Andy Kirk and I did the 2016 #AskAndy anything webinar today. We hope you enjoyed it. Let us know your thoughts on Twitter using #AskAndy. This post contains the slides and links to the resources we shared.
I went super simple this week: all you can do in my viz is select a country and see where people went to. You can only see one origin country and I only exposed the most recent year.
The original chord diagram lets you see a very large amount of the dataset simultaneously.
I used to dislike chord diagrams: too complex. really messy, incomprehensible. But then this chord diagram was presented at Graphical Web in 2014, and it changed my mind. Why?
The designers took the time to explain how the chord diagram worked. Once you have worked out the mechanisms, the data pops out and becomes clear. Taking the time to read the instructions and learn how to read a chord diagram is time worth investing
Chord diagrams require interactivity, and that’s fine. The initial state is an overhelming confusion of lines. Interacting brings it to life. Charts that require interactivity can still be valid.
I do not believe there is another way to visualise flow that has so much detail. My own makeover this week is an admission of that: I’m using filters to show only a part of the data. Andy’s own makeover is a massive simplification of the dataset. It’s fine, as a matrix, but comes at the expense of detail, which the chord does contain. Almost all of this week’s makeovers show only a slice of the full data. Only the chord diagram allows you to access it all with ease.
People shouldn’t shy away from complex charts. Chord diagams do not provide instant insight: you need to invest time to read it. That is not a reason to shy away from a chart. Alan Smith discussed this on the PolicyViz podcast: he explained why they used a chord diagram in the FT this summer, knowing it was a chart that needed time to digest. That’s well worth a listen.
Chord diagrams cope with a range within your measures. Some countries have really huge numbers of people moving, while others have tiny. The outliers dwarf everything else when you encode with colour or length. I think width is a more successful encoding in this case.
I love the original chart. It’s visually striking, it’s engaging and there is a vast amount of detail available in one view, once you’ve devoted the time to learn how it works.
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.
Andy and I are very excited to be doing MakeoverMonday live in Austin at the Tableau Customer Conference, on Monday November 7th. This is our chance to say thanks to everyone involved, welcome some new friends, and play with data.
Here’s everything you need to know.
Where and when is it?
It’ll be at Level 2, Westin. We start at 2.30pm and finish at 4pm. Full details are in the data16 app (available on Android, iOS and Windows phone)
Will it be full?
YES. Due to many other factors, we could only secure a room for 100 people.
If you want a spot, get there early!
What if there’s no room?
We’re sorry we couldn’t get a bigger room. But all is not lost! Here’s what we recommend you do:
Say hello to the three people standing nearest to you.
My first thought on seeing this week’s original was to try another way to show distribution. I turned to the boxplot, an under-appreciated chart. Steve Wexler, friend and co-author of The Big Book of Dashboards, really dislikes them, suggesting that laypeople don’t understand them. I disagree, and think that a lack of understanding is only caused by lack of exposure to them.
Hopefully my “How to read a boxplot” instructional image at the top helps those unfamiliar with them!
Boxplots pack a large amount of useful info:
The whiskers spread to show outliers. Glasgow has a high SIMD score, but the data is very spread.
Comparing location is much easier. Consider Glasgow/Dundee in the original and the boxplot: It’s much easier to compare the two cities in the boxplot.
My boxplot still needs more work, which I would do with more time. I think it’s important to know how many data points are in each category. The Shetland Islands has a really narrow box, but that’s partly because there are only 7 items, compared to, say, 133 in Glasgow.
We threw you a curveball today. Only two numbers? This is a great challenge.
I did play in Tableau for a while, but then began to think about what these numbers really mean, and what the goal of the original infographic was. The problem is that comparing national debt to anything else is like comparing apples to oranges. And if you do compare it to other things, you run the risk of suggesting that because it’s so large relative to other things that there’s a problem.
That isn’t necessarily true. National debt isn’t like household debt, or currency, or assets. It’s a funny old beast. Here’s 3 of many amazing articles about this:
All of which isn’t too say that high levels of national debt aren’t a problem: they are.
My makeover’s goal was to make it clear that the different values aren’t the same kind of thing. Since there were only two numbers, it seemed right to pull out the pen and paper!
This week’s original
All of the above is one thing, but at the same time, I concede that the original wasn’t explicitly trying to say that US National Debt and, say, all the currency in the world, are similar. They were just trying to give you an idea of what the value represents. I do think there’s a lot of scaffolding around just a few numbers, but as an infographic to sit down and digest, it was a compelling read.