地球信息科学学报

地球信息科学学报 ›› 2016, Vol. 18 ›› Issue (4): 486-492.doi: 10.3724/SP.J.1047.2016.00486

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城市人群聚集消散时空模式探索分析——以深圳市为例

杨喜平1(), 方志祥1,**(), 赵志远1, 萧世伦1, 尹凌2   

  1. 1. 武汉大学 测绘遥感信息工程国家重点实验室,武汉 430079
    2. 中国科学院深圳先进技术研究院,深圳 518055
  • 收稿日期:2015-06-24 修回日期:2015-10-07 出版日期:2016-04-20 发布日期:2016-04-19
  • 作者简介:

    作者简介：杨喜平（1986-）,男,博士生,研究方向为时空数据挖掘和交通地理信息系统。E-mail: 0yangxiping0@163.com

  • 基金资助:
    国家自然科学基金项目(41231171、41371420);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开放基金项目(2013);深圳市科技创新委基础研究“从大规模手机定位数据中挖掘居民出行的关键技术研究”(JCYJ20140610151856728);武汉大学自主科研项目拔尖创新人才类资助项目（2042015KF0167）

Exploring Urban Human Spatio-temporal Convergence-Dispersion Patterns: A Case Study of Shenzhen City

YANG Xiping1(), FANG Zhixiang1,*(), ZHAO Zhiyuan1, SHAW Shih-Lung1, YIN Ling2   

  1. 1. State Key Laboratory of Information Engineering in Surveying, Mapping, Remote and Sensing, Wuhan University, Wuhan 430079, China
    2. Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
  • Received:2015-06-24 Revised:2015-10-07 Online:2016-04-20 Published:2016-04-19
  • Contact: FANG Zhixiang

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被引次数

10 | 9

摘要：

城市中人群的移动是带有目的性的,城市空间结构功能也存在差异,导致人群在城市中出现聚集或消散的现象,而且该现象会随着时间不断变化。本文基于海量的手机位置数据,以深圳市为例,采用自相关分析识别出城市中人群聚集与消散的区域,然后将这些区域一天中人群聚散组合成时间序列矩阵,采用自组织图聚类方法（SOM）进行聚类得到9种典型的人群聚集、消散时空模式,结合土地利用现状数据,分析解释了每种聚散模式最可能出现的土地利用组合。该研究从聚集和消散的角度探索了城市人群移动的时空模式,进一步帮助理解城市不同区域人群的移动模式以及与城市空间结构功能之间的关系,对城市规划、交通管理具有参考和指导意义。

关键词: 手机位置数据, 人群聚集消散, 时空模式, 城市空间结构

Abstract:

People′s movement in a city is driven by purpose. Moreover, the distribution of urban spatial structure can cause the phenomenon of human convergence or dispersion, and this phenomenon is always changing over time. Therefore, understand the spatio-temporal patterns of human convergence and dispersion could provide us a good knowledge of human travel demand in the urban context, so that the better decisions can be carried out to meet the demands of citizens. With the rapid development and widespread use of location-aware devices, it becomes relatively easy to collect the large-scale human sensor datasets and to bring new opportunities and challenges to the study of urban human mobility. Especially in recent years, mobile phone data has become a rich resource for research and it is widely used to study the human mobility patterns from various aspects, with regard to its advantage in tracking the long-term and large-volume of urban citizens with low cost. In this paper, taking Shenzhen City as an example, we firstly extracted the origin-destination flow matrix from the mobile phone location data and employed Local Moran′s I to identify people’s convergence or dispersion areas. And then a time series matrix was constructed according to the temporal signatures of these areas. SOM algorithm was selected to cluster the matrix into nine typical human convergence-dispersion patterns. Based on the land use data, we have calculated the percentage of different land use types for each pattern to explain the human convergence-dispersion phenomenon, thus we could understand the relationship between human mobility patterns and urban spatial function. This study helps us to acquire a good knowledge of the daily human convergence and dispersion patterns within different urban functional areas. The findings derived from this study could give us the insights about where and when the convergence and dispersion of people would occur in Shenzhen. This knowledge is helpful for the city planners to improve the urban local planning and makes it more suitable for human mobility applications, such as making targeted adjustments to optimize the urban transportation facilities to improve their service efficiency.

Key words: mobile phone location data, human convergence and dispersion, spatio-temporal pattern, urban spatial structure