A republic of information designers
Jos de Bruin and Remko Scha
Institute of Artificial Art Amsterdam
Information Design requires system architects, not information architects.
It will be about developing algorithms for automatic and semi-automatic
visualization, not about creating specific designs. It will be about
design systems and design theories, not about deciding for the rest
of us what the world should look like.
Anyone who can speak or write or draw is by definition involved
in the design of information. In the information republic that we
envisage, all citizens will have access to all raw information,
and use automatic design tools to represent that information for
themselves or for others in whatever way they want.
Existing automatic visualization techniques point the way, but are
not yet up to this task. This paper stresses the distinction between
the communicative and the esthetic dimension of visual information.
It argues that indeterminacy is inherent in communication and essential
to esthetics, and that this indeterminacy should be incorporated
into the generation processes used by visual design systems. It
points to some of the relevant research that is going on already,
and sketches the architecture of the interactive visualization systems
of the future.
Talk of a "Republic of Information" raises some uneasy feelings.
Its solemn capitals evoke images of a disembodied network in Cyberspace,
in which information has taken on a life of its own and individual
persons have been reduced to replaceable I/O-devices, controlled
by the spin doctors who used to work for Orwell's Department of
Information. Such an authoritarian cyber-state is completely at
odds with the idea that the digital revolution could lead to more
The introduction of an information elite does little to reassure
us. Wurman (1995) sees a heroic role for "a group of people, small
in number, deep in passion, called Information Architects", struggling
forward through the "field of black volcanic ash" constituted by
current design, in order to save humanity from the "tsunami of data
that is crashing onto the beaches of the civilized world". This
sounds more like a blurb for the next Spielberg blockbuster, with
Information Architects as the good guys, than as a serious proposal
about the role of information design. However, the conference brochure
similarly suggests that the "Republic of Information" is "going
to be laid out and planned by a new breed of architects, informed
with a new level of understanding and purpose".
We would like to propose an alternative approach. Let's drop the
capitals. The "republic of information" could then refer to a worldwide
community of people, freely engaged in producing and processing
information -- a community that includes virtually everybody. After
all, anyone who can speak or write or draw is by definition involved
in the design of information. In contrast to what the term Information
Age suggests, information is not a commodity, like iron or steam
or electricity, that mankind has only recently learned to harness
for its needs, and that requires a new type of experts. Humans have
always spent most of their lives processing information. What is
new today is the role of technology. This is not the Information
Age, but the Information Technology Age.
The information-infrastructure of the world is developing at a very
fast rate. The Web epitomizes this trend. Huge databases are or
will soon be instantaneously accessible to large numbers of people.
These people will increasingly be able to create large and complex
databases of their own, by selecting and recombining subsets from
existing databases, by employing automatic procedures for collecting
their own data, and by performing complex computations. In order
to inspect, distribute and publish these databases, people will
have to be able to represent them in a visual way.
The need for data visualizations is thus going to explode. Ever
more people will regularly be involved in visualizing complex data
sets. These people do not want to become professional Information
Designers. Nor do they want to hire professional Information Designers,
because they want their visualizations immediately. They need computer-aided,
interactive or fully automatic data visualization.
In this paper we investigate what is involved in accomplishing this.
To do that, we must distinguish two dimensions of information design.
The first is the "communicative" dimension: is the visual representation
easy to decode, and is it likely to give rise to the interpretation
that was actually intended? The second is the "esthetic" dimension:
does it give rise to visual pleasure? Does it look fashionable?
Does it fit in with the "corporate identity" of the sender?
Note that these two concerns are necessarily in conflict. If communication
would be the only goal or have unassailable priority, we should
set ISO standards and Deutsche Industrie Norms and stick to them.
It is clear that nobody wants to do that. We have seen what happened
with functionalism in architecture.
The R&D tradition aiming at automatic visualization algorithms
has been almost exclusively concerned with the communicative dimension.
It has largely ignored many relevant aspects of visual cognition,
and it has completely ignored the esthetic dimension. That is why
existing automatic visualization techniques do not yet answer the
challenge of providing the world with automatic information design
systems. They still need human design experts to tune the systems,
to tweak their inputs, to modify their outputs, or even to redo
things from scratch when the system fails completely.
