DUX'07 location:Chicago, Illinois date:5-7 November 2007
Through results gathered from a large-scale online survey, this paper empirically investigates the assessment of aesthetic in 11 common data visualization techniques. Visualizations represented in this study were generated from an identical hierarchical dataset and visually normalized to avoid unwanted implications of default application parameters or personal preferences. Results from subjective participant response shows data visualizations that portray non-orthogonal, organic qualities, receive higher aesthetic rankings. Provided rationale further correlate these qualities with animate attributes of motion, growth and evolution, positively affecting the perception of their beauty.