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1.
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.  相似文献   

2.
基于多目标遗传算法的土地利用空间结构优化配置   总被引:12,自引:0,他引:12  
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。  相似文献   

3.
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.  相似文献   

4.
Urban growth boundaries (UGBs) have been applied in many rapid urbanizing areas to alleviate the problems of urban sprawl. Although empirical research has stressed the importance of ecological protection in UGB delineation, existing UGB models lack a component for the assessment of ecologically sensitive areas. To address this problem, we develop an innovative method that is capable of simulating UGB alternatives with economic and ecological constraints. Our method employs a patch-based cellular automaton (i.e. SA-Patch-CA) for simulating future urban growth, constrained by the ecological protection areas produced by an agent-based land allocation optimization model (AgentLA). The delineation of UGBs is also based on the estimated future urban land demand derived from support vector regression (SVR). The proposed method is applied in the Pearl River Delta (PRD), China. Three scenarios are designed to represent different objectives of future industrial transitions. The results indicate that increasing the shares of low energy consumption industries and tertiary industries can effectively reduce urban land demand. By overlapping the simulations, we found that the areal agreement of the simulated UGBs among the three scenarios accounts for approximately 88% of the total area. These areas can then be considered as the primary locations for establishing the UGBs.  相似文献   

5.
土地利用优化配置中系列模型的应用——以乐清市为例   总被引:44,自引:12,他引:32  
土地利用优化配置,既包括宏观数量与空间结构格局的优化,也包括微观尺度生产要素的合理比配,是一个多目标、多层次的持续拟合与决策过程。本文结合乐清市实证研究,提出了运用系列模型研究县域土地利用优化配置的新方法。系列模型由空间分区模型、结构优化模型和微观设计模型,按照土地资源优化配置目标的内在联系性组合而成。系列模型既能够发挥单个模型的作用,也能充分利用它们在土地利用优化配置与决策中所具有的同一性与互补性,在科学协调土地利用配置数量与空间、宏观与微观之间的关系中更好地发挥其综合优势,因而具有广阔的应用前景。  相似文献   

6.
基于耦合地理模拟优化系统GeoSOS的农田保护区预警   总被引:4,自引:0,他引:4  
陈逸敏  黎夏  刘小平  李少英 《地理学报》2010,65(9):1137-1145
依据农田保护区规划与城市发展模拟相耦合的新思路,开展农田保护区预警研究。以广东番禺为实验区,首先利用基于多智能体的空间优化配置模型(AgentLA) 自动生成农田保护区范围。AgentLA模型能避免产生分散、破碎的空间格局,有利于保护区的管理和维护。通过地理模拟优化系统GeoSOS中的神经网络CA模型对研究区2025 年的城市发展格局进行三个情景的模拟:低速增长情景、基准情景和高速增长情景。最后利用GIS空间分析方法将预测结果与农田保护区相结合,提取农田保护与城市扩张产生冲突的区域。总体上,冲突的严重程度随着空间上城市扩张强度的增大而加剧,高速增长情景中冲突区域的面积较大。冲突区域的存在一方面说明了农田保护区需要相应的法律、法规支持,否则可能会因城市扩张而被侵占;另一方面也反映了农田保护与地区发展之间的冲突。因此,可以根据所提取的冲突区域面积大小、空间位置等特征,采取某种形式补偿,以此取得农田保护与地方利益之间的平衡。  相似文献   

7.
基于神经网络的元胞自动机及模拟复杂土地利用系统   总被引:57,自引:9,他引:57  
黎夏  叶嘉安 《地理研究》2005,24(1):19-27
本文提出了基于神经网络的元胞自动机(CellularAutomata),并将其用来模拟复杂的土地利用系统及其演变。国际上已经有许多利用元胞自动机进行城市模拟的研究,但这些模型往往局限于模拟从非城市用地到城市用地的转变。模拟多种土地利用的动态系统比一般模拟城市演化要复杂得多,需要使用许多空间变量和参数,而确定模型的参数值和模型结构有很大困难。本文通过神经网络、元胞自动机和GIS相结合来进行土地利用的动态模拟,并利用多时相的遥感分类图像来训练神经网络,能十分方便地确定模型参数和模型结构,消除常规模拟方法所带来的弊端。  相似文献   

8.
Many resource allocation issues, such as land use- or irrigation planning, require input from extensive spatial databases and involve complex decisionmaking problems. Spatial decision support systems (SDSS) are designed to make these issues more transparent and to support the design and evaluation of resource allocation alternatives. Recent developments in this field focus on the design of allocation plans that utilise mathematical optimisation techniques. These techniques, often referred to as multi-criteria decision-making (MCDM) techniques, run into numerical problems when faced with the high dimensionality encountered in spatial applications. In this paper we demonstrate how simulated annealing, a heuristic algorithm, can be used to solve high-dimensional non-linear optimisation problems for multi-site land use allocation (MLUA) problems. The optimisation model both minimises development costs and maximises spatial compactness of the land use. Compactness is achieved by adding a non-linear neighbourhood objective to the objective function. The method is successfully applied to a case study in Galicia, Spain, using an SDSS for supporting the restoration of a former mining area with new land use.  相似文献   

