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。     

基于元胞自动机模型的土地利用变化模拟——以大连经济技术开发区为例

元胞自动机模型已经成为模拟土地利用变化的重要方法。传统土地模拟方法中侧重于通过分析影响土地利用变化的因素来构建预测模型,较少从土地利用类型变化及其相互作用的空间角度来关注模型构建。本文以1998年、2004年和2009年1:10000土地利用数据,利用Python语言结合GDAL与Numpy类库实现局部土地利用竞争的元胞自动机模型原型开发,并用于模拟大连市经济技术开发区1998-2009年土地利用变化模拟。研究结果:1建立了发掘多地类之间相互作用关系的试验方法,研究适用于具有明确物理意义的多地类元胞自动机模拟模型;2该模型具有好的模拟精度,对建设用地、农用地和林地等3种不同类型用地进行同时模拟,其对应Kappa系数分别为0.762,0.634和0.678;3该模型建立了研究不同种地类协调作用的基本方法,可以用于进一步研究土地利用变化地类之间驱动原理。     

Coupling fuzzy clustering and cellular automata based on local maxima of development potential to model urban emergence and expansion in economic development zones

ABSTRACT

Modeling urban growth in Economic development zones (EDZs) can help planners determine appropriate land policies for these regions. However, sometimes EDZs are established in remote areas outside of central cities that have no historical urban areas. Existing models are unable to simulate the emergence of urban areas without historical urban land in EDZs. In this study, a cellular automaton (CA) model based on fuzzy clustering is developed to address this issue. This model is implemented by coupling an unsupervised classification method and a modified CA model with an urban emergence mechanism based on local maxima. Through an analysis of the planning policies and existing infrastructure, the proposed model can detect the potential start zones and simulate the trajectory of urban growth independent of the historical urban land use. The method is validated in the urban emergence simulation of the Taiping Bay development zone in Dalian, China from 2013 to 2019. The proposed model is applied to future simulation in 2019–2030. The results demonstrate that the proposed model can be used to predict urban emergence and generate the possible future urban form, which will assist planners in determining the urban layout and controlling urban growth in EDZs.     

基于GeoSOS的城市扩展模拟——以旅顺口区为例

运用MCE-CA和Logistic-CA两种基本的元胞自动机模型作为理论模型,考虑边界到市中心、镇中心、铁路和主要公路等作为区位因素的空间距离约束条件,以及地形和禁止建设区作为区位因素的全局限制约束条件,在地理模拟优化系统(Geographical Simulation and Optimization System,GeoSOS)的支持下,对1990~2000年和2000~2010年辽宁省大连市旅顺口区的城市空间扩展进行了模拟,并取得较好效果。结果表明,MCE-CA模型的Kappa系数分别为0.71和0.64,Logistic-CA模型分别为0.54和0.55,两者均达到较好的模拟精度;MCECA模型适用于主观变量较多的CA模型,Logistic-CA模型更适合于客观因素较多的CA模型;利用合理的CA模型模拟旅顺口区城市未来土地利用变化,可为今后的土地规划以及制定有效的土地管理措施和方针政策提供依据。     

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 CA 模型的土地利用变化研究

土地利用变化趋势及驱动力分析研究是目前全球变化研究的热点问题之一,如何合理并准确地模拟预测出土地利用变化的趋势是研究的核心。土地利用动态变化十分复杂,传统的GIS模型能很好地解决部分空间相关问题,但对复杂的时空动态变化地理现象却难以模拟。元胞自动机（ CA ）是“自下而上”的动态模拟建模框架,其时间、空间、状态都离散,是一种空间相互作用和时间因果关系都为局部的网格动力学模型,具有模拟复杂系统时空演化过程的能力。 CA模型的这些特点在土地利用演化的模拟方面较为合适,集成于GIS下的CA模型将会改善CA的模拟环境,使模拟分析的结果更加准确。     

Modeling the spread of spatio-temporal phenomena through the incorporation of ANFIS and genetically controlled cellular automata: a case study on forest fire

