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.
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.
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.
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.
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.
Today’s post is, I think, the first time I’ve featured the Politico on my blog. Politico is, I confess, a regular part of my daily media diet. But I never thought of it as a great publication for data visualisation. Maybe that is changing?
Anyway, today’s post highlights an article on how the Irish shipping/logistics industry could be affected by Brexit. To do so, they looked at data sets including destinations, port volume, and travel times. Basically, the imposition of customs controls at the Irish border will mean increased travelling times, which are not so great for time-sensitive shipments.
This screenshot if of an animated .gif showing how pre-Brexit transit was conducted through the UK to English Channel ports and then on into the continent. Post-Brexit, to maintain freedom of movement, freight would have to transit the Irish Sea and then the English Channel before arriving on the continent. The piece continues with a few other charts.
My only question would be, is the animation necessary? From the scale of the graphic—it is rather large—we can see an abstracted shape of the European coastlines—that is to say it’s rather angular. I wonder if a tighter cropping on the route and then subdividing the space into three different ‘options’ would have been at least as equally effective.
Credit for the piece goes to Politico’s graphics department.
Today we look at income in American cities and in particular the middle class disappearance. The Guardian published the graphics, but they originate with Metrocosm, LTDB at Brown, and IPUMS National Historical Geographic Information System. So what are we looking at? Well, the big one is a set of small multiples of cities and their income breakdowns as percentages of city census tracts. This screenshot is static, but the original is an animated .gif.
I have a few issues with the design of the graphic, the most important of which is the colour palette. If the goal is to focus on the decline of the middle class—and I admit that may be the point of the Guardian’s authors and not the original authors—why are the most visually striking colours at the top of the income distribution. Instead, you would want to draw attention to the middle of each chart, not the right. And if the idea was that the darker colours represent the higher income groups, well the positioning of each bar on the chart and the axis labelling does that already. After all, if anything, the story is that in a number of cities the middle class has shrunk while the lower income groups have grown. And you can barely see that with the lower income groups coloured yellow.
My other issues are more minor design things such as the city labelling. I kept reading the label as being below the bars, not above as it actually is.
And then I wonder if a different chart form would be more effective at showing the decline in the middle class. Perhaps a line chart plotting the beginning and end points for each cohort?
Then the piece gets into some three-dimensional maps that you can spin and rotate.
Yeah. Shall I count the ways? A more conventional choropleth would have served the purpose far more effectively. The dimensionality hides lower income tracts behind higher ones. The solution? Allow the user to rotate and spin the map? No, get rid of the dimensionality. It offers little to the understanding of the underlying data. Not to mention, are the areas of shadows shadows? Or are they another bin or cohort of income?
And then you have to read the piece to get a fuller understanding of my criticism.
But don’t worry, I can quote it.
Chicago was largely successful transitioning away from manufacturing to a service-based economy. This shift is evident in the bifurcated pattern present in 2015 – a heavy concentration of wealth in the business/financial district and marked decline in the surrounding area.
Those of you who read this blog from Chicago or who have lived in Chicago will pick up on it. The rest of you not so much. The concentration of wealth is not located in the business/financial district. Those dark red skyscrapers are not actual skyscrapers, they are census tracts located not in the financial district, but the areas of River North, Old Town, Gold Coast, &c. Thinking of the issue more logically, yes incomes are up in cities that are doing well. But how many of those very wealthy live on the same block as their office? Not many. Your higher income is going to be concentrated in residential or mixed-residential neighbourhoods near, but not in the business/financial district.
The data behind this work fascinates me. I just wish the final graphics had been designed with a bit more consideration for the data and the stories therein. And a little bit of proper understanding of the cities and their geography would help the text.
Credit for the piece goes to Metrocosm, LTDB at Brown University, and IPUMS National Historical Geographic Information System.
Emmanuel Macron won the French presidential election yesterday. So Guess what we have a graphic or two of this week? If you guessed Mongolian puppies, you were wrong.
Thursday afternoon the Wall Street Journal—they seem to really be upping their game of late—published an article breaking down the connection between a Le Pen support in the first round and unemployment. For me, the key to the article was the following graphic, which plots those two variables by department. The departments that she won, generally speaking, suffer higher unemployment.
Colour coding relates to the winner of the department. I am not certain that the size of the voters in the department matters as much. But the annotation of particular departments, qualified as being limited to the French mainland—see my problem back in April about when France is more than France—flows through the several graphics in the piece.
This is a piece from the Thursday running up to Sunday’s vote. Tomorrow we will look at a piece from the day before the vote that looked at another key component of Macron’s win.
Credit for the piece goes to Martin Burch and Renée Rigdon.