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.

Congressional District Population Density

Tomorrow is Election Day here in the United States and this morning I wanted to look at a piece I’ve had in mind on doing from City Lab. I held off because it looks at the election and what better time to do it than right before the election.

Specifically, the article looks at the density of the different congressional districts across the United States. Whilst education level appears to be the most predictive attribute of today’s political climate—broadly speaking those with higher levels of formal education support the Democrats and those with lower or without tend to support President Trump—the growing urban–rural divide also works. But what about the in-between? The suburbs? The exurbs? And how do we then classify the congressional districts that include those lands.

For that purpose City Lab created its City Lab Congressional Density Index. Very simplistically it scores districts based on their mixture of low- to medium- to high-density neighbourhoods. But visually, which is where this blog is concerned, we get maps with six bins from pure urban to pure rural and all the mixtures in-between. This cartogram will show you.

All the urban and rural seats
All the urban and rural seats

Now, there are a couple of things I probably would have done differently in terms of the visualisation. But the more I look at this, one of those things would not be to design the hexagons to all fit together nicely. Instead, you get this giant gap right where the plains states begin west of the Mississippi River stretching through the Rockies over to California. If you think about it, however, that is a fairly accurate description of the population distribution of the United States. With a few exceptions, e.g. Denver, there are not many people living in that space. Four geographically enormous states—North Dakota, South Dakota, Montana, and Wyoming—have only one congressional district. Idaho has two. Nebraska three. And then Iowa and Kansas four. So why shouldn’t a map of the United States display the plains and Rocky Mountain interior as a giant people hole?

Like I said, initially I took umbrage at that design decision, but the more I thought about it, the more it made sense. But there are a few others with which I quibble. The labelling here is a big one. First, we have the district labels. They are small, because they have to be to fit within the five hexagons that define the districts’ shapes. But every label is black. Unfortunately, that makes it difficult to read the labels on the darker colours, most notably the dark purple. I probably would have switched out the black labels in those instances for white ones.

But then the state labels are white with black outlines, which makes it difficult to read on either dark or light backgrounds. The designer made the right decision in making the labels larger than the districts, but they need to be legible. For example, the labels of Alaska and Hawaii need not be white with black outlines. They could just be set in black type to be legible. Conversely, Florida’s, sitting atop darker purple districts, could be made white.

The piece makes use of more standard geographic map divided into congressional districts—the type you will see a lot tomorrow night. And it makes use of bar charts to describe the demographics of the various density types. I like the decision there to use a new colour to fill in the bars. They use a dark green because it can cut across each of the six types.

Credit for the piece goes to David H. Montgomery.

Georgia 6th Special Election

Wow do we have a lot to talk about this week. Probably bleeding into next week to be honest. But, last night was the special election for the Georgia 6th.

For those of you not following politics, the congressman representing it was Tom Price; he is now the Secretary of Health and Human Services. Consequently, Georgia needed to elect a fill-in for the Atlanta-suburbs district. That election was between 18 candidates last night. The race could have been won outright, but it would have required a vote total over 50%.

That did not happen—and realistically with 18 people running was not likely. But, Democrats hoped they could get their candidate in at 50+%.

The live results from early in the evening
The live results from early in the evening

This screenshot is from a nice piece by the New York Times. As you all know by now, I am not a huge fan of choropleth maps. They distort geographic area and population. But, I like the arrangement of these small multiples. It does a nice job of comparing the results for the five major candidates. I particularly like the addition of the 2016 presidential election result. With the cratering poll approvals of Donald Trump, could some of the paler red precincts flip in June?

The results from later in the evening
The results from later in the evening

The above screenshot comes from BuzzFeed, whose coverage I followed via live streaming last night. They used a cartogrammic approach, assuming that cartogrammic is actually a word. The colours could use a bit more sophistication—the best example being the Democratic–Republican margin map where the blues are darker than the reds and have a hopefully unintended greater visual weight.

Credit for the piece

What Does Europe Want from Brexit?

