Gender Inequality in Nobel Prizes

Category : Vizzing
Date : March 18, 2018

I had a dilemma with the viz for the Midlands TUG in Nottingham. It screams for a rich infographic approach to present in front of an eager audience. While I admire the skill in creating those type of graphics, I find they often lack cohesion. A random series of charts to fill the available space. As a user I don’t know where to look first, and the onus of the viz seems to be on me to find the connection between the graphics.

But to be honest, I’m no good at that design heavy vizzing and I’m also trying to reduce my already simple vizzing style this year to the basics: to focus on storytelling (which sounds more pretentious than it is.)

Back to the data. First, the data was a mess. So much so I gave up and downloaded the data from Kaggle. For the first hour, I wandered down a path of looking at the age the person received the prize, and then the countries that produced the most winners. Neither of which seemed to be useful to promote International Women’s Day, the reason the data was released for the TUG meeting.

So getting back on track, and after some random moving of dimensions around (aka the Tableau shuffle) I ended up with an idea. One that showed all the data, while at the same time showing the lack of female winners.

When creating data graphics, we apparently specify a mapping of data items to visual channels (see Tamara Munzner Visualisation Analysis and Design). So looking at it that way I ended up with :

  • X & Y Planar – X(Year) & Y(Category)
  • Marks – I used circles. Perhaps not the most effective encoding of data, but it allowed me the option to use several other visual variables. And everyone likes circles (Ok, not Stephen Few)
  • Size – The number of awards for each year. Important to know if you don’t. This doesn’t get crazy as there is a maximum of three awards per category. The issue was whether to allow the circles to overlap. If I didn’t, they were too small, too big, and the overlapping would be distracting. So they overlapped slightly, but seeing the orange in all that grey was more important.
  • Colour – Push the male winners back in the visual hierarchy by using light grey. Make the few female winners pop out by using two shades of blue: one a dark blue for all winners being female, and a light blue if only some of the prize was won by females.  This required an LOD calculation, and then grouping the of data. Hang on. What colours should be used to represent gender? Ok, see Visualising Data’s article. Blue and grey are a great combination, but the associations with gender are too much. So I switched to orange.
  • Annotation Layer –  a couple of interesting comments to interest and guide the reader, and use viz tools tips to show the names of the winners for each circle so the viz can work as an interactive graphic. Add a summary explaining the data, most vizzing seems to assume that it’s obvious what the story is, and point out that it’s a disgrace Vera Rubin didn’t win.
  • Typography – I like using Georgia for headlines. It’s Tableau Public safe, and has a newspaper feel and look. Finally, a take-away headline as recommended by storytelling with data.

And a whole Sunday afternoon later I finished. I’m working slow, in the age of instant everything.

Click image below for interactive version:

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