中心城区城市增长的情景模拟与空间格局演化

基于1993-2008 年间4 个时相的遥感影像,应用SLEUTH模型模拟与预测自组织和规划引导两类情景下泉州中心城区的城市用地增长过程,并借助空间关联法分析其城市增长的空间格局演化特征,为“两规”空间协调提供科学依据。结果表明：① SLEUTH模型适用于研究区的城市增长模拟与预测,其对城市用地扩展的数量拟合要优于空间匹配,可作为多方案情景模拟的一个技术手段。② 规划引导预案的MPS、ED、AWMSI、MPI 四类景观指数均优于自组织预案,城市用地斑块的整体性、连接性较优,未来城市发展较为紧凑,利于实现土地的集约利用与城市的集聚发展。③ 随预测时间推移,研究区城市用地扩展的速率以及空间集聚性将有所减弱,城市增长的热点区也会发生演变与迁移。2008-2020 年,热点区分布总体呈现“圈层式”结构,局部以“跨江发展”为主要特征;2020-2030 年,热点区总体布局较为发散,局部则呈“环湾发展”与“孤立分布”特征。本研究将情景模拟、景观指数、空间分析等方法有效结合,有助于深刻理解研究区的城市空间增长过程,可为城市管理工作提供决策支持。     

低碳视角下城市空间形态紧凑度研究

全球气候变化背景下,构建功能紧凑的低碳城市空间形态已成为世界各地城市实现低碳化可持续发展的主要途径之一。但目前的城市形态研究主要集中在城市外部空间,缺乏对城市内部功能空间紧凑度的量化。从实现低碳城市的视角,提出了城市内部功能空间形态紧凑度的量化指标,用以探讨实现低碳城市的城市空间形态特征;以商业、医疗、教育和文化娱乐等4种重要服务设施布局为基础,构建了城市功能空间紧凑度指数,对不同规模、不同地理条件类型的8个案例城市进行定量研究,实证了城市内部“功能空间紧凑度”指标较城市外部“形态紧凑度”指标能够更好的反映城市形态的紧凑性内涵,适用于评价各类城市空间形态的低碳发展模式。     

基于多智能体的土地利用模拟与规划模型

利用多智能体和元胞自动机对城市土地资源的可持续利用进行了规划。根据环境经济学资源分配原理和可持续发展理论,提出结合多智能体及元胞自动机的微观规划模型,在时间和空间上合理分配及规划城市土地资源的利用,以避免浪费不可再生的土地资源。该模型由相互作用的多智能体层、元胞自动机层和环境因素层组成,可方便地探索不同土地利用政策下城市土地利用发展情景,能够为城市规划提供有用的决策依据。以广州市海珠区为实验区,在可持续发展为前提的规划下,模拟了1995-2010年的城市扩展的动态变化,并讨论了在不同规划情景下城市土地资源的利用效率及合理性。     

规划导向的CA约束方法与城市用地格局变化预测——以广州市白云区为例

城市发展是一个复杂系统的演化过程,掌握其发展规律并用于预测城市形态变化在城市理论和城市规划管理中有重要意义.一般约束性元胞自动机(CA)无法模拟存在时空分异的约束条件,文中提出一种时、空可变的规划导向约束条件表达方法,将城市发展战略调整、建设周期等外部条件变化用时间变量和空间变量表达,模拟出动态变化的CA外部环境,逼近城市受动态规划条件影响下的演化过程.以广州市白云区为例,对“十二五”规划中城市发展战略继承和调整并存的现象进行了情景模拟;构造了地铁线路、重点发展区两个规划方案在2010-2015年间建设过     

基于约束性CA方法的北京城市形态情景分析

约束性元胞自动机(Constrained cellular automata,Constrained CA) 在模拟城市增长方面已有诸多实践,已有文献常利用各种反映土地利用需要、区位影响、管理制度等的政策参数,设定不同的城市空间发展情景以反映未来的城市形态。在基于约束性CA进行城市形态情景分析时,以各种政策参数作为情景条件的研究实例较多。而在中国的城市规划实践中,城市规划用于建设管理的审批,对应明确的城市形态,因而如何利用城市形态作为情景条件分析所需政策,也有实践意义,可以解决一些广为关注的重要问题,例如已有的发展政策是否与期望的城市形态相匹配,不同规划方案所需采取的政策有什么区别等。本文提出了以城市形态作为情景条件(即形态情景分析) 的新方法,该方法基于约束性CA,改变了传统的约束性CA情景条件的设置方法,以期望的未来城市形态为目标,识别相应需要的发展政策。并将北京市域的4 个规划城市形态作为情景条件进行了实证分析,对多个规划方案实施的可能性和政策参数进行对比分析,证明该方法具有较好的适用性和可行性,是将约束性CA的应用扩展到城市规划领域的又一尝试。     

