基于元胞自动机模型的土地利用变化模拟——以大连经济技术开发区为例

元胞自动机模型已经成为模拟土地利用变化的重要方法。传统土地模拟方法中侧重于通过分析影响土地利用变化的因素来构建预测模型,较少从土地利用类型变化及其相互作用的空间角度来关注模型构建。本文以1998年、2004年和2009年1:10000土地利用数据,利用Python语言结合GDAL与Numpy类库实现局部土地利用竞争的元胞自动机模型原型开发,并用于模拟大连市经济技术开发区1998-2009年土地利用变化模拟。研究结果:1建立了发掘多地类之间相互作用关系的试验方法,研究适用于具有明确物理意义的多地类元胞自动机模拟模型;2该模型具有好的模拟精度,对建设用地、农用地和林地等3种不同类型用地进行同时模拟,其对应Kappa系数分别为0.762,0.634和0.678;3该模型建立了研究不同种地类协调作用的基本方法,可以用于进一步研究土地利用变化地类之间驱动原理。     

基于区块特征的元胞自动机土地利用演化模型研究

针对传统元胞自动机模型中栅格式规则空间模拟复杂地理元素精度不高的问题,提出一种基于土地区块特征的非规则空间元胞自动机模型,以地理单元实质不规则实体形状作为元胞空间单元,进行土地利用变化的仿真模拟,运用MapInfo建立非规则空间元胞自动机模型的应用软件.对头灶镇土地利用演化的实证研究表明,非规则空间元胞自动机模型可以更真实地描述元胞地理信息、局部空间关系和演化规则,可为城市规划提供决策支持.     

元胞自动机和多主体模型在土地利用变化模拟中的应用

土地利用变化模拟研究是土地利用变化研究的重要分支。复杂系统理论的完善和系统模拟平台的发展使得复杂系统模拟方法在土地利用变化模拟研究中的优势越来越明显,元胞自动机模型和多主体模型正是基于复杂系统理论模拟研究的代表。本文简要回顾了土地利用变化模拟研究进展,探讨了土地利用变化的内在机理,着重分析了元胞自动机和多主体模型的内涵、研究热点、模拟平台和优缺点等,并对其今后在土地利用变化模拟研究的发展趋势进行了展望,以期为土地利用变化模拟研究提供参考。     

基于局部化转换规则的元胞自动机土地利用模型

传统土地利用元胞自动机(Cellular automata,CA)模型基于空间同质性假设,使用全局性模型建立元胞转换规则,忽略了土地利用变化驱动因素的驱动作用在空间上的变化。以美国佛罗里达州的橙县(Orange County)2003-2009年土地利用变化为例,提出了基于局部化转化规则的CA土地利用模型,其中元胞的土地利用类型适宜性由地理加权多项logit模型(Geographically weighted multinomial logit,GWML)获得。结果表明:GWML模型较传统全局性多项logit(Multinomial logit,MNL)模型有更高的数据解释能力。基于GWML模型的土地利用CA模型能反映局部土地利用变化模式,因而较基于MNL模型的CA模型具有更高的模拟精度。所得结论对未来国内地区的研究有借鉴意义。     

海湾型半城市化地区空间形态演化模拟

元胞自动机（CA）是模拟城市土地利用演变过程的有效工具,转换规则和元胞邻域是元胞模型的核心。综合考虑元胞邻域的距离衰减效应,基于模拟退火算法（SA）挖掘最优的转换规则,文章构建了一种考虑邻域衰减的城市演化模型（SA-NDCA）。模型以负幂指数函数作为元胞邻域的衰减曲线表示元胞邻域的距离衰减效应;运用模拟退火优化算法计算城市CA模型模拟结果与样本点的累积差异,在目标解空间快速搜索以提取最优的转换规则;最后以厦门市半城市化地区为研究案例,模拟了研究区域1995―2010年期间的城市空间形态演化,通过混淆矩阵和Kappa系数评价了模型的模拟精度,1995―2010年期间的建设用地模拟精度为68.5%,总体精度达到86.2%,Kappa系数达到66.3,取得了较好的模拟效果。利用提出的SA-NDCA模型,成功模拟了研究区2010―2020年期间的城市空间形态演化,结果显示,所预测的演化情景与中国当前实施的新型城镇化战略十分契合。     

