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.
The National Oceanic and Atmospheric Administration (NOAA) released its 2022 report, Sea Level Rise Technical Report, that details projected changes to sea level over the next 30 years. Spoiler alert: it’s not good news for the coasts. In essence the sea level rise we’ve seen over the past 100 years, about a foot on average, we will witness in just thirty years to 2050.
Now I’ve spent a good chunk of my life “down the shore” as we say in the Philadelphia dialect and those shore towns will all have a special place in my life. But that looks more to be like a cherished memory fading into time. I took a screenshot of the Philadelphia region and South Jersey in particular.
To be fair, that big blob of blue is Delaware Bay. That’s already the inlet to the Atlantic. But the parts that ought to disturb people are just how much blue snakes into New Jersey and Delaware, how much/little space there is between those very small ribbons of land land off the Jersey coast.
You can also see little blue dots. When the user clicks on those, the application presents the user with a small interactive popup that models sea level rise on a representative photograph. In this case, the dot nearest to my heart is that of the Avalon Dunes, with which I’m very familiar. As the sea level rises, more and more of the street behind protected by the dunes disappears.
My only real issue with the application is how long it takes to load and refresh the images every single time you adjust the zoom or change your focus. I had a number of additional screenshots I wanted to take, but frankly the application was taking too long to load the data. That could be down to a million things, true, but it frustrated me nonetheless.
Regardless of my frustration, I do highly recommend you check out the application, especially if you have any connection to the coast.
Earlier this week marked the 70th anniversary of Queen Elizabeth’s accession to the throne of the United Kingdom and many Commonwealth realms. There are many graphics about the length of her reign and the numerous prime ministers and presidents she has met over the years. But I actually enjoyed this article from the BBC as it dovetails nicely with my interest in genealogy, which frequently looks at the same sort of materials.
In genealogy we often want to find photos, illustrations, or really any kind of documentation that ties an ancestor to a particular place at a particular time. What I never realised is that the birthplace of Her Majesty, the Queen, no longer exists.
It kind of makes sense, however, when you consider that as the daughter of the younger son she was never expected to take the throne. When her uncle abdicated, however, her father took the throne and then she became next in line and we all know the rest. But because of that lack of expectation her birthplace was just another London townhome. The article details how development changed the location, not the Blitz as is often thought.
You can see from the screenshot above how the article uses a slider device to compare the neighbourhood in London today vs. what it was in 1895, about 30 years before the Queen’s birth.
At this point we’re all familiar with sliders, but they do work really well when it comes to this kind of before-after comparison.
Credit for the piece goes to the BBC graphics department.
This is an older piece that I stumbled across doing some other work. I felt like it needed sharing. The interactive graphic shows the high and low note vocal ranges of major musical artists.
Interactive controls allow the user to sort the bars by the greatest vocal range, high notes, or low notes. Colour coding distinguishes male from female vocalists.
In particular I enjoy the bottom of the piece that uses the keyboard to show the range of notes. When the user mouses over a particular singer, the ends of the range display the particular song in which the singer hit the note.
Again, this is an older piece that I just discovered, but I did enjoy it. I would be curious to see how these things could change over time. As an artist ages, how does that change his or her vocal range? Are there differences between albums? This could be a fascinating point at which branching out for further research could be done.
On Friday, I mentioned in brief that the East Coast was preparing for a storm. One of the cities the storm impacted was Boston and naturally the Boston Globecovered the story. One aspect the paper covered? The snowfall amounts. They did so like this:
This graphic fails to communicate the breadth and literal depth of the snow. We have two big reasons for that and they are both tied to perspective.
First we have a simple one: bars hiding other bars. I live in Greater Centre City, Philadelphia. That means lots of tall buildings. But if I look out my window, the tall buildings nearer me block my view of the buildings behind. That same approach holds true in this graphic. The tall red columns in southeastern Massachusetts block those of eastern and northeastern parts of the state and parts of New Hampshire as well. Even if we can still see the tops of the columns, we cannot see the bases and thus any real meaningful comparison is lost.
Second: distance. Pretty simple here as well, later today go outside. Look at things on your horizon. Note that those things, while perhaps tall such as a tree or a skyscraper, look relatively small compared to those things immediately around you. Same applies here. Bars of the same data, when at opposite ends of the map, will appear sized differently. Below I took the above screenshot and highlighted two observations that differed in only 0.5 inches of snow. But the box I had to draw—a rough proxy for the columns’ actual heights—is 44% larger.
This map probably looks cool to some people with its three-dimensional perspective and bright colours on a dark grey map. But it fails where it matters most: clearly presenting the regional differences in accumulation of snowfall amounts.
Compare the above to this graphic from the Boston office of the National Weather Service (NWS).
No, it does not have the same cool factor. And some of the labelling design could use a bit of work. But the use of a flat, two-dimensional map allows us to more clearly compare the ranges of snowfall and get a truer sense of the geographic patterns in this weekend’s storm. And in doing so, we can see some of the subtleties, for example the red pockets of greater snowfall amounts amid the wider orange band.
Credit for the Globe piece goes to John Hancock.
Credit for the NWS piece goes to the graphics department of NWS Boston.
Yesterday we looked at a piece by