基于动态邻域思想的ACO-CA城市动态模拟——以重庆市沙坪坝区为例

邻域因子是城市动态变化的重要驱动因子,该文提出了动态邻域约束思想,在借助蚁群优化(ACO)算法提取城市用地转换规则的基础上,结合元胞自动机(CA)模型构建了基于动态邻域约束的ACO-CA城市动态模拟模型,实现了对城市用地的动态模拟,并以重庆市沙坪坝区为例,设计不同方案验证了该模型的有效性。研究结果显示:当采用动态邻域方案时,总的Kappa系数比静态邻域方案高1.70%;城市用地的Kappa系数比采用静态邻域方案时的模拟精度高出6.37%。研究结果表明:构建的基于动态邻域思想的ACO-CA模型能够有效模拟城市用地的动态变化;采用动态邻域约束条件时,尽管算法的复杂度有所增加,但与静态邻域约束方案相比,城市用地模拟精度要高,且更符合城市发展演变规律。     

基于CA模型的乌鲁木齐都市圈城市用地扩展模拟研究

借助CA模型技术,基于乌鲁木齐都市圈城市用地发展的特点,构建了模拟多个城市群体扩展的CA模型,重点对模型的转换规则进行了扩展,把用地适宜性约束、城市内部社会经济驱动、城市之间相互作用力、区域生态格局限制、城市发展规模有限这几个方面的作用机制融入到模型的规则制定中,通过宏观约束、中观调节、微观驱动3个层次逐步实现;为了使社会经济数据与CA模型中的空间数据相匹配,对社会经济数据进行了空间化表达;在历史数据的参照下,所构建的模型经过反复调试与修正,模拟结果达到了较为满意的效果;应用此模型模拟了经济优先、生态优先与规划优先3种不同情景下的城市用地扩展,对都市圈城市用地今后的发展起到重要的启示作用。     

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。     

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。     

中部崛起与城市收缩二元情境下河南省城市用地结构演变及驱动机制

构建城市用地结构分析框架,基于2010—2020年河南省17个地级市的社会经济数据和城市用地数据,引入动态度模型、信息熵模型定量剖析城市用地结构时空演变特征,并运用DPS处理软件中的灰色关联模型厘清中部崛起与城市收缩二元情境下城市用地结构演变的驱动机制。结果表明：(1)2010—2020年,河南省城市用地总面积持续增长,工业用地有所减少,其他地类涨幅存在显著差异。城市用地结构信息熵呈现先上升后下降的趋势,城市用地结构有序性增强,多样性和均衡度显著提升,单一地类优势度明显下降。(2)2010—2020年,河南省17个地级市城市用地结构信息熵在时间维度和空间类型方面的地域差异性显著。多数城市逐渐进入城市用地平稳收缩期,呈现省会城市和重点城市平稳收缩、中等城市明显收缩、小城市平稳扩张的空间演化过程。(3)人口变迁、经济增长、城镇化发展与产业结构升级对城市用地结构演变具有显著的正向驱动作用。中部崛起与城市收缩二元情境下,城市用地结构演变的驱动因素呈现不同程度的减弱,导致城市用地结构呈现新的阶段特征和驱动机制。     

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。     

基于核主成分元胞模型的城市演化重建与预测

通过元胞自动机(CA)模拟和重建城市演化的复杂非线性过程,对于城市土地利用规划和决策具有指导意义。利用传统线性方法获取的地理CA转换规则,较难刻画城市演化的时空动力学过程。基于核主成分分析方法(KPCA),通过核函数映射,在高维特征空间下不仅能够对多重共线的空间变量进行非线性降维,且由此建立的地理元胞模型KPCA-CA参数物理意义明确,能够较好地体现城市化过程的非线性本质。基于GIS环境下自主研发的地理模拟框架SimUrban,利用该KPCA-CA模型模拟和重建了快速城市化区域上海市嘉定区1989-2006年城市演化过程,并预测了研究区2010年的城市空间格局。模拟结果显示,嘉定区城市主要沿中心区域及主干道路而扩展,体现了KPCA方法提取的前两个主成分的作用,与城市实际发展情况相符。利用混淆矩阵和面积控制精度等指标,对模拟结果进行了评价,得到总体精度为80.67%、Kappa系数为61.02%,表明模拟结果与遥感分类结果及统计结果符合程度较好;与传统基于线性方法的地理CA模型比较,KPCA-CA模型模拟结果精度更高。     

