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Perceptual Guidelines for Creating Rectangular Treemaps Nicholas Kong, Jeffrey Heer, and Maneesh Agrawala
 

Summary: Perceptual Guidelines for Creating Rectangular Treemaps
Nicholas Kong, Jeffrey Heer, and Maneesh Agrawala
Fig. 1. A node-link tree with corresponding treemap and bar chart representations. Treemap design parameters that can affect
perception of rectangle area include the aspect ratios of rectangles (top middle, top right), rectangle luminance (bottom left) and
border thickness (bottom middle). Bar charts are an alternative encoding of leaf nodes' data that use length rather than area. At lower
data densities bar charts can be easier to read, but as the amount of data increases bar charts become less effective because they
are not as space-efficient as treemaps. In addition, bar charts do not directly encode the hierarchical structure of a tree.
Abstract--Treemaps are space-filling visualizations that make efficient use of limited display space to depict large amounts of hi-
erarchical data. Creating perceptually effective treemaps requires carefully managing a number of design parameters including the
aspect ratio and luminance of rectangles. Moreover, treemaps encode values using area, which has been found to be less accurate
than judgments of other visual encodings, such as length. We conduct a series of controlled experiments aimed at producing a set
of design guidelines for creating effective rectangular treemaps. We find no evidence that luminance affects area judgments, but
observe that aspect ratio does have an effect. Specifically, we find that the accuracy of area comparisons suffers when the com-
pared rectangles have extreme aspect ratios or when both are squares. Contrary to common assumptions, the optimal distribution
of rectangle aspect ratios within a treemap should include non-squares, but should avoid extreme aspect ratios. We then compare
treemaps with hierarchical bar chart displays to identify the data densities at which length-encoded bar charts become less effective
than area-encoded treemaps. We report the transition points at which treemaps exhibit judgment accuracy on par with bar charts for
both leaf and non-leaf tree nodes. We also find that even at relatively low data densities treemaps result in faster comparisons than
bar charts. Based on these results, we present a set of guidelines for the effective use of treemaps.
Index Terms--Graphical Perception, Visualization, Treemaps, Rectangular Area, Visual Encoding, Experiment, Mechanical Turk.

  

Source: Agrawala, Maneesh - Department of Electrical Engineering and Computer Sciences, University of California at Berkeley
O'Brien, James F. - Department of Electrical Engineering and Computer Sciences, University of California at Berkeley

 

Collections: Computer Technologies and Information Sciences