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 language | English (US) |
---|---|
Title of host publication | Geographic Information Science - 7th International Conference, GIScience 2012, Proceedings |
Pages | 43-56 |
Number of pages | 14 |
DOIs | |
State | Published - 2012 |
Event | 7th International Conference on Geographic Information Science, GIScience 2012 - Columbus, OH, United States Duration: Sep 18 2012 → Sep 21 2012 |
Publication series
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
---|---|
Volume | 7478 LNCS |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Other
Other | 7th International Conference on Geographic Information Science, GIScience 2012 |
---|---|
Country/Territory | United States |
City | Columbus, OH |
Period | 9/18/12 → 9/21/12 |
Bibliographical note
Funding Information:M. Buchin and B. Speckmann are supported by the Netherlands Organisation for Scientific Research (NWO) under project no. 612.001.106 and no. 639.022.707, respectively. S. Dodge was supported in parts by Forschungskredit University of Zurich (Credit No. 57060804), and NASA grant number NNX11AP61G.
Keywords
- Movement data
- geographic context
- similarity measures