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 NHS Winter Crisis

In the United Kingdom, the month of January has been less than stellar for the National Health Service, the NHS, as surgeries have been cancelled or delayed, patients left waiting in corridors, and a shortage of staff to cope with higher-than-usual demand.

But another problem is the shortage of hospital beds, which compounds problems elsewhere in hospitals and health services. The Guardian did a nice job last week of capturing the state of bed capacity in some hospitals. Overall, the piece uses line charts and scatter plots to tell the story, but this screenshot in particular is a lovely small multiples set that shows how even with surge capacity, the beds in orange, many hospitals are running at near 100% capacity.

Some of the worst hospitals
Some of the worst hospitals

Credit for the piece goes to the Josh Holder.

Where It’ll Be Too Warm for the Winter Olympics

The Winter Olympics are creeping ever closer and so this piece from the New York Times caught my eye. It examines the impact of climate change on host cities for the Winter Olympics. Startlingly, a handful of cities from the past almost century are no longer reliable enough, i.e. cold and snow-covered, to host winter games.

This screenshot is of a bar chart that looks at temperatures, because snow and ice obviously require freezing temperatures. The reliability is colour-coded and at first I was not a fan—it seemed unnecessary to me.

At first I did not care for the colours in the bars
At first I did not care for the colours in the bars

But then further down the piece, those same colours are used to reference reliability on a polar projection map.

But then this map changed my mind
But then this map changed my mind

That was a subtle, but well appreciated design choice. My initial aversion to the graphic and piece was changed by the end of it. That is always great when designers can pull that off.

Credit for the piece goes to Kendra Pierre-Louis and Nadja Popovich

The World Grows On

January is the month of forecasts and projections for the year to come. And the Economist is no different. Late last week it published a datagraphic showcasing the GDP growth forecasts of the Economist Intelligence Unit. I used to make this exact type of datagraphic a lot. And I mean a lot. But what I really enjoy is how successfully this piece integrates the map, the bar chart, and the tables to round out the story.

Take a note at how the chart distributes the bins as well
Take a note at how the chart distributes the bins as well

The easy thing to do is always the map, because people like maps. They can be big, and if the data set is robust, full of data and colour. But maps hide and obscure geographically small countries. And then you have to assume that people know all the countries in the world. Problem is, most people do not.

So the bar chart does a good job of showing each country as equals, a slim vertical bar. In such a small space, labelling every country is impossible, but the designers chose a select number of countries that might be of interest and called them out across the entire series.

Lastly, people always like to know who is #winning and who is a #loser. So the tables at the extreme ends of the chart showcast the top and last five.

I may have rearranged some of the elements, and dropped the heavy black rules between the bins on the legend, but overall I consider this piece a success.

Credit for the piece goes to the Economist Data Team.

Data Displays

This past weekend I saw the film Darkest Hour with one of my mates. The film focuses on Winston Churchill at the very beginning of his term as prime minister. Coincidentally I was walking through some of the very rooms and corridors depicted in the film—and rather accurately I should say—just one week prior.

One of the things in the real place that caught my eye in particular was the Map Room Annex. Most people know about the Map Room proper, from which the British Empire’s war effort was coordinated, but the annex contained data on wartime casualties, material production, &c. Consequently the walls were lined with displays of that data. But this was also the early 1940s and so none of it was computerised. Instead, we had handmade charts.

Alas, the space is quite narrow and the museum was quite crowded. So I only managed a snapshot or two, but I think this one does some justice to the hardworking folks producing charts about the war.

They were not made to be mobile-friendly or responsive…
They were not made to be mobile-friendly or responsive…

Credit for the piece goes to some junior officer/staffer back in the day.

Flying for Thanksgiving

This is a piece from a few years ago, but the New York Times cleverly brought it to the front of their Upshot page. And it seemed just so appropriate. Many of you are likely travelling today—I’m not, I’m headed to work—and many of you will be driving or taking the train. But some will be flying. But to and from where?

If only it captured other travel data
If only it captured other travel data

The map has some nice features that allow you to selectively few particular cities. Philadelphia has relatively few travellers by air, but that’s probably because places in the Northeast are more easily accessible by road or rail.

Chicago also has relatively few travellers, though more than Philadelphia. I would posit that is because most people are not flying to visit their relatives, but rather driving to places in Wisconsin, Iowa, and Indiana.

