Integrating a Raster Geographical Information System with Multi‐Objective Land Allocation Optimization for Conservation Reserve Design

The multi‐objective land allocation problem is to optimize the selection of land for different uses based on a set of decision objectives. For most applications, a geographical information system (GIS) is either absent or loosely coupled through file exchange. In this article the evolutionary algorithm (EA), a heuristic solution method for optimization problems, is integrated with a raster GIS to form a spatial decision support system (SDSS) for multi‐objective conservation reserve design. The SDSS effectively combines the functions of a GIS for data management, analysis, and visualization, with the optimization capability of the EA; and provides a uniform way to solve conservation reserve design problems with different types of constraints and objectives. The SDSS is demonstrated through application to the creation of conservation reserves in Bolivia to protect 17 endemic mammals.     

“三规合一”服务的空间信息技术:地理模拟与优化

中国的国民经济和社会发展规划、土地利用总体规划以及城乡规划都是法定规划,但由于规划主体、技术标准和编制办法、实施手段和监督机制等的不同,导致"三规分离"、各个规划之间相互冲突的问题较为突出。虽然国家为了消除冲突,正在开展"三规合一"的有关工作,但缺乏有关技术手段的支持。本文以地理信息科学为出发点,对地理过程建模在国内外研究中的应用进行了总结,阐述了地理模拟与优化的框架体系可以成为目前中国正在进行的"三规合一"工作的重要理论和方法支撑。     

Incorporating sprawl and adjacency measures in land‐use forecasting model: A case study of Collin County,TX

Sprawl measures have largely been neglected in land‐use forecasting models. The current approach for land‐use allocation using optimization mostly utilizes objective functions and constraints that are non‐spatial in nature. Application of spatial constraints could take care of the contiguity and compactness of land uses and can be utilized to address urban sprawl. Because a land‐use model is used as an input to transportation modeling, a better spatial allocation strategy for more compact land‐use projections will promote better transportation planning and sustainable development. This study formulates a scenario‐based approach to normative modeling of urban sprawl. In doing so, it seeks to improve the land‐use projections by employing a spatial optimization model with contiguity and compactness consideration. This study incorporates urban sprawl measures based on smart growth principles together with a mixed‐use factor, and adjacency consideration of nearby land uses. The objective function used in the study maximizes net suitability based on imposed constraints. These constraints are based on smart growth principles that enhance walkability in neighborhoods, promote better health for residents, and encourage mixed‐use development. The formulated model has been applied to Collin County, TX, a fast‐developing suburban county located to the north of the Dallas–Fort Worth metroplex. The suitability of land cells indicates the probability of conversion, which is calculated using spatial discrete choice analysis with Moran eigenvector spatial filtering for vacant cells at a resolution of 150 × 150 m employing factors of the built environment, and socioeconomic and demographic characteristics. This study demonstrates how spatial proximity between land uses, which has been ignored to date, can be used to control sprawl, resulting in better mixing of different land uses based on constraints imposed in a spatial optimization problem.     

An open source GIS‐based Planning Support System: Application to the land use plan of La Troncal,Ecuador

Planning Support Systems (PSS) comprise a wide variety of geo‐technological tools related to GIS and spatial modeling aimed at addressing land planning processes. This article describes the OpenRules system, a PSS based on a previous system called RULES. Among OpenRules new features are its architecture, based exclusively on free and open source software, and its applicability to all land use types, including rural and urban uses. In addition, OpenRules incorporates an unlimited number of land evaluation factors and a new objective in land use spatial allocation. OpenRules has been programmed in Java and implemented as a module of the free GIS software gvSIG, with full integration between the GIS and the decision support tools. Decision support tools include multicriteria evaluation, multiobjective linear programming and heuristic techniques, which support three basic stages of land use planning processes, namely land suitability evaluation, land use area optimization and land use spatial allocation. The application of OpenRules to the region of La Troncal, Ecuador, demonstrates its capability to generate alternative and coherent solutions through a scientific and justified procedure at low cost in terms of time and resources.     

