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.     

基于GA-MCE算法的不规则邻域CA土地利用模拟

不规则邻域元胞自动机通过定义一定的邻域规则,将对中心元胞影响较大的邻域元胞进行识别与计算从而确定邻域形态与影响范围,与传统元胞自动机模型相同尺寸邻域形态相比,模拟更加真实有效。基于不规则邻域识别算法对元胞邻域范围进行划分,再通过遗传算法和多准则评价相结合获取转化规则参数,继而对大连市金石滩国家旅游度假区2004年和2010年土地利用变化进行模拟研究,通过比对分析以及Kappa系数检验法对模拟精度做一检验,研究模拟结果总体Kappa系数为81.62%,具有一定的可靠性,研究显示该模型在多地类碎小斑块之间的转化模拟具有一定的优势,对于模拟土地利用/覆盖变化模型具有一定的改进。     

球面元胞自动机框架的比较与选择

在球面构建基本的元胞自动机框架是球面元胞自动机研究的基础.该文从球面空间特性出发,分析比较了四边形、三角形和六边形网格的不同几何特性和元胞运动机制,提出了基于六边形网格的元胞自动机框架模型,描述了其基本定义、运动机制和坐标系统,并构建了球面六边形元胞自动机原型系统进行模拟实验,结果表明,该元胞自动机框架能有效支持球面元胞自动机的深入研究.     

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。     

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。     

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。     

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。     

基于随机森林CA的东莞市多类土地利用变化模拟

城市土地利用及其变化对城市环境有着重要影响。很多学者已经结合元胞自动机和机器学习算法对城市扩张进行了相关的模拟研究,但针对复杂的多类土地利用相互变化过程的研究仍然较少。该文提出了一种基于随机森林算法的多类元胞自动机(RFA-CA)模型,并将其用于模拟和预测复杂的多类土地利用变化。该模型使用随机森林算法提取元胞自动机的转换规则,并计算了各空间变量的重要性,在东莞市2000-2014年土地利用动态模拟结果中,Kappa系数和整体精度分别为0.73和84.7%。针对每一种土地利用类型,计算了影响东莞市土地利用变化的各空间变量的重要性,结果显示,交通、区位因素对东莞市土地利用变化格局的形成有重要影响。文中引入的POIs邻近因素反映了城市空间开发程度的高低,同样对多类土地利用格局的形成具有重要作用。     

基于多尺度空间ANN - CA模型的遥感影像超分辨率制图方法研究

为获取遥感影像混合像元中各组分的空间分布状况,提出一种新的遥感影像超分辨率制图方法,用于继混合像元分解之后的亚像元定位。将元胞自动机理论移植到不同空间尺度的演化上,建立基于神经网络的多尺度元胞自动机模型(ANN-CA),并利用该模型提取北京市海淀区城镇用地超分辨率信息。结果表明,该方法能有效表达图像像元之间的空间自相关性。     

顾及风向和风速的气温空间插值方法

当前气温插值方法多将高程、经纬度等作为影响因素,无法解决风向、风速对气温空间分布的影响问题。该文提出一种顾及风向和风速的气温空间插值方法:1)根据气象观测站点的风向、风速数据进行插值,生成连续的风场表面;2)基于该风场数据利用高斯扩散模型构建成本表面;3)利用Dijkstra最短路径算法计算观测点与待求点的累计移动成本最短路径(SPOCMC);4)将SPOCMC作为协变量进行薄盘光滑样条插值以实现气温插值。为验证该方法的有效性和可靠性,选取山东省109个气象观测点样本数据,以SPOCMC、DEM和SPOCMC-DEM分别作为协变量对气温数据进行薄盘光滑样条插值,结果显示:SPOCMC-DEM法的MAE和RMSE均值(分别为0.517、0.779)略低于SPOCMC法(0.583、1.016),显著低于DEM法(0.809、1.231),表明添加SPOCMC作为协变量可有效提高气温空间插值的准确性。在江苏省与贵州省的插值实验结果证明了该方法的普适性。     

A hybrid genetic algorithm with local optimiser improves calibration of a vegetation change cellular automata model

Cellular automata (CA) models are commonly used to model vegetation dynamics, with the genetic algorithm (GA) being one method of calibration. This article investigates different GA settings, as well as the combination of a GA with a local optimiser to improve the calibration effort. The case study is a pattern-calibrated CA to model vegetation regrowth in central Victoria, Australia. We tested 16 GA models, varying population size, mutation rate, and level of allowable mutation. We also investigated the effect of applying a local optimiser, the Nelder?Mead Downhill Simplex (NMDS) at GA convergence. We found that using a decreasing mutation rate can reduce computational cost while avoiding premature GA convergence, while increasing population size does not make the GA more efficient. The hybrid GA-NMDS can also reduce computational cost compared to a GA alone, while also improving the calibration metric. We conclude that careful consideration of GA settings, including population size and mutation rate, and in particular the addition of a local optimiser, can positively impact the efficiency and success of the GA algorithm, which can in turn lead to improved simulations using a well-calibrated CA model.     

