The US Flies Alone

On Sunday, a Boeing 737 Max 8 aircraft crashed shortly after taking off from the airport in Addis Ababa, Ethiopia. This was the second crash in less than a year, since the another 737 Max 8 crashed into the sea shortly after taking off from Jakarta, Indonesia. And in the intervening months, there have been numerous reports to American regulators from pilots of problems with aircraft in flight. Unsurprisingly, international regulators have begun to take steps to protect their skies and their passengers from what might be an unsafe aircraft. American regulators, the Federal Aviation Administration, remains unconvinced.

Consequently, the New York Times put together a graphics-driven article that details just how extensive the global grounding of 737 Max 8 aircraft has been in the last 24 hours.

There's a lot more orange than blue.
There’s a lot more orange than blue.

It’s a route map to headline the article. And it shows that almost all aircraft on 737 Max 8 routes, except for those in Canada and the United States, have been grounded.

The rest of the article makes use of more maps highlighting the countries who civil aviation authorities have grounded flights and popular routes. It also includes a bar chart showing how many 737 Max 8 aircraft are in use with each airline and how many of those airlines have had their fleets grounded.

Overall, it’s a strong article that makes great use of graphics to illustrate its point about the magnitude of the grounding and the isolation of the United States and Canada.

Credit for the piece goes to Denise Lu, Allison McCann, Jin Wu, and K.K. Rebecca Lai.

The Stunted Growth of North Korea

This piece from the BBC is a few years old, but it provides some interesting nuggets about North Korea. Unsurprisingly it appeared on my radar because of the coverage of the Trump–Kim summit in Vietnam. The article says it is nine charts that tell you all you need to know about North Korea. Now, I do not think that is quite true, but it does contain the following graphic—I hesitate to call it a chart—that illustrates one of my favourite details.

It's just a matter of inches
It’s just a matter of inches

The two figures illustrate the average height of a person from North Korea and then South Korea. What do you see? That the North Korean is shorter. This is despite the fact that the populations were the same just a few decades ago. The impact of years of malnutrition, undernourishment, and general lack of well-being have manifested themselves in the physical reduction of size of human beings compared to their nearly identical population to the south.

Thankfully the rest of the piece contains data on things like GDP, birth rates, and life expectancy. So there are some things in there that one should know about North Korea. As much as I find the story of height interesting, I struggle to think it is one of the nine things you should really know about the state.

Credit for the piece goes to Mark Bryson, Gerry Fletcher, and Prina Shah.

Where’s All the Oil Going?

Hint: not China.

Today’s piece is a nice little graphic from the Economist about the oil and natural gas industry in the United States. We have a bar chart that does a great job showing just how precipitous the decline in Chinese purchases of oil and liquid natural gas has been. Why the drop off? That would be the trade war.

Will they take it? For all the tea in China?
Will they take it? For all the tea in China?

The second graphic, on the right, is far more interesting. The data comes from BP, so the proverbial grain of salt, but it compares expected GDP and demand for energy by source from a baseline model of pre-Trumpian trade war policies to a future of “less globalisation”. Shockingly (sarcasm), the world is worse off when global trade is hindered.

You all know where I stand on stacked bar charts. They are better than pie charts, but still not my favourite. If I really want to dig in and look at the change to, say, coal demand, I cannot. I have to mentally remove that yellow-y bit from the bottom of the bar and reposition to the 0 baseline. Or, I could simply have coal as a separate bar next to the other energy sources.

Credit for the piece goes to the Economist Data Team.

Individualistic Immigrants

As many of you know, genealogy and family history is a topic that interests me greatly. This past weekend I spent quite a bit of time trying to sort through a puzzle—though I am not yet finished. It centred on identifying the correct lineages of a family living in a remote part of western Pennsylvania. The problem is the surname was prevalent if not common—something to be expected if just one family unit has 13 kids—and that the first names given to the children were often the same across family units. Combine that with some less than extensive records, at least those available online, and you are left with a mess. The biggest hiccup was the commonality of the names, however. It’s easier to track a Quinton Smith than a John Smith.

