Allocation of urban land uses by Multi-Objective Particle Swarm Optimization algorithm

Considering the ever-increasing urban population, it appears that land management is of major importance. Land uses must be properly arranged so that they do not interfere with one another and can meet each other's needs as much as possible; this goal is a challenge of urban land-use planning. The main objective of this research is to use Multi-Objective Particle Swarm Optimization algorithm to find the optimum arrangement of urban land uses in parcel level, considering multiple objectives and constraints simultaneously. Geospatial Information System is used to prepare the data and to study different spatial scenarios when developing the model. To optimize the land-use arrangement, four objectives are defined: maximizing compatibility, maximizing dependency, maximizing suitability, and maximizing compactness of land uses. These objectives are characterized based on the requirements of planners. As a result of optimization, the user is provided with a set of optimum land-use arrangements, the Pareto-front solutions. The user can select the most appropriate solutions according to his/her priorities. The method was tested using the data of region 7, district 1 of Tehran. The results showed an acceptable level of repeatability and stability for the optimization algorithm. The model uses parcel instead of urban blocks, as the spatial unit. Moreover, it considers a variety of land uses and tries to optimize several objectives simultaneously.     

基于“多规合一”的市级国土空间优化方法——以烟台市为例

针对现阶段各规划边界交叉、空间重叠的问题,本文从提高空间价值、减少空间破碎度和协调各类空间的角度出发,在梳理现有空间优化模型及智能算法的基础上,改进多目标规划模型并适应性改造遗传算法,以此二者构建市级国土空间优化模型。以烟台市为例,设置3种情景为决策者提供方案集进行3类规划主导下的2020年国土空间优化研究,结果显示：①优化后烟台市农业、城镇和生态空间价值分别增加了23.24%、29.27%、6.30%;②不同情景下空间分布合理且较为集中,生态保护情景有利于国土空间集合连片,经济发展情景更适合于多空间协调发展。研究表明：该模型能有效地解决国土空间内容重叠问题,明显提高了国土空间价值,同时优化模型适应性强,为“多规合一”背景下市级国土空间优化提供技术支撑。     

湘江新区土地利用结构多目标规划及决策偏好分析

土地利用规划需要综合考虑经济效益和生态效益。构建了经济最大化和生态效益最大化2个目标,并运用基于模糊集理论的最大(小)算子法求得兼顾经济效益和生态效益的土地利用模糊最优规划方案。随后重点探讨了不同决策偏好情景下的土地利用优化方案和综合效益的变化。研究结果表明:(1)对经济效益和生态效益的偏好程度不同,主要会导致林地、城镇建设用地和水域面积发生变化;(2)随着决策者对生态效益偏好程度的增加,城镇建设用地面积逐渐减少,林地和水域面积逐渐增加;(3)当经济效益比生态效益重要时,最优的用地规划方案主要是在城镇用地和水域面积之间进行权衡;当生态效益比经济效益重要时,最优的用地规划方案主要是在林地和城镇建设用地之间进行权衡。研究结果对湘江新区进行"资源节约型和环境友好型社会"建设中的多目标土地利用规划具有一定的决策参考价值。     

基于多目标遗传算法的土地利用空间结构优化配置

针对如何将土地利用数量结构落实到具体的地域空间，以实现土地资源的优化配置的土地利用总体规划编制难点，以及常规的叠加法等土地利用配置方法难以根据土地适宜性评价结果，有效地将土地利用数量结构匹配到具体的土地单元问题，该文利用遗传算法的内在并行机制及其全局优化的特性，提出了基于多目标遗传算法的土地利用空间结构优化配置方法。实例分析表明，该方法具有客观性强、灵活性高、操作简便等优点。     

不同生态保育尺度下铜陵市土地利用结构优化

与传统发展模式相比,新阶段的土地利用应该与区域自然基础和生态环境格局相匹配,科学判别和保护土地空间增长的生态底线,引导地区土地利用结构的优化具有重要意义。以铜陵市为例,从生态环境约束角度出发,利用土地利用的生态环境敏感性评价方法对区域用地进行分等定级,设定了不同生态保育尺度和不同发展情景下的用地约束界限。在此基础上对铜陵市土地利用结构进行多目标情景分析与优化配置研究,从保障生态环境约束并实现效益最优的角度提出铜陵市土地利用结构优化的方案。     

