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.
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.
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Buchin, M., Dodge, S., Speckmann, B. (2012). Context-Aware Similarity of Trajectories. In: Xiao, N., Kwan, MP., Goodchild, M.F., Shekhar, S. (eds) Geographic Information Science. GIScience 2012. Lecture Notes in Computer Science, vol 7478. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33024-7_4
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DOI: https://doi.org/10.1007/978-3-642-33024-7_4
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