Abstract

Virtual representation and simulation of spatio-temporal phenomena is a promising goal for the production of an advanced digital earth. Spread modeling, which is one of the most helpful analyses in the geographic information system (GIS), plays a prominent role in meeting this objective. This study proposes a new model that considers both aspects of static and dynamic behaviors of spreadable spatio-temporal in cellular automata (CA) modeling. Therefore, artificial intelligence tools such as adaptive neuro-fuzzy inference system (ANFIS) and genetic algorithm (GA) were used in accordance with the objectives of knowledge discovery and optimization. Significant conditions in updating states are considered so traditional CA transition rules can be accompanied with the impact of fuzzy discovered knowledge and the solution of spread optimization. We focused on the estimation of forest fire growth as an important case study for decision makers. A two-dimensional cellular representation of the combustion of heterogeneous fuel types and density on non-flat terrain were successfully linked with dynamic wind and slope impact. The validation of the simulation on experimental data indicated a relatively realistic head-fire shape. Further investigations showed that the results obtained using the dynamic controlling with GA in the absence of static modeling with ANFIS were unacceptable.     

MonoLoop:CA城市模型状态转换规则获取的一种方法

状态转换规则是元胞自动机(Cellular Automata,CA)的核心,如何获取并建立CA的状态转换规则是构建CA模型的关键。邻域作用是CA能够模拟复杂物理现象的核心驱动力,而在已有的用于城市增长模拟的CA城市模型中,因为邻域作用在模拟的过程中为时间动态的变量,其系数很难通过常用的Logistic回归方法识别,致使已有的CA城市模型的状态转换规则中,往往仅通过Logistic回归获取邻域作用之外的空间变量的模型参数,而邻域作用的参数通常采用主观赋值的方法。本文提出了CA城市模型的多指标评价(Multi-Criteria Evalua-tion,MCE)形式状态转换规则获取的一种新方法 MonoLoop,并针对北京市域1976～2006年的城市增长开展了该方法的实验。基于这种方法,一方面利用历史数据可以建立更为客观的状态转换规则;另一方面也可以大大降低模型参数识别的时间。     

耦合遥感观测和元胞自动机的城市扩张模拟

在传统元胞自动机(CA)模型中,静态的模型参数和模型误差不能释放是影响城市扩张模拟效果的两个重要原因。文中引入集合卡尔曼滤波方法到CA模型中,提出了基于联合状态矩阵的地理元胞自动机。该模型在模拟过程中可以通过同化遥感观测数据,动态地调整模型参数和纠正模拟结果,使模型参数能够反映转换规则的时空变化,同时也能较好地释放积累的模型误差。将模型应用于东莞市的城市扩张模拟中,实验结果表明,模型能够准确地调整模型参数使之符合城市发展模式,同时也能有效地控制模型误差,其模拟的空间格局与真实情况吻合。     

Modeling Spatial Processes of Urban Growth in African Cities: A Case Study of Nairobi City

Africa's urban population growth has been especially rapid, averaging about 5% per year over the past two decades. As a result, many urban areas have experienced dramatic growth that is seriously outstripping the capacity of most cities to provide adequate services for their residents. Although population growth and urbanization rates in Africa have slowed recently due to a number of factors including HIV/AIDS, urban growth is still expected to double by 2030, leading to dramatic sprawl with serious environmental and social consequences. Using Nairobi as an example of a rapidly urbanizing African city, we studied the dynamics of land use and land cover change using satellite data and addressed the need for models and urban management tools that can guide sustainable urban planning policies. Cellular Automata, which integrate biophysical factors with dynamic spatial modeling, are used in this study. The model was calibrated and tested using time series of urbanized areas derived from land use/cover maps, produced from remotely sensed imagery, with future urban growth projected to 2030. Model assessment results showed high levels of accuracy, indicating that simulation findings were realistic, thereby confirming the effectiveness of the model. Results further showed that the model is a useful and effective tool to foresee the spatial consequences of planning policies in the context of many African cities. The forecast for Nairobi showed unsustainable sprawl.