吉林市城市扩展监测及模拟研究

随着我国城市化进程的加快,其引起的城市数量的增加和城市规模的扩大已经引起学术界广泛的重视,定期或不定期地获得城市扩展信息、了解城市动态变化趋势,可为城市土地资源的规划和管理提供有力的依据。本文基于元胞自动机(CA)原理,充分利用CA在土地利用空间格局演化模拟和空间局部优化方面的优势特点,结合遥感和GIS 技术建立城市空间扩展 CA 模型,对吉林市建成区的演化过程进行模拟。结果表明,开发的CA模型具有较好的模拟效果。     

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...     

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。     

数字城市地理空间框架研究

现有城市规划管理系统的数据格式具有多样性,同时存储系统较多,信息交互复杂,信息的共享困难,利用率不高。基于城市空间框架的城市规划管理系统可有效实现规划标准体系的统一、规划数据资源的整合以及规划应用系统的合并。本文在分析城市规划管理系统的现状和问题的基础上,探讨了城市空间框架的体系结构,并讨论了基于城市空间框架的城市规划管理系统的建设目标与关键技术,最后对其城市规划专题数据整理进行了实践示例。     

基于CA模型的西安城市空间扩展模拟及误差分析

元胞自动机(CA)是城市发展动态模拟的重要工具。本文以西安市为例,利用基于遗传算法的CA模型对西安市1990-2007年的城市发展进行模拟,得到了较好的效果。结果表明,运用遗传算法建立的CA模型能够较好地模拟城市发展状态;对模拟误差分析表明,影响城市土地利用变化机制的尺度特征,城市规划调整、重大事件、重大建设项目和行政区划调整等过程,城市不同发展阶段和不同区位的扩展类型等都将影响确定转换规则、寻找最佳参数和模拟精度的结果。     

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。     

基于集合卡尔曼滤波动态优化CA模型参数的方法

传统元胞自动机(CA)模型的转换规则不随模拟过程的时间和空间而变化,难以模拟和表达非线性地理过程.提出基于集合卡尔曼滤波(EnKF)动态优化CA模型参数的方法,以提高模型对复杂地理过程模拟的适应能力.通过引入集合卡尔曼滤波到CA模型中,将模型参数与模型状态整合成一个联合状态矩阵(joint state matrix).再把该矩阵与观测数据输入到EnKF更新方程中,计算出新的参数值,并自动更新到模型中,从而实现动态调整模型运行轨迹,以更好地适应城市发展的过程模拟.将此方法应用于东莞市的城市模拟试验中,优化后的CA模型能在单参数和多参数优化中正确地调整模型参数,使其迅速地收敛于真值并趋于平缓,也能降低模型误差并获得更好的模拟结果.     

集成GIS的元胞自动机在城市扩展模拟中的应用

元胞自动机CA(Cellular Automata)是一种"自下而上"的动态模拟模型,具有模拟城市复杂系统时空演化过程的能力。CA和GIS的集成使二者在时空建模方面相互补充,能使CA模拟结果可视化显示。这里以郑州市为例,设计了城市CA模型,借助VB 6.0和MapX 5.0建立了一个与GIS无缝集成的2维CA模拟系统,并结合郑州市2005~2007年遥感影像图和土地利用图,对郑东新区进行了模拟,取得较好的效果。     

集成GIS的元胞自动机在城市扩展模拟中的应用

元胞自动机CA(Cellular Automata)是一种"自下而上"的动态模拟模型,具有模拟城市复杂系统时空演化过程的能力.CA和GIS的集成使二者在时空建模方面相互补充,能使CA模拟结果可视化显示.这里以郑州市为例,设计了城市CA模型,借助VB 6.0和MapX 5.0建立了一个与GIS无缝集成的2维CA模拟系统,并结合郑州市2005～2007年遥感影像图和土地利用图,对郑东新区进行了模拟,取得较好的效果.     

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。     

基于CA模型的城市空间扩展研究——义乌市为例

本文引入地理信息系统(GIS)和元胞自动机的有机集成而构筑的GeoCA-Urban模型模拟义乌市城市空间扩展的动态演化过程。结果表明:从发展速度分析,义乌市城市发展经历了一个起步—缓慢发展—爆炸发展的过程,而且爆炸式发展还在继续,从发展的空间布局上分析,义乌市在交通(主要是浙赣铁路和主要公路)、水系(义乌江)和城市中心辐射作用下,经历了带状(东北西南向)—椭圆—圆形的发展过程。通过设置转换规则、参数,真实直观地再现展示了城市空间系统的演化过程,为城市规划等提供了辅助决策。     

Integration of neural networks and cellular automata for urban planning

This paper presents a new type of cellular automata (CA) model for the simulation of alternative land development using neural networks for urban planning. CA models can be regarded as a planning tool because they can generate alternative urban growth. Alternative development patterns can be formed by using different sets of parameter values in CA simulation. A critical issue is how to define parameter values for realistic and idealized simulation. This paper demonstrates that neural networks can simplify CA models but generate more plausible results. The simulation is based on a simple three-layer network with an output neuron to generate conversion probability. No transition rules are required for the simulation. Parameter values are automatically obtained from the training of network by using satellite remote sensing data. Original training data can be assessed and modified according to planning objectives. Alternative urban patterns can be easily formulated by using the modified training data sets rather than changing the model.     

