Beauty Is As Beauty Does

Infographics is one of two new fashionable terms used nowadays to refer to statistical charts and graphs. The other term is visualizations, which replaces such archaic words as graphs, charts, pictures, diagrams, and illustrations. Sometimes the term is affectionately shortened to data viz by its really cool practitioners.

In the infographics/visualization/data viz movement there are two basic schools of thought. One school emphasizes principles of artistic design and the other emphasizes information clarity. The first prizes graphics that are beautiful and appealing, while the other judges visualizations based on how informative they are.1 Many adherents of the first approach to graphics are marketing and advertising professionals. Lest you presume that they subscribe to the motto ars gratia artis, I must point out that their interest is purely utilitarian. To them beautiful graphics that don’t persuade audiences and reel in more customers are useless.

Infographics can have quantitative or qualitative content or both. In librarydom you may have seen infographics mentioned in Library Journal’s TheDigitalShift, in this issue of AL Direct, or in this YALSA blog. Many of the tools recommended by these sources pertain to qualitative content. Here, though, I will be writing just about quantitative graphics, that is, data visualizations. And I want to show you how efforts to decorate and beautify these can lead to trouble.

3-D Horrors

I begin with a good example of bad results as seen in the chart below. This chart shows libraries’ responses about the value of statistical measures, expressed as percentages. I created the chart for a small survey I conducted in 2008. At the time I felt it to be a respectable graphic. In hindsight I realize better. Style-wise, its color choices and shading are garish.

But the chart’s main shortcoming is that it communicates really wrong information. Notice how none of the bars reaches the 100% mark. (In this type of chart each bar is supposed to total 100%.) When you create what Microsoft Excel calls a 100% stacked bar in 3-D, the software adjusts the perspective so the 100% line hovers above the actual data.

Click for larger image showing legend and horizontal axis labels.2

So now you have to wonder, since the bar tops are wrong, how about the lines dividing the bar segments? Are they off too? I’m not sure what Microsoft programmers were thinking, but the final result is misinformation.

Even without Excel’s display defects, 3-D stacked bar charts are difficult to read in general. It’s really hard to judge the relative heights of the segments. Take a look at the chart again. In the second bar from the left, would you say the aqua segment is longer than the aqua segment in the third bar from the right? Which bar edges should you judge by? Should you match up the front edges of the bars with the gridlines next to the percentage labels? Or the back edges to the rear wall?

The unadorned graph below displays the same data as my 3-D chart does. Creating one bar chart per response category (click image above to see chart legend) and arranging these left to right makes for easier comparisons of the bar lengths. No need to worry about being deceived by ill-designed 3-D chart scaling.

Click for larger image.

Visual effects added to a graphic, like 3-D, bright colors, elaborate fonts, ornamental borders and such are meant to enhance a graphic’s message. But quite often these fall flat. Consider the chart below, which tries to be tastefully unflat. Note that the non-perpendicular left axis exaggerates the lengths of the lower bars, pushing them further to the right than they should be. A minor defect, for sure. But there’s something else about this chart that makes it appear vaguely off-kilter.

Source: ALA, Condition of U.S. Libraries: Trends, 1999-2010.3 Click for larger image.

The graphic below shows the image rotated to a perspective our eyes are more accustomed to. Here the incline in the chart axis (which looks like a candlestick base) is a visual cue that the right-most bars recede slightly into the distance. In the prior chart the bars cascade downward, as if the viewer were looking from above the image. That is one reason for that chart’s visual oddness. Another reason is easier to see in the rotated chart.

90° counter-clockwise rotation of bars from prior chart. Click for larger image.

If you ignore the chart axis (the base) and look only at the right two or three bars, they appear to be advancing into the foreground. This is because in a 3-D perspective rendered on a 2-D page, larger objects appear closer and smaller ones appear more distant. The chart is, in effect, an optical illusion.

Click on the graphic to view a larger image and then stare at that for a moment. You should be able to make the left end of the axis recede into the background and the higher right edge shift into the foreground.

Rather than burden the audience with peculiar visual effects, I suggest a plain vanilla graphic something along these, er, lines:

Click for larger image.

More 3-D Horrors

The next chart produces some curious optical illusions. First, notice how the three right bar segments appear to have non-parallel edges, as if the stack were bending leftward. And when you first look at the left axis, it sometimes appears concave rather than flat. Finally, if you gaze a minute or so at the entire chart, you can get the bars to flip inside out! (Try it.)