Sorry about last week, everyone. I had some trouble with the database powering the blog here. Great week for things to go down, right? Well, either way, we’re back and it’s not like the news is stopping. This week? Brexit’s back, baby.

I’m never using the word “baby” again on this blog.

I have been saving this piece until the announcement of Article 50 by the UK government. I know the British and Europeans among my audience likely know what that means, but for the rest of you, Article 50 is the formal mechanism by which the United Kingdom starts the two-year process to leave the European Union.

Think of it like signing the divorce papers, except that the divorce isn’t unofficial for two years until after that date. The interim period is figuring out who gets which automobile, the dinnerware, and that ratty-old sofa in the basement. Except that instead of between two people, this divorce is more like a divorce between polygamists with multiples husbands and wives. So yeah, not really like a divorce at all.

What the EU wants from Brexit at a desktop scale
What the EU wants from Brexit at a desktop scale

This piece from the Guardian attempts to explain what the various parties want from the United Kingdom and from the eventual settlement between the UK and the EU. It leads off with a nice graphic about the importance of a few key issues in a cartogram. And then several voting blocs run down the remainder of the page with their key issues.

What the EU wants from Brexit at a mobile scale
What the EU wants from Brexit at a mobile scale

I really like this piece as the small multiples for each section refer back to the opening graphic. But then on a narrow window, e.g. your mobile phone, the small multiples drop off, because really, the location of the few countries mentioned on a cartogram is not terribly important to that part of the analysis. It shows some great understanding of content prioritisation within an article. In a super ideal world, the opener graphic would be interactive so the user could tap the various squares and see the priorities immediately.

But overall, a very solid piece from the Guardian.

Credit for the piece goes to the Guardian’s graphics department.

US Foreign Aid

One of the big news stories yesterday centred on the Trump administration’s budget outline that would expand US defence spending by 9%, or $54 billion. That is quite a lot of money. More worrying, however, was the draft’s directive that it be accompanied by equal spending cuts in neither security nor entitlement programmes like Social Security and Medicare. Nor, obviously, the trillions allocated for mandatory spending, e.g. debt repayment.

White House officials—worth noting of the Trump-despised anonymous type that I suppose that only matters if reporting unflattering news—declined to get into specifics, but pointed out foreign aid as an area likely to receive massive cuts.

Problem is, foreign aid is one of the smallest segments of the federal budget. How small? Well, let’s segue into today’s post—see how smooth that was—from the Washington Post. The article dates from October, but was just brought to my attention to one of my mates.

Foreign aid spending is a small fraction of the budget
Foreign aid spending is a small fraction of the budget

Beyond this graphic that leads the piece, the Post presents numerous cartograms and other graphics that detail spending patterns. Hint, there is a pattern. But those patterns could also make it difficult to slash said spending.

The reason foreign aid spending is important is that it ties nicely into that concept of soft power. No surprise that over 120 retired generals and admirals told Congress that spending on diplomacy and foreign aid is “critical to keeping America safe”.

But for now this remains a budget outline sent to federal agencies to review. The actual budget fight is yet to come. So I’m sure this won’t be the last time we look at this topic here on Coffeespoons.

Credit for the piece goes to Max Bearak and Lazaro Gamio.

The Affordable Care Act You Likely Know as Obamacare

It just won’t die. Grandma, that is, in front of the death panels of Obamacare. Remember those? Well, even if you don’t, the Affordable Care Act (the actual name for Obamacare) is still around despite repeated attempts to repeal it. So in this piece from Bloomberg, Obamacare is examined from the perspective of leaving 27 million people uninsured. In 2010, there were 47 million Americans without insurance and so the programme worked for 20 million people. But what about those remaining 27?

I am not usually a fan of tree maps, because it is difficult to compare areas. However, in this piece the designers chose to animate each section of the tree as they move along their story. And because the data set remains consistent, e.g. the element of the 20 million who gained insurance, the graphic becomes a familiar part of the article and serves as a branching off point—see what I did there?—to explore different slices of the data.

