Urban Modeling, Spatial Simulation and Fine-scale Projection

This output forms part of the Beijing City Lab research direction on Urban Modeling, Spatial Simulation and Fine-scale Projection. Download the publication below.

城市模型是城市规划学科定量研究的重要方法与工具，经过半个多世纪的发展，城市模型可以为城市政策的执行及城市规划方案的制定和评估提供可行的技术支持。在经历30 年快速城市化发展的背景下，我国城市正面临一系列变革与挑战，城市模型如何在其中支持与适应是亟待解决的问题。因此，文章首先介绍城市模型的背景， 包括发展过程与基本分类；其次对城市模型发展现状进行梳理，介绍主流经典城市模型与城市模型研究相关的学术会议；最后，结合我国城市的主要变革趋势，重点对城市模型的未来发展提出五点展望，分别是：①加快研究颠覆性技术对城市的影响并纳入城市模型中；②构建面向收缩城市的城市模型；③加强人本尺度的城市模型构建；④加强数据驱动型城市模型的开发；⑤客观认识城市定义，更科学地构建城市模型。在现有研究不足的基础上提出模型研究与构建的具体策略，以期为规划工作者使用城市模型解决城市问题以及城市模型研究人员更好地构建城市模型提供可靠与详细的参考。

自 2009 年北京空间发展模型第一阶段顺利完成以来， 北京市范围内的地块尺度的空间数据（地块和建筑）和精细化的社会经济数据（居民和企业等）均有了大量的积累；同时，在技术方法层面也有了一定程度的发展，具备了进行微观尺度模型研究的条件。因此本文以北京市空间发展的大量数据为依托，直接面向北京城乡规划实践，对空间发展模型的理论和技术架构进行了深入研究，建立了一套多尺度、多维度的城乡空间发展模型：BUDEM（Beijing Urban Development Model） 。该模型由土地开发模块（宏观与微观） 、人口空间化与属性合成模块、 居住区位选择模块、企业区位选择模块以及基于活动的交通出行模块构成，可以对城市扩张与再开发进行模拟，并进行相应空间政策的评估。

This output forms part of the Beijing City Lab research direction on Urban Modeling, Spatial Simulation and Fine-scale Projection. Download the publication below.

准确把握城镇扩张过程中空间形态的变化规律对城镇规划水平的提高具有重要的现实意义 。当前，京津冀的协同发展已上升为国家战略 ，为了对京津冀远景的城镇增长进行预测 ，本文将北京城市空间发展分析模型 (BUDEM)扩展到京津冀(JJJ)区域，构建了京津冀城镇空间发展分析模型(BUDEM-JJJ)。该模型以城市增长理论和约束性元胞自动机 (Constrained Cellular Automata, Constrained CA) 模型为基础 ，以京津冀区域为研究对象 ，以 Lo- gistic 回归和单一参数循环方法 (Monoloop)为模型参数识别方法 ，利用 2000-2005 年和 2005-2010 年两个历史阶段的城镇建设用地数据对模型进行了参数识别 ，并进行了模型验证。在此基础上，对京津冀区域 2049 年的城镇形态进行了多种情景模拟，分析了不同发展情景(Scenario)下京津冀地区城镇空间发展格局的变化 ，可为当前京津冀城镇发展方向的调整和下一轮的城镇规划提供参考。

