Originally published: Feb 22, 2011
This is a continuation from a previous article, Making Beautiful Visualization - How Do We Achieve Beauty?
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.
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.
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.
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.
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.
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.
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.
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.
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