PROGRESS IN GEOGRAPHY ›› 2018, Vol. 37 ›› Issue (7): 880-889.doi: 10.18306/dlkxjz.2018.07.002

Special Issue: 地理大数据

• Reviews • Previous Articles     Next Articles

Recent progress in studying human mobility and urban spatial structure based on mobile location big data

Xiping YANG1(), Zhixiang FANG2,*()   

  1. 1. School of Geography and Tourism, Shaanxi Normal University, Shaanxi Key Laboratory of Tourism Informatics, Xi'an 710119, China
    2. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
  • Received:2017-09-05 Revised:2018-01-22 Online:2018-07-28 Published:2018-07-28
  • Contact: Zhixiang FANG;
  • Supported by:
    National Natural Science Foundation of China, No.41231171, No.41771473, No.41571135;China Postdoctoral Science Foundation, No.2017M623112;Fundamental Research Funds for the Central Universities, No.GK201803049, No.2042017kf0235;National Key Research and Development Program of China, No.2017YFB0503802


Understanding human mobility patterns and spatial structure is of great important to urban planning, traffic management, emergence response, and so on. With the development of information and communication technologies, it is possible to collect large-scale, long-term human tracking data, which brings great opportunities and challenges for human mobility behavior studies. This article first introduces the main datasets being used for studying human mobility patterns, then reviews the recent progress from the perspectives of human travel behavior and urban spatial structure respectively. We found that most studies follow the route of "data-human travel behavior-urban spatial structure," mining and understanding human mobility patterns from the datasets and further giving insights on the characteristics of urban spatial structures. However, there exist few studies on the influence of urban spatial structure on human travel behavior. In the future, it is necessary to integrate multi-source spatiotemporal data to understand the interaction between human mobility and urban spatial structure, develop spatiotemporal analysis theory and models for dealing with mobile location big data, and focus on understanding the coupling relationship between human mobility and urban spatial structure.

Key words: mobile location big data, human mobility, urban spatial structure, progress