人工免疫系统与嵌入规划目标的城市模拟及应用

人工免疫系统(AIS) 具有强有力的计算能力, 可以通过免疫识别、克隆选择、免疫学 习、免疫记忆等功能来进行模式识别和自适应学习。AIS 所具有的自学习、自适应和记忆的能力非常适合于复杂地理过程的研究。而元胞自动机(CA) 是研究复杂系统非常方便和有效的工具。将人工免疫系统和元胞自动机相结合, 建立了城市演变的模拟和规划模型。该模型通过改变抗体的进化变异机制, 把规划目标嵌入到AIS 算法中, 抗体将会逐渐朝着规划目标“进化”, 从而模拟出基于不同规划情景的城市发展空间格局, 为城市和土地利用规划提供决 策支持。设计了6 种不同的城市发展方案, 利用该模型模拟了不同规划方案下珠江三角洲城市的发展情景(1988-2002 年)。并比较了不同模拟情景结果城市的紧凑性: “城市中心” 和 “城市中心-高速公路”发展模式的城市形态更为紧凑, 破碎度较低; 而“镇中心” 和“道路”发展模式形成的城市形态则比较凌乱和分散。模拟结果和分析表明: “城市中心-高速 公路”是珠江三角洲最适合的城市发展模式。     

Zoning farmland protection under spatial constraints by integrating remote sensing,GIS and artificial immune systems

Currently, with rapid expanding of urban area, the rate of conversion of agricultural land to nonagricultural uses in China is increasing. Zoning farmland protection is an important measure to protect limited land resource. This article presented an innovative approach based on the integrated use of remote sensing, GIS, and artificial immune systems (AIS) for generating farmland protection areas. Some modifications have been made for conventional AIS so that it can be further extended to the solution of zoning problems. The optimal objective is to generate farmland protection areas that minimize development potential and maximize agricultural suitability and spatial compactness. First, utility function by addressing the criteria of farmland protection is incorporated into AIS algorithm. Second, encoding and mutation of antibodies is modified so that it can be suited to the solution of spatial optimization problems. The AIS-based zoning model was then applied to a case study in Guangzhou, Guangdong, China. The experiments have demonstrated that the proposed method was an efficient and effective spatial optimization technique, which took only about 194 seconds to generate satisfied farmland protection patterns. Furthermore, the AIS-based zoning model can explore various alternatives conveniently, and it can yield better performances than nonprotection scenario in the utility efficiency of land resources and the site condition for farmland.     

模拟退火算法用于产生城市土地空间布局方案

本文发展了一种模拟退火算法辅助产生城市土地空间布局方案。首先建立了城市土地空间配置问题的数学模型 ,目标函数是最小化土地开发费用、最大化不同功能地块间的空间协调性。由于问题存在的诸多空间约束条件使得可行的土地利用布局方案的搜寻过程变得十分缓慢 ,采用了将一些空间约束条件结合到目标函数中的方法 ,如结合了距离约束、方向约束、相同土地利用单元的邻近约束和地块形状的紧凑约束等约束条件。应用于湖南省长沙市暮云工业区用地规划的结果表明 ,模拟退火算法是一种辅助城市土地利用规划的有用的、有潜力的优化方法     

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.     

A travel time-based variable grid approach for an activity-based cellular automata model

Urban growth and population growth are used in numerous models to determine their potential impacts on both the natural and the socio-economic systems. Cellular automata (CA) land-use models became popular for urban growth modelling since they predict spatial interactions between different land uses in an explicit and straightforward manner. A common deficiency of land-use models is that they only deal with abstract categories, while in reality, several activities are often hosted at one location (e.g. population, employment, agricultural yield, nature…). Recently, a multiple activity-based variable grid CA model was proposed to represent several urban activities (population and economic activities) within single model cells. The distance-decay influence rules of the model included both short- and long-distance interactions, but all distances between cells were simply Euclidean distances. The geometry of the real transportation system, as well as its interrelations with the evolving activities, were therefore not taken into account. To improve this particular model, we make the influence rules functions of time travelled on the transportation system. Specifically, the new algorithm computes and stores all travel times needed for the variable grid CA. This approach provides fast run times, and it has a higher resolution and more easily modified parameters than the alternative approach of coupling the activity-based CA model to an external transportation model. This paper presents results from one Euclidean scenario and four different transport network scenarios to show the effects on land-use and activity change in an application to Belgium. The approach can add value to urban scenario analysis and the development of transport- and activity-related spatial indicators, and constitutes a general improvement of the activity-based CA model.     

