Depending upon where you live, autumn presents us with a spectacular tapestry of colour with bright piercing yellows, soft warm oranges, and attention-grabbing reds all situated among still verdant green grasses and calming blue skies. But this technicolour dreamcoat that drapes the landscape disappears after only a few weeks. For those that chase the colour, the leaf peepers, they need to know the best time to travel.
For that we have this interactive timeline/map from SmokyMountains.com. It’s pretty simple as far as graphics go. We have a choropleth map coloured by a county’s status from no change to past peak, when the colours begin to dull.
All the colour
The map itself is not interactive, i.e. you cannot mouse over a county and get a label or some additional information. But the time slider at the bottom does allow you to see the progression of colour throughout the autumn.
Normally, as my longtime readers know, I am not a fan of the traffic light colour palette: green to red. Here, however, it makes sense in the context of changing colours of plant leaves. No, not all trees turn red, some stay yellow. Broadly speaking, though, the colours make sense.
And to that end, the designers of the map chose their colours well, because this map avoids the issues we often see—or don’t—when it comes to red-green colour blindness. This being the reason why a default of green-to-red is a poor choice. Their green is distinct from the red, as these two proof colour screenshots show (thanks to Photoshop’s Proof Colour option).
ProtanopiaDeuteranopia
The choice isn’t great, don’t get me wrong. You can see how the green still falls into the shades of red. A blue would be a better choice. (And that’s why I always counsel people to stick to a blue-to-red palette.) Compare, for example, what happens when I add a massive Borg cube of blue to the area of Texas and Oklahoma—not that you have a choice, because resistance is futile.
A bit of blue
Here the blue is very clearly different than the reds. That makes it very distinct, but again, I think in the context of a map about the changing of leaf colours from greens to reds, a green-to-red map is appropriate. But only if, as these designers have, the colours are chosen so that the green can be distinguished from the reds.
As I always say, know the rules—don’t use red-to-green as one—so that you know the few instances when and where it’s appropriate to break them. As this map is.
Credit for the piece goes to the SmokyMountains.com
After twenty years out of power, the Taliban in Afghanistan are back in power as the Afghan government collapsed spectacularly this past weekend. In most provinces and districts, government forces surrendered without firing a shot. And if you’re going to beat an army in the field, you generally need to, you know, fight if you expect to beat them.
I held off on posting anything about the Taliban takeover of Afghanistan simply because it happened so quick. It was not even two months ago when they began their offensive. But whenever I started to prepare a post, things would be drastically different by the next morning.
And so this timeline graphic from the BBC does a good job of capturing the rapid collapse of the Afghan state. It starts in early July with a mixture of blue, orange, and red—we’ll come back to the colours a bit later. Blue represents the Afghan government, red the Taliban, and orange contested areas.
The start of the summer offensive
The graphic includes some controls at the bottom, a play/pause and forward/backward skip buttons. The geographic units are districts, sub-provincial level units that I would imagine are roughly analogous to US counties, but that’s supposition on my part. Additionally the map includes little markers for some of the country’s key cities. Finally in the lower right we have a little scorecard of sorts, showing how many of the nearly 400 districts were in the control of which group.
Skip forward five weeks and the situation could not be more different.
So much for 20 years.
Almost all of Afghanistan is under the control of the Taliban. There’s not a whole lot else to say about that fact. The army largely surrendered without firing a shot. Though some special forces and commando units held out under siege, notably in Kandahar where a commando unit held the airport until after the government fell only to be evacuated to the still-US-held Hamid Karzai International Airport in Kabul.
My personal thoughts, well you can blame Biden and the US for a rushed US exodus that looks bad optically, but the American withdrawal plan, initiated by Trump let’s not forget, counted on the Afghan army actually fighting the Taliban and the government negotiating some kind of settlement with the Taliban. Neither happened. And so the end came far quicker than anyone thought possible.
But we’re here to talk graphics.
In general I like this. I prefer this district-level map to some of the similar province-level maps I have seen, because this gives a more granular view of the situation on the ground. Ideally I would have included a thicker line weight to denote the provinces, but again if it’s one or the other I’d opt for district-level data.
