On map-matching vehicle tracking data
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Towards a taxonomy of movement patterns
Information Visualization
Information Processing Letters
Constrained free space diagrams: a tool for trajectory analysis
International Journal of Geographical Information Science
Space-time density of trajectories: exploring spatio-temporal patterns in movement data
International Journal of Geographical Information Science - Geospatial Visual Analytics: Focus on Time Special Issue of the ICA Commission on GeoVisualization
Time-geographic density estimation for moving point objects
GIScience'10 Proceedings of the 6th international conference on Geographic information science
Detecting Regular Visit Patterns
Algorithmica
Interactive visualization of multivariate trajectory data with density maps
PACIFICVIS '11 Proceedings of the 2011 IEEE Pacific Visualization Symposium
Computing similarity of coarse and irregular trajectories using space-time prisms
Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Map matching with inverse reinforcement learning
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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In trajectory data a low sampling rate leads to high uncertainty in between sampling points, which needs to be taken into account in the analysis of such data. However, current algorithms for movement analysis ignore this uncertainty and assume linear movement between sample points. In this paper we develop a framework for movement analysis using the Brownian bridge movement model (BBMM), that is, a model that assumes random movement between sample points. Many movement patterns are composed from basic building blocks, like distance, speed or direction. We efficiently compute their distribution over space and time in the BBMM using parallel graphics hardware. We demonstrate our framework by computing patterns like encounter, avoidance/attraction, regular visits, and following. Our motivation to study the BBMM stems from the rapidly expanding research paradigm of movement ecology. To this end, we provide an interface to our framework in R, an environment widely used within the natural sciences for statistical computing and modeling, and present a study on the simultaneous movement of groups of wild and free-ranging primates.