/
article

# Data Presentation: Tapping the Power of Visual Perception

Why do we quickly comprehend some forms of data presentation, and not others? The answer is vital to designers of decision-support applications. This installment of our series connects insight into the process of vision with presentation best practices.

Does intuition or even convention dictate that red equates to low values, black to mid-level values, and blue to high values? Even with the legend below the graph, which attempts to equate hues with quantitative values ranging from \$6,848.70 at the low end to \$419,867.24 at the high end (ignoring the inappropriate six decimal digits of precision), this graph is far too difficult to interpret. Only two of the preattentive attributes can be accurately used to encode quantitative values: 2-D location (for example, the location of data points in a scatter plot) and line length (for example, the length of a bar in a bar graph).

Color intensity, such as different shades of gray ranging from white to black (that is, "grayscale") can be quantitatively perceived to a degree — by making one value darker, for example, we can tell that it is greater than another — but not well enough to decode specific shades into specific values without a lot of work. An object's size, as in its 2-D area (simultaneous perception of both length and width), is another attribute that can be perceived quantitatively, in that we can tell that one object is bigger than another. However, it's difficult to determine by how much they differ. Our inability to reliably compare the sizes of 2-D areas makes pie charts difficult to interpret. It's hard to accurately compare the slices of a pie. It doesn't seem like it should be, but it is. Our ability to perceive differences in 2-D areas hasn't evolved to the same level of accuracy as our perception of differences in 2-D position, perhaps because it was more important for survival that our ancestors could detect the exact location of the saber-toothed tiger, rather than its exact size.

FIGURE 6 - Example of a misuse of hue for the display of quantitative values. (Notes: This is a screen capture of a graph that was constructed using interactive examples on Visualize's Web site.

Attributes that can't be perceived quantitatively can still be used in graphs, but their use is restricted to distinguishing categorical differences, such as the use of hue to distinguish different lines in a line graph, sets of bars in a bar graph, or sets of points in a scatter plot. Of these attributes, some convey stronger categorical distinctions than others. In Figure 7, which preattentive attribute does a better job of grouping the two data sets in the scatter plot: orientation on the left or hue on the right? They both work to a degree, but hue works better.

FIGURE 7 - Comparison of the relative strength of two preattentive attributes: orientation and hue.

### Thriving With Information

The better you understand the strengths and weaknesses of visual perception, the better equipped you'll be to make use of your readers' abilities to detect structure and patterns in data when it's visually displayed. Edward R. Tufte, the foremost authority of visual data presentation, says:

"We thrive in information-thick worlds because of our marvelous and everyday capacities to select, edit, single out, structure, highlight, group, pair, merge, harmonize, synthesize, focus, organize, condense, reduce, boil down, choose, categorize, catalog, classify, list, abstract, scan, look into, idealize, isolate, discriminate, distinguish, screen, pigeonhole, pick over, sort, integrate, blend, inspect, filter, lump, skip, smooth, chunk, average, approximate, cluster, aggregate, outline, summarize, itemize, review, dip into, flit through, browse, glance into, leaf through, skim, refine, enumerate, glean, synopsize, winnow the wheat from the chaff, and separate the sheep from the goats.

"Visual displays rich with data are not only an appropriate and proper complement to human capabilities, but also such designs are frequently optimal."

What science has discovered about visual perception and the many ways that this knowledge can be applied to data presentation extends far beyond this article. I hope I've sparked enough interest to inspire you to study it further. According to William Wright (see Resources), "There is virtually unlimited freedom in how we represent data. The difficult question is how best to represent it."

Stephen Few is the founder of Perceptual Edge, a consulting firm that specializes in information design for analysis and communication. His new book, Show Me the Numbers: Designing Tables and Graphs to Enlighten, is now available from Analytics Press.

### REFERENCES

• Ware, C., Information Visualization: Perception for Design, Academic Press, 2000.
• Tufte, E. R., Envisioning Information, Graphics Press, 1990.
• Wright, W., from the research report "Information Animation Applications in the Capital Markets," published in Information Visualization, Stuart K. Card, Jock D. Mackinlay, and Ben Shneiderman, Academic Press, 1999.
• Editor's Choice