9.
Currently, with rapid expanding of urban area, the rate of conversion of agricultural land to nonagricultural uses in China is increasing. Zoning farmland protection is an important measure to protect limited land resource. This article presented an innovative approach based on the integrated use of remote sensing, GIS, and artificial immune systems (AIS) for generating farmland protection areas. Some modifications have been made for conventional AIS so that it can be further extended to the solution of zoning problems. The optimal objective is to generate farmland protection areas that minimize development potential and maximize agricultural suitability and spatial compactness. First, utility function by addressing the criteria of farmland protection is incorporated into AIS algorithm. Second, encoding and mutation of antibodies is modified so that it can be suited to the solution of spatial optimization problems. The AIS-based zoning model was then applied to a case study in Guangzhou, Guangdong, China. The experiments have demonstrated that the proposed method was an efficient and effective spatial optimization technique, which took only about 194 seconds to generate satisfied farmland protection patterns. Furthermore, the AIS-based zoning model can explore various alternatives conveniently, and it can yield better performances than nonprotection scenario in the utility efficiency of land resources and the site condition for farmland.  相似文献   

10.
杨青生  黎夏 《地理学报》2006,61(8):882-894
为了更有效地模拟地理现象的复杂演变过程,提出了用粗集理论来确定元胞自动机 (CA)不确定性转换规则的新方法。CA可以通过局部规则来有效地模拟许多地理现象的演变过程。但目前缺乏很好定义CA转换规则的方法。往往采用启发式的方法来定义CA的转换规则,这些转换规则是静态的,而且其参数值多是确定的。在反映诸如城市扩张、疾病扩散等不确定性复杂现象时,具有一定的局限性。利用粗集从GIS和遥感数据中发现知识,自动寻找CA的不确定性转换规则,基于粗集的CA在缩短建模时间的同时,能提取非确定性的转换规则,更好地反映复杂系统的特点。采用所提出的方法模拟了深圳市的城市发展过程,取得了比传统MCE方法更好的模拟效果。  相似文献   

11.
This paper presents a new method to discover transition rules of geographical cellular automata (CA) based on a bottom‐up approach, ant colony optimization (ACO). CA are capable of simulating the evolution of complex geographical phenomena. The core of a CA model is how to define transition rules so that realistic patterns can be simulated using empirical data. Transition rules are often defined by using mathematical equations, which do not provide easily understandable explicit forms. Furthermore, it is very difficult, if not impossible, to specify equation‐based transition rules for reflecting complex geographical processes. This paper presents a method of using ant intelligence to discover explicit transition rules of urban CA to overcome these limitations. This ‘bottom‐up’ ACO approach for achieving complex task through cooperation and interaction of ants is effective for capturing complex relationships between spatial variables and urban dynamics. A discretization technique is proposed to deal with continuous spatial variables for discovering transition rules hidden in large datasets. The ACO–CA model has been used to simulate rural–urban land conversions in Guangzhou, Guangdong, China. Preliminary results suggest that this ACO–CA method can have a better performance than the decision‐tree CA method.  相似文献   

12.
The case study based on Qinling Mountains in Shaanxi Province of China, in virtue of the information from TM image, classifies the land types and analyzes their spatial and temporal differential law, and puts forward three structural patterns of land types in mountainous areas, namely, spatial, quantitative and qualitative structures of mountainous land types. Furthermore, it has been noticed that the analysis of structural patterns can disclose the heterogeneity and orderliness of combination of land types, which can lay the theoretic foundation for comprehensively recognizing ecological characteristics and succession law of structure and function of land types. After the all-around comparative analysis, an optimal allocation of land use in Qinling Mountains has been put forward according to the principle of sustainable development and landscape ecology, which can lay the scientific foundation in practice for the structural adjustment and distribution optimization from the macro level to micro level.  相似文献   

13.
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.  相似文献   

14.
1 IntroductionMountains,as one of the huge ecosystems in the world, play a significant role during human evolution and development[1]. As the treasury of ecological resources[2], mountains have faced the challenge of the degradation of their eco-environment and the difficulty in their regional poverty relief because of human unsuitable exploitation. The 53rd Conference of United Nations has claimed the year 2002 as the International Mountains Year and called on each country to pay attention…  相似文献   

