Color is one of the most important visual variables since it can be combined with any other visual mapping to encode information without using additional space on the display. Encoding one or two dimensions with color is widely explored and discussed in the field. Also mapping multi-dimensional data to color is applied in a vast number of applications, either to indicate similar, or to discriminate between different elements or (multi-dimensional) structures on the screen. A variety of 2D colormaps exists in literature, covering a large variance with respect to different perceptual aspects. Many of the colormaps have a different perspective on the underlying data structure as a consequence of the various analysis tasks that exist for multivariate data. Thus, a large design space for 2D colormaps exists which makes the development and use of 2D colormaps cumbersome. According to our literature research, 2D colormaps have not been subject of in-depth quality assessment. Therefore, we present a survey of static 2D colormaps as applied for information visualization and related fields. In addition, we map seven devised quality assessment measures for 2D colormaps to seven relevant tasks for multivariate data analysis. Finally, we present the quality assessment results of the 2D colormaps with respect to the seven analysis tasks, and contribute guidelines about which colormaps to select or create for each analysis task.

Date Published: 8 February 2015
PDF: 16 pages
Proc. SPIE 9397, Visualization and Data Analysis 2015, 93970M (8 February 2015); doi: 10.1117/12.2079841

Published in SPIE Proceedings Vol. 9397:
Visualization and Data Analysis 2015
David L. Kao; Ming C. Hao; Mark A. Livingston; Thomas Wischgoll, Editor(s)