1 大数据时代的城市模型研究及其机遇龙瀛，北京市城市规划设计研究院，北京 100045 （《时空行为研究前沿》章节，修改稿，应编者北京大学柴彦威教授约稿提交） 1、城市模型发展历程纵观城市科学的发展历史，从对城市现象的记载、描述，到对其进行归纳、总结，再到对城市事物之间的关系描述，最后发展到用系统的观点看待城市，其发展历程经历了一个从定性到定量的过程。现阶段，定量化程度已经越来越成为衡量该学科发展程度的标志。城市模型”（Urban Model）是在对城市系统进行抽象和概化的基础上，对城市空间现象与过程的抽象数学表达，是理解城市空间现象变化、对城市系统进行科学管理和规划的重要工具，可以为城市政策的执行及城市规划方案的制定和评估提供可行的技术支持。 城市模型研究始于20世纪初期，20世纪初到50年代中期是城市模型发展的初级阶段，主要经历了一般概念模型、数学（或分析）模型和计算机模拟模型等几个阶段（图1）。从1990年代开始，随着计算机硬件技术和GIS技术的日益成熟，GIS在城市模型研究中的应用及其与城市模型的集成已经成为侧重于计算机模拟的城市模型发展的重要趋势。根据城市模型是否具有时间维，可以把城市模型分为静态模型和动态模型两大类。静态城市模型不考虑时间维，主要分为两类，一是城市统计模型，二是空间相互作用模型（如劳瑞模型） 。从建模方法看， 应该分为自上而下和自下而上两种建模方法， 前者主要是以系统动力学 （System Dynamics）为代表的城市模型，一般是基于微分方程的形式，往往从宏观的空间尺度出发，研究对象也往往是对城市居住区、商业区等的机械划分及其相互作用，或区位选择，无法反映造成城市动态性、自组织性和突变性等城市微观结构和理性人的个体行为；后者是以离散动力学 （Discrete Dynamics）如元胞自动机 （Cellular Automata，CA）、多主体系统 （Multi-agent Systems）、分形理论（Fractal）和自组织理论（Self-organization）等为代表的城市模型。目前MAS为代表的动态城市模型是研究的重点和近期的趋势。 2 图1 城市模型发展历程一览1 2、城市模型的基本分类动态城市模型从不同角度可以有不同的分类。 从建模的方法看，常用的方法有基于空间相互作用理论（Spatial Interaction）的重力模型2（Gravity Model）、最大熵理论模型（Entropy Maximizing），来自经济学的Alonso/Mills/Muth地租理论 （Rent Models）、离散选择模型（Discrete Choice Model）、空间投入产出模型（Spatial Input-output Model）、 回归分析 （Regression）， 来自复杂科学的元胞自动机 （CA）、 基于个体建模（Agent-based Modelling，ABM）3，以及微观模拟（Microsimulation Model，MSM）和地理信息系统（GIS）等技术(Pagliara和Wilson, 2010)。 从模型应用的具体领域看，有区域模型、城市土地模型、土地使用与交通模型、土地使用－交通－环境模型等(郑思齐等, 2010)。

Big Models: From Beijing to the whole China 1 Ying Long, Beijing Institute of City Planning 2 Kang Wu, Capital University of Economics and Business, China 3 Jianghao Wang, Chinese Academy of Sciences, China 4 Zhenjiang Shen, Kanazawa University, Japan Abstract: This paper propose the concept of big model as a novel research paradigm for regional and urban studies. Big models are fine -scale regional/urban simulation models for a large geographical area, and they overcome the trade-off between simulated scale and spatial unit by tackling both of th em at the same time enabled by emerging big/open data , increasing computation power and matured regional/urban modeling methods. The concept, characteristics, and potential applications of big models have been elaborated. We addresse several case studies to illustrate the progress of research and utilization on big models , including mapping urban areas for all Chinese cities, performing parcel-level urban simulation, and several ongoing research projects. Most of these applications can be adopted across the country, and all of them are focusing on a fine -scale level, such as a parcel, a block, or a township (sub-district), which is not the same with the existing studies using conventional models that are only suitable for a certain single or two cities o r regions, or for a larger area but have to significantly sacrifice the data resolution.

】基于对城市模型领域重要学者麦克·巴蒂的访谈，对城市模型的发展历程、现状与前景进行了回顾、评述与展望。介绍城市模型的发展脉络，分析20世纪第一次发展高潮中城市模型在规划应用中的实际困难与受到批判的原因。梳理了当前第二次城市模型发展高潮期的模型门类， 并指出精细化城市模型是当前研究热点，模型建模方法将更加侧重“自下而上”与“自上而下” 的结合、 “问题导向型”以及应对城市日益增长的复杂性与不确定性。同时大数据时代的来临也为城市模型的发展带来了重要契机，但相关研究需要吸取第一轮研究热潮中的经验教训，促进城市模型与规划实践的结合。最后介绍了麦克·巴蒂的城市“新科学”思想、城市定量研究教育的起步以及新近成立的以定量城市研究为着力点的北京城市实验室。 【