城市交通与土地利用一体化模型的理论基础与发展趋势

城市交通与土地利用间的协调发展是科学调控城市发展的重要环节。在基于地租理论的微观区位决策与土地利用与交通系统宏观动态交互理论基础上,回顾了城市交通与土地利用一体化模型的发展过程及不同依据下的分类特征,对空间相互作用和重力模型理论模型、空间经济学理论模型、随机效用和离散选择模型和基于行为的微观模型4类一体化模型的优劣进行了系统梳理分析。分析表明,具有合理理论基础、科学数据处理方法、较高模型精度和政策评估适用性的一体化模型仍存在巨大发展空间。近年来西方学者研究趋向微观化、综合化和简约化,而国内一体化模型发展相对滞后,要求模型理论与综合实证开发的进一步突破。特别是在新数据、新交通技术、国土空间规划体系背景下,城市交通与土地利用一体化模型将从“空间”模型向“时空间”模型转变;从“宏观”集计模型向“微观”行为模型转变;从“交通-土地”模型向“综合系统”模型转变;从“样本数据”模型向“大数据”模型转变;从“交通规划”应用向“国土空间规划”应用转变。旨在对城市交通与土地利用一体化模型的一般结构、原理类型、当前面临的问题和挑战进行深入剖析,以推进一体化模型在中国的研究与开发。     

土地利用结构多目标优化遗传算法

传统数学方法难以有效解决土地利用结构多目标优化问题,针对土地利用结构优化的多目标性和遗传算法在多目标优化求解方面的优势,以经济效益和生态效益为目标函数建立土地利用结构优化数学模型,采用遗传算法进行模型求解.以重庆市合川区为例详细介绍了具体应用过程,得出了4个可行方案,对方案进行论证择优,选出满意的最佳方案,证明采用遗传算法进行土地利用结构多目标优化求解是科学可行的.     

Improving the performance of genetic algorithms for land-use allocation problems

Multi-objective optimization can be used to solve land-use allocation problems involving multiple conflicting objectives. In this paper, we show how genetic algorithms can be improved in order to effectively and efficiently solve multi-objective land-use allocation problems. Our focus lies on improving crossover and mutation operators of the genetic algorithms. We tested a range of different approaches either based on the literature or proposed for the first time. We applied them to a land-use allocation problem in Switzerland including two conflicting objectives: ensuring compact urban development and reducing the loss of agricultural productivity. We compared all approaches by calculating hypervolumes and by analysing the spread of the produced non-dominated fronts. Our results suggest that a combination of different mutation operators, of which at least one includes spatial heuristics, can help to find well-distributed fronts of non-dominated solutions. The tested modified crossover operators did not significantly improve the results. These findings provide a benchmark for multi-objective optimization of land-use allocation problems with promising prospectives for solving complex spatial planning problems.     

Book Review

In this paper, we examine the applicability of spatial optimization as a generative modelling technique for sustainable land‐use allocation. Specifically, we test whether spatial optimization can be used to generate a number of compromise spatial alternatives that are both feasible and different from each other. We present a new spatial multiobjective optimization model, which encourages efficient utilization of urban space through infill development, compatibility of adjacent land uses, and defensible redevelopment. The model uses a density‐based design constraint developed by the authors. The constraint imposes a predefined level of consistent neighbourhood development to promote contiguity and compactness of urban areas. First, the model is tested on a hypothetical example. Further, we demonstrate a real‐world application of the model to land‐use planning in Chelan, a small environmental amenity town in the north‐central region of the State of Washington, USA. The results indicate that spatial optimization is a promising method for generating land‐use alternatives for further consideration in spatial decision‐making.     

Map-based multicriteria analysis to support interactive land use allocation

This article focuses on the use of map-based multicriteria analysis to develop a negotiation support tool for land use allocation. Spatial multicriteria analysis is used to make explicit trade-offs between objectives and to provide guidance and feedback on the land use changes negotiated by the participants. Digital maps are the means of communication among workshop participants, and an interactive mapping device (the ‘Touch table’) is used as the interface. Participants are informed about the relevant trade-offs on the map and use this information to change the land use maps. The approach is tested during a negotiation session as part of the land use planning process of the Bodegraven polder, a peat meadow area in the Netherlands.     

模拟退火算法用于产生城市土地空间布局方案

本文发展了一种模拟退火算法辅助产生城市土地空间布局方案。首先建立了城市土地空间配置问题的数学模型 ,目标函数是最小化土地开发费用、最大化不同功能地块间的空间协调性。由于问题存在的诸多空间约束条件使得可行的土地利用布局方案的搜寻过程变得十分缓慢 ,采用了将一些空间约束条件结合到目标函数中的方法 ,如结合了距离约束、方向约束、相同土地利用单元的邻近约束和地块形状的紧凑约束等约束条件。应用于湖南省长沙市暮云工业区用地规划的结果表明 ,模拟退火算法是一种辅助城市土地利用规划的有用的、有潜力的优化方法     

土地规划的自然过程基础与大数据时代的方法探索

随着城市化与工业化程度不断加深,产业空间结构不断重组,导致土地供需矛盾日益突出,国土资源无序开发日益严重,因而优化国土资源空间格局,成为生态文明建设的首要任务。同时,科学合理配置土地资源对土地规划提出了更高要求,传统土地规划方法有待改革。在基于尊重自然、顺应自然的开发理念下,总结土地自然过程基础对土地规划的影响,实现“山水路林田生命共同体”的协调规划发展。其次,“大数据”时代的来临,云计算、空间数据整合、云分析等技术对土地规划方法提供新的技术支撑;最后,针对土地规划数据特殊性、移动用户终端的广泛性,提出应创建土地规划云服务平台,使土地规划实现数据集成管理和更新,从而提高土地规划质量。     

Planning with complexity—how do we deal with stakeholder and spatial heterogeneity in land use planning?

