Where People Vote

Voting is not compulsory in the United States. Consequently a big part of the strategy for winning is increasing your voters’ turnout and decreasing that of your opponent. In other words, demotivate your opponent’s supporters whilst simultaneously motivating your own base. But what does that baseline turnout map look like? Well, thankfully the Washington Post created a nice article that explores who votes and who does not. And there are some clear geographic patterns.

A lot of people don't vote
A lot of people don’t vote

The piece uses this map as the building block for the article. It explores the difference between the big rural counties that dominate the map vs. the small urban counties where there can be hundreds of thousands of voters, a large number of whom do not vote. It uses the actual map to compare states that differ drastically. For example, look at the border between Tennessee and North Carolina. On the Tennessee side you have counties with low turnout abutting North Carolinian counties with high turnout.

And towards the end of the piece, the article reuses a stripped down version of the map. It overlays congressional districts that will likely be competitive and then has the counties within that feature low turnout highlighted.

Overall the piece uses just this one map to walk the reader through the geography of voting. It’s really well done.

Credit for the piece goes to Ted Mellnik, Lauren Tierney and Kevin Uhrmacher.

Pages of Polls and Forecasts

We are now one week away from the midterm elections here in the United States. Surprisingly, we are going to be looking at election-y things over the course of the next week or so. But before we delve into that, I wanted to focus on the homepage for FiveThirtyEight, the below screenshot is from my laptop.

The homepage as of 30 October
The homepage as of 30 October

The reason I wanted to call attention to it is that right-most column of content. The site does a great job of succinctly providing the latest forecasts and polling number on the two main midterm results, federal representation in the House and Senate, along with polling numbers for President Trump.

Starting from the bottom, the polling numbers chart works really well. It clearly and effectively shows the latest approval/disapproval numbers and their longer term trend whilst providing a link to a page of deeper data. It’s very effective.

Moving up we have the House forecasts. These are tricker to see because so many of the more urban and suburban districts are inherently small geographically ergo very difficult to see in a small map. But the map does the job of at least providing some data along with the key takeaway of the odds of the Democrats flipping or Republicans retaining the House. Again, not surprisingly, it offers a link into the data.

The Senate map is the one where I have the most difficulty. Now when we get to the actual page—hopefully later this week—the map shown makes perfect sense because it exists in a large space. That space is needed to show two hexagons that represent each state’s two senators. But, similar to the problem with the House districts, the Northeast is so geographically cramped that it is difficult to show the senators from Maine through Maryland clearly. I wonder if some of the other visualisations on their Senate forecast page would have been a better choice. However, they do at least provide those odds at the top of the graphic.

Credit for the piece goes to the FiveThirtyEight design department.

First Florence, Now Michael

You may recall a few weeks ago there was a hurricane named Florence that slammed into the Carolina before stalling and dumping voluminous amounts of rain that inundated inland communities in addition to the damage by the storm surge in the coastal communities. At the time I wrote about a New York Times piece that explored housing density in coastal areas, specifically around the Florence impact area.

Well today the New York Times has a print graphic about something similar. It uses the same colours and styles, but swaps in a different data set and then uses a small multiple setup to include the Florida Panhandle. Of course the Florida Panhandle was just struck by Hurricane Michael, a Category 4 storm when it made landfall.

Of course that track for Michael also brought significant rainfall to the areas recovering from Florence for a double whammy
Of course that track for Michael also brought significant rainfall to the areas recovering from Florence for a double whammy

This one instead looks at median income per zip code to highlight the disparity between those living directly on the coast and those inland. In these two most recent landfall areas, the reader can clearly see that the zip codes along the coast have far greater incomes and, by proxy, wealth than those just a few zip codes further inland.

The problem is that rebuilding lives, communities, and infrastructure not only takes time, but also money. And with lower incomes, some of the hardest hit areas over the past several weeks could have a very difficult time recovering.

Regardless, the recoveries on the continental mainlands of the Carolinas and Florida will likely be far quicker and more comprehensive than they have been thus far for Puerto Rico.

The only downside with this graphic is the registration shift, which is why the graphic appears fuzzy as colours are ever so slightly offset whereas the single ink black text in the upper right looks clear and crisp.

Credit for the piece goes to the New York Times graphics department.

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.

T Minus 12 Weeks

Today is Tuesday, 14 August. We are now 12 weeks away from the 2018 midterms. That is just three months away. Coverage will only intensify in the weeks to come, and you can be certain that if there are pieces worth noting, I will do that. But to mark the date I went with this choropleth map from the New York Times.

The nation will turns its eyes to you…in 12 weeks
The nation will turns its eyes to you…in 12 weeks

Nothing too crazy here. Likelihood of results colour the districts. The darker the blue, the more solid the Democratic seat. The darker the red, the more solid the Republican one. But what this map does really well is it excludes the likely’s and the solids and sets them to a light, neutral grey. You can still hover over a district if you are curious about where it falls, but, in general those have been excluded from the consideration set because they are not the districts of the most national attention.

Secondly, note the state labels. States like Wyoming that have no competitive seats have no label. After all, why are we labelling things that have no impact on this story, again, the competitive races. Fewer labels means fewer distracting elements in the graphic.

Finally, the piece includes the ability to zoom into a region. After all, for those of us living in urban areas, our districts are geographically tiny compared to the at-large or state-wide seats like in Wyoming, the Dakotas, and Alaska. Otherwise, good luck trying to find the Illinois 5th or Pennsylvania 3rd.

Credit for the piece goes to Jasmine C. Lee.

The Decline of the Media

Everybody loves maps. Unfortunately this is not a map to love. The Economist looked at the global status of the free press and its decline around the world.

If only it were a larger map
If only it were a larger map

The graphic is a neat little package of a map to anchor the narrative and a few callout countries with their general declines—or in Tunisia’s case the reversal thereof—highlighted. But I do have a few issues with the piece.

Do the lines need to be curved? Some certainly make sense, e.g. how do you get from the Turkey box to the outline of Turkey? But then for Afghanistan, a straight line through Balochistan, Pakistan would mean the line would not have to cover Pakistan, India, curve around Sri Lanka, and then finally reach the box.

In the little boxes, I also wonder if the lines need to be as thick as they are. Could a lighter stroke weight improve the legibility of the charts?

And to be super picky, I wonder if the stroke outlines of the countries are complete. My trained eye fails to register an outline of both the European part of Turkey and of the Russian oblast of Kaliningrad.

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

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.