In the United Kingdom, the month of January has been less than stellar for the National Health Service, the NHS, as surgeries have been cancelled or delayed, patients left waiting in corridors, and a shortage of staff to cope with higher-than-usual demand.
But another problem is the shortage of hospital beds, which compounds problems elsewhere in hospitals and health services. The Guardian did a nice job last week of capturing the state of bed capacity in some hospitals. Overall, the piece uses line charts and scatter plots to tell the story, but this screenshot in particular is a lovely small multiples set that shows how even with surge capacity, the beds in orange, many hospitals are running at near 100% capacity.
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.
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.
I’ve worked on a few scatter plots of late and so this piece from the Economist grabbed my attention. It examines the correlation between unemployment rates and inflation rates. Broadly speaking, the theory has been that low unemployment rates lead to high inflation rates. But the United States has had low unemployment rates now for a few years, but inflation is around that ideal 2% realm. This theory is called the Phillips Curve.
The graphic does a nice job of showing three data series all in one plot. Normally, I would argue for splitting the chart into three smaller plots, a la the small multiples. But here, the data aligns just well enough that the overlapping is minimal. And smart colour choices mean that each data range appears clearly separate from the rest. A nice thoughtful addition is the annotations to the time period are set in the same colour as the dots themselves.
My only two quibbles: One, I would probably increase the height of the chart to better show the trend line. I find that for scatter plots, a more squarish profile works better than the long rectangle. Overall, though, a really well done chart. Second, I would consider adding a zero line to the x-axis to show 0% cyclical unemployment. But that might also not be terribly useful, because you can see how the curve should move regardless of that natural line.
Full disclosure: the Economist article cites a paper from the Philadelphia Fed Research Department, which employs me.
Credit for the piece goes to the Economist Data Team.
When I lived in Chicago, people back East would always ask if I was worried about murder and gun crime in Chicago. My reply was always, “no, not really”. Why? Because I lived in generally safe neighbourhoods. But on that topic, the second most numerous question/comment was always, why are the strict gun laws in Chicago not preventing these crimes? More often than not the question had more to do with saying gun control laws were ineffective.
But in Chicago, it seemed to me to be fairly common knowledge that most of the guns people used to commit crimes were, in fact, not purchased in Illinois. Rather, criminals imported them from neighbouring states that had far looser regulations on firearms.
They bring back more than just cheese from Wisconsin…I am not the biggest fan of the maps that they use, although I understand why. Most Americans would probably not be able to name the states bordering Illinois, California, or Maryland—the two other states examined this way—and this helps ground the readers in that geographically important context. But, thankfully the designers opted for another further down in the article that explores the data set in a more nuanced approach. Surprise, surprise, it’s not that simple of an issue.
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.
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.
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.
This has been a busy week. I am working on a small piece on the Red Sox managers in the free agency period—I thought it would be ready yesterday, but not so much—but news continues to happen outside of the baseball world. Some of the biggest, at least in the US, would have to be the speech by Senator Flake of Arizona who announced he would not seek re-election in 2018.
So cue the politically-themed graphics. Today’s piece comes from the Washington Post. The graphic itself is not terribly complex as it is a scatter plot comparing the liberal/conservativeness of senators with how their respective state voted in 2016.
But what the piece does really well is weave a narrative through the chart. Scrolling down the page locks the graphic in place while the text changes to provide new context. And then different dots are highlighted or called out.
It proves that not all the best graphics need to be terribly complex.
Credit for the piece goes to Kevin Schaul and Kevin Uhrmacher.
Last week I covered a lot of Red Sox data. And your feedback has been fantastic. I think you can look forward to more visualisation of sportsball data. But since this is not a sports blog, let us dive back into some other topics. Like today’s piece on economic growth.
It comes from the Economist and explores the development history of national economies relative to that of the United States. The point of the chart was to illustrate what the researchers determined was the middle income trap, a space in which countries develop and become semi-rich, but then can never quite escape.
The Economist makes the point that the definition of middle income matters. The range is enormous and one statistic says that it could take 48 years to graduate at a healthy rate of economic growth. I wonder is this bit, however, could also have been charted. The show don’t tell mantra works well here for setting up the problem, but a chart or two showing that exact range could have supplemented the text and perhaps made it more digestible.
Credit for the piece goes to the Economist Data Team.
Did you really think we were done with eclipse coverage? (Actually we still will not be, there were some other neat eclipse coverage pieces I want to look at.) But today is Friday and so we look to lighter (and I find humourous) coverage, this week from xkcd.
My only quibble would be that the partial solar eclipse could be nudged further up the y-axis. I thought it was rather fantastic to witness. (But you better bet that come 2024 I will be travelling to see totality.)
About two weeks ago, Michael Phelps raced a shark. What will they not do for television ratings? The Economist took the basic premise and then had an insightful piece about the speed of animals compared to their size. The whole notion of animals get faster the larger they get. Well, to a point, the Economist found. The graphic is a bit complex, perhaps, in their use of a log scale on both the x and y axes. But they have cute little illustrations of everyone’s favourite animals. So it all balances out in the end.
But there is real science in the piece and it is worth a quick read.
Credit for the piece goes to the Economist graphics department.
In this piece from the Guardian, we have one of my favourite types of charts. But, the piece begins with a chart I wonder about. We have a timeline of countries creating universal healthcare coverage, according to the WHO definition—of which there are only 32 countries. But we then plot their 2016 population regardless of when the country established the system. It honestly took me a few minutes to figure out what the chart was trying to communicate.
However, we do get one of my favourite charts: the scatter plot over time. And in it we look at the correlation between spending on healthcare compared to life expectancy. And, as I revealed in the spoiler, for all the money we spend on healthcare—it is not leading to longer lives as it broadly does throughout the world. And care as you might want to blame Obamacare, the data makes clear this problem began in the 1980s.
And of course Obamacare is why the Guardian published this piece since this is the week of the Vote-a-rama that we expect to see Thursday night. The Republicans will basically be holding an open floor to vote on anything and everything that can get some measure of repeal and/or replace 50 votes. And to wrap the piece, the Guardian concludes with a simple line chart showing the number of uninsured out to 2026. To nobody’s surprise, all the plans put forward leave tens of millions uncovered.
It is a fantastic piece that is well worth the read, especially because it compares the systems used by a number of countries. (That is largely the text bit that we do not cover here at Coffeespoons.) I found the piece very informative.
Credit for the piece goes to the Guardian graphics department.