Remember how just last week I posted a graphic about the number of under-18 year olds killed by under-18 year olds? Well now we have an 18-year-old shooting up an elementary school killing 19 students and two teachers. Legally the alleged shooter, Salvador Ramos, is an adult given his age. But he was also a high-school student, reportedly more of a loner type. Legally an adult, perhaps, but I’d argue still more of a child. At least a young adult.
Well, as I noted above, here we are again, kids killing kids. With guns!
And it does look like it correlates with those state with more liberal gun laws, including Texas.
If you keep doing the same thing, but expect different results…
Last month, a 2-year old shot and killed his 4-year old sister whilst they sat in a car at a petrol station in Chester, Pennsylvania, a city just south of Philadelphia.
Not surprisingly some people began to look at the data around kid-involved shootings. One such person was Christopher Ingraham who explored the data and showed how shootings by children is up 50% since the pandemic. He used two graphics, one a bar chart and another a choropleth map.
The map shows where kid-involved shootings have occurred. Now what’s curious about this kind of a map is that the designer points out that toddler incidents are concentrated around the Southeast and Midwest. And that appears to be true, but some of the standouts like Ohio and Florida—not to mention Texas—are some of the most populated states in the country. More people would theoretically mean more deaths.
So if we go back to the original data and then grab a 2020 US Census estimate for the under-18 population of each state, I can run some back of the envelope maths and we can take a look at how many under-18 deaths there had been per 100,000 under-18 year-olds. And that map begins to look a little bit different.
If anything we see the pattern a bit more clearly. The problem persists in the Southeast, but it’s more concentrated in what I would call the Deep South. The problem states in the Midwest fade a bit to a lower rate. Some of the more obvious outliers here become Alaska and Maine.
As the original author points out, some of these numbers likely owe to lax gun regulation in terms of safe storage and trigger locks. I wonder if the numbers in Alaska and Maine could be due to the more rural nature of the states, but then we don’t see similar rates of kid deaths in places like Wyoming, Montana, and Idaho.
Credit for the original piece goes to Christopher Ingraham.
This past weekend the United States surpassed one million deaths due to Covid-19. To put that in other terms, imagine the entire city of San Jose, California simply dead. Or just a little bit more than the entire city of Austin, Texas. Estimates place the number of those infected at about 80 million. Back of the envelope maths puts that fatality rate at 1.25%. That’s certainly lower than earlier versions of the virus, which has evolved to be more transmissible, but thankfully less lethal than its original form.
Sunday morning I opened the door to my flat and found the Sunday edition of the New York Times waiting for me with a sobering graphic not just above the fold, nor across the front page. No, the graphic—a map where each dot represents one Covid-19 death—wrapped around the entire paper.
You don’t need to do much more here. Black and white colour sets the tone simply enough. Of course, a bit more critically, these maps mask one of the big issues with the geographic spread of not just this virus but many other things: relatively few people live west of the Mississippi River.
Enormous swathes of the plains and Rocky Mountains have but few farmers and ranchers living there. Most of the nation’s populous cities are along the coast, particularly the East Coast, or along rivers or somewhat arbitrary transport hubs. You can see those because this map does not actually plot the locations of individual deaths, but rather fills county borders with dots to represent the deaths that occurred within those limits. That’s why, particularly west of the Mississippi, you see square-shaped concentrations of deaths.
A choropleth map that explores deaths per capita, that is after adjusting for population, shows a different story. (This screenshot comes from the New York Times‘ data centre for Covid-19.
The story here is literally less black and white as here we see colours in yellows to deep burnt crimsons. Whilst the big map yesterday morning concentrated deaths in the Northeast, West Coast, and around Chicago we see here that, relative to the counties’ populations, those same areas fared much better than counties in the plains, Midwest, and Deep South.
A quick scan of the Northeast and Mid-Atlantic states shows that only one county, Juniata in Pennsylvania, fell into the two worst deaths per capita bins—the deeper reds. Juniata County sits squarely in the middle of Pennsyltucky or Trumpsylvania, where Covid countermeasures were not terribly popular. No other county in the region shares that deep red.
