2015 IEEE Pacific Visualization Symposium (PacificVis) (2015)
April 14, 2015 to April 17, 2015
Vsevolod Peysakhovich , ISAE, Toulouse, France
Christophe Hurter , DGAC, Toulouse, France
Alexandru Telea , University of Groningen, Netherlands
Edge bundling methods reduce visual clutter of dense and occluded graphs. However, existing bundling techniques either ignore edge properties such as direction and data attributes, or are otherwise computationally not scalable, which makes them unsuitable for tasks such as exploration of large trajectory datasets. We present a new framework to generate bundled graph layouts according to any numerical edge attributes such as directions, timestamps or weights. We propose a GPU-based implementation linear in number of edges, which makes our algorithm applicable to large datasets. We demonstrate our method with applications in the analysis of aircraft trajectory datasets and eye-movement traces.
Image edge detection, Clutter, Aircraft, Visualization, Trajectory, Kernel, Instruments
V. Peysakhovich, C. Hurter and A. Telea, "Attribute-driven edge bundling for general graphs with applications in trail analysis," 2015 IEEE Pacific Visualization Symposium (PacificVis)(PACIFICVIS), Hangzhou, China, 2015, pp. 39-46.