That said, I’d probably have used white lines instead of black. If you look in the east, especially south and east of Kabul, the geographically small areas begin to clump up into a mass of shapes made dark by the black outlines. That black is, of course, darker than the reds, blues, and yellows. If the designers had opted for white or even a light shade of grey, we would enhance the user’s ability to see the district-level data by dropping the borders to the back of the visual hierarchy.
Finally with colours, I’m not sure I understand the rationale behind the red, blue, yellow here. Let’s compare the BBC’s colour choice to that of the Economist. (Initially I was going to focus on the Economist’s graphics, but last minute change of plans.)
Another day, more losses for the government
Here we see a similar scheme: red for the Taliban, blue for the government. But notably the designers coloured the contested areas grey, not yellow. We also have more desaturated colours here, not the bright and vibrant reds, blues, and yellows of the BBC maps above.
First the grey vs. yellow. It depends on what the designers wanted to show. The grey moves the contested districts into the background, focusing the reader’s attention on the (dwindling) number of districts under government control. If the goal is to show where the fighting is occurring, i.e. the contest, the yellow works well as it draws the reader’s attention. But if the goal is to show which parts of the country the Taliban control and which parts the government, the grey works better. It’s a subtle difference, I know, but that’s why it would be important to know the designer’s goal.
I’ll also add that the Economist map here shows the provincial capitals and uses a darker, more saturated red dot to indicate if they’d fallen to the Taliban. Contrast that with the BBC’s simple black dots. We had a subtler story than “Taliban overruns country” in Afghanistan where the Taliban largely did hold the rural, lower populated districts outside the major cities, but that the cities like the aforementioned Kandahar, Herat, Mazar-i-Sharif held out a little bit longer, usually behind commando units or local militia. Personally I would have added a darker, more saturated blue dot for cities like Kabul, which at the time of the Economist’s map, was not under threat.
Then we have the saturation element of the red and blue.
Should the reds be brighter, vibrant and attention grabbing or ought they be lighter and restrained, more muted? It’s actually a fairly complex answer and the answer is ultimately “it depends”. I know that’s the cheap way out, but let me explain in the context of these maps.
Choropleth maps like this, i.e. maps where a geographical unit is coloured based on some kind of data point, in this instance political/military control, are, broadly speaking, comprised of large shapes or blocks of colour. In other words, they are not dot plots or line charts where we have small or thin instances of colour.
Now, I’m certain that in the past you’ve seen a wall or a painting or an advert for something where the artist or designer used a large, vast area of a bright colour, so bright that it hurt your eyes to look at the area. I mean imagine if the walls in your room were painted that bright yellow colour of warning signs or taxis.
That same concept also applies to maps, data visualisation, and design. We use bright colours to draw attention, but ideally do so sparingly. Larger areas or fields of colours often warrant more muted colours, leaving any bright uses to highlight particular areas of attention or concern.
Imagine that the designers wanted to highlight a particular district in the maps above. The Economist’s map is better designed to handle that need, a district could have its red turned to 11, so to speak, to visually separate it from the other red districts. But with the BBC map, that option is largely off the table because the colours are already at 11.
Why do we have bright colours? Well over the years I’ve heard a number of reasons. Clients ask for graphics to be “exciting”, “flashy”, “make it sizzle” because colours like the Economist’s are “boring”, “not sexy”.
The point of good data visualisation, however, is not to make things sexy, exciting, or flashy. Rather the goal is clear communication. And a more restrained palette leaves more options for further clarification. The architect Mies van der Rohe famously said “less is more”. Just as there are different styles of architecture we have different styles of design. And personally my style is of the more restrained variety. Using less leaves room for more.
Note how the Economist’s map is able to layer labels and annotations atop the map. The more muted and desaturated reds, blues, and greys also allow for text and other artwork to layer atop the map but, crucially, still be legible. Imagine trying to read the same sorts of labels on the BBC map. It’s difficult to do, and you know that it is because the BBC designers needed to move the city labels off the map itself in order to make them legible.
