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.

The Shitholes

Today’s post is a very quick reaction to the news last night about President Trump calling Haiti, El Salvador, and African countries “shitholes” and trying to get rid of immigrants from those countries in favour of immigrants from places like Norway.

Norwegian contributions to American immigrants peaked well before the 21st century. At that time, Norway was poor and lesser developed. The data was hard to find, but on a GDP per capita level, Norway was one of the least developed countries in Western Europe. On a like dollar-for-dollar basis, El Salvador of 2008 is not too far from Norway 1850.

I wish I had more time to develop this graphic for this morning. Alas, it will have to do as is.

I'm just really hoping Africa isn't a country again…
I’m just really hoping Africa isn’t a country again…

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 Internationalism in Sport

Whilst away, I came upon this piece in the following of my offseason baseball news. The New York Times published it between Christmas and New Years and the piece looks at the origins of sports persons in European football leagues compared to several American sports leagues, including American football, baseball, and basketball.

I was most confused by US women's football, which I had not realised has not been a single continuous organisation
I was most confused by US women’s football, which I had not realised has not been a single continuous organisation

The piece features an opening set of small multiples comparing all the leagues. Maddeningly, I wanted details and mouseovers and annotations at the start. Fortunately, as the reader continues through the article, each small multiple becomes big and the reader can explore the details of the league.

Credit for the piece goes to Gregor Aisch, Kevin Quealy, and Rory Smith.

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.

The Sinking of the Vasa

In 1628, Sweden launched one of its largest and most powerful warships not just in Sweden, but in all of Europe. She was to participate in the wars with Poland and Lithuania as Sweden sought to expand her growing empire. After two years of construction in Stockholm’s naval yard she set sail into a calm day with a light breeze.

After a strong gust pushed her hard to port, she righted herself and continued to set sail to a fortress to load 300 troops for the war. But only 20 minutes into her maiden voyage, a second gust of wind pushed her again hard to port so much so that water began to flood in via her open lower gunports. As the continued to rush in, she never righted herself and sank, not to be recovered for 300 years.

The recovery itself is a great story, but the question was why did she sink? This model in the large Vasa museum, built to host the recovered and preserved ship, shows just how dangerously she was designed. Take careful note of the faint blue waves signifying the waterline of the ship and how close they are to the lower gunports.

Note the waterline on the lower crossbeam of the barrel to which the model is connected
Note the waterline on the lower crossbeam of the barrel to which the model is connected

The short takeaway is that the ship was top-heavy and she needed to be both wider and deeper to support her displacement. I like the model here, but my one complaint with it is the waterline. Even when I was standing in front of it, I did not notice the waves at first. A little bit more emphasis or paint, perhaps to show the water beneath the ship, would really help to convey just how little of the ship was below the waterline.

Credit for the piece goes to the Vasa Museum design staff.

Below Stockholm’s Streets

I survived my holidays and hopefully you did as well. My holiday included a two-week trip to Stockholm, Copenhagen, London, and York. Over the next few weeks, you can expect to see posts with graphics and diagrams that I captured whilst on holiday.

Today’s post is about a rather large piece from the Medieval Museum in Stockholm. The city dates probably from the 13th century, but there is no definite date nor any definite explanation of the origin of the name Stockholm. A lot of work thus has to be done via archaeology and this piece, easily twice as tall as me, shows just how deep those artifacts are buried. The years can be seen to the right for a sense of scale.

Layers of history
Layers of history

But why did I love it? Because Converse trainers. And did I ever see so many black Converse walking around.

Jones–Moore Election Results

Apologies for the lack of posts over the last week or so, I have alternately been on holiday or sick while spending other time on my annual Christmas card. This will also be the last post for 2017 as I am on holiday until the new year. But before I go, I want to take a look at the election night graphics for the Alabama US Senate special election yesterday.

I am going to start with the New York Times, which was where I went first last night after returning from work. What was really nice was there graphic on their homepage. It provided a snapshot fo the results before I even got to the results page.

The homepage of the New York Times last night
The homepage of the New York Times last night

The results page then had the standard map and table, but also this little dashboard element.

I'm spinning my wheels…
I’m spinning my wheels…

We all know how I feel about dashboard things. To put it tersely: not a fan. But what I did enjoy about the experience was its progression. The bars below filled in as the night progressed, and the range in the vote-ometers narrowed. But that same sort of design could be applied to other graphics representing the narrowing of likely outcomes.

The second site I visited was the Washington Post. Like the Times, their homepage also featured an interactive graphic, another choropleth map.

A different page, a different map
A different page, a different map

There are two key differences between the maps. The Times map uses four bins for each party whereas the Post simplifies the page to two: leading and won. The second difference is the placement of the map. The Post’s map is a cropping of a larger national map versus the Times that uses a sole map of the state.

For a small homepage graphic, bits of both work rather well. The Times cuts away the unnecessary map controls and neighbouring states. But the space is small and maybe not the best for an eight-binned choropleth. In the smaller space, the Post’s simplified leading/won tells the story more effectively. But on a larger space that is dedicated to the results/story, the more granular results are far more insightful.

On a quick side note, the Post’s page included some context in addition to the standard results graphics. This map of the Black Belt and how it correlates to regions of Democratic votes in 2016 provides an additional bit of background as to how the votes played out.

Note, the Black Belt was named for the black soil, not the slaves.
Note, the Black Belt was named for the black soil, not the slaves.

Credit for the piece goes to the design teams of the New York Times and the Washington Post.