An Evaluation of Perceptually Complementary Views for Multivariate Data

We evaluate the relative merits of three techniques for visualising multivariate data: parallel coordinates; scatterplot matrix; and a side-by-side, coordinated combination of these views. In particular, we report on: (1) the most effective visual encoding of multivariate data for each of the six common tasks considered; (2) common strategies that our participants used when the two views were combined based on eye-tracking data analysis; (3) the finding that these views are perceptually complementary in the sense that they both show the same information, but with different and complementary support for different types of analysis. For the combined view, our studies show that there is a perceptually complementary effect in terms of significantly improved accuracy for certain tasks, but that there is a small cost in terms of slightly longer completion time than the faster of the two techniques alone. Eye-movement data shows that for many tasks participants were able to swiftly switch their strategies after trying both in the training phase.