Both sets of maps are strong in their own right. But the ultimate loser here is going to be the Afghan people. Though it is pretty clear that this was the ultimate result. There just wasn’t enough support in the broader country to prop up a Western style liberal democracy. Or else somebody would have fought for it.
Credit for the BBC piece goes to the BBC graphics department.
Credit for the Economist piece goes to the Economist graphics department.
First, I should say that I don’t have a lot to say about this graphic because I went back to the source because I was interested in another city and I wanted to compare the two. In other words, expect a small graphic follow up to this maybe tomorrow.
Anyways, over the last few years since returning to Philadelphia after eight years away in Chicago, I’ve had numerous conversations with different people about how “I don’t remember it always being this hot before”, which is particularly relevant as the Philadelphia region endures excessive heat. Thankfully, it’s not nearly as bad as the Pacific Northwest. Also I have air conditioning blasting next to me as I type this out, so, you know.
The common refrain in these conversations, however, tends to be less about how we have high temperatures and more about how it’s difficult to sleep at night. And there’s a reason for that as this article from the Philadelphia Inquirer explains, our average summer low temperatures are rising, and rising faster than our average summer high temperatures.
Yep, definitely getting warmer.
Of course you can probably already see where I was going with this. The Inquirer linked to their source and that’s where I’ve spent my time this morning, alas, I didn’t quite have enough to finish what I started and so this post will have to do.
Credit for the piece goes to the Philadelphia Inquirer graphics department.
It’s no secret that Americans—and likely at least Western communities more broadly—live in bubbles, one of which being our political bubbles. And so I want to thank one of my mates for sending me the link to this opinion piece about political bubbles from the New York Times.
The piece is fairly short, but begins with an interactive piece that allows you to plot your address and examine whether or not you live in a political bubble. Using my flat in Philadelphia, the map shows lots of little blue dots, representing Democratic voters, near the marker for my address and comparatively few red dots for Republicans.
An island of blue in a sea of red.
If you then look a bit more broadly, you can see that by summing up the dots, my geographic bubble is largely a political bubble, as only 13% of my neighbours are Republicans. Not terribly surprising for a Democratic city.
A certain lack of diversity in political thought.
And while the piece does then zoom back out a wee bit, it tries to show me that I don’t live too far from a politically integrated bubble. Except in this case, it’s across a decent sized river and getting there isn’t the easiest thing in the world. I’m not headed to Gloucester anytime soon.
Things are better in Jersey?
These interactives serve the purpose of drawing the user into the article, which continues explaining some of the causes of this political segregation, by both policy, redlining, and personal choice, lifestyle. The approach works, because it gives us the most relatable story in a large dataset, ourselves. We’re now emotionally or intellectually invested in the idea, in this case political bubbles, and want to learn all about it. Because the more you know…
The piece uses the same type of map to showcase the bubbles more broadly from the Bay Area to the plains of Wyoming. (No surprises in the nature of those political bubbles.) It wraps up by showing how politicians can use the geography of our political bubbles to create political geographies via gerrymandering that shore up their political careers by creating safe districts. The authors use a gerrymandered northeastern Ohio district that encompasses two cities, Cleveland and Akron, to make that point.
That’s in part why I’m in favour of apolitical, independent boundary commissions to create more competitive congressional districts. Personally, I would have been fascinated to see how Pennsylvania’s congressional districts, redrawn in 2018 by the Pennsylvania Supreme Court, after the court found the gerrymandered districts of 2011 unconstitutional, created political competition between parties instead of within parties. But I digress.
And then for kicks, I looked at how my flat in Chicago compared.
Less island of blue and sea of red, because a lake of blue water alters that geography.
Not surprisingly, my neighbourhood in Lakeview was another political bubble, though this one even more Democratic than my current one.
Lakeview is even more Democratic than Logan Square, Philly’s Logan Square that is.
But if I had wanted to move to an integrated political bubble, instead of Philadelphia, I could have moved to…Jefferson Park.
