Chinese Urban Clusters

Yesterday the Economist posted a graphic about Chinese urban clusters, of which the Chinese government is planning to create 19 as part of a development strategy. In terms of design, though, I saw it and said, “I remember doing something like that several years ago”.

The Economist piece looks at just the geography of the Chinese clusters. It highlights three in particular it discusses within the article while providing population numbers for those clusters. Spoiler: they are large.

The Economist graphic does little else beyond labelling the cities and the highlighting of the three features clusters. But that is perfectly okay, because that was probably all the graphic was required to do. I am actually impressed that they were able to label every city on the map. As you will see, we quickly abandoned that design idea.

The Chinese government's new urban cluster plan
The Chinese government’s new urban cluster plan

So back in 2015, using 2014 data, my team worked on a series of graphics for a Euromonitor International white paper on Chinese cities. The clusters that the analysts identified, however, were just that, ones identified by researchers. Since the Chinese government had not yet created this new plan.

We added some context to our cluster map
We added some context to our cluster map

We also looked at more cities and added some vital context to the cluster map by working to identify the prospects of the various Chinese provinces. Don’t ask me what went into that metric, though, since I forget. The challenge, however, was identifying the four different tiers of Chinese city and then differentiating between the three different cluster types while overlaying that on a choropleth. Then we added a series of small multiples to show how now all provinces are alike despite having similar numbers of cities.

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

Credit for the Euromonitor piece is mine. I would gladly give a shoutout to those that worked with me on that project…but it’s been so long I forget. But I’m almost certain both Lindsey Tom and Ciana Frenze helped out, if not on that graphic, on other parts of the project.

Primarily California

Today is primary day and everyone will be looking to the California results. Although probably not quite me, because Eastern vs. Pacific time means even I will likely be asleep tonight. But before we get to tonight, we have a nice primer from last Friday’s New York Times. It examines the California House of Representatives races that we should be following.

53 districts are a lot to follow in one night…
53 districts are a lot to follow in one night…

Like most election-related pieces, it starts with a map. But it uses some scrolling and progressive data disclosure. The map above, after a bit of scrolling, finally reveals the districts worth following and their 2016 vote margins.

Out of all 53, these are the districts the Times says to watch
Out of all 53, these are the districts the Times says to watch

From there the article moves onto a bit of an exploration of those few districts. You should read the full article—it’s a short read—for the full context on the California votes today. But it does make some nice of bar and line charts to plot the differences in presidential race vs. congressional race margins and the slow Democratic shift.

Credit for the piece goes to Jasmine C. Lee and Karen Yourish.

Turning the Midwest Red

Continuing with election-y stuff, I want to share a fascinating map from the Washington Post. The article came out last week, and it is actually incredibly light in terms of data visualisation. By my count, there were only two maps. The article’s focus is on interviews with Trump voters in 2016 and how their opinions of the president have changed over the last year or so. If you want to read it, and you should as it is very well written, I will warn you that it is long. But, to the map.

I may have used an even lighter shade for 2012 counties…
I may have used an even lighter shade for 2012 counties…

What I loved about this map is how it flips the usual narrative a bit on its head. We talk about how much a candidate won a county in 2016, or even how much the vote shifted in 2016. And anecdotally we talk about “ancestral Democrats” flipping to Trump. But this map actually tries to chart that. It reveals the last time a county actually voted for a Republican presidential candidate—the darker the red, the further back in time one has to go.

Counties that vote Democratic are white, because why do we need them for this examination. Omitting them was a great design decision. Much of the country, as we know or can intuit, voted Republican in 2012 for Mitt Romney. But what about before then? You can see how the upper Midwest, along the Mississippi River, was a stronghold for Democrats with some counties going as far back as the 1980s or earlier. And then in 2016 they all flipped and that flipping was most significant there—of some additional interest to me are the counties in Maine, the Pacific Northwest, and along Lake Erie near Cleveland.

In short, this was just a brilliantly done map. And it sets the tone for the rest of the article, which is interviews with residents of those counties called out on the map.

Credit for the piece goes to Andrew Braford, Jake Crump, Jason Bernert and Matthew Callahan.

Pennsylvania Primary Night

Surprise, surprise. This morning we just take a quick little peak at some of the data visualisation from the Pennsylvania primary races yesterday. Nothing is terribly revolutionary, just well done from the Washington Post, Politico, and the New York Times.

