One story I’m following on Tuesday night is Texas. The state’s early voting—still with Monday to go—has surpassed the state’s total 2016 vote. Polling suggests that early votes lean Biden due to President Trump’s insistence that his supporters vote in person on Election Day as he lies about the integrity of early and mail-in voting.
The Texas Tribune looked at what we know about that turnout and what it may portend for Tuesday’s results. And, to be honest, we don’t—and won’t—really know until the votes are counted. They put together a great piece that divided Texas counties into four groups (their terminology): big blue cities, fast-changing counties, solidly red territory, and border counties. They then looked at the growth in registered voters in those counties from 2016, and looked at how they voted in the 2016 presidential election (Hillary Clinton vs. Donald Trump) and the 2018 US Senate election (Beto O’Rourke vs. Ted Cruz).
The piece uses the above stacked bar chart to show that Texas’ 1.8 million new registered voters’ largest share belongs to the big blue cities. The second largest group is the competitive suburbs in the fast-changing counties. The third largest, though quite close to second, was the solidly red territory. The border counties, still important for the margins, ranks a distant fourth.
I’m not normally a fan of stacked bar charts, because they do not allow for great comparisons of the constituent elements. For example, try comparing any of of those solidly red territory counties to one another. But here, the value is more in the stacked set as a group rather than the decomposition of the set, because you can see how the big blue cities have, as a group, a greater number of those 1.8 million new voters.
Those fast-changing counties include a lot of the suburbs for Texas’ largest cities. And those are areas where, across the country, Republicans are losing voters by the tens of thousands to the Democrats. As battlegrounds, these presented a challenge, because as swing counties, they split their votes between Clinton and Cruz and Trump and Beto. And so the designers chose purple to represent them in the stacked design. I think it’s a solid choice and works really well here.
But in terms of the story, I’ll add that in 2016, Trump won Texas by 807,000 votes. Texas added 1,800,000 new voters since then. And turnout before Election Day is already greater than it was in 2016.
It’s still a state likely to go for Trump on Tuesday. But, if Biden has a good night, it’s not inconceivable that Texas flips. FiveThirtyEight’s polling average has Trump with only a 1.2 point lead.
Credit for the piece goes to Mandi Cai, Darla Cameron and Anna Novak.
Last night I published a graphic on Instagram that I think people may find helpful if they try to follow Election Day results on Tuesday. I wanted to explain the concept of a red shift or blue shift. (I’ve also seen it described as states having a red mirage or a blue mirage.)
For my non-American readers, it’s important to understand that while this is a national election, the United States’ federal system means that each state runs its own election with its own rules and they can vary some state to state. For example, early or mail-in voting can vary significantly from state to state with some states allowing it only in emergencies (and some of those this cycle will not allow people to cite COVID-19 as an emergency).
Another factor for everyone to consider is that polling indicates President Trump’s fraudulent messaging about, well, voting fraud has shifted a normally split use of early/mail-in voting to a Democratic advantage. In other words, Democrats are far more likely to vote early, either in person or by post. Republicans are far more likely to vote on Election Day.
Combine those two factors and we get Red Shift vs. Blue Shift.
Some states allow election officials to begin counting their early votes prior to Election Day. Other states forbid counting until Election Day morning, or in some cases until after the polls close.
In states where early votes can be counted—the swing states Arizona, Florida, and North Carolina are among this group—it is possible that when the polls close, or shortly thereafter, we will see an instant and enormous lead for Joe Biden. But, as the states begin to count in-person day-of votes, which again favour Republicans, Trump may begin to eat into those margins. The question will be, can Trump’s numbers eat in so much that when the final counts are complete, he can overtake those Biden numbers? This is the Red Shift.
Conversely we have the Blue Shift. In these states—swing states like Georgia, Michigan, Pennsylvania, Texas, and Wisconsin are in this group—election officials cannot begin to count early votes either until the morning or when the polls close. In these states we may see the in-person day-of votes, largely expected to be for Republicans, run up to high totals fairly quickly. At that time, Trump may have a significant lead. Then when officials pivot to counting the early votes, Biden will begin to eat into those margins. And again, the question will be, can Biden eat into those margins sufficiently to shift the outcome after all the votes are counted?
Be prepared to hear about these scenarios Tuesday night.
In case you weren’t aware, the US election is in less than a week, five days. I had written a long list of issues on the ballot, but it kept getting longer and longer so I cut it. Suffice it to say, Americans are voting on a lot of issues this year. But a US presidential election is not like many other countries’ elections in that we use the Electoral College.
