Name
Abstract | ||
|---|---|---|
Multivariate visualization techniques have been applied to a wide variety of visual analysis tasks and abroad 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. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1117/12.2005728 | VISUALIZATION AND DATA ANALYSIS 2013 |
Keywords | Field | DocType |
Quantitative evaluation, multivariate visualization, visual task design, texture perception, relative texture density, user study | Data mining,Computer vision,Static data,Computer science,Visualization,Texture perception,Multivariate statistics,Data type,Artificial intelligence,Test data,Multivariate analysis,Multivariate visualization | Conference |
Volume | ISSN | Citations |
8654 | 0277-786X | 3 |
PageRank | References | Authors |
0.47 | 17 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mark A. Livingston | 1 | 399 | 33.58 |
| Jonathan W. Decker | 2 | 71 | 7.60 |
| Zhuming Ai | 3 | 124 | 13.15 |