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.     

Simulating urban growth by integrating landscape expansion index (LEI) and cellular automata

Traditional urban cellular automata (CA) model can effectively simulate infilling and edge-expansion growth patterns. However, most of these models are incapable of simulating the outlying growth. This paper proposed a novel model called LEI-CA which incorporates landscape expansion index (LEI) with CA to simulate urban growth. Urban growth type is identified by calculating the LEI index of each cell. Case-based reasoning technique is used to discover different transition rules for the adjacent growth type and the outlying growth type, respectively. We applied the LEI-CA model to the simulation of urban growth in Dongguan in southern China. The comparison between logistic-based CA and LEI-CA indicates that the latter can yield a better performance. The LEI-CA model can improve urban simulation accuracy over logistic-based CA by 13.8%, 10.8% and 6.9% in 1993, 1999 and 2005, respectively. Moreover, the outlying growth type hardly exists in the simulation by logistic-based CA, while the proposed LEI-CA model performs well in simulating different urban growth patterns. Our experiments illustrate that the LEI-CA model not only overcomes the deficiencies of traditional CA but might also better understand urban evolution process.     

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.     

元胞自动机在城市模拟中的误差传递与不确定性的特征分析

元胞自动机(Cellular Automata,简称CA)已越来越多地用于地理现象的模拟中,如城市系统的演化等。城市模拟经常要使用GIS数据库中的空间信息,数据源中的误差将会通过CA模拟过程发生传递。此外,CA 模型只是对现实世界的近似模拟,这就使得其本身也具有不确定性。这些不确定因素将对城市模拟的结果产生较大的影响,有必要探讨CA在模拟过程中的误差传递与不确定性问题。本文采用蒙特卡罗方法模拟了CA误差的传递特征,并从转换规则、邻域结构、模拟时间以及随机变量等几个方面分析了CA不确定性产生的根源。发现与传统的GIS模型相比,城市CA模型中的误差和不确定性的很多性质是非常独特的。例如,在模拟过程中由于邻域函数平均化的影响,数据源误差将减小;随着可用的土地越来越少,该限制也使城市模拟的误差随时间而减小;模拟结果的不确定性主要体现在城市的边缘。这些分析结果有助于城市建模和规划者更好地理解CA建模的特点。     

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.     

Simulating urban growth in a metropolitan area based on weighted urban flows by using web search engine

As a consequence of rapid and immoderate urbanization, simulating urban growth in metropolitan areas effectively becomes a crucial and yet difficult task. Cellular automata (CA) model is an attractive tool for understanding complex geographical phenomena. Although intercity urban flows, the key factors in metropolitan development, have already been taken into consideration in CA models, there is still room for improvement because the influences of urban flows may not necessarily follow the distance decay relationship and may change over time. A feasible solution is to define the weights of intercity urban flows. Therefore, this study presents a novel method based on weighted urban flows (CA_WeightedFlow) with the support of web search engine. The relatedness measured by the co-occurrences of the cities’ names (toponyms) on massive web pages can be deemed as the weights of intercity urban flows. After applying the weights, the gravitational field model is integrated with Logistic-CA to fulfill the modeling task. This method is employed to the urban growth simulation in the Pearl River Delta, one of the most urbanized metropolitan areas in China, from 2005 to 2008. The results indicate that our method outperforms traditional methods with respect to two measures of calibration goodness-of-fit. For example, CA_WeightedFlow can yield the best value of ‘figure of merit’. Moreover, the proposed method can be further used to explore various development possibilities by simply changing the weights.     

基于动态约束的元胞自动机与复杂城市系统的模拟

为获得复杂城市系统更理想的模拟效果,提出时空动态约束的城市元胞自动机(CA)模型。用不同区域、不同时间新增加的城市用地总量作为CA模型的约束条件,形成时空动态约束的CA模型,并利用该模型模拟1988—2010年东莞市和深圳市城市扩张过程。结果表明,利用CA模型模拟的1993年城市用地总精度比静态CA模型提高了5.86%,而且模型中的动态约束条件可以反映城市发展的时空差异性。     

Simulating urban land use change by integrating a convolutional neural network with vector-based cellular automata

ABSTRACT

Vector-based cellular automata (VCA) models have been applied in land use change simulations at fine scales. However, the neighborhood effects of the driving factors are rarely considered in the exploration of the transition suitability of cells, leading to lower simulation accuracy. This study proposes a convolutional neural network (CNN)-VCA model that adopts the CNN to extract the high-level features of the driving factors within a neighborhood of an irregularly shaped cell and discover the relationships between multiple land use changes and driving factors at the neighborhood level. The proposed model was applied to simulate urban land use changes in Shenzhen, China. Compared with several VCA models using other machine learning methods, the proposed CNN-VCA model obtained the highest simulation accuracy (figure-of-merit = 0.361). The results indicated that the CNN-VCA model can effectively uncover the neighborhood effects of multiple driving factors on the developmental potential of land parcels and obtain more details on the morphological characteristics of land parcels. Moreover, the land use patterns of 2020 and 2025 under an ecological control strategy were simulated to provide decision support for urban planning.     

How much past to see the future: a computational study in calibrating urban cellular automata

The objective of this computational study was to investigate to which extent the availability and the way of use of historical maps may affect the quality of the calibration process of cellular automata (CA) urban models. The numerical experiments are based on a constrained CA applied to a case study. Since the model depends on a large number of parameters, we optimize the CA using cooperative coevolutionary particle swarms, which is an approach known for its ability to operate effectively in search spaces with a high number of dimensions. To cope with the relevant computational cost related to the high number of CA simulations required by our study, we use a parallelized CA model that takes advantage of the computing power of graphics processing units. The study has shown that the accuracy of simulations can be significantly influenced by both the number and position in time of the historical maps involved in the calibration.     

