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.     

基于粗集的知识发现与地理模拟——以深圳市土地利用变化为例

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。     

A bottom‐up approach to discover transition rules of cellular automata using ant intelligence

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.     

Simulating urban growth by integrating landscape expansion index (LEI) and cellular automata

Traditional urban cellular automata (CA) model can effectively simulate infilling and edge-expansion growth patterns. However, most of these models are incapable of simulating the outlying growth. This paper proposed a novel model called LEI-CA which incorporates landscape expansion index (LEI) with CA to simulate urban growth. Urban growth type is identified by calculating the LEI index of each cell. Case-based reasoning technique is used to discover different transition rules for the adjacent growth type and the outlying growth type, respectively. We applied the LEI-CA model to the simulation of urban growth in Dongguan in southern China. The comparison between logistic-based CA and LEI-CA indicates that the latter can yield a better performance. The LEI-CA model can improve urban simulation accuracy over logistic-based CA by 13.8%, 10.8% and 6.9% in 1993, 1999 and 2005, respectively. Moreover, the outlying growth type hardly exists in the simulation by logistic-based CA, while the proposed LEI-CA model performs well in simulating different urban growth patterns. Our experiments illustrate that the LEI-CA model not only overcomes the deficiencies of traditional CA but might also better understand urban evolution process.     

基于约束性CA的大都市郊区城镇增长的情景模拟与管控——以武汉市江夏区为例

大都市郊区是快速城镇化进程中空间演变最为频繁、人地矛盾最为突出的区域,尤其在中国加快推进“就地就近”城镇化战略的背景下,把握大都市郊区小城镇土地利用时空变化过程及其演变机制,对制定科学合理的管控政策和优化都市空间结构具有重要的现实意义。约束性元胞自动机(constrained Cellular Automata, constrained CA)能够通过简单的规则模拟复杂的城市动态演化过程。本文将土地利用总体规划指标、城镇空间发展战略布局、土地利用开发适宜性等,嵌入约束性CA的转换规则中,采用Logistic逐步回归法分析土地利用空间影响因素,对严格约束下的武汉市江夏区2020年土地利用进行情景模拟分析,并提出城市增长管控手段。结果表明：①研究时段内,江夏区城镇用地呈低效外延式扩张,土地利用集约节约程度较低,其人口规模并未有较大增长,对主城区人口的分散作用尚未真正形成;②约束性CA在模拟大都市郊区演化方面具有较高的可靠性,能够真实反映近郊小城镇的未来空间布局与结构,模拟结果与土地利用规划和城市规划较为契合;③将规划目标导向与现实发展趋势下的模拟结果进行叠加分析,可确定城镇增长需求与规划指标调控间冲突的空间分布,从而划定土地督察的重点监测区域,为加强大都市近郊区的违法用地监查和土地利用管控提供先验的预警知识。     

基于CA-ABM模型的福州城市用地扩张研究

以中国海西地区重要门户福州市为研究区,结合其地理位置多层次约束性条件,以地理加权回归模型作为元胞自动机(CA)层的转换规则,同时以2000-2015年多期LandsatTM/ETM+影像的城市用地情况为参照,借助GIS空间分析技术,对CA和多智能体(ABM)相耦合的城市用地扩张模型进行改进。然后利用传统的和改进后的CA-ABM模型,多角度、多层次地模拟福州市2000年、2005年、2010年、2015年城市用地扩张在微观格局上的变化。结果表明,传统的和改进后的CA-ABM模型的整体精度均在80%以上,模拟结果具有较强的可信度;改进的 CA-ABM模型模拟的点对点总体精度和Kappa系数均高于传统的CA-ABM模型,而且模拟结果更加接近实际的城市用地扩张分布情况。结论可为平衡城市化进程和合理规划城市用地提供重要的理论技术支撑。     

A partitioned and asynchronous cellular automata model for urban growth simulation