But let’s start with my district, which was super exciting.

The only thing to write home about is how the Republican incumbent dropped out at the last moment and was replaced by this guy…
The only thing to write home about is how the Republican incumbent dropped out at the last moment and was replaced by this guy…

Moving on.

Each of the three I chose to highlight did a good job. The Post was very straightforward and presented each office with a toggle to separate the two parties. Usually, however, this was not terribly interesting because races like the Pennsylvania governor had one incumbent running unopposed.

Mango is represented by what colour?
Mango is represented by what colour?

But Politico was able to hand it differently and simply presented the Democratic race above the Republican and simply noted that the sitting governor ran unopposed. This differs from the Post, where it was not immediately clear that Tom Wolf, the governor, was running unopposed and had already won.

Clean and simple design. No non-sense here.
Clean and simple design. No non-sense here.

The Times handled it similarly and simultaneously displayed both parties, but kept Wolf’s race simple. The neat feature, however, was the display of select counties beneath the choropleth. This could be super helpful in the midterms in several months when key races will hinge upon particular counties.

The Republican primary for the PA governorship has been ugly
The Republican primary for the PA governorship has been ugly

But where the Times really shines is the race for Pennsylvania’s lieutenant governor. Fun fact, in Pennsylvania the governor and lieutenant governor do not run as a ticket and are voted for separately. This year’s Democratic incumbent, Mike Stack, does not get on with the governor and had a few little scandals to his name, prompting several Democrats to run against him. And the Times’ piece shows the two parties result, side-by-side.

Pennsylvania's oddest race this time 'round
Pennsylvania’s oddest race this time ’round

Credit for the Post’s piece goes to the Washington Post graphics department.

Credit for Politico’s piece goes to Politico’s graphics department.

Credit for the Times’ piece goes to Sarah Almukhtar, Wilson Andrews, Matthew Bloch, Jeremy Bowers, Tom Giratikanon, Jasmine C. Lee and Paul Murray, and Maggie Astor.


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.

Deaths in America

Yesterday was murders in London and New York. Today, we have a nice article from FiveThirtyEight about deaths more broadly in America. If you recall, my point yesterday was that not all graphics need to be full column width. And this article takes that approach—some graphics are full width whereas others are not.

This screenshot shows a nice line chart that, while the graphic sits in the full column, the actual chart is only about half the width of the graphic. I think the only thing that does not sit well with me is the alignment of the chart below the header. I probably would align the two as it creates an odd spacing to the left of the chart. But I applaud the restraint from making the line charts full width, as it would mask the vertical change in the data set.

The screenshot is of the graphic's full width, note the lines only go a little over half the width.
The screenshot is of the graphic’s full width, note the lines only go a little over half the width.

Meanwhile, the article’s maps all sit in the full column. But my favourite graphic of the whole set sits at the very end of the piece. It examines respiratory deaths in a tabular format. But it makes a fantastic use of sparklines to show the trend leading towards the final number in the row.

Loving the sparklines…
Loving the sparklines…

Credit for the piece goes to Ella Koeze and Anna Maria Barry-Jester.

Pennsylvania 18th Congressional District: The Special Election

Today is Tuesday, 12 March. And that means a special election in the 18th congressional district of Pennsylvania, located in the far southwest of the state, near Pittsburgh.

Long story short, the district is uber Republican. But, the long-time Republican congressman, the avowedly pro-life type, was caught urging his mistress to abort their unborn child. Needless to say, that did not go over so well and so he resigned and now here we are with a veteran state legislator and veteran who calls himself “Trump before there was Trump” running for the Republicans and another veteran but also former federal prosecutor involved with fighting the opioid epidemic running for the Democrats.

Now about that uber Republican-ness. It is so much so that Democrats didn’t even run candidates in 2014 and 2016. And then in 2016, Trump won the district by 20 percentage points. But the polls show the Republican, Rick Saccone, with a very narrow lead within the margin of error. That in and of itself is tremendous news for Democrats in Pennsylvania. But what if Conor Lamb, the Democrat, were to actually somehow pull off a victory?

Well the Washington Post put together a great piece about how it really might not matter. Why? Because of that whole redistricting thing that I talked about. Neither Saccone nor Lamb live in the district that will replace today’s 18th.

The piece has several nice graphics showing just how much this area of the state will change and how that will impact these two candidates. But my favourite piece was actually this dot plot.

