Election Day

The 2018 midterm elections are finally here. Thankfully for political nerds like myself, the New York Times homepage had a link to a guide of when what polls close (as early as 18.00 Eastern).

I'm not saying you can't keep voting. You just can't keep voting here.
I’m not saying you can’t keep voting. You just can’t keep voting here.

It makes use of small multiples to show when states close and then afterwards which states have closed and which remain open. It also features a really nice bar chart that looks at when we can expect results. Spoiler: it could very well be a late night.

But what I really wanted to look at was some of the modelling and forecasts. Let’s start with FiveThirtyEight, because back in 2016 they were one of the only outlets forecasting that Donald Trump had a shot—although they still forecast Hillary Clinton to win. They have a lot of tools to look at and for a number of different races: the Senate, the House, and state governorships. (To add further interest, each comes in three flavours: a lite model, the classic, and the deluxe. Super simply, it involves the number of variables and inputs going into the model.)

The Deluxe House model
The Deluxe House model

The above looks at the House race. The first thing I want to point out is the control on the left, outside the main content column. Here is where you can control which model you want to view. For the whimsical, it uses different burger illustrations. As a design decision, it’s an appropriate iconographic choice given the overall tone of the site. It is not something I would have been able to get away with in either place I have worked.

But the good stuff is to the right. The chart at the top shows the percentage of likelihood of a particular outcome. Because there are so many seats—435 are up for vote—every additional seat is between almost 0 and 3%. But taken in total, the 80% confidence band puts the likely Democratic vote tally at what those arrows at the bottom show. In this model that means picking up between 20 and 54 seats with a model median of 36. You will note that this 80% says 20 seats. The Democrats will need 23 to regain the majority. A working majority, however, will require quite a few more. This all goes to show just how hard it will be for the Democrats to gain a workable majority. (And I will spare you a review of the inherent difficulties faced by Democrats because of Republican gerrymandering after the 2010 election and census.) Keep in mind with FiveThirtyEight’s model that they had Trump with a 29% chance of victory on Election Day 2016. Probability and statistics say that just because something is unlikely, e.g. the Democrats gaining less than 20 seats (10% chance in this model), it does not mean it is impossible.

The cartogram below, however, is an interesting choice. Fundamentally I like it. As we established yesterday, geographically large rural districts dominate the traditional map. So here is a cartogram to make every district equal in size. This really lets us see all the urban and suburban districts. And, again, as we talked about yesterday, those suburban districts will be key to any hope of Democratic success. But with FiveThirtyEight’s design, compared to City Lab’s, I have one large quibble. Where are the states?

As a guy who loves geography, I can roughly place, for example, Kentucky. So once I do that I can find the Kentucky 6th, which will have a fascinating early closing race that could be a predictor of blue waviness. But where is Kentucky on the map? If you are not me, it might be difficult to tell. So compared to yesterday’s cartogram, the trade-off is that I can more easily see the data here, but in yesterday’s piece I could more readily find the district for which I wanted the data.

Over on the Senate side, where the Democrats face an even more uphill battle than in the House, the bar chart at the top is much clearer. You can see how each seat breakdown, because there are so fewer seats, has a higher percentage likelihood of success.

In the Senate, things don't look good for the Democrats
In the Senate, things don’t look good for the Democrats

The take away? Yeah, it looks like a bad night for the Democrats. The only question will be how bad does it go? A good night will basically be the vote split staying as it is today. A great night is that small chance—20%, again compared to Trump’s 29% in 2016—the Democrats narrowly flip the Senate.

Below the bar chart is a second graphic, a faux-cartogram with a hexagonal bar chart of sorts sitting above it. This shows the geographic distribution of the seats. And you can quickly understand why the Democrats will not do well. They are defending a lot more seats in competitive states than Republicans. And a lot of those seats are in states that Trump won decisively in 2016.

That's a lot of red states…
That’s a lot of red states…

I have some ideas about how this type of data could be displayed differently. But that will probably be a topic for another day. I do like, however, how those seats up for election are divided into their different categories.

Unfortunately my internet was down this morning and so I don’t have time to compare FiveThirtyEight to other sites. So let’s just wrap this up.

Overall, what this all means is that you need to go vote. Polls and modelling and guesswork is all for nought if nobody actually, you know, votes.

Credit for the poll closing time map goes to Astead W. Herndon and Jugal K. Patel.

Credit for the FiveThirtyEight goes to the FiveThirtyEight graphics department.

