#MakeoverMonday

#MakeoverMonday Week 39 – Child Marriage

1. Oktober 2020 /

THE PROCESS

This week’s dataset consisted of only 4 fields: the country name, percentage of girls married by 15, percentage of girls married by 18 and percentage of boys married by 18. I decided to concentrate on the girls and not use the boys percentage. The next thing I noticed was that the girls married by 15 are a subset of the girls married by 18 and I wanted my viz to reflect that somehow. That’s how the idea of the squares within squares formed in my head.

But how to get there?
I knew I had to use polygons – something I’ve never really worked with before. Luckily, the Flerlage Twins are there to help you out with a handy blog post. Some data prep was needed (pivot the measure columns and quadruple the data to be able to draw the 4 corners of the square), do some calculations to get the positions of the corners – and voilà!

Next, I had to get the country labels look nice. For that, I used another trick that was in Kevin’s blog post mentioned above: create the labels on a separate sheet and float them behind the polygon viz. I positioned the labels using transparent shapes – another trick I learned from the Flerlage’s blog. This has quickly become one of my favorite and most-used little tricks ever since I read the 14 use cases for transparent shapes blog post. 

All that was left after that was implementing the sort functionality. I actually handled this in data prep by creating index fields based on the three sort options. Depending on the sorting parameter I would then use those indexes to calculate the X and Y position of each country in the small multiples grid. I then created the sort buttons and configured the parameter action.

THE VERDICT

All in all, I really liked how this turned out. It was really fun to try something new and explore polygons. I also rarely do small multiples, so that was a good opportunity for me to get more used to them. What’s also cool is that this was one of those cases where I had an idea in my head and I actually managed to recreate that exact idea – something that sadly doesn’t happen all the time. I do feel like this viz got more attention than I would usually get – so I guess other people out there liked it, too.

However, let’s be very clear about something: This is not necessarily what I would call a best practice approach. Is it compelling to look at? I would say so. But is it the best way to communicate the data? Maybe not. Because check out the example on the right: If the white square is 100% – how much do you think the grey square represents? The correct answer is 76%! The first time I saw this I thought my calculations must be wrong. But I double-checked. They’re not. It’s just that we are super bad at estimating and comparing areas!