Today is a great World Cup day. The two teams for which I am rooting are playing—thankfully not yet against each other. Later this afternoon England takes on Colombia. But this morning Sweden will play Switzerland. (Neutrality is no longer an option.) And in the spirit of Sweden, I figured I would return to my winter trip to Stockholm and dig out a graphic. This one seemed particularly relevant.
It may be difficult to read, because it is in Swedish along with being large, but it shows medieval trade routes connecting Sweden to Europe. For example, Stockholm received cloth from East Anglia in modern-day England and from Bruges in Flanders, beer from modern-day Germany, and wine from modern day France and Spain.
Even in the Medieval period, international trade was vital to the economies of the emerging European cities and states.
Credit for the piece goes to the Medieval Museum design department.
Brexit is bad for Britain. Here is some proof from an article by Bloomberg that looks at where London-based banking jobs are headed post-Brexit. Spoiler alert, not elsewhere in Britain. The article purports to be more of a tracker in that they will add on data about jobs moving places when news breaks. But I cannot verify that part of the piece.
What I can verify is a sankey diagram. Underused, but still one of my favourite visualisation forms. This one explores where companies’ London-based banking jobs are moving. Right now, it clearly says Frankfurt, Germany is winning.
As sankeys go, this one is pretty straightforward. Aesthetically I wonder about the colour choice. I get the blues and that the banks are coloured by their ultimate destination. But why the gradient?
But conceptually the big question would be what about London? I probably would have kept London in the destination set. While many jobs are likely to leave Britain, some will in fact stay, and those lines will need to go somewhere in this graphic.
The piece also makes nice use of some small multiple maps and tables. All in all, this is a really solid piece. It tells a great—well, not great as in good news—story and does it primarily through visuals.
Credit for the piece goes to Gavin Finch, Hayley Warren and Tim Coulter.
Yesterday the Economist posted a graphic about Chinese urban clusters, of which the Chinese government is planning to create 19 as part of a development strategy. In terms of design, though, I saw it and said, “I remember doing something like that several years ago”.
The Economist piece looks at just the geography of the Chinese clusters. It highlights three in particular it discusses within the article while providing population numbers for those clusters. Spoiler: they are large.
The Economist graphic does little else beyond labelling the cities and the highlighting of the three features clusters. But that is perfectly okay, because that was probably all the graphic was required to do. I am actually impressed that they were able to label every city on the map. As you will see, we quickly abandoned that design idea.
So back in 2015, using 2014 data, my team worked on a series of graphics for a Euromonitor International white paper on Chinese cities. The clusters that the analysts identified, however, were just that, ones identified by researchers. Since the Chinese government had not yet created this new plan.
We also looked at more cities and added some vital context to the cluster map by working to identify the prospects of the various Chinese provinces. Don’t ask me what went into that metric, though, since I forget. The challenge, however, was identifying the four different tiers of Chinese city and then differentiating between the three different cluster types while overlaying that on a choropleth. Then we added a series of small multiples to show how now all provinces are alike despite having similar numbers of cities.
Credit for the Economist piece goes to the Economist Data Team.
Credit for the Euromonitor piece is mine. I would gladly give a shoutout to those that worked with me on that project…but it’s been so long I forget. But I’m almost certain both Lindsey Tom and Ciana Frenze helped out, if not on that graphic, on other parts of the project.
Yesterday we looked at trade with China. Today, we look at Canada, allegedly ripping off America. But what does the data say? Thankfully the Washington Post put together a piece looking at just that topic. And it uses a few interesting graphics to explore the idea.
The easiest and least controversial graphic is that below, which breaks down constituent parts of our bilateral trade.
Note that the graphic does not just show the traditional goods part of the equation, but also breaks out services. And as soon as you consider that part of the economy the US trade deficit with Canada turns from deficit into surplus.
But the graphic also uses a pair of maps to look at that same goods vs. goods and services split.
Parts of the design of the map like the colours, meh. But the designers did a great job by breaking the standard convention of placing the Prime Meridian at the centre of the map. Instead, because the United States is the story here, the map places North America at the map’s centre. It does lead to a weird fracturing of the Asian continent, but so long as China is largely intact, that is all that matters to the trade story.
This all just goes to show that it is important to begin a conversation about policy with facts and understand the actual starting point rather than the perceived starting point.
On Saturday Ireland announced the results of a referendum on changing its constitution to remove Article 8, which had made abortion illegal except in the case of risk of death to the mother. And that was it, none of the usual rape or incest clauses. I want to look at a little coverage of the results and we will start with the Irish Times.