For my non-American readers, the Electoral College, very briefly, was created by the country’s founding fathers (Washington, Jefferson, Adams, Franklin, et al.) to do two things. One, restrict selection of the American president to a class of individuals who theoretically had a broader/deeper understanding of the issues—but who also had vested interests in the outcome. The founders did not intend for the American people to elect the president. The second feature of the Electoral College was to prevent the largest states from dominating smaller states in elections. Why else would Delaware and Rhode Island surrender their sovereignty to join the new United States if Virginia, Pennsylvania, and New York make all the decisions? (The founders went a step further and added the infamous 3/5 clause, but that’s another post.)
So Americans don’t elect the president directly and larger states like California, New York, and Texas, have slightly less impact than smaller states like Wyoming, Vermont, and Delaware. Each state is allotted a number of Electoral College votes and the key is to reach 270. (Maybe another time I’ll get into the details of what happens in a 269–269 tie.) Many Americans are probably familiar with sites like 270 To Win, where you can determine the outcome of the election by saying who won each state. But, even though the US election is really 50 different state elections, common threads and themes run through all those states and if one candidate or another wins one state, it makes winning or losing other states more or less likely. FiveThirtyEight released a piece that attempts to link those probabilities and help reveal how decisions voters in one state make may reflect on how other voters decide.
The interface is fairly straightforward—I’m looking at this on a desktop, though it does work on mobile—with a bunch of choices at the top and a choropleth map below. There we have a continually divergent gradient, meaning the states aren’t grouped into like bins but we have incredibly subtle differences between similar states. (I should also point out that Maine and Nebraska are the two exceptions to my above description of the Electoral College. They divide their votes by congressional district, whoever wins the district gets that Electoral College vote and then the state overall winner receives the remaining two votes.)
Below that we have a bar chart, showing each state, its more/less likely winner state and the 270 threshold. Below that, we have what I’ve read/heard described as a ball plot. It represents runs of the simulation. As of Thursday morning, the current FiveThirtyEight model says Trump has an 11 in 100 chance of winning, Biden, conversely, an 89-in-100 chance.
But what happens when we start determining the winners of states?
Well, for my non-American readers, this election will feature a large number of voters casting their ballots early. (I voted early by mail, and dropped my ballot off at the county election office.) That’s not normal. And I cannot emphasise this next point enough. We may not know who wins the election Tuesday night or by the time Americans wake up on Wednesday. (Assuming they’re not like me and up until Alaska and Hawaii close their polls. Pro-tip, there’s a potentially competitive Senate race in Alaska, though it’s definitely leaning Republican.)
But, some states vote early and/or by mail every year and have built the infrastructure to count those votes, or the vast majority of them, on or even before Election Day. Three battleground states are in that group: Arizona, Florida, and North Carolina. We could well know the result in those states by midnight on Election Day—though Florida is probably going to Florida.
So what happens with this FiveThirtyEight model if we determine the winners of those three states? All three voted for Trump in 2016, so let’s say he wins them again next week.
We see that the states we’ve decided are now outlined in black. The remainder of the states have seen their colours change as their odds reflect the set electoral choice of our three states. We also now have a rest button that appears only once we’ve modified the map. I’m also thinking that I like FiveyFox, the site’s new mascot? He provides a succinct, plain language summary of what the user is looking at. At the bottom we see what the model projects if Arizona, Florida, and North Caroline vote for Trump. And in that scenario, Trump wins in 58 out of 100 elections, Biden in only 41. Still, it’s a fairly competitive election.
So what happens if by midnight we have results from those three states that Biden has managed to flip them? And as of Thursday morning, he’s leading very narrowly in the opinion polls.
Well, the interface hasn’t really changed. Though I should add below this screenshot there is a button to copy the link to this outcome to your clipboard if, like me, you want to share it with the world or my readers.
As to the results, if Biden wins those three states, Trump has less than a 1-in-100 chance of winning and Biden a greater than 99-in-100.
This is a really strong piece from FiveThirtyEight and it does a great job to show how states are subtly linked in terms of their likelihood to vote one way or the other.
Credit for the piece goes to Ryan Best, Jay Boice, Aaron Bycoffe and Nate Silver.
I’m not working for a good chunk of the next few days. But, I did want to share with my readers an analysis of Pennsylvania’s missing votes. Broadly, Trump needs to win the Commonwealth of Pennsylvania next week—yes, the US election is now one week away. Though, Pennsylvania allows mail-in ballots postmarked on Election Day to arrive within a few days and still be counted. So we may not have final tallies for the state until the weekend or Monday after Election Day.
Pennsylvania, of course, narrowly voted for Donald Trump over Hillary Clinton in 2016 with 44,000+ votes making the difference. In 2020, polling has consistently placed Joe Biden above Donald Trump by 5+ points. But, can Trump again pull off an upset victory?
