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Journal of Computer Science and Technology

, Volume 33, Issue 4, pp 849–862 | Cite as

Mining Semantic Trajectory Patterns from Geo-Tagged Data

  • Guochen Cai
  • Kyungmi Lee
  • Ickjai Lee
Regular Paper
  • 38 Downloads

Abstract

User-generated social media data tagged with geographic information present messages of dynamic spatio-temporal trajectories. These increasing mobility data provide potential opportunities to enhance the understanding of human mobility behaviors. Several trajectory data mining approaches have been proposed to benefit from these rich datasets, but fail to incorporate aspatial semantics in mining. This study investigates mining frequent moving sequences of geographic entities with transit time from geo-tagged data. Different from previous analysis of geographic feature only trajectories, this work focuses on extracting patterns with rich context semantics. We extend raw geographic trajectories generated from geo-tagged data with rich context semantic annotations, use regions-of-interest as stops to represent interesting places, enrich them with multiple aspatial semantic annotations, and propose a semantic trajectory pattern mining algorithm that returns basic and multidimensional semantic trajectory patterns. Experimental results demonstrate that semantic trajectory patterns from our method present semantically meaningful patterns and display richer semantic knowledge.

Keywords

semantic trajectory spatio-temporal geo-tagged data trajectory pattern mining 

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Supplementary material

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Information Technology Academy, College of Business, Law and GovernanceJames Cook UniversityQueenslandAustralia

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