Journal of Geo-information Science ›› 2014, Vol. 16 ›› Issue (5): 665-672.doi: 10.3724/SP.J.1047.2014.00665

Special Issue: 地理大数据

• Orginal Article •     Next Articles

Research on Human Mobility in Big Data Era

LU Feng*(), LIU Kang, CHEN Jie   

  1. State Key Lab of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
  • Received:2014-07-08 Revised:2014-08-18 Online:2014-09-10 Published:2014-09-04
  • Contact: LU Feng
  • About author:

    *The author: CHEN Nan,


Human mobility has received much attention in many research fields such as geography, sociology, physics, epidemiology, urban planning and management in recent years. On the one hand, trajectory datasets characterized by a large scale, long time series and fine spatial-temporal granularity become more and more available with rapid development of mobile positioning, wireless communication and mobile internet technologies. On the other hand, quantitative studies of human mobility are strongly supported by interdisciplinary research among geographic information science, statistical physics, complex networks and computer science. In this paper, firstly, data sources and methods currently used in human mobility studies are systematically summarized. Then, the research is comprehended and divided into two main streams, namely people oriented and geographical space oriented. The people oriented research focuses on exploring statistical laws of human mobility, establishing models to explain the appropriate kinetic mechanism, as well as analyzing human activity patterns and predicting human travel and activities. The geographical space oriented research focuses on exploring the process of human activities in geographical space and investigating the interactions between human movement and geographical space. Followed by a detailed review of recent progress around these two streams of research, some research challenges are proposed, especially on data sparsity, data skew processing and heterogeneous data mining, indicating that more integration of multidiscipline are required in human mobility studies in the future.

Key words: human mobility, big data, data mining, statistical physics, complex network