基于Logistic回归的CA模型改进方法——以广州市为例

基于Logistic回归的CA模型因其结构简单和数据要求相对较小的优势,被广泛应用于城市模拟领域,但数据的空间自相关性影响了模型机制挖掘与模拟精度。通过将影响城市发展演变的各种约束条件划分为强制和普通约束条件,以及运用主成分分析降低普通约束条件的数据相关性,构建了改进型Logistic回归CA模型,并在2000~2008年广州市城市增长模拟研究中进行应用。结果表明:与传统型Logistic回归CA模型相比,改进型Logistic回归CA模型在模型拟合度和精度上均有4%左右的提高。其中约束条件划分对非城市像元模拟精度约有6%的提高,对整体精度有3%的提高。更为重要的是,降低数据相关性后,Logistic回归CA模型对于城市扩展机制的解释更符合实际。本研究旨在寻求一种简单可行且易于构建的CA模型,探求城市发展机理,为城市规划管理提供更为准确的科学依据。     

基于近邻传播聚类元胞自动机模型的武汉城市扩散和聚合过程同步模拟

元胞自动机（CA）作为城市时空动态模拟应用最广泛的模型,可以有效模拟填充式和边缘式城市扩张过程,但是在飞地式扩张模拟方面稍显不足。本文提出一种改进CA模型—APCA,在传统CA基础上利用近邻传播聚类（AP）搜寻城市扩散增长的“种子点”,实现城市增长扩散过程和聚合过程的同步模拟。以武汉市为研究区域,使用APCA模拟其在2005—2025年间城市扩张的时空过程。结果显示：① APCA在设置“种子点”数量为1~8个时模拟总体精度均高于Logistics-CA,当“种子点”数量为6时,模拟新增部分精度最高,达到0.5217;② 2015—2025年武汉市飞地型增长面积约为8.67 km²,占新增城市用地总面积比例为6.30%;③ 武汉市1995—2025年间“先扩散后聚合”的城市扩张过程符合城市增长相位理论。APCA在一定程度上了完善了传统二维平面CA框架,将城市扩张模拟维度由面维扩展到点维,为准确展现城市用地空间扩展规律提供参考。     

基于MCE-Markov-CA的郑州市土地利用时空变化模拟研究

以中原经济区核心城市郑州市为研究对象,以1988年、2002年和2017年遥感影像及其他辅助资料为数据源,从元胞适宜性、空间全局约束和邻域局部控制3个角度构建CA转换规则,并基于MCE-Markov-CA模型模拟近30年土地利用时空变化,在此基础上预测其未来15年(至2032年)的演变格局。结果发现:1)郑州市近30年间土地利用转换频繁,草地、耕地、水域和未利用地的面积分别减少19.18%、38.72%、50.23%和27.44%,建设用地增加97.31%,土地城市化进程不断加快;2)利用MCE-Markov-CA模型模拟得到的2017年土地利用数据与实际解译土地利用数据的一致性(Kappa系数)达到0.8234,证明了模型的适用性,可有效应用在与郑州市类似的特大城市土地变化模拟中;3)模型预测2032年郑州市耕地面积将减少12.56%,林地和建设用地分别增加16.97%和4.83%,草地、水域和未利用地变化不大。研究结果可为郑州市土地利用规划、管理及城市弹性可持续发展提供科学支撑,目前亟须平衡城市发展与耕地保护之间的关系,可采用省内异地代补等措施补偿耕地。     

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。     

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.     

