My New Toast

I am a millennial. That broadly means I am destroying and/or ruining everything. It also means I am obsessed with things like avocado toast. It also means I am not buying a house. Thankfully the Economist is on top of my next fad: indoor houseplants.

Plant things
Plant things

Your author will admit to having a few: a hanging plant, an Easter lily, an aloe plant and its children, and a dwarf conifer. Just don’t ask me how they’re doing. (Hint: not well.) Turns out I am not a plant person.

In terms of the graphic, though, what we have is a straight up set of small multiples of line charts. The seasonality mentioned in the article text appears quite clearly in a number of plants.

But is Swiss Cheese really a plant?

Credit for the piece goes to the Economist Data Team.

Development Languages

Last week the Economist published an article sort of about my industry. Now I am a designer and more familiar with the front-end design and some HTML and CSS, but a lot of the things I have designed over the last few years have needed some serious developers with some serious skills. And those guys were the ones who would truly understand this graphic, which looks at the popularity of Python relative to other languages like C++, Java, Javascript, .NET, &c.

Python has certainly climbed in importance
Python has certainly climbed in importance

I really like what the designers did here. First and foremost the key chart is a ranking chart showing the popularity of languages since 1988—Java and C have consistently been at the top. But other languages no longer relevant are not even shown. (Where are you, Actionscript?) Those that are both relevant and also mentioned are colour coded within the set.

But the truly nice thing is being able to use the empty space of the lower-left area of the chart to add some context. It shows the growth in Google searches since 2010 in searches for Python.

Bonus note, look at that rise in R since 2008.

Credit for the piece goes to the Economist Data Team.

The Decline of the Media

Everybody loves maps. Unfortunately this is not a map to love. The Economist looked at the global status of the free press and its decline around the world.

If only it were a larger map
If only it were a larger map

The graphic is a neat little package of a map to anchor the narrative and a few callout countries with their general declines—or in Tunisia’s case the reversal thereof—highlighted. But I do have a few issues with the piece.

Do the lines need to be curved? Some certainly make sense, e.g. how do you get from the Turkey box to the outline of Turkey? But then for Afghanistan, a straight line through Balochistan, Pakistan would mean the line would not have to cover Pakistan, India, curve around Sri Lanka, and then finally reach the box.

In the little boxes, I also wonder if the lines need to be as thick as they are. Could a lighter stroke weight improve the legibility of the charts?

And to be super picky, I wonder if the stroke outlines of the countries are complete. My trained eye fails to register an outline of both the European part of Turkey and of the Russian oblast of Kaliningrad.

Credit for the piece goes to the Economist’s Data Team.

The Rising Tide of Jihadist Violence in Africa

The other day somebody mentioned to me that Africa is big, to which I agreed. It is big. It contains, depending upon how you count, about 55 countries and over one billion people. It stretches from Mediterranean climates and deserts in the north to rainforests around the equator and then back down through steppe climates to the southern coast of South Africa.

But in that vast territory also comes jihadist violence, and in this article by the Economist, it points out that despite that vastness, the violence can be found in two main areas: first, along the Mediterranean coast and, second, along the Sahel and savannah.

At least it's not spiking?
At least it’s not spiking?

The map uses dots to nice effect here, pinpointing the actual locations of violence and then providing additional detail by colouring the dots according to the perpetrators of the violence. But what I really enjoyed was the simple effect of tying together the dot colours to the stacked area chart in the lower left. It shows the number of people killer per year. And while significantly up from 2010, at least the number of people killed by Boko Haram is down from its heights in 2014–15.

But the reason I brought up the vastness at the beginning is that while these are all groups following a jihadist ideology, many are also driven by very local concerns. Consequently they likely have local solutions. And we need to be careful about how much lumping together we do about jihadist violence in Africa.

Credit for the piece goes to the Economist data team.

Penalty Shoot Outs

Well, football is not coming home. But the World Cup continues. And should we get another final match tied at the end of extra time, that means penalty shoot outs. Thankfully, the Economist did a nice job detailing the success rates on goal by placement of the ball.

