地理科学进展

地理科学进展 ›› 2012, Vol. 31 ›› Issue (2): 248-254.doi: 10.11820/dlkxjz.2012.02.014

• 理论与方法研究 • 上一篇    下一篇

基于居民日常出行的生活空间单元的划分

季珏1,2,3, 高晓路1,2   

  1. 1. 中国科学院区域可持续发展分析和模拟重点实验室, 北京 100101;
    2. 中国科学院地理科学与资源研究所, 北京 100101;
    3. 中国科学院研究生院, 北京 100049
  • 收稿日期:2011-05-01 修回日期:2011-10-01 出版日期:2012-02-25 发布日期:2012-02-25
  • 通讯作者: 高晓路(1969-),女,研究员,博士生导师,主要从事城市环境评价、城市空间分析研究。E-mail: gaoxl@igsnrr.ac.cn
  • 基金资助:
    国家科技支撑计划项目(2008BAH31B01);国家自然科学基金项目(41171138)。

Identifying the Scope of Daily Life in Urban Areas Based on Residents’Travel Behaviors

JI Jue1,2,3, GAO Xiaolu1,2   

  1. 1. Key Laboratory of Regional Sustainable Development Modeling, CAS, Beijing 100101, China;
    2. Institute of Geographical Sciences and Natural Resources Research, CAS, Beijing 100101, China;
    3. Graduate University of Chinese Academy of Sciences, Beijing 100049, China
  • Received:2011-05-01 Revised:2011-10-01 Online:2012-02-25 Published:2012-02-25

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37 | 10

摘要： 本研究从行为地理学的视角,以空间稳定性假设为出发点,提出辨识生活空间单元的新方法。借鉴行为地理学关于微观个体的数据采集方法,在北京市清河永泰居住区随机选择了100 位居民为对象,对其日常活动空间的驻点信息进行了调查。通过对居住地和驻点联系的K-Means 空间聚类分析,对生活空间单元的范围进行了划分。分析表明:①属于相同生活空间单元的居民日常出行范围和频率比较接近;②小区建成年代、规划设计、房价等空间环境因素,对生活空间单元的形成具有显著影响;③共用的商业设施和交通服务设施是影响生活空间划分的主要因素。利用这一方法得出的生活空间范围可应用到地区环境性能评价、城市空间管制等多个研究领域。

关键词: 居民出行, 空间单元, 空间聚类分析, 生活空间

Abstract: This paper proposed a method for determining the scope of daily lives of urban residents based on travel behaviors; with the purpose of exploring the heterogeneity of urban structure from perspective of behavior geography. It was assumed that there exists a certain scale, where the people of inside the scale with outside are very active, and it turns significantly inactive beyond the scale. This attribute helps to identify urban areas belonging to same units of urban life. Taking Qinghe Yongtai area in Beijing as an example; we made a survey on residents’travel frequencies to 24 label places in and surrounding the study area, which showed their familiarities with those places. Then, K-means clustering analysis was conducted with the travel frequency data and the spatial distribution data of the samples. By Voronoi transformation of sample clusters, the whole study area was divided into different parts and the spatial scope for the separation was obtained. Further analysis on the social and economic attributes of different parts reveals that, indices of second-hand housing prices, attributes of residential blocks including building ages, planning and management, and the coverage of same commercial and transportation facilities are key factors for identifying the boundaries of urban communities. This identifying method can make reference for scale choosing in fields of residential environmental evaluation and so on.

Key words: residents’travel behavior, scope of daily life, spatial cluster analysis, spatial scale