Because everyone can agree Polish food is good food.
Credit for the piece goes to Gus Wezerek, Ryan D. Enos and Jacob Brown.
Admittedly, I was trying to find a data set for a piece, but couldn’t find one. So instead for today’s post I’ll turn to something that’s been sitting in my bookmarks for a little while now. It’s a choropleth map from the US Census Bureau looking at population change between the censuses.
Unequal growth
The reason I have it bookmarked is for the apportionment map, but I will save apportionment for another post because, well, it’s complicated. But map colours are a thing we’ve been discussing of late and we can extend that conversation here.
What I find interesting about this map is how they used a very dark blue-grey colour for their positive growth and an orange that is a fair bit brighter for negative growth, or population loss. And because of that difference in brightness, the orange really jumps out at you.
To be fair, that’s ideal if you’re trying to talk about where state populations are shrinking, because it focuses attention on declines. But, if you’re trying to present a more neutral position, like this seems to be, that colour choice might not be ideal.
Another issue is that if you look at the legend it simply says loss for that orange. But, look above and you’ll see four bins clearly delimited by ranges of percents for the positive growth. If we are trying to present a more neutral story, the use of the orange places it visually somewhere near the top of that blue-grey spectrum.
If you look at the percentages, however, Michigan’s population decline was 0.6% and Puerto Rico’s 2.2%. If this map used a legend that treated positive and negative growth equally, you would place that one state and one should-be state in a presumably light orange. The scale of their negative growth is equal to something like Ohio, which is in the lightest blue-grey available.
Consequently, this map is a little bit misleading when it comes to negative growth.
Credit for the piece goes to the Census Bureau graphics team.
As many of my long-term readers know, I am really only a one sport kind of guy. And that sport is baseball. American football, well, I’ve seen one match live and in person and it was…boring. But it’s a big deal in America. And this is the time of the year when teams begin signing free agents.
I happened to be reading the Boston Globe for news on the Red Sox, my team, when I saw a link to this interactive tool allowing users to build their own roster with free agent signings.
Go Pats
Conceptually, the piece is fairly simple. There is a filterable list of free agents, broken out by whether their forecast signing values falls into the high-, middle-, or low-end of the range. Plus a draft pick.
I root for the Patriots. However, if you asked me to name a single player on last season’s roster, I could only name Cam Newton. Apparently he wasn’t great. I really and truly don’t follow the sport.
The piece displays the available free agents, along with those no longer available. (Though, the piece does offer you the option to go back to the beginning of free agent season and pretend reality didn’t happen.)
I have no idea who any of these people are.
I went through and began semi-randomly picking names. I’d heard of some of them, and others were blind choices. Once you’ve selected within the budget, you can choose a draft pick. They all appear in list format to the right with the ability to remove them via a small X button.
Nope, not a clue.
Once you’ve confirmed your choices you’re taken to a screen that reviews your selection. You are able to either tweet it to the world—which I did not do—or start over again. I would do that, but I wouldn’t do any better than how I just did.
I hope I did at least okay.
Overall, the piece felt intuitive and I never had any issues selecting my free agents. Of course, it would help if I knew anything about the sport. But that’s a user problem.
No two rivers are the same, though they certainly can be similar. Rivers have their own ecosystems and when I was at school, I learned of the different classifications of rivers by the colour of their water: black, white, and clear. Broadly speaking, that just means the amount of sediment dissolved in the river’s water. Black colours appear when slow moving water has absorbed lots from its environment, think swamps. White waters resemble tea or coffee with added milk or cream. This happens when sediments enter and dissolved into the water. Clear water is that, relatively clear and free of sediment.
But a team of scientists at University of North Carolina at Chapel Hill (UNC Chapel Hill) recently released some work where they used shifts in blue to yellow and green to help classify rivers. Their classification differs, but broadly can point to a change from healthy (blue) to unhealthy (yellow and green). The novelty in their work, however, focuses on using satellite imagery to capture the colour of rivers and their evolution since the mid 1980s.
