Modeling urban growth with GIS based cellular automata and least squares SVM rules: a case study in Qingpu-Songjiang area of Shanghai, China
Modeling urban growth with GIS based cellular automata and least squares SVM rules: a case study in Qingpu-Songjiang area of Shanghai, China
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DOI：
10.1007/s00477-015-1128-z
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发表时间：
2016-05-01
影响因子：
4.2
通讯作者：
Batty, Michael
中科院分区：
文献类型：
--
作者：
Feng, Yongjiu;Liu, Yan;Batty, Michael
城市元胞自动机（CA）建模的一个关键问题涉及识别生成现实城市土地利用模式的转换规则。最近的研究表明，线性方法无法充分描绘城市和非城市地区之间异常复杂的边界，并且由于大多数城市CA模型模拟跨越这些边界的转变，因此迫切需要好的方法来促进这种划分。本文提出了一种基于最小二乘支持向量机 (LS-SVM) 的具有非线性转换规则的机器学习 CA 模型（称为 MachCA）来模拟此类城市增长。通过使用LS-SVM方法将输入数据集投影到高维空间，构建最优超平面来分离城市和非城市土地之间的复杂边界，从而能够检索非线性CA转换规则。在 MachCA 模型中，转换规则是关于单元是否更改其状态的是/否决策，规则会针对模型实现的每次迭代动态更新。 MachCA 在中国上海青浦-松江地区城市增长模拟中的应用表明，城乡格局的空间配置是可以建模的。 MachCA 模型与通过对数回归拟合的传统 CA 模型（称为 LogCA）的比较表明，MachCA 模型由于能够捕获城市动态的空间复杂性，因此产生更多的命中、更少的漏报和误报。这提高了模拟精度，尽管 MachCA 和 LogCA 模型产生的总体误差之间的偏差只有不到 1%。尽管如此，MachCA模型用于检索过渡规则的方式为模拟城市增长的动态过程提供了一种新的方法。
A critical issue in urban cellular automata (CA) modeling concerns the identification of transition rules that generate realistic urban land use patterns. Recent studies have demonstrated that linear methods cannot sufficiently delineate the extraordinary complex boundaries between urban and non-urban areas and as most urban CA models simulate transitions across these boundaries, there is an urgent need for good methods to facilitate such delineations. This paper presents a machine learning CA model (termed MachCA) with nonlinear transition rules based on least squares support vector machines (LS-SVM) to simulate such urban growth. By projecting the input dataset into a high dimensional space using the LS-SVM method, an optimal hyper-plane is constructed to separate the complex boundaries between urban and nonurban land, thus enabling the retrieval of nonlinear CA transition rules. In the MachCA model, the transition rules are yes-no decisions on whether a cell changes its state or not, the rules being dynamically updated for each iteration of the model implementation. The application of the MachCA for simulating urban growth in the Shanghai Qingpu-Songjiang area in China reveals that the spatial configurations of rural-urban patterns can be modeled. A comparison of the MachCA model with a conventional CA model fitted by logarithmic regression (termed LogCA) shows that the MachCA model produces more hits and less misses and false alarms due to its capability for capturing the spatial complexity of urban dynamics. This results in improved simulation accuracies, although with only less than 1 % deviation between the overall errors produced by the MachCA and LogCA models. Nevertheless, the way MachCA model use in retrieving the transition rules provides a new method for simulating the dynamic process of urban growth.