地球信息科学学报 ›› 2014, Vol. 16 ›› Issue (5): 727-734.doi: 10.3724/SP.J.1047.2014.00727

• • 上一篇    下一篇

一种考虑空间增长潜力的城市扩张灰度CA模型与应用

马世发1,2(), 艾彬3,,A;*(), 赵克飞1,2   

  1. 1. 中山大学地理科学与规划学院,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
    3. 中山大学海洋学院,广州 510275
  • 收稿日期:2014-02-28 修回日期:2014-04-15 出版日期:2014-09-10 发布日期:2014-09-04
  • 通讯作者: 艾彬 E-mail:whuma@163.com;abin@mail.sysu.edu.cn
  • 作者简介:

    作者简介:马世发(1985-),男,湖北宜昌人,博士生,主要从事地理建模与空间规划研究。 E-mail: whuma@163.com

  • 基金资助:
    国家自然科学基金项目(41371376、41301418)

Gradient Cellular Automata with the Consideration of Spatial Growth Potentiality for Urban Sprawling Simulation

MA Shifa1,2(), AI Bin3,*(), ZHAO Kefei1,2   

  1. 1. Geography and Planning School, Sun Yat-sen University, Guangzhou 510275, China
    2. Key Laboratory of Guangdong Province in Urbanization and Geography Environment Simulation, Guangzhou 510275, China
    3. School of Marine Sciences, Sun Yat-sen University, Guangzhou 510275, China
  • Received:2014-02-28 Revised:2014-04-15 Online:2014-09-10 Published:2014-09-04
  • Contact: AI Bin E-mail:whuma@163.com;abin@mail.sysu.edu.cn
  • About author:

    *The author: CHEN Nan, E-mail:fjcn99@163.com

摘要:

元胞自动机(Cellular Automata,CA)是进行城市空间演变模拟的重要建模工具。经典城市扩张CA模拟规则提取,主要利用一段历史变化样本对城市化(1值)和未城市化(0值)进行双向拟合,存在0值过度拟合现象,即历史观测不变化的元胞样本并不代表其没有转变的潜在可能性。为此,本文将城市空间增长潜力引入CA模型,重新构建CA规则学习样本和参数拟合目标,并利用粒子群优化算法进行参数挖掘,弥补传统CA规则提取的局限性。研究以广州市为案例区,基于主体功能区规划思想构建空间开发潜力,对改进的城市扩张CA模拟模型进行实例应用。结果表明,本文改进的CA模型不论在整体格局还是细节呈现上,均比传统CA模型表现出更高的可信度,模型整体评估精度高于70%,结果可为中长期城市规划提供更好的参考。

关键词: 元胞自动机, 粒子群, 城市规划, 主体功能区划

Abstract:

Geography simulation model such as Cellular Automata (CA) is one of the most important tools for simulating and early warning the urban growth. The CA model can simulate urban sprawling accurately only when suitable conversion rules for every cell are achieved. Hence, the core of CA is to derive the conversion rules, and many researchers have been interested in discovering the rules. However, the conversion rules of traditional CA are mainly derived from historic samples, in which both changed samples and unchanged ones are considered for function fitting to retrieve parameters simultaneously. In this approach, it is assumed that if the urban sprawling occurred, samples were labeled as 1; otherwise, samples were accordingly labeled as 0. However, it will result in over fitting for the unchanged samples, because those samples with labels of 0 may have the potentiality to transform in future, especially for those located at the rural-urban fringe. Therefore, we proposed a gradient CA for simulating urban sprawling. In this model, whether or not urban growth would occur was determined by the developing probability instead of its developed or undeveloped status. Accordingly, the unchanged samples were set to the values ranging from 0 to 1. And in this research, the developing potentiality was estimated according to present planning maps. Compared with traditional CA, the gradient CA could avoid the over fitting problem for the unchanged samples to a certain degree. Moreover, the fitting objective was distinguished from traditional CA for its ability in retrieving conversion rules. In addition, particle swarm optimization algorithm was used to obtain the parameters of spatial indices. Finally, Guangzhou City, which locates in the Pearl River Delta of China, was chosen as the study area for model implementation and validation. In this case study, the spatial developing potentiality was allocated referring to the major function zone (MFZ) planning, because MFZ is currently one of the most significant planning policies for Chinese government to control the chaotic urbanization. In order to evaluate the model’s efficiency, a comparison analysis was carried out between the gradient CA and traditional CA. Global and local patterns of the simulation results were analyzed respectively in details. Results demonstrate that the model modified in this paper can perform efficiently and the overall accuracy of the model is greater than 70%, which can provide better and reasonable spatial scenarios for medium-and long-term urban planning.

Key words: cellular automata, particle swarm optimization, urban planning, major function zone