That's an enormous gap for Lamb to overcome
That’s an enormous gap for Lamb to overcome

It speaks more to today’s election than the future of the district. Everyone will undoubtedly be looking to see if Lamb can eke out a victory of Saccone this evening. But even if he loses narrowly, the Democrats can still take a glimmer of hope because of just how insurmountable the challenge was. It would require an enormous swing just to crack 50.1%.

Credit for the piece goes to Reuben Fischer-Baum and Kevin Uhrmacher.

Changes to Immigration Enforcement

Almost two weeks ago I read a piece in City Lab that used three maps to look at the changes to immigration enforcement in the first year of the Trump administration. I was taken by this final map in particular.

Some geographic patterns do emerge…
Some geographic patterns do emerge…

While the map does have some large areas of N/A, it still does show some interesting geographic patterns. I think New York showcases it the best. Counties that are less involved in enforcement operations are in the southern part, near New York City. But then you can begin to get a clear sense of what is “upstate” by that break roughly parallel to both the Connecticut and Pennsylvania northern borders.

To a lesser extent you can see the same pattern play out in Pennsylvania. While far more white—as in no change on the map—the counties of orange—more involvement—are located in the interior and western counties. That is perhaps somewhat in the same space as Pennsyltucky.

Immigration is clearly an engaging topic these days, and I found this map interesting not because of its design, but because of the geographic stories it tells.

Credit for the piece goes to Victoria Beckley.

The 2017–18 Flu Season

Last week I covered the Pennsylvania congressional district map changes quite a bit. Consequently I was not able to share a few good pieces of work. Let’s hope nothing goes terribly wrong this week and maybe we can catch up.

From last Friday we have this nice piece from FiveThirtyEight looking at the spread of influenza this season.

Red is definitely bad
Red is definitely bad

The duller blues and greens give way to a bright red from south to north. Very quickly you can see how from, basically, Christmas on, the flu has been storming across the United States. It looks as if your best bets are to head to either Maine or Montana. Maybe DC, it’s too small to tell, but I kind of doubt that.

As you all know, I am a fan of small multiples and so I love this kind of work. To play Devil’s advocate, however, I wonder if an interactive piece that featured one large map could have worked better? Could the ability to select the week and then the state yield information on how the flu has spread across each state? I am always curious what other other forms and options were under consideration before they chose this path.

Credit for the piece goes to the FiveThirtyEight graphics department.

Gerrymandering Pennsylvania Part III

Almost a month ago I wrote about how the Pennsylvania Supreme Court was considering a case involving the state’s heavily gerrymandered US congressional districts, which some have called among the worst in the nation. About a week later the Pennsylvania Supreme Court decided that the map is in fact so gerrymandered it violates the Pennsylvania Constitution. It ordered the Republican-controlled legislature to create a new, non-gerrymandered map that would have to be approved by the Democratic governor. I did not write up that then Pennsylvanian Republicans appealed to the US Supreme Court—no graphics for that story. That appeal was rejected by Justice Alito, but with only days to spare the state legislature then created this new map and sent in this new one on Friday.

The proposed congressional districts, black lines, overlaid atop electoral precincts, the pretty colours.
The proposed congressional districts, black lines, overlaid atop electoral precincts, the pretty colours.

The problem, according to the governor and outside analysts, is that the map is just as gerrymandered as the previous one. Consequently, yesterday the governor rejected the new map and so now the Pennsylvania Supreme Court, working with outside experts in political redistricting, will create a new congressional map for Pennsylvania. Hopefully before May when the state has its first primaries.

But just how do we know that the new map, despite looking different, was just as gerrymandered. Well, the Washington Post plotted the election margins for districts in 2016 using precinct data versus their proposed 2018 map overlaid atop those same precincts. What did they get? Almost identical results. The districts are no longer Goofy Kicking Donald Duck-esque, but they exhibit the same Republican bias of the previous map.

Trying to do the same thing to get a different or the same result?
Trying to do the same thing to get a different or the same result?

For the purposes of design, I probably would have dropped the “PA-” labels, as they are redundant since the whole plot examines Pennsylvania congressional districts. I think that, perhaps with a marker, and maybe a line of no-change would go a bit further in more clearly showing how the ultimately rejected map was nearly identical to its previous incarnation.

Credit for the map borders goes to the Pennsylvania state legislature, the version here to the Washington Post Wonkblog. All Wonkblog for the scatterplot.