This paper proposes to overcome these limitations by accepting that
indeterminacy is inherent in communication and essential to esthetics,
and incorporating this indeterminacy into the generation processes
used by visual design systems. It points to some of the relevant
research that is going on already, and sketches the architecture
of the interactive visualization systems of the future.
The problem of automatic visualization
Information design as a discipline is based on the assumption that
information can be visualized in a systematic manner, i.e. that
there are rules that govern the interpretation of visualizations.
By taking these rules into account, a designer can create effective
visualizations that reflect the relevant properties of the underlying
information design takes this idea literally:
it tries to make these rules explicit and formal, in order to make
it possible to automate their application. This would allow end-users
to focus on the information they want to express, leaving decisions
on how to express this information to a computer program.
To be able to develop such automatic design systems, we have to
be formal, i.e. precise and unambiguous, about each of the steps
needed to create an effective visualization. We have to describe
in mathematical terms:
Mathematical data characterization: relational databases
- the information we want to express
- the graphical means that are available
- which designs are appropriate for what type of
- what procedure we will use to select a particular
design for a given piece of information,
- and how we will implement that design and produce
an actual visual rendering.
To understand current automatic visualization systems, it is useful
to begin by looking in some detail at the first of these issues:
how to characterize the information that is to be visualized. This
characterization has to be independent of the analysis of graphical
attributes, otherwise we will not be able to specify the relations
between these two levels clearly and unambiguously. Twymans
otherwise thought-provoking Schema for the Study of Graphic Language
(1979) illustrates how a failure to distinguish clearly between
these two levels limits the usefulness of a classification scheme.
To specify the data characteristics, we can make use of the formal
semantics of information as developed in logic, statistics, linguistics
and computer science. One particularly useful formalism is relational
algebra (Codd, 1970; Date, 1975), the formalism underlying relational
databases. Relational databases specify the extensions of n-place
predicates: e.g. the employees of a company are enumerated in a
table together with their values for the functions Name, Salary,
Department. This sort of information is by far the most common type
of information considered for visualization.
A basic distinction among the attributes of relations is the way
their values can be ordered. In nominal attributes such as Name,
the set of values is unordered. The values can be distinguished,
but not compared further in any meaningful way. In an ordinal data
set, values can be ranked, but their differences or distances can
not. An interval attribute does allow such comparison of differences,
while values on a ratio scale can even be compared in absolute terms.
Automatic visualization: the state of the art
Typical automatic visualization systems such as APT (Mackinlay,
1986) or SAGE (Roth e.a., 1994) are built to exploit the properties
of certain kinds of relational data sets. These systems typically
assume a rather limited set of well-understood graphical representations
(graphs, bar-charts, maps) as potential graphical structures. Esthetic
details are fixed arbitrarily or must be specified by the end-user.
The visualization problem is dealt with in these systems as a matter
of finding the best mapping from the data to the available graphical
structures. Systems of this sort take the basic scale types discussed
above as a point of departure. In this they follow in the footsteps
of Bertin (1983), who used these types as the basis for his seminal
treatment of graphs and maps. The scale types have clear implications
for the kind of graphical attributes that are appropriate for expressing
the data. For instance, shape or color are effective to distinguish
items, but not to rank them, saturation is good to rank a limited
number of values, and position along a scale is a good representation
for quantative values.
While the distinction between these scale types is a useful starting
point, it is not enough. Many researchers have felt the need to
add more specific semantic types. Time, space, temperature, money
etc. all have their own special characteristics, which influence
the appropriate choice of graphical attributes (see Roth, 1990 for
some additional types). This is an open issue, with each special
domain introducing additional distinctions (see e.g. Zhou and Feiner,
1996 for types required within the medical domain).
The scope of this approach can be extended by distinguishing different
types of predicates or relations. Sets, functions, trees, lists,
can all be specified as n-place predicates with special properties.
Each of these has different implications for effective presentations.
Kamps et al. (1996) give a taxonomy of binary predicates and their
appropriate graphical representations.
