An intelligent spatial land use planning support system using socially rational agents

This research presents an intelligent planning support system based on multi-agent systems for spatial urban land use planning. The proposed system consists of two main phases: a pre-negotiation phase and an automated negotiation phase. The pre-negotiation phase involves interaction between human actors and intelligent software agents in order to elicit the actors’ social preferences. The agents employ social value orientation theory, which is rooted in social psychology, in order to model actors’ social preferences. The automated negotiation phase involves negotiation among autonomous software agents, the aim being to achieve consensus about the spatial problem on behalf of the relevant actors and using the information obtained.

This study employs a computationally effective Bayesian learning technique, along with social value orientation theory, to design socially rational intelligent agents who work on behalf of real actors. The proposed system is applied to a real world urban land use planning case study. Human actors participate in a pre-negotiation phase, and their social preferences are elicited by intelligent software agents through a number of interactions. Then, software agents come together to engage in an automated negotiation phase and eventually reach an agreement on the spatial configuration of urban land uses on behalf of the actors. The results of the study show that the proposed system is effective at performing an automated negotiation, plus that the final plan – which is the output of the automated negotiation – produces higher social utility and better spatial land use configurations for the agents.     

Spatial multi-objective land use optimization: extensions to the non-dominated sorting genetic algorithm-II

A spatial multi-objective land use optimization model defined by the acronym ‘NSGA-II-MOLU’ or the ‘non-dominated sorting genetic algorithm-II for multi-objective optimization of land use’ is proposed for searching for optimal land use scenarios which embrace multiple objectives and constraints extracted from the requirements of users, as well as providing support to the land use planning process. In this application, we took the MOLU model which was initially developed to integrate multiple objectives and coupled this with a revised version of the genetic algorithm NSGA-II which is based on specific crossover and mutation operators. The resulting NSGA-II-MOLU model is able to offer the possibility of efficiently searching over tens of thousands of solutions for trade-off sets which define non-dominated plans on the classical Pareto frontier. In this application, we chose the example of Tongzhou New Town, China, to demonstrate how the model could be employed to meet three conflicting objectives based on minimizing conversion costs, maximizing accessibility, and maximizing compatibilities between land uses. Our case study clearly shows the ability of the model to generate diversified land use planning scenarios which form the core of a land use planning support system. It also demonstrates the potential of the model to consider more complicated spatial objectives and variables with open-ended characteristics. The breakthroughs in spatial optimization that this model provides lead directly to other properties of the process in which further efficiencies in the process of optimization, more vivid visualizations, and more interactive planning support are possible. These form directions for future research.     

现代土地利用规划的理论演变

从19 世纪末、20 世纪初诞生以来,现代土地利用规划(简称规划)涌现出很多理论流派,充分理解和掌握代表性理论的主要特征及其流变,对于开展规划研究和实践有重要的指导意义。本文在文献综述的基础上,对国内外现代规划理论的演变历程进行梳理和探究,以期有助于促进对现代规划理论的理解和应用。按时间顺序将现代规划理论演变分为4个阶段,阐述了每一阶段不同规划理论流派的产生背景、主要观点和实践影响,具体包括20世纪50 年代前的"物质形态规划论"、"马克思主义规划论",60 年代的"综合理性规划论"、"渐进规划论"、"人本主义规划论"、"自由主义规划论",70-80 年代的"新马克思主义规划论"和"新自由主义规划论",以及90 年代以来的"沟通规划论"、"可持续规划论"、"新制度主义规划论"和"公共政策规划论"等。这些理论派别有着不同的视角和侧重点,相互之间存在着或创新、或继承、或反叛、或补充的复杂关系。最后指出,现代规划理论深受多学科知识和规划实践的影响,具有深刻的时代烙印,并伴随产生了丰富的文献成果;尽管中国学者在现代规划理论构建中作出了一定贡献,但与欧美学者相比仍有较大差距,未来尚需付出更大努力。     

生态系统服务在土地利用规划研究和应用中的进展——基于文献计量和文本分析法

生态系统服务与人类福祉和区域可持续发展息息相关,是区域土地利用规划的重要考量因素。为此,论文利用文献计量和文本分析法,分析了1997年以来生态系统服务概念、方法和相关高频关键词在土地利用规划研究及实践中的应用。结果表明,近年来土地利用规划研究对生态系统服务的关注度越来越高,相关论文发表量和引用量均呈逐年稳步增长趋势。1997—2017年间,共有121篇中文论文涉及生态系统服务在规划中的运用,总引用量达到2252。与此同时,从全国到地方的《土地利用总体规划》文本中,与生态系统服务相关的关键词的词频总量在升高。生态系统服务相关关键词出现的频次从1997—2010版规划的14630次增长到2006—2020版规划的25467次,增长率达到74%。其中,规划文本对供给及支持服务关注度较高,而对调节及文化服务关注度较低。未来需要进一步发展科学有效的方法,将生态系统服务应用到规划实践中,并在规划文本中加强对调节及文化服务的重视,切实为生态文明建设服务。     

