元胞空间分区及其对GeoCA模型模拟精度的影响

采用双约束空间聚类方法对元胞空间进行分区,在此基础上对不同的分区分别求取元胞转换规则,从而提高 元胞自动机的模拟精度。以杭州市土地利用变化为例,采用本文提出的基于双约束空间聚类的分区元胞自动机模型对 研究区域2000年—2005年的土地利用变化进行模拟,并利用逐点对比法和Moran I指数对模拟结果进行精度评估。结果 表明：(1)采用双约束空间聚类算法对元胞空间进行分区,可以保证同一分区内的元胞既在空间上邻近,又具有相对一 致的非空间属性信息,分区效果较好;(2)与不分区元胞自动机模型和基于空间聚类的分区元胞自动机模型相比,双约 束空间聚类元胞自动机模型具有较高的模拟精度,尤其是在空间形态和整体结构上具有较好的模拟效果。     

基于支持向量机的元胞自动机及土地利用变化模拟

提出了利用遥感数据，并采用支持向量机来确定元胞自动机非线性转换规则的新方法。元胞自动机在模拟复杂地理现象时，需要采用非线性转换规则。目前元胞自动机主要采用线性方法来获取转换规则，在反映复杂的非线性地理现象时有一定的局限性。以城市扩张的模拟为例，将模拟城市系统的主要特征变量映射到Hilbert空间后，通过SVM建立最优分割超平面，分割超平面的分类决策函数由径向基核（Radial Basis Kernel）构造。利用历史遥感数据校正超平面的决策函数，确定城市元胞自动机的非线性转换规则，计算出城市发展概率。利用所提出的方法，对深圳市1988-2010年的城市发展进行了模拟，取得了较理想的模拟效果。研究结果表明，基于SVM-CA模型的模拟精度比传统MCE方法模拟精度高，MoranⅠ指数与实际更为接近。     

城市扩张CA模型的参数敏感性分析

以广州市番禺区为研究区,构建了相应的城市扩张CA模型,从采样、邻域结构和微观元胞尺度等方面研究了CA模型的敏感性。首先通过改变模型采样比例、样本各个类别的比例等研究样本对模型参数的影响。然后分析不同的邻域结构与模型模拟精度的关系,并从微观尺度分析邻域元胞对中心元胞的影响。最后从空间尺度上分析CA模型在各种不同分辨率下的模拟结果,用景观指数剖析模拟结果的形态,同时在元胞摩尔邻域内分析其3×3邻域的城市发展密度变化情况。实验表明:(1)适当提高采样比例,会得到精度较高的权重,但训练样本中城市用地的比例应该与城市用地的转变量在全区的占比相匹配。(2)不论是采用摩尔邻域还是冯诺依曼邻域,模拟精度均随着空间尺度的增加而降低。在同一空间尺度下,采用摩尔邻域的模拟结果略好。相比冯诺依曼4个邻域元胞,摩尔邻域中的角点对中心元胞具有更大的影响。(3)随着空间分辨的降低,模拟结果的斑块数、斑块密度、聚集度和分形维度值在减少,结构变得简单,而且在微观的摩尔邻域中城市发展密度正在减少,即由高密度向低密度转换。     

基于元胞自动机和多智能体系统的城市人口空间分布研究

城市人口预测和空间分布模拟是人口社会学、城市规划、地理信息科学等领域的热门问题,其在灾害评估、资源配置、智慧城市建设等方面应用广泛。但小尺度人口数据部分缺失的问题经常出现,城市人口空间分布模型分辨率的提升仍需进一步研究。为此,提出一种基于元胞自动机和多智能体的方法,并考虑土地分类和公共设施的位置信息。通过确定元胞属性值,多智能体进行自主移动以确定人口空间分布,继而生成北京市人口空间分布图。为验证模型模拟结果,对北京市部分街道级行政区人口数据进行检验,结果表明:该方法生成的人口空间分布模型精度较高,结果可信;该模型对人口空间分布预测精度在街道尺度普遍优于6%,空间分辨率达到30m×30m。同时,提出可结合人口预报数值推定未来数年城市人口空间分布。经验证,模型所采用的人口预报方法精度和可信度较好。     