基于CLUE模型的2030年江西省土地利用变化情景分析

引入“一切照常情景”、“规划情景"和”优化情景"三种生态系统变化情景,采用CLUE模型,对江西省2001～2030年的土地利用空间变化进行模型模拟,并用2005年的土地利用现状图对模拟结果进行了验证。研究表明:在“一切照常情景”中,耕地持续减少;建设用地急剧增加;林地面积基本保持稳定。在“规划情景”中,耕地持续增加;建设用地略有增加,并在2020年后基本保持不变;林地总面积变化不大,但是高密度森林面积比重增大;河流和湖泊面积略微减少,湿地面积大幅增加。在“优化情景”中,森林增加趋势较“规划情景”有所减缓,河流和湖泊,湿地均有显著增加。本研究对研究区今后的土地利用规划修编和土地可持续性管理具有重要的指导意义。     

城市收缩时代的适应战略和空间重构 ——基于日本网络型紧凑城市规划

通过对日本网络型紧凑城市规划的发展机制、发展政策和代表性案例--富山市和宇都宫市的探讨，归纳总结了日本中央和地方城市政府的收缩城市规划战略政策和空间重构规划方案，借此为陷入收缩境况的中国城市在战略取向和空间重构方面提出有益启示：1）勇于接纳和面对人口减少和城市收缩这一客观事实，积极采取激活城市收缩所带来的机遇的适应性战略，而不是采取促使人口再度增加的应对性战略；2）充分考虑人口减少和老龄化社会并存的城市收缩时代特征，通过城市功能集聚的“据点化”战略和公共交通系统主导的“网络化”战略，构建多中心连接的网络型紧凑城市空间结构；3）根据人口数量变化预测结果，动态地推动城市规模的精明收缩，开展匹配存量规划或减量规划的规模适当化规划。     

水资源约束下西北干旱区河西走廊城市化发展模式

西北干旱区城市化进程受到水资源短缺及其用水结构不合理的双重约束。本文以甘肃河西走廊为例 ,针对河西走廊未来 30年总需水量阈值、经济总量阈值、总人口阈值和城市化水平阈值 ,认为最经济、最节水的城市化发展方针为禁止发展特大城市 ,有选择地培育一个大城市 ,积极发展中小城市 ,科学发展小城镇。据此提出了与水资源约束相适应的西北干旱区河西走廊城市化发展的 6种模式。即 :推行“产业移人”和“环境移民”为主、“人移产业”为辅的城市扩展模式 ;推行“分步到位”为主 ,“一步到位”为辅的城市生态移民模式 ;推行“退一进二”、“退乡进城”的城市用水转换模式 ;推行“节水量城”为主 ,“借水养市”为辅的节水型城市化模式 ;推行“稳粮兴草” ,“稳农强工” ,“活商旺城”的城市产业转换模式 ;推行“一核四极” ,“一轴三带”、“乡镇同兴”的“卅”字型城市空间推进模式     

中国东北地区城市地理基本框架

从东北地区城市地理框架的形成和演变过程出发,对东北地区城市体系框架、中心城市和大都市区、城市群和城市组群等城市地理问题进行了研究。结果表明,东北地区城市地理框架的形成演变过程分为三个时期,即古代城镇形成时期-近代城市形成时期-现代城镇体系形成发展时期。将城市体系结构框架归纳为：自上而下先快后缓的城市化进程,“弱金字塔”型的等级规模结构,沿铁路分布的“T”型空间结构,由“单一”向“新型”发展的城市职能结构。东北地区以哈尔滨、长春、沈阳、大连为中心城市的“四城市时代”已经到来,三省GDP向四城市高度集中,并且依托四市正在形成发展为“大哈尔滨”、“大长春”、“沈阳经济区”和“大大连”四个大都市区。作为城市化发展到一定阶段的产物,东北地区的城市群和城市组群也发育得相对完备而密集,已初步形成“三圈”的基本格局,即辽中南城市群以及吉中、哈大齐等2个大型城市组群。     

