Evaluating Multivariate Visualizations on Time-Varying Data
Abstract
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 five visualizations of our synthetic test data: three previously evaluated techniques (Data-driven Spots, Oriented Slivers, and Attribute Blocks), one hybrid of the first 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 five techniques. Our previous finding (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.
Document Details
- Document Type
- Technical Report
- Publication Date
- Feb 01, 2013
- Accession Number
- ADA602771
Entities
People
- Jonathan W. Decker
- Mark A. Livingston
- Zhuming Ai
Organizations
- United States Naval Research Laboratory