A spatial parallel heuristic approach for solving very large‐scale vehicle routing problems

The vehicle routing problem (VRP) is one of the most prominent problems in spatial optimization because of its broad applications in both the public and private sectors. This article presents a novel spatial parallel heuristic approach for solving large‐scale VRPs with capacity constraints. A spatial partitioning strategy is devised to divide a region of interest into a set of small spatial cells to allow the use of a parallel local search with a spatial neighbor reduction strategy. An additional local search and perturbation mechanism around the border area of spatial cells is used to improve route segments across spatial cells to overcome the border effect. The results of one man‐made VRP benchmark and three real‐world super‐large‐scale VRP instances with tens of thousands of nodes verify that the presented spatial parallel heuristic approach achieves a comparable solution with much less computing time.     

基于Spatialite轻量级空间数据库的GIS数据管理

针对目前大多数GIS系统在处理空间数据和属性数据时都是将两者分开存放和管理,而这种方式不能保证数据的一致性和完整性,本文将Spatialite空间数据库嵌入到GPS/PDA的GIS外业土地调查系统中,实现了空间数据和属性数据的集中管理,从而确保了数据的共享性和完整性,并结合外业土地调查系统,分析了在数据存储、查询与显示过程中空间数据库的操作。     

Evaluation and development of sustainable urban land use plans through spatial optimization

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.     

A grey wolf optimizer–cellular automata integrated model for urban growth simulation and optimization

This article proposes a grey wolf optimizer (GWO) and cellular automata (CA) integrated model for the simulation and spatial optimization of urban growth. A new grey wolf‐inspired approach is put forward to determine the urban growth rules of CA cells by using the GWO algorithm, which is suitable for solving optimization problems. The inspiration for GWO comes from the social leadership of wolf groups, as well as their hunting behavior. The GWO‐optimized urban growth rules for CA describe the relationship between the spatial variables and the urban land‐use status for each cell in the formation of “if–then.” The GWO algorithm and CA model are then integrated as the GWO–CA model for urban growth simulation and optimization. By taking Nanjing City as an example, the simulation accuracy in terms of urban cells is 86.6%, and the kappa coefficient is 0.715, indicating that the GWO algorithm is efficient at obtaining urban growth rules from spatial variables. The validation of the GWO–CA model also illustrates that it performs well in terms of the simulation and spatial optimization of urban growth, and can further contribute to urban planning and management.     

A Comparative Analysis of Traveling Salesman Solutions from Geographic Information Systems

The Traveling Salesman Problem is one of the most prominent problems in combinatorial optimization, and is regularly employed in a wide variety of applications. The objective of this article is to demonstrate the extent of sub‐optimality produced by Traveling Salesman solution procedures implemented in the context of Geographic Information Systems and to discuss the consequences that such solutions have for practice. Toward that end, an analysis is made of Traveling Salesman solutions from implementations in four Geographic Information System packages. These implementations are tested against the optimal solution for a range of problem sizes. Computational results are presented in the context of a school bus routing application. This analysis concludes that no Traveling Salesman implementation in GIS is likely to find the optimal solution when problems exceed 10 stops. In contrast, optimal solutions can be generated with desktop linear programming software for up to 25 cities. Moreover, one GIS implementation consistently found solutions that were closer to optimal than its competitors. This research strongly suggests that for applications with fewer than 25 stops, the use of an optimal solution procedure is advised, and that GIS implementations can benefit from the integration of more robust optimization techniques.     

A spatial‐knowledge‐enhanced heuristic for solving the p‐median problem

The p‐median problem (PMP) is one of the most applied location problems in urban and regional planning. As an NP‐hard problem, the PMP remains challenging to solve optimally, especially for large‐sized problems. A number of heuristics have been developed to obtain PMP solutions in a fast manner. Among the heuristics, the Teitz and Bart (TB) algorithm has been found effective for finding high‐quality solutions. In this article, we present a spatial‐knowledge‐enhanced Teitz and Bart (STB) heuristic method for solving PMPs. The STB heuristic prioritizes candidate facility sites to be examined in the solution set based on the spatial distribution of demand and service provision. Tests based on a range of PMPs demonstrate the effectiveness of the STB heuristic. This new algorithm can be incorporated into current commercial GIS packages to solve a wide range of location‐allocation problems.     

