Acta Geographica Sinica ›› 2020, Vol. 75 ›› Issue (7): 1523-1538.doi: 10.11821/dlxb202007014

• Geospatial Theory and Application • Previous Articles     Next Articles

Analytical methods and applications of spatial interactions in the era of big data

LIU Yu1(), YAO Xin1, GONG Yongxi2, KANG Chaogui3,4, SHI Xun5, WANG Fahui6, WANG Jiao'e7, ZHANG Yi1, ZHAO Pengfei1, ZHU Di1, ZHU Xinyan8   

  1. 1. Institute of Remote Sensing and Geographical Information Systems, School of Earth and Space Sciences, Peking University, Beijing 100871, China
    2. Shenzhen Key Laboratory of Urban Planning and Decision Making, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, Guangdong, China
    3. School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
    4. Center for Urban Science and Progress, New York University, Brooklyn, NY 11201, USA
    5. Department of Geography, Dartmouth College, Hanover, NH 03755, USA
    6. Department of Geography and Anthropology, Louisiana State University, Baton Rouge, LA 70803, USA
    7. Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
    8. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
  • Received:2019-07-23 Revised:2020-04-14 Online:2020-07-25 Published:2020-09-25
  • Supported by:
    National Natural Science Foundation of China(41830645);National Natural Science Foundation of China(41625003)


Spatial interaction is a critical basis of understanding human processes on the land surface. Together with spatial dependence, it embodies the uniqueness and relatedness of geographical space, as well as the impact on the embedded geographical distribution patterns. Spatial interaction also has distinctive space-time attributes, and thus it is significant to geographical research. Big data bring new opportunities for the studies of spatial interaction, which enables us to sense and observe spatial interaction patterns at different spatial scales, and simulate and predict their dynamic evolution. This provides great support for the research of human activity regularities and regional spatial structures. In this article, we first demonstrated the relationship between spatial interaction and geospatial patterns, and introduced how to sense spatial interaction with big geodata. Then, we generalized the progress of relevant models and analytical methods, and introduced the corresponding applications in fields of spatial planning, urban transportation, public health and tourism. Some key issues were also discussed. We hope this review can provide guidance for the studies of spatial interaction supported by big data.

Key words: spatial interaction, big data, model, analytical method, application, social sensing