A mathematical characterization of the space of possible graphical
Current automatic visualization systems consider rather limited
sets of rather prototypical visualizations. What would it mean to
try to take the whole range of possibilities into account? How can
all possible graphics be systematically described? Spreadsheets,
statistical and mathematical packages, Geographical Information
Systems and other tools with facilities for data presentation offer
an ever-increasing set of templates or graph-types. Can we describe
these in terms of a shared set of structural properties?
Visualizations are useful for communication to the extent that their
interpretations are shared. Some interpretations can only be shared
if all participants learn the direct and arbitrary associations
between pictures and meanings. Like symbols or words, most iconic
pictures can only be understood if their meaning is given. However,
the real power of visualizations lies in their ability to express
new meanings that go beyond the given meanings of their components
or attributes, just like a sentence or larger text expresses more
than an unstructured collection of words.
Therefore a first pre-requisite for specifying design rules is a
good way to describe pictures in terms of their structure. Automatic
design presupposes the availability of a set of graphical categories
and a generative formalism in which specific visualizations can
be described. Just as a language can be concisely specified in terms
of a grammar and a lexicon, we can specify an unlimited number of
possible graphics by using a limited set of basic terms, plus a
set of operators with which new terms or categories can be constructed
out of these basic terms.
One place to look for the type of operators required is in drawing
tools such as MacPaint or AutoCAD. These contain a large number
of operators that are suitable candidates for inclusion in a generative
image grammar. Relevant also are interface builders, such as systems
for computational steering (Van Liere, 1996), which provide users
with low-level graphical objects from which to construct interfaces
that provide direct control over selected parameters of a simulation.
Mappings from visual Gestalts to information structures
While constructive operators are sufficient to define the space
of graphical structures, they provide a misleading angle on the
visualization problem. Visualization is not so much a matter of
finding the right visual structure for a piece of information as
of judging whether a given visual structure does indeed cause the
intended perceivers to draw the intended inferences. It is not enough
that there is an isomorphism between the information structure and
description of its graphical representation; what matters
is that there is such an isomorphism between the information structure
and the structure of the graphical representation that is actually
perceived. Existing automatic visualization systems do not embody
theories about the way in which visual input is perceived by humans.
This is an important limitation, even if existing theories are still
Gestalt Psychology (Wertheimer, 1938) has distinguished some of
the organizing principles, such as proximity, continuity and similarity,
that underlie perceptual structures or Gestalts. Gestalt Psychology
was brought to the US by German psychologists fleeing the Third
Reich. While they survived, their psychology succumbed to the behavioristic
mainstream of the time. It resurfaced in the work of Leeuwenberg
et al (1971), who explained the Prägnanz of certain Gestalts
(i.e. the perceptual preference for certain structural interpretations)
in terms of a complexity measure over expressions that specify the
operations needed to recreate the pictures that cause these Gestalts.
This work was initially restricted to "turtle graphics",
i.e. line-based structures that are essentially one-dimensional.
Dastani is currently extending it to real 2-D by developing a mathematical
characterization of the space of all possible Gestalts, and a mathematical
description of the mappings from visual inputs to visual Gestalts
as performed by the human visual/cognitive system.
Mappings are sets of rules that specify how the elements and operators
that compose a structure in one domain can be translated into the
elements and operators that compose the corresponding structure
in the other domain. Once we understand which visual structures
are perceived, these structures have to be mapped to their corresponding
information structures. A mathematical characterization of the space
of possible mappings from Gestalts to information structures has
to include a preference measure on this space that reflects how
easily the human cognitive system performs this mapping. (Cf. the
rules for "good" or "effective" design.)
One of the first designers to formulate such mappings, was the cartographer
Bertin (1983). He based his firm prescriptions for good design on
a number of intuitively appealing correspondences between the way
people can distinguish colors, saturation levels, positions, shapes,
sizes etc. on the one hand and the properties of different types
of data (continuous, ordinal, nominal) on the other hand. Dastani
(1997) is formalizing these mappings using isomorphisms between
relational and graphical algebras. Engelhardt et al. (1996,
1998) elaborate the semantic use of space. Wang et al. (1997) formalize
these ideas in a design theory based on mappings between data signatures
and graphical signatures. Design systems such as APT and SAGE use
these mappings in a more pragmatic way, and in the other direction,
to generate designs based on data characteristics.
