地理科学进展 ›› 2016, Vol. 35 ›› Issue (12): 1433-1446.doi: 10.18306/dlkxjz.2016.12.001

• •    下一篇

城市与区域定量研究进展

顾朝林1,**(), 张悦1, 翟炜1, 管卫华2, 李强3, 赵娜4, 刘晨5   

  1. 1. 清华大学建筑学院,北京 100084
    2. 南京师范大学地理科学学院,南京 210023
    3. 中国人民解放军95806部队,北京 100076
    4. 中国科学院地理与资源科学研究所,北京 100101
    5. 北京师范大学地理学与遥感科学学院,北京100875
  • 出版日期:2016-12-20 发布日期:2016-12-20
  • 通讯作者: 顾朝林 E-mail:gucl@tsinghua.edu.cn
  • 作者简介:

    作者简介:顾朝林(1958-),男,教授,研究方向为城市与区域规划,E-mail:gucl@tsinghua.edu.cn

  • 基金资助:
    国家自然科学重大项目(41590844);清华大学自主科研计划项目(2015THZ01)

Progress in urban and regional quantitative research

Chaolin GU1,*(), Yue ZHANG1, Wei ZHAI1, Weihua GUAN2, Qiang LI3, Na ZHAO4, Chen LIU5   

  1. 1. School of Architecture, Tsinghua University, Beijing 100084, China
    2. School of Geography, Nanjing Normal University, Nanjing 210023, China
    3. The Chinese People's Liberation Army 95806 Troops, Beijing 100076, China
    4. Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
    5. School of Geography, Beijing Normal University, Beijing 100875, China
  • Online:2016-12-20 Published:2016-12-20
  • Contact: Chaolin GU E-mail:gucl@tsinghua.edu.cn
  • Supported by:
    Major Program of the National Natural Science Foundation of China, No.41590844;Tsinghua University Initiative Scientific Research Program, No.2015THZ01

摘要:

本文系统地梳理了城市与区域的定量研究方法,主要包括数理模型和模拟方法、单个城市增长和动力系统研究、城市系统动力学模型及其发展过程模拟研究、空间一般均衡模型研究以及多模型复合/集成系统研究等。主要结论为:1950年以前的城市与区域研究主要采取时间序列预测法,依靠历史资料的时间数列进行趋势外推研究;1960-1970年代主要采用数理统计分析方法,应用人口统计学模型和线性回归方法进行研究;1970-1980年代,随着系统科学的发展,采用系统分析方法发展了诸如交通、人口、土地等专业模型,展开对城市与区域问题的深入分析;1990-2000年代,研究数据的获取手段得到极大的改善,利用数据模型、地计算分析、模拟预测等分析手段使得研究的深度和广度均有了长足进步。近年来,由于空间数据挖掘的突破,大数据、云计算和巨型模型系统开始进入城市与区域研究领域,多模型复合/集成系统研究成为主要研究方向。

关键词: 城市, 区域, 定量研究, 数学模型, 元胞自动机, 多智能体, 数量经济模型, 系统动力学模型

Abstract:

This article systematically reviews some quantitative research methods about urbanization, including mathematical and simulation models of the process of urbanization, single urban growth models (cellular automata, agent and multi-agent), system dynamics model and simulation of urbanization, spatial computable general equilibrium model of urbanization, and integrated multi-model system of urbanization. The article concludes that before 1950, the main method of urbanization forecast was time series model that predicts urbanization trend based on historical data. During the 1960s, the main method was mathematical statistics model and population statistics model; regression model was also applied in urbanization research. During the 1970s, with the development of system science, system analysis method contributed to the creation of transportation, population, and land use models, which all facilitated in-depth analysis of urbanization. Since 1990, access to urbanization data has been greatly improved and data-intensive simulation models expanded the scope and depth of urbanization research. In recent years, integrated multi-model system becomes a popular research area because of the breakthrough in spatial data mining, big data, cloud computing, and large-scale model system.

Key words: urban, region, quantitative research, mathematical model, cellular automata, multi-agent system, numerical economic model, system dynamics model