@inproceedings{9656c86cfb274130b7123fcf5a0e93ee,
title = "Context-aware similarity of trajectories",
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. Keywords: Movement data {\textendash} geographic context {\textendash} similarity measures",
author = "M. Buchin and S. Dodge and B. Speckmann",
year = "2012",
doi = "10.1007/978-3-642-33024-7\_4",
language = "English",
isbn = "978-3-642-33023-0",
series = "Lecture Notes in Computer Science",
publisher = "Springer",
pages = "43--56",
editor = "N. Xiao and M.P. Kwan and M.F. Goodchild and S. Shekhar",
booktitle = "Geographic Information Science (7th International Conference, GIScience 2012, Columbus, OH, USA, September 18-21, 2012. Proceedings)",
address = "Germany",
note = "conference; 7th International Conference on Geographic Information Science; 2012-09-18; 2012-09-21 ; Conference date: 18-09-2012 Through 21-09-2012",
}