Modern visualization methods are needed to cope with very highdimensional data. Efficient visual analytical techniques are required to extract the information content in these data. The large number of possible projections for each method, which usually grow quadratically or even exponentially with the number of dimensions, urges the necessity to employ automatic reduction techniques, automatic sorting or selecting the projections, based on their informationbearing content. Different quality measures have been successfully applied for several specified user tasks and established visualization techniques, like Scatterplots, Scatterplot Matrices or Parallel Coordinates. Many other popular visualization techniques exist, but due to the structural differences, the measures are not directly applicable to them and new approaches are needed. In this paper we propose new quality measures for three popular visualization methods: Radviz, Pixel-Oriented Displays and Table Lenses. Our experiments show that these measures efficiently guide the visual analysis task.

Density differences are one of the main features users perceive in 2D scatter plots. However, because of pixels’ collisions, some areas become saturated and such differences are lost. To solve this problem, several proposals rely on sampling the dataset before visualizing it. Some of these introduce precise measures to understand the image degradation and use numerical differences in pixels to estimate density differences. It is our opinion that this issue deserves a deeper analysis, taking into account perceptual issues. In this paper we describe a study we conducted to understand the relationship between numerical pixel density and the perceived density. The results obtained were used to refine a sampling technique we developed to preserve relative densities in the context of 2D scatter plots

Information Visualization produces a visual representation of abstract data in order to facilitate a deeper level of understanding of the data under investigation. This paper introduces ViCo, a metric for assessing Information Visualization complexity. The proposed metric allows for the measurement of Information Visualization complexity with respect to tasks and users. The algorithm for developing such a metric for any chosen collection of visualizations is described in general and then applied to two examples for purposes of illustration.

Information Visualization produces a visual representation of abstract data in order to facilitate a deeper level of understanding of the data under investigation. This paper introduces ViCo, a metric for assessing Information Visualization complexity. The proposed metric allows for the measurement of Information Visualization complexity with respect to tasks and users. The algorithm for developing such a metric for any chosen collection of visualizations is described in general and then applied to two examples for purposes of illustration.