基于元胞自动机复合模型的土地利用演化模拟——以北京市海淀区为例

为了在土地利用空间格局演化模拟的基础上,为未来城市土地利用规划及管理提供更为科学合理的决策依据,本文以北京市海淀区1996年、2002年及2008年3期土地利用数据为数据源,重点采用元胞自动机复合模型CA-Markov模型与多标准评价方法相结合的手段,构建元胞转移数量规则及空间位置转化规则,并分别构造3种不同大小的元胞邻域集合,进行土地利用格局的模拟及预测。试验中2008年土地利用模拟结果与实际土地利用数据Kappa系数高达0.856 1,表明CA-Markov模型结合多标准评价方法的模拟手段可行性较高,同时元胞邻域空间大小对模拟结果的精度有明显的影响。土地利用结构数据及模拟预测结果表明城市集约化现象明显,建设用地迅速扩张,占用大量耕地、园地用地,因此迫切需要促进城市土地利用的可持续发展。     

顾及空间交互作用的城市群联动空间增长模拟——以武汉都市区为例

随着区域城市化与城市区域化的发展,城市群成为中国城市化进程中最引人注目的地区,研究城市群联动空间增长动态也成为了当前研究热点。在城市扩张的研究中,传统以元胞自动机(cellular automaton,CA)为代表的城市扩张模拟方法主要针对单一的城市展开,缺乏对城市群空间交互作用和联动增长效应的建模,难以真实反映城市群的空间扩张过程。通过引入城市流模型来量化城市之间的空间交互作用,并将其作为转换规则嵌入CA模型,构建了一种顾及空间交互作用的城市群联合空间增长扩张过程分析模型,并以武汉都市区为例,模拟了武汉都市区(1个主城区+6个远郊区)的城市扩张过程。与传统的Logistic回归CA模型对比,结果表明,模型模拟精度更高,可以在一定程度上反映城市发展,尤其是城市群联动增长扩张的特征和规律。     

基于SLEUTH模型的长江口北岸土地利用演化模拟研究

采用经典的元胞自动机城市扩展与土地利用演化SLEUTH模型,改进元胞自动机控制系数的筛选方法,结合驱动力的研究调整模型控制系数,根据长江口北岸流域实际情况设置模型校准参数,提高模型在长江口北岸流域应用的实用性。研究表明SLEUTH模型方法可以获得精度较高的土地利用变化模拟结果。     

城市扩展元胞自动机多结构卷积神经网络模型

传统的城市扩展元胞自动机(CA)模型是基于单个元胞的变量信息挖掘来构建转换规则的。针对这一问题,本文基于多结构卷积神经网络提出从区域特征出发且顾及区域多尺度特征挖掘转换规则的城市扩展元胞自动机模型(MSCNN-CA),并以武汉主城区和上海浦东新区为例,模拟了两个试验区2005—2015年期间城市扩展过程。模型验证表明:与逻辑回归和神经网络相比,本文构建的3个单一结构的卷积神经网络元胞自动机(CNN-CA)模型在4个指标(Kappa系数、FoM(figure of merit)值、命中率(h)和错误率(m))上都有不同程度的提高。特别是FoM指数,在武汉主城区提高了23.3%~29.4%,在上海浦东新区提高了20.3%~28.5%。此外,MSCNN-CA模型与3个单一结构的CNN-CA模型相比,在各个指标上也有所改善,FoM指数在武汉主城区提高了0.8%~4.8%,上海浦东新区提高了2.8%~7.8%。两个试验区的模拟结果表明:相比传统CA模型,基于多结构卷积神经网络的城市扩展元胞自动机模型(MSCNN-CA)能够有效提高城市扩展模拟的精度,更真实地反映城市扩展空间演变过程。相比单结构的卷积神经网络CA模型,多结构卷积神经网络CA模型的稳定性和模拟结果准确性有所提升。     

元胞自动机和克隆选择算法的蓝藻水华预测模型

针对蓝藻水华频发影响水体质量和生态环境的问题,该文以太湖为例,探讨了元胞自动机和克隆选择算法在蓝藻水华预测方面的可行性。使用遥感经验算法计算水体叶绿素a浓度;将叶绿素a浓度分布数据作为预测模型的输入数据,将基于平衡方程的预报模型与元胞自动机进行耦合,对蓝藻水华时空变化过程进行了动态模拟;在预测方面实现了蓝藻水华空间分布情况的可视化,并采用克隆选择算法优化了预测模型中的重要参数。研究结果表明:利用克隆选择算法优化的元胞自动机模型能够在蓝藻水华预测中直观地显示蓝藻水华的时空变化特征;能够在短时间内重复性地预演蓝藻水华的变化过程,为决策者提供有用的参考。     