ÌÆÌÆ Ò“Ò“ £”£” 以地块作为基本空间单元并以城市活动主体作为模拟对象的精细化模拟是未来城市模型研究的重要方向 ， 大数据 （ big data ） 时代的到来也为其提供了重要发展机遇 。 本文重点对精细化城市模型的主流建模方法进行了介绍 ， 包括元胞自动机 （ Cellular Automata ， CA ） 、 基于主体建模 （ Agent-based Model - ling ， ABM ） 和传统的微观模拟 （ Microsimulation ， MSM ） 这三种自下而上的微观模拟方法 。 之后结合精细化城市模型的高标准数据需求问题 ， 对国际上通行的用于精细化模拟数据合成 （ population synthesis ） 的方法进行了综述 ， 并给出笔者近年来在精细化城市模拟方面的多项实践案例 ， 最后提出了以 GIS 为平台 ， 结合 CA/ABM/MSM 方法 ， 构建我国精细化城市模型的框架体系和关键技术 ， 以期支持我国大城市地区空间政策的制定和评估 。 „Ø…ü·˚„Ø…ü·˚ £”£” 城市模型 ； 大数据 ； 精细化 ； 规划支持系统 （ PSS ） ； 北京 Ö—˝…•ÖÀà”¯Ö—˝…•ÖÀà”¯ £”£” TU 984 ˛˜ˇ×–Œ˚¶´º˛˜ˇ×–Œ˚¶´º £”£” A ·ó˚ý¾Ý˚–·œµ˜¾« ˇ‚»fl‡˙˚—˜£˜â £”•‰•¤ ¡¢˚ý¾Ý”˝°‚Àý `œ å› 1 , 2 £‹ˆ'ˆ÷î£ 1 £‹ˆ«˘äÖ˙ 3 £‹ÉòÕ扛 4 £‹Õ¯ÓÀ˘‰ 5 , 6 £¤ 1 . ––¾'˚—‡˙˚—„æ»fiÉŁ…˘Ñ—¾¿Ô” £‹ ––¾' 100045 £» 2 . ‰£˙¯·óѧ ‰¤Öþˇµ £‹ ‰£˙¯ Ó¢„œ £» 3 . ˙廓·óѧ ‰¤ÖþѧԔ £‹ ––¾' 100084 £» 4 . ‰ðÔó·óѧ »•¾‡ÉŁ…˘Ñ§Ô” £‹ ‰ðÔó¨Õ–¾ £» 5 . ¿¤µˇ•ò·óѧ £‹ ¿¤µˇ•ò Ó¢„œ £» 6 . ˜Úˆ•”à·óѧ £‹ ˜Úˆ•”à ”ÉÀ… £' FINE-SCALE URBAN MODELING AND ITS OPPORTUNITIES IN THE "BIG DA TA" ERA: METHODS, DA TA AND EMPIRICAL STUDIES LONG Ying 1 , 2 , MAO Ming-rui 1 , MAO Qi-zhi 3 , SHEN Zhen-jiang 4 , ZHANG Y ong-ping 5 , 6 ( 1 . B e i j i n g I n s t i t u t e o f C i t y p l a n n i n g , B e i j i n g 1 0 0 0 4 5 , C h i n a ; 2 . D e p a r t m e n t o f A r c h i t e c t u r e , U n i v e r s i t y o f C a m b r i d g e , C a m b r i d g e , U K ; 3 . S c h o o l o f A r c h i t e c t u r e , T s i n g h u a U n i v e r s i t y , B e i i n g 1 0 0 0 8 4 , C h i n a ; 4 .

This article proposes the concept of big model as a novel research para - digm for regional and urban studies. Big models are fine-scale regional/urban sim - ulation models for a large geographical area. With the widespread use of big/open data, the increased computation capacity, as well as the advanced regional and ur - ban modeling methodologies, big models make it possible to overcome the trade-off between geographical scale and simulation resolution. In this paper, the concept, characteristics, and potential applications of big models have been elabo - rated. We also presented several case studies to illustrate the progress of our re - search and the application of big models. Most of these applications can be adopt - ed across the country, and all of them are focusing on a fine-scale level, such as a parcel, a block, or a township (sub-district). It is expected that big models will mark a promising new era for the urban and regional study in the age of big da - ta.

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Monol_00p，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。

Landscape and Urban Planning 108 (2012) 103– 111 Contents lists available at SciVerse ScienceDirect Landscape and Urban Planning jou rn al h om epa ge: www.elsevier.com/locate/landurbplan Research paper Spatiotemporal heterogeneity of urban planning implementation effectiveness: Evidence from five urban master plans of Beijing Ying Long a, Yizhen Gu b, Haoying Han c,∗ a Beijing Institute of City Planning, Beijing 100045, PR China b Department of City and Regional Planning, University of California, Berkeley 94720, USA c Department of Land Management, College of Public Administration, Zhejiang University, 268 Kaixuan Road, Hangzhou 310029, PR China h i g h l i g h t s ▶ We identified spatiotemporally heterogeneities of UPI effectiveness. ▶ We made our analysis using concrete data of five urban master plans of Beijing. ▶ The effectiveness of UPI was visualized by an urban expansion simulation model. a r t i c l e i n f o Article history: Received 18 August 2011 Received in revised form 19 July 2012 Accepted 20 August 2012 Available online 14 September 2012 Keywords: Urban expansion Urban planning implementation Conformity Spatiotemporal heterogeneity Beijing a b s t r a c t Evaluation of urban planning implementation (UPI) has attracted extensive attention from urban plan- ners and researchers in recent years. Existing literature, however, mainly focuses on the conformity approach and does not take the spatial and temporal heterogeneity of UPI into account.

