共查询到19条相似文献，搜索用时 156 毫秒
1.
空间异质性主要表现为时空非平稳性,引入空间异质性用于提取用地变化转换规则是学术界一直探讨的热点问题。该文针对城市群空间扩张模拟过程中的空间非平稳性和建模过程中的因子优化问题,运用K-means空间聚类算法和因子分析(FA)方法,耦合多层感知机(MLP)与元胞自动机(CA),构建基于城市扩张速率分区的FA-MLP-CA模型,并利用该模型预测2030年成渝地区双城经济圈城市扩张。研究结果显示:模型总体精度(OA)和品质因数(FoM)分别达到0.9785和0.3801,相较于未引入因子优化的常规分区MLP-CA模型和城市扩张速率分区MLP-CA模型,总体精度分别提高0.0043和0.0014,FoM分别提高0.0298和0.0091;相较于未分区的传统模型,OA和FoM分别提高0.0076和0.0992;城市扩张速率分区MLP-CA模型与常规分区MLP-CA模型相比,OA和FoM分别提高0.0029和0.0207。研究表明,分区建模能改善常规建模过程中因空间异质性导致的对局部特征学习不充分的问题,其中采用城市扩张速率分区能在一定程度上提升模拟效果;而引入因子分析后不仅可提升模型精度,而且能很好地增强模型可解释性。 相似文献
2.
基于动态约束的元胞自动机与复杂城市系统的模拟 总被引：2，自引：0，他引：2
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。 相似文献
3.
顾及城市空间结构信息的元胞自动机模型构建及其应用 总被引：2，自引：2，他引：0
利用衡量新增斑块空间邻接关系的多阶景观扩张指数定量识别城市组团的空间特征,结合城市组团所表现出的城市空间结构信息,构建基于MLEI的元胞自动机城市扩展（MLEI-CA）模型。针对武汉市1990、2000、2013年3期遥感影像数据,运用MLEI-CA对武汉市城市扩展进行模拟,通过与Logistic-CA模型对比验证该模型的适用性。研究结果表明,MLEI-CA模型更加准确地揭示城市扩展的空间演变过程,MLEI-CA模型精度优于Logistic-CA模型,Kappa系数、城市用地的精度分别提高6%和4%。 相似文献
4.
《地理与地理信息科学》2016,(5)
该文构建了一种基于空间自回归的地理元胞自动机(CA)模型——SARCA模型,该模型能够较好融合地理系统模拟中的空间自相关特性,且获取的CA参数具有明确的物理意义。以1995-2015年上海城市土地利用为案例,验证了该模型的有效性。CA参数和城市土地转化概率表明,上海市外环线对于CA参数的贡献相比其他空间变量具有压倒性优势,到外环线距离越近则土地发展为城市的概率就越大。将基于Logistic回归的CA模型(LogCA)作为比较对象,模拟同期上海全域城市土地利用变化过程。CA规则表明,SAR在赤池信息量准则(AIC)、残差的描述性统计量和空间自相关指标等方面均优于Logistic回归。同时,SARCA模型在2005年和2015年的土地利用模拟结果总体精度分别为86.3%和82.0%,均优于LogCA模型的模拟结果(总体精度分别为79.8%和76.3%)。 相似文献
5.
地理元胞自动机模型研究进展 总被引：6，自引：0，他引：6
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。 相似文献
6.
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。 相似文献
7.
《地理科学进展》2015,(8)
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,RFCA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。 相似文献
8.
基于核主成分元胞模型的城市演化重建与预测 总被引：3，自引：1，他引：2
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。 相似文献
9.
基于Logistic回归的CA模型改进方法——以广州市为例 总被引：7，自引：1，他引：6
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。 相似文献
10.
元胞邻域对空间直观模拟结果的影响 总被引：2，自引：1，他引：1
作为一种空间直观模拟模型,地理元胞自动机(Geo-CA)能够模拟及预测城市扩展与土地利用情景.地理CA模拟中,元胞邻域及其空间构型会对转换规则的挖掘与空间直观模拟结果的可靠性产生显著影响,从模拟进度和精度、景观格局及运行效率等角度可以定量分析这种影响.以logistic回归CA模型为例,基于Von:Neumann型和M... 相似文献
11.
Mining transition rules of cellular automata for simulating urban expansion by using the deep learning techniques 总被引：3，自引：0，他引：3
Jialv He Yao Yao Ye Hong Zhang Jinbao 《International journal of geographical information science》2018,32(10):2076-2097
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. 相似文献
12.