地理元胞自动机模型的尺度敏感性及原因

地理元胞自动机模型的模拟精度会受到元胞尺度的影响。以杭州市土地利用变化模拟为例,分析了元胞尺度分别为50m×50m、100m×100m、150m×150m和200m×200m时地理元胞自动机模型的模拟精度,对地理元胞自动机模型的尺度敏感性进行了分析;并从元胞转换规则入手,研究了元胞自动机模型尺度敏感性产生的原因:(1)元胞尺度会对地理元胞自动机模型的模拟精度产生影响,元胞尺度越精细模拟精度越高;(2)元胞自动机模型的尺度敏感性与元胞尺度相关,在有些尺度区间上表现得明显,而在有些尺度区间上表现并不明显;(3)孤立元胞是元胞自动机模型尺度敏感性产生的主要原因。研究表明,随着元胞尺度的增大,元胞空间的孤立元胞增多,这些孤立元胞本身及其周围元胞具有较低的邻域函数值和较小的转换概率值,并影响了地理元胞自动机模型的模拟精度。     

土地利用动态模拟模型的构建及其应用——以北京市海淀区为例

CLUE-S模型是一个基于经验统计原理的模拟多土地利用类型空间变化的动态模拟模型,它在世界多个国家和地区的区域尺度农业、森林为主的土地利用变化模拟中得到应用。作者对CLUE-S模型进行了改进,改进后的模型 (CLUE-SII) 引入了动态计算的邻域分析因子,可以对土地利用变化中的自发过程、自组织过程和土地利用类型间的竞争进行模拟,还可以根据研究区域特点构建不同的模拟方案,在这些模拟方案中,局地因子和邻域因子在土地利用变化中的作用方式不同。应用CLUE-SII对北京市海淀区1991~2001年土地利用变化进行了多方案模拟,结果表明邻域因子对城镇用地变化具有重要作用,其中将邻域因子看作自发过程放大因子的模拟方案获得了较好的模拟结果,整图符合比达到77%,其中城镇用地符合比达到82%,Kappa值达到0.754。CLUE-SII在北京市海淀区的应用实例表明,该模型可以对高分辨率和多土地利用类型下的城市扩展进行有效模拟,扩展了CLUE-S的应用领域;通过构建多模拟方案,不但可以探索最佳的模拟结果,还可以研究和分析不同土地利用驱动因子在土地利用变化中的作用模式。     

城市元胞自动机扩展邻域效应的测量与校准研究

城市元胞模型由于在定量分析与预测城市动态的潜力而受到众多研究者的持续关注.邻域规则是主导城市元胞模型模拟过程的关键组件.研究表明,不同土地利用组合间存在显著的邻域效应,且邻域效应具有惯性、排斥和吸引等影响.然而,传统城市元胞模型主要考虑的是特定分辨率下较小窗口的邻域范围.本文尝试刻画更大窗口的邻域效应及其对元胞模型的影响.基于测量的扩展邻域因子,应用粒子群优化算法校准大窗口邻域规则,并创建了考虑扩展邻域效应的城市元胞模型.为验证模型有效性,将其应用于模拟厦门市1995-2010年期间的城市扩张动态.与3×3摩尔邻域的逻辑回归模型相比较,1995-2010年期间的建设用地模拟精度从80.7％提高到83.9％,总体精度从87.8％提高到89.6％,Kappa系数从70.0％提高到74.5％,表明考虑扩展邻域效应的城市模型取得了更好的模拟效果.     

地理元胞自动机模型研究进展

元胞自动机(Cellular Automata,简称CA)是一种基于微观个体的相互作用空间离散动态模型,其强大的计算功能、固有的平行计算能力、高度动态及空间概念等特征,使它在模拟空间复杂系统的时空动态演变研究具有较强的优势。文章回顾了元胞自动机的发展历程,阐述了CA在地理学中的主要应用领域和研究进展,在此基础上,以现实世界地理实体及现代城市扩张特征为视角,分析目前CA研究所面临的问题,并对其未来的研究趋势进行了初步探讨,认为以下3个方面将是未来CA研究的热点：① 利用不规则元胞及可控邻域的CA模型,对不同规则或不同邻域地理实体的模拟研究;② 采用三维元胞自动机对现代城市扩张进行立体化模拟,以克服二维CA模型的缺陷;③ 将矢量元胞自动机模型应用于地理实体的模拟研究,进一步提高模拟精度。     

CA-Markov模型的空间尺度敏感性研究

以广州市花都区为研究区,研究利用CA-Markov模型进行土地利用变化模拟的空间尺度敏感性特征,结论如下:①元胞尺寸的选择会明显影响模拟结果,元胞尺寸越大,模拟结果精度越低。模型中存在元胞尺寸的阈值,当元胞尺寸超出该阈值时,模拟结果的精度急剧下降,因此对于元胞尺寸的选择必须要慎重。②邻域类型的选择也会对模拟结果产生影响。采用3×3冯诺依曼邻域的模拟结果会比3×3摩尔邻域和5×5摩尔邻域生成更多的斑块数量和更高的斑块密度,但是模拟结果的Kappa系数值相差不大。     

Simulating urban growth under planning policies through parcel-based cellular automata (ParCA) model

