As most of you know, I am what would have been called a loyalist. That is, I disagree with the premise of the American Revolution. People often mistake that as saying I think Americans should be British. No, although I personally would not mind that. Instead, America would likely have been a lot more like Canada and it would have obtained its independence peacefully through an organic, evolutionary process leading to, likely, some kind of parliamentary democracy.
Every year, somebody digs up articles people have written about why the Revolution was a bad idea. I have seen a lot of them. But I had not seen this Washington Post article that looked at constitutional monarchies. It was published during the whole royal baby buzz back in 2013. It examines why constitutional monarchies are not so bad, and might even be better than presidential republics.
The above graphic is far from great. The same goes for the other graphic in the article. I probably would have added more emphasis on the constitutional monarchies as they get overwhelmed by the number of non-constitutional monarchies s in the scatter plot. That could be through a brighter blue or keeping the pink and setting the rest to a light grey. I perhaps would have added a trend line.
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.
Late last week I was explaining to someone in the pub why the World Cup matches are played beyond their 90 minute booking. For those among you that do not know, basically the referees add up all the stoppage time, i.e. when play stops for things like injuries or people dilly dallying, and then tack that on to the end of the match.
But it turns out that after I explained this, FiveThirtyEight published an article exploring just how accurate this stoppage time was compared to the amount of stopped time. Spoiler: not very.
In design terms, the big takeaway was the dataset of recorded minutes of actual play in all the matches theretofore. It captured everything but the activity totals where they broke down stoppage time into categories, e.g. injuries, video review, free kicks, &c. (How those broke out across an average game are a later graphic.)
The setup is straightforward: a table organises the data for every match. The little spark chart in the centre of the table is a nice touch that shows how much of the 90 minutes the ball was actually in play. The right side of the table might be a bit too crowded, and I probably would have given a bit more space particularly between the expected and actual stoppage times. On the whole, however, the table does its job in organising the data very well.
Now I just wonder how this would apply to a baseball or American football broadcast…
Today is Friday. We all made it through yet another week. So let us look up into the evening sky tonight and see the Hertzsprung–Russel diagram in action. Or, we can take xkcd’s expanded version and just enjoy ourselves.
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.
I found myself doing a bit of summer cleaning yesterday and I stumbled upon a few graphics of interest. This one comes from a September 2016 Wall Street Journal article about the changes in the S&P 500, a composite index of American stocks, some of the 500 largest.
In terms of the page design, if it were not for that giant 1/6 page advert in the lower right corner, this graphic could potentially dominate the visual page. The bulk of it sits above the page’s fold and the only other competing element is a headshot to the upper-right. Regardless, it was clearly enough to grab my attention as I was going through some papers.
As for the graphic itself, I probably would have some done things differently.
To start, are these actual tree maps? Or are they things attempting to look like tree maps? It is difficult to tell. In an actual tree map, the rectangles are not just arranged by convenience, as they appear to be here. Instead, they are in descending—or perhaps occasionally ascending—area, within groupings.
The groupings would have been particularly powerful here. Imagine instead of disparate blue boxes for industrials and utilities in the latter two years that they were combined into a single box. In 2001, that box may have been larger than the orange financials. Then by 2016, you would have seen those boxes switch places—in both years well behind the green boxes of 2001 debuts. If instead the goal was to show the percentages, as it might be given each percentage is labelled, a straight bar chart would have sufficed.
I am not always a fan of the circle for sizes along the bottom. But the bigger problem I have here is the alignment of the labelling and the pseudo-tree maps. One of my first questions was “how big are these years?”. However, that was one of the last points displayed, and it is separated from the tree maps from the listing of the largest company in the index from that year. I would have kept the total market cap closer to the trees, and perhaps used the whole length of line beneath the trees and instead pushed the table labels somewhere between the rather large gap from 1976 and 2001.
Credit for the piece goes to the Wall Street Journal graphics department.
On Saturday I attended an all-day seminar by the New England Historic Genealogical Society (NEHGS) at the new Museum of the American Revolution here in Philadelphia. Just fantastic. One of the lectures included some maps that looked at the distribution of families over a year span—turns out families did not stand still. Instead, they tended to “fill in” the sparser areas near their settlements and as land became sparser, the younger sons with less to inherit began to move further west, primarily, but also sometimes north into the rest of the nascent United States.
In terms of design, these work in a black and white book, so we do not get any fancy colours. Consequently, the location markers are well chosen as distinct shapes. I also liked the limitation of state outlines to only those states where descendants were present to limit the amount of distracting black lines on the graphic.
Perhaps not surprisingly I then decided to take my own stab at something similar late last night and this morning. I looked at only one line of ancestors, the Millers, and their descendants with the caveat that there are very much indeed holes in lines of the cousins and second cousins that I have not followed yet. But those I have included show, to a lesser degree, that patterns of movement west and north. The key difference is I extended mine to 1900, because the pattern becomes a little bit clearer over time in my family. I also stopped writing out names of individuals and just started writing out families, because it gets out of control pretty quickly.
Credit for the originals goes to Lois Kimball Matthews.
Credit for the coloured one is mine and my Miller family ancestors and their descendants.
The World Cup continues. Well for a few teams. Some have already been eliminated from the Round of 16. But for those Americans rooting for Team America, well, if you have not yet figured it out, you got knocked out well before the World Cup even started by…Panama. And so you are stuck in the question of who’s next? Thankfully FiveThirtyEight, in addition to their fantastic live probabilities that we looked at the other day, put together a little quiz to help you find your new team.
You answer seven questions and you are told your new allegiance. Questions like this:
Naturally I took the quiz and discovered that in addition to England, I am cheering for…
Yep. Fantastic since I was just there in December and happened to love Stockholm. But what I love about this piece is how it uses data to create the newfound bond I have with Sweden. Often times you take a quiz and are given an answer without any sense of why the answer was correct. Here, FiveThirtyEight plots the seven different variables used to create your newfound personality and then shows you how you scored.
It’s Friday, it’s the World Cup. Have a great weekend. And in addition to England on Sunday, I’ll now be cheering for Sweden against Germany on Saturday.
Credit for the piece goes to Michael Caley, Rachael Dottle, Geoff Foster, Gus Wezerek, Daniel Levitt, Emily Scherer, and Jorge Lawerta.
The World Cup has had some impressive matches and some stunners. (And the two are not mutually exclusive.) But if you are like me and have to work during most of the broadcasts, how can you follow along? Well thankfully FiveThirtyEight put together a nice statistical model that provides the probability of a team winning—or drawing—in real time.
The design is fairly simple: a small table with the score and probability followed by a chart drawn as the match goes on. (Clearly I took this image at the half.)
I included a snippet of the table below to show the other work the FiveThirtyEight team put out there. You can explore the standings, the screenshot above, as well as the matches and then the brackets later in the competition.
The table makes nice use of the heat map approach to show is likely to make easy of the different stages of the competition. Like I said the other day, they are high on Brazil, because Brazil. But a little lower on Germany. But never count Germany out.
The only unclear thing to me in the table? The sorting mechanism. In Group B, at least whilst the Portugal match is ongoing, should probably have Iran at the top. After all, as of writing, it is the only team in the group to have won a match. The only thing I can guess is that it has to do with an overall likelihood to advance to the next round. I highly doubt that Iran will defeat either Spain or Portugal. But as with many knockout-style championships, anything can happen in a single match sample size.
Credit for the piece goes to Jay Boice, Rachael Dottle,Andrei Scheinkman, Gus Wezerek, and Julia Wolfe.