Comparing 2D Vector Field Visualization Methods: A User Study

Abstract

Abstract-We present results from a user study that compared six visualization methods for two-dimensional vector data. Users performed three simple but representative tasks using visualizations from each method: 1) locating all critical points in an image, 2) identifying critical point types, and 3) advecting a particle. Visualization methods included two that used different spatial distributions of short arrow icons, two that used different distributions of integral curves, one that used wedges located to suggest flow lines, and line-integral convolution (LIC). Results show different strengths and weaknesses for each method. We found that users performed these tasks better with methods that: 1) showed the sign of vectors within the vector field, 2) visually represented integral curves, and 3) visually represented the locations of critical points. Expert user performance was not statistically different from nonexpert user performance. We used several methods to analyze the data including omnibus analysis of variance, pairwise t-tests, and graphical analysis using inferential confidence intervals. We concluded that using the inferential confidence intervals for displaying the overall pattern of results for each task measure and for performing subsequent pairwise comparisons of the condition means was the best method for analyzing the data in this study. These results provide quantitative support for some of the anecdotal evidence concerning visualization methods. The tasks and testing framework also provide a basis for comparing other visualization methods, for creating more effective methods and for defining additional tasks to further understand the tradeoffs among the methods. In the future, we also envision extending this work to more ambitious comparisons, such as evaluating two-dimensional vectors on two-dimensional surfaces embedded in three-dimensional space and defining analogous tasks for three-dimensional visualization methods.

From Template to Image: Reconstructing Fingerprints from Minutiae Points

“…Streamlines and Line Integral Convolution (LIC) have been widely used for imaging arbitrary two and threedimensional vector fields [4], [22]. Given a vector field, streamlines are the curves that are tangential to the vector field at every point.…”

Evaluating and Validating Non-photorealistic and Illustrative Rendering

“…The critique by experts as an evaluation strategy promises to provide rich qualitative feedback which can not only suggest which type of technique is better suited for a given purpose but also, in particular, why this is the case. Acevedo et al [1] also compared the critique-based results by Jackson et al [31] to those of a previous controlled experiment [37]. They found in their pilot study that the results exhibited the same patterns for both studies, but that the designer critique generally took less time.…”

Generative Data Models for Validation and Evaluation of Visualization Techniques

“…Laidlaw et al [64] employed random vector field generation: they selected randomly nine positions within a unit square where the vectors were again chosen randomly. From these points, the vector field on the whole domain was generated by interpolation.…”