Regions of German Nationalism

The Economist has an interesting piece looking at the areas of support for the far-right AfD German political party, arguably a neo-fascist nationalist party. It turns out that

Historical analogies are dangerous, but fascinating.
Historical analogies are dangerous, but fascinating.

The piece does a great job of setting the case through the demographics map at the top of the piece. It shows how the two areas where the largest AfD support experienced the least changes from prior to the war. And with those demographics in place, the support for hardline nationalism might still be present, as is indicated by the support for the AfD.

In terms of the municipality maps, I would be curious if the hexagon tile map is because those borders have changed. Obviously 84 years can change political boundaries.

But I wonder if a single map could have been done showing the correlation between the 1933 vote and the 2017 vote. Of course, the difficulty could well be in that political boundaries may have changed.

And of course, we should not go so far as to compare the AfD to Nazism.

Credit for the piece goes to the Economist graphics department.

Congressional District Population Density

Tomorrow is Election Day here in the United States and this morning I wanted to look at a piece I’ve had in mind on doing from City Lab. I held off because it looks at the election and what better time to do it than right before the election.

Specifically, the article looks at the density of the different congressional districts across the United States. Whilst education level appears to be the most predictive attribute of today’s political climate—broadly speaking those with higher levels of formal education support the Democrats and those with lower or without tend to support President Trump—the growing urban–rural divide also works. But what about the in-between? The suburbs? The exurbs? And how do we then classify the congressional districts that include those lands.

For that purpose City Lab created its City Lab Congressional Density Index. Very simplistically it scores districts based on their mixture of low- to medium- to high-density neighbourhoods. But visually, which is where this blog is concerned, we get maps with six bins from pure urban to pure rural and all the mixtures in-between. This cartogram will show you.

All the urban and rural seats
All the urban and rural seats

Now, there are a couple of things I probably would have done differently in terms of the visualisation. But the more I look at this, one of those things would not be to design the hexagons to all fit together nicely. Instead, you get this giant gap right where the plains states begin west of the Mississippi River stretching through the Rockies over to California. If you think about it, however, that is a fairly accurate description of the population distribution of the United States. With a few exceptions, e.g. Denver, there are not many people living in that space. Four geographically enormous states—North Dakota, South Dakota, Montana, and Wyoming—have only one congressional district. Idaho has two. Nebraska three. And then Iowa and Kansas four. So why shouldn’t a map of the United States display the plains and Rocky Mountain interior as a giant people hole?

Like I said, initially I took umbrage at that design decision, but the more I thought about it, the more it made sense. But there are a few others with which I quibble. The labelling here is a big one. First, we have the district labels. They are small, because they have to be to fit within the five hexagons that define the districts’ shapes. But every label is black. Unfortunately, that makes it difficult to read the labels on the darker colours, most notably the dark purple. I probably would have switched out the black labels in those instances for white ones.

But then the state labels are white with black outlines, which makes it difficult to read on either dark or light backgrounds. The designer made the right decision in making the labels larger than the districts, but they need to be legible. For example, the labels of Alaska and Hawaii need not be white with black outlines. They could just be set in black type to be legible. Conversely, Florida’s, sitting atop darker purple districts, could be made white.

The piece makes use of more standard geographic map divided into congressional districts—the type you will see a lot tomorrow night. And it makes use of bar charts to describe the demographics of the various density types. I like the decision there to use a new colour to fill in the bars. They use a dark green because it can cut across each of the six types.

Credit for the piece goes to David H. Montgomery.

Europe is More than the Big States

First, I want to start with a housekeeping note. Your author will be travelling for work and then a short autumn holiday. And so while I may be able to sneak a post or two in, I generally would not expect anything until next Friday, 12 October.

But let’s end this string of posts with a map. It is a choropleth, so in one sense there is nothing crazy going on here. The map comes from the Economist, which published an article on life expectancy throughout Europe and the big takeaway is that it is lower in the east than the west.

Apparently life is pretty good in northern Spain
Apparently life is pretty good in northern Spain

The great part of the map, however, is that we get to see a more granular level of detail. Usually we just get a view of the European states, which presents them as an even tone of one shade or one colour. Here we can see the variety of life expectancy in the UK, France, and Belgium, and then still compare that to eastern Europe.

Of course creating a map like this demands data to drive it. Do data sets exist for the sub-national geographic units of EU or European states? Sometimes not. And in those cases, if you need a map, the European state choropleth is the choice you have to make. I just hope that we get to see more data sets like this with more granular data to present a more complex and patterned map.

Credit for the piece goes to the Economist Data Team.

The Carolinas and Florence

As you all probably know, Hurricane Florence crashed into the Carolinas this past weekend. And while I was on holiday, I did see a few articles about the storm and its impact. This one from the New York Times captured my attention because of its use of—surprise, surprise—maps.

Hurricanes are just not fun
Hurricanes are just not fun

In particular, as the user scrolls through the experience, he or she sees the change in population density of the region from 1990 to 2010. Spoiler, a lot more people now live near the coast.

In terms of the graphic, however, I wonder if a simpler approach could have communicated that part of the story more clearly. Could the map have simply shown the change in density instead of visually transforming from one number to the next? Or maybe a summary map could have followed those transitions?

Credit for the piece goes to Stephen M. Strader and Stuart A. Thompson.

Germany

Last week Angela Merkel, the German chancellor, visited President Trump in Washington. This post comes from the Economist and, while not specifically about that trip, describes Germany in a few different metrics. Back in the day it would be what I called a country-specific datagraphic. That is, it shows metrics not necessarily connected to each other, but all centred around a country. In theory, the framework can then be used to examine a number of different countries.