No post tomorrow, because I intend on sleeping in. But you can expect something on Friday.

Credit for the piece goes to Josh Katz and Quoctrung Bui.

How to Choose the Match to Broadcast

I was reading the Sunday paper yesterday and whilst I normally skip the sports section, especially during baseball’s offseason, this time a brightly coloured map caught my attention. Of course then I had to read the article, but I am glad that I did.

On Sunday the New York Times ran a print piece—I mean I assume I can find it online (I did.)—about CBS chooses which American football matches to air in the country’s markets. It is a wee bit complicated. And if you can find it, you should read it. The process is fascinating.

But I want to quickly talk about the design of the thing. Remember how I said a map caught my attention. That was pretty important, because the map was not the largest part of the article. Instead that went to a nice big photo. But the information designer I am, well, my eyes went straight to the map below that.

The story dominated the section page
The story dominated the section page

There is nothing too special about the map in particular. It is a choropleth where media markets are coloured by the game being aired yesterday. (The piece explains the blackout rules that changed a few years ago from what I remember growing up.)

But then on the inside, the article takes up another page, this time fully. It runs maps down the side to highlight the matches and scenarios the author discusses, reusing the same map as above, but because this is an interior page, in black and white. It probably looks even better online as they likely kept the colour. (They did. But the maps are smaller.)

To have that much space in which to design an article…
To have that much space in which to design an article…

Overall, I really enjoyed the piece and the maps and visuals not only drew me into the piece, but helped contextualise the story.

Credit for the piece goes to Kevin Draper.

Trumping (Most) All on Twitter

Initially I wanted today’s piece to be coverage of the apparent coup d’état in Zimbabwe over night. But while I have found some coverage of the event, I have not yet seen a single graphic trying to explain what happened. Maybe if I have time…

In the meantime, we have the Economist with a short little piece about Trump on Twitter and how he has bested his rivals. Well, most of them at least.

Trumping one's rivals
Trumping one’s rivals

The piece uses a nice set of small multiples to compare Trump’s number of followers to those of his rivals. The multiples come into play as the rivals are segmented into three groups: political, sport, and media. (Or is that fake media?)

Small multiples of course prevent spaghetti charts from developing, and you can easily see how that would have occurred had this been one chart. But I like the use of the reddish-orange line for Trump being the consistent line throughout each. And because the colour was consistent, the labelling could disappear after identifying the data series in the first chart.

And worth calling out too the attention to detail. Look at the line breaks in the chart for the labelling of Fox News and NBA. It prevents the line from interfering with and hindering the legibility of the type. Again, a very small point, but one that goes a long way towards helping the reader.

I think the only thing that could have made this a really standout, stellar piece of work is the inclusion of another referenced data series: the followers of Barack Obama. At 97 million followers, Obama dwarfs Trump’s 42.2 million. Would it not be fantastic to see that line soaring upwards, but cutting away towards the side of the graphic would be the text block of the article continuing on? Probably easier for them to do in their print edition.

Regardless, this is another example of doing solid work at small scale. (Because small multiples, get it?)

Credit for the piece goes to the Economist Data Team.

Why So Many Mass Shootings?

Well, the data speaks for itself. I wanted to use this screenshot, however, to show you the story because I think it does a fantastic job. Without having to read the article, the image encapsulates what is to come in the article.

Just the visual impact of the outlier
Just the visual impact of the outlier

That said, there are a few other scatter plots worth checking out if the topic is of interest. And the explanation of the data makes all the more sense.

But I really loved the impact of that homepage.

Credit for the piece goes to Max Fisher and Josh Keller.

Murder Rates in the US

Yesterday we looked at an article about exporting guns from one state to another. After writing the article I sat down and recalled that the copy of the Economist sitting by the sofa had a small multiple chart looking at murders in a select set of US cities. It turns out that while there was a spike, it appears that lately the murder rate has been flat.

Chicago is higher than Philly, to be fair
Chicago is higher than Philly, to be fair

It’s a solid chart that does its job well. That is probably why I neglected to mention it until I realised it fit in with the map of Illinois and talk about gun crimes yesterday. Because there is plenty of other news through data visualisation that we can talk about this week.

Credit for the piece goes to the Economist Data Team.