Their presentation is straightforward, a parliamentary-like slider and a small choropleth. All the colours link to each other and you will note that at first glance there is no variation in the colours on the map. Instead they present the binary choice, yes or no. To get the details of the vote, the user needs to select the Yes% or No%. From those we see not a lot of variation—probably not unsurprising given the overwhelmingness of the vote—as the Dublin area had the most yes, the rest of Ireland fairly solidly yes, and only Donegal in the northwest voting no, and even then, barely so.
But then we have the Guardian’s results map. And I am a wee bit lost. The bin definitions offer a bit more granular detail and so the sweeping results from the Irish Times results can here be seen as a bit more simplified. I probably would have shifted the colours and kept the yes on one side of the spectrum and not mixed the yellows and oranges into the positive, or yes, side. The stunning part of the result was, after all, that only Donegal voted no. So I would expect the colour of the choropleth to reflect that sharp break and less the gradation seen here. It’s a curious choice.
But more importantly, I am left wondering about the data, the titles, or the descriptions—I cannot be sure. The key bit is the callout of Roscommon-Galway. The text says the constituency voted 57.2% yes. But the colour would seem to indicate that it voted 65–69% no. A simple mistake? Perhaps. But then I look at the wording of the legend and maybe not. Could percentage of yes vote mean something more like the expected total or the percentage of registered voters? Probably not, but I cannot quite figure out what is going on in Roscommon-Galway. And if it is a data error, it is only made more noticeable because they point out that is one of only two constituencies described in the text.
Post script: After writing this and doing some more investigation over the long holiday weekend, I found a different map that appears to be more in sync with the results. The above was probably a mistake that just didn’t get pulled down and replaced. Below is the correct one. But it goes to show you how an incorrect graphic can cause confusion.
Credit for the Irish Times piece goes to the Irish Times graphics department.
Credit for the Guardian piece goes to the Guardian graphics department.
Here in Philadelphia, I think yesterday was the first day it had not rained in over a week. Not that everyday was a drenching storm, but at least showers passed through along with some downpours and definitely grey skies. But what about my old home, Chicago?
Well, FiveThirtyEight turned to a longer-term look and examined how over the century the amount of rainfall in the upper Midwest has been increasing. We are actually looking at the same places the Post looked at a few days ago. But instead of political maps, we have rainfall maps.
This one in particular is weird.
I get why they have the map, to show the geographic distribution of the rain gauges that collect the data. And those are site specific, not statewide. But did the designer have to choose area?
We know that area is a less than ideal way of allowing users to compare data points. And as I just noted, a choropleth, even at say the county level, is out of the question. But what about little squares? Or circles? Could colour have been used to encode the same data instead of size? And then we would likely have fewer overlapping triangles.
I suppose the argument is that the big triangles make a bigger visual impact. But they do so at the cost of comparable data points across the Midwest. Maybe the designer chose the area of triangles because there were too few gauges across the country. I am not sure, but for me the triangles are not quite on point.
That said, the graphics throughout the rest of the article are quite good, especially the opening scatterplots. They are not the sexiest of charts, but they clearly show a trends towards a wetter climate.
Continuing with election-y stuff, I want to share a fascinating map from the Washington Post. The article came out last week, and it is actually incredibly light in terms of data visualisation. By my count, there were only two maps. The article’s focus is on interviews with Trump voters in 2016 and how their opinions of the president have changed over the last year or so. If you want to read it, and you should as it is very well written, I will warn you that it is long. But, to the map.
What I loved about this map is how it flips the usual narrative a bit on its head. We talk about how much a candidate won a county in 2016, or even how much the vote shifted in 2016. And anecdotally we talk about “ancestral Democrats” flipping to Trump. But this map actually tries to chart that. It reveals the last time a county actually voted for a Republican presidential candidate—the darker the red, the further back in time one has to go.
Counties that vote Democratic are white, because why do we need them for this examination. Omitting them was a great design decision. Much of the country, as we know or can intuit, voted Republican in 2012 for Mitt Romney. But what about before then? You can see how the upper Midwest, along the Mississippi River, was a stronghold for Democrats with some counties going as far back as the 1980s or earlier. And then in 2016 they all flipped and that flipping was most significant there—of some additional interest to me are the counties in Maine, the Pacific Northwest, and along Lake Erie near Cleveland.
In short, this was just a brilliantly done map. And it sets the tone for the rest of the article, which is interviews with residents of those counties called out on the map.
