Detecting Regular Visit Patterns
ESA '08 Proceedings of the 16th annual European symposium on Algorithms
Decentralized Movement Pattern Detection amongst Mobile Geosensor Nodes
GIScience '08 Proceedings of the 5th international conference on Geographic Information Science
Detecting single file movement
Proceedings of the 16th ACM SIGSPATIAL international conference on Advances in geographic information systems
Evaluation of the visibility of vessel movement features in trajectory visualizations
EuroVis'11 Proceedings of the 13th Eurographics / IEEE - VGTC conference on Visualization
Detecting movement patterns using Brownian bridges
Proceedings of the 20th International Conference on Advances in Geographic Information Systems
Semantic trajectories modeling and analysis
ACM Computing Surveys (CSUR)
Mining group movement patterns
Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Efficient identification and approximation of k-nearest moving neighbors
Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Pervasive and Mobile Computing
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Widespread availability of location aware devices (such as GPS receivers) promotes capture of detailed movement trajectories of people, animals, vehicles and other moving objects, opening new options for a better understanding of the processes involved. In this paper we investigate spatio-temporal movement patterns in large tracking data sets. We present a natural definition of the pattern `one object is leading others', which is based on behavioural patterns discussed in the behavioural ecology literature. Such leadership patterns can be characterised by a minimum time length for which they have to exist and by a minimum number of entities involved in the pattern. Furthermore, we distinguish two models (discrete and continuous) of the time axis for which patterns can start and end. For all variants of these leadership patterns, we describe algorithms for their detection, given the trajectories of a group of moving entities. A theoretical analysis as well as experiments show that these algorithms efficiently report leadership patterns.