Authors
Andrew S. Forsberg
Jian Chen
David H. Laidlaw

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TVCG.2009.126

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Abstract
In a user study comparing four visualization methods for three-dimensional vector data, participants used visualizations from each method to perform five simple but representative tasks: 1) determining whether a given point was a critical point, 2) determining the type of a critical point, 3) determining whether an integral curve would advect through two points, 4) determining whether swirling movement is present at a point, and 5) determining whether the vector field is moving faster at one point than another. The visualization methods were line and tube representations of integral curves with both monoscopic and stereoscopic viewing. While participants reported a preference for stereo lines, quantitative results showed performance among the tasks varied by method. Users performed all tasks better with methods that: 1) gave a clear representation with no perceived occlusion, 2) clearly visualized curve speed and direction information, and 3) provided fewer rich 3D cues (e.g., shading, polygonal arrows, overlap cues, and surface textures). These results provide quantitative support for anecdotal evidence on visualization methods. The tasks and testing framework also give a basis for comparing other visualization methods, for creating more effective methods, and for defining additional tasks to explore further the tradeoffs among the methods.

Authors
Jibonananda Sanyal
Song Zhang
Gargi Bhattacharya
Phil Amburn
Robert J. Moorhead

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TVCG.2009.114

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Abstract
Many techniques have been proposed to show uncertainty in data visualizations. However, very little is known about their effectiveness in conveying meaningful information. In this paper, we present a user study that evaluates the perception of uncertainty amongst four of the most commonly used techniques for visualizing uncertainty in one-dimensional and two-dimensional data. The techniques evaluated are traditional errorbars, scaled size of glyphs, color-mapping on glyphs, and color-mapping of uncertainty on the data surface. The study uses generated data that was designed to represent the systematic and random uncertainty components. Twenty-seven users performed two types of search tasks and two types of counting tasks on 1D and 2D datasets. The search tasks involved finding data points that were least or most uncertain. The counting tasks involved counting data features or uncertainty features. A 4×4 full-factorial ANOVA indicated a significant interaction between the techniques used and the type of tasks assigned for both datasets indicating that differences in performance between the four techniques depended on the type of task performed. Several one-way ANOVAs were computed to explore the simple main effects. Bonferronni’s correction was used to control for the family-wise error rate for alpha-inflation. Although we did not find a consistent order among the four techniques for all the tasks, there are several findings from the study that we think are useful for uncertainty visualization design. We found a significant difference in user performance between searching for locations of high and searching for locations of low uncertainty. Errorbars consistently underperformed throughout the experiment. Scaling the size of glyphs and color-mapping of the surface performed reasonably well. The efficiency of most of these techniques were highly dependent on the tasks performed. We believe that these findings can be used in future uncertainty visualization design. In addition, the framework developed in this user study presents a structured approach to evaluate uncertainty visualization techniques, as well as provides a basis for future research in uncertainty visualization.