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Can information be beautiful when information doesn't exist?

Reader Steve S. sent in this article that displays nominations for the "Information is Beautiful" award (link). I see "beauty" in many of these charts but no "information". Several of these charts have appeared on our blog before.

Junkcharts_trifecta_checkupLet's use the Trifecta checkup on these charts. (More about the Trifecta checkup here.)

 

Info_beaut_plot_linesThe topic of this chart is both tangible and interesting. As someone who loves books, I do want to know what genres of books typically win awards.

However, both the data collection and graphical design make no sense.

The data collection problem presents a huge challenge and it's easy to get wrong. The problem is how narrow should a theme be. If it's too narrow, you can imagine every book has its own set of themes. If it's too wide, each theme maps to lots of books. The challenge is how to select the themes such that they have similar "widths". For example, "death" is a very wide theme and lots of books contain it, as indicated by the black lines. "Nanny trust issues" is a very narrow theme, and only one of those books deals with this theme. When there is such a theme, is its lack of popularity due to its narrow definition or due to writers not being interested in it?

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Info_beaut_coversThe caption of this chart said "Cover stars: Charting 50 years up until 2010, this graphic shows The Beatles to be the most covered act in living memory." If that is the message, a much simpler chart would work a lot better.

Since the height of the chart indicates the number of covers sold in that year, the real information being shown is the boom and bust cycles of the worldwide economy. So, a lot more records were sold in 2005, and then the market tanked in 2008, for example.

That's why the data analyst should think twice before plotting raw data. Most data like these should be adjusted. In this case, you could either compare artists against one another in each year (by using proportions) or you have to do a seasonal and trend adjustment. I also don't see the point of highlighting year-to-year fluctuations. Nor do I understand why only in certain years is the top-rated cover identified by name and laurel wreath.

 

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I talked about this stream graph of 311 calls back in 2010. See the post here.

Info_beaut_311calls

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I featured this set of infographics/pie charts back in 2011. See the post here.

Info_beaut_refugees

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This chart is a variant of the one from New York Times that I discussed here. I like the proper orientation on the NYT's version. The color scheme here may be slightly more attractive.

Info_beaut_trackfield

 

Comments

Michele

I'm the author of "Cover-Mania", the visualisation you've analysed in this post.

you say: >"The caption of this chart said "Cover stars: Charting 50 years up until 2010, this graphic shows The Beatles to be the most covered act in living memory." If that is the message, a much simpler chart would work a lot better."

I think this kind of critic cannot be avoided by any visualisation. Anyway, in this particular case, the visualisation was intended to be printed on a 27x27 cm square.
Trying to visualise a timeline in a square, i ended up with this chart. It might have been done better, but it is a mediation between data and physical constraints, and not only an aesthetic quirk.

>"Since the height of the chart indicates the number of covers sold in that year, the real information being shown is the boom and bust cycles of the worldwide economy. So, a lot more records were sold in 2005, and then the market tanked in 2008, for example."

Actually the height of the chart indicates the number of cover songs recorded in that year. So, nothing about worldwide economy.

>"That's why the data analyst should think twice before plotting raw data. Most data like these should be adjusted. In this case, you could either compare artists against one another in each year (by using proportions) or you have to do a seasonal and trend adjustment. I also don't see the point of highlighting year-to-year fluctuations."

The point is that this visualisation is part of a series of visual experiments on new data sources, in this case whosampled.com database. It is not intended for a deep analysis. Also, the visualisation is completed by an article, in which is told that the represented data has been collected by a collaborative platform, and it's not fully representative of all the cover songs.

>"Nor do I understand why only in certain years is the top-rated cover identified by name and laurel wreath."

In the legend there's written that the laurel shows the most covered song, if there's one. This means, if in a year a song has a significant amount of covers compared to the others, it have a laurel.

Kaiser

Hi Michele, Thanks for coming here and explaining your work. I appreciate it. I certainly see your point about exploratory graphics. For me, such graphics are like artists' sketches. What I like to see in a complex graphic are more complex messages. Not all data sets contain such messages so I can understand that if you have to work with a specific source, it's difficult.
As I said elsewhere, I do appreciate that you're trying something new. Some experiments work, some work less well. Continue the good work!

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