Simulating urban growth under planning policies through parcel-based cellular automata (ParCA) model

In recent decades, the cellular automata model, among the urban development prediction models, has been applied considerably. Studies show that the output of conventional cellular automata models is sensitive to cell size and neighborhood structure, and varies with changes in the size of these parameters. To solve this problem, vector-based cellular automata models have been introduced which have overcome the mentioned limitations and presented better results. The aim of this study was to present a parcel-based cellular automata (ParCA) model for simulating urban growth under planning policies. In this model, undeveloped areas are first subdivided into smaller parcels, based on some geometric parameters; then, neighborhood effect of parcels is defined in a radial structure, based on a weighted function of distance, area, land-use, and service level of irregular cadastral parcels. After that, neighborhood effect is evaluated using three components, including compactness, dependency and compatibility. The presented model was implemented and analyzed using data from municipal region 22 of Tehran. The obtained results indicated the high ability of ParCA model in allocating various land-uses to parcels in the appropriateness of the layout of different land-uses. This model can be used in decision-making and urban land-use planning activities, since it provides the possibility of allocating different urban land-use types and assessing different urban-growth scenarios.     

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。     

城市交通与土地利用一体化模型的理论基础与发展趋势

城市交通与土地利用间的协调发展是科学调控城市发展的重要环节。在基于地租理论的微观区位决策与土地利用与交通系统宏观动态交互理论基础上,回顾了城市交通与土地利用一体化模型的发展过程及不同依据下的分类特征,对空间相互作用和重力模型理论模型、空间经济学理论模型、随机效用和离散选择模型和基于行为的微观模型4类一体化模型的优劣进行了系统梳理分析。分析表明,具有合理理论基础、科学数据处理方法、较高模型精度和政策评估适用性的一体化模型仍存在巨大发展空间。近年来西方学者研究趋向微观化、综合化和简约化,而国内一体化模型发展相对滞后,要求模型理论与综合实证开发的进一步突破。特别是在新数据、新交通技术、国土空间规划体系背景下,城市交通与土地利用一体化模型将从“空间”模型向“时空间”模型转变;从“宏观”集计模型向“微观”行为模型转变;从“交通-土地”模型向“综合系统”模型转变;从“样本数据”模型向“大数据”模型转变;从“交通规划”应用向“国土空间规划”应用转变。旨在对城市交通与土地利用一体化模型的一般结构、原理类型、当前面临的问题和挑战进行深入剖析,以推进一体化模型在中国的研究与开发。     

Modeling urban expansion scenarios by coupling cellular automata model and system dynamic model in Beijing, China

Spatially explicit urban expansion models that can trace urban development in the past and predict the expansion scenarios in the future are indispensable for examining urban planning policies. This paper demonstrates a new urban expansion scenario (UES) model by coupling one “bottom-up” cellular automata (CA)-based model and one “top-down” system dynamics (SD)-based model. By implementing the UES model in Beijing, the urban evolution from 1991 to 2004 was simulated and the UESs from 2004 to 2020 were predicted. The results suggest that a dilemma of urban expansion versus limited water resource and environment deterioration exists. Dealing with such a dilemma remains a challenge for the local government.     