空间信息系统移动终端设计与实现技术

空间信息技术与移动通信技术的有机结合,加快了空间信息社会化的进程,越来越多的传统方法不适用现代信息化进程。本文分析了在LBS社会化服务需求背景下空间信息获取技术的缺陷,探讨了移动GIS环境下移动终端的关键技术、实现的技术路线等,提出了一种利用基于移动GIS方法、遥感栅格影像技术、GPS定位、无线网络互联移动计算技术的移动终端设计思想,并给出其实现的技术路线。     

GIS在空间选址中的应用现状分析

空间选址问题涉及因素错综复杂，模型建立困难，GIS的发展为空间选址问题提供了新的技术手段，其应用于空间选址可以克服以往一些模型的不足之处，具有广阔的应用前景。本文主要对影响空间选址的因素进行了归纳总结，并对GIS技术在空间选址中的应用现状进行了一定的探讨分析。     

Selecting conservation reserves using species-covering models: Adapting the ARC/INFO GIS

Conflicts between human development of the landscape and conservation of biodiversiry will continue to grow. Given this reality, there have been a number of attempts to model the optimal selection of conservation reserve sites such that maximum biodiversity protection can be attained within a limited budget for land acquisition. Here we adapt the Location-Allocation module of ARC/INFO to solve the problem of representing, or covering, as many species as possible in a fixed number of selected reserve sites. Resident ARC/INFO solution routines are applied to an innovative 'logical' network that converts the problem of optimal reserve selection into a problem of optimal facility placement, which the Location-Allocation module can recognize and solve. Use of this unique logical network structure as input to ARC/INFO's internal solvers makes possible, compared to previous methods, a much tighter integration of spatial optimization tools with mapping and database tools, all of which are internal to the GIS and accessed via a menu-driven interface. The main advantage is that users of public domain data (such as the US Gap Analysis data) can conduct their own explorations of possible reserve systems without having to acquire and master optimization packages and reformat model output data for GIS display and post-analysis of solutions. Our sample application uses species data from south-western California. We also present a second major form of species-covering model grounded in the same logical network. This enhanced modcl accommodates weighting of species by their conservation importance, thus allowing reserve systems to be designed around the protection of the most threatened or vulnerable biota.     

Modeling Collaborative GIS Processes Using Soft Systems Theory, UML and Object Oriented Design

The evidence from collaborative GIS problem solving indicates that although environmental problems are context dependent, meaningful solutions are framed around the core issues of multiple stakeholder interests, complexity, wicked problems, ill‐defined problem specification, and their spatial characteristics. Based on subsets of these issues, there exists a number of overlapping collaborative GIS designs and processes. The goal of this study is to reconcile the overlap by modeling a core collaborative GIS design and process. General systems theory is used to classify core technical components of the collaborative GIS design, and soft systems theory characterizes the human activity dynamics of the collaborative process. Further, object oriented principles are used to generate a flexible problem domain design, and the unified modeling language (UML) visually describes the structure and behavior of the collaborative process. The core formalism facilitates GIS process integration, standardization, reusability, ontology design, and rapid solution design in new problem contexts. The collaborative spatial Delphi (CSD) methodology is a proof of concept. This research contributes to the design and specification of a core collaborative GIS model, a reusable pattern, and their ontological impacts.     

征供一体化土地批后监管系统研建

在传统的土地批后监管工作中,由于缺乏动态监管的有效途径和技术,存在信息获取速度慢、共享程度低、流通性差等问题。征供一体化土地批后监管系统依托多源遥感数据,结合GPS定位技术与GIS空间信息技术,对土地征收征用、土地供应建设用地项目实施动态监测,对监测成果进行信息化管理,实现了对建设用地供应、储备、利用信息的收集、整合与更新管理等功能;并构建了征地、出让土地之间的土地流转关系,从而形成了征、供、管一体化的批后监管工作模式。     

A Spatial Analysis System for Integrating Data,Methods and Models on Environmental Risks and Health Outcomes