Integration of neural networks and cellular automata for urban planning

This paper presents a new type of cellular automata (CA) model for the simulation of alternative land development using neural networks for urban planning. CA models can be regarded as a planning tool because they can generate alternative urban growth. Alternative development patterns can be formed by using different sets of parameter values in CA simulation. A critical issue is how to define parameter values for realistic and idealized simulation. This paper demonstrates that neural netowrks can simplify CA models but generate more plausible results. The simulation is based on a simple three-layer network with an output neuron to generate conversion probability. No transition rules are required for the simulation. Parameter values are automatically obtained from the training of network by using satellite remote sensing data. Original training data can be assessed and modified according to planning objectives. Alternative urban patterns can be easily formulated by using the modified training data sets rather than changing the model.     

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.     

A grey wolf optimizer–cellular automata integrated model for urban growth simulation and optimization

This article proposes a grey wolf optimizer (GWO) and cellular automata (CA) integrated model for the simulation and spatial optimization of urban growth. A new grey wolf‐inspired approach is put forward to determine the urban growth rules of CA cells by using the GWO algorithm, which is suitable for solving optimization problems. The inspiration for GWO comes from the social leadership of wolf groups, as well as their hunting behavior. The GWO‐optimized urban growth rules for CA describe the relationship between the spatial variables and the urban land‐use status for each cell in the formation of “if–then.” The GWO algorithm and CA model are then integrated as the GWO–CA model for urban growth simulation and optimization. By taking Nanjing City as an example, the simulation accuracy in terms of urban cells is 86.6%, and the kappa coefficient is 0.715, indicating that the GWO algorithm is efficient at obtaining urban growth rules from spatial variables. The validation of the GWO–CA model also illustrates that it performs well in terms of the simulation and spatial optimization of urban growth, and can further contribute to urban planning and management.     

元胞自动机城市增长模型的空间尺度特征分析

基于元胞自动机模拟城市系统的复杂行为时,空间尺度是一个非常重要的概念,模型的模拟结果往往会随着输入数据的空间尺度变化而发生变化。然而,目前的元胞自动机城市增长模型大多没考虑数据的空间尺度特征,本文拟通过改变模型中输入数据的空间尺度来验证元胞自动机城市增长模型对尺度的敏感性及其空间尺度特征,并以长沙市为例进行实证研究。研究结果表明:元胞自动机城市增长模型只有在一定的尺度范围内才具有较高的模拟精度,并且模型对尺度具有一定的敏感性,因此为了使模型能够具有较高的模拟精度,并较好地反映城市形态特征,应认真选择模型中输入数据的空间尺度。     

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.     

Developing a geospatial web-GIS system for landscape and urban planning

This study discusses a geographical information system (GIS) for operating a local government's landscape and urban planning activities via a website. Implementing this web-GIS system will help build a more realistic landscape and urban planning model that includes citizen participation and city marketing. The approach is applicable to ubiquitous city (u-city) development based on geospatial web and its related systems. The approach presented is built on six selected elements of a u-city system. The outcome of the study includes sustainable analysis, environmental planning, urban planning, and city marketing. The outcome is applicable to cities that are planning to adopt the u-city system or advanced telecommunication or planning tools into their urban frameworks.     

城市公共服务设施优化配置模型研究的近期进展与展望

城市公共服务设施优化配置是实现城市公共服务均等化的必要途径。近年来,时空大数据、智能决策与仿真等技术蓬勃发展,引发了城市公共服务设施配置模型研究的革命。基于以上时代背景,本文对城市公共服务设施优化配置模型的近期研究进展进行了总结和展望：①优化决策模型朝着精细化方向快速发展,优化决策目标将更加多元化;②多源地理大数据和时空数据挖掘方法的城市公共服务需求建模动态化;③多目标群智能优化算法使城市公共服务设施优化决策朝着智能化的方向发展。随着大数据、云计算、并行计算和人工智能等新技术在GIS领域的广泛应用与集成,城市公共服务设施优化配置模型将越来越朝着精细化和智能化的方向发展。     

基于时空数据挖掘的城市体检评估方法与实践——以无锡市为例

时空数据挖掘作为当前研究的热点之一,为城市研究提供了新的思路和技术手段。本文以无锡市为例,利用手机信令数据、车辆轨迹实时数据、基础地理信息等多源异构时空数据,构建相应的数据挖掘模型,进行了职住平衡、区域联系度、交通拥堵等城市特征的挖掘与分析,构建多源数据挖掘的城市评估体系,探索了时空数据挖掘在城市体检评估中的应用,为城市规划和运行管理提供了科学评估与决策。