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.
Whilst away, I came upon this piece in the following of my offseason baseball news. The New York Times published it between Christmas and New Years and the piece looks at the origins of sports persons in European football leagues compared to several American sports leagues, including American football, baseball, and basketball.
The piece features an opening set of small multiples comparing all the leagues. Maddeningly, I wanted details and mouseovers and annotations at the start. Fortunately, as the reader continues through the article, each small multiple becomes big and the reader can explore the details of the league.
Credit for the piece goes to Gregor Aisch, Kevin Quealy, and Rory Smith.
While I am still looking for a graphic about Zimbabwe, I also want to cover the tax reform plans as they are being discussed visually. But then the Senate went and threw a spanner into the works by incorporating a repeal of Obamacare’s individual mandate. “What is that?”, some of you may ask, especially those not from the States. It is the requirement that everyone have health insurance and it comes with tax penalties if you fail to have coverage.
Thankfully the New York Times put together a piece explaining how the mandate is needed to keep premiums low. Consequently, removing it will actually only increase the premiums paid by the poor, sick, and elderly. The piece does this through illustrations accompanying the text.
Overall the piece does a nice job of pairing graphics and text to explain just why the mandate, so reviled by some quarters, is so essential to the overall system.
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.
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.
Today is Election Day here in the States, but neither for the presidency nor for Congress. 2017 is an off-year, but it does have a few interesting races worth following. One is the New Jersey gubernatorial election across the river here from Philadelphia. Further down the Northeast Corridor we have the gubernatorial election in Virginia. And then I am going to be following the special election for a Seattle suburb’s state-level district. Why? Because it all gets to setting the table for 2022.
These three elections are all important for one reason, they relate to the idea of solid political control of a state government. The analogy is what we have in Washington, DC where the Republicans control the executive branch and both chambers of the legislative branch. In New Jersey, Democrats control the state legislature while (in?)famous Chris Christie, a Republican, is governor. In Virginia, Terry McAuliffe, a Democrat, is governor whilst the General Assembly is solidly Republican—we will get to that in a minute, trust me—and finally in Washington, the governorship is Democratic, the lower chamber of the state legislature is Democratic, but the state senate is Republican by one seat. And one of those very seats is up for a special election today.
So why am I making the big deal about this? Because solid political control of a state allows for biased redistricting, or gerrymandering, in 2020, when the US Census will reapportion seats to states, and thereby electoral college votes. If the Republicans win in Virginia, which is possible in what the polls basically have as a toss-up, they can redistrict Virginia to make it even harder for Democrats to win. And if the Democrats win in New Jersey and Washington, as they are expected to, they will be able to redistrict the state in their favour. Conversely, if the Democrats win in Virginia, and Republicans in New Jersey and Washington, they can thwart overly gerrymandered districts.
Which gets us to Virginia and today’s post. (It took awhile, apologies.) But as the state of Virginia changes, look at the dynamic growth in northern part of the state over the past decade, how will the changing demographics and socio-economics impact the state’s vote? Well, we have a great piece from the Washington Post to examine that.
It does a really nice job of showing where the votes are, in northern Virginia, and where the jobs are, again in northern Virginia. But how southern Virginia and Republicans in the north, might have just enough votes to defeat Democratic candidate Ralph Northam. The last polls I saw showed a very narrow lead for him over Republican Ed Gillespie. Interestingly, Gillespie is the very same Gillespie who architected the Republican’s massive victory in 2010 that obviously shifted the House of Representatives to the Republicans, but more importantly, shifted state legislatures and governorships to the Republicans.
That shift allowed for the Republicans to essentially stack the deck for the coming decade. And so even though in 2016, Democrats won more votes for the House of Representatives, they have far fewer seats. Even if there is a groundswell of new support for them in 2018, that same gerrymandering will make it near impossible for the Democrats to win the House. And so these votes in Virginia, New Jersey, and Washington state are fun to follow tonight—I will be—but they could also lay the groundwork for the elections in 2022 and 2024.
Basically, I just used today’s post to talk about why these three elections are important not for today, but for the votes in a few years’ time. But you really should check out the graphic. It makes nice use of layout, especially with the job bar chart organised by Virginia region. Overall, a solid and terrific piece.
Credit for the piece goes to Darla Cameron and Ted Mellnik.
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.
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.
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.
Last week the Economist published an article looking at the attitudes of the young at university in the United States. The examination was sparked by the recent-ish waves of news about stifled speech on campuses. Thankfully, we have a long-running survey from those on the ground in our universities and it reveals some interesting facts. You should head on over to the article if you want the full set, but in general, to perhaps nobody’s surprise, the media is exaggerating the confrontations we have seen.
My only quibble with the graphic is the height of the small multiples. I probably would have increased the height a little bit to allow any real fluctuations over the years to show more readily. But, for all I know, that could have been a limitation of the space in which the designers had to work, i.e. converting a print graphic to work on their blog.
Credit for the piece goes to the Economist’s Data Team.
Following on yesterday’s post about the Red Sox offence, I wanted to follow up and look into their power numbers. So here we have a smaller scale graphic. Nothing too fancy, but the data backs what my eyes saw all year. A definite power drain up and down the Red Sox lineup in 2017.