15.
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.  相似文献   

16.
多智能体区域土地利用优化配置模型及其应用   总被引:12,自引:0,他引:12  
土地利用优化配置对促进区域可持续土地利用具有重要意义,然而现有的土地利用优化配置模型引导可持续土地利用的能力有待提高。本文从整体考量区域土地利用优化配置数量、空间、时间三维特征的角度,定义了区域土地利用优化配置多智能体系统及其决策行为规则,构建了基于多智能体系统的区域土地利用优化配置RLUOA (Regional Land Use OptimizationAllocation) 模型,并以中部地区典型城市--长沙市为例开展了实证应用研究。研究结果表明:该模型能够将规划时间段内多目标约束的区域土地利用规模的数量结构合理配置到不同的空间单元,实现土地利用数量结构、空间布局、效益的协同优化,构建整体上经济可行、社会可接受、生态环境友好的土地利用格局,并明显提高区域整体土地利用经济、生态和社会效益,从而能够为促进区域土地资源可持续利用和制定土地利用总体规划提供参考借鉴。  相似文献   

17.
山地土地结构格局与土地利用优化配置   总被引:16,自引:1,他引:15  
刘彦随 《地理科学》1999,19(6):504-509
山地是具有高度和坡度的自然综合体,由此决定了土地芬异的序列及其结构格局的特殊性,形成土地空间格局,土地数量格局和土地格局等3种基本形式,格局分析可以揭示土地类型与功能的异质性和有序性,从而为全面认识土地类型群体的生态属笥与演替规律提供理论依据,为土地利用优化布置的实践提供科学指导。  相似文献   

18.
The reconstruction of arable land patterns over historical periods is one of critical research issues in the study of land use and land cover change (LUCC). Taking into account the continuous distribution of arable land and spatial constraints, this paper proposes a constrained cellular automata model to reconstruct historical arable land patterns. The paper describes model establishment, parameter calibration, and results validation in detail. The model was applied to Jiangsu Province, China, and was compared with a conventional spatial allocation method. The results showed that the methodology developed in this study can more objectively reflect the evolution of the pattern of arable land over historical periods, in terms of similarity with contemporary pattern, than the spatial allocation methods and can provide an effective basis for the historical study of arable land.  相似文献   

19.
The efficiency of taxi services in big cities influences not only the convenience of peoples’ travel but also urban traffic and profits for taxi drivers. To balance the demands and supplies of taxicabs, spatio-temporal knowledge mined from historical trajectories is recommended for both passengers finding an available taxicab and cabdrivers estimating the location of the next passenger. However, taxi trajectories are long sequences where single-step optimization cannot guarantee the global optimum. Taking long-term revenue as the goal, a novel method is proposed based on reinforcement learning to optimize taxi driving strategies for global profit maximization. This optimization problem is formulated as a Markov decision process for the whole taxi driving sequence. The state set in this model is defined as the taxi location and operation status. The action set includes the operation choices of empty driving, carrying passengers or waiting, and the subsequent driving behaviors. The reward, as the objective function for evaluating driving policies, is defined as the effective driving ratio that measures the total profit of a cabdriver in a working day. The optimal choice for cabdrivers at any location is learned by the Q-learning algorithm with maximum cumulative rewards. Utilizing historical trajectory data in Beijing, the experiments were conducted to test the accuracy and efficiency of the method. The results show that the method improves profits and efficiency for cabdrivers and increases the opportunities for passengers to find taxis as well. By replacing the reward function with other criteria, the method can also be used to discover and investigate novel spatial patterns. This new model is prior knowledge-free and globally optimal, which has advantages over previous methods.  相似文献   

20.
Understanding the complexity of urban expansion requires an analysis of the factors influencing the spatial and temporal processes of rural–urban land conversion. This study aims at building a statistical land conversion model to assist in understanding land use change patterns. Specifically, GIS coupled with a logistic regression model and exponential smoothing techniques is used for exploring the effects of various factors on land use change. These factors include population density, slope, proximity to roads, and surrounding land use, and their influence on land use change is studied for generating a predictive model. Methods to reduce spatial autocorrelation in a logistic regression framework are also discussed. Primarily, an optimal sampling scheme that can eliminate spatial autocorrelation while maintaining adequate samples to allow the model to achieve the comparable accuracy as the spatial autoregressive model is developed. Since many of the previous studies on modeling the spatial complexity of urban growth ignored temporal complexity, a modified exponential smoothing technique is employed to produce a smoothed model from a series of bi‐temporal models obtained from different time periods. The proposed model is validated using the multi‐temporal land use data in New Castle County, DE, USA. It is demonstrated that our approach provides an effective option for multi‐temporal land use change modeling and the modeling results help interpret the land use change patterns.  相似文献   

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