Source: Council on Library Information and Resources (CLIR), Census of Institutional Repositories in the United States, 2007.4

A simple bar chart gets the same information across without visual incident:

In the next chart can you tell whether the bars are truly horizontal or not?

Source: CLIR, Census of Institutional Repositories in the United States, 2007.5

The illusion is kind of fun, even. If the 3-D effects in these examples were pleasant and appealing, with no side effects, they’d be fine. But they aren’t pleasant or appealing. They are distracting, sometimes subliminally so.

Artistic License

Some proponents of the aesthetic approach to visualization will disagree with me. And they’ll probably dismiss my vanilla charts as boring and stupid. Escheresque side effects won’t bother them since they don’t see viewer bewilderment as a problem. When a viewer is bewildered, at least he’s engaged. What really worries artistic vizualists is graphical ordinariness, blandness, and potential tedium−ills they combat with entertaining and provocative images. For these purposes decorations of any and all kinds will do, like the cartoonish icons in this example:

Click for larger image.

The infographic is not very informative. We know there are some proportions or other of people, houses, cars, and animals in the U.K. But the pictures interfere with our appreciating these otherwise quite specific proportions.

The idea in a graphic like this one is that all of the icons are quantitatively equal, like tally marks. However, as you see, visually they are unequal. The thickly inked houses overpower the delicately drawn automobiles, suggesting at first glance that the U.K has more houses per 100 persons than automobiles. (The opposite is true.) Children are tinier than adults. Canes make older men and women wider than able-bodied adults. And chickens are tiny chicks. (I don’t think those are eggs.) Incidentally, although the chickens are the most, shall we say, populous (275 chickens/100 people), they are allotted about the same space as dogs, cats, and sheep. (There is no eggscuse for that.)

The creator of the infographic did include group counts in gray circles at the right. (Numbers can be such useful annotations to infographics.) Except these are mostly illegible. You can also try viewing the graphic in its original location if you like.

Chaneling Data

Because graphic designers are more comfortable with geometry than arithmetic, they believe that shapes and forms automatically illuminate any subject matter. This occasionally leads them to underestimate the intelligence of their audiences. (Readers generally know what houses and chickens are shaped like.) But sometimes their arithmetical mucking around enters the realm of the sublime. Like this astounding graphic by a data viz expert depicting unoccupied airline seats in the U.S. in a single year:

Click to view article. Scroll down to this image.

An intriguing mandala, to say the least. Can you unravel its clues? Maybe you guessed that the circle’s circumference represents 365 days of the year. Do you get that the pastel blue partial border represents 217 days? The white portion, which is 148 days long, has no meaning. So it’s a convenient place to put a labeling phrase. What do you suppose that blank areas in beige near January and July mean? That there are no unoccupied seats during these months?

Evidently, the spatial arrangement of the icons is connected to the numbers inscribed in the center of the image. There we learn that the icons symbolize certain cryptic quantities: 1 seat icon = 2.5 million seats and 1 plane icon = 6.5K empty 747 jets. Add the 2.5 and 6.5 together and you get 9. Add 7 + 4 + 7 together you get 18, and adding 1 + 8 together yields a second 9! Does the mandala reveal an esoteric numerology, some sort of Da Vinci code of airline statistics?

Hardly. I’m afraid the only relevant numeral here is zero, the informational content of this viz. You can only make sense of it by referring back to the supposedly inferior chart for which the mandala is intended as an improvement. This chart can be seen in the article under the heading ‘If the Client Wanted an Excel Chart, They Wouldn’t Need You.’ (That heading says it all.)

Engaging Designs

Good visualizations should not require viewers to solve puzzles, at least not without their prior consent. But standards of this sort are stifling to the more radical data viz artists. Take a look at how the designer of the mandala graphic above turned this humdrum chart…

Click to view article. Scroll down to this image.

…into this:

Click to view article. Scroll down to this image.

Who, you may ask, wouldn’t prefer a Tweet-o-meter over a dull bar chart? Well, actually, anyone wishing to understand the data. The Tweet-o-meter is more of a hindrance to this aim than a help. Note how its scale only extends to the largest data value (7200). This makes the dial’s outer light blue band look more like background than the Women’s World Cup data. Then, two of the needles are almost invisible and the one for New Year’s Eve 2010 in Japan is invisible. And we have to extrapolate the scale’s peak value (as I did already) because it’s hidden by a stylishly shadowed needle. Oh, wait. Could the shadow be the missing needle?

