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Proceedings Paper

Exploring hierarchical visualization designs using phylogenetic trees
Author(s): Shaomeng Li; R. Jordan Crouser; Garth Griffin; Connor Gramazio; Hans-Jörg Schulz; Hank Childs; Remco Chang
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Paper Abstract

Ongoing research on information visualization has produced an ever-increasing number of visualization designs. Despite this activity, limited progress has been made in categorizing this large number of information visualizations. This makes understanding their common design features challenging, and obscures the yet unexplored areas of novel designs. With this work, we provide categorization from an evolutionary perspective, leveraging a computational model to represent evolutionary processes, the phylogenetic tree. The result - a phylogenetic tree of a design corpus of hierarchical visualizations - enables better understanding of the various design features of hierarchical information visualizations, and further illuminates the space in which the visualizations lie, through support for interactive clustering and novel design suggestions. We demonstrate these benefits with our software system, where a corpus of two-dimensional hierarchical visualization designs is constructed into a phylogenetic tree. This software system supports visual interactive clustering and suggesting for novel designs; the latter capacity is also demonstrated via collaboration with an artist who sketched new designs using our system.

Paper Details

Date Published: 8 February 2015
PDF: 14 pages
Proc. SPIE 9397, Visualization and Data Analysis 2015, 939709 (8 February 2015); doi: 10.1117/12.2078857
Show Author Affiliations
Shaomeng Li, Univ. of Oregon (United States)
R. Jordan Crouser, Tufts Univ. (United States)
Garth Griffin, Tufts Univ. (United States)
Connor Gramazio, Brown Univ. (United States)
Hans-Jörg Schulz, Fraunhofer-Institut für Graphische Datenverarbeitung (Germany)
Hank Childs, Univ. of Oregon (United States)
Remco Chang, Tufts Univ. (United States)


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

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