The thin red line…
The thin red line…

That sort of works here, except the choropleth is for the Alternative for Germany political party. That only real works as a metric in, you know, Germany.

Overall, I like the piece. The layout works well, but Germany is fortunate in that the geographic shape works here. Try it with Russia and good luck.

First let us dispense with the easy criticism: do we need the box map in the lower right corner to show where in the world Germany is? For Americans, almost certainly yes. But even if you cannot identify where Germany is, I am not certain its location in Europe is terribly important for the data being presented.

But the pie charts. I really wish they had not done that. Despite my well-known hatred of pie charts, they do work in a very few and specific instances. If you want to show a reader 1/4 of something, i.e. a simple fraction, a pie chart works. You could stretch and argue that is the case here: what is the migrant percentage in Bavaria? But the problem is that by having a pie party and throwing pie charts all over the map, the reader will want to compare Bavaria to the Rhineland-Palatinate.

Just try that.

Mentally you have to take out the little red slice from Bavaria and then transpose it to Rhineland-Palatinate. So which slice is larger? Good luck.

Instead, I would have left that little fact out as a separate chart. Basically you need space for 16 lines, presumably ranked, maybe coloured by their location in former East or West Germany, and then set in the graphic. Nudge Germany to the left, and eat up the same portion of Bavaria the box map, cover the Czech Republic, and you can probably fit it.

Or you could place both metrics on a scatter plot and see if there is any correlation. (To the designers’ credit, perhaps they did and found there is none. Although that in and of itself could be a story to tell.)

The point is that I still hate pie charts.

Credit for the piece goes to the Economist’s graphics department.

Natural Decrease

The New York Times has posted a nice piece with an animated graphic. No, not that piece, I’ll probably cover that next week. This one looks at demographic changes in the United States, specifically in the population change at county levels. A number you arrive at by subtracting deaths from births and excluding migration.

That is a lot of red, especially in the Northeast and Midwest…
That is a lot of red, especially in the Northeast and Midwest…

Basically what we are seeing is a whole lot of red outside the major cities, i.e. the outer suburbs. The article does a nice job of explaining the factors going into the declines and is well worth its quick read.

Credit for the piece goes to Robert Gebeloff.

Russia Tomorrow

In news that surprises absolutely nobody, Russia “re-elected” Vladimir Putin as president for another six-year term. The Economist recently looked at what they termed the Puteens, a generation of Russians born starting in 1999 who have no memory of a Russia pre-Vladimir Putin.

This piece features a set of interactive dot plots that capture survey results on a number of topics that are segmented by age. It attempts to capture the perspective of Puteens on a range of issues from their media diet to foreign policy outlook to civil rights.

The ideas of youth…
The ideas of youth…

The design is largely effective. The Puteen generation sticks out clearly as the bright red to the cool greys. And more importantly, when the dots would overlap they move vertically away from the line so users can clearly see all the dots. And on hover, all the dots of the same age cohort’s interest are highlighted. I think one area of improvement would have been to apply that same logic to the legend to allow the user to scroll through the whole dataset without always having to interact with the chart. But that is a minor bit on an otherwise really nice piece.

Credit for the piece goes to the Economist’s graphics department.

Gerrymandering Pennsylvania

Here in Pennsylvania this week, the state Supreme Court will hear arguments on the legality of congressional districts drawn by Republicans in 2010. The state is rather evenly split between Republicans and Democrats, e.g. Donald Trump won by less than one percentage point or less than 45,000 votes. But 13 of its 18 congressional districts are represented by Republicans, roughly 72%.

This graphic is from the New York Times Upshot and it opens a piece that explores gerrymandering in Pennsylvania. The graphic presents the map today as well as a nonpartisan map and an “extreme” gerrymander. The thing most noticeable to me was that even with the nonpartisan geography, the Democrats are still below what they might expect for a near 50-50 split. Why? One need only look at Philadelphia and Pittsburgh where, using the Times’ language, the Democrats “waste” votes with enormous margins, leaving the suburban and rural parts of the state open for Republican gains.

Three different ways of drawing Pennsylvania's congressional districts
Three different ways of drawing Pennsylvania’s congressional districts

Credit for the piece goes to Quoctrung Bui and Nate Cohn.

The Middle Income Trap?

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.

It's a trap! (Unless it isn't.)
It’s a trap! (Unless it isn’t.)

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.

How Bad is the Rohingya Crisis?

Pretty bad.

Less than a week after posting about the satellite views showing entire villages razed to the ground, we have a piece from the Economist looking at refugee outflows. And they are worse than the outflow of refugees during the Rwandan genocide back in 1994.

To be clear, they are not saying that nearly a million people have been killed—though there is quite a bit of evidence to say the Burmese security forces are cleansing the state of Rakhine of one of its primary ethnic groups.

That is a lot of people fleeing Burma
That is a lot of people fleeing Burma

But when it comes to the chart, I am not quite sure what I feel about it. It uses both the x and y axis to show the impact of the refugee outflow. But the problem is that we are generally rubbish at comparing areas. Compounding that, we have the total number of refugees represented by circles, another notorious way of displaying areas. (Often people will confuse the circle’s area with its radius or diameter and get the scale wrong.)

I wonder, would a more straight forward display that broke the dataset into two charts would be clearer? What if the designers had kept the Marimekko-like outflow display, but represented each crisis and its total outflow as a straight bar chart to the right of the timeline? (I do think the timeline is particularly good context, especially since it highlights the earlier persecution of the Rohingya.)

Credit for the piece goes to the Economist’s Data Team.