Modeling urban land-use dynamics in a fast developing city using the modified logistic cellular automaton with a patch-based simulation strategy

Cellular automata (CA) have been used to understand the complexity and dynamics of cities. The logistic cellular automaton (Logistic-CA) is a popular urban CA model for simulating urban growth based on logistic regression. However, this model usually employs a cell-based simulation strategy without considering the spatial evolution of land-use patches. This drawback largely constrains the Logistic-CA for simulating realistic urban development. We proposed a Patch-Logistic-CA to deal with this problem by incorporating a patch-based simulation strategy into the conventional cell-based Logistic-CA. The Patch-Logistic-CA differentiates new developments into spontaneous growth and organic growth, and uses a moving-window approach to simulate the evolution of urban patches. The Patch-Logistic-CA is tested through the simulation of urban growth in Guangzhou, China, during 2005–2012. The cell-based Logistic-CA was also implemented using the same set of data to make a comparison. The simulation results reflect that the Patch-Logistic-CA has slightly lower cell-level agreement than the cell-based Logistic-CA. However, visual inspection of the results reveals that the cell-based Logistic-CA fails to reflect the actual patterns of urban growth, because this model can only simulate urbanized cells around the edges of initial urban patches. Actually, the pattern-level similarities of the Patch-Logistic-CA are over 18% higher than those of the cell-based Logistic-CA. This indicates that the Patch-Logistic-CA has much better performance of simulating actual development patterns than the cell-based Logistic-CA. In addition, the Patch-Logistic-CA can correctly simulate the fractal structure of actual urban development patterns. By varying the control parameters, the Patch-Logistic-CA can also be used to assist urban planning through the exploration of different development alternatives.     

城市规划CA模型及其应用

基于CA模型,从城市规划角度,通过引入控制性详细规划层,将整个城市空间划分为不同组团片区,构建了自下而上和自上而下相结合的城市规划CA模型(UPCA),并以长沙市为例进行实证研究。结果表明:该模型简化了传统CA城市模拟模型的数据量,提高了模型的模拟精度,并增强了模型的规划决策功能,使模型具备一定的社会经济分析能力。     

Experiences and issues of using cellular automata for assisting urban and regional planning in China

Since the late 1990s, there are growing studies on the development of cellular automata (CA) as a simulation tool for assisting urban and regional planning in China. Rapid urban development is the main reason that this country has become one of the best places to test the methodology of CA and analyze the effectiveness of using these models. This paper attempts to summarize the experiences and issues of using CA to solve various environmental and planning problems in China. The analysis is based on the literature review using the search engines of ISI Web of Science and Google Scholar. These experiences could be important for those who want to apply CA in developing countries. For example, which environmental and ecological problems can be solved by using this bottom-up approach? What are the data inputs to these models and how can they be calibrated? Our analyses indicate that CA have the great potential to support land-use planning and policy analysis for fast-growing regions. Some specific features of using CA in China are also identified in the literature review, including delineation of urban growth boundary, prevention of illegal development and formulating zoning schemes. The CA studies in this fast-growing country provided valuable experiences for other developing countries to solve a series of simulation and planning problems by using this bottom-up approach.     

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.     

面向高质量发展的中国城市体检方法体系探讨

改革开放40 a来,中国的城市规划、建设和管理水平发生了质的改变,但快速城镇化所引发的城市问题日益突出。城市体检概念应运而生,并逐渐从国家战略落实到城市高质量发展的具体实践中。在梳理和解析城市体检背景及其作用的基础上,详细阐述城市体检指标设计的逻辑框架,梳理城市体检各指标间的关系,辨析不同尺度间城市体检的异同,提出中国城市体检的方法,旨在对中国城市体检提供理论依据。     

The influencing factors of urban land expansion in Changsha

This research systematically analyses land-use map of Changsha city in different periods of time. The spatial form and structural evolution was analysed by studying indices such as city land-use structure proportion, expansion intensity, economic flexibility, population flexibility, changing compactness index and so on. The dynamic mechanism of urban land expansion has been discussed by integrating the regional social economy development situation and many aspects such as the physiographical surrounding, population and eco-nomic development, traffic infrastructure, planning and regional development tactic and system innovation. The research indicates that the urban land expansion speed and intensity have steadily increased in Changsha from 1949 to 2004. The expansion form has been from a single external expansion to a combination form of external and internal expansion, from a circular or linear continuous form to a blocky or agglomeration shape. Overall, the urban land expansion of Changsha city is a phasic, diversified and complex process. And no matter what the stage is, it is an organic system containing multiple speed, pattern and shape, which are driven by multiple impetuses. The dominant feature at different stages was highlighted be-cause of the balance and fluctuation between different forces, and the existing urban land border and shape have resulted from the joint efforts of these phasic forces.     