Running Up the Debt

I was reading the paper this morning and stumbled across this graphic in a New York Times article that focused on the increasing importance of debt payments.

Those interest payment lines are headed in the wrong direction.
Those interest payment lines are headed in the wrong direction.

The story is incredibly important and goes to show why the tax cuts passed by the administration are fiscally reckless. But the graphic is really smart too. After all, it is designed to work in a single colour.

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

Kavanaugh’s Fading in Competitive House Seats

Another day, another allegation of sexual misconduct against Brett Kavanaugh. We are presently at two and are expecting a third tomorrow. But the question is, will these allegations sink his nomination? Probably not. But could that confirmation hurt Republicans in the mid-terms? Possibly.

The New York Times posted an article about how Kavanaugh’s support in battleground congressional districts is slipping. To be fair, the chart is simple, but it does its job. And usually that’s all we want a chart to do.

Just a few points can make all the difference…
Just a few points can make all the difference…

Me the person interested in politics, however, will take this a bit further. If Kavanaugh’s support continues to fade—this survey was taken before these new allegations were public—will Republicans supporting the nomination face a backlash from their constituents?

Credit for the piece goes to Nate Cohn.

Swedish Election Results

Sweden went to the polls this past weekend and the results are mostly in, with overseas ballots left to be counted. But the results are clear, a stark rise for the nationalist Sweden Democrats, though not as high as some had feared late last week.

Not surprisingly we had the standard parliamentary seat chart, seen below by the BBC. The nice twist this time is the annotations stating the seat change. (More on that later.)

An unnerving amount of yellow
An unnerving amount of yellow

It does a good job of showing the parties and how they are laid out, though I am sometimes more partial to a straight-up bar chart like below at Reuters.

Here the Sweden Democrats are grey.
Here the Sweden Democrats are grey.

However, both do not do a great job in showing what would traditionally be a kingmaker result for Sweden Democrats. When stacked at each end, neither the centre-left bloc, led by the Social Democrats, nor the centre-right, led by the Moderates, are in control of a majority of seats in the Riksdag. Imagine that neutral colour straddling a 50% benchmark line or sitting in the middle of the seats. It makes it far clearer just how pivotal the Sweden Democrats would usually be. Because, usually, Sweden Democrats or parties like it—in the sense of it won a large number of seats—that help the main coalition cross that 50% threshold would have an enormous sway in the next governing coalition. But here, the Sweden Democrats are an anti-immigrant, nationalist party that both the centre-left and centre-right have said with whom they will not enter talks.

Here the Sweden Democrats are brown.
Here the Sweden Democrats are brown.

But graphically, the thing I always find lacking in charts like those above are just how dramatic the rise of the Sweden Democrats has been. And so for that, we have this little piece of mine that complements the two. Because not all members of the coalitions experienced the declines of their major parties, the Social Democrats and the Moderates. In fact, with the exception of the Green Party, all others rose or, in the case of the Liberals, stayed flat. A more thorough defeat would have probably seen the whole of the coalition falling in the number of seats. Unfortunately for Sweden, in this case, the nationalists took the lion share of the seats lost by the top two parties.

Credit for the BBC piece is mine.

Credit for my work is mine.

The Toll of the Trolls

This is an older piece that I’ve been thinking of posting. It comes from FiveThirtyEight and explores some of the data about Russian trolling in the lead up to, and shortly after, the US presidential election in 2016.

They're all just ugly trolls. Nobody loves them.
They’re all just ugly trolls. Nobody loves them.

The graphic makes a really nice use of small multiples. The screenshot above focuses on four types of trolling and fits that into the greyed out larger narrative of the overall timeline. You can see that graphic elsewhere in the article in its total glory.

From a design standpoint this is just one of those solid pieces that does things really well. I might have swapped the axes lines for a dotted pattern instead of the solid grey, though I know that seems to be FiveThirtyEight’s house style. Here it conflicts with the grey timeline. But that is far from a dealbreaker here.

Credit for the piece goes to Oliver Roeder.

The Rise of Online Dating

This past weekend I cited this article from the Economist that looked at the rise of online dating as a way of couples meeting. There was some debate about which channels of interaction/attraction still worked or were prevalent. And it turns out that, in general, the online world is the world today.

Meeting your partner in primary/secondary school has clearly gone out of fashion since the 40s.
Meeting your partner in primary/secondary school has clearly gone out of fashion since the 40s.