Integrating data on health outcomes with methods of disease mapping and spatially explicit models of environmental contaminants are important aspects of environmental health surveillance. In this article, we describe a modular, web‐based spatial analysis system that uses GIS, spatial analysis methods and software services delivered over computer networks to achieve this end. The Environmental Health Surveillance System (EHSS) is a prototype system that is designed to serve three purposes: a secure environment for producing maps of disease outcomes from individual‐level data while preserving privacy; an automated process of linking environmental data, environmental models, and GIS tasks like geocoding for the purposes of estimating individual exposures to environmental contaminants; and mechanisms to visualize the spatial patterns of disease outcomes via Web‐based mapping interfaces and interactive tools like Google Earth.     

Positioning Disaster Relief Teams Given Dynamic Service Demand: A Hybrid Agent‐Based and Spatial Optimization Approach

During extreme events, like hurricanes, it is essential to position relief services for non‐evacuees throughout the affected region in optimal locations. While previous research has explored a variety of spatial optimization models to accomplish such a task, most work assumes that the population with demand is relatively static. However, this assumption neglects to account for potential feedbacks in the relief distribution system. For example, a population's behavior can both affect and be affected by the placement of relief services, resulting in a dynamic spatial distribution of demand. This article presents a hybrid modeling approach that utilizes GIS data with agent‐based and spatial optimization models to position emergency relief teams in Bay County, Florida during a hurricane event. Non‐evacuating household agents choose to remain at home or seek shelter, while relief team agents are periodically repositioned to account for changes in the spatial distribution of household agents. A total of 220 simulations are run to explore a variety of scenarios. Results show different repositioning timing strategies and the magnitude of the feedback effect drastically changes the level of access households have to relief teams. Ultimately, this work demonstrates the importance of accounting for behavioral and spatial dynamics in disaster relief distribution systems.     

城市人口GIS中数据的不确定性研究

针对系统数据源、数据模型的不确定性以及分析过程中引入的不确定性等问题进行了系统的研究，并归纳了各种不确定性的影响模型，进而提出了一套人口GIS数据优化建模以及分析过程中人口密度中心建模的思想和实用方法，用于克服人口GIS中数据的不确定性的影响。     

土地利用优化配置建模研究进展与展望

土地利用优化配置是解决土地资源供需矛盾、协调社会经济发展与生态保护的重要技术手段,也是土地资源可持续利用需要解决的关键问题之一。随着空间模拟和智能优化技术的发展,土地利用优化的理论基础和方法体系也在不断更新。在文献计量的基础上,系统总结了国内外土地利用优化配置的驱动理论和方法,重点分析了空间模拟优化模型和智能寻优模型在土地利用优化配置建模应用中的进展和存在的问题,对土地利用优化配置未来的发展和面临的挑战进行了讨论和展望,提出自然与人文过程的耦合驱动、多模型的空间化协同、大数据知识挖掘方法的嵌入和数字孪生技术的支撑,应是土地利用优化配置领域下一阶段的研究方向。     

Usability Issues in Applying Participatory Mapping for Neighborhood Infrastructure Planning

Public participation is required in neighborhood infrastructure planning and problem‐solving. Although Participatory GIS methods are considered important to help urban community groups identify problems and express their needs and concerns, usable means to help groups produce their maps remain difficult to be realised. Further, an effective means to facilitate the integration of government spatial plans and participatory maps also remains unclear. This article addresses usability issues in participatory mapping activities by exploring group collaboration mechanics and accomplishing use assessments. The study aims at testing the usefulness and the impact of participatory mapping for community development. User studies including questionnaire surveys, interviews, group usability testing, scenario assessments, and the scaling‐up activity were executed. Transparent photomaps, Mobile GIS, and a Web map were implemented and assessed in the study area. A use scenario for community and official spatial data integration was also developed. The results of user studies show that the use of transparent photomaps is more effective to complete group tasks to discuss, draw, and annotate their infrastructure problems. For group participants, the transparent maps are more engaging, easy to learn, and more error tolerant than the use of Mobile GIS. A combination use of simple and advanced PGIS methods is necessary to be implemented to reach informed priority‐decision making.