Indeterminacy and effective designs
Given the mappings, a visualization algorithm can be developed.
This algorithm will have to make a number of indeterminate choices,
since the mappings will be neither sufficient nor necessary, i.e.
they do not cover all details, and are only probable. In other words,
there will always be many different ways to convey the same information,
and this leaves an enormous space for variation.
There are three sources for this indeterminacy. The first has to
do with what we have called the esthetic dimension: a design generated
by an automatic visualization system will never be complete. The
data characteristics will never completely determine fonts, colors,
the shape of marks, the use of illustrations and so on. The same
design (e.g. a line graph with time as X-axis, temperature on the
Y-axis) can be rendered in many ways.
Two other sources of indeterminacy lie within the communicative
realm. Firstly, more than one mapping might be applicable, i.e.
more than one design might be appropriate. The type of graph, the
coupling of data dimensions to scales etc., each may well allow
multiple choices. Secondly, the information itself will normally
be more or less indeterminate. Initially users will not know exactly
what they want to express or what they are looking for.
A visualization algorithm should not only find and apply the appropriate
mappings, but should also have a strategy for dealing with the large
set of alternative possibilities for visualizing any particular
piece of information. The usual approaches are: (1) forced determinism
(the system makes certain fixed choices) or (2) user control (the
system lets the end-user decide on the steps to take). Both of these
strategies are problematic. In the first approach, the set of possibilities
is limited in an arbitrary way. If the second approach is taken
seriously, end-users must make too many choices which they do not
understand, with a similar result: the set of possibilities ends
up being limited in an equally arbitrary way.
The VISAGE framework developed by Roth c.s. (1997) follows the second
approach. In VISAGE the SAGE design engine is augmented with SAGEBRUSH,
a sketch pad for new designs, and SAGEBOOK, a repository of previous
designs, both of which are intended to give the user more control
over the process. We expect that these will lead to less variation
and less surprising designs.
Giving both designer and end-user more control was also the aim
of the M modeling tool (de Bruin, 1996). The M package consists
of an integrated simulation and visualization system. It is used
to develop user interfaces to simulation models of environmental
and public health issues, such as climate change. These complex
issues require the involvement of many different experts, affect
many people and are surrounded with much uncertainty and controversy.
It should be easy for designers of such models to adapt their presentations
to different audiences, be they policy makers, other experts or
members of the general public. These audiences should also be able
to explore these models by themselves, e.g. by focusing on details
of particular concern to them.
To achieve this, the M interface design engine uses its knowledge
of the model variables and their dependencies to produce appropriate
visualizations for different data types, and knowledge of the model
equations to produce network diagrams that reflect the dependencies
between variables. Users can always override these suggestions by
direct manipulation of the interface design. As different occasions
or users may require different designs, multiple views on the same
model can be generated and presented simultaneously.
M has been used successfully in large modeling efforts involving
up to 10 designers over periods of 4-5 years. While users are generally
satisfied with the resulting interfaces, they often shy away from
taking full advantage of the options provided. They tend to stick
to basic templates and pre-selected settings, instead of actively
taking advantage of the possibility to generate alternative views
that might provide new or more effective insights.
Therefore, we propose a different approach, which allows users to
take better advantage of the myriad possibilities for expressive
designs. The inspiration for this approach comes from the use of
mathematical randomness in "chance art", which was introduced in
music and visual art in the late fifties. (Cf. Brecht, 1957; Cage,
1973; Morellet, 1962; Nake, 1974.) Artists practicing this genre
do not design individual works of art; instead, they specify constructive
definitions of large classes of possible artworks, and then execute
randomly selected samples from these classes. Chance art was first
developed by artists with a "minimalist" frame of mind; their definitions
of "artwork schemata" were therefore extremely simple. Typical examples
would be: the set of all possible colorings of a grid of squares,
using a very small set of colors; the set of all possible positionings
on a plane of a random number of identical black dots; or the set
of all possible ways to connect randomly chosen points on the plane
by means of straight lines with a fixed width and a fixed color.