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.     

Simulating urban dynamics in China using a gradient cellular automata model based on S-shaped curve evolution characteristics

Cellular automata (CA) have been efficiently used to express the complexity and dynamics of cities at different scales. However, those large-scale simulation models typically use only binary values to represent urbanization states without considering mixed types within a cell. They also ignore differences among the cells in terms of their temporal evolution characteristics at different urbanization stages. This study establishes a gradient CA for solving such problems while considering development differences among the cells. The impervious surface area data was used to detect the urbanization states and temporal evolution trends of the grid cells. Transition rules were determined with the incorporation of urban development theory expressed as an S-shaped curve. China was selected as the case study area to validate the performance of the gradient CA for a national simulation. A comparison was also made to a traditional binary logistic-CA. The results demonstrated that the gradient CA achieved higher accuracies in terms of both spatial patterns and quantitative assessment indices. The simulation pattern derived from the gradient CA can better reflect the local disparity and temporal characteristics of urban dynamics. A national urban expansion for 2050 was also simulated, and is expected to provide important data for ecological assessments.     

Parallel cellular automata for large-scale urban simulation using load-balancing techniques

Cellular automata (CA), which are a kind of bottom-up approaches, can be used to simulate urban dynamics and land use changes effectively. Urban simulation usually involves a large set of GIS data in terms of the extent of the study area and the number of spatial factors. The computation capability becomes a bottleneck of implementing CA for simulating large regions. Parallel computing techniques can be applied to CA for solving this kind of hard computation problem. This paper demonstrates that the performance of large-scale urban simulation can be significantly improved by using parallel computation techniques. The proposed urban CA is implemented in a parallel framework that runs on a cluster of PCs. A large region usually consists of heterogeneous or polarized development patterns. This study proposes a line-scanning method of load balance to reduce waiting time between parallel processors. This proposed method has been tested in a fast-growing region, the Pearl River Delta. The experiments indicate that parallel computation techniques with load balance can significantly improve the applicability of CA for simulating the urban development in this large complex region.     

珠江三角洲城市空间形态及其演化机制对比

改革开放以来,珠江三角洲地区的经济增长和城市空间形态的演变引人注目。利用RS与GIS技术方法,并结合经济、政治、规划等因素,对比回顾了改革开放30年来珠江三角洲六大城市——广州、深圳、佛山、东莞、中山和珠海城市形态的时空演化。研究发现,1979-2008年间,各主要城市在各自的地理条件和制度实践的基础上,演变出不同的空间形态。广州、深圳和珠海是自上而下的发展管治模式,其城市用地随着产业发展和优化而扩张并调整,但不同的城市发展政策造成了各异的空间形态;佛山、东莞和中山是自下而上的发展管治模式,空间形态总体上较为分散破碎,并在不同地理区位呈现不同程度的空间联系。     

An agent-integrated irregular automata model of urban land-use dynamics

Urban growth models are useful tools to understand the patterns and processes of urbanization. In recent years, the bottom-up approach of geo-computation, such as cellular automata and agent-based modeling, is commonly used to simulate urban land-use dynamics. This study has developed an integrated model of urban growth called agent-integrated irregular automata (AIIA) by using vector geographic information system environment (i.e. both the data model and operations). The model was tested for the city of San Marcos, Texas to simulate two scenarios of urban growth. Specifically, the study aimed to answer whether incorporating commercial, industrial and institutional agents in the model and using social theories (e.g. utility functions) improves the conventional urban growth modeling. By validating against empirical land-use data, the results suggest that a holistic framework such as AIIA performs better than the existing irregular-automata-based urban growth modeling.     

Incorporation of intra-city human mobility into urban growth simulation: A case study in Beijing

Journal of Geographical Sciences - The effective modeling of urban growth is crucial for urban planning and analyzing the causes of land-use dynamics. As urbanization has slowed down in most...     

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。     

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。     

顾及空间尺度效应的城市土地利用变化精细化模拟

城市土地利用变化模拟是优化土地资源配置的科学依据,提高其精细化程度和可靠性有助于准确把握城市用地发展趋势,对城市土地资源精准调控具有重要意义。基于宏观遥感分类的土地利用变化模拟,难以在街区尺度上揭示城市用地社会功能变化及精细化模拟中空间尺度效应来源和作用机理。本文联合遥感影像和POI数据识别出城市土地利用精细化特征,运用响应面法率定土地利用精细化模拟的最优空间尺度组合,在此基础上,利用CA-Markov模型开展了未来土地利用变化的精细化模拟。以武汉市中心城区为应用案例,研究结果表明：基于POI 的城市土地利用精细化识别方法,可以深度解析城市建设用地的社会功能,极大改善了传统基于遥感的土地覆被宏观解译效果;研究区土地利用变化元胞自动机精细化模拟的最优空间尺度组合是30 m元胞、7×7邻域以及冯诺依曼邻域类型,采用最优空间尺度组合能够提高土地利用变化精细化模拟的可靠性。响应面试验设计结果可有效识别精细化模拟过程中空间尺度效应的主要来源,并区分其对模拟精度的影响程度与正负效应;预计到2025年,研究区建设用地范围将继续向周边扩张,各类型用地之间互为交织,土地利用空间格局将呈更加破碎化趋势。