Quantitative comparative evaluation of 2D vector field visualization methods

Abstract

We present results from a user study that compared six visualization methods for 2D vector data. Two methods used different distributions of short arrows, two used different distributions of integral curves, one used wedges located to suggest flow lines, and the final was line-integral convolution (LIC). We defined three simple but representative tasks for users to perform using visualizations from each method: 1) locating all critical points in an image, 2) identifying critical point types, and 3) advecting a particle.Results show different strengths and weaknesses for each method. We found that users performed 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.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 tradeoffs among methods. They may also be useful for evaluating 2D vector on 2D surfaces embedded in 3D and for defining analogous tasks for 3D visualization methods.

Neural modeling of flow rendering effectiveness

“…This produces a neural map in which continuous contours have an enhanced representation. The model or cortical processing we chose to apply is adapted from (Li 1998 We apply the model to a set of 2D flow visualization methods that were previously studied by (Laidlaw et al 2001). This allows us to carry out a qualitative comparison between the model and how humans actually performed.…”

“…Next we show how a slightly modified version of Li's model differentially enhances various flow rendering methods. Following this, we develop a perceptual model of advection tracing and show how it predicts different outcomes for an advection path-tracing task based on the prior work of (Laidlaw et al 2001). Finally we discuss how this work relates to other work that has applied perceptual modeling to data visualization and suggest other uses of the general method.…”

“…But which is best and why? (Laidlaw et al 2001) showed that the ``which is best'' question can be answered by means of user studies in which participants are asked to carry out tasks such as tracing advection pathways or finding critical points in the flow field. (Note: An advection pathway is the same as a streamline in a steady flow field.)…”

An Icon-Based Synoptic Visualization of Fully Polarimetric Radar Data

“…The new visualization technique presented here allows five variables to be mapped to a single location through the use of colored ellipses, in a manner similar to the technique used by Laidlaw et al [20,21] for visualizing diffusion tensors in a mouse's spinal column. In Figure 1, the visual parameterization of the ellipse is illustrated.…”

“…Instead of using the column/row structure of the data, the ellipses were placed at geographic locations based on the sorting of the entropy channel from high to low values. This produces a 'jitter' effect [21] which acts to apparently randomize the placement of icons.…”