More recently, this approach has been developed further in two different
directions. On the one hand, some artists have implemented complex
algorithms that emulate specific artistic styles (cf. Cohen, 1979).
More relevant for our current discussion, however, is the "postmodernist"
generalization of chance art exemplified by the program "Artificial"
(Eberson, 1993; Kluitenberg and Harry, 1998; Scha, 1989.) Here the
goal is precisely not
to emulate a particular style, but
to develop an algorithm that can generate any
image, in any
possible style -- an algorithm that embodies absolute meaninglessness
and total arbitrariness, by implementing an all-encompassing "style
to end all styles".
The project of fully automatic, completely random "Artificial Art"
is of course not finished yet, and it is not even clear whether
it ever will be. But it has been demonstrated that significant steps
in this direction can be taken. "Artificial" employs a mathematical
picture-description language that incorporates notions from programs
like MacPaint and from Gestalt-perception theories like Leeuwenberg's.
By executing randomly generated expressions from this language,
"Artificial" displays an interesting caricature of art-generating
behavior. It is obvious that this technique can be usefully applied
to deal with what we have called the "esthetic" dimension of the
automatic visualization problem. Because here we have the same situation:
a wealth of possible choices which are ultimately arbitrary, but
which the end-user may nevertheless find significant for unfathomable
reasons. Rather than making one fixed ill-motivated choice, the
ideal system should operate with an explicit representation of the
whole "choice-space" that is available, and present the user with
samples from that space.
The approach we suggest can thus be summarized as "generate and
test". Choices should be made by the system, not deterministically
and once, but randomly and repeatedly. The system should thus generate
many variations of appropriate visualizations, possibly taking into
account preference measures and user-defined constraints. The task
of the users is to pick the ones they like best.
A requirement is that the visualizations generated are reasonable
candidates: they should express the underlying data faithfully and
take the known Gestalt principles into account. However, even so
there will exist many reasonable variants, some of which will be
surprisingly better at conveying particular aspects to particular
users. Finding these will be an iterative process: the user can
interactively modify constraints and preferences.
By enlisting the unlimited capacity of the computer to apply the
design rules without prejudice, i.e. in a truly random fashion,
there will be much more variation. The algorithm we have in mind
will not be tempted to stick to previously successful designs, except
of course when it is told to do so. This freedom to explore uncharted
areas is an obvious advantage in art, where there is no need for
communication, and the esthetic experience of the observer is the
only criterion. Artists perform their function in a suboptimal way,
because of their need for subjective expression and story-telling,
and their tendency to follow fads and fashions (cf. Harry, 1992;
Vreedenburgh and Scha, 1994).
To the extent that esthetic pleasure also plays a role in information
design, the same argument applies. But does it also apply to the
communicative aspects of information design? Seen as communications,
visualizations are expressions of something and not just l'art-
pour-l'art, and one could argue that a search for all possible variants
is irrelevant or perhaps even counter-productive. If a visualization
works, it works, so why confuse people by straying from generally
accepted conventions and familiar images? Why not leave it up to
the users to adapt a design to their esthetic tastes, or to change
the data specification in order to get another view that might correspond
more closely to what they could be looking for?
There are two closely related reasons why the freedom to explore
the full space of potential designs is an important advantage for
information design: (a) designers exploring a data set seldom know
in advance what exactly they are looking for, and (b) even if designers
know exactly what they want to tell their audience, they normally
have neither full knowledge of this audience nor full control over
the circumstances in which this audience will be looking at their
In other words, the visualization cannot be fully specified in advance
and a single, once-and-for-all design of the information is not
sufficient. In these cases and they are by far the most common
automatic design has to be part of an iterative search for
the right way to present some piece of information. Whether communicating
with oneself (i.e. exploring information) or communicating information
to others, the computer can help in the search for the most effective
presentation by a) reducing the space of potential forms and b)
quickly testing candidates from the remaining space. Using the computer,
this testing does not necessarily have to be done by the sender.