伪满时期长春城市社会空间结构研究

遵循长春城市空间发展变化过程,揭示了伪满之前长春社会空间结构演化特征,发现至伪满成立前,长春已经形成了“中-俄-日”三极分化的社会空间结构。伪满成立后,在“国都”城市性质、“消费”经济功能、城市空间扩张、外来移民流入等背景下,长春城市社会空间开始呈现出“中日分化”的总体格局,城市中轴线成为这一格局的空间标识。总体来看,伪满时期长春社会空间已分化为伪满高级官署区、日本人居住区、民族商业区和中国贫困农民居住区等社会区类型,空间结构模型呈现出围绕城市中心形成的同心圆与扇形结构和城市边缘“孤岛”相结合的形态模式。城市空间发展的历史基础,人口城市化的畸形发展,城市建设的殖民地本质以及“新京”城市规划等是伪满时期长春社会空间结构形成的主要机制。     

Simulating land-use dynamics under planning policies by integrating artificial immune systems with cellular automata

Cellular automata (CA) have been increasingly used in simulating urban expansion and land-use dynamics. However, most urban CA models rely on empirical data for deriving transition rules, assuming that the historical trend will continue into the future. Such inertia CA models do not take into account possible external interventions, particularly planning policies, and thus have rarely been used in urban and land-use planning. This paper proposes to use artificial immune systems (AIS) as a technique for incorporating external interventions and generating alternatives in urban simulation. Inspired by biological immune systems, the primary process of AIS is the evolution of a set of ‘antibodies’ that are capable of learning through interactions with a set of sample ‘antigens’. These ‘antibodies’ finally get ‘matured’ and can be used to identify/classify other ‘antigens’. An AIS-based CA model incorporates planning policies by altering the evolution mechanism of the ‘antibodies’. Such a model is capable of generating different scenarios of urban development under different land-use policies, with which the planners will be able to answer ‘what if’ questions and to evaluate different options. We applied an AIS-based CA model to the simulation of urban agglomeration development in the Pearl River Delta in southern China. Our experiments demonstrate that the proposed model can be very useful in exploring various planning scenarios of urban development.     

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。     

基于元胞自动机的城市发展密度模拟

元胞自动机CA越来越多地被用于模拟复杂的城市系统,但这些模拟基本不考虑城市的发展密度。不同的城市发展密度会对城市的形态有很大的影响,有必要将城市的发展密度引进CA的城市模拟中,以获得更好的模拟结果。本文将密度梯度函数引进了CA模型的转换规则中,并定义‘灰度’来反映状态的转换。利用该模型对不同可能的城市发展组合进行了模拟,为城市规划提供了辅助依据。     

Mining transition rules of cellular automata for simulating urban expansion by using the deep learning techniques

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.     

基于粗集的知识发现与地理模拟——以深圳市土地利用变化为例

为了更有效地模拟地理现象的复杂演变过程,提出了用粗集理论来确定元胞自动机 (CA)不确定性转换规则的新方法。CA可以通过局部规则来有效地模拟许多地理现象的演变过程。但目前缺乏很好定义CA转换规则的方法。往往采用启发式的方法来定义CA的转换规则,这些转换规则是静态的,而且其参数值多是确定的。在反映诸如城市扩张、疾病扩散等不确定性复杂现象时,具有一定的局限性。利用粗集从GIS和遥感数据中发现知识,自动寻找CA的不确定性转换规则,基于粗集的CA在缩短建模时间的同时,能提取非确定性的转换规则,更好地反映复杂系统的特点。采用所提出的方法模拟了深圳市的城市发展过程,取得了比传统MCE方法更好的模拟效果。     

Modeling dynamic urban land-use change with geographical cellular automata and generalized pattern search-optimized rules

A novel generalized pattern search (GPS)-based cellular automata (GPS-CA) model was developed to simulate urban land-use change in a GIS environment. The model is built on a fitness function that computes the difference between the observed results produced from remote-sensing images and the simulated results produced by a general CA model. GPS optimization incorporating genetic algorithms (GAs) searches for the minimum difference, i.e. the smallest accumulated residuals, in fitting the CA transition rules. The CA coefficients captured by the GPS method have clear physical meanings that are closely associated with the dynamic mechanisms of land-use change. The GPS-CA model was applied to simulate urban land-use change in Kunshan City in the Yangtze River Delta from 2000 to 2015. The results show that the GPS method had a smaller root mean squared error (0.2821) than a logistic regression (LR) method (0.5256) in fitting the CA transition rules. The GPS-CA model thus outperformed the LR-CA model, with an overall accuracy improvement of 4.7%. As a result, the GPS-CA model should be a superior tool for modeling land-use change as well as predicting future scenarios in response to different conditions to support the sustainable urban development.     