In recent decades, the cellular automata model, among the urban development prediction models, has been applied considerably. Studies show that the output of conventional cellular automata models is sensitive to cell size and neighborhood structure, and varies with changes in the size of these parameters. To solve this problem, vector-based cellular automata models have been introduced which have overcome the mentioned limitations and presented better results. The aim of this study was to present a parcel-based cellular automata (ParCA) model for simulating urban growth under planning policies. In this model, undeveloped areas are first subdivided into smaller parcels, based on some geometric parameters; then, neighborhood effect of parcels is defined in a radial structure, based on a weighted function of distance, area, land-use, and service level of irregular cadastral parcels. After that, neighborhood effect is evaluated using three components, including compactness, dependency and compatibility. The presented model was implemented and analyzed using data from municipal region 22 of Tehran. The obtained results indicated the high ability of ParCA model in allocating various land-uses to parcels in the appropriateness of the layout of different land-uses. This model can be used in decision-making and urban land-use planning activities, since it provides the possibility of allocating different urban land-use types and assessing different urban-growth scenarios.     

Simulating urban land-use changes at a large scale by integrating dynamic land parcel subdivision and vector-based cellular automata

Cellular automata (CA) have been widely used to simulate complex urban development processes. Previous studies indicated that vector-based cellular automata (VCA) could be applied to simulate urban land-use changes at a realistic land parcel level. Because of the complexity of VCA, these studies were conducted at small scales or did not adequately consider the highly fragmented processes of urban development. This study aims to build an effective framework called dynamic land parcel subdivision (DLPS)-VCA to accurately simulate urban land-use change processes at the land parcel level. We introduce this model in urban land-use change simulations to reasonably divide land parcels and introduce a random forest algorithm (RFA) model to explore the transition rules of urban land-use changes. Finally, we simulate the land-use changes in Shenzhen between 2009 and 2014 via the proposed DLPS-VCA model. Compared to the advanced Patch-CA and RFA-VCA models, the DLPS-VCA model achieves the highest simulation accuracy (Figure-of-Merit = 0.232), which is 32.57% and 18.97% higher respectively, and is most similar to the actual land-use scenario (similarity = 94.73%) at the pattern level. These results indicate that the DLPS-VCA model can both accurately split the land during urban land-use changes and significantly simulate urban expansion and urban land-use changes at a fine scale. Furthermore, the land-use change rules that are based on DPLS-VCA mining and the simulation results of several future urban development scenarios can act as guides for future urban planning policy formulation.     

A heuristic cellular automata approach for modelling urban land-use change based on simulated annealing

This article presents a novel cellular automata (CA) approach to simulate the spatio-temporal process of urban land-use change based on the simulated annealing (SA) algorithm. The SA algorithm enables dynamic optimisation of the CA's transition rules that would otherwise be difficult to configure using conventional mathematical methods. In this heuristic approach, an objective function is constructed based on a theoretical accumulative disagreement between the simulated land-use pattern and the actual land-use pattern derived from remotely sensed imagery. The function value that measures the mismatch between the actual and the simulated land-use patterns would be minimised randomly through the SA process. Hence, a set of attribution parameters that can be used in the CA model is achieved. An SA optimisation tool was developed using Matlab and incorporated into the cellular simulation in GIS to form an integrated SACA model. An application of the SACA model to simulate the spatio-temporal process of land-use change in Jinshan District of Shanghai Municipality, PR China, from 1992 to 2008 shows that this modelling approach is efficient and robust and can be used to reconstruct historical urban land-use patterns to assist with urban planning policy-making and actions. Comparison of the SACA model with a typical CA model based on a logistic regression method without the SA optimisation (also known as LogCA) shows that the SACA model generates better simulation results than the LogCA model, and the improvement of the SACA over the LogCA model is largely attributed to higher locational accuracy, a feature desirable in most spatially explicit simulations of geographical processes.     

城市土地利用变化中邻域相互作用的特征——日本三大都市圈的比较研究

Local spatial interaction between neighborhood land-use categories (i.e. neighborhood interaction) is an important factor which affects urban land-use change patterns. Therefore,it is a key component in cellular automata (CA)-based urban geosimulation models towards the simulation and forecast of urban land-use changes. Purpose of this paper is to interpret the similarities and differences of the characteristics of neighborhood interaction in urban land-use changes of different metropolitan areas in Japan for providing empirical materials to understand the mechanism of urban land-use changes and construct urban geosimulation models. Characteristics of neighborhood interaction in urban land-use changes of three metropolitan areas in Japan,i.e. Tokyo,Osaka,and Nagoya,were compared using such aids as the neighborhood interaction model and similarity measure function. As a result,urban land-use in the three metropolitan areas was found to have had similar structure and patterns during the study period. Characteristics of neighborhood interaction in urban land-use changes are quite different from land-use categories,meaning that the mechanism of urban land-use changes comparatively differs among land-use categories. Characteristics of neighborhood interaction reveal the effect of spatial autocorrelation in the spatial process of urban land-use changes in the three metropolitan areas,which correspond with the characteristics of agglomeration of urban land-use allocation in Japan. Neighborhood interaction amidst urban land-use changes between the three metropolitan areas generally showed similar characteristics. The regressed neighborhood interaction coefficients in the models may represent the general characteristics of neighborhood effect on urban land-use changes in the cities of Japan. The results provide very significant materials for exploring the mechanism of urban land-use changes and the construction of universal urban geosimulation models which may be applied to any city in Japan.     

