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Beautiful Visualization: How To Make it Efficient

by Noah Iliinsky
on February 22, 2011

This is a continuation from a previous article, Making Beautiful
Visualization – How Do We Achieve Beauty?

Make It Efficient

After ensuring that a visualization will be informative, the next
step is to ensure that it will be efficient. The most important
consideration when designing for efficiency is that every bit of
visual content will make it take longer to find any particular
element in the visualization. The less data and visual noise there
is on the page, the easier it will be for readers to find what
they’re looking for. If your clearly stated goal can’t justify the
existence of some of your content, try to live without it.

Visually Emphasize What Matters

When you’ve identified the critically necessary content, consider
whether some portion of it—a particular relationship or data
point—is especially relevant or useful. Such content can be visually
emphasized in a number of ways. It can be made bigger, bolder,
brighter, or more detailed, or called out with circles, arrows, or
labels. Alternately, the less-relevant content can be de-emphasized
with less intense colors, lighter line weight, or lack of detail.
The zones in the Tube map, for example, are visually de-emphasized:
they exist, but clearly aren’t as relevant as the Tube lines and
stations.

The London Underground (“Tube”) map; 2007 London Tube Map © TfL from
the London Transport Museum collection (used with permission)

Note that this strategy of emphasizing relevance typically applies
to presentation data, not research data: by changing the emphasis,
the designer is intentionally changing the message. However,
highlighting different facets or subsets of unknown data is a valid
way to discover relationships that might otherwise be lost in the
overall noise.

Use Axes to Convey Meaning and Give Free Information

One excellent method for reducing visual noise and the quantity of
text while retaining sufficient information is to define axes, and
then use them to guide the placement of the other components of the
visualization. The beauty of defining an axis is that every node in
a visualization can then assume the value implied by the axis, with
no extra labeling required. For example, the periodic table is made
up of clearly defined rows (periods) and columns (groups). A lot of
information can be learned about an element by looking at what
period and group it occupies. As a result, that information doesn’t
have to be explicitly presented in the element’s table cell. Axes
can also be used to locate a portion or member of the dataset, such
as looking for an element in a particular period, southern states,
or a Tube station that is known to be in the northwest part of
London.

Well-defined axes can be effective for qualitative as well as
quantitative data. In qualitative contexts, axes can define
(unranked or unordered) areas or groupings. As with quantitative
axes, they can provide information and support the search for
relevant values.

Slice Along Relevant Divisions

One last way to reduce visual clutter and make information more
accessible is to divide larger datasets into multiple similar or
related visualizations. This works well if the information available
can be used independently and gains little (or infrequent) value
from being shown in conjunction with the other data in the set. The
risk here is that there may be relevant, unsuspected correlations
among seemingly unrelated datasets that will only become evident
when all the data is displayed together.

Use Conventions Thoughtfully

After the influences of the intended message, context of use, and
data have been taken into consideration for your unique situation,
it’s worth looking into applying standard representations and
conventions. Intentional and appropriate use of conventions will
speed learning and facilitate retention on the part of your readers.
In situations where a convention does exist, and doesn’t conflict
with one of the aforementioned considerations, applying it can be
extremely powerful and useful. The examples we’ve examined have used
default, conventional representations for element symbols, subway
line colors, and compass directions. Most of these seem too obvious
to mention or notice, and that’s the point. They are easily
understood and convey accurate information that is integrated
extremely rapidly, while requiring almost no cognitive effort from
the user and almost no creative effort from the designer. Ideally,
this is how defaults and conventions should work.

Leverage the Aesthetics

Once the requirements for being informative and efficient have been
met, the aesthetic aspects of the visual design can finally be
considered. Aesthetic elements can be purely decorative, or they can
be another opportunity to increase the utility of the visualization.
In some cases visual treatments can redundantly encode information,
so a given value or classification may be represented by both
placement and color, by both label and size, or by other such
attribute pairings. Redundant encodings help the reader
differentiate, perceive, and learn more quickly and easily than
single encodings.

There are other ways in which aesthetic choices can aid
understanding: familiar color palettes, icons, layouts, and overall
styles can reference related documents or the intended context of
use. A familiar look and feel can make it easier or more comfortable
for readers to accept the information being presented. (Care should
be taken to avoid using familiar formats for their own sake, though,
and falling into the same traps as the designers of the unfortunate
periodic tables and Tube-style maps.)

At times, designers may want to make choices that could interfere
with the usability of some or all of the visualization. This might
be to emphasize one particular message at the cost of others, to
make an artistic statement, to make the visualization fit into a
limited space, or simply to make the visualization more pleasing or
interesting to look at. These are all legitimate choices, as long as
they are done with intention and understanding of their impact on
the overall utility.

Get more on turning data into beautiful visualizations

Noah has spent the past several years thinking about effective
approaches to creating diagrams and other types of information
visualization. How he views information design is fascinating.

In his UIE Virtual Seminar,
Information Visualization: Letting Data Tell the Story, Noah discusses the types of visualizations in common use, why
and when they are useful, what types to use in different situations,
how to think about different types, and who’s doing good work. Learn more
about this virtual seminar.

Share your thoughts with us

How do you make your visualizations more efficient? Do you have specific
methods for cutting out the quantity of text? Share your thoughts with us in
the UIE Brain Sparks blog.