This is a tree map I actually think works well
This is a tree map I actually think works well

So in the end, this becomes one of those cases where I actually think the tree map worked to great effect. Now there is a cartogram in the article, that I am less sure about. It uses squares within squares to represent the number of uninsured and ineligible for assistance as a share of the total uninsured.

I'm not sure the map is necessary here
I’m not sure the map is necessary here

Some of the visible patterns come from states that refused to expand Medicaid. It was supposed to cover the poorest, but the Supreme Court ruled it was optional not mandatory and 19 states refused to expand the coverage. But surely that could have been done in a clearer fashion than the map?

Credit for the piece goes to Jeremy Scott Diamond, Zachary Tracer, and Chloe Whiteaker.

Predicting the Electoral College

Well the Democratic DC primaries were last Tuesday and Hillary Clinton won. So now we start looking ahead towards the July conventions and then the November elections. Consequently, if a day is an eternity in politics we have many lifespans to witness before November. But that does not mean we cannot start playing around with electoral college scenarios.

The Wall Street Journal has a nice scenario prediction page that leads with the 2012 results map, in both traditional map and cartogram form. You can play god and flip the various states to either red or blue. But from the interaction side the designers did something really interesting. Flipping a state requires you to click and hold the state. But the speed with which it then flips is not equal for all states. Instead, the length of hold time depends upon the state’s likelihood to be a flippable state, based on the state’s partisan voter index. For example, if you try and flip Kansas, you will have to wait awhile to see the state turn blue. But try and flip North Carolina and the flip is near instantaneous.

Starting with the 2012 cartogram
Starting with the 2012 cartogram

While the geographic component remains on the right, the left-hand column features either text, or as in this other screenshot, smaller charts that illustrate the points more specifically.

Charts and cartograms and text, oh my
Charts and cartograms and text, oh my

Taken all together, the piece does a really nice job of presenting users with a tool to make predictions of their own. The different sections with concepts and analysis guide the user to see what scenarios fall within the realm of reason. But, what takes the cake is that flipping interaction. Using a delay to represent the likelihood of a flip is brilliant.

Credit for the piece goes to Aaron Zitner, Randy Yeip, Julia Wolfe, Chris Canipe, Jessia Ma, and Renée Rigdon.

The History and Future of Data Visualisation

From time to time in my job I hear the desire or want for more different types of charts. But in this piece by Nick Brown over on Medium, we can see that there are really only a few key forms and some are already terrible—here’s looking at you, pie charts. How new are some of these forms? Turns out most are not that new—or very new depending on your history/timeline perspective. Brown illustrated that timeline by hand.

A timeline of chart forms
A timeline of chart forms

Worth the read is his thoughts on what is new for data visualisation and what might be next. No spoilers.

Credit for the piece goes to Nick Brown.

It’s All the Hex

If you have not noticed, lots of news sites are using a variant of the cartogram lately. Basically, the idea is that geographic maps have the limitation of accurately representing landmass. And that means small polities, e.g. Rhode Island or Belgium, that might be quite important are visibly not so much, because they are geographically small. These pseudo-cartograms sort of do the trick by making all polities the same size. The trade off? Geographic fidelity. Anyway, there is an intelligent piece worth reading over at the NPR blogs explaining the thought process going on there about why to use the form. (You may recall I wrote about a similar project for London boroughs back in February.)

Hex map
Hex map

Credit for the piece goes to Danny DeBelius.

Old Healthcare Policy Renewal

Let’s start this week off with cartograms. Sometimes I like the idea, sometimes not so much. Here is a case where I really do not care for the New York Times’ visualisation of the data. Probably because the two cartograms, a before and after of health policy renewals, do not really allow for a great side-by-side comparison. I imagine there is probably a way of condensing all of that information into a single chart or graphic component.

The before map
The before map

Credit for the piece goes to Keith Collins, Josh Katz, Katie Thomas, Archie Tse, and Karen Yourish.