I am a graphic designer who focuses on information design. My day job? I am the data visualisation manager for the Federal Reserve Bank of Philadelphia. (This blog is my something I do on my own time and does not represent the views of the Fed, blah blah blah legal stuff.) And with my main interest in information design—be it in the shape of clear charts, maps, diagrams, or wayfinding systems—I am fortunate that my day job focuses on data visualisation. Outside of work, I try to stay busy with personal design work. Away from the world of design, I enjoy cooking and reading and am interested in various subjects from history and geography to politics to science to the arts. And I allow all of them to influence my work.
Last week we talked a lot about trade—and we will get back to it. But the World Cup is now in full swing and I want to take a look at a couple of things this week. But to begin, the Economist published an article about the difficulty of predicting the outcome of World Cups. It looks at the quirks of random events alongside more quantitative things like ranking systems and their differences.
But one graphic in particular caught my attention. It explore the difference between the ranking in individual players versus the teams as a whole. In short, some teams are valued more highly than their constituent players and others vice versa. The graphic is fairly straightforward in that it plots the team value on the y-axis and the players’ on the x.
Personally? I would never bet against Germany. Or Brazil.
But if your author is lucky, he’s going to enjoy the England–Tunisia match this afternoon for lunch—rooting for England, of course. Though thanks to some online tools that’s not the only team I’m rooting for this year. But more on that later this week.
Credit for the piece goes to the Economist graphics department.
If you live under a rock or in America, the World Cup starts today. (Go England.) So what else to have but a chart-driven piece from the BBC from last week about the World Cup. It features seven charts encapsulating the competition. But the one I want to focus on? It’s all about the host nations, in this case Russia.
On its design, I could go without the football icons to represent points on the dot plot, but I get it. (Though to be fair, they work well as icons depicting the particular World Cup event in another set of graphics elsewhere in the article.) In particular, I really like the decision to include the average difference between a host nation’s points in non-hosting matches vs. hosting matches.
It does look like the host nation scores more points per match than when they are not hosting. And that—shameless plug—reminds me of some work I did a few years back now looking at the Olympics and the host nation advantage in that global competition.
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.
Following up on yesterday’s post about the facts on tariffs, today we look at an article from Politico that polled voters on their feelings about trade and trade policy. Now the poll dates from the beginning of June and unfortunately a lot of things have changed since then. But, the data overwhelmingly supports the conclusion that voters, at that time at least, do not support placing tariffs on goods coming into the US.
Let’s take a look at another component of the article, however, a chart exploring the infamous trade deficit. First of all, trade deficits do not work like how the president says they do—but we will come back to that in another post. In short, trade deficits are neither good nor bad. They are just one way of describing one facet of a trade relationship between two countries.
This piece looks at the trade balance between the United States and China.
Now, from the topical standpoint, it does a really nice job of showcasing how our imports have surged above our experts. From a topical standpoint, however, we do not know if this is a total trade deficit or just in goods, like the president prefers to talk about, or in goods and services, the latter of which accounts for way more than half of the US economy.
From a design perspective, I have a few thoughts and the first is labelling. The chart does label the endpoints of the data set, 1985 and 2017. But aside from a grey bar representing the Financial Crisis, there are few other markers to indicate the year. In smaller charts, I often do this myself, because space. But here there is enough space for at least a few intervening years to be labelled.
Secondly, the white outline of the red line. I have talked before of a trend to showcase a line over other lines with that thin stroke. But this is the first time I can recall the effect being used over an area filled with colour. Is it necessary? Because the area is light and the line dark and bright, probably not.
Then the outline appears on the text in the graphic, in particular the labels of imports, exports, and the trade deficit label. The labels for the imports and exports likely are necessary because of that light grey used for the text. But, as with the line for the trade deficit, its label likely provides sufficient contrast the thin white outline isn’t necessary.
Unless you avoid the news, we all heard a lot about tariffs this weekend. So this morning, instead of going with some other things I found, I decided I wanted to look and see just what the data is on tariffs. Turns out Trump is wrong on the data about tariffs. In short, in 2016 the US had a slightly higher average tariff for all products at 1.61%. The EU was at 1.6%. And the Canadians? They charged an outrageous 0.8%.
The data comes from the World Bank.
And over breakfast, I did not really have the time to clean this graphic up, so it shows the whole world. Though it goes to show you, the western countries against which Trump raged this weekend generally have low tariffs, some lower than what the US.
Yo. C’mon, bro. This jawn is getting tired. Just stop already.
