城市地理

大都市区城市扩展模型— 以北京城市扩展模拟为例

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  • 北京师范大学环境演变与自然灾害教育部重点实验室,北京师范大学资源科学研究所,北京 100875
何春阳 (1975-), 男, 博士生, 四川射洪人, 主要从事遥感与土地利用研究。E-mail: hcy@bnu.edu.cn

收稿日期: 2002-07-08

  修回日期: 2002-10-27

  网络出版日期: 2003-03-25

基金资助

国家重点基础研究发展规划项目 (G1999043406-03)

City Expansion Model of Metropolitan Area in China: a Case Study of Beijing

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  • Key Laboratory of Environmental Change and Natural Disaster, Ministry of Education of China, Beijing Normal University; Institute of Resources Science, Beijing Normal University, Beijing 100875, China

Received date: 2002-07-08

  Revised date: 2002-10-27

  Online published: 2003-03-25

Supported by

The National Key Project for Basic Sciences, No.G1999043406-03

摘要

在分析大都市区城市扩展特征的基础上,从宏观外部约束性因素和局部城市单元自身扩展能力变化共同作用影响城市发展演变的角度,构建了一个模拟和预测大都市区城市发展演变过程的城市扩展模型 (City Expanding Model in Metropolitan Area; CEM)。首先对北京1975~1997年的城市发展过程进行模拟重建,然后在此基础上从城镇用地数量最优和位置最佳相结合的角度对北京2005~2015年的城市发展格局进行了预测。结果表明,在对各种影响因素进行严格标准化并利用自适应Monte-Carlo方法多次模拟确定最佳影响权重的基础上,该模型可以在一定程度上反映城市发展,尤其是大都市区城市发展演变的特征和规律。

本文引用格式

何春阳,陈晋,史培军,范一大 . 大都市区城市扩展模型— 以北京城市扩展模拟为例[J]. 地理学报, 2003 , 58(2) : 294 -304 . DOI: 10.11821/xb200302017

Abstract

Based on the understanding that the city expansion is the result of the collective effects of exogenous constraints and internal expanding ability of urban pixel, a city expansion model in metropolitan area (CEM) based on CA and Tietenberg model is developed to understand the city development in metropolitan area in China and Beijing is selected as the case study city here. At first, the city development in Beijing from 1975 to 1997 was simulated. Then according to the simulation, the city expansion from 1998 to 2015 was predicted under the condition that the optimal amount and optimal location of the urban land use in the future should be both satisfied. The results suggested that the model can represent the characteristics and rules of the city development, especially the city expansion in the metropolitan area to a certain extent if the influencing factors are standardized strictly and the optimal weights are given by adaptive Monte Carlo method newly developed in the paper.

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