Cellular automata (CA) models are used to analyze and simulate the global phenomenon of urban growth. However, these models are characterized by ignoring spatially heterogeneous transition rules and asynchronous evolving rates, which make it difficult to improve urban growth simulations. In this paper, a partitioned and asynchronous cellular automata (PACA) model was developed by implementing the spatial heterogeneity of both transition rules and evolving rates in urban growth simulations. After dividing the study area into several subregions by k-means and knn-cluster algorithms, a C5.0 decision tree algorithm was employed to identify the transition rules in each subregion. The evolving rates for cells in each regularly divided grid were calculated by the rate of changed cells. The proposed PACA model was implemented to simulate urban growth in Wuhan, a large city in central China. The results showed that PACA performed better than traditional CA models in both a cell-to-cell accuracy assessment and a shape dimension accuracy assessment. Figure of merit of PACA is 0.368 in this research, which is significantly higher than that of partitioned CA (0.327) and traditional CA (0.247). As for the shape dimension accuracy, PACA has a fractal dimension of 1.542, which is the closest to that of the actual land use (1.535). However, fractal dimension of traditional CA (1.548) is closer to that of the actual land use than that of partitioned CA (1.285). It indicates that partitioned transition rules play an important role in the cell-to-cell accuracy of CA models, whereas the combination of partitioned transition rules and asynchronous evolving rates results in improved cell-to-cell accuracy and shape dimension accuracy. Thus, implementing partitioned transition rules and asynchronous evolving rates yields better CA model performance in urban growth simulations due to its accordance with actual urban growth processes.     

从高维特征空间中获取元胞自动机的非线性转换规则

元胞自动机 (CA) 具有强大的空间模拟能力,能够模拟和预测复杂的地理现象演变过程。CA 的核心是如何定义转换规则,但目前CA转换规则获取往往是基于线性方法来进行,例如采用多准则判断 (MCE) 技术。这些方法较难反映地理现象所涉及的非线性等复杂特征。为此提出了利用新近发展的核学习机来获取地理元胞自动机非线性转换规则的新方法。该方法是通过核函数产生隐含的高维特征空间,把复杂的非线性问题转化成简单的线性问题,为解决复杂非线性问题提供了一种非常有效的途径。利用所提出的方法自动获取地理元胞自动机的转换规则,不仅大大减少了建模所需的时间,也较好地反映地理现象复杂的特性,从而改善了CA模拟的效果。     

基于随机森林的元胞自动机城市扩展模拟——以佛山市为例

本文提出一种基于随机森林的元胞自动机城市扩展(RF-CA)模型。通过在多个决策树的生成过程中分别对训练样本集和分裂节点的候选空间变量引入随机因素,提取城市扩展元胞自动机的转换规则。该模型便于并行构建,能在运算量没有显著增加的前提下提高预测的精度,对城市扩展中存在的随机因素有较强的容忍度。RF-CA模型可进行袋外误差估计,以快速获取模型参数;也可度量空间变量重要性,解释各空间变量在城市扩展中的作用。将该模型应用于佛山市1988-2012年的城市扩展模拟中,结果表明,与常用的逻辑回归模型相比,RF-CA模型进行模拟和预测分别能够提高1.7%和2.6%的精度,非常适用于复杂非线性特征的城市系统演变模型与扩展研究;通过对影响佛山市城市扩展的空间变量进行重要性度量,发现对佛山城市扩张模拟研究而言,距国道的距离与距城市中心的距离具有最重要的作用。     

基于动态约束的元胞自动机与复杂城市系统的模拟

为获得复杂城市系统更理想的模拟效果,提出时空动态约束的城市元胞自动机(CA)模型。用不同区域、不同时间新增加的城市用地总量作为CA模型的约束条件,形成时空动态约束的CA模型,并利用该模型模拟1988—2010年东莞市和深圳市城市扩张过程。结果表明,利用CA模型模拟的1993年城市用地总精度比静态CA模型提高了5.86%,而且模型中的动态约束条件可以反映城市发展的时空差异性。     

An intelligent method to discover transition rules for cellular automata using bee colony optimisation