Characteristics of neighborhood interaction in urban land-use changes: A comparative study between three metropolitan areas of Japan

Local spatial interaction between neighborhood land-use categories (i.e. neighborhood interaction) is an important factor which affects urban land-use change patterns. Therefore, it is a key component in cellular automata (CA)-based urban geosimulation models towards the simulation and forecast of urban land-use changes. Purpose of this paper is to interpret the similarities and differences of the characteristics of neighborhood interaction in urban land-use changes of different metropolitan areas in Japan for providing empirical materials to understand the mechanism of urban land-use changes and construct urban geosimulation models. Characteristics of neighborhood interaction in urban land-use changes of three metropolitan areas in Japan, i.e. Tokyo, Osaka, and Nagoya, were compared using such aids as the neighborhood interaction model and similarity measure function. As a result, urban land-use in the three metropolitan areas was found to have had similar structure and patterns during the study period. Characteristics of neighborhood interaction in urban land-use changes are quite different from land-use categories, meaning that the mechanism of urban land-use changes comparatively differs among land-use categories. Characteristics of neighborhood interaction reveal the effect of spatial autocorrelation in the spatial process of urban land-use changes in the three metropolitan areas, which correspond with the characteristics of agglomeration of urban land-use allocation in Japan. Neighborhood interaction amidst urban land-use changes between the three metropolitan areas generally showed similar characteristics. The regressed neighborhood interaction coefficients in the models may represent the general characteristics of neighborhood effect on urban land-use changes in the cities of Japan. The results provide very significant materials for exploring the mechanism of urban land-use changes and the construction of universal urban geosimulation models which may be applied to any city in Japan.     

Examining the sensitivity of spatial scale in cellular automata Markov chain simulation of land use change

Understanding the spatial scale sensitivity of cellular automata is crucial for improving the accuracy of land use change simulation. We propose a framework based on a response surface method to comprehensively explore spatial scale sensitivity of the cellular automata Markov chain (CA-Markov) model, and present a hybrid evaluation model for expressing simulation accuracy that merges the strengths of the Kappa coefficient and of Contagion index. Three Landsat-Thematic Mapper remote sensing images of Wuhan in 1987, 1996, and 2005 were used to extract land use information. The results demonstrate that the spatial scale sensitivity of the CA-Markov model resulting from individual components and their combinations are both worthy of attention. The utility of our proposed hybrid evaluation model and response surface method to investigate the sensitivity has proven to be more accurate than the single Kappa coefficient method and more efficient than traditional methods. The findings also show that the CA-Markov model is more sensitive to neighborhood size than to cell size or neighborhood type considering individual component effects. Particularly, the bilateral and trilateral interactions between neighborhood and cell size result in a more remarkable scale effect than that of a single cell size.     

A neighbor decay cellular automata approach for simulating urban expansion based on particle swarm intelligence

Simulation and quantitative analysis of urban land use change are effective ways to investigate urban form evolution. Cellular Automata (CA) has been used as a convenient and useful tool for simulating urban land use change. However, the key issue for CA models is the definition of the transition rules, and a number of statistical or artificial intelligence methods may be used to obtain the optimal rules. Neighborhood configuration is a basic component of transition rules, and is characterized by a distance decay effect. However, many CA models do not consider the neighbor decay effect in cellular space. This paper presents a neighbor decay cellular automata model based on particle swarm optimization (PSO-NDCA). We used particle swarm optimization (PSO) to find transition rules and considered the decay effect of the cellular neighborhood. A negative power exponential function was used to compute the decay coefficient of the cellular neighborhood in the model. By calculating the cumulative differences between simulation results and the sample data, the PSO automatically searched for the optimal combination of parameters of the transition rules. Using Xiamen City as a case study, we simulated urban land use changes for the periods 1992–1997 and 2002–2007. Results showed that the PSO-NDCA model had a higher prediction accuracy for built-up land, and a higher overall accuracy and Kappa coefficient than the urban CA model based on particle swarm optimization. The study demonstrates that there exist optimal neighborhood decay coefficients in accordance with the regional characteristics of an area. Urban CA modelling should take into account the role of neighborhood decay.