An extended cellular automaton using case‐based reasoning for simulating urban development in a large complex region

Rule‐based cellular automata (CA) have been increasingly applied to the simulation of geographical phenomena, such as urban evolution and land‐use changes. However, these models have difficulties and uncertainties in soliciting transition rules for a large complex region. This paper presents an extended cellular automaton in which transition rules are represented by using case‐based reasoning (CBR) techniques. The common k‐NN algorithm of CBR has been modified to incorporate the location factor to reflect the spatial variation of transition rules. Multi‐temporal remote‐sensing images are used to obtain the adaptation knowledge in the temporal dimension. This model has been applied to the simulation of urban development in the Pearl River Delta which has a hierarchy of cities. Comparison indicates that this model can produce more plausible results than rule‐based CA in simulating this large complex region in 1988–2002.     

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。     

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.     

长沙城市土地扩张模型及其应用

Although traditional urban expansion simulation models can simulate dynamic features, these models fail to address complex changes produced by different agents’ behaviors. The paper has built up a set of spatial-temporal land resource allocation rules and developed a dynamic urban expansion model based on a multi-agent system, which can simulate the interaction among different agents, such as residents, peasants, and governments. This model is applied to simulate urban expansion process taking Changsha City, in China as a study area. The results show that this model can not only reflect basic characteristics of urban expansion, but also help explain the reasons for urban expansion process and understand the effect of agents’ behavior on the expansion process, and provide insights into the causing factors behind the expansion. In addition, in contrast to simulation results with land use classification map from remote sensing images, the precision of the simulation reached over 68% with higher precision than cellular automata model according to the cell-by-cell comparison. The results suggest that the model can help to provide land use decision making support to government and urban planners.     

基于神经网络的元胞自动机及模拟复杂土地利用系统

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。     

Modelling urban expansion using a multi agentbased model in the city of Changsha

Although traditional urban expansion simulation models can simulate dynamic features, these models fail to address complex changes produced by different agents' behaviors. The paper has built up a set of spatial-temporal land resource allocation rules and developed a dynamic urban expansion model based on a multi-agent system, which can simulate the interaction among different agents, such as residents, peasants, and governments. This model is applied to simulate urban expansion process taking Changsha City, in China as a study area. The results show that this model can not only reflect basic characteristics of urban expansion, but also help explain the reasons for urban expansion process and understand the effect of agents' behavior on the expansion process, and provide insights into the causing factors behind the expansion. In addition, in contrast to simulation results with land use classification map from remote sensing images, the precision of the simulation reached over 68% with higher precision than cellular automata model according to the cell-by-cell comparison. The results suggest that the model can help to provide land use decision making support to government and urban planners.     

Modeling different urban growth patterns based on the evolution of urban form: A case study from Huangpi,Central China

Spatial patterns of urban expansion mainly include infilling, edge expansion, and outlying growth patterns. The cellular automata (CA) model, is an important spatio-temporal dynamic model and effectively simulates infilling and edge-expansion urban expansion. but is evidently lacking in outlying scenarios. In addition, CA cannot explain the causes and processes of urban land expansion. Given these limitations, we proposed a novel urban expansion model called simulation model of different urban growth pattern (SMDUGP), which can work well in both adjacent (i.e., infilling and edge expansion) and outlying growth patterns. SMDUGP has two main components. First, we divided the non-urban region into two categories, namely, candidate region for adjacent pattern urban growth (CRFAP) and candidate region for outlying pattern urban growth (CRFOP). Second, different methods were utilized to simulate urban expansion in the different categories. In CRFAP, a CA model based on the potential of urban growth was proposed to simulate urban growth in relatively randomly selected urban growth regions based on the discrete selection model and Monte Carlo method as the expansion area was implemented in CRFOP. Huangpi, a suburban area in Wuhan, is utilized as the case study area to simulate the spatial and temporal dynamics of urban growth from 2004 to 2024. SMDUGP can effectively simulate outlying urban growth with a highly improved simulation precision compared with the traditional CA model and can explain the causes and processes of urban land expansion.