地球信息科学学报 ›› 2019, Vol. 21 ›› Issue (3): 384-397.doi: 10.12082/dqxxkx.2019.180608

• 地理空间分析综合应用 • 上一篇    下一篇

北京市多尺度中心特征识别与群聚模式发现

宋辞1,2(), 裴韬1,2,*()   

  1. 1. 中国科学院地理科学与资源研究所 资源与环境信息系统国家重点实验室, 北京 100101
    2. 中国科学院大学,北京,100049
  • 收稿日期:2018-11-26 修回日期:2018-12-30 出版日期:2019-03-15 发布日期:2019-03-15
  • 通讯作者: 裴韬 E-mail:songc@lreis.ac.cn;peit@lreis.ac.cn
  • 作者简介:

    作者简介:宋辞(1986-),男,湖北赤壁人,助理研究员,主要研究方向为时空数据挖掘。E-mail: songc@lreis.ac.cn

  • 基金资助:
    国家自然科学基金项目(41601430、41421001、41525004);国家重点研发计划项目(2017YFB0503801)

Exploring Polycentric Characteristic and Residential Cluster Patterns of Urban City from Big Data

Ci SONG1,2(), Tao PEI1,2()   

  1. 1. State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijin 100101, China
    2. University of Chinese Academy of Sciences, Beijing 100101, China
  • Received:2018-11-26 Revised:2018-12-30 Online:2019-03-15 Published:2019-03-15
  • Supported by:
    National Natural Science Foundation of China, No.41601430, 41421001, 41525004;National Key R&D Program of China, No.2017YFB0503801.

摘要:

城市多中心的研究是揭开城市系统复杂空间结构的重要研究内容,然而受传统调查数据时空精度的局限,现有研究缺乏足够细致的实证分析。LBS服务的广泛应用为高效、实时、微观地刻画个体和城市之间的空间活动提供了充足的信息,使得对城市多中心的微观量化机制的研究成为可能。基于此原因,本文结合兴趣点数据以及微博签到数据和出租车GPS轨迹数据对北京市五环内的不同尺度、不同社会功能的城市中心进行了识别,并对其居民行为的群聚模式进行了分析。结果表明,北京市五环内包括4个市级中心,16个区级中心以及45个街道级中心。市级中心包括海淀科教中心,西单休闲中心,北京文化中心和国贸金融中心4大中心;区级中心主要包括文娱中心、商业中心、教育中心以及交通中心4类中心;街道级中心除了区级的四类中心外,还包括行政中心、购物中心、商住中心等9类中心。不同中心在同一时段呈现相似的群聚模式,其中工作日城市不同尺度中心的群聚模式持续时间较长,群聚密度较大,主要以通勤和饮食为主;非工作日城市不同尺度中心的群聚模式相对较分散,持续时间较短,主要以休闲娱乐为主。

关键词: 多尺度城市中心, 居民活跃度, 社会功能, 丛聚过程, 群聚模式

Abstract:

With the development of urbanization, the problem of "big city disease" is becoming more and more prominent in recent years. Since the strategy of polycentric city has been proposed, polycentric characteristics of city has become one of the most important issue in revealing spatial structure of a city. Due to the rough resolution of traditional census data, most studies have not get an insight into the fine structure of city centers and lacked empirical researches. With the widely application of LBS services, most trajectory data of human activities have been recorded to provide a profile of daily urban dynamics in real time. This information has opened up a new way to analyze the mechanism of polycentric city. Based on the reason, this study proposes a new method to identify the multi-centers in Beijing within rings, and analyzes the spatial-temporal characteristics and interactions in each center, using sina micro blog check-in data and taxi GPS data with POI data. In our study, groups of centers with different social functions can be identified from the clusters of residents' activities. From these results, 4 city centers, 16 district centers and 45 community centers have been identified within 5th rings in Beijing. The district centers can be divided into 4 groups, including cultural and recreational center, business center, education center and transportation center. And the representativeness for each types of centers are Sanlitun, Guomao, Beijing Normal University and Beijing West Railway Station. The community centers can be divided into 9 groups, including political center, residential center, administration center and business-residential center. The representativeness for each types of centers are Xidan, Wudaokou, Wanliu, Qianmen, Zhongguancun, Tianmen square, Chaoyang District Government, Beijing West Railway Station and Yonganli Community. The residential clusters can be observed in some daily hours. Number of residential clusters for district centers are commonly higher than number for community centers, while number of residential clusters in workdays area higher than that in holidays. For district centers, there are 4 classes of common cluster pattern in workdays and 5 classes of common cluster pattern in holidays. For community centers, there are 5 classes of common cluster pattern in workdays and 3 classes of common cluster pattern in holidays, respectively.

Key words: polycentric center, residents' activities, social function, clustering process, residential cluster patterns