Graphical Encoding for Information Visualization:
Using Icon Color, Shape, and Size To Convey
Nominal and Quantitative Data
Lucille Terry Nowell
ABSTRACT
In producing a user interface design to visualize search results for a digital library called Envision [Nowell, France, Hix, Heath, & Fox, 1996] [Fox, Hix, Nowell, et al., 1993] [Nowell & Hix, 1993], we found that choosing graphical devices and document attributes to be encoded with each graphical device is a surprisingly difficult task. By graphical devices we mean those visual display elements (e.g., color, shape, size, position, etc.) used to convey encoded, semantic information.
Research in the areas of psychophysics of visual search and identification tasks, graphical perception, and graphical language development provides scientific guidance for design and evaluation of graphical encodings which might otherwise be reduced to opinion and personal taste. However, literature offers inconclusive and often conflicting viewpoints, suggesting a need for further research.
The goal of this research was to determine empirically the effectiveness of graphical devices for encoding nominal and quantitative information in complex visualization displays. Using the Envision Graphic View, we conducted a within-subjects empirical investigation of the effectiveness of three graphical devices - icon color, icon shape, and icon size - in communicating nominal (document type) and quantitative (document relevance) data.
Our study provides empirical evidence regarding the relative effectiveness of icon color, shape, and size for conveying both nominal and quantitative data. While our studies consistently rank color as most effective, the rankings differ for shape and size. For nominal data, icon shape ranks ahead of icon size by all measures except time for task completion, which places shape behind size. For quantitative data, we found, by all measures, that encodings with icon shape are more effective than with icon size. We conclude that the nature of tasks performed and the relative importance of measures of effectiveness are more significant than the type of data represented for designers choosing among rankings.
