Visualization combining space and time in a single display called “space time cube (STC)” is used for visualizing spatio-temporal movement data. An STC enables us to explore not only shapes and positions of vehicle trajectories but also their temporal distributions. However, it is difficult for users to manually find optimal viewpoints for understanding such characteristics of trajectories. In this research, we propose an optimal viewpoints selection method for visualizing the spatio-temporal characteristics of vehicle trajectories on a large set of crossroads using an STC. For this purpose, we provide an algorithm based on viewpoint entropy weighted by angles of trajectories with a horizontal line as a measure of viewpoint quality on a projected 2D image. We then argue that our method can be adapted to crossroads with different trajectory shapes.
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| Ranking of viewpoints with proposed viewpoint entropy based method. Each row represents results for different types of crossroads having different characteristic trajectory shapes: (a) t-shaped, (b) l-shaped, and (c) s-shaped. |
■References
- Masahiko Itoh, Daisaku Yokoyama, Masashi Toyoda, and Masaru Kitsuregawa, Optimal Viewpoint Finding for 3D Visualization of Spatio-Temporal Vehicle Trajectories on Caution Crossroads, the IEEE Big Spatial Data Workshop (BSD) 2017 (Workshop on IEEE BigData 2017), pp.3344-3352, Dec.2017
- Masahiko Itoh, Daisaku Yokoyama, Masashi Toyoda, and Masaru Kitsuregawa, Optimal Viewpoint Finding for Space Time Cube to Explore Spatio-temporal Characteristics of Vehicle Trajectories on Crossroads, In Proceedings of the 7th IEEE Symposium on Large Data Analysis and Visualization (LDAV2017)(in conjunction with IEEE VIS 2017), pp.94-95, Oct.2017