If you did not catch it this week, the most important news was Donald Trump disinviting the Super Bowl champions Eagles to the White House to celebrate their victory over the Patriots. He then lied about Eagles players kneeling during the US anthem—no player did during the 2017 season. He then claimed that the Eagles abandoned their fans. Yeah, good luck convincing the city of that.
So naturally we have a Friday graphic for youse.
Full disclosure: I root for the Patriots. But I mean, seriously, can’t youse guys do the math?
Today’s post references a short article from the BBC about some YouGov survey results that examine English respondents’ pride in being English.
The post uses numerous bar charts to examine the demographic and political splits of the results as well as to try and come to a sense of what defines “English”. But the thing that struck me the most? A map of the results.
The most obvious result is in the title: Londoners identify the least as being part of an English county. That sort of regional association firms up the further one travels from London. The exceptions, however, are still urban areas. There are pockets of that light yellow-ish colour to be found also in the areas of Birmingham, the UK’s second-largest city, and, to a lesser extent, Manchester. Then there are other areas around the bigger universities like Cambridge and Oxford.
It makes me wonder how a similar question would play out across the United States. How much to do you identify with being a Pennsylvanian, or an Illinoisan? A New Yorker? And then I would probably take it a step further as well as a step backward in this political climate. How much does one identify with their local community or city? Are you a Philadelphian? A Chicagoan? A New Yorker? And then do you identify more with a city/community or a state more than you do as an American? How much is today’s divisiveness stoking regionalism or tribalism?
Alas, I am not a surveyor nor do I own a company that does surveys. So these sorts of questions are likely to remain curiosities for me.
Credit for the piece goes to the BBC graphics department.
Today is primary day and everyone will be looking to the California results. Although probably not quite me, because Eastern vs. Pacific time means even I will likely be asleep tonight. But before we get to tonight, we have a nice primer from last Friday’s New York Times. It examines the California House of Representatives races that we should be following.
Like most election-related pieces, it starts with a map. But it uses some scrolling and progressive data disclosure. The map above, after a bit of scrolling, finally reveals the districts worth following and their 2016 vote margins.
From there the article moves onto a bit of an exploration of those few districts. You should read the full article—it’s a short read—for the full context on the California votes today. But it does make some nice of bar and line charts to plot the differences in presidential race vs. congressional race margins and the slow Democratic shift.
Credit for the piece goes to Jasmine C. Lee and Karen Yourish.
Last weekend I enjoyed several days off for Memorial Day. But that Sunday I enjoyed a nice, full-spread graphic in the New York Times. The clue that I was in for a treat was on the front page, beneath the fold, with a small map with some green, magenta, and orange.
When I looked more closely I could see that the piece was about the location of disasters. But the actual graphic itself served more as an advertisement than informative graphic. The closeup here shows only that there is a lot of green in the American southeast. But nowhere do we get a sense of what it means other than probably some bad disasters.
But that is the point of an advertisement—to get you to turn to the page, click on the banner, fork over your e-mail address. To be fair, I did not jump straight to the spread as I was going to read the entire section. But when I did finally get there, I got map overload.
The piece uses small multiples across the top. One for each of the last 16 years. Clearly we get the legend explaining what each circle means. But we also have the added context of storm tracks for the tropical systems. And with those in particular, it is fascinating to see how unpredictable tropical systems can be in terms of their impact on America’s coastal regions.
Along with the text of the article, we also get a bar chart exploring the actual dollar value of the largest disasters, major disasters being defined as those over $1 billion in damages. I was a bit surprised to see that Harvey lower than Katrina, as I heard a lot about Harvey surpassing Katrina, but maybe the full data is not in yet? Or maybe it needs to account for the changing value of the dollar?
Regardless, the big thing is the map, as in the big map. Conceptually we get nothing terribly complex, just a choropleth for US postal codes. But keep in mind that more often than not, we want simpler forms because they work the best at showing clearly and concisely what the data is trying to tell us.
The only thing I could not figure out is why some cities were labelled and not others. After all, the map did stretch across 2/3 of the spread. There was clearly enough room to label Philadelphia. Maybe it is just because there looks to be comparatively few losses reported in its postal codes.
But lastly, I absolutely loved the inclusion of Puerto Rico here. No, it is not technically a US state. But it would be the 30th largest if it were. And given that at least 1,000 people died from Hurricane Maria, it is one of the deadliest hurricanes to have hit the United States. And the more attention that gets, the more likely it is Puerto Rico will get the federal assistance it needs.
Overall, this was just a great piece to sit and absorb over a cup of tea on a Sunday morning.