Credit for the piece goes to Andrew Braford, Jake Crump, Jason Bernert and Matthew Callahan.
Surprise, surprise. This morning we just take a quick little peak at some of the data visualisation from the Pennsylvania primary races yesterday. Nothing is terribly revolutionary, just well done from the Washington Post, Politico, and the New York Times.
But let’s start with my district, which was super exciting.
Each of the three I chose to highlight did a good job. The Post was very straightforward and presented each office with a toggle to separate the two parties. Usually, however, this was not terribly interesting because races like the Pennsylvania governor had one incumbent running unopposed.
But Politico was able to hand it differently and simply presented the Democratic race above the Republican and simply noted that the sitting governor ran unopposed. This differs from the Post, where it was not immediately clear that Tom Wolf, the governor, was running unopposed and had already won.
The Times handled it similarly and simultaneously displayed both parties, but kept Wolf’s race simple. The neat feature, however, was the display of select counties beneath the choropleth. This could be super helpful in the midterms in several months when key races will hinge upon particular counties.
But where the Times really shines is the race for Pennsylvania’s lieutenant governor. Fun fact, in Pennsylvania the governor and lieutenant governor do not run as a ticket and are voted for separately. This year’s Democratic incumbent, Mike Stack, does not get on with the governor and had a few little scandals to his name, prompting several Democrats to run against him. And the Times’ piece shows the two parties result, side-by-side.
Credit for the Post’s piece goes to the Washington Post graphics department.
Credit for Politico’s piece goes to Politico’s graphics department.
Credit for the Times’ piece goes to Sarah Almukhtar, Wilson Andrews, Matthew Bloch, Jeremy Bowers, Tom Giratikanon, Jasmine C. Lee and Paul Murray, and Maggie Astor.
As a kid, volcanoes fascinated me. The idea that the molten core of the Earth can bubble its way up to and then erupt from the cold crusty surface of the planet still fascinates me. Of course, volcanoes can also have drastic impacts on people, both at the grand scale of impacting global climate to the smaller and more personal scale of someone’s home destroyed by a lava flow.
And unfortunately for residents of Hawai’i that personal destruction is unfolding across a development called Leilani Estates. The Washington Post has a nice piece detailing the geography of the area and showing how quickly things can change.
The article uses the photo above to illustrate the distance the lava flow travelled in only a few days. It also shows how precariously sited the homes are.
Only because I am so fascinated by these kinds of stories, I hope the Post continues to expand its content with pieces like this exploring the eruption and those of other volcanoes in the area.
Credit for the piece goes to Laris Karklis and Lauren Tierney.
Last week Angela Merkel, the German chancellor, visited President Trump in Washington. This post comes from the Economist and, while not specifically about that trip, describes Germany in a few different metrics. Back in the day it would be what I called a country-specific datagraphic. That is, it shows metrics not necessarily connected to each other, but all centred around a country. In theory, the framework can then be used to examine a number of different countries.
That sort of works here, except the choropleth is for the Alternative for Germany political party. That only real works as a metric in, you know, Germany.
Overall, I like the piece. The layout works well, but Germany is fortunate in that the geographic shape works here. Try it with Russia and good luck.
First let us dispense with the easy criticism: do we need the box map in the lower right corner to show where in the world Germany is? For Americans, almost certainly yes. But even if you cannot identify where Germany is, I am not certain its location in Europe is terribly important for the data being presented.
But the pie charts. I really wish they had not done that. Despite my well-known hatred of pie charts, they do work in a very few and specific instances. If you want to show a reader 1/4 of something, i.e. a simple fraction, a pie chart works. You could stretch and argue that is the case here: what is the migrant percentage in Bavaria? But the problem is that by having a pie party and throwing pie charts all over the map, the reader will want to compare Bavaria to the Rhineland-Palatinate.
Just try that.
Mentally you have to take out the little red slice from Bavaria and then transpose it to Rhineland-Palatinate. So which slice is larger? Good luck.
Instead, I would have left that little fact out as a separate chart. Basically you need space for 16 lines, presumably ranked, maybe coloured by their location in former East or West Germany, and then set in the graphic. Nudge Germany to the left, and eat up the same portion of Bavaria the box map, cover the Czech Republic, and you can probably fit it.
Or you could place both metrics on a scatter plot and see if there is any correlation. (To the designers’ credit, perhaps they did and found there is none. Although that in and of itself could be a story to tell.)
The point is that I still hate pie charts.
Credit for the piece goes to the Economist’s graphics department.