广州市城市边缘区土地利用优化配置(英文)

In response to the strong drive for social and economic development, local governments have implemented urban master plans, providing measures and timeframes to address the continuous demand for land and to alleviate urban problems. In this paper, a multi-objective model was constructed to discuss the problem, including economic benefits and ecological effectiveness, in terms of land use optimization. A genetic algorithm was then adopted to solve the model, and a performance evaluation and sensitivity analysis were conducted using Pareto optimality. Results showed that a set of tradeoffs could be acquired by the allocation of land use. In addition, the Pareto solutions proved the model to be efficient; for example, a limit of 13,500 ha of urban area conformed to plan recommendations. The reduction in crop land, orchard land, grassland, and unused land provided further efficiencies. These results implied that further potential regional land resources remain and that the urban master plan is able to support sustainable local development in the years to come, as well as verified that it is feasible to use land use allocation multi-objective modeling and genetic algorithms.     

Optimal land use allocation of urban fringe in Guangzhou

In response to the strong drive for social and economic development, local governments have implemented urban master plans, providing measures and timeframes to address the continuous demand for land and to alleviate urban problems. In this paper, a multi-objective model was constructed to discuss the problem, including economic benefits and ecological effectiveness, in terms of land use optimization. A genetic algorithm was then adopted to solve the model, and a performance evaluation and sensitivity analysis were conducted using Pareto optimality. Results showed that a set of tradeoffs could be acquired by the allocation of land use. In addition, the Pareto solutions proved the model to be efficient; for example, a limit of 13,500 ha of urban area conformed to plan recommendations. The reduction in crop land, orchard land, grassland, and unused land provided further efficiencies. These results implied that further potential regional land resources remain and that the urban master plan is able to support sustainable local development in the years to come, as well as verified that it is feasible to use land use allocation multi-objective modeling and genetic algorithms.     

基于因子分析的中国西部土地利用程度分区

利用多元统计方法中的因子分析法,对我国西部地区县市级行政区的土地利用程度进行了综合评价。基于评价单元的含有结构性映射的综合得分值,以中国生态地理区域系统为宏观控制性框架,利用GIS技术对评价单元进行了归并,从而尝试了自上而下和自下而上区划方法的结合。研究结果将西部12个省、自治区和直辖市的所有县市级行政区归并成东部季风区、西北干旱区和青藏高寒区3个大区及30个小区。     

国土空间规划的土地利用分类方案研究

“统一用地分类,系统整合土地利用现状分类、城市用地分类,形成空间规划用地分类标准”是新时期国家推进空间规划和统一规划基础的重要任务之一。在梳理总结国内外土地利用分类体系演化特点和解析中国现行土地利用分类体系存在问题的基础上,结合空间规划和不同部门对用地分类的要求,通过历史继承、部门融合和综合集成,提出既希望服务空间规划、又能满足部门行业需求的基础性土地利用分类方案。研究表明：20世纪20年代以来国内外土地利用分类体系的演化进程大致以1960年和1985年为界分为前、中、后三个不同时期;中国现行的土地利用/覆被分类体系在分类方法、分类主体、分类层级、类型构成、类型命名以及适用的数据采集方式和精度等方面存在着巨大差异,不同体系的成果数据相互之间缺乏可比性;研究建立的空间规划土地利用分类方案由7个一级类型、27个二级类型、79个三级类型构成,与中国现行的分类体系相比,该分类除了在林地三级类型、建设用地部分三级类型吸纳了现行林地分类体系和城市用地分类体系中的合理成分外,对耕地的三级类型、园地的三级类型、草地的二级和三级类型、水域的二级类型等做了重大改进和调整。     