Geografisk Tidsskrift, Danish Journal of Geography 105(2):23–37, 2005

As a reaction to the 'top-down 'approach to rural development during the 1970's and 1980's, and in line with the increased focus on ‘putting farmers first’ (bottom-up) through local participation, new and integrated approaches to natural resource management (NRM) have been launched to generate sustainable development. This paradigmatic change has greatly influenced land use planning. Some of the implications involved in this change are discussed in this paper and based on empirical data from Tanzania and Botswana. In this paper we will discuss three critical issues related to land use planning for sustainable NRM: the complexity of livelihood strategies at the local level, the role of different stakeholder interests and the importance of spatial scale considerations. The discussion emphasizes the importance of understanding that the different and often erratic resource endowment of the rural households influences the strategies these households follow in order to meet their overall needs, rather than ‘simply’ maximizing agricultural yields. Furthermore, the importance of spatial scale, in relation to both different stakeholder interests and their needs will be discussed. The paper highlights the need to take community heterogeneity into consideration in land use planning if sustainable NRM is to be achieved in the future.     

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.     

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。     

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.     

流域土地可持续利用规划方法及应用研究

土地利用既受自然环境条件的种种制约,又对生态环境,尤其是水资源和水环境,产生深刻影响。因此,进行系统规划优化土地利用结构、协调土地利用和其他经济活动及生态环境保护的关系是非常必要的。基于土地利用系统是一个本征性灰色系统,本文以灰色多目标规划(GMOP)方法为核心,提出了一套流域土地利用灰色多目标规划模型。该方法既可有效地处理信息的不完备性和系统的动态性,又可全面地表达土地利用的多目标性。在对密云土地利用规划研究中,通过应用交互式调整和情景分析方法,得到该模型在两种情景下的优化方案,并结合密云实际情况综合分析,进行了方案筛选。     

土地利用规划环境影响评价

土地利用规划的环境影响评价对提高政府决策的科学化具有重要意义。国外相关方面的研究已有30年之久,但我国刚刚起步。对于评价内涵及内容的探讨是开展这一领域研究的基础。本文从现阶段土地利用规划的需求出发,在综合分析项目环境影响评价的经验及相关的研究成果基础上提出土地利用规划环境影响评价的主要内容和研究的建议。     

新农村建设村级土地利用规划研究进展

新农村建设促进了村级土地利用规划的开展。该文分析了村级土地利用规划的内涵,论述了国内村级土地利用规划的研究现状,指出目前村级土地利用规划编制存在缺乏理论研究、规划内容不统一、规划基础资料不足及缺少规划实施后评价等问题。小尺度研究将成为土地利用规划研究的总体趋势,未来应加大试点村的规划编制,深化村级土地利用规划的空间布局优化,推进村级土地利用规划理论体系和实践方法的完善和成熟,促成国家制定统一的规划规程。     

不同非空间模拟方法下CLUE-S模型土地利用预测——以秦淮河流域为例

基于快速城镇化背景下秦淮河流域土地利用历史状况,选择CLUE-S模型对其2020年土地利用情况进行模拟预测。分别使用线性回归、Markov模型、灰色GM（1,1）模型预测CLUE-S模型非空间模块的土地利用需求量,再嵌入CLUE-S中得到3种预测结果,对预测结果进行比较。另外设定“自然发展”情景与考虑规划政策影响的“优化格局”情景,模拟2020年不同情景下秦淮河流域土地利用格局情况,并进行景观格局分析。结果表明：线性回归模型、Markov模型、灰色GM（1,1）模型的Kappa指数分别为0.866、0.849、0.867,3种方法均满足模型精度要求;自然发展情景中2020年水域、水田、林地、城镇用地、旱地面积相对于2010年分别变化21.5%、-15.3%、-9.0%、51.5%、-28.9%,而优化格局情景下水域、水田、林地、城镇用地、旱地面积分别变化3.1%、-1.6%、10.8%、6.3%、-10.6%,相比于自然发展情景,优化情景土地利用状况更符合保护基本农田、增加生态用地连通性、提高雨水下渗能力以及缓解城市热岛效应的要求,为后期土地利用规划提供了依据。