In recent years, simulating urban growth using cellular automata (CA) has attracted extensive attenti on for investigating the dynamic urban system. Distinguished from the pure CA, the CA model applied for urban growth simulation should not only in clude the neighbourhood influence, but other factors related with urban devel opments. We proposed the term ‘complex constrained CA’ (CC-CA), which inte grates constraints of neighbourhood, macro socioeconomic, spatial and institutional types. Particularly, the constructing constrained zoning planning, as an institutional constraint, is introduced into the CC-CA model. We applied the CC-CA model for the Beijing Metropolitan Area. Parameters of constraints are calibrated for various historical phases of Beijing, with which the baseline scenario with current developing trend is build to simulate the long-term urban form of Beijing. In addition, we set the planning controlled scenario via adjusting institutional constraints’ parameters, and compare the results with the baseline scenario. The impact of institutional constraints on futu re urban form is then visualised for policy makers. The CC-CA is proved to have its policy implications for Beijing.

规划实施评价是近年来中国城市规划领域的研究热点 。 已有研究主要侧重于以整个规划范围为研究对象 ， 在一定时间范围内对比规划实施结果和发展现状的一致性 ， 属于时空静态的针对规划实施结果的评价方法 。 但城市规划的实施效果在时间维度和空间维度都具有异质性 ， 而且城市扩展同时受到市场力量和政府因素的影响 ， 城市规划作为一种政府主导的制度性因素 ， 并不是制约城市扩展的唯一因素 。 本文提出了基于 Logistic 回归和 GIS 的时空动态的规划实施评价方法 ， 其在考虑实施结果的同时更侧重对规划实施过程的评估 ， 考虑了城市扩展的框架下城市规划对空间控制的实施效果 ， 并以北京市域 1958 、 1973 、 1982 、 1992 和 2004 年编制的五版城市总体规划为例进行实证分析 。 根据规划执行的时间跨度 ， 将 1947 年至 2008 年分为 5 个时间阶段 ， 分析各个阶段城市扩展的驱动因素 ， 识别各版城市规划在不同阶段所起到的城市扩展促进作用 。 并以 2004 - 2008 年为例 ， 以各个区 ( 县 ) 为子区域 ， 探讨 2004 版总规在中心城市 、 近郊区和远郊区实施效果的空间变化 ， 识别总规在城市扩展中的空间异质性 ， 并给出相应的政策含义 。 结果显示 ， 随着时间的推移 ， 建国后北京市城市规划的控制作用呈现出逐渐增强的趋势 。 远郊区县的规划作用效果明显高于近郊和中心地区 。

约束性元胞自动机（Cellular automata，CA）在模拟城市增长方面已有诸多实践，一方面，城市规划空间形态可以作为约束性 CA 的制度性约束条件，另一方面，也可以利用约束性 CA 模拟不同的城市发展情景，以辅助城市规划空间形态的制定。然而如何利用约束性 CA 模拟城市规划空间形态并没有文献探讨。 在城市规划领域，需要哪些发展政策支持来实现城市规划空间形态，规划期限内不同阶段的城市空间的可能形态是什么，是广为关注的重要问题。本文提出了基于约束性 CA 城市模型模拟城市规划空间形态的方法， 并可以给出相应需要的发展政策，并基于此方法开发了一个约束性 CA 模型，并以北京市域城市规划空间形态为例进行了模型应用，证明该方法具有较好的适用性，是将约束性 CA 的应用扩展到城市规划领域的又一尝试。

In recent years, it is prevailing to simulate urban growth by means of cellular automata (CA in short) modeling, which is based on self- organizing theories and different from the system dynamic modeling. Since the urban system is definitely complex, the CA models applied in ur - ban growth simulation should take into consideration not only the neighborhood influence, but also other factors influencing urban development. We bring forward the term of complex constrained CA (CC-CA in short) model, which integrates the constrained conditions of neighborhood, macro socio-economy, space and institution. Particularly, the constrained construction zoning, as one institutional constraint, is considered in the CC-CA modeling. In the paper, the conceptual CC-CA model is introduced together with the transition rules. Based on the CC-CA model for Beijing, we discuss the complex constraints to the urban development of , and we show how to set institutional constraints in planning scenario to control the urban growth pattern of Beijing.

