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.
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.
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.
Last Thursday, Facebook’s share price plunged on the news of some not so great numbers from the company on its quarterly earnings report. The data and number itself is not terribly surprising—it is a line chart. But what I loved is how the New York Times handled this on the front of the Business section on Friday morning.
I found the layout of the page and that article striking. In particular, each day of the share price is almost self-contained in that the axis lines start and stop for each day. I question the thickness of the stroke as something a little thinner might have been a bit clearer on the data. However, it might also have not been strong enough to carry the attention at the top of the page. As it is, that attention is needed to draw the reader down the page and then down across the fold.
Additionally, the designers were sensitive to the need to draw that attention down the page. In order to do that they kept the white space around the graphic and kept the text to two small blocks before moving on to the interior of the section.
Credit for the story goes to Matthew Philips. Although I’m pretty sure the page layout goes to somebody else.
The weather in Philly the past week has been just gross. It reminds of Florida in that it has been hot, steamy, storms and downpours pop up out of nowhere then disappear, and just, generally, gross. I do not understand how people live in Florida year round. Anyway, that got me thinking about this piece from a month ago in the New York Times. It looked at the impact of climate change and living conditions in South Asia. Why is South Asia important? Well, it is home to nearly a billion people, a large number of whom are poor and demanding resources, and oh yeah, has a few countries that have fought several wars against each other and are armed with nuclear weapons. South Asia is important.
The map from the piece—it also features a nice set of small multiples of rising temperatures in six countries—shows starkly how moderate emissions and the high projection of emissions will impact the region. Spoiler: not well. It notes how cities like Karachi, for example, will be impacted as hotter temperatures mean lower labour productivity means worse public health means lower standard of living. And it doesn’t take a rocket scientist to see how things like demand for water in desert or arid areas could spark a conflict between Pakistan and India. Although, to be very clear, the article does not go there.
As to the design of the graphic, I wonder about the use of white for no impact and grey for no data. Should they have been reversed? As it is, the use of white for no impact makes the regions of impact, most notably central India, stand out all the more clearly. But it then also highlights the regions of no data.
Credit for the piece goes to Somini Sengupta and Nadja Popovich.
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.
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.
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.
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.
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.
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.
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.
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.
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.