My problem with the graphic is that it is a bit too spaghettified for my liking. Too many lines, too many colours, and they are all overlapping. I probably would have tried a few different tricks. One, small multiples. The drawback to that method is that while it allows you to clearly analyse one particular series, you lose the overlap that might be of some interest to readers.

Second, maybe don’t highlight every single channel? Again, you could lose some audience interest, but it would allow the reader to more clearly see the online trend, especially in the heterosexual couple section of the data. You could accomplish this by either greying out uninteresting lines or removing them entirely, like that primary/secondary school series.

Third, I would try a bit more consistent labelling. Maybe increase the overall height of the graphic to give some more vertical space to try and label each series to the right or left of the graphic. You might need a line here or there to connect the series to its label, but that is already happening in this chart.

However, I do like how the designers kept the y-axis scale the same for both charts. It allows you to clearly see how much of an impact the online dating world has been for homosexual couples. My back-of-the-envelope calculations would say that is more than three times as successful than it is for heterosexual couples. But that insight would be lost if both charts were plotted on separate axis scales.

But lastly, note how the dataset only goes as far as 2010. I can only imagine how these charts would look if the data continued through 2018.

Credit for the piece goes to the Economist Data Team.

Joblessness in the Developed World

  • We have been looking at tariffs a little bit this week, but unfortunately one of the side effects of tariffs is job losses. And of course when it comes to people losing jobs, not all countries in the  developed world handle them the same. Last month the Washington Post published an article examining how those countries compare in a number of related metrics such as unemployment compensation, notice for termination, and income inequality.
Not all countries give people the short stick.
Not all countries give people the short stick.

It uses a series of bar charts to show the dataset and reveal how the United States fares poorly compared to its peers. The chart above looks at the earning needed for termination from employment and the differences are stark. The outlined bar chart shows longer tenured employees and the full bars as coloured. Of course this makes it look like a stacked bar chart or filled bar chart. Instead I wonder if a dot plot would be clearer. It would eliminate the confusion in determining what if any share of the empty bar is held by the full bar.

The US offers shockingly little assistance to people
The US offers shockingly little assistance to people

The chart for unemployment insurance versus assistance is a bit better. Here the bar represents insurance and the lines assistance. I like how the lines continue off beyond the margins to indicate an unlimited timeframe for assistance. However, for those countries where assistance is short-lived, the bars versus lines again begin to look like an instance of a share of a total, which they are not.

My New Toast

I am a millennial. That broadly means I am destroying and/or ruining everything. It also means I am obsessed with things like avocado toast. It also means I am not buying a house. Thankfully the Economist is on top of my next fad: indoor houseplants.

Plant things
Plant things

Your author will admit to having a few: a hanging plant, an Easter lily, an aloe plant and its children, and a dwarf conifer. Just don’t ask me how they’re doing. (Hint: not well.) Turns out I am not a plant person.

In terms of the graphic, though, what we have is a straight up set of small multiples of line charts. The seasonality mentioned in the article text appears quite clearly in a number of plants.

But is Swiss Cheese really a plant?

Credit for the piece goes to the Economist Data Team.

Apple Hits One Trillion

Last Tuesday we looked at a print piece from the New York Times detailing the share price plunge of Facebook after the company revealed how recent scandals and negative news impacted its financials. Well, today we have a piece from last week that shows how large Apple is after it hit a market capitalisation of one trillion US dollars.

The piece itself is not big on the data visualisation, but it functions much like the Facebook piece, as a blend of editorial design and data visualisation. The graphic falls entirely above the fold and combines a factette and maybe we could classify it as a deconstructed tree map. It uses squares where, presumably, the area equates to the company’s value. And the sum total of those squares equals that of one trillion dollars, or the value of Apple.

Looking at the full page
Looking at the full page

In terms of design it does it well. The factette is large enough to just about stretch across the width of the page and so matches the graphic below it in its array of colours. Why the colours? I believe these are purely aesthetic. After all, it is unclear to me just what Ford, Hasbro, and General Mills all have in common. In a more straight data visualisation piece, we might see colour used to classify companies by industry, by growth in share price or market share. Here, however, colour functions in the editorial space to grab the reader’s attention.

The design also makes use of white space surrounding the text, much like the Facebook piece last week, to quiet the overall space above the fold and focus the reader’s attention on the story. Note that the usual layout of stories on the page continues, but only after the fold.

When we keep in mind the function of the piece, i.e. it is not a straight-up-explore-the-data type of piece, we can appreciate how well it functions. All in all this was a really nice treat last Friday morning.

Credit for the piece goes to Karl Russell and Jon Huang.