This output forms part of the Beijing City Lab research direction on Urban Modeling, Spatial Simulation and Fine-scale Projection. Download the publication below.

Along with the prosperity of Beijing macro-economy, especially as a result of the Olympics, the future form of the Beijing metropolitan ar ea needs to be analyzed in the post-Olympic games period in 2009, at the end of the urban master planning in 2020, and for the 100th anniversary of the founding of the P. R. China’s capital in 2049. Furthermore, long-term forecast of the urban form is essential for the next round of urban master planning. Comprehensive urban models have been de- veloped to simulate urban systems for some major metropolitan areas, such as the London metropolitan area, the San Francisco Bay area, and the California area. However, there is no urban model for Beijing or most other cities in China. Urban models, which were developed in the early years of the twentieth century, have progressed from structural models to static models to dynamic models. Traditional urban models, based on differential equa- tions or quasi dynamic equations, usually simulate the urban system at a macro level, so they cannot accu- rately reflect the dynamic, self-organizing, or emerging characteristics of urban sys tems. The development of GIS and other complex adaptive models has led to ur- ban models based on artificial life or discrete dynamics. In recent years, urban growth models have used the cellular automata (CA) appro ach which is based on self-organizing theory.

It is in great need of identifying the future urban form of Beijing, which faces challenges of rapid growth in urban development projects implemented in Beijing. We develop Beijing Urban Developing Model (BUDEM in short) to support urban planning and corresponding policies evaluation. BUDEM is the spatio-temporal dynamic model for simulating urban growth in Beijing metropolitan area, using cellular automata (CA) and Multi-agent system (MAS) approaches. In this phase, the computer simulation using CA in Beijing metropolitan area is conducted, which attempts to provide a premise of urban activities including different kinds of urban development projects for industrial plants, shopping facilities, houses. In the paper, concept model of BUDEM is introduced, which is established basing on prevalent urban growth theories. The method integrating logistic regression and MonoLoop is used to retrieve weights in the transition rule by MCE. After model sensibility analysis, we apply BUDEM into three aspects of urban planning practices: (1) Identifying urban growth mechanism in various historical phases since 1986; (2) Identifying urban growth policies needed to implement desired urban form (BEIJING2020), namely planned urban form; (3) Simulating urban growth scenarios of 2049 (BEIJING2049) basing on the urban form and parameter set of BEIJING2020.

With the development of GIS and complex adaptive system , the urban model, based on artificial li fe or discrete dynamics, is dominating relevant academic researches. In recent years, it is prevailing to simulate urban growth by means of cellular automata (CA in short) , which is based on self-organizing theory and different from the system dynamic type models. Distinguished from pure CA, the CA mo del applied for urban growth simulation should not only consider neighborhood influence, but other factors with respect to urban developing, since urban system is definitely quite complex. Generally, the status transition rule except neighborhood is define d as the term of constraint of constrained condition (White, 1997). Some researchers are inclined to control the urban growth simulation via introducing constrained conditions to CA model. In SLEUTH model, exclusive layer is set to constrain urban growth ( Clark and Gaydos, 1998); White et al (1997) initiated the concept of constrained CA, in which suitability (spatial constraint) is considered; Engelen et al (1997) takes macro socio -economic constraint into account in the urban planning support system integrating CA, GIS and other decision support toolkit; In Simland model using CA by Wu (1998), variable con is set to signify the constraints for land development;

This output forms part of the Beijing City Lab research direction on Urban Modeling, Spatial Simulation and Fine-scale Projection. Download the publication below.

In recent years，it is prevailing to simulate urban growth by means of cellular automata(CA for short)，which is based on self-organizing theory and dif- ferent from the system dynamic type models．Distinguished from pure CA，the CA model applied for urban growth simulation should not only consider neighborhood influence，but also other factors with respect to urban developing．We bring forward the term of comprehensive constrained CA(cc—CA for short)，which integrates the constrained conditions of neighborhood，macro socio-economic，spatial and institu· tional．Particularly，constructing constrained zoning planning，as one institutional constrained condition，is considered in CC—CA model．In this paper,the conceptual model of comprehensive constrained CA model is introduced together with the form of transition rule．The CC-CA model for Beijing is developed，and we discuss the comprehensive constrained conditions in Beijing．Furthermore，we show how to set institutional constraints in the scenario of planning control to control the urban growth pattern in Beijing．