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.     

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.     

长沙城市土地扩张特征及影响因素

This research systematically analyses land-use map of Changsha city in different periods of time. The spatial form and structural evolution was analysed by studying indices such as city land-use structure proportion, expansion intensity, economic flexibility, population flexibility, changing compactness index and so on. The dynamic mechanism of urban land expansion has been discussed by integrating the regional social economy development situation and many aspects such as the physiographical surrounding, population and economic development, traffic infrastructure, planning and regional development tactic and system innovation. The research indicates that the urban land expansion speed and intensity have steadily increased in Changsha from 1949 to 2004. The expansion form has been from a single external expansion to a combination form of external and internal expansion, from a circular or linear continuous form to a blocky or agglomeration shape. Overall, the urban land expansion of Changsha city is a phasic, diversified and complex process. And no matter what the stage is, it is an organic system containing multiple speed, pattern and shape, which are driven by multiple impetuses. The dominant feature at different stages was highlighted be- cause of the balance and fluctuation between different forces, and the existing urban land border and shape have resulted from the joint efforts of these phasic forces.     

多层次矢量元胞自动机建模及土地利用变化模拟

城市规划对土地利用变化起着重要的引导作用,各层次规划相互支撑、互为补充,自上而下影响着土地利用格局的演变。矢量元胞自动机以不规则的地理实体作为基本单元,可以更逼真地表达客观复杂的城市用地空间结构。然而,当面向具有层次协同性、空间引导性和管控传导性等特征的城市规划时,其元胞多层次体系构造、层次间协同方法和转换规则获取等关键问题凸显出来。本文以江阴市2007年、2012年、2017年3期土地利用现状数据为基础,在多层次矢量元胞自动机建模基础上,模拟了2017年土地利用变化,通过模拟结果与用地现状对比分析,对模型个别参数进行了修正,进一步提高了模型的可行性与适用性,进而预测了2022年城市土地利用格局。模拟结果显示,中心城片区建设用地发展已经趋于饱和,澄南、澄东南和澄东片区建设用地扩张较为明显,有逐步形成“中心城区—城镇组团—村庄”三级城乡空间聚落体系的趋势。最后利用FoM指标对模拟结果进行了评价,得到整体和各片区的精度基本都大于或接近于0.21,表明模拟结果精度较高,其构建的模型在面向多层次规划的用地变化模拟方面具有更好的效果。     

基于地理模拟方法的昆明市空间拓展情景分析

改革开放以来,西部高原湖滨城市经历的快速城镇化进程给湖泊流域带来了较为严重的生态环境问题,未来城市空间发展政策的调整需要关注城市空间拓展对区域生态环境的影响。以位于滇池湖滨地区的昆明市为例,设定6种不用的城市空间拓展政策情景,应用SLEUTH模型预测了6种情景下未来20年的城市空间格局,采用空间指数和空间分析方法对预测结果进行了分析评价。结果表明：昆明市城市建成区具有典型的摊大饼式空间拓展模式,城市道路网对城市形态具有重要的影响。6种情景模式下未来昆明市建成区空间格局既有相似性,也表现出显著的差异。城市建设用地空间格局总体上呈集约、紧凑型的发展趋势。生态保护与城市发展管制相结合的政策情景对滇池湖滨地区的景观影响最小。多中心城市发展格局和城市发展管制相结合的政策情景对城市总体空间规模的控制具有明显的效果,但不宜在湖滨地区实施。滇池湖滨地区需要划定景观或生态保护区,严格禁止城市建设用地对湖滨用地景观的占用与分割。滇池湖滨以外的区域,适宜执行生态保护与城市发展管制相结合的多中心发展模式。