New insights into the suitability of the third dimension for visualizing multivariate/multidimensional data: A study based on loss of quality quantification

Most visualization techniques have traditionally used 2D, instead of 3D representations to visualize multidimensional and multivariate data. In this paper, a way to demonstrate the underlying superiority of 3D, with respect to 2D representations, is proposed. Specifically, it is based on the inevitable quality degradation produced when reducing the data dimensionality. The problem is tackled from two different approaches: a visual and an analytical approach. Firstly, a set of statistical tests (point classification, distance perception and outlier identification) using the 2D and 3D visualization are carried out on a group of 40 users. The results indicate that there is an improvement in the accuracy introduced by the inclusion of a third dimension, however these results do not allow to obtain definitive conclusions on the superiority of 3D. Therefore, in order to draw further conclusions, a deeper study based on an analytical approach is proposed. The aim is to quantify the real loss of quality produced when the data is visualized in 2D and 3D spaces, in relation to the original data dimensionality, to analyze the difference between them. To achieve this, a recently proposed methodology [1] is used. The results obtained by the analytical approach reported that the loss of quality reaches significantly high values only when switching from 3D to 2D. The considerable quality degradation suffered in the 2D visualization strongly suggests the suitability of the third dimension to visualize data.