TrajectoryLenses – A Set-based Filtering and Exploration Technique for Long-term Trajectory Data
Article first published online: 1 JUL 2013
© 2013 The Author(s) Computer Graphics Forum © 2013 The Eurographics Association and Blackwell Publishing Ltd.
Computer Graphics Forum
Volume 32, Issue 3pt4, pages 451–460, June 2013
How to Cite
Krüger, R., Thom, D., Wörner, M., Bosch, H. and Ertl, T. (2013), TrajectoryLenses – A Set-based Filtering and Exploration Technique for Long-term Trajectory Data. Computer Graphics Forum, 32: 451–460. doi: 10.1111/cgf.12132
- Issue published online: 1 JUL 2013
- Article first published online: 1 JUL 2013
- H.3.3 [Information Storage and Retrieval]: Information Search and Retrieval—Information filtering, Query formulation, Selection process;
- H.5.2 [Information Interfaces and Presentation]: User Interfaces—GUI
The visual analysis of spatiotemporal movement is a challenging task. There may be millions of routes of different length and shape with different origin and destination, extending over a long time span. Furthermore there can be various correlated attributes depending on the data domain, e.g. engine measurements for mobility data or sensor data for animal tracking. Visualizing such data tends to produce cluttered and incomprehensible images that need to be accompanied by sophisticated filtering methods. We present TrajectoryLenses, an interaction technique that extends the exploration lens metaphor to support complex filter expressions and the analysis of long time periods. Analysts might be interested only in movements that occur in a given time range, traverse a certain region, or end at a given area of interest (AOI). Our lenses can be placed on an interactive map to identify such geospatial AOIs. They can be grouped with set operations to create powerful geospatial queries. For each group of lenses, users can access aggregated data for different attributes like the number of matching movements, covered time, or vehicle performance. We demonstrate the applicability of our technique on a large, real-world dataset of electric scooter tracks spanning a 2-year period.