Look to the southeast and south, however, and you see lots of deep and burnt crimsons dotting the landscape. This doesn’t mean people didn’t die in the Northeast, because of course they did. Rather, a greater percentage of the population died elsewhere when, as the policies enacted by the Northeast and West Coast show, they didn’t need to.
After all, injecting bleach was never a good idea.
Last night we had breaking news on two very big fronts. The first is that somebody inside the Supreme Court leaked an entire draft of the majority opinion, written by Justice Alito, to Politico. Leaks from inside the Supreme Court, whilst they do happen, are extremely rare. This alone is big news.
But let’s not bury the lede, the majority opinion is to throw out Roe v. Wade in its entirety. For those not familiar, perhaps especially those of you who read me from abroad, Roe v Wade is the name of a court case that went before the United States Supreme Court in 1971 and was decided in 1973. It established the woman’s right to an abortion as constitutionally protected, allowing states to enact some regulations to balance out the state’s role in concern for women’s public health and the health of the fetus as it nears birth. Regardless of how you feel about the issue—and people have very strong feelings about it—that’s largely been the law of the United States for half a century.
To be fair, the draft opinion is just that, a draft. And the supposed 5-3 vote—Chief Justice Roberts is reportedly undecided, but against the wholesale overthrow of Roe—could well change. But let’s be real, it won’t. And even if Roberts votes against the majority he would only make the outcome 5-4. In other words, it looks like at some point this summer, probably June or July, tens of millions of American women will lose access to reproductive healthcare.
And to the point of this post, what will that mean for women?
This article by Grid runs down some of the numbers, starting with laying out the numbers on who chooses to have abortions. And then ultimately getting to this map that I screenshot.
The map shows how far women in a state would need to travel for an abortion with Roe active as law and without. I’ve used the toggle to show without. Women in the south in particular will need to travel quite far. The article further breaks out distances today with more granularity to paint the picture of “abortion deserts” where women have to travel sometimes well over 200 miles to have a safe, legal abortion.
I am certain that we will be returning to this topic frequently in coming months, unfortunately.
Today we have an interesting little post, a choropleth map in a BBC article examining the changes occurring in the voting systems throughout the United States. Broadly speaking, we see two trends in the American political system when it comes to voting: make it easier because democracy; make it more restrictive because voter fraud/illegitimacy. The underlying issue, however, is that we have not seen any evidence of widespread or concerted efforts of voter fraud or problems with elections.
Think mail-in ballots are problematic? They’ve been used for decades without issues in many states. That doesn’t mean a new state could screw up the implementation of mail-in voting, but it’s a proven safe and valid system for elections.
Think that were issues of fraudulent voters? We had something like sixty cases brought before the courts and I believe in only one or two instances were the issues even remotely proven. The article cites some Associated Press (AP) reporting that identified only 500 cases of fraudulent votes. Out of over 14 million votes cast.
500 out of 14,000,000.
Anyway, the map in the article colours states by whether they have passed expansive or restrictive changes to voting. Naturally there are categories for no changes as well as when some expansive changes and some restrictive changes were both passed.
Normally I would expect to see a third colour for the overlap. Imagine we had red and blue, a blend of those colours like purple would often be a designer’s choice. Here, however, we have a hatched pattern with alternating stripes of orange and blue. You don’t see this done very often, and so I just wanted to highlight it.
I don’t know if this marks a new stylistic design direction by the BBC graphics department. Here I don’t necessarily love the pattern itself, the colours make it difficult to read the text—though the designers outlined said text, so points for that.
But I’ll be curious to see if I, well, see more of this in coming weeks and months.
Credit for the piece goes to the BBC graphics department.
When I was in the Berkshires, one thing I noticed was signs about bears. Bear crossing. Don’t feed the bears. Be beary careful. Okay, not so much the latter. But it was nonetheless odd to a city dweller like myself where I just need to be wary of giant rats.
Less than a month later, I read an article in the Boston Globe about how the black bear population in Massachusetts is expanding from the western and central portions of the state to those in the east.
The graphic in the article actually comes from the Massachusetts Division of Fisheries and Wildlife, so credit goes to them, but it shows the existing range and the black bears’ new range.
I understand the inclusion of the highways in red, green, and black, but I wish they had some even simple labelling. In the article they mention a few highways, but my familiarity with the highway system in Massachusetts is not great. Also, because the designer used thin black lines to demarcate the towns, one could think that the black lines, especially out west, represent counties or other larger political geography units.
