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
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.
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…
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.
You said what?
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.
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 may recall how over two years ago I posted about a piece from the New York Times that explored the levels of Arctic sea ice. It showed how the winter sea ice of 2015 was the lowest level ever recorded. Well last week the Times updated that piece with new data. And instead of the static graphic we enjoyed last time around, this time the piece began with a nice animation. It really helps you see the pattern, so you should click through and check out the whole piece.
Go hit play and you’ll see the trend
But this isn’t just a visually top heavy piece. No, the remainder of the article continues to explore the state of Arctic sea ice through a number of other charts and maps.
Credit for the piece goes to Nadja Popovich, Henry Fountain, and Adam Pearce.
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
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.
But to be honest, it never really went anywhere. As you know, your humble author visited Boston this past weekend and got to see two games of his Red Sox against Tampa Bay. Tampa, of course, is not the rivalry to which I am referring, but things were heated back in the days when Maddon managed Tampa.
No, I am of course talking about the Red Sox–Yankees rivalry. Two weeks ago FiveThirtyEight posted an article about the rivalry and how it has returned. Admittedly, they meant not from the perspective of bitter hatred for all things Yankees, but rather that the Yankees are attempting to be good again.
This chart from the article is nothing more than a line chart. But I just wanted to point out that the rivalry lives, though in my mind it never really went away.
Down with the Empire…
Credit for the piece goes to the FiveThirtyEight graphics department.
One more day of Harvey-related content. At least I hope. (Who knows? Maybe someone will design a fantastic retrospective graphic?) Today, however, we look at a piece from the Economist about the rising number of weather-related disasters, but thankfully falling numbers of deaths. The piece has all the full suite of graphics: choropleths, line charts, and bar charts (oh my!). But I want to look at the bar chart.
A timeline of disaster causes around the world
I cannot tell from this chart whether there has been any change in the individual elements, the meteorological, hydrological, or climatological disasters. And unfortunately stacked bar charts do not let us see that kind of detail. They only really allow us to see total magnitude and the changes in the element at the bottom of the stack, i.e. aligned with the baseline. So I took their chart and drew the shapes as lines and realigned everything to get this.
My take
You can begin to see that meteorological might be overtaking hydrological, but it is too early to tell. And that right now, climatological causes are still far behind the other two.
Credit for the piece goes to the Economist Data Team.
Initially I was not going to post this work, if only because other things came up and I do have to prioritise what I post on my site. It had nothing to do with the work’s quality, which I think is actually quite good. What am I talking about? Well today’s piece is from a Pro Public article about the impact of immigration on economic growth. And it turns out the two are linked. Why? Well, the overly simplistic explanation is that we will need immigrants to pick up the slack in the labour force that will otherwise begin shrinking in years to come.
But why take my word for it when you can take charts’ word for it. The piece does a great job of showing how changes in immigration numbers can help grow or shrink economic growth. And if you recall, President Trump has promised growth rates of 4%. But, and this is why I decided to post this, yesterday it was announced that Trump will support legislation intending to halve immigration to the United States over the next ten years. As my screenshot captured, a reduction in immigration will actually lead to lower economic growth and put us further away from the 4% rate.
Reducing immigration takes GDP growth further away from the 4% target
