4 February 2013 Evaluating multivariate visualizations on time-varying data
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Multivariate visualization techniques have been applied to a wide variety of visual analysis tasks and a broad range of data types and sources. Their utility has been evaluated in a modest range of simple analysis tasks. In this work, we extend our previous task to a case of time-varying data. We implemented ve visualizations of our synthetic test data: three previously evaluated techniques (Data-driven Spots, Oriented Slivers, and Attribute Blocks), one hybrid of the rst two that we call Oriented Data-driven Spots, and an implementation of Attribute Blocks that merges the temporal slices. We conducted a user study of these ve techniques. Our previous nding (with static data) was that users performed best when the density of the target (as encoded in the visualization) was either highest or had the highest ratio to non-target features. The time-varying presentations gave us a wider range of density and density gains from which to draw conclusions; we now see evidence for the density gain as the perceptual measure, rather than the absolute density.
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Mark A. Livingston, Mark A. Livingston, Jonathan W. Decker, Jonathan W. Decker, Zhuming Ai, Zhuming Ai, } "Evaluating multivariate visualizations on time-varying data", Proc. SPIE 8654, Visualization and Data Analysis 2013, 86540N (4 February 2013); doi: 10.1117/12.2005728; https://doi.org/10.1117/12.2005728


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