Taking a break from that for a bit yesterday, I was reminded of this piece from the Economist about two weeks ago. It looked at the individualism of the United States and how that might track with names. The article is a fascinating read on how the commonness or lack thereof for Danish names can be used as a proxy to measure the individualism of migrants to the United States in the 19th century. It then compares that to those who remained behind and the commonness of their names.

But where are the Brendans?
But where are the Brendans?

The scatter plot above is what the piece uses to introduce the reader to the narrative. And it is what it is, a solid scatter plot with a line of best fit for a select group of rich countries. But further on in the piece, the designers opted for some interesting dot plots and bar charts to showcase the dataset.

Now I do have some issues with the methodology. Would this hold up for Irish, English, German, or Italian immigrants in the 19th century? What about non-European immigrants? Nonetheless it is a fascinating idea.

Credit for the piece goes to the Economist Data Team.

Be Like Mike?

Back in 2012 the New York Times ran what is a classic data visualisation piece on Mariano Rivera. It tracked the number of saves the legendary Yankees closer had over his career and showed just how ridiculous that number was—and how quickly he had attained it. Last week, the Washington Post ran a piece that did something very similar about LeBron James, a future basketball legend, and Michael Jordan, definitely a basketball legend.

They might have game.
They might have game.

The key part of the piece is the line chart tracking points scored, screenshot above. It takes the same approach as the Rivera piece, but instead tracks scored points. Unlike the Rivera piece, which was more “dashboard” like in its appearance and function, allowing users to explore a dataset, this is more narratively constructed. The user scrolls through and reads the story the authors want you to read. Thankfully, for those who might be more interested in exploring the dataset, the interactivity remains intact as the user scrolls down the article.

While the main thrust of the piece is the line chart, it does offer a few other bar and line charts to put James’ career into perspective relative to the changing nature of NBA games. The line chart breaking down the composition of James’ scoring on a yearly basis is particularly fascinating.

But, don’t ask me about how he fits into the history of basketball or how he truly compares to Michael Jordan. Basketball isn’t my sport. But this is a great piece overall.

Credit for the piece goes to Armand Emamdjomeh and Ben Golliver.

Trump’s Executive Time

Tonight President Trump will give his State of the Union address, the annual speech about the president’s goals and agenda. Today I have a work meeting about management practices. So when I read this piece yesterday by Axios on Trump’s schedule (from a leak of November and December dates), I figured what better piece to highlight here on Coffeespoons.

All the orange…
All the orange…

To be fair, the concept is pretty straightforward. We have a stacked bar chart with each type of time block represented by a colour. Because the focus of the piece is the Executive Time blocks, I really think the designer did a great job summing the other types of time, e.g. travel and meetings, into one bin. And by being a lighter colour on nearly the same scale as the grey, it helps the orange Executive Time pop. Clearly Executive Time dominates the schedule, which as many analysts have been pointing out, is a departure from recent past presidents.

And, if you’re curious how the time blocks compare, elsewhere in the piece is a stacked bar chart summing all the types of time. Not surprisingly, most of his schedule is Executive Time.

Credit for the piece goes to Lazaro Gamio.

A Not so Dry January

January has ended, and with it for, apparently, a very few Britons, Dry January. The Economist looked at alcohol consumption, using a proxy of beer sales, and compared that against the number of times people searched for “Dry January” on Google.

Not so dry after all…
Not so dry after all…

What I really like about this chart is that it does not try to combine the two series into one. Instead, by keeping the series separate on different plots, the reader can clearly examine the trends in both searches and consumption.

You also run into the problem of how to overlay two different scales. By placing one line atop the other, the user might implicitly understand that as higher or better than the lower series when, one, that may not be true. Or, two, the scales are so different they prevent the direct comparison the chart would otherwise imply as possible.

Here, the designers rightly chose to separate the two plots, and then highlighted the month of January. (I also enjoy the annotation of the World Cup.) I might have gone so far as to further limit the palette and make both series the same colour, but I understand the decision to make them distinct.

