Context-aware similarity of trajectories

Maike Buchin, Somayeh Dodge, Bettina Speckmann

Research output: Chapter in Book/Report/Conference proceedingConference contribution

32 Scopus citations

Abstract

The movement of animals, people, and vehicles is embedded in a geographic context. This context influences the movement. Most analysis algorithms for trajectories have so far ignored context, which severely limits their applicability. In this paper we present a model for geographic context that allows us to integrate context into the analysis of movement data. Based on this model we develop simple but efficient context-aware similarity measures. We validate our approach by applying these measures to hurricane trajectories.

Original languageEnglish (US)
Title of host publicationGeographic Information Science - 7th International Conference, GIScience 2012, Proceedings
Pages43-56
Number of pages14
DOIs
StatePublished - Oct 23 2012
Event7th International Conference on Geographic Information Science, GIScience 2012 - Columbus, OH, United States
Duration: Sep 18 2012Sep 21 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7478 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other7th International Conference on Geographic Information Science, GIScience 2012
CountryUnited States
CityColumbus, OH
Period9/18/129/21/12

Keywords

  • Movement data
  • geographic context
  • similarity measures

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