城市扩展模拟可为城市可持续发展与国土空间规划提供参考。智能体模型(ABM)与元胞自动机(CA)结合可兼顾城市空间增长的自组织性和不同决策主体的决策过程,人工神经网络(ANN)可描述智能体与城市扩展之间复杂的非线性关系。该文基于ANN-ABM-CA耦合模型,在构建CA转换规则时基于ABM刻画人类决策行为的影响,并采用ANN挖掘不同类型的智能体在城市扩展过程中的偏好差异,同时考虑宏观和微观层面的智能体决策行为,结合城市扩展的10个驱动因素,模拟武汉市主城区2005-2015年的扩展情况,结果表明:1)相比传统的ANN-CA模型,ANN-ABM-CA模型模拟性能更优,从宏观与微观相结合的角度更好地解释了城市扩展的驱动机制,OA值为97.46%,Kappa系数为0.9176,FoM值为0.4375,结果可靠且合理;2)不同收入层级的居民智能体对城市扩展的决策偏好不同;3)武汉主城区城市扩展模式主要为边缘型扩展,洪山区西南部有少部分填充型扩展、东南部出现飞地型扩展,与实际扩展情况相符。 相似文献
13.
基于城市流和层级性的城市群扩展模型构建 总被引：1，自引：1，他引：0
城市群是具有网络关系与层级性的区域空间,一直是中国推进城镇化与区域协调发展的主体单元。本文综合考虑城市群的网络与层级性特征,运用城市流刻画城际网络交互作用,采用分层广义线性模型（HGLM）揭示城市群分层驱动机制。同时,选取长江中游城市群开展实证研究,通过与元胞自动机（CA）耦合,构建HGLM-CA模型模拟城市群空间扩展。将模拟结果与Logistic-CA模型、BBO-CA模型进行比对,据此评析HGLM-CA模型的优劣与改进方向。实证结果表明：城市群空间扩展是多层驱动因素共同作用的结果,城市流不仅会推动城市群空间扩展,而且对元胞层因素起到重要的调节作用,使之具有城际分异性;HGLM-CA模型相比Logistic-CA模型模拟精度更高,说明顾及城市流与层级性的城市群空间扩展模拟结果更为精准;与智能模型BBO-CA相比,HGLM-CA模型模拟精度较低,但其便于从层级性角度把握城市群空间扩展机制。 相似文献
14.
多智能体与元胞自动机结合及城市用地扩张模拟 总被引：15，自引：3，他引：12
运用多智能体(Agent)和元胞自动机(CA)结合来模拟城市用地扩张的方法,将影响和决定用地类型转变的主体作为Agent引进元胞自动机模型中,Agent在CA确定的城市发展概率的基础上,通过自身及其周围环境的状况,综合各种因素的影响做出决策,决定元胞下一时刻的城市发展概率。运用Agent的决策结果,对CA模型中以随机变量体现的不确定性通过Agent决策行为给予地理意义的新解释。以城市郊区—樟木头镇为例,对1988～1993年城市用地扩张进行了模拟研究,取得了良好的模拟效果。 相似文献
15.
Simulating urban growth by integrating landscape expansion index (LEI) and cellular automata 总被引：4，自引：0，他引：4
Xiaoping Liu Lei Ma Xia Li Bin Ai Shaoying Li Zhijian He 《International journal of geographical information science》2014,28(1):148-163
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. 相似文献
16.
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. 相似文献
17.
Xinli Ke Lingyun Qi Chen Zeng 《International journal of geographical information science》2016,30(4):637-659
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. 相似文献
18.
基于CA-ABM模型的福州城市用地扩张研究 总被引：3，自引：2，他引：1
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。 相似文献
19.
Simulating urban dynamics in China using a gradient cellular automata model based on S-shaped curve evolution characteristics 总被引：1，自引：0，他引：1
Xiaoping Liu Bin Ai Xia Li Guangjin Tian Yimin Chen 《International journal of geographical information science》2018,32(1):73-101
Cellular automata (CA) have been efficiently used to express the complexity and dynamics of cities at different scales. However, those large-scale simulation models typically use only binary values to represent urbanization states without considering mixed types within a cell. They also ignore differences among the cells in terms of their temporal evolution characteristics at different urbanization stages. This study establishes a gradient CA for solving such problems while considering development differences among the cells. The impervious surface area data was used to detect the urbanization states and temporal evolution trends of the grid cells. Transition rules were determined with the incorporation of urban development theory expressed as an S-shaped curve. China was selected as the case study area to validate the performance of the gradient CA for a national simulation. A comparison was also made to a traditional binary logistic-CA. The results demonstrated that the gradient CA achieved higher accuracies in terms of both spatial patterns and quantitative assessment indices. The simulation pattern derived from the gradient CA can better reflect the local disparity and temporal characteristics of urban dynamics. A national urban expansion for 2050 was also simulated, and is expected to provide important data for ecological assessments. 相似文献