But, overall, as the piece points out, drinking in Britain seems to correlate to the weather/temperature. People go out to the pubs more on warmer days than colder. But regardless of any post-holiday hangover, they still consumer beer in January.

I’ll drink to that.

Credit for the piece goes to the Economist Data Team.

Border Arrests

We move from one manufactured crisis to another today as we look at a piece by the Economist on the number of illegal immigrants arrested at the US southern border. Lately, here in the United States we have been hearing of an invasion on our southern border. Illegal immigrants streaming across the border. Except, that is not true. In fact, illegal immigration is at or near its lowest rate in recent years.

Note how few there have been in recent years…
Note how few there have been in recent years…

The graphic does one thing really well and that is its unorthodox placement of the map. Instead of the usual orientation, here the designers chose to “tilt” the map so that the border segments roughly align with the sets of charts below them. I might have desaturated the map a little bit and cut off the gradient so Mexico does not bleed through underneath the bars, but the concept overall is really nice.

On the other hand, we have the bar charts arranged like funnels. This does allow the reader to see the slopes trending towards zero, however, it makes it incredibly difficult to see changes in smaller numbers. And without a scale on the axis, the reader has to take the bars and mentally transpose them on top of the grey bars in the bottom right corner. I wonder if a more traditional set of bar charts in small multiples could have worked better beneath the map.

Overall, however, I really do like this piece because of the way the map and the bar charts interact in their positioning.

Credit for the piece goes to the Economist Data Team.

The World Grows On and On

I mentioned this this time last year, but I used to make a lot of datagraphics about GDP growth. The format here has not changed and so there is nothing new to look at there. But, the content is still interesting. And the accompanying Economist article makes the point that high growth rates are not always what they seem. After all, Syria’s high growth rate is because its base is so small.

The 2019 GDP growth forecasts
The 2019 GDP growth forecasts

Credit for the piece goes to the Economist Data Team.

PECO Outages Five Years Ago

Christmas time is a time when people receive gifts. Well this year was no different and I received a few. One, however, was in a box stuffed with old newspaper pages. And it turns out one of said pages had a graphic on it. So let us spend today looking at this little blast from the past.

The piece looks at PECO outages, PECO being the Philadelphia region’s main electricity supplier. The article is full page and is both headed and footed with photography, the graphic in which we are interested sits centre stage in the middle of the page.

Full page design.
Full page design.

Overall the graphic is fairly compact and works well at showing the distribution of the outages, which the bar chart below the choropleth shows was historically significant. (Despite my years in Chicago, I was somehow in the area for all but the storm written about and can confirm that they were, in fact, disruptive.)

Ice storms suck.
Ice storms suck.

The choropleth works, but I question the colour scheme. The bins diverge at about 50%, which to my knowledge marks no special boundary other than “half”. If that yellow bin represented, say, the average number of outages per storm or the acceptable number of outages per storm, sure, I could buy it. Otherwise, this is really just degrees of severity along one particular axis. I would have either kept the bins all red or all blue and proceeded from a light of either to a dark of either.

I probably would have also dropped Philadelphia entirely from the map, but I can understand how it may be important to geographically anchor readers in the most populous county to orientate themselves to a story about suburbia.

Lastly, I have one data question. With power lines down during an ice storm, I would be curious to see less of the important roadways as the map depicts and other variables. What about things like average temperature during the storm? Was the more urban and built-up Delaware County less susceptible because of an urban heat bubble preventing water from freezing? Or what about trees? Does the impact in the more rural areas have anything to do with increasing numbers of trees as one heads away from the city?

Those last data questions were definitely out of scope for the graphic, but I nevertheless remain curious. But then again, this piece is almost five years old. Just a look at how some graphical forms remain in use because of their solid ability to communicate data. Long live the bar chart. Long live the choropleth.

Credit for the piece goes to the Philadelphia Inquirer graphics department.