Some theory behind the likability of XKCD style charts



Recently on the stackexchange sites there was a wave of questions regarding how to make XKCD style charts (see example above). Specifically, the hand-drawn imprecise look about the charts.

There also appear to be a variety of other language examples floating around, like MATLAB, D3 and Python.

What is interesting about these exchanges, in some highly scientific/computing communities, is that they are excepted (that was a weird Freudian slip) accepted with open arms. Some dogmatic allegiance to Tufte may consider these to be at best chart junkie, and at worst blatant distortions of data (albeit minor ones). For an example of the fact that at least the R community on stackoverflow is aware of such things, see some of the vitriol to this question about replicating some aesthetic preferences of gradient backgrounds and rounded edges (available in Excel) in R. So what makes these XKCD charts different? Certainly the content of the information in XKCD comics is an improvement over typical horrific 3d pie charts in Excel, but this doesn’t justify there use.

Wood et al. (2012) provide some commentary as to perhaps why people like the charts. Such hypothesis include that the sketchy rendering evokes some mental model of simplicity, and thus reduces barriers to first interpreting the image. The actual sketchy rendering also makes one focus on more obvious global characteristics of the graphic, and thus avoid spending attention on minor imperceivable details. This should also lead into why it is a potentially nice tool to visualize uncertainty in the data presented. The concept of simplifying and generalizing geographic shapes has been known for awhile in cartography (I’m skeptical it is much known in the more general data-viz community), but this is a bit of a unique extension.

Besides the implementations noted at the prior places, they also provide a library, Handy for making sketchy drawings from any graphics produced in Processing. Below are two examples.





So there isn’t just a pretty picture behind the logic of why everyone likes the XKCD style charts. It is a great example of the divide between classical statistical graphics (ala Tufte and Cleveland) versus current individuals within journalism and data-viz who attempt to make charts aesthetically pleasing, attention grabbing, and for the masses. Wood and company take great lengths to show the relative error in the paper cited when using such sketchy rendering, but weighing the benefits of readability vs. error in graphics is a difficult question to address going forward.


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  1. How art can influence info viz. | Andrew Wheeler

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