地理学报 ›› 2010, Vol. 65 ›› Issue (6): 665-675.doi: 10.11821/xb201006004

• 土地利用 • 上一篇    下一篇

基于核主成分元胞模型的城市演化重建与预测

冯永玖1, 刘妙龙2, 童小华2, 刘艳3, 韩震1   

  1. 1. 上海海洋大学海洋科学学院,上海201306;
    2. 同济大学测量与国土信息工程系,上海200092; 
    3. 南洋理工大学,新加坡637616
  • 收稿日期:2009-08-07 修回日期:2010-03-15 出版日期:2010-06-25 发布日期:2010-08-04
  • 通讯作者: 刘妙龙(1944-), 男, 教授, 博士生导师, 中国地理学会会员(S110001469M), 主要从事城市地理学、城市模拟、GIS理论、方法与应用研究。E-mail: liuml@tongji.edu.cn
  • 作者简介:冯永玖(1981-), 男, 讲师, 工学博士, 主要从事遥感与GIS、地学信息模型研究。E-mail: yjfeng@shou.edu.cn
  • 基金资助:

    国家自然科学基金项目(40771174); 教育部科学技术研究重点项目(209047); 上海市科学技术委员会重点项目(08230510700), 上海高校选拔培养优秀青年教师科研专项基金(ssc09018)

Kernel Principal Components Analysis Based Cellular Model for Restructuring and Predicting Urban Evolution

FENG Yong-jiu1, LIU Miao-long2, TONG Xiao-hua2, LIU Yan3, HAN Zhen1   

  1. 1. College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China;
    2. Department of Surveying and Geo-informatics, Tongji University, Shanghai 200092, China;
    3. National Institute of Education, Nanyang Technological University, Singapore 637616
  • Received:2009-08-07 Revised:2010-03-15 Online:2010-06-25 Published:2010-08-04
  • Supported by:

    National Natural Science Foundation of China, No.40771174; Key Project on Science & technology of Ministry of Education of China, No.209047; Key Program of Science and Technology Commission of Shanghai Municipality, No.08230510700; Special Research Funds for Selection and Cultivation of Outstanding Young Teachers of Shanghai Universities, No.ssc09018

摘要:

通过元胞自动机(CA)模拟和重建城市演化的复杂非线性过程,对于城市土地利用规划和决策具有指导意义。利用传统线性方法获取的地理CA转换规则,较难刻画城市演化的时空动力学过程。基于核主成分分析方法(KPCA),通过核函数映射,在高维特征空间下不仅能够对多重共线的空间变量进行非线性降维,且由此建立的地理元胞模型KPCA-CA参数物理意义明确,能够较好地体现城市化过程的非线性本质。基于GIS环境下自主研发的地理模拟框架SimUrban,利用该KPCA-CA模型模拟和重建了快速城市化区域上海市嘉定区1989-2006年城市演化过程,并预测了研究区2010年的城市空间格局。模拟结果显示,嘉定区城市主要沿中心区域及主干道路而扩展,体现了KPCA方法提取的前两个主成分的作用,与城市实际发展情况相符。利用混淆矩阵和面积控制精度等指标,对模拟结果进行了评价,得到总体精度为80.67%、Kappa系数为61.02%,表明模拟结果与遥感分类结果及统计结果符合程度较好;与传统基于线性方法的地理CA模型比较,KPCA-CA模型模拟结果精度更高。

关键词: 城市演化模拟, 元胞自动机, 核主成分分析, 精度分析, 上海市嘉定区

Abstract:

Simulating and restructuring complex non-linear process of urban evolution with cellular automata plays a significant role in urban land use planning and decision-making. By using conventional methods, it is difficult to retrieve reasonable CA transition rules to capture the dynamic process of urban expansion and evolution. Based on kernel principal components analysis approaches (KPCA), non-linear dimension reduction can be executed on spatial variables with multi-collinearity by kernel method projection in the high-dimensional feature space, therefore, a novel CA model based on KPCA with explicit CA parameters is built which can well reflect the nonlinear nature of urbanization. In a geographical modelling framework called as SimUrban developed in a GIS environment, a fast growing area, Jiading District of Shanghai Municipality, is successfully simulated from 1989 to 2006, and the spatial pattern of the urban areas of 2010 is predicted. The simulation results demonstrate that the urban expansion occurred on the fringe areas of urban center and main roads, which reflects the impacts of the first two components extracted from KPCA approaches and highly accords with the actual development. To evaluate the performances of the KPCA-CA model, confusion matrix and area control indexes are used to assess the accuracies of the simulation results. The overall accuracy 80.67% and Kappa coefficient 61.02% illustrate that the simulation results produced by the KPCA-CA model are well matched with the actual urban evolution of Jiading District. Compared with a cellular model based on linear PCA approach, the simulated results generated by the cellular model based on KPCA have higher accuracies.

Key words: urban evolution simulation, cellular automata, kernel principal components analysis, accuracy analysis, Jiading District of Shanghai