Coin flips
Coin flips

The only thing I am unsure about is whether the dots represent the actual placement or just positioning within the aggregate zone. The colours work well together and the graphic of the goal is not overpowering.

Credit for the piece goes to the Economist Data Team.

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.

When the Whole Is Less Than the Sum of its Parts

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.

When sums are greater or less than the whole…
When sums are greater or less than the whole…

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.

Spanish Silver

A few weeks back now the Economist posted a graphic about the link between lead, silver, and the rise and fall of the Roman Empire. But not in the way you probably think. Instead, they graph the appearance of lead deposits in the glaciers of Greenland.

I believe that final Iberian power is meant to be the Moops.
I believe that final Iberian power is meant to be the Moops.

For the full explanation you should read the short article. But this piece was right up my alley. We have ancient history, economics, science, and a timeline. And all in one neat little chart.

Credit for the piece goes to the Economist’s Data Team.

Forecasting the American Midterm Elections

We are inching ever closer to the US midterm elections in November. In less than a week the largest state, California, will go to the polls to elect their candidates for their districts. So late last week whilst your author was on holiday, the Economist released its forecast model for the results. They will update it everyday so who knows what wild swings we might see between now and the election.

I will strike out against the common knowledge that this is a wave election year and Democrats will sweep swaths through Republican districts in an enormous electoral victory. Because while Democrats will likely win more overall votes across the country, the country’s congressional districts are structurally designed to favour Republicans as a result of gerrymandering after the 2010 Census redistricting. The Economist’s modelling handles this fairly well, I think, as it prescribes only a modest majority and gives that likelihood as only at 2-in-3. (This is as of 30 May.)

But how is it designed?

The big splashy piece is an interactive map of districts.

The overall state of the US in the 30 May run of the model
The overall state of the US in the 30 May run of the model

It does a good job of connecting individual districts to the dots below the map showing the distribution of said seats into safe, solid, likely, leaning, and tossup states. However, the interactivity is limited in an odd way. The dropdown in the upper-right allows the user to select any district they want and then the district is highlighted on the map as well as the distribution plot below. Similarly, the user can select one of the dots below the map to isolate a particular district and it will display upon the map. But the map itself does not function as a navigation element.

Selecting the newly drawn Pennsylvania 6th
Selecting the newly drawn Pennsylvania 6th

I am unsure why that selection function does not extend to the map because clearly the dropdown and the distribution plot are both affecting the objects on the map. Redeeming the map, however, are the district lines. Instead of simply plopping dots onto a US state-level map, the states are instead subdivided into their respective congressional districts.

But if we are going so far as to display individual districts, I wonder if a cartogram would have been a better fit. Of course it is perfectly plausible that one was indeed tried, but it did not work. The cartogram would also have the disadvantage of, in this case, not exhibiting geographically fidelity and thus being unrecognisable and therefore being unhelpful to users.

Now the piece also makes good use of factettes and right-left divisions of information panels to show the quick hit numbers, i.e. how many seats each party is forecast to win in total. But the map, for our purposes, is the big centrepiece.

Overall, this is solid and you better bet that I will be referencing it again and again as we move closer to the midterms.

Credit for the piece goes to the Economist Data Team.

Open Door Cabinets

Here in the States we are accustomed to unstable governments—the Trump administration has set records for the most departures so early in its term. But the United Kingdom is not to be outdone as Amber Rudd, the Home Secretary, resigned in response to an immigration scandal. She makes six the number of cabinet officials who have left the British government.

The Economist put together a small graphic showing how long it took various governments, British and otherwise, to reach the level of so many departures. May’s government has been the fastest to reach so many departures in recent years.

I wonder where the US administration falls…
I wonder where the US administration falls…

The key thing to note here is what I pointed out last week, which is the use of a thin white stroke on the outside of the lines being highlighted with the Theresa May government using a bolder weight to make it stand out just a wee bit more. This is a bit different than the Times version which uses the outline approach for only what would here be the May line, but it still works overall to draw attention to the British governments.

Credit for the piece goes to the Economist graphics department.