A look at the broader lower-48 of the United States
They published their findings as an interactive application driven primarily by a clickable map. Clearly not all rivers are available, but a large number are, and you can see some obvious patterns at a national scale—their work excludes Alaska and Hawaii. If blue represents healthy rivers, we see healthy rivers in New England and the Pacific Northwest with a host of green rivers in the Mid-Atlantic and Upper Midwest with yellow in the Mississippi basin and southeast.
I wanted to look at Pennsylvania a bit more specifically given my familiarity with the Commonwealth and zoomed in a bit on the map.
The colour of Pennsylvania’s rivers
You can see that using that above scale, Pennsylvania’s rivers are in okay, not great state. Some of the upper stretches of the Delaware and Susquehanna Rivers are coloured blue, but we mostly see a lot of green.
To the right of the map, the designers placed three smaller charts driven by the user’s selection of river. Let’s take a look at the Juniata River as an example—my grandfather grew up living alongside a tributary that emptied into the Frankstown Branch just a short walk from his house.
A look at the Schuylkill River south of the Fairmount Water Works
We can see that the chart on the upper right shows the colour shift over the decades for that observed section of the river. The legend provides the information that the section of the river has shifting blue—gotten healthier—and then below it looks for any seasonal changes. Here the chart is grey, indicating the system lacks enough data for a clear trend. This examines the short changes we might see in a river based on seasonal effects like rainy season, dry season, and human-driven effects—perhaps we pollute more in the spring and then use rivers recreationally in the summer.
Finally a distribution of the river section’s colour, all in wavelengths of light.
My biggest critique here would be the wavelengths. Users likely will not the colour spectrum by wavelength, and adding some labels like blue, yellow, and green could go a long ways to help users understand at what they are looking.
Overall, though, this is a really fascinating project.
Yesterday we looked at an article from the Inquirer about the 2020 election and how Biden won because of increased margins in the suburbs. Specifically we looked at an interactive scatter plot.
Today I want to talk a bit about another interactive graphic from the same article. This one is a map, but instead of the usual choropleth—a form the article uses in a few other graphics—here we’re looking at three-dimensional pyramids.
All the pyramids, built by aliens?
Yesterday we talked about the explorative vs. narrative concept. Here we can see something a bit more narrative in the annotations included in the graphic. These, however, are only a partial win, though. They call out the greatest shifts, which are indeed mentioned in the text. But then in another paragraph the author writes about Bensalem and its rightward swing. But there’s no callout of Bensalem on the map.
But the biggest things here, pun intended, are those pyramids. Unlike the choropleth maps used elsewhere in the article, the first thing this map fails to communicate is scale. We know the colour means a county’s net shift was either Democratic or Republican. But what about the magnitude? A big pyramid likely means a big shift, but is that big shift hundreds of votes? Thousands of votes? How many thousands? There’s no way to tell.
Secondly, when we are looking at rural parts of Bucks, Chester, and Montgomery Counties, the pyramids are fine. They remain small and contained within their municipality boundaries. Intuitively this makes sense. Broadly speaking, population decreases the further you move from the urban core. (Unless there’s a secondary city, e.g. Minneapolis has St. Paul.) But nearer the city, we have more population, and we have geographically smaller municipalities. Compare Colwyn, Delaware County to Springfield, Bucks County. Tiny vs. huge.
In choropleth maps we face this problem all the time. Look at a classic election map at the county level from 2016.
Wayb ack when…
You can see that there is a lot more red on that map. But Hillary Clinton won the popular vote by more then 3,000,000 votes. (No, I won’t rehash the Electoral College here and now.) More people are crowded into smaller counties than there are in those big, expansive red counties with far, far fewer people.
And that pattern holds true in the Philadelphia region. But instead of using the colour fill of an area as above, this map from the Inquirer uses pyramids. But we face the same problem, we see lots of pyramids in a small space. And the problem with the pyramids is that they overlap each other.
At a glance, you cannot see one pyramid beind another. At least in the choropleth, we see a tiny field of colour, but that colour is not hidden behind another.