This paper presents a new, intelligent approach to discover transition rules for geographical cellular automata (CA) based on bee colony optimisation (BCO–CA) that can perform complex tasks through the cooperation and interaction of bees. The artificial bee colony miner algorithm is used to discover transition rules. In BCO–CA, a food source position is defined by its upper and lower thresholds for each attribute, and each bee searches the best upper and lower thresholds in each attribute as a zone. A transition rule is organised when the zone in each attribute is connected to another node by the operator ‘And’ and is linked to a cell status value. The transition rules are expressed by the logical structure statement ‘IF-Then’, which is explicit and easy to understand. Bee colony optimisation could better avoid the tendency to be vulnerable to local optimisation through local and global searching in the iterative process, and it does not require the discretisation of attribute values. Finally, The BCO–CA model is employed to simulate urban development in the Xi’an-Xian Yang urban area in China. Preliminary results suggest that this BCO approach is effective in capturing complex relationships between spatial variables and urban dynamics. Experimental results indicate that the BCO–CA model achieves a higher accuracy than the NULL and ACO–CA models, which demonstrates the feasibility and availability of the model in the simulation of complex urban dynamic change.     

Mining transition rules of cellular automata for simulating urban expansion by using the deep learning techniques

Along with the gradually accelerated urbanization process, simulating and predicting the future pattern of the city is of great importance to the prediction and prevention of some environmental, economic and urban issues. Previous studies have generally integrated traditional machine learning with cellular automaton (CA) models to simulate urban development. Nevertheless, difficulties still exist in the process of obtaining more accurate results with CA models; such difficulties are mainly due to the insufficient consideration of neighborhood effects during urban transition rule mining. In this paper, we used an effective deep learning method, named convolution neural network for united mining (UMCNN), to solve the problem. UMCNN has substantial potential to get neighborhood information from its receptive field. Thus, a novel CA model coupled with UMCNN and Markov chain was designed to improve the performance of simulating urban expansion processes. Choosing the Pearl River Delta of China as the study area, we excavate the driving factors and the transformational relations revealed by the urban land-use patterns in 2000, 2005 and 2010 and further simulate the urban expansion status in 2020 and 2030. Additionally, three traditional machine-learning-based CA models (LR, ANN and RFA) are built to attest the practicality of the proposed model. In the comparison, the proposed method reaches the highest simulation accuracy and landscape index similarity. The predicted urban expansion results reveal that the economy will continue to be the primary factor in the study area from 2010 to 2030. The proposed model can serve as guidance in urban planning and government decision-making.     

基于遗传算法自动获取CA模型的参数

本文提出了基于遗传算法来寻找CA模型最佳参数的方法。CA被越来越多地应用于城市和土地利用等复杂系统的动态模拟。CA模型中变量的参数值对模拟结果有非常重要的影响。如何获取理想的参数值是模型的关键。传统的逻辑回归模型运算简单,常常用来获取模型的参数值,要求解释变量间线性无关,所以获取的城市CA模型参数具有一定的局限性。遗传算法在参数优化组合、快速搜索参数值方面有很大的优势。本文利用遗传算法来自动获取优化的CA模型参数值,并获得了纠正后的CA模型。将该模型应用于东莞1988~2004年的城市发展的模拟中,得到了较好的效果。研究结果表明,遗传算法可以有效地自动获取CA模型的参数,其模拟的结果要比传统的逻辑回归校正的CA模型模拟精度高。     

Simulating land-use dynamics under planning policies by integrating artificial immune systems with cellular automata