Credit for the piece goes to the Massachusetts Division of Fisheries and Wildlife.
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 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.
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.)
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.
Today I want to highlight a print article from the New York Times I received about two weeks ago. It’s been sitting in a pile of print pieces I want to sit down, photograph, and then write up. But as we begin to return to normal, I need my second dining room chair back because at some point I’ll have guests over.
The article in question examined the rates of Covid-19 vaccination across the United States. And on the front page, above the fold no less, we can compare the vaccination rates for Covid-19 to those of the 2019–2020 flu and if you unfold it to its full-length glory we can add in the 2009–2010 H1N1 swine flu outbreak.
First thing I want to address is the obvious. Look at those colours. Who loves a green-to-red scale on a choropleth? Not this guy. They are a pretty bad choice because of green-to-red colour blindness. (There’s two different types as well as other types of colour blindness, but I’m simplifying here.) But here’s what happen when I pull the photo into Photoshop and test for it. (This is a screenshot, because I’m not aware of a means of exporting a proof image.)
You can still see the difference between the reds and greens. That’s good. And it’s because colour is complicated. In red-green colour blindness, the issue is sensitivity to picking up reds and greens. (Again, oversimplifying for the sake of a blog post.) Between those two colours in the spectrum we have yellow. To the other side of green we have blue.
So if a designer needs to use a red-green colour scheme—and any designer who has worked in data visualisation will have undoubtedly have had a client asking for the map/chart/whatever to be in red and green—there’s a trick to making it work.
I don’t know if this is true, but growing up, I learned that green was the one colour the human eye evolved to distinguish the most. Now for a print piece like this, you are working in what we call CMYK space (cyan, magenta, yellow, and black). Red is a mixture of magenta and yellow. Green a mixture of cyan and yellow. If you remember your school days, it’s similar to—but not the same as—mixing your primary colours. So if you need to make red and green work, what can you do? First, you can subtract a bit of yellow from your green, because that exists between red and green. But then, and this is why CMYK is different from your primary school primary colours, we can adjust the amount of magenta. Magenta is not a “pure” red, instead it’s kind of purplish and that means has some blue in it. Adding a little bit of magenta, while it does add “red” into the green, it’s also adding more blue to the blue present in the cyan. Now you can spend quite a bit of time tweaking these colours, but very quickly I can get these two options.
Great, you can still see them as both red and green. Your client is probably happy and probably accepts this greenish-blue as green, because we have that ability to distinguish so many types of green. But what about those with red-green colour blindness? Again, I can’t quite do a straight export, so the best is a screenshot, but we can compare those two options like so.
You can probably still tweak the green, but by going for that simple tweak, you can make the client happy—even though it’s still just better to avoid the red and green altogether—and still make the graphic work.
There’s a bit more to say about the rest of the article, which has some additional graphics inside. But that’ll have to wait for another day. As will clearing down the pile of print pieces to share, because that keeps on growing.
Credit for the piece goes to Lazaro Gamio and Amy Schoenfield Walker.
I’ve been trying to limit the amount of Covid-19 visualisations I’ve been covering. But on Sunday this image landed at my front door, above the fold on page 1 of the New York Times. And it dovetails nicely with our story about the pandemic’s impact on Pennsylvania, New Jersey, Delaware, Virginia, and Illinois.
For most of 2020, the United States was one of the worst hit countries as the pandemic raged out of control. Since January 2021, however, the United States has slowly been coming to grips with the virus and the pandemic. Its rate is now solidly middle of the pack—no longer is America first.
And if you compare the chart at the bottom to those that I’ve been producing, you can clearly see how our five states have really gotten this most recent wave under control to the point of declining rates of new cases.
However, you’ve probably heard the horror stories from India and Brazil where things are not so great. It’s countries like those that account for the continual increase in new cases at a global level.
Credit for the piece goes to Lazaro Gamio, Bill Marsh, and Alexandria Symonds.
Yesterday I wrote my usual weekly piece about the progress of the Covid-19 pandemic in the five states I cover. At the end I discussed the progress of vaccinations and how Pennsylvania, Virginia, and Illinois all sit around 25% fully vaccinated. Of course, I leave my write-up at that. But not everyone does.
