Trumping (Most) All on Twitter

Initially I wanted today’s piece to be coverage of the apparent coup d’état in Zimbabwe over night. But while I have found some coverage of the event, I have not yet seen a single graphic trying to explain what happened. Maybe if I have time…

In the meantime, we have the Economist with a short little piece about Trump on Twitter and how he has bested his rivals. Well, most of them at least.

Trumping one's rivals
Trumping one’s rivals

The piece uses a nice set of small multiples to compare Trump’s number of followers to those of his rivals. The multiples come into play as the rivals are segmented into three groups: political, sport, and media. (Or is that fake media?)

Small multiples of course prevent spaghetti charts from developing, and you can easily see how that would have occurred had this been one chart. But I like the use of the reddish-orange line for Trump being the consistent line throughout each. And because the colour was consistent, the labelling could disappear after identifying the data series in the first chart.

And worth calling out too the attention to detail. Look at the line breaks in the chart for the labelling of Fox News and NBA. It prevents the line from interfering with and hindering the legibility of the type. Again, a very small point, but one that goes a long way towards helping the reader.

I think the only thing that could have made this a really standout, stellar piece of work is the inclusion of another referenced data series: the followers of Barack Obama. At 97 million followers, Obama dwarfs Trump’s 42.2 million. Would it not be fantastic to see that line soaring upwards, but cutting away towards the side of the graphic would be the text block of the article continuing on? Probably easier for them to do in their print edition.

Regardless, this is another example of doing solid work at small scale. (Because small multiples, get it?)

Credit for the piece goes to the Economist Data Team.

Why So Many Mass Shootings?

Well, the data speaks for itself. I wanted to use this screenshot, however, to show you the story because I think it does a fantastic job. Without having to read the article, the image encapsulates what is to come in the article.

Just the visual impact of the outlier
Just the visual impact of the outlier

That said, there are a few other scatter plots worth checking out if the topic is of interest. And the explanation of the data makes all the more sense.

But I really loved the impact of that homepage.

Credit for the piece goes to Max Fisher and Josh Keller.

Murder Rates in the US

Yesterday we looked at an article about exporting guns from one state to another. After writing the article I sat down and recalled that the copy of the Economist sitting by the sofa had a small multiple chart looking at murders in a select set of US cities. It turns out that while there was a spike, it appears that lately the murder rate has been flat.

Chicago is higher than Philly, to be fair
Chicago is higher than Philly, to be fair

It’s a solid chart that does its job well. That is probably why I neglected to mention it until I realised it fit in with the map of Illinois and talk about gun crimes yesterday. Because there is plenty of other news through data visualisation that we can talk about this week.

Credit for the piece goes to the Economist Data Team.

An Ailing Graphic on the Healthcare Labour Force

I know I have said it before, but I like the increasing number of graphics-led articles published by Politico. Many policy and politics stories are driven—or should be driven—by data. But, myself included, we cannot hit it out of the park at every plate appearance. And that is what we have from Politico today, actually last week.

The graphic focuses on the healthcare industry and its need for a larger labour force in coming years as the baby boomers continue to age and start to retire. If their own doctors retire along with them, who will be their new doctors?

But there are two components of the graphic on which I want to focus. The first is the projection of the number of registered nurses (RNs) in 2024 compared to a 2014 baseline.

We need more. Just more.
We need more. Just more.

The story focuses on the future condition, but that colour is set to the lighter green thus drawing the reader’s eyes to the 2014 data point. Flipping those two colours would shift the focus of the chart to the 2024 timeframe, which would better match the text above.

Then we have the design decision to include a line chart for the growth rate, presumably total, for each category of RN from 2014 to 2024. The problem is that the chart itself does not sit on any baseline. While I do not care for the dual axis chart, that format at least keeps an axis legend on the right side of the chart. (You still have the problem of implying certain things based on what scale you choose to use relative to the first data series.) Here, because there is no chart lines associated with the growth data, I wonder if a table below the x-axis labels would be more efficient? Home health care, a very small category, will have the highest growth (a small change from a small base will beat the same small change or even slightly bigger changes from a far larger base) but the eye has the furthest to travel to reach the 61% number from the top of the bars or the labelling.

