Perceptually Motivated Constraints on 3D Visualizations

Abstract: This thesis addresses some important characteristics of human visual perception and their implications for three-dimensional (3D) information visualization. The effort can be divided into two parts. First, findings from vision science are explored and validated. As a starting point, the compilation of perceptually motivated evidence about what constitutes an effective and efficient method for mapping of data is undertaken. Second, the knowledge obtained is used to create candidate visualizations and to demonstrate the predictive power of the findings.Results indicate a general difficulty to convey metric, i.e. quantitative, information in 3D visualizations. Structure as defined by Euclidean geometry is not perceived with accuracy and information encoded by such distinctions are misunderstood or overlooked. On the other hand, qualitative properties as defined by affine geometry are salient are perceived with accuracy (paper I). These findings are also characteristic of two-dimensional (2D) visualizations when these need to be rapidly examined (paper II). A novel method (3D surface glyphs) for abstract multivariate data sets was developed to investigate the possible merit of encoding information by qualitative distinctions, (paper III). Evaluations showed that the information conveyed was successfully utilized and that these types of glyph have great potential. The study also illustrated the predictive power of the earlier findings. These issues were further demonstrated when investigating how 3D perspective displays are unaffected by distortions in data when the patterns displayed were defined by affine properties (paper IV). In addition, a new metric for measuring the efficiency of visualizations is presented (paper III). It is concluded that as long as visualizations are specified by qualitative properties, they could most probably be effectively and efficiently used. The need for user studies to determine if, when and how to choose a certain visualization technique for a given task is thereby significantly reduced.