Inferring spatial interaction patterns from sequential snapshots of spatial distributions

Di Zhu, Zhou Huang, Li Shi, Lun Wu, Yu Liu

Research output: Contribution to journalArticlepeer-review

48 Scopus citations


Spatial interactions underlying consecutive sequential snapshots of spatial distributions, such as the migration flows underlying temporal population snapshots, can reflect the details of spatial evolution processes. In the era of big data, we have access to individual-level data, but the acquisition of high-quality spatial interaction data remains a challenging problem. Most research has been focused on distributions of movable objects or the modelling of spatial interaction patterns, with few attempts to identify hidden spatial interaction patterns from temporal transitions of spatial distributions. In this article, we introduced an approach to infer spatial interaction patterns from sequential snapshots of spatial population distributions by incorporating linear programming and the spatial constraints of human movement. Experiments using synthetic data were conducted using four simple scenarios to explore the characteristics of our method. The proposed method was used to extract interurban flows of migrants during the Chinese Spring Festival in 2016. Our research demonstrated the feasibility of using discrete multi-temporal snapshots of population distributions in space to infer spatial interaction patterns and offered a general analytical framework from snapshot data to spatial interaction patterns.

Original languageEnglish (US)
Pages (from-to)783-805
Number of pages23
JournalInternational Journal of Geographical Information Science
Issue number4
StatePublished - Apr 3 2018
Externally publishedYes

Bibliographical note

Funding Information:
This research was supported by the National Key Research and Development Program of China [Grant Number: 2017YFB0503602] and the National Natural Science Foundation of China [Grant Number: 41625003].

Publisher Copyright:
© 2017 Informa UK Limited, trading as Taylor & Francis Group.


  • big geodata
  • linear programming
  • migration flow
  • spatial heterogeneity
  • Spatial interaction


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