Almost all of the Tweet-o-meter’s data are difficult to see. Plus, the concentric pastel arcs depicting the categories are totally wrong. They suggest that the peak value, 7200, is so large that it surrounds the other four values. A quick glance at the original bar chart debunks this. Comparisons of arc lengths in the Tweet-o-meter are invalid because the arcs are from circles that have unequal diameters.6

Graphical beautification enthusiasts iconic metaphors like the mechanical gauge, quantitative reasoning be damned. After all, this particular metaphor has been blessed by the Balanced Scorecard movement. So it’s definitely state-of-the-art. However, like any tool, this metaphor has strengths and weaknesses. One weakness is that it is prone to misinterpretation. It can give viewers really wrong ideas about the data. Take the image below for example:

Click for larger image.

The pinpoint accuracy of the needles implies high-precision data. Yet, the data are anything but! Measurement scales for the reported propensity of human beings to behave one way or another are nothing like the scales used in altimeters, barometers, speedometers, and thermometers. Neither are scales measuring attitudes and beliefs, which library advocacy surveys often use.

As with any survey, in this example the two data values are estimates of true figures in the population under study. Each will have some margin of error. So, the actual numbers in the population may or may not be nearly equal. They could easily differ by 0.5, 1.0 or more. Let’s say, though, that we know that the true figures from the population differ by 0.5, and that the dial needles show this difference. With these types of scales 0.5 might still be a negligible amount. Perhaps any number between 8.0 and 9.0 might have about the same meaning. For these kinds of scales pin-pointing numbers on dials is overkill.

Now, pretend that the dials are applause meters for competing acts in a live TV show, so that none of the issues I just raised applies. The dials are still difficult to read. First, our eyes must scan and then ignore the prominent numerals and tick-marks that occupy most of the graphic. The dials’ green regions help some by leading us to the needles (though the meaning of these regions is unexplained). On the left dial the needle points just shy of the first tick-mark past 8, on the right a tad beyond that same mark. But the tick-marks are weirdly spaced so that each one is 0.4 units away from its neighbors. In the left gauge the graphical software has fouled up. The needle should rest exactly on the tick-mark.

Struggling to decipher these miscues, we discover that the graphic’s most reliable information appears in the perimeter: the numbers 8.4 and 8.5. So, the question is, what is the purpose of the dials? Just to look pretty on the page? Though graphical beauty and fanciness might capture viewer attention, if these make information incomprehensible, they’re a waste of time.


1  Of course, it is possible for graphics to be simultaneously beautiful and informational. Well-designed graphics can be elegant in their clarity and visual appeal. See Edward Tufte’s book Beautiful Evidence.
2  In the larger image, notice that the angle and close spacing of the horizontal axis labels make them difficult to visually trace to their corresponding bars. This is a good reason to avoid angled labeling. And beware of Microsoft Excel on this. When you narrow the overall width of a bar chart, Excel may decide to realign your labels at an angle. Be kind to your readers and don’t let the software get away with it.
3  Davis, D. (2009). The condition of U.S. libraries: Trends, 1999‐2009, Chicago: American Library Association, p. 12.
4  Markey, K. et al. (2007). Census of institutional repositories in the United States, Washington, DC: Council on Library and Information Resources, p. 34.
5  Markey, K. et al. (2007). p. 24.
6  An example of the effective use of circular graphs to illuminate data is the beautifully informative rose charts designed by Florence Nightingale.

4 thoughts on “Beauty Is As Beauty Does

  1. The bars above DO reach 100% – it’s just that the bars are not as thick as the bounding volume, so you are interpreting the setback in depth as a ‘hovering above’ in height. The flat tops of the bars do fully reach the imaginary 100% roof, though.

    1. Thanks Joseph. Very helpful comment. There may have been method in the madness of those Microsoft programmers? You just have to adjust your eyes to “see” the way they do. But my eyes won’t cooperate. They want to see the left bar as flush-left to the angled left axis. Only if I study the base of that bar can I see that there’s a space between the two. And my eyes are blind to the spaces behind the bars; so they want to gauge bar height using bar segment demarcations against the back gridlines. The way that “normal” statistical charts are read. If I’m willing to mentally draw upward angled lines from each bar demarcation, I can guess at the values each segment represents. But how inconsiderate to ask readers to do this extra work!! Plus, it’s a recipe for gross misinterpretation, the trap you have enabled me to realize I fell into. People like me are another reason to avoid using 3-D charts.

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