I argue that yes, he can. And fairly easily too. (If you want to see why I think Pennsylvania is really Trumpsylvania, I recommend checking out my longer, more in-depth analysis.) So where would the votes come from? I mapped the 2016 difference between votes cast and registered voters, i.e. people who could have voted, but did not for whatever reason. I then coloured the map by the county’s winner in 2016. Red counties voted for Trump by more than 10 points and blue for Clinton by more than 10 points. The purple counties are those that were competitive, plus or minus 10 points for either candidate.
In the purple counties, both candidates will want to drive out as many voters as possible. But in the blue counties, Biden has reliably Democratic votes and in red Trump has reliably Republican votes. So why on Monday did Trump visit Allentown, Lititz, and Martinsburg? Because that’s where those votes are.
Allentown, in Lehigh County, is competitive. In fact, neighbouring Northampton Co. will be a key swing county next week and one I will be following closely as the returns come in. But Lititz, Lancaster Co., and Martinsburg, Blair Co., are in reliably red counties. (Though in my Trumpsylvania piece I argue Lancaster Co. is undergoing a transition to a competitive, albeit lean Republican county.)
In Lancaster Co., which went to Trump by nearly 20 percentage points in 2016, there were still just short of 100,000 voters who didn’t vote in 2016. Not all of those voters would have voted for Trump, but for sake of argument, just say 50% would have. That makes just short of 50,000 potential Trump votes—more than Trump’s entire state margin.
Blair Co. is in the Pennsyltucky region of the state, relatively rural, but in Blair’s case, its county seat Altoona is the state’s 10th largest city. While the total number of votes—and the total number of non-voting voters—are smaller than in Lancaster Co., add up all the available votes and it’s a large number.
If you add up all those red counties’ missing votes, you get a total of just shy of 840,000 missing votes. Far more than enough to drastically swing the Commonwealth to Trump in 2020.
Of course, Biden’s counting on driving out turnout in Philadelphia and Pittsburgh and their suburbs, along with other cities in the state, like Allentown, Scranton, Harrisburg, and Erie. In those blue counties, there were 927,000 missing votes, so the potential for a Biden win is also there.
But, if Democratic voters don’t vote again in 2016, Trump has plenty of potential votes to pick up across the state.
Just before Halloween, NBC News published an article by political analyst David Wasserman that examined what airports could portend about the 2020 American presidential election. For those interested in politics and the forthcoming election, the article is well worth the read.
The tldr; Democrats have been great at winning over cosmopolitan types in global metropolitan areas in the big blue states, e.g. New York and California. But the election will be won in the states where the metropolitan areas that sport regional airports dominate, i.e. Pennsylvania, Michigan, Wisconsin, and North Carolina. And in those districts, support for Democrats is waning.
The closing line of the piece sums it up nicely:
…to beat Trump, Democrats will need to ask themselves which candidates’ proposals will fly in Erie, Saginaw and Green Bay.
But what about the graphics?
We have a line chart that shows how support for Democrats has been increasing amongst those in the global and international airport metros.
It uses four colours and I don’t necessarily love that. However, it smartly ties into an earlier graphic that did require each series to be visualised in a different colour. And so here the consistency wins out and carries on through the piece. (Though as a minor quibble I would have outlined the MSA being labelled instead of placing a dot atop the MSA.)
The kicker, however is one of those maps with trend arrows. It shows the increasing Republican support by an arrow anchored over the metropolitan area.
The problem here is many-fold. First, the map is actually quite small in the overall piece. Whereas the earlier maps sit centred, but outside the main text block, this fits neatly within the narrow column of text (on a laptop display at least). That means that these labels are all crowded and actually make it more difficult to realise which arrow is which city. For example, which line is Canton, Ohio? Additionally with the labels, because they are set in black text and a relatively bolder face, they standout more than the red lines they seek to label. Consequently, the users’ focus falls not on the lines, but actually on the labels—the reverse of what a good graphic should do.
Second, length vs. angle. If all lines moved away from their anchor at the same angle, we could simply measure length and compare the trending support that way. However, it is clear from Duluth and Green Bay that the angles are different in addition to their sizes. So how does one interpret both variables together?
Third, I wonder if the map would not have been made more useful with some outlines or shading. I may know what the forthcoming battleground states are. And I might know where they are on a map. But Americans are notorious for being, well, not great when it comes to geography. A simple black outline of the states could have been useful, though it in this design would have conflicted with the heavy black labelling of the arrows. Or maybe a purple shading could have been used to show those states.
Overall, the piece is well worth a read and the graphics generally help tell the narrative visually. But that final graphic could have used a revision or two.
Credit for the piece goes to Jiachuan Wu and Jeremia Kimelman.
This is not exactly data visualisation or graphic design. But it made me laugh the other day. And since we all made it to Friday, we could all do for a good laugh. Classify this under my interest in branding and visual identities.