基于主成分分析法的江苏省土地利用综合分区研究

在回顾国内外关于土地利用分区研究的基础上,以县市为研究单元,从土地资源利用的结构、投入、产出、生态、动态和潜力等6个方面,遴选出25个指标,组成区域土地利用分区的综合指标评价体系,借助SPSS软件,利用主成分分析法,对江苏省各县市的土地利用程度进行综合评价。结果表明:江苏省县市土地利用程度呈现明显的城乡差异和南北区域不平衡,经济发达的县市,土地利用的综合水平相对较高,并由南向北逐渐递减,梯度差异地域特征显著;参考综合自然条件、土地利用特点和区划单元空间位置的连续性,将江苏省划分为3个一级土地利用区域,即江南地区、江淮地区和淮北地区;并进一步划分为环太湖地区、宁镇山地丘陵地区、江淮沿江地区、江淮沿海地区、里下河平原地区、淮北沿海地区和沿黄河故道地区等7个二级土地利用区域。     

An analysis of land use pattern in the industrial development city using high resolution satellite imagery

The spatial differentiation of land use changes of Tuticorin is studied using high resolution LISS III satellite imagery and Maximum Likelihood algorithms. The classification accuracy of 95.2% was obtained. In this study, the land use of Tuticorin is classified as settlement, salt pan, agricultural land, wasteland, water bodies and shrubs. The settlement area is increased to 4.6 km2 during the year 2001 and 2006. The settlement area change is mainly driven by growth of industries and migration of people from peripheral villages. Shrub is increased to 3.63 km2 in the six year period. Water logging due to growth of shrubs in Tuticorin leads to several environmental and health hazard. This study warrants proper urban planning for Tuticorin for sustainable use of resource and environment.     

利用高分辨率卫星影像分析工业城市的土地利用

The spatial differentiation of land use changes of Tuticorin is studied using high resolution LISS III satellite imagery and Maximum Likelihood algorithms. The classification accuracy of 95.2% was obtained. In this study,the land use of Tuticorin is classified as settlement,salt pan,agricultural land,wasteland,water bodies and shrubs. The settlement area is increased to 4.6 km2 during the year 2001 and 2006. The settlement area change is mainly driven by growth of industries and migration of people from peripheral villages. Shrub is increased to 3.63 km2 in the six year period. Water logging due to growth of shrubs in Tuticorin leads to several environmental and health hazard. This study warrants proper urban planning for Tuticorin for sustainable use of resource and environment.     

昆明市土地利用变化的强度分析与稳定性研究

在遥感、地理信息系统和统计定量分析结合的基础上,引进土地利用变化强度分析理论方法。作为一种解释性的数学框架,通过不同时间间隔的土地利用转移矩阵计算了每个时间间隔和每种地类的变化面积和变化强度。强度分析自上而下分为3个层次：间隔层次、地类层次和转变层次,将观察变化强度与平均变化强度比较,揭示不同层次的变化特征,并依次用快速的与缓慢的、活跃的与平稳的、来源与转变三对术语来描述。本文以昆明市的土地利用变化为例,深入阐述了不同层次的面积变化和强度变化,并对不同层次的稳定性进行了深入分析。结果表明：在间隔层次,2000年后昆明市的土地利用变化强度逐渐加大;在地类层次,建设用地的变化强度是最活跃的,林地的变化强度是最平稳的;在转变层次,增加的建设用地主要来源于耕地,减少的林地主要转变为草地。强度分析在土地利用变化过程分析中具有系统性的优势,对深入挖掘土地利用变化信息、理解土地利用变化过程具有重要作用。     

土地利用规划环境影响评价指标与案例

土地利用规划环境影响评价,是针对土地利用的宏观结构调整与布局对环境与生态的可能影响作出的预测性评估。本文从生态保护、土地退化防治、耕地资源保障、建设用地增长的适度性与后效、耕地占补平衡的生态风险等5个方面,设计了11个指标用于预测和评估土地利用规划对环境、生态和土地资源的可能影响程度,并以《1997~2010年全国土地利用总体规划纲要》为案例进行了实际应用。结果表明,所提出的指标能较好的预估土地利用规划方案的实施对环境、生态和土地资源的潜在影响,为规避土地利用调整可能出现的不良环境与生态影响,提供有益的决策支持。     

基于CLUE模型的2030年江西省土地利用变化情景分析

引入“一切照常情景”、“规划情景"和”优化情景"三种生态系统变化情景,采用CLUE模型,对江西省2001～2030年的土地利用空间变化进行模型模拟,并用2005年的土地利用现状图对模拟结果进行了验证。研究表明:在“一切照常情景”中,耕地持续减少;建设用地急剧增加;林地面积基本保持稳定。在“规划情景”中,耕地持续增加;建设用地略有增加,并在2020年后基本保持不变;林地总面积变化不大,但是高密度森林面积比重增大;河流和湖泊面积略微减少,湿地面积大幅增加。在“优化情景”中,森林增加趋势较“规划情景”有所减缓,河流和湖泊,湿地均有显著增加。本研究对研究区今后的土地利用规划修编和土地可持续性管理具有重要的指导意义。     

