The Disappearing Urban Middle Class

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.

The flattening of the curve
The flattening of the curve

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.

Just stop
Just stop

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.

Vive la France

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.

Unemployment and Len Pen support
Unemployment and Len Pen support

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.

US Steel Imports

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.

The state of US steel imports
The state of US steel imports

Credit for the data goes to the US Census Burea. The graphic is mine.

Those Who Will Lose Subsidies for Trumpcare

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.

Trump won the vast majority of counties that will be hardest hit
Trump won the vast majority of counties that will be hardest hit

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.

Credit for the piece goes to Priya Krishnakumar.

UK Spring Budget

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.

Quarterly growth
Quarterly growth

Credit for the piece goes to the Guardian graphics department.

Declining British Wages

Now for the actual piece for today.

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.

I would have designed this a little bit differently
I would have designed this a little bit differently

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.

A Look Back

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.”

Deaths by terrorism
Deaths by terrorism

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.

But yeah, one day away now.

Credit for the piece goes to Steven Rattner.

Get Ready Folks

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.

A look at advanced economies' GDPs
A look at advanced economies’ GDPs

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.

Credit for the piece goes to Andrew Van Dam.

Mapping the Country’s Brain Drain

Alternatively known as the zombie food map. Sorry, but I couldn’t resist that one. Today we look at a piece from Bloomberg that maps brain drain across the country. What is brain drain? Basically it is the exodus of people with advanced degrees and education employed in science-y industries and fields. So this map shows us where the brains are moving from and where they are moving to.

Zombies, pay heed for feeding zones
Zombies, pay heed for feeding zones

Credit for the piece goes to Vincent Del Giudice and Wei Lu.

Income Inequality

On the lighter side of things we have today’s post on income inequality. Always a lighter subject, no? Thanks to Jonathan Fairman for the link.

Herwig Scherabon designed the Atlas of Gentrification as a project at the Glasgow School of Art and it was picked up by Creative Review. It displays income as height and so creates a new cityscape of skyscrapers for the wealthy and leaves lower income residents looking straight up. His work covered the US cities of New York, Los Angeles, and Chicago. The image below is of Chicago. I probably was living in a cluster of mid-rise buildings despite living in a five-story building.

A look at Chicago
A look at Chicago

Credit for the piece goes to Herwig Scherabon.