Innovation trajectories for information visualizations: comparing treemaps, cone trees, and hyperbolic trees

  • Authors:

  • Ben Shneiderman;Cody Dunne;Puneet Sharma;Ping Wang

  • Affiliations:

  • Department of Computer Science and Human-Computer Interaction Lab, University of Maryland, College Park, MD;Department of Computer Science and Human-Computer Interaction Lab, University of Maryland, College Park, MD;Department of Computer Science and Human-Computer Interaction Lab, University of Maryland, College Park, MD;College of Information Studies and Human-Computer Interaction Lab, University of Maryland, College Park, MD

  • Venue:
  • Information Visualization
  • Year:
  • 2012

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Abstract

This paper reviews the trajectory of three information visualization innovations: treemaps, cone trees, and hyperbolic trees. These three ideas were first published around the same time in the early 1990s, so we are able to track academic publications, patents, and trade press articles over almost two decades. We describe the early history of each approach, problems with data collection from differing sources, appropriate metrics, and strategies for visualizing these longitudinal data sets. This paper makes two contributions: (1) it offers the information visualization community a history of how certain ideas evolved, influenced others, and were adopted for widespread use and (2) it provides an example of how such scientometric trajectories of innovations can be gathered and visualized. Guidance for designers is offered, but these conjectures may also be useful to researchers, research managers, science policy analysts, and venture capitalists.