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 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.     

中国城乡建设占用耕地的时空格局及形成机制

描绘了1996-2006年间中国城乡建设用地增长及其占用耕地的时空特征,探讨了城乡建设占用耕地的驱动机制。研究发现：人口流动为主的城镇化模式导致农业转移人口在城乡“两头占地”,这是中国城乡建设用地持续扩张的主要根源;各类开发区的设立和扩张都以占用耕地为主,对城乡建设占用耕地的贡献超过一半;而城市和镇的扩张速度更慢、土地来源更为多元。实证结果表明,投资和产出增长的差异性对城乡建设及其耕地占用的时空格局起到了关键作用;中西部地区的基础设施建设占用了大量耕地,但对区域经济发展和人口集聚的带动作用有限;以开发区为载体的产业集聚发展模式有利于中西部地区的土地集约利用和耕地保护,而东部沿海地区的省级开发区则容易成为地方政府的圈地运动。土地集约利用和耕地保护政策应重点解决农业转移人口城乡同时占地的问题,发挥城镇化的节地效应,并强化政策的区域差异性和针对性。     

Urban growth simulation by incorporating planning policies into a CA-based future land-use simulation model

Urban land-use change is affected by urban planning and government decision-making. Previous urban simulation methods focused only on planning constraints that prevent urban growth from developing in specific regions. However, regional planning produces planning policies that drive urban development, such as traffic planning and development zones, which have rarely been considered in previous studies. This study aims to design two mechanisms based on a cellular automata-based future land-use simulation model to integrate different planning drivers into simulations. The first update mechanism considers the influence of traffic planning, while the second mechanism can model the guiding effect of planning development zones. The proposed mechanisms are applied to the Pearl River Delta region, which is one of the fastest growing areas in China. The first mechanism is validated using simulations from 2000–2013 and demonstrates that simulation accuracy is improved by the consideration of traffic planning. In the simulation from 2013–2052, the two mechanisms are implemented and yield more realistic urban spatial patterns. The simulation outcomes can be employed to identify potential urban expansion inside the master plan. The proposed methods can serve as a useful tool that assists planners in their evaluation of urban evolvement under the impact of different planning policies.     

Modeling different urban growth patterns based on the evolution of urban form: A case study from Huangpi,Central China

Spatial patterns of urban expansion mainly include infilling, edge expansion, and outlying growth patterns. The cellular automata (CA) model, is an important spatio-temporal dynamic model and effectively simulates infilling and edge-expansion urban expansion. but is evidently lacking in outlying scenarios. In addition, CA cannot explain the causes and processes of urban land expansion. Given these limitations, we proposed a novel urban expansion model called simulation model of different urban growth pattern (SMDUGP), which can work well in both adjacent (i.e., infilling and edge expansion) and outlying growth patterns. SMDUGP has two main components. First, we divided the non-urban region into two categories, namely, candidate region for adjacent pattern urban growth (CRFAP) and candidate region for outlying pattern urban growth (CRFOP). Second, different methods were utilized to simulate urban expansion in the different categories. In CRFAP, a CA model based on the potential of urban growth was proposed to simulate urban growth in relatively randomly selected urban growth regions based on the discrete selection model and Monte Carlo method as the expansion area was implemented in CRFOP. Huangpi, a suburban area in Wuhan, is utilized as the case study area to simulate the spatial and temporal dynamics of urban growth from 2004 to 2024. SMDUGP can effectively simulate outlying urban growth with a highly improved simulation precision compared with the traditional CA model and can explain the causes and processes of urban land expansion.     

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。     

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。     

沈阳市城市扩展与土地利用变化多情景模拟

利用基于遥感手段获取的沈阳市城市扩展与土地利用变化历史数据,对SLEUTH城市扩展模型进行校正,对未来(2005~2030年)不同管理情景下的城市扩展与土地利用变化过程进行模拟,并对其发展变化趋势和生态环境影响进行分析与比较。结果显示,在三种管理情景下,未来的沈阳市城市建设用地都将持续增加,大量的耕地资源被侵占;但不同管理情景下,城市景观格局和区域面临的景观生态风险却表现出明显差异。SLEUTH模型的模拟结果较好地反映了沈阳市不同土地利用政策、规划方案等对未来城市扩展和土地利用变化以及区域景观生态风险的潜在影响,同时也指出了当前城市增长管理政策中存在的不足之处。     

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.     

城市建设对暴雨内涝空间分布的影响研究——以武汉市主城区为例

城市内涝灾害频繁,用地开发与空间扩张被普遍认为是其致因之一。对比武汉市遥感数据,1984—2017年,超过30%的自然水体被填占开发,城市建设开发活跃、填湖造陆强度大。论文以武汉市为例,采用二项Logistic模型,定量分析不同降雨强度情景下的内涝影响因素。研究表明,填湖造陆将极大地增加极端降雨情景下城市滨水区域的内涝风险。城市地形地势、排水管网条件、用地类型以及邻域用地结构等因素,也直接影响内涝风险。基于2种不同的用地开发策略,预测城市内涝风险结果显示,城市用地的不当开发将引致严重内涝风险。依据内涝风险的空间分布预测结果,论文提出了相应的改善策略,以为科学地制定防涝减灾规划提供参考。