cognition of information. We commonly encounter a variety of different types of information, including cooking recipes, budgets and financial data, dance steps, tutorials on using software, explanations of strategies and plays in sports, and political polling numbers. Effective visualizations of such everyday information could empower citizens to make better decisions.

We have focused our work on identifying domain-specific design principles. An open challenge is to generalize them across multiple domains. One approach might be to first identify domain-specific design principles in very different domains, then look for commonalities between the domain-specific principles; for example, we recently developed an automated system for generating tutorials explaining how to manipulate photographs using Photoshop and GIMP. 7 The design principles for photo-manipulation tutorials are similar to those we identified for assembly instructions and include step-by-step sequences of screenshots and highlighting actions through arrows and other diagrammatic elements. Finding such similarities in design principles across multiple domains may indicate more general principles are at work.

Though we presented three strategies for identifying design principles, other strategies may be possible as well. The strategies we presented all require significant human effort to identify commonalities in hand-designed visualizations, synthesize the relevant prior studies in perception and cognition, and conduct such studies. Moreover, the Internet makes a great deal of visual content publicly available, often with thousands of example visualizations within an individual information domain. Thus, a viable alternative strategy for identifying design principles may be to learn them from a large collection of examples using statistical machine-learning techniques. We have taken an initial step in this direction, with a project designed to learn how to label diagrams from a few examples. 26 One advantage of this approach is that skilled designers often find it easier to create example visualizations than explicitly describe design principles.

Techniques for evaluating the effectiveness of visualizations and validating the design principles could also be improved. Design principles are essentially models that predict how visual techniques affect perception and cognition. However, as we noted, it is not always clear how to check the effectiveness of a visualization. More sophisticated evaluation methodology could provide stronger evidence for these models and thereby experimentally validate the design principles.

 

acknowledgments

We would like to thank David Bargeron, Michael Cohen, Brian Curless, Pat Hanrahan, John Haymaker, Julie Heiser, Olga Karpenko, Jeff Klingner, Johannes Kopf, Niloy Mitra, Mark Pauly, Doantam Phan, Lincoln Ritter, David Salesin, Chris Stolte, Robert Sumner, Barbara Tver-sky, Dong-Ming Yan, and Yong-Liang Yang for their contributions to this research. Jeff Heer, Takeo Igarashi, and Tamara Munzner provided excellent suggestions and feedback on early drafts of this article. Figure 4a is from The New Way Things Work by David Macaulay: compilation copyright © 1988, 1998 Dorling Kindersley, Ltd., London; illustrations copyright © 1988, 1998 David Macaulay. Used by permission of Houghton Mifflin Harcourt Publishing Company. All rights reserved.

 

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Maneesh Agrawala ( [e-mail]) is an associate professor in the electrical engineering and computer sciences department of the university of california, berkeley.

Wilmot Li ( [e-mail]) is a research scientist in the creative technologies lab of adobe systems Inc., san Francisco, ca.

Floraine Berthouzoz ( floraine.berthouzoz@gmail. com) is a Ph.d. candidate in the electrical engineering and computer sciences department of the university of california, berkeley.

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