地球信息科学学报 ›› 2017, Vol. 19 ›› Issue (4): 467-474.doi: 10.3724/SP.J.1047.2017.00467

• 地球信息科学理论与方法 • 上一篇    下一篇

基于大规模手机定位数据的群体活动时空特征分析

曹劲舟1(), 涂伟2,3,4,*(), 李清泉1,2,3,4, 曹瑞5   

  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. 深圳大学 土木工程学院空间信息智能感知与服务深圳市重点实验室,深圳 518060
    3. 海岸带地理环境监测国家测绘地理信息局重点实验室,深圳 518060
    4. 深圳大学智慧城市研究院,深圳 518060
    5. 宁波诺丁汉大学国际博士创新研究中心,宁波 315100
  • 收稿日期:2016-11-04 修回日期:2016-12-24 出版日期:2017-04-20 发布日期:2017-04-20
  • 通讯作者: 涂伟 E-mail:caojinzhou@whu.edu.cn;tuwei@szu.edu.cn
  • 作者简介:

    作者简介:曹劲舟(1991-),男,湖南益阳人,博士生,主要从事时空大数据分析与挖掘研究。E-mail:caojinzhou@whu.edu.cn

  • 基金资助:
    国家自然科学基金项目(41401444、41371377、41671387);深圳大学青年教师科研启动项目(2016065);国土资源部城市土地资源监测与仿真重点实验室开放基金资助课题(KF-2016-02-009)

Spatio-temporal Analysis of Aggregated Human Activities Based on Massive Mobile Phone Tracking Data

CAO Jinzhou1(), TU Wei2,3,4,*(), LI Qingquan1,2,3,4, CAO Rui5   

  1. 1. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
    2. Shenzhen Key Laboratory of Spatial Smart Sensing and Services, College of Civil Engineering, Shenzhen University, Shenzhen 518060, China
    3. Key Laboratory for Geo-Environmental Monitoring of Coastal Zone of the National Administration of Surveying, Mapping and GeoInformation, Shenzhen 518060, China
    4. Smart City Institute, Shenzhen University, Shenzhen 518060, China
    5. International Doctoral Innovation Centre, University of Nottingham, Ningbo 315100, China
  • Received:2016-11-04 Revised:2016-12-24 Online:2017-04-20 Published:2017-04-20
  • Contact: TU Wei E-mail:caojinzhou@whu.edu.cn;tuwei@szu.edu.cn

摘要:

城市空间与居民行为不断交互,相互影响。探究城市空间中的群体活动分布及其时空变化能够帮助数据驱动的城市规划与城市治理。基于大数据的时空间群体活动研究是当前时空大数据研究的一个热点。本文以深圳市为例,基于约1000万手机用户在某一工作日的基站尺度的手机定位数据,识别用户停留位置和停留活动,重建活动语义信息,分析用户的停留点和停留活动的分布差异,研究群体活动的时空分布模式,探讨人群活动模式的多样分布特征。研究表明:停留位置和活动分布存在差异,每人每天平均的停留个数约为2.1个,而每人每天平均从事的活动约为3.4个;不同类型的活动在时间上存在波动;群体活动存在空间分异特征,整体上服从“空间幂律”。本研究揭示了城市空间中群体活动的多样性及其时空分布特征,对于城市居民活动研究、城市交通优化和城市规划具有重要的意义。

关键词: 手机定位数据, 轨迹分析, 时空大数据, 群体活动, 时空特征

Abstract:

Urban space and the behavior of human activities constantly interact with each other. Investigation on distribution of aggregated human activities and spatio-temporal change benefits data-driven policy-making in urban planning and urban governing. In the era of big data, with the development of information and communication technologies, it is possible to collect city-scale data with high resolution in space and time by various location-aware devices and sensors. Exploration of spatial-temporal activities attracts a lot of attention. By taking about 10 million one-day tracking data of mobile phone users in Shenzhen, China as an example, this paper firstly identified their stay locations according to spatial and temporal rules to generate stay trajectory for each individual and recovered activity semantic information by labelling activity types for each stay locations. Then, the significant differences in patterns of distributions of stay locations and their activities were analyzed. Spatial and temporal distributions of different human activities were explored, respectively. The study shows that the distribution of stay locations and activities is obviously heterogeneous. The average number of stay locations of an individual per day is 2.1, while the average number of activities an individual engaged in per day is 3.4. This study furthermore suggests that different types of activities have temporal variance and spatial heterogeneity. The temporal distribution fluctuates significantly over 24 hours, which is in accordance with daily routine. The spatial distribution overall obeys “space power law”, and the spatial distribution of social activity, which has a faster-down tail, shows a more obvious pattern of spatial segregation than the other two activities. The study revealed the diversity and heterogeneity of spatial and temporal distribution of human aggregated activities in urban space, which is meaningful in analyzing human activities research and facilitating urban traffic optimization and urban planning.

Key words: mobile phone tracking data, trajectory analysis, spatial-temporal big data, aggregated human activities, spatial-temporal pattern