大数据时代的空间交互分析方法和应用再论

Translated title of the contribution: Analytical methods and applications of spatial interactions in the era of big data

Yu Liu, Xin Yao, Yongxi Gong, Chaogui Kang, Xun Shi, Fahui Wang, Jiao'e Wang, Yi Zhang, Pengfei Zhao, Di Zhu, Xinyan Zhu

Research output: Contribution to journalReview articlepeer-review

16 Scopus citations

Abstract

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.

Translated title of the contributionAnalytical methods and applications of spatial interactions in the era of big data
Original languageChinese (Traditional)
Pages (from-to)1523-1538
Number of pages16
JournalActa Geographica Sinica
Volume75
Issue number7
DOIs
StatePublished - Jul 25 2020
Externally publishedYes

Bibliographical note

Funding Information:
National Natural Science Foundation of China, No.41830645, No.41625003.

Publisher Copyright:
© 2020, Science Press. All right reserved.

Keywords

  • Analytical method
  • Application
  • Big data
  • Model
  • Social sensing
  • Spatial interaction

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