The other component I wanted to discuss is the scatter plot that compares the number of jobs to their average salary.

Bursting these bubbles…
Bursting these bubbles…

But this is a bubble chart, not a scatter plot, and so we have a third variable encoded in the size of the dot/bubble. The first thing I looked for was a scale for the size of the circles. What magnitude is the RN circle vs. the Personal Care Aides circle? There is none, but unfortunately that seems to be a common practice with bubble chart. But after failing to find that, I noticed that the circles decrease in size from right to left. That was when I looked to the legend and saw the y-axis in numbers of jobs and the x-axis in average salary. But then the circles are sized in proportion to the average salary of each profession to the other. In other words, the circles are basically re-plotting the x-axis. The physical therapist circle should be roughly twice as large, by area, than the vocational nurses. But we can also just see by the x-axis coordinates. The bubble chart-ness of the chart is unnecessary and the data could be told more clearly by stripping that away and making a straight-up scatter plot where all the circles are sized the same.

Credit for the piece goes to Christina Animashaun.

The Economic Impact of Hurricanes

Yesterday Hurricane Ophelia hit Ireland and the United Kingdom. Yes, the two islands get hit with ferocious storms from time to time, but rarely do they enjoy the hurricanes like we do on the eastern seaboards of Canada, Mexico, and the United States.

Earlier this hurricane season the US had to deal with Harvey, Irma, and Maria. And in early October the Wall Street Journal published a piece that looked at the economic impact of the former two hurricanes as exhibited in economic data.

Overall the piece does a nice job explaining how hurricanes impact different sectors of the economy, well, differently. And wouldn’t you know it that leisure and hospitality is the hardest hit? But then they put together this stacked bar chart showing the impact of the hurricanes on both Florida/Georgia and Texas for Irma and Harvey, respectively.

I just want a common baseline…
I just want a common baseline…

The problem is that the stacked bar chart does not allow us to examine each hurricane as a specific data set. Because the Harvey data set is first, we have the common baseline and can compare the lengths of the magenta-ish bars. But what about the blue sets for Irma? How large is natural resources and mining compared to professional and business services? It is incredibly difficult to tell because neither bar starts at the same point. You must mentally move the bars to the same baseline and then hope your brain can accurately capture the length.

Instead, a split bar chart with each sector having two bars would have been preferable. Or, barring that, two plots under the same title. Then you could even sort the data sets and make it even easier to see which sectors were more important in the impacted areas.

Stacked bar charts work when you are trying to show total magnitude and the breakdowns are incidental to the point. But as soon as the comparison of the breakdowns becomes important, it’s time to make another chart.

Credit for the piece goes to Andrew Van Dam.

The Red Sox Offence in 2017

Like I said yesterday, the Red Sox season is over. And the coverage on offseason needs began in the morning papers. But I wanted to follow up on the data from yesterday and delve a bit more deeply into the offence.

Yes, we know it was roughly league average across the team. And we know it took a hit with David Ortiz’s retirement at the end of last year. But what happened? Well, I took those same OPS+ numbers for the starting nine and compared 2017 to 2016. I then looked further back to see how those same players performed throughout their careers (admittedly I skipped Hanley Ramirez’s 2 plate appearances in 2005.)

You should take a look at the full graphic, but the short version, pretty much everyone had an off year. And when everyone has an off year, it is a pretty safe bet the team will have an off year.

You can't all take a break…
You can’t all take a break…

How Serious Is the Rise in Violent Crime?

On Monday, Attorney General Jeff Sessions and the Justice Department released figures for violent crimes in 2016. The administration talked about the rise in violent crime. And yes, such crime did rise in 2016. But, what was not raised nearly as much is that we are also living in an era of historically low crime. FiveThirtyEight broke down the crime numbers through a series of charts and put them in their historical context.

