Joblessness in the Developed World

  • We have been looking at tariffs a little bit this week, but unfortunately one of the side effects of tariffs is job losses. And of course when it comes to people losing jobs, not all countries in the  developed world handle them the same. Last month the Washington Post published an article examining how those countries compare in a number of related metrics such as unemployment compensation, notice for termination, and income inequality.
Not all countries give people the short stick.
Not all countries give people the short stick.

It uses a series of bar charts to show the dataset and reveal how the United States fares poorly compared to its peers. The chart above looks at the earning needed for termination from employment and the differences are stark. The outlined bar chart shows longer tenured employees and the full bars as coloured. Of course this makes it look like a stacked bar chart or filled bar chart. Instead I wonder if a dot plot would be clearer. It would eliminate the confusion in determining what if any share of the empty bar is held by the full bar.

The US offers shockingly little assistance to people
The US offers shockingly little assistance to people

The chart for unemployment insurance versus assistance is a bit better. Here the bar represents insurance and the lines assistance. I like how the lines continue off beyond the margins to indicate an unlimited timeframe for assistance. However, for those countries where assistance is short-lived, the bars versus lines again begin to look like an instance of a share of a total, which they are not.

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.

Apple Hits One Trillion

Last Tuesday we looked at a print piece from the New York Times detailing the share price plunge of Facebook after the company revealed how recent scandals and negative news impacted its financials. Well, today we have a piece from last week that shows how large Apple is after it hit a market capitalisation of one trillion US dollars.

The piece itself is not big on the data visualisation, but it functions much like the Facebook piece, as a blend of editorial design and data visualisation. The graphic falls entirely above the fold and combines a factette and maybe we could classify it as a deconstructed tree map. It uses squares where, presumably, the area equates to the company’s value. And the sum total of those squares equals that of one trillion dollars, or the value of Apple.

Looking at the full page
Looking at the full page

In terms of design it does it well. The factette is large enough to just about stretch across the width of the page and so matches the graphic below it in its array of colours. Why the colours? I believe these are purely aesthetic. After all, it is unclear to me just what Ford, Hasbro, and General Mills all have in common. In a more straight data visualisation piece, we might see colour used to classify companies by industry, by growth in share price or market share. Here, however, colour functions in the editorial space to grab the reader’s attention.

The design also makes use of white space surrounding the text, much like the Facebook piece last week, to quiet the overall space above the fold and focus the reader’s attention on the story. Note that the usual layout of stories on the page continues, but only after the fold.

When we keep in mind the function of the piece, i.e. it is not a straight-up-explore-the-data type of piece, we can appreciate how well it functions. All in all this was a really nice treat last Friday morning.

Credit for the piece goes to Karl Russell and Jon Huang.

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 James Webb Telescope: Delayed Again

A few weeks ago it was announced that NASA’s James Webb space telescope would see its launch delayed again. The successor to the Hubble telescope was originally supposed to launch several years ago, but now it won’t fly until at least 2021. Thankfully xkcd covered this slipping launch date.

Sad trombone
Sad trombone

Credit for the piece goes to Randall Munroe.

Kavanaugh the Conservative

Last night President Trump nominated Merrick Garland to fill the seat left by Anthony Kennedy. Just kidding. But he is up for a vote in the Senate. Also just kidding.

No, instead, President Trump nominated a very conservative judge for the Supreme Court, Brett Kavanaugh. How conservative? Well, FiveThirtyEight explained in a piece that plotted the judge against his probably peers on the bench, based upon one measure of judicial ideology. And it turns out, spoiler, Kavanaugh sits just to the left of Clarence Thomas. And he sits pretty well to the right.

To the right, to the right, to the right goes the Court
To the right, to the right, to the right goes the Court

The graphic itself is an evolution of a piece from last Friday that looked at what were thought to be the four main candidates on Trump’s shortlist.

A definite lean to the right
A definite lean to the right

The final piece, with only Kavanaugh plotted, removes the other potential candidates. And it functions well, using the brighter orange to draw attention from the black dots of the sitting bench and the open dot of the vacant seat. My slight issue is with the predecessor graphic that shows the four candidates.

I probably would have just left off Barrett as she did not have a score. While I have no doubt that she would score to the right based upon all the reading I have done over the past several days, it feels a bit odd to place her on the graphic at all. Instead, I probably would have used an asterisk or a footnote to say that she did not have a score and thus was not placed.

Credit for the piece goes to Oliver Roeder and Amelia Thomson-DeVeaux.

Going Over (But Actually Under)

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.)

Through 27 June
Through 27 June

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…

Credit for the piece goes to David Bunnell.

The London Job Exodus

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.

Look at all those job…
Look at all those job…

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.

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.