Collision Detection and Response for Computer Animation
SIGGRAPH '88 Proceedings of the 15th annual conference on Computer graphics and interactive techniques
Analyzing Relative Motion within Groups of Trackable Moving Point Objects
GIScience '02 Proceedings of the Second International Conference on Geographic Information Science
Foundations of Multidimensional and Metric Data Structures (The Morgan Kaufmann Series in Computer Graphics and Geometric Modeling)
OpenStreetMap: User-Generated Street Maps
IEEE Pervasive Computing
Towards a taxonomy of movement patterns
Information Visualization
A graph-based approach to vehicle trajectory analysis
Journal of Location Based Services - GeoVA(t) - Geospatial visual analytics: focus on time. Special issue of the ICA Commission on GeoVisualisation
Comparing Vessel Trajectories Using Geographical Domain Knowledge and Alignments
ICDMW '10 Proceedings of the 2010 IEEE International Conference on Data Mining Workshops
A conceptual framework and taxonomy of techniques for analyzing movement
Journal of Visual Languages and Computing
A description logic approach to discover suspicious itineraries from maritime container trajectories
GeoS'11 Proceedings of the 4th international conference on GeoSpatial semantics
Interactive visualization of multivariate trajectory data with density maps
PACIFICVIS '11 Proceedings of the 2011 IEEE Pacific Visualization Symposium
An event-based conceptual model for context-aware movement analysis
International Journal of Geographical Information Science
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The widespread adoption of location-aware devices is resulting in the generation of large amounts of spatiotemporal movement data, collected and stored in digital repositories. This forms a fertile ground for domain experts and scientists to analyze such historical data and discover interesting movement behavioral patterns. Experts in many domains, such as transportation, logistics and retail, are interested in detecting and understanding movement patterns and behavior of objects in relation to each other. Their insights can point to optimization potential and reveal deviations from planned behavior. In this paper, we focus on the detection of the encounter patterns as one possible type in movement behavior. These patterns refer to objects being close to one another in terms of space and time. We define scalability as a core requirement when dealing with historical movement data, in order to allow the domain expert to set parameters of the encounter detection algorithm. Our approach leverages a designated data structure and requires only a single pass over chronological data, thus resulting in highly scalable and fast technique to detect encounters. Consequently, users are able to explore their data by interactively specifying the spatial and temporal windows that define encounters. We evaluate our proposed method as a function of its input parameters and data size. We instantiate the proposed method on urban public transportation data, where we found a large number of encounters. We show that single encounters emerge into higher level patterns that are of particular interest and value to the domain. © 2012 Wiley Periodicals, Inc.