This past weekend, the New York Times published an article looking at the correlation between Biden–Trump support and rates of vaccination. Perhaps I should not be surprised this kind of piece exists, let alone the premise.
From a design standpoint, the piece makes use of a number of different formats: bars, lines, choropleth maps, and scatter plots. I want to talk about the latter in this piece. The article begins with two side by side scatter plots, this being the first.
The header ends in an ellipsis, but that makes sense because the next graphic, which I’ll get to shortly, continues the sentence. But let’s look at the rest of the plot.
Starting with the x-axis, we have a fairly simple plot here: votes for the candidates. But note that there is no scale. The header provides the necessary definition of being a share of the vote, but the lack of minimum and maximum makes an accurate assessment a bit tricky. We can’t even be certain that the scales are consistent. If you recall our choropleth maps from the other day, the scale of the orange was inconsistent with the scale of the blue-greys. Though, given this is produced by the Times, I would give them the benefit of the doubt.
Furthermore, we have five different colours. I presume that the darkest blues and reds represent the greatest share. But without a scale let alone a legend, it’s difficult to say for certain. The grey is presumably in the mixed/nearly even bin, again similar to what I described in the first post about choropleths from my recent string.
Finally, if we look at the y-axis, we see a few interesting decisions. The first? The placement of the axis labels. Typically we would see the labelling on the outside of the plot, but here, it’s all aligned on the inside of the plot. Intriguingly, the designers took care for the placement—or have their paragraph/character styles well set—as the text interrupts the axis and grid lines, i.e. the text does not interfere with the grey lines.
The second? Wyoming. I don’t always think that every single chart needs to have all the outliers within the bounds of the plot. I’ve definitely taken the same approach and so I won’t criticise it, but I wonder what the chart would have looked like if the maximum had been 35% and the grid lines were set at intervals of 5%. The tradeoff is likely increased difficulty in labelling the dots. And that too is a decision I’ve made.
Third, the lack of a zero. I feel fairly comfortable assuming the bottom of the y-axis is zero. But I would have gone ahead and labelled it all the same, especially because of how the minimum value for the axis is handled in the next graphic.
Speaking of, moving on to the second graphic we can see the ellipsis completes the sentence.
We otherwise run into similar issues. Again, there is a lack of labelling on the x-axis. This makes it difficult to assess whether we are looking at the same scale. I am fairly certain we are, because when I overlap the graphics I can see that the two extremes, Wyoming and Vermont, look to exist on the same places on the axis.
We also still see the same issues for the y-axis. This time the axis represents vaccination rates. I wish this graphic made a little clearer the distinction between partial and full vaccination rates. Partial is good, but full vaccination is what really matters. And while this chart shows Pennsylvania, for example, at over 40% vaccinated, that’s misleading. Full vaccination is 15 points lower, at about 25%. And that’s the number that needs to be up in the 75% range for herd immunity.
But back to the labelling, here the minimum value, 20%, is labelled. I can’t really understand the rationale for labelling the one chart but not the other. It’s clearly not a spacing issue.
I have some concerns about the numbers chosen for the minimum and maximum values of the y-axis. However, towards the middle of the article, this basic construct is used to build a small multiples matrix looking at all 50 states and their rates of vaccination. More on that in a moment.
My last point about this graphic is on the super picky side. Look at the letter g in “of residents given”. It gets clipped. You can still largely read it as a g, but I noticed it. Not sure why it’s happening, though.
So that small multiples graphic I mentioned, well, see below.
Note how these use an expanded version of the larger chart. The y-minimum appears to be 0%, but again, it would be very helpful if that were labelled.
Also for the x-axis in all the charts, I’m not sure every one needs the Biden–Trump label. After all, not every chart has the 0–60% range labelled, but the beginning of each row makes that clear.
In the super picky, I wish that final row were aligned with the four above it. I find it super distracting, but that’s probably just me.
Overall, this is a strong piece that makes good use of a number of the standard data visualisation forms. But I wish the graphics were a bit tighter to make the graphics just a little clearer.
Credit for the piece goes to Danielle Ivory, Lauren Leatherby and Robert Gebeloff.