Additionally, the way this is constructed, what happens if in a municipality there was a small net shift? The pyramid’s height will be minimal. But to determine the direction of the shift we need to see the colour, and if the area under the line creating the pyramid is small, we may be unable to see the colour. Again, compare that to a choropleth where there would at least be a difference between, say, a light blue and light red. (Though you could also bin the small differences into a single neutral bin collecting all small shifts be them one way or the other.)
I really think that a more straight forward choropleth would more clearly show the net shifts here. And even then, we would still need a legend.
The article overall, though, is quite strong and a great read on the electoral dynamics of the Philadelphia region a month ago.
The thing with election results is that we don’t have the final numbers for a little while after Election Day. And that’s normal.
There are a few things I want to look at in the coming weeks and months once my schedule eases up a bit. But for now, we can use this nice piece from the Philadelphia Inquirer to look at a story close to home: the vote in the Philadelphia suburbs.
It’s all happening in the yellow.
I’ve already looked at some analysis like this for Wisconsin and I shared it on my social. But there I looked at the easy, county-level results. What the Inquirer did above is break down the Pennsylvania collar counties of Philadelphia, i.e. the suburbs, into municipality level results. It then plotted them 2020 vs. 2016 and the results were—as you can guess since we know the result—Biden beat Trump.
What this chart does well is colours the municipalities that Biden flipped yellow. It’s a great choice from a colour standpoint. As the third of the primaries, with both blue and red well represented, it easily contrasts with the Biden- and Trump-won towns and cities of the region. The colour is a bit “darker” than a full-on, bright yellow, but that’s because the designers recognised it needs to stand out on a white field.
Let’s face it, yellow is a great colour to use, but it’s difficult because it’s so light and sometimes difficult to see. Add just the faintest bit of black to your mix, especially if you’re using paints, and voila, it works pretty well. So here the designer did a great job recognising that issue with using yellow. Though you can still see the challenge, because even though it is a bit darker, look at how easy it is to read the text in the blue and the red. Now compare that to the yellow. So if you’re going to use yellow, you want to be careful how and when you do.
The other design decision here comes down to what I call the explorative vs. the narrative. Now, I don’t think explorative is a word—and the red squiggle agrees—but it pairs nicely with narrative. And I’ve been talking about this a lot in my field the last several works, especially offline. (In the non-blog sense, because obviously all my work is done online these days. Oh, how I miss my old office.)
Explorative works present the user with a data set and then allow them to, in this case, mouse over or tap on dots and reveal additional layers of information, i.e. names and specific percentages. The idea is not to tell a specific story, but show an overall pattern. And if the piece is interactive, as this is, potentially allow the user to drill down and tease out their own stories.
Compare that to the narrative, my Wisconsin piece I referenced above is more in this category. Here the work takes you through a guided tour of the data. It labels specific data points, be them on trend or outliers and is sometimes more explicit in its analysis. These can also be interactive—though my static image is not—and allow users to drill down, and critically away, from the story to see dots of interest, for example.
This piece is more explorative. The scatter plot naturally divides the municipalities into those that voted for Biden, Trump, and then more or less than they voted for Trump in 2016. The labels here are actually redundant, but certainly helpful. I used the same approach in my Wisconsin graphic.
But in my Wisconsin graphic, I labelled specific counties of interest. If I had written an accompanying article, they would have been cited in the textual analysis so that the graphic and text complemented each other. But here in the Inquirer, it’s a bit of a missed opportunity in a sense.
The author mentions places like Upper Darby and Lower Merion and how they performed in 2020 vis-a-vis 2016. But it’s incumbent on the user to find those individual municipalities on the scatter plot. What if the designer had created a version where the towns of interest were labelled from the start? The narrative would have been buttressed by great visualisations that explicitly made the same point the author wrote about in the text. And that is a highly effective form of communication when you’re not just telling, but also showing your story or argument.
Overall it’s a great article with a lot to talk about. Because, spoiler, I’m going to be talking about it again tomorrow.
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.
