Burden Sharing in NATO

Late last week we heard a lot about contributions to NATO. Except, that was not true. Because the idea of spending 2% of GDP on NATO is actually about a NATO member spending 2% of its GDP on its military. And within that 2%, at least 20% must be spent on hardware or R&D. There is a separate operating budget to which countries actually contribute funds. But before we look at all of this as a whole, I wanted to explore the burden sharing, which is what NATO terms the 2% of GDP defence expenditures.

I did something similar a couple of years ago back in 2014 during the height of the Russia–Ukraine crisis. However, here I looked at a narrower data set from 2011 to 2018 and then across all the NATO members. In 2014, NATO met in Wales and agreed that over the next ten years all members would increase their defence spending to 2% of GDP. We are only four years into that ten year plan and so most of these countries still have time to reach that level.

That's still a whole lot spent on defence
That’s still a whole lot spent on defence

Credit for the piece is mine.

Chinese Urban Clusters

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.

The Chinese government's new urban cluster plan
The Chinese government’s new urban cluster plan

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 added some context to our cluster map
We added some context to our cluster map

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.

Family Migration Patterns

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.

1671, 1721, and 1771
1671, 1721, and 1771
By 1821, the families are spread quite a bit further
By 1821, the families are spread quite a bit further

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.

1650
1650
1700
1700
1750
1750
1800
1800
1850
1850
1900
1900

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 Facts on Tariffs

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%.

Apologies for the length on this one
Apologies for the length on this one

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.

Credit for this one is mine.

Philly Rules

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.

That's 25,304.
That’s 25,304.

Full disclosure: I root for the Patriots. But I mean, seriously, can’t youse guys do the math?

Lunar Observations

Monday night I was doing some work outside and when I turned around to head inside I was struck by the brilliance of an object in the night sky. I had seen the Moon rise earlier in the evening, but this was far to the east. It was identifiable as a dot, not just a speck in the night sky. As I was now intrigued I went to grab my binoculars to see if I could see Venus.

Turns out I was wrong and it was Jupiter. But then I turned my binocular-aided eyes to the west and examined the Moon. That was then I decided to try and sketch my observations, as I had done with the Eclipse.

Unfortunately, it turns out it is far more difficult to sketch in the dark then under a still semi-sunny sky. But these are my attempts to digitise those observations. And as I sat and watched, I began to notice that some faint twinkling specks near Jupiter had also moved. After I came inside, I discovered that the movement and positions hewed close to the orbits of Jupiter’s moons Ganymede and Calisto. The moving speck near the Moon I had also observed was actually the bright star Regulus. (And to be fair, it had not really moved, the Moon had moved, but I was not redrawing the Moon.)

The Moon and Regulus. The cool part is the thin ring of one of the seas that could be spotted beyond the line separating lunar day from night.

Capturing the exact shapes of the lunar "seas" was difficult in the darkness.
Capturing the exact shapes of the lunar “seas” was difficult in the darkness.

Jupiter and two of its moons. The cool thing about Jupiter is just being able to see it as a round ball in space and not a distant twinkling speck.

Jupiter and two of its moons, as they orbit the distant planet
Jupiter and two of its moons, as they orbit the distant planet

Credit for these is mine.

The Short Arc of Pub Trivia Scores Bends Towards Victory

Well, at least over the last three weeks it did. In previous examples of my pub trivia team’s performance, we have had a lacklustre performance. But a few weeks we had an epic collapse. Having been in 4th place out of 10 in the penultimate round we ultimately finished in 8th out of 9—somebody left early—and 14 points out of first place. It probably didn’t help we put Beyonce down as the artist for three different songs. It probably really didn’t help that none of those artists were Beyonce. And it probably definitely didn’t help that we had no idea who those three artists were.

Then after a middling performance two weeks ago, last week we shocked even ourselves with our first victory since last autumn. Just how shocking? 19 points in that oft ill-fated music round. (It’s not really, but I’ll have to make another graphic about that.)

We were perhaps the most shocked of all that week.
We were perhaps the most shocked of all that week.

Credit for the piece is mine. Credit for the score goes to my teammates.

Pub Trivia Performance—the Long View

Last week my pub trivia team was debating whether our high score, although only good for second place—we lost by one point—was the highest. So this past weekend I scoured my sketchbooks for the last year and a half and reviewed our scores.

Alas, the earliest appearances were tally-free. And I did not record them consistently until this past autumn, but I had developed a decent system by last summer for the sake of comparing weeks.

Over the summer (not entirely captured) and autumn, we had a string of first-place finishes. Then we cratered towards the new year. And while we have strung together a couple of second-place finishes, we haven’t finished in first since last autumn.

We've been on a steady climb up since the new year…
We’ve been on a steady climb up since the new year…

London Beats New York

In murders. Not the best of news, no. But this past March London saw more murders than New York. But as I was reading the BBC article this weekend, I wondered why the graphic they chose to use received as much prominence in the article as it did.

London moves ahead of New York
London moves ahead of New York

The chart as you can see occupied a full column width. But keep in mind, we are looking at a total of six datapoints: the murders for two cities in three months. While the story and data is significant, does the display of the data need to be?

My version
My version

The important point in the story is that in the past three months, London has surpassed New York in the number of murders. But the graphic supporting those six data points should not be overwhelming the significance of the text explaining the trend. After all, the data consists of only three points for two cities. If the data is displayed on an extended horizontal axis, it flattens the change and minimises the increase. To counteract that, the y axis should be increased, but then the amount of screen real estate being devoted to six data points is enormous. The better approach is to use a smaller graphic that displays the data in a better proportion, but also in a proportion that does not blow out the text of the story. The graphic to the right (and maybe above this blurb of text) shows how that can be done in a smaller space.

Credit for the original goes to the BBC graphics department.

Bitcoin Land

Sorry, I ran into some technical problems this morning so this is going up this afternoon with an added bit at the end.

I’m not really sure this piece should go onto the blog. But I like it. And this is still my blog. So what the hell.

I grew up a big fan of games like Sim City, where you could create your own universes. And in the world of infographics, you do occasionally see the isometric drawings of cities, but I find they often lack representative value. Here, in this piece from Politico Magazine, we have the Bitcoin landscape.

The different buildings represent different elements of the cryptocurrency’s ecosystem, from supporting markets, regulators, utility companies, &c. Later on in the article, the different sections are broken out and labelled and annotated. Additional elements are also brought in to explain ancillary parts of the Bitcoin landscape. All the while keeping the same style. Very well done.

Reticulating splines
Reticulating splines

This detail looks at some of the things existing outside the specific Bitcoin environment, e.g. other cryptocurrencies. And the aforementioned utility companies that provide the necessary power for the computations.

It even has a tram system…
It even has a tram system…

I kind of wish the universe was larger, though. If only for the purely selfish purpose of getting lost in the illustrations.

Since I’ve had today to think more about this, it reminded me of one of my favourite projects I got to work on from a couple of years ago.

Unfortunately for me, my illustration skills are not quite top-notch. But I did get to direct a similar project, working with a talented designer—now expert craftsman—who can in fact draw. And since it’s not often I get to show this work, why not. We used consumer survey data describing the average middle class household to, well, visualise said middle class household. It took a lot longer than I think anyone thought, so we never attempted the style again. But the designer did some great work on this.

One of my favourite projects that I oversaw as Captain Art Director (not my real title).
One of my favourite projects that I oversaw as Captain Art Director (not my real title).

Credit for the Politico piece goes to Patterson Clark and Todd Lindeman.

Credit for the Euromonitor piece goes to Benjamin Byron and myself.