Scenarios analysis based on CLUE model in of land use change Jiangxi Province by 2030

引入“一切照常情景”、“规划情景”和“优化情景”三种生态系统变化情景,采用CLUE模型,对江西省2001～2030年的土地利用空间变化进行模型模拟,并用2005年的土地利用现状图对模拟结果进行了验证。研究表明：在“一切照常情景”中,耕地持续减少;建设用地急剧增加;林地面积基本保持稳定。在“规划情景”中,耕地持续增加;建设用地略有增加,并在2020年后基本保持不变;林地总面积变化不大,但是高密度森林面积比重增大;河流和湖泊面积略微减少,湿地面积大幅增加。在“优化情景”中,森林增加趋势较“规划情景”有所减缓,河流和湖泊,湿地均有显著增加。本研究对研究区今后的土地利用规划修编和土地可持续性管理具有重要的指导意义。     

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.     

A WOE method for urban growth boundary delineation and its applications to land use planning

The aim of this study is to develop a new approach for delineating urban growth boundaries (UGBs) by applying the weight of evidence (WOE) method to land suitability assessments. Rapid urbanization is causing urban areas to encroach on agricultural land in China, posing a threat to national food security. Land use planning with clear delineation of UGBs is an effective method for controlling urban expansion. However, existing methods for delineating UGBs are typically complex or involve arbitrary decision-making. To address these drawbacks, we introduced the WOE method to develop a new UGB delineation approach, and applied this approach to a case study in the city of Jinan, China. This application achieved satisfactory accuracy; therefore, we concluded that the WOE method was an objective and effective approach to land use suitability assessments and UGB delineation. Land use planning could be benefitted considerably from the application of this method to land allocation and other planning decisions.     

Spatial features of land use/land cover change in the United States

With the classification data covering American land-use/land-cover (LUCC) with 30 m resolution from the project of National Land Cover Data (NLCD), we normalized them and made their resolution changed into 1 km ×1 km, created the data of American land-use grade and analyzed the spatial distribution and features of American LUCC as well as the influence of population and altitude on the land-use grade in light of methods of sampling analysis and correlation study. Based on the analysis, we concluded that forestry and grassland, accounting for 71.24% of the whole country, has taken the main part of American land cover, and besides, construction and arable land has occupied 19.22% of the total land, the rest of land cover types, including water area, wetland and underdeveloped land, is 9.54% of the country's total. The developing potential of American land resources is enormous with less destroyed and disturbed ecological environment. Although, in some sense, the population and altitude influence the spatial variation of American land-use grade respectively, the influence of spatial variation of altitude and population density on that of land-use grade is not significanct.     

美国土地利用/土地覆盖变化的空间特征

1IntroductionInrecentyears,studyonglobalchangehasbecomethefocus,onwhichstresshasbeenlaidbytodayhumansociety.Inordertodealwithseriesofenvironmentalproblemssuchasdegradationoflandresources,globalwarming,destructionofozonosphereandsoon,andassurethestabilizationandsustainabledevelopmentofworldeconomy,IGBPhasestablishedthestudyframeforglobalchange.Withavastrangeofstudy,researchonglobalchangehasbroughtforwardagreatdealofimportantscientificquestionsrelatedtofeasibilityforhumanelivingoftheEarth.Inth…     

Spatially-explicit sensitivity analysis of an agent-based model of land use change

The complexity of land use and land cover (LULC) change models is often attributed to spatial heterogeneity of the phenomena they try to emulate. The associated outcome uncertainty stems from a combination of model unknowns. Contrarily to the widely shared consensus on the importance of evaluating outcome uncertainty, little attention has been given to the role a well-structured spatially explicit sensitivity analysis (SSA) of LULC models can play in corroborating model results. In this article, I propose a methodology for SSA that employs sensitivity indices (SIs), which decompose outcome uncertainty and allocate it to various combinations of inputs. Using an agent-based model of residential development, I explore the utility of the methodology in explaining the uncertainty of simulated land use change. Model sensitivity is analyzed using two approaches. The first is spatially inexplicit in that it applies SI to scalar outputs, where outcome land use maps are lumped into spatial statistics. The second approach, which is spatially explicit, employs the maps directly in SI calculations. It generates sensitivity maps that allow for identifying regions of factor influence, that is, areas where a particular input contributes most to the clusters of residential development uncertainty. I demonstrate that these two approaches are complementary, but at the same time can lead to different decisions regarding input factor prioritization.