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.
On Thursday President Trump announced that the Commerce Department would investigate imports of steel to the United States. This falls under the Buy American campaign pledge. A lot of talk in the media is, of course, about the threat of Chinese steel to the United States. So I dug into the Census Bureau’s website and found their data on steel imports.
Well, it turns out that steel imports were already down by over 5 million tons before Trump took office. And from 2015 to 2016, China fell sharply from 7th to 10th in a ranking of our import partners. In fact the only country from whom we import significant amounts of steel to see a rise over that period was Mexico.
But we’ll probably need their steel to build the wall to keep out their steel.
I visualised the data in this datagraphic. Enjoy. And look for a later post today in the usual, light-hearted vein.
Credit for the data goes to the US Census Burea. The graphic is mine.
As much as I like trains…we need to get back to Trumpcare. As you probably know, it will cover fewer people than Obamacare. Just how many fewer people? Somewhere in the ten to twenty million range. The poor, the elderly, and the sick are those who will be worse off. Because the poor, the elderly, and the sick are the ones who clearly do not need healthcare. Higher-income young people, your subsidies are about to go up.
But I digress, the Los Angeles Times looked at county electoral and tax data to see just where the pain falls geographically, and more importantly where it falls politically. So they took a look specifically at the bracket that will be hurt the most: the poor and elderly, 60 and earning $30,000.
Well, it looks like all those people who voted against the idea of Obamacare just voted themselves to get even less assistance. Trumpcare’s going to be great, guys. Unless you’re old. Or poor. Or sick.
The British government is delivering its budget statement today. So as a teaser, the Guardian published this article with six charts to help understand where things are at. Chart-wise there is nothing radical or revolutionary here, but I have a soft spot for articles driven by data visualisation.
Credit for the piece goes to the Guardian graphics department.
We have a scatterplot from the Financial Times that looks at wage and economic growth across the OECD, focusing on the exception that is the United Kingdom. And that is not an exception in the good sense.
The UK had the rare privilege of experiencing economic growth—that’s good—while simultaneously wages fell—that’s bad. But I wanted to comment on the chart today.
Straight off the bat, the salmon-coloured background does not bother me. That is FT’s brand and best to stick to it and make your graphics work around it. Possibly the colours in the plot could use a bit of a push to increase separation, but that is more a design quibble. Instead, I am not too keen on the colour coding here.
Not that the colours need not be applied, but why to the dots? Note how the dots of a colour fall into one of the quadrants. Instead of having people refer to the legend, incorporate the legend into the chart by moving the labels to the plot background. You could colour code the labelling or even colour the quadrants to make it a bit clearer.
Credit for the piece goes to the Financial Times graphics department.
Well, we are one day away now. And I’ve been saving this piece from the New York Times for today. They call it simply 2016 in Charts, but parts of it look further back while other parts try to look ahead to new policies. But all of it is well done.
I chose the below set of bar charts depicting deaths by terrorism to show how well the designers paid attention to their content and its placement. Look how the scale for each chart matches up so that the total can fit neatly to the left, along with the totals for the United States, Canada, and the EU. What it goes to show you is best summarised by the author, whom I quote “those 63 [American] deaths, while tragic, are about the same as the number of Americans killed annually by lawn mowers.”
I propose a War on Lawn Mowers.
The rest of the piece goes on to talk about the economy—it’s doing well; healthcare—not perfect, but reasonably well; stock market—also well; proposed tax cuts—good for the already wealthy; proposed spending—bad for public debt; and other things.
The commonality is that the charts work really well for communicating the stories. And it does all through a simple, limited, and consistent palette.
Well have we got an interesting week this week. Friday begins Trump Time. So hold onto your Twitter accounts, folks. But before we get there, I wanted to do a short week of some data-driven graphics that take a look at the state of things.
Instead of what I had intended for today, let us take a look at a new post from the Wall Street Journal that examines GDP, inflation, industrial production, and the unemployment rate in advanced economies. At its most basic level, the graphics show how many of the 39 advanced economies have a value within a one-percentage point range. The size of the dots indicates how many countries fall within the bin.
What keeps getting me, however, is the colour. Nowhere does the piece explain what the colour represents. Does it represent anything? I think it might only be used to show the ranges in the values, not the number of countries sharing said values. And if that is the case, it is a poor design decision.
My eye goes to the colour first before it goes to the dot density let alone the size of the dots. Like a Magic Eye, when I stare at the piece long enough, I begin to see the overall trend for each metric. But blink and the colours reassert their visual dominance.
I wonder what would happen if the graphic settled on a single colour? My instinct says that the patterns would become far clearer, because colour change would no longer be a visual pattern needing interpretation—even though it needs no interpretation from a data standpoint. By limiting the number of visual patterns, the piece would make the data stand out more clearly and make for clearer communication.
If an editor screams something like “It needz more colourz!!1!”, I would reserve four separate colours and then use one and only one for each of the four metrics.
That all said, what the piece does really well is explain segments of the data. In the above screenshot, you can clearly see and get the overall GDP story. But then from there you read down and get explanations or callouts of the overall to provide more context and information. The designer greys out the remainder of the dots and allows the colour to emphasise those countries in focus. A lightly transparent overlay allows for the background dots to remain faintly visible while the text can clearly be read.
All in all, I am not sure where I fall on this particular piece. It does some things well, others not so much. But either way, the piece does paint an interesting portrait of populism’s potential causes.