Simulating urban dynamics by coupling top-down and bottom-up strategies

ABSTRACT

Cellular automata (CA) are effective tools for simulating urban dynamics. Coupling top-down and bottom-up CA models are often used to address macro-scale demand and micro-scale allocation in the simulation of urban dynamics. However, those models typically ignore spatial differences in terms of the coupling process between macro-scale demand and micro-scale allocation. Herein, a novel approach for combining top-down and bottom-up strategies based on simulating urban dynamics is proposed. An optimizing strategy was used to predict the parameter of the inverse S-shaped function of future urban land use pattern and further deduce urban land increment within each concentric ring. The maximum probability transformation rule was incorporated into the CA model to address the micro-scale allocation. Wuhan was selected to test the performance of the proposed approach, and the conventional and the proposed approaches were compared. The results demonstrated that the proposed approach can not only retain the model’s accuracies but also better simulate the macro morphology of urban development dynamics and generate more realistic urban dynamic pattern in the urban sub-center and fringe regions. The proposed coupling approach can also be used to generate different development scenarios. The approach is expected to provide new perspectives for coupling top-down and bottom-up CA models in modeling urban expansion.     

基于遗传算法自动获取CA模型的参数

本文提出了基于遗传算法来寻找CA模型最佳参数的方法。CA被越来越多地应用于城市和土地利用等复杂系统的动态模拟。CA模型中变量的参数值对模拟结果有非常重要的影响。如何获取理想的参数值是模型的关键。传统的逻辑回归模型运算简单,常常用来获取模型的参数值,要求解释变量间线性无关,所以获取的城市CA模型参数具有一定的局限性。遗传算法在参数优化组合、快速搜索参数值方面有很大的优势。本文利用遗传算法来自动获取优化的CA模型参数值,并获得了纠正后的CA模型。将该模型应用于东莞1988~2004年的城市发展的模拟中,得到了较好的效果。研究结果表明,遗传算法可以有效地自动获取CA模型的参数,其模拟的结果要比传统的逻辑回归校正的CA模型模拟精度高。     

Simulating urban land-use changes at a large scale by integrating dynamic land parcel subdivision and vector-based cellular automata

Cellular automata (CA) have been widely used to simulate complex urban development processes. Previous studies indicated that vector-based cellular automata (VCA) could be applied to simulate urban land-use changes at a realistic land parcel level. Because of the complexity of VCA, these studies were conducted at small scales or did not adequately consider the highly fragmented processes of urban development. This study aims to build an effective framework called dynamic land parcel subdivision (DLPS)-VCA to accurately simulate urban land-use change processes at the land parcel level. We introduce this model in urban land-use change simulations to reasonably divide land parcels and introduce a random forest algorithm (RFA) model to explore the transition rules of urban land-use changes. Finally, we simulate the land-use changes in Shenzhen between 2009 and 2014 via the proposed DLPS-VCA model. Compared to the advanced Patch-CA and RFA-VCA models, the DLPS-VCA model achieves the highest simulation accuracy (Figure-of-Merit = 0.232), which is 32.57% and 18.97% higher respectively, and is most similar to the actual land-use scenario (similarity = 94.73%) at the pattern level. These results indicate that the DLPS-VCA model can both accurately split the land during urban land-use changes and significantly simulate urban expansion and urban land-use changes at a fine scale. Furthermore, the land-use change rules that are based on DPLS-VCA mining and the simulation results of several future urban development scenarios can act as guides for future urban planning policy formulation.     

2003-2013年中国城市形态研究评述

根据2003-2013年间在国内主要地理学、城市规划和建筑学期刊上发表的研究文献,对近10年间的城市形态相关研究的内容、方法和进展进行评述。文章在辨析城市“布局形态”、“结构形态”、“肌理形态”三大概念的基础上,将现有研究成果按城市形态影响要素分解、新技术方法应用、时间—空间维度探讨、可持续城市形态、城市形态规划、管理和控制,以及分城市、地区案例研究六大主线进行归纳,并评析现有成果的研究视角、技术方法、实践应用特点及其局限。比较中西方城市形态研究轨迹,国内研究习惯于通过实证主义的因果演绎和经验主义的案例归纳来理解现有城市的形态发展历程,并在借鉴历史经验和西方经验的基础上,寻求城市形态发展的“理想城市形态”。而西方研究则致力于寻求“可持续城市形态”的可操作性和实现方式,侧重于可持续发展的路径探索和规划应对。本文提出未来城市形态研究应更关注微观层面的肌理形态分析,应从更加温和、建设性的“时间—空间演化”视角探讨城市形态演进规律,为包容性、共生性的“可持续形态”演变发展构建理论基础。