顾及空间多尺度邻域效应和时间依赖性的城市扩展模拟

在推进新型城镇化和实施新时代国土空间规划的战略背景下,城市扩展研究逐渐成为热点问题。当前基于元胞自动机(CA)的城市扩展模拟对城市空间多尺度邻域效应解析不足,且在转换规则中对城市长时间演变过程的时间依赖性影响表达不够完善,简化了城市扩展的时空依赖性,无法真实模拟推演未来规划实施情景以服务于国土空间规划。针对上述问题,本文构建一种兼顾空间多尺度邻域效应(3DCNN)和时间依赖性(ConvLSTM)的城市扩展深度学习CA模型(下文称“Deep-CA”)。首先通过组合普通卷积和空洞卷积的3DCNN来提取城市空间多尺度邻域效应,再利用ConvLSTM神经网络将历史信息同化,考虑长时间序列的时间依赖性,从而得到城市扩展的适宜性概率。北京市1995—2015年的土地利用数据及其驱动因素数据用于验证所提CA模型的科学性与适用性,1995—2010年数据用于模型训练,模拟2015年的城市范围。同时将模拟结果精度与ANN-CA、LR-CA和ME-CA 3种传统方法进行对比。与传统CA模型相比,Deep-CA的北京市2015年模拟FoM指数提高了4%左右,且对于城市全局和局部形态模拟效果较好,斑块破碎度低...     

Modelling urban growth in the Indo-Gangetic plain using nighttime OLS data and cellular automata

The present study demonstrates the applicability of the Operational Linescan System (OLS) sensor in modelling urban growth at regional level. The nighttime OLS data provides an easy, inexpensive way to map urban areas at a regional scale, requiring a very small volume of data. A cellular automata (CA) model was developed for simulating urban growth in the Indo-Gangetic plain; using OLS data derived maps as input. In the proposed CA model, urban growth was expressed in terms of causative factors like economy, topography, accessibility and urban infrastructure. The model was calibrated and validated based on OLS data of year 2003 and 2008 respectively using spatial metrics measures and subsequently the urban growth was predicted for the year 2020. The model predicted high urban growth in North Western part of the study area, in south eastern part growth would be concentrated around two cities, Kolkata and Howrah. While in the middle portion of the study area, i.e., Jharkhand, Bihar and Eastern Uttar Pradesh, urban growth has been predicted in form of clusters, mostly around the present big cities. These results will not only provide an input to urban planning but can also be utilized in hydrological and ecological modelling which require an estimate of future built up areas especially at regional level.     

Assessing the effect of temporal dynamics on urban growth simulation: Towards an asynchronous cellular automata

Time is a fundamental dimension in urban dynamics, but the effect of various definitions of time on urban growth models has rarely been evaluated. In urban growth models such as cellular automata (CA), time has typically been defined as a sequence of discrete time steps. However, most urban growth processes such as land‐use changes are asynchronous. The aim of this study is to examine the effect of various temporal dynamics scenarios on urban growth simulation, in terms of urban land‐use planning, and to introduce an asynchronous parcel‐based cellular automata (AParCA) model. In this study, eight different scenarios were generated to investigate the impact of temporal dynamics on CA‐based urban growth models, and their outputs were evaluated using various urban planning indicators. The obtained results show that different degrees of temporal dynamics lead to various patterns appearing in urban growth CA models, and the application of asynchronous (event‐driven) CA models achieves better simulation results than synchronous models.     

An Artificial-Neural-Network-based,Constrained CA Model for Simulating Urban Growth

Insufficient research has been done on integrating artificial-neural-network-based cellular automata (CA) models and constrained CA models, even though both types have been studied for several years. In this paper, a constrained CA model based on an artificial neural network (ANN) was developed to simulate and forecast urban growth. Neural networks can learn from available urban land-use geospatial data and thus deal with redundancy, inaccuracy, and noise during the CA parameter calibration. In the ANN-Urban-CA model we used, a two-layer Back-Propagation (BP) neural network has been integrated into a CA model to seek suitable parameter values that match the historical data. Each cell's probability of urban transformation is determined by the neural network during simulation. A macro-scale socio-economic model was run together with the CA model to estimate demand for urban space in each period in the future. The total number of new urban cells generated by the CA model was constrained, taking such exogenous demands as population forecasts into account. Beijing urban growth between 1980 and 2000 was simulated using this model, and long-term (2001–2015) growth was forecast based on multiple socio-economic scenarios. The ANN-Urban-CA model was found capable of simulating and forecasting the complex and non-linear spatial-temporal process of urban growth in a reasonably short time, with less subjective uncertainty.     

