Spatiotemporal data model for network time geographic analysis in the era of big data

Bi Yu Chen, Hui Yuan, Qingquan Li, Shih Lung Shaw, Hing Keung William Lam, Xiaoling Chen

Research output: Journal article publicationJournal articleAcademic researchpeer-review

50 Citations (Scopus)


There has been a resurgence of interest in time geography studies due to emerging spatiotemporal big data in urban environments. However, the rapid increase in the volume, diversity, and intensity of spatiotemporal data poses a significant challenge with respect to the representation and computation of time geographic entities and relations in road networks. To address this challenge, a spatiotemporal data model is proposed in this article. The proposed spatiotemporal data model is based on a compressed linear reference (CLR) technique to transform network time geographic entities in three-dimensional (3D) (x, y, t) space to two-dimensional (2D) CLR space. Using the proposed spatiotemporal data model, network time geographic entities can be stored and managed in classical spatial databases. Efficient spatial operations and index structures can be directly utilized to implement spatiotemporal operations and queries for network time geographic entities in CLR space. To validate the proposed spatiotemporal data model, a prototype system is developed using existing 2D GIS techniques. A case study is performed using large-scale datasets of space-time paths and prisms. The case study indicates that the proposed spatiotemporal data model is effective and efficient for storing, managing, and querying large-scale datasets of network time geographic entities.
Original languageEnglish
Pages (from-to)1041-1071
Number of pages31
JournalInternational Journal of Geographical Information Science
Issue number6
Publication statusPublished - 2 Jun 2016


  • compressed linear reference
  • spatiotemporal big data
  • spatiotemporal data model
  • spatiotemporal query
  • Time geography

ASJC Scopus subject areas

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

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