Similarity of trajectories taking into account geographic context

Maike Buchin, Somayeh Dodge, Bettina Speckmann

Research output: Contribution to journalArticlepeer-review

28 Scopus citations

Abstract

The movements of animals, people, and vehicles are embedded in a geographic context. This context influences the movement and may cause the formation of certain behavioral responses. Thus, it is essential to include context parameters in the study of movement and the development of movement pattern analytics. Advances in sensor technologies and positioning devices provide valuable data not only of moving agents but also of the circumstances embedding the movement in space and time. Developing knowledge discovery methods to investigate the relation between movement and its surrounding context is a major challenge in movement analysis today. In this paper we show how to integrate geographic context into the similarity analysis of movement data. For this, we discuss models for geographic context ofmovement data. Based on this we develop simple but efficient context-aware similarity measures for movement trajectories, which combine a spatial and a contextual distance. These are based on well-known similarity measures for trajectories, such as the Hausdorff, Fréchet, or equal time distance. We validate our approach by applying these measures to movement data of hurricanes and albatross.

Original languageEnglish (US)
Pages (from-to)101-124
Number of pages24
JournalJournal of Spatial Information Science
Volume9
Issue number2014
DOIs
StatePublished - 2014

Bibliographical note

Publisher Copyright:
© by the author(s).

Keywords

  • Environmental factors
  • Equal time distance
  • Fréchet distance
  • Geographic context
  • Hausdorff distance
  • Modeling context
  • Movement data
  • Similarity measures
  • Spatiotemporal analytics
  • Trajectory analysis

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