Two weeks ago President Trump gave a speech at a conference for young conservatives. Uncontroversially, the organisation hosting the event projected on the screen an image of the seal of the President of the United States.
Or did they?
According to the report from the New York Times, it turns out some careless audiovisual guy lifted the wrong image from the internet. Instead of the presidential seal, he took an anti-Trump merchandise image.
He was fired.
So remember, properly source your images. A Google search isn’t the solution.
Happy Friday, all.
Credit for the imitation piece goes to Charles Leazott. I have no idea who designed the original seal.
Tuesday night/Wednesday morning, the New York Times broke the story that they had some of President Trump’s tax return information. For decades now, US presidents and candidates for that office have released their tax returns for the public to inspect. Trump has refused, often claiming that they are under audit from the IRS and then adding, and falsely claiming, they cannot be released whilst under audit. Consequently, when the Times publishes an article at the secret world of Trump’s finances, it’s a big news thing.
Unfortunately, the Times only had access to what are essentially summary transcripts of the returns. And only for a period in the mid-1980s through mid-1990s. So we cannot get the granular data and make deeper insights. But what we did get was turned into this bold graphic in the middle of the article.
Conceptually, there is not much to say. The bar charts are a solid choice to represent this kind of data. Red makes sense given the connotation of “being in the red”. And the annotations providing quotes from Trump about his finances for the years highlighted provide excellent context.
What the screenshot does not truly capture, however, is the massiveness of the chart in the context of the rest of the article. It’s big, bold, and red. That design choice instead of, say, making it a smaller sidebar-like graphic, goes a long way in hitting home the sheer magnitude of these business losses.
Sometimes it’s not always fancy and shiny charts that garner the most attention. Sometimes an old staple can do wonders.
Credit for the piece goes to Rich Harris and Andrew Rossback.
About a week and a half ago the Economist published an article about the retaliatory actions of the European Union and China against the tariffs imposed by the Trump administration. Of course last week we had a theme of sorts with lineages and ancestry. So this week, back to the fun stuff.
What makes today’s piece particularly relevant is that over the weekend, Trump announced he might increase the tariffs proposed, but not yet implemented, upon Chinese goods. So some economists looked at the retaliatory tariffs proposed by the EU and China.
Each targets Trump voters, albeit of different types. But China appears more willing to engage in a brutal fight. Its tariff proposal would not just harm Trump voters, but would also harm Chinese citizens. The EU’s plan appears tailored to maximise the pain on Trump voters, but minimise that felt by its own citizens.
A few minor points. I like how the designers chose to highlight high impact categories with colour. Lower impact shares are two shades of light grey. But after that, the scale changes. I wonder how the maps would compare if each had been set to the same scale. It looks doable as the bottom range of the maximum bin is 6% for the EU and 8% for China. (Their high limit is much higher at 22% compared to the EU’s 10%.)
That said, it does a good job of showing the different geographic footprints of the two retaliatory tariff packages. Tomorrow—barring breaking news—we will look at why that is important.
Credit for the piece goes to the Economist Data Team.
So today’s piece is not a revolutionary piece of information design, but it is fascinating. For two or so years now, we have all heard about the Robert Mueller investigation into potential contacts between the Trump campaign, early administration, and the administration of Russian president Vladimir Putin.
To be clear, thus far, this has been an incredibly productive special counsel.
34: the number of indictments
6: guilty pleas from associates of the Trump campaign
But what happens when the whole thing is done, especially since prevailing Justice Department rules state sitting presidents cannot be indicted? Well to answer that, we have this piece from the Washington Post.
Ultimately it is nothing more than a flow chart broken into pieces, separated by a textual narrative explaining the process. Now, I’m not certain how critical to the design each headshot is—especially Barr’s that looks especially frowny faced. However, the context in the above screenshot is crucial. The public does not necessarily have the right to the findings of the report if individuals in the report are not charged.
This means that design wise, we are looking at snippets of a larger chart interspersed with text. I would be interested to see the entire thing stitched together, but the textual breaks make a lot of sense. Overall, much like the sports pieces we looked at recently, this does a nice job of weaving textual story together with information design or data-driven content.
Credit for the piece goes to Dan Keating and Aaron Steckelberg.
Yesterday the New York Times published a fascinating piece looking at the data on how often President Trump has gone after the Special Counsel’s investigation. (Spoiler: over 1100 times.) It makes use of a number of curvy line charts showing the peaks of mentions of topics and people, e.g. Jeff Sessions. But my favourite element was this timeline.
It’s nothing crazy or fancy, but simple small multiples of a calendar format. The date and the month are not particular important, but rather the frequency of the appearances of the red dots. And often they appear, especially last summer.
Credit for the piece goes to Larry Buchanan and Karen Yourish.