This week provided a good challenge. It’s difficult to present data which divides one percentage (US Wealth) into categories about another percentage (household income).
My first try is with an Area chart. I like the area chart because it shows part-to-whole for the entirety of US Wealth:
But it doesn’t quite punch home the differential between bottom 90 and top 0.5. Could I do that another way?
I chose to drop the history and focus on just the most recent year.
How about a stacked bar?
Or a bar chart?
They’re ok but the fundamental problem is that this approach doesn’t capture the size of “Bottom 90%”. The words “Bottom 90” don’t capture that magnitude of the inequality.
To tell this story in the most powerful way, I think we’d need a way to encode the 90%/0.5% households, too. And rather than spend time making that viz, I’ll share this video instead. It does one of the best jobs of showing the extent of inequality I’ve ever seen:
We got to makeover with my favourite dataset this week. The full wildlife strike dataset is one of the best to explore. It has a great mix of measures and dimensions, and seemingly endless stories to find in it.
Our source was Kelly Martin’s excellent take on the data. Is it an amazing dashboard? Yes, without a doubt. Is it perfect? Of course not: the perfect visualization does not exist.
There is much to love about Kelly’s dashboard. Here’s a few things that stick out:
Lovely title – a play on Superman motto and sets up the viewer for exploration
Really nice layout with enough intrigue and flow to keep the viewer’s eye moving around the views to find out more
Effective use of colour: only one view has colour, which I find very pleasing to look at
Great annotations add some focus where it’s needed.
Log scale on y-axis condenses the data nicely (but be honest, did you notice it?!)
But all dashboards can be improved. Here are some of the challenges with this dashboard:
The axes are ‘invisible’. I think it took me several return visits to this dashboard to even notice the x and y axes. They seem to be a long way from the data, but there are actually some data points right at the left hand side. Have you ever noticed them?
The floating map and extent of the axes make me wonder how many data points are hidden behind the map? I suspect not many because low velocity strikes must surely happen on the ground.
It’s not clear what the marks on the scattterplot represent. They’re beautiful, for sure, but ask yourself (without using tooltips): what does each mark represent? I was unable to answer that question. Even with the tooltip it’s hard to describe what each mark shows. Don’t believe me? Then tell me in the comments what each mark shows, and how long it took you to work it out.
I loved Chris’ original treemap (for reasons explained below). When it was made Viz of the Day, I heard lots of people say that it was a terrible choice: “you can’t make any insight out of that treemap”, they said. However, I sat in a big group of customers and partners that day, and showed the viz on a screen. What happened? They engaged in it – the treemap generated curiosity in a way my bar chart doesn’t. The subtle use of highlighting on Chris’ original teases people into exploring the data.
However, one of the first things I’d noticed was that the most common words were also the most common words in English. For my makeover, I wanted to exclude those words. I downloaded that data from Wikipedia.
It turns out that ‘baby’, ‘oh’ and ‘yeah’ are the most common of the uncommon words (if I did this again, I probably exclude the next few hundred common words to start getting to the uncommon ones). I like that “Na” is in this list solely because of Hey Jude.
We needed some Austin-related data for MakeoverMonday live at Tableau Conference. We turned to Restaurant Inspection scores from Austin’s data site.
I went in search of lunch in order to do the makeover, and found myself in Franks, home of hot dogs and cold beer. I sat down and ordered a bacon-infused Bloody Mary. Seriously? Bacon in a Bloody Mary? It was amazing.
Anyway, it got me wondering how well Frank’s had performed in recent inspections. That led my direction. I reduced the entire dataset to just Frank’s inspections. Turns out their last inspection was right on the borderline of failure.
My conclusion? Wonderful Bloody Mary. They passed my Restaurant Inspection!
Andy and I hope you all enjoyed MakeoverMonday live, wherever in Austin you ended up doing it.