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地理科学进展  2015, Vol. 34 Issue (8): 937-946    DOI: 10.18306/dlkxjz.2015.08.001
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基于随机森林的元胞自动机城市扩展模拟——以佛山市为例
陈凯1,2(),刘凯1,2*(),柳林1,2,朱远辉1,2
1. 中山大学地理科学与规划学院,综合地理信息研究中心,广州 510275
2. 广东省城市化与地理环境空间模拟重点实验室,广州 510275
Urban expansion simulation by random-forest-based cellular automata: a case study of Foshan City
CHEN Kai1,2(),LIU Kai1,2,*(),LIU Lin1,2,ZHU Yuanhui1,2
1. Center of Integrated Geographic Information Analysis, School of Geography and Planning, Sun Yat-Sen University, Guangzhou 510275, China
2. Guangdong Key Laboratory for Urbanization and Geo-simulation, Guangzhou 510275, China
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摘要 

本文提出一种基于随机森林的元胞自动机城市扩展(RF-CA)模型。通过在多个决策树的生成过程中分别对训练样本集和分裂节点的候选空间变量引入随机因素,提取城市扩展元胞自动机的转换规则。该模型便于并行构建,能在运算量没有显著增加的前提下提高预测的精度,对城市扩展中存在的随机因素有较强的容忍度。RF-CA模型可进行袋外误差估计,以快速获取模型参数;也可度量空间变量重要性,解释各空间变量在城市扩展中的作用。将该模型应用于佛山市1988-2012年的城市扩展模拟中,结果表明,与常用的逻辑回归模型相比,RF-CA模型进行模拟和预测分别能够提高1.7%和2.6%的精度,非常适用于复杂非线性特征的城市系统演变模型与扩展研究;通过对影响佛山市城市扩展的空间变量进行重要性度量,发现对佛山城市扩张模拟研究而言,距国道的距离与距城市中心的距离具有最重要的作用。

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陈凯
刘凯
柳林
朱远辉
关键词 随机森林元胞自动机城市扩展佛山    
Abstract

Cellular automata (CA) has been frequently used to investigate the logical nature of self-reproducible systems and simulate the evolution of complex geographical phenomena such as urban expansion. The core of cellular automata is to define transition rules. Traditionally, approaches for defining the transition rules of cellular automata had difficulty to balance the interpretability, accuracy, and convenience. This article presents a new cellular automata model for simulating urban expansion based on random forest algorithm. The proposed model extracts CA transition rules of urban expansion by introducing random factors in training samples and candidate spatial variables that split nodes during the multiple decision trees building process. One significant advantage of our approach is that it can be easily adopted for parallel implementation and has high prediction accuracy and tolerance to random factors in urban expansion. Another strength of the proposed approach is that it can estimate out-of-bag errors to obtain model parameters quickly and measure the importance of spatial variables and explain the contribution of each variable in urban expansion. The model was applied to simulate urban expansion in Foshan City, Guangdong Province. We used the urban land change of 1988 and 2000 as the dependent variable and the spatial variables as the independent variables to construct the CA model based on random forests, then simulate and predict urban expansion of 2000 and 2012. The results show that random forest model can improve the simulation and prediction accuracies by 1.7% and 2.6%, respectively, when compared to the logistic regression model commonly used in CA simulation. This suggests that random forest model is superior for modeling complex nonlinear urban evolution. Urban expansion of Foshan City in 2024 was also predicted according to its urban development trend. Through measuring the importance of some spatial variables that affect urban expansion, we found that distance to national roads and to the city center are the two most important spatial variables for urban expansion simulation in Foshan City.

Key wordsrandom forest    cellular automata    urban expansion    Foshan City
     出版日期: 2015-08-25
基金资助:国家高技术研究发展计划(863)项目(2012AA121402);国家自然科学基金项目(41001291);中央高校基本科研业务费专用资金项目(13lgpy61)
引用本文:   
陈凯,刘凯,柳林,朱远辉. 基于随机森林的元胞自动机城市扩展模拟——以佛山市为例[J]. 地理科学进展, 2015, 34(8): 937-946.
CHEN Kai,LIU Kai,LIU Lin,ZHU Yuanhui. Urban expansion simulation by random-forest-based cellular automata: a case study of Foshan City. PROGRESS IN GEOGRAPHY, 2015, 34(8): 937-946.
链接本文:  
http://www.progressingeography.com/CN/10.18306/dlkxjz.2015.08.001      或      http://www.progressingeography.com/CN/Y2015/V34/I8/937
Fig. 1  随机森林算法示意图(方匡南等, 2011)
Fig.2  基于随机森林CA模型的城市扩展模拟
变量类型 变量 获取方法
因变量 是否转变为城市元胞 遥感分类
空间距离变量 距市中心的距离(x1) 利用ArcGIS的Distance获取
距区中心的距离(x2)
距镇中心的距离(x3)
距高速公路的距离(x4)
距省道的距离(x5)
距县道的距离(x6)
自然因素变量 高程栅格图(x7)
坡度栅格图(x8)
限制因素变量 不可发展区域图(x9)
局部变量 3×3邻域已城市化元胞数(x10) 用ArcGIS的Focal函数
Tab.1  空间变量及获取方法
Fig.3  分类精度与预测变量个数之间的关系(树的数量为1000)
Fig. 4  分类精度与树的数量之间的关系(预测变量个数为4)
Fig. 5  空间变量重要性度量
Pthreshold 0.4 0.5 0.6 0.7 0.8 0.9
精度/% 82.23 82.55 82.79 82.98 83.10 83.07
Tab. 2  不同城市发展阈值对应的模拟精度
Fig. 6  佛山市城市用地模拟结果与实际情况对比图
年份 类型 随机森林模型 逻辑回归模型
非城市 城市 精度/% 非城市 城市 精度/%
1988-2000年 遥感分类图 非城市 3444759 220694 94.0 3406151 259302 92.9
城市 220008 383507 63.6 253128 350387 58.1
总精度 89.7 88.0
Kappa 0.580 0.508
2000-2012年 遥感分类图 非城市 2901659 360751 88.9 2844209 418201 87.2
城市 360647 645911 64.2 416346 590212 58.6
总精度 83.1 80.5
Kappa 0.531 0.458
Tab. 3  随机森林模型与逻辑回归模型模拟的混淆矩阵
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