Cellular automata (CA) have been increasingly used in simulating urban expansion and land-use dynamics. However, most urban CA models rely on empirical data for deriving transition rules, assuming that the historical trend will continue into the future. Such inertia CA models do not take into account possible external interventions, particularly planning policies, and thus have rarely been used in urban and land-use planning. This paper proposes to use artificial immune systems (AIS) as a technique for incorporating external interventions and generating alternatives in urban simulation. Inspired by biological immune systems, the primary process of AIS is the evolution of a set of ‘antibodies’ that are capable of learning through interactions with a set of sample ‘antigens’. These ‘antibodies’ finally get ‘matured’ and can be used to identify/classify other ‘antigens’. An AIS-based CA model incorporates planning policies by altering the evolution mechanism of the ‘antibodies’. Such a model is capable of generating different scenarios of urban development under different land-use policies, with which the planners will be able to answer ‘what if’ questions and to evaluate different options. We applied an AIS-based CA model to the simulation of urban agglomeration development in the Pearl River Delta in southern China. Our experiments demonstrate that the proposed model can be very useful in exploring various planning scenarios of urban development.     

A travel time-based variable grid approach for an activity-based cellular automata model

Urban growth and population growth are used in numerous models to determine their potential impacts on both the natural and the socio-economic systems. Cellular automata (CA) land-use models became popular for urban growth modelling since they predict spatial interactions between different land uses in an explicit and straightforward manner. A common deficiency of land-use models is that they only deal with abstract categories, while in reality, several activities are often hosted at one location (e.g. population, employment, agricultural yield, nature…). Recently, a multiple activity-based variable grid CA model was proposed to represent several urban activities (population and economic activities) within single model cells. The distance-decay influence rules of the model included both short- and long-distance interactions, but all distances between cells were simply Euclidean distances. The geometry of the real transportation system, as well as its interrelations with the evolving activities, were therefore not taken into account. To improve this particular model, we make the influence rules functions of time travelled on the transportation system. Specifically, the new algorithm computes and stores all travel times needed for the variable grid CA. This approach provides fast run times, and it has a higher resolution and more easily modified parameters than the alternative approach of coupling the activity-based CA model to an external transportation model. This paper presents results from one Euclidean scenario and four different transport network scenarios to show the effects on land-use and activity change in an application to Belgium. The approach can add value to urban scenario analysis and the development of transport- and activity-related spatial indicators, and constitutes a general improvement of the activity-based CA model.     

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.     

Self-modifying CA model using dual ensemble Kalman filter for simulating urban land-use changes

There are many different methods to calibrate cellular automata (CA) models for better simulation results of urban land-use changes. However, few studies have been reported on combination of parameter update and error control using local data in CA calibration procedures. This paper presents a self-modifying CA model (SM-CA) that uses the dual ensemble Kalman filter (dual EnKF), which enables the CA model to simultaneously update model parameters and simulation results by merging observation data (local data). We applied the proposed model to simulate urban land-use changes in a 13-year period (1993–2005) in Dongguan City, a rapidly urbanizing region in south China. Simulation results indicate that this model yields better simulation results than the conventional logistic-regression CA and decision-tree CA models. For example, the validation is carried out using cross-tabulation matrix. The simulation results of SM-CA have allocation disagreement of 10.18%, 19.64%, and 30.03% in 1997, 2001, and 2005, respectively, which are 2.12%, 2.47%, and 6% lower than conventional logistic-regression CA models.     

基于元胞自动机的城市用地变化研究

城市土地利用演化的实质是人为干预下城市生态景观的自组织机制作用过程；元胞自动机（CA）所独有的特征和构模方式使其在模拟复杂性系统如城市系统等方面表现出强大的模拟能力。利用ASTER影像数据，在CA模型下对福州市2010年和2020年的城市用地进行了模拟。从而为城市规划提供决策支持服务，对真正合理地利用城市土地，实现城市可持续发展具有重要意义。     

分析学习智能元胞自动机及优化的城市模拟

元胞自动机被广泛应用于城市及其他地理现象的模拟,模拟过程中的最大问题是如何确定模型的结构和参数。该文提出一种基于分析学习的智能优化元胞自动机,该模型在逻辑回归模型的基础上,基于分析学习的智能方法,寻找元胞自动机模型的最佳参数。该方法允许用户控制空间变量影响权重,进而模拟出不同的城市发展模式,可为城市规划提供重要参考。