地理学报 ›› 2011, Vol. 66 ›› Issue (3): 416-426.doi: 10.11821/xb201103014

• GIS应用 • 上一篇    下一篇

城市系统微观模拟中的个体数据获取新方法

龙瀛1,2, 沈振江3, 毛其智1   

  1. 1. 清华大学建筑学院,北京100084;
    2. 北京市城市规划设计研究院,北京100045;
    3. 日本金泽大学环境设计学院,日本金泽920-1192
  • 收稿日期:2010-01-31 修回日期:2010-07-25 出版日期:2011-03-20 发布日期:2011-05-15
  • 作者简介:龙瀛(1980-), 男, 博士研究生, 高级工程师, 中国地理学会会员(S110007674M), 主要研究方向为规划支持系统和城市系统微观模拟。E-mail: longying1980@gmail.com
  • 基金资助:

    国家自然科学基金项目(51078213); 国家“十一五”科技支撑计划项目(2006BAJ14B08)

Retrieving Individual Attributes from Aggregate Dataset for Urban Micro-simulation: A Preliminary Exploration

LONG Ying1,2, SHEN Zhenjiang3, MAO Qizhi1   

  1. 1. School of Architecture, Tsinghua University, Beijing 100084, China;
    2. Beijing Institute of City Planning, Beijing 100045, China;
    3. School of Environment Design, Kanazawa University, Kanazawa 920-1192, Japan
  • Received:2010-01-31 Revised:2010-07-25 Online:2011-03-20 Published:2011-05-15
  • Supported by:

    National Natural Science Foundation of China, No.51078213; Technical Supporting Programs Funded by Ministry of Science & Technology of China, No.2006BAJ14B08

摘要: 目前自上而下的城市系统宏观模拟并不能解决城市这一复杂系统中出现的部分问题,城市系统微观模拟(如多主体系统MAS) 已经成为城市系统模拟的新思路,其主要是基于个体数据(如个人、家庭、公司或建筑物) 开展的。国际国内这方面的应用都受到个体样本稀缺的限制。微观模拟所需要的个体样本数据是原有的统计制度所不能适应的,尤其是在中国,个体样本在统计公报或年鉴中不公开,仅可通过典型调查来补充。本文旨在探索稀疏数据环境下构建城市系统微观模拟的个体样本数据的新方法。该方法基于已有的多源统计数据、典型调查数据以及个体的通用规则,反演出个体样本的属性信息和空间分布,进而可以以GIS图层的形式直接作为微观模拟的数据基础。通过本方法获取的样本,能够符合已有的统计资料,并遵照了样本的基本特征,可以作为现有数据条件下的微观模拟模型的数据输入。同时该方法的应用简单,统计意义上的准确度高,适合我国统计制度下的微观模拟模型的构建。

关键词: 微观模拟, 反演, 多主体系统, 统计数据, 北京

Abstract: As the traditional top-bottom based macro-simulation models can not properly adapt to the present research of spatiotemporal dynamic urban system, the bottom-up micro-simulation models using individual data have gradually become a novel perspective for investigating urban systems recently. However, one of the factors restraining the wide application of micro-simulation models is the limited individual data due to the difficulties of data fetching and processing. Such a situation is especially serious in China, where individual dataset is not available from the official census and can only be obtained via various surveys in small scale conducted by separate units. This paper proposes a new solution to retrieve individual dataset from aggregate dataset, e.g. statistical data, for urban micro-simulation under the current sparse-data environment. Based on the existing multi-resource official statistical data, non-official surveys, as well as general relationships among individual attributes, our approach can disaggregate the individual attributions and location obeying the input aggregation observations data. This approach has proved to be significantly accurate at the statistical level, and can be conveniently adopted for urban micro-simulation under the present statistical institutions of China.

Key words: micro-simulation, disaggregation, multi-agent system, statistical data