The screenshot below looks just at murder rates. And again, nobody denies that the murder rate is up. But it still below the level it was in the 2000s, 1990s, and 1980s. One has to go back to the 1960s to find murder rates so low.

Murder is up, but still historically low
Murder is up, but still historically low

The point is really just to reiterate that context matters. If we were to look at the rise over the last year, yes an increase from 4.9 to 5.3 would look bad. But, really, we are still living in a far safer country than we were for most of the latter half of the 20th century. You just need to extend the endpoints of the chart to see it.

Credit for the piece goes to Jeff Asher.

How Bad is the Rohingya Crisis?

Pretty bad.

Less than a week after posting about the satellite views showing entire villages razed to the ground, we have a piece from the Economist looking at refugee outflows. And they are worse than the outflow of refugees during the Rwandan genocide back in 1994.

To be clear, they are not saying that nearly a million people have been killed—though there is quite a bit of evidence to say the Burmese security forces are cleansing the state of Rakhine of one of its primary ethnic groups.

That is a lot of people fleeing Burma
That is a lot of people fleeing Burma

But when it comes to the chart, I am not quite sure what I feel about it. It uses both the x and y axis to show the impact of the refugee outflow. But the problem is that we are generally rubbish at comparing areas. Compounding that, we have the total number of refugees represented by circles, another notorious way of displaying areas. (Often people will confuse the circle’s area with its radius or diameter and get the scale wrong.)

I wonder, would a more straight forward display that broke the dataset into two charts would be clearer? What if the designers had kept the Marimekko-like outflow display, but represented each crisis and its total outflow as a straight bar chart to the right of the timeline? (I do think the timeline is particularly good context, especially since it highlights the earlier persecution of the Rohingya.)

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

Irma’s Impending Arrival

Your author is on holiday today and is actually writing today’s post on a Thursday night train to Boston. But by the time he returns late Sunday night—a Monday morning post is not guaranteed—Hurricane Irma will have likely made landfall somewhere along the Florida coast.

Thursday the Guardian published a nice article looking at the potential tracks for Irma. And while the specific routes will certainly be amended and updated over the weekend, the article is worth looking at prior to Irma’s arrival at Florida. As of my writing the track has shifted ever slightly westward and the current predicted path looks for Irma to land south and west of Miami. Ergo this screenshot is already a little outdated.

The three little wolves will huff and puff…
The three little wolves will huff and puff…

The remarkable thing about this graphic, which is just a cleaner version of the standard meteorological maps through more a more considered palette, is that there is not just one path of winds, but three. Following quickly on the heels of Irma are Katia and Jose, the latter the one taking the nearly same path as Irma while Katia spins towards Mexico.

But the graphic I really wanted to look at is the one ending the piece.

A very wide range of countries
A very wide range of countries

This looks at the countries in Irma’s path as of Thursday morning. What I do not understand is the vertical axis of the bars. What does the height represent? To simply show the rank of countries able to cope with natural disasters, a more straight-forward table could have been used. A dot plot would also make some sense, but again, it would require an understanding of the underlying metrics driving the chart.

The graphic is saved by the annotations, in particular the more/less vulnerable directional arrows. Because I do not understand why countries are grouped into the particular buckets, I find the coloured bins out of place.

I think the concept of showing the most vulnerable countries is terribly important, however, the graphic itself needed a little more thought to be a little more clear in presenting the concept.

Credit for the piece goes to the Guardian graphics department.

Plotting Cries for Help

So I thought I would be done with Harvey coverage, but this morning I saw this map from the New York Times that plotted out requests for aid throughout the storm.

You can really see the storm’s movement through the impacts upon the people. It’s especially true later in the timeline as the storm moved further to the east.

Early on the focus was in Houston
Early on the focus was in Houston

Credit for the piece goes to Gregor Aisch, Sarah Almukhtar, Jeremy Ashkenas, Matthew Bloch, Audrey Carlsen, Jose A. Delreal, Ford Fessenden, K.K. Rebecca Lai, Adam Pearce, Anjali Singhvi, and Karen Yourish.