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.
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
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
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Maneesh Agrawala ( email@example.com) is
an associate professor in the electrical engineering and
computer sciences department of the university of
Wilmot Li ( firstname.lastname@example.org) is a research scientist in
the creative technologies lab of adobe systems Inc., san
Floraine Berthouzoz ( floraine.berthouzoz@gmail.
com) is a Ph.d. candidate in the electrical engineering
and computer sciences department of the university of
© 2011 acM 0001-0782/11/04 $10.00