High-effort graphics

Jon Quinton made a chart for Cancer Research UK, which is quite an eyeful.

The full infographic is here.

Below is a close-up of the key of this chart:

Where would this chart fall in my "return on effort matrix"? It is an extremely high-effort chart; I got tired trying to figure out what all those dimensions mean.

Is it a high-reward or a low-reward chart? It depends on why you're reading the chart. For most readers, I suspect it's low-reward.

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In my view, the best charts are high-reward, low-effort. I'd emphasize that by effort, I mean effort by the reader. In general, the effort by the chart designer is inversely proportional to that by the reader.

In some special cases, high-effort charts may have high reward justifying the destruction of some brain cells.

Low-effort, low-reward charts are harmless.

More on the return-on-effort matrix here.

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One simple improvement to a chart like this one is to produce separate charts for men and women. Outside academia, it seems to me almost all use cases for this chart would involve only one gender.

 

Comments

[Matt Warren]

Wow. I got sick of it, too, and I generally *like* explorable infographics. In this case, it's just too busy for me to be more patient.

[Alan S.]

Hi, I'm the designer of this chart. Thanks for the feedback. Here are some comments to hopefully stimulate useful discussion.

First of all, the image you have linked to is actually the web preview image, and Jon Quinton is a member of the public who liked the image and uploaded it to visual.ly.

• Here's the full version. (a pdf because it was designed to be printable) http://info.cancerresearchuk.org/prod_consump/groups/cr_common/@nre/@sta/documents/generalcontent/cr_082466.pdf
• Here's a blog post which sets the context: http://scienceblog.cancerresearchuk.org/2011/12/07/the-causes-of-cancer-you-can-control/
• ...and more on the data for a statistical audience http://info.cancerresearchuk.org/cancerstats/causes/comparing-causes-of-cancer/.

It is high effort, as you say, and I am separately working on a lower effort interactive version designed for engaging people without a specific prior interest in the causes of cancer.

To evaluate a design, it is important to understand its purpose, aim, and target audience.

This was produced to be an objective visual overview of the results of a major, 90-page, 16-chapter study on the causes of cancer ( http://www.nature.com/bjc/journal/v105/n2s/index.html ). We needed something that could be printed, passed around, discussed, scribbled on and pinned to a desk - something that a journalist could take from a press conference and discuss with their editor, a clinician could pin to a wall and discuss with a patient, and that health professionals, policy makers and others with an interest (including the public) could use as a reference.

It has been very successful in this regard. Feedback has been very positive on its usefulness and we've had a high number of requests and enquiries. Among interested members of the public it's circulated well on social media, it's raised comfortably more than it cost for the charity through spontaneous donations alone, and it's proving useful as a face-to-face discussion aid. I'd say this is evidence that it is high reward among people with some interest in the topic, which is the audience it is optimised for.

You're right that it could do more to lower effort and to reward exploration among people without any prior interest. I'd argue that when different audiences have opposing requirements, it's better to optimise different views for different requirements, rather than making one compromise. Hence I'm working on an alternate version that this can sit alongside, using progressive disclosure, a 'martini glass' ( http://vis.stanford.edu/files/2010-Narrative-InfoVis.pdf ) storytelling structure, filtering by personal relevance (e.g. by sex) and other such techniques that draw viewers in gradually and prompt (rather than await) questions and exploration. Such structures help casual audiences, but would obstruct those wanting to look up a specific risk in context.

That said, there is always room for improvement especially in a difficult visualisation challenge like this. It'd be interesting to see any practical suggestions you or your readers have that would still be home-printable, show all the data with an impartial voice, and work as a reference.

(p.s. what kind of audience did you have in mind when you referred to "most readers"? And what were the use cases you had in mind?)

[Dani]

This is hilarious. I love the dry sense of humor. Some of these charts actually remind me of grad school. I used to see so many off the wall charts that passed as "academic" it was sickening. A lot of them were actually just opinion charts, not even poll charts, or statistical survey results, but actually just the author's opinion displayed in the form of a chart! Thanks for sharing. Such a fun read.