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.
Browsing the internets, I often find these little adverts saying something about “10 Things You Didn’t Know About Your Name” or “10 Things Your Name Says About You”. They grab my attention because, as you all know, genealogy is kind of a thing that I do and I am curious where lots of names in my family come from.
But where do countries names originate? We all probably know America comes from Amerigo Vespucci. But how about Mexico? Thankfully Quartz put together a piece exploring country name origins. And it turns out that most can be grouped into four different types. Being named after a man, like America, well you guessed it, that’s one of the four.
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.
And I’m not talking about walking into a bar late at night. Instead, I am talking about the ratio of likes to retweets to replies, which, for those of you unfamiliar with the service, refers to engagement with a person’s tweets on Twitter.
The Ratio does not come from FiveThirtyEight—read the article for the full background on the concept, it is well worth the read—but they applied it to President Trump, whom we all know has a penchant for tweeting. The basic premise of the ratio is that you want more retweets and likes than replies. Think of it like customer reviews. Rarely do people bother to put the effort in to complement good service, but they will often write scathing reviews if something does not fit their expectations. Same in Twitter. If I do not care for what you say, I will let you know. But if I do, it is easy for me to like it, or even retweet it.
Anyway, the point is they took this and applied it to the tweets of Donald Trump and received this chart.
What I truly enjoy is the interactivity. Each dot reflects a tweet, and you can reveal that tweet by hovering over it. (I would be curious to know if the dots move. That is, do they, say, refresh daily with new tabulations on the updated numbers of likes, retweets, and replies?)
But the post goes on using the same chart form, in both other interactive displays and as static, small multiple pieces, to explore the political realm of previous tweeting presidents and current senators.
A solid article with some really nice graphics to boot.
Credit for the piece goes to Oliver Roeder, Dhrumil Mehta, and Gus Wezerek.
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.
So today we enjoy an xkcd post about how graphic designers would change the country if they seized control.
Though to be fair, if this graphic designer seized control of the country, he would not be interested in just adjusting state borders. He’d probably work on the margins and bounds and then establish a whole new baseline grid.
Yesterday the New York Times published a piece looking at the potential impacts of the proposed tax reforms on Americans. Big caveat, not a lot has been detailed about what the reforms entail. Instead, much remains vague. But using the bits that are clear, the Tax Policy Centre has explored some possible impacts and the Times has visualised them.
I like the opening graphic, though all are informative, that cycles through various proposals. It highlights which group benefits most from the proposals. The quick takeaway is that while all would moderately benefit, the rich do really well.
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.
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.
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.