A multi-hierarchical method to extract spatial network structures from large-scale origin-destination flow data

Xingxing Zhou, Haiping Zhang, Xinyue Ye

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


Extracting spatial network structure (SNS) from large-scale origin-destination flow data is an important approach for understanding interregional association patterns and interaction laws. Currently, the extraction of SNS primarily relies on complex network clustering or aggregated statistics with predefined regional constraints. However, these methods often overlook one or more fundamental principles essential for ensuring correctness and accuracy: 1) Aggregation of spatially proximate nodes is necessary when strong interactions exist, whereas separation is preferred in the absence of such interactions. 2) It is crucial to maintain strong interactions between non-spatially proximate nodes. 3) Ultimately, nodes within each group should exhibit spatial continuity. To address these challenges, a multi-hierarchical SNS extraction method is proposed, which focuses on raw node aggregating and generalization, measurement of interaction volume and strength between node groups and strategies for node/edge filtering. The effectiveness and value of the proposed method are demonstrated through a case study using city population migration data. Furthermore, the method provides a general approach for extracting SNSs from any origin-destination flow dataset that includes locations and weights, facilitating effective flow map generalization through aggregation of origin destination (OD) flow data.

Original languageEnglish (US)
Pages (from-to)577-602
Number of pages26
JournalInternational Journal of Geographical Information Science
Issue number3
StatePublished - 2024
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Geography, Planning and Development
  • Library and Information Sciences


  • intelligent optimization
  • map generalization
  • Spatial complex network
  • spatial interaction
  • spatial network structure


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