A sender could obviously send multiple versions of a message in
the hope of thus increasing the chances of conveying his message
to a diverse audience. However, it is simpler and more appropriate
to send the contents, and let recipients use design algorithms and
decide for themselves on the precise form in which to look at the
The role of style in the generation of graphics
Information design wears two hats, often at the same time. Wearing
one hat, information design is about expressing information in an
objective, scientific manner, about "not lying with visualizations"
and improving insight (see Tufte, 1997, for some rather high-minded
opinions on this point - and some nice illustrations). Wearing its
other hat, information design is about helping customers to find
a corporate style, i.e. forms that express their individual and
distinguishing characteristics: what they stand for or, at least,
what they would like their customers to believe about them. In this
role designers play the facilitator and act as a kind of medium
for the corporation-as-artist.
The term information architect suggests that information designers
feel that they should look at their colleagues of the bricks-and-mortar
variety for inspiration. Whether by inclination or because the technology
for another approach has not yet been fully developed, real architects
(the ones that do not need a qualifier) generally believe that they
can objectively translate the functions required of a building into
a particular, fixed form. This presumption often this leads to arbitrary
straight-jackets for the users of their buildings.
Instead of taking this approach to design as their inspiration,
information designers should take up the challenge and translate
requirements not into final answers, but into further restrictions
on the rules or mappings that specify the space of possible answers.
Information design thus becomes a two-step process:
- based on characteristics of the data, a goal-driven process
generates (under-specified) visual structures that are optimal
for conveying these characteristics;
- fully specified instances of these visual structures are generated
randomly, possibly subject to further "arbitrary"
constraints of style.
This last step is crucial. In the current approach to design,
a designer either specifies a design down to the last pixel, or
puts together a style guide that is so complex that it can only
be used by other designers to create variants within a style.
We suggest that these styles can be, and should be, formulated
as (additional) mapping rules. Design systems can then impose
specific style guides by applying these additional rule sets.
Technological progress makes it possible to incorporate the creation
of these variants directly into the production process of documents.
Documents, letters, bills etc. are no longer printed on pre-printed
forms, but as a whole, including letterheads and other design
elements, so the technology is there for much more customized
information design, not only on screen, but also on paper. Random
design is needed to make optimal use of these possibilities.
Information design is about providing users with tools for extending
their means of communication, with others and with themselves.
The essence of communication is self-correction, the ability to
start speaking or writing and home in on a subject by continually
listening to oneself or ones partner and correcting the
misunderstandings and gaps present in the expression so far. To
allow this to take place also during visualization, indeterminacy
should not be removed from the system, but be used as a force
for exploration and creativity.
Information design can thus profit in at least two ways from the
approach embodied in chance art, i.e. the random application of
formal design rules. To the extent that design is involved with
creating pleasing decorations, randomness can create much more
variation e.g. a different illustration in each individual
letterhead and thus lead to more diverse and more enjoyable
esthetic experiences. By using a restricted grammar, this diversity
can still remain recognizable as belonging to a specific style.
To the extent that information design is about effective data
visualization, the introduction of randomness in the process of
automatic design increases the chances of finding more effective
In the information republic that we envisage, all citizens will
have access to all raw information, digitally represented in standard
database formats; and everyone will be able to represent that
information for themselves or for others in whatever way they
want. Visualizing individual data sets will be obsolete as a professional
activity. It will become a nostalgic art form, like oil painting
after the invention of photography. Information Design will be
about developing algorithms for automatic and semi-automatic visualization.
It will be about design systems and design theories, not about
deciding for the rest of us what the world should look like.
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Jos de Bruin is a scientist at the Dutch
National Institute of Public Health and the Environment, Bilthoven.
His research is concerned with data visualization and interactive
Remko Scha is head of the Department
of Computational Linguistics in the Faculty of Humanities of the
University of Amsterdam. His research is concerned with computational
models of language processing and perception. He also developed
an automatic guitar band ("The
Machines") and an art generation algorithm ("Artificial").