Spatial Differences in Multi-Resolution Urban Automata Modeling

The last decade has seen a renaissance in spatial modeling. Increased computational power and the greater availability of spatial data have aided in the creation of new modeling techniques for studying and predicting the growth of cities and urban areas. Cellular automata is one modeling technique that has become widely used and cited in the literature; yet there are still some very basic questions that need to be answered with regards to the use of these models, specifically relating to the spatial resolution during calibration and how it can impact model forecasts. Using the SLEUTH urban growth model ( Clarke et al. 1997 ), urban growth for San Joaquin County (CA) is projected using three different spatial grains, based on four calibration routines, and the spatial differences between the model outputs are examined. Model outputs show that calibration at finer scaled data results in different parameter sets, and forecasting of urban growth in areas that was not captured through the use of more coarse data.     

Simulating urban growth in a megalopolitan area using a patch‐based cellular automata

Although traditional cellular automata (CA)‐based models can effectively simulate urban land‐use changes, they typically ignore the spatial evolution of urban patches, due to their use of cell‐based simulation strategies. This research proposes a new patch‐based CA model to incorporate a spatial constraint based on the growth patterns of urban patches into the conventional CA model for reducing the uncertainty of the distribution of simulated new urban patches. In this model, the growth pattern of urban patches is first estimated using a developed indicator that is based on the local variations in existing urban patches. The urban growth is then simulated by integrating the estimated growth pattern and land suitability using a pattern‐calibrated method. In this method, the pattern of new urban patches is gradually calibrated toward the dominant growth pattern through the steps of the CA model. The proposed model is applied to simulate urban growth in the Tehran megalopolitan area during 2000–2006–2012. The results from this model were compared with two common models: cell‐based CA and logistic‐patch CA. The proposed model yields a degree of patch‐level agreement that is 23.4 and 7.5% higher than those of these pre‐existing models, respectively. This reveals that the patch‐based CA model simulates actual development patterns much better than the two other models.     

Modeling sprawl of unauthorized development using geospatial technology: case study in Kuantan district,Malaysia

Abstract

The paper explores a framework combining remote sensing and GIS-cellular automata (CA) concepts aimed at improving the modeling of unauthorized land use sprawl. Remote sensing data have been used in urban modeling and analysis, the use of high-resolution remote sensing data in assessing unauthorized development is quite unexplored. This work has demonstrated systematic combination utilization of geospatial analyses tools to acquire a new level of information to enable urban modeling and sprawl analysis in assisting urban sustainable management. In this study, Kuantan city, Malaysia was selected in simulation of the unauthorized land use with CA concept for a period of 15 years (2000–2015), with main input time-series land use observation from 1995 to 2005. The 2000 and 2005 land use input was also used as calibrated and test assessment of the simulation. The results show excellent agreement between in-situ changes of the unauthorized land use classes and the corresponding simulated classes within the same periods. In conclusion, CA model can lead to new levels of understanding of how urban areas grow and change as in view of digital earth aspiration.     

Exploring land use policy scenarios with the use of a cellular automata-based model: urban sprawl containment and sustainable development in Thessaloniki

This study addresses the issue of urban sprawl through the application of a cellular automata (CA)-based model in the area of Thessaloniki, Greece. The model integrates a multiple regression model at the regional level with a CA model at the local level. New urban land is allocated in a disaggregated field of land units (cells) taking into account a wide range of data. Particular emphasis is placed on the way zoning regulations and land availability data are inserted into the model, so that alternative land use policy scenarios could be examined. Thessaloniki, a typical Mediterranean city, is used as a case study. The model is used to compare two scenarios of urban growth up to year 2030; the first one assuming a continuation of existing trends, whereas the second one assuming the enactment of various land use zoning regulations in order to contain urban sprawl.     

Simulating urban growth processes by integrating cellular automata model and artificial optimization in Binhai New Area of Tianjin,China

This study presents an optimized algorithm into the cellular automata (CA) models for urban growth simulation in Binhai New Area of Tianjin, China. The optimized CA model by particle swarm optimization (PSO) was compared with the logistic-based cellular automata (LOGIT-CA) model to see the effects of the simulation. The study evaluated the stochastic disturbance in the development of urban growth using the Monte Carlo method; the coefficient d determined the state of urban growth. The validation was conducted by both cross-tabulation test and structural measurements. The results showed that the simulations of PSO-CA were better than LOGIT-CA model, indicating an improvement in the spatio-temporal simulation of urban growth and land use changes in study area. Since the simulations reached their best values when the coefficient was between 1 and 2, the urban growth in the study area was in the period of conversion from spontaneous growth to edge-expansion and infilling growth.