Classifying visual knowledge representations: a foundation for visualization research

Authors:
Jerry Lohse;Henry Rueter;Kevin Biolsi;Neff Walker
Affiliations:
University of Michigan, Ann Arbor, Michigan;University of Michigan, Ann Arbor, Michigan;University of Michigan, Ann Arbor, Michigan;University of Michigan, Ann Arbor, Michigan
Venue:
VIS '90 Proceedings of the 1st conference on Visualization '90
Year:
1990

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Abstract

This research is an exploratory effort to classify visual representations into homogeneous clusters. Our goal is to determine the type of knowledge conveyed by various visual representations. We collected hierarchical sorting data from twelve subjects. Five principal groups of visual representations emerged from a cluster analysis of sorting data: graphs and tables, maps, diagrams, networks, and icons. Two dimensions appear to distinguish these clusters: the amount of spatial information and cognitive processing effort. Our future research will continue to identify properties that characterize knowledge expressed with visual representations.