基于栅格数据和图论算法的生态廊道识别

利用图论中最短路算法思想和ArcGIS的空间分析功能,对生境中动物迁徙廊道的识别与构建进行研究。通过对具有不同空间自相关特征的模拟景观进行分析,发现该方法具有较好的适应性,能识别出不同类型模拟景观的廊道。在模拟景观分析的基础上,将该算法应用于长沙市大河西先导区,计算了先导区重要栖息地之间的廊道。研究表明该计算方法提取的生态廊道具有明显的冗余性,充分反映了物种在不同栖息地之间迁徙的需求,具有一定的生态学意义。同时廊道提取结果在一定程度上反映了城市发展对生态系统尤其是生态廊道的胁迫与挤占。通过对廊道空间结构的分析还发现生态廊道存在一定的瓶颈区域,对该区域的生态恢复与重建将对物种多样性的保持起到关键作用。该方法为区域土地管理以及生态环境保护规划提供一定的科学依据。     

Estimation of Blast-Induced Air Overpressure in Quarry Mines Using Cubist-Based Genetic Algorithm

In the present work, blast-induced air overpressure is estimated by an innovative intelligence system based on the cubist algorithm (CA) and genetic algorithm (GA) with high accuracy, called GA–CA model. Herein, CA initialization model was developed first and the hyper-parameters of the CA model were selected randomly. Subsequently, the GA procedure was applied to perform a global search for the optimized values of the hyper-factors of the CA model. Root-mean-square error (RMSE) is utilized as a compatibility function to determine the optimal CA model with the lowest RMSE. Gaussian process (GP), conditional inference tree (CIT), principal component analysis (PCA), hybrid neural fuzzy inference system (HYFIS) and k-nearest neighbor (k-NN) models are also developed as the benchmark models in order to compare and analyze the quality of the proposed GA–CA algorithm; 164 blasting works were investigated at a quarry mine of Vietnam for this aim. The results revealed that GA significantly improved the performance of the CA model. Based on the statistical indices used for model assessment, the proposed GA–CA model was confirmed as the most superior model as compared to the other models (i.e., GP, CIT, HYFIS, PCA, k-NN). It can be applied as a robust soft computing tool for estimating blast-induced air overpressure.

     

A method for finding a least-cost wide path in raster space

Given a grid of cells each having an associated cost value, a raster version of the least-cost path problem seeks a sequence of cells connecting two specified cells such that its total accumulated cost is minimized. Identifying least-cost paths is one of the most basic functions of raster-based geographic information systems. Existing algorithms are useful if the path width is assumed to be zero or negligible compared to the cell size. This assumption, however, may not be valid in many real-world applications ranging from wildlife corridor planning to highway alignment. This paper presents a method to solve a raster-based least-cost path problem whose solution is a path having a specified width in terms of Euclidean distance (rather than by number of cells). Assuming that all cell values are positive, it does so by transforming the given grid into a graph such that each node represents a neighborhood of a certain form determined by the specified path width, and each arc represents a possible transition from one neighborhood to another. An existing shortest path algorithm is then applied to the graph. This method is highly efficient, as the number of nodes in the transformed graph is not more than the number of cells in the given grid and decreases with the specified path width. However, a shortcoming of this method is the possibility of generating a self-intersecting path which occurs only when the given grid has an extremely skewed distribution of cost values.     

A bat-inspired approach to define transition rules for a cellular automaton model used to simulate urban expansion

A new metaheuristic approach is presented to discover transition rules for a cellular automaton (CA) model using a novel bat movement algorithm (BA). CA is capable of simulating the evolution of complex geographical phenomena, and transition rules lie at the core of these models. An intelligence algorithm based on the echolocation behavior of bats is used to discover explicit transition rules for use in simulating urban expansion. CA transition rules are formed by links between attribute constraint items and classification items. The transition rules are derived using the BA to optimize the lower and upper threshold values for each attribute. The BA-CA model is then constructed for the simulation of urban expansion observed for Nanjing City, China. The total accuracy of newly formulated BA-CA model for this application is 86.9%, and the kappa coefficient is 0.736, which strongly suggest that the interactions of bats are effective in capturing the relationships between spatial variables and urban dynamics. It is further demonstrated that this bat-inspired BA-CA model performs better than the null model, the particle swarm optimization-based CA model (PSO-CA), and the ant colony optimization-based CA model (ACO-CA) using the same dataset. The model validation and comparison illustrate the novel capability of BA for discovering transition rules of CA during the simulation of urban expansion and potentially for other geographic phenomena.     

How much past to see the future: a computational study in calibrating urban cellular automata

The objective of this computational study was to investigate to which extent the availability and the way of use of historical maps may affect the quality of the calibration process of cellular automata (CA) urban models. The numerical experiments are based on a constrained CA applied to a case study. Since the model depends on a large number of parameters, we optimize the CA using cooperative coevolutionary particle swarms, which is an approach known for its ability to operate effectively in search spaces with a high number of dimensions. To cope with the relevant computational cost related to the high number of CA simulations required by our study, we use a parallelized CA model that takes advantage of the computing power of graphics processing units. The study has shown that the accuracy of simulations can be significantly influenced by both the number and position in time of the historical maps involved in the calibration.     

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.     

地理系统模拟的CA模型理论探讨

在系统认识和理解地理元胞自动机（CA）模型的基本性质基础上,重点从自然与人文综合的复杂地理系统模拟研究角度,对地理元胞模型所涉及的基本理论与方法问题进行了进一步的探讨。研究表明：从地理系统的模拟看,CA模型的研究和应用提供了一种从地理系统的微观出发、将自然与人文统一的地理系统模拟的新视角与新途径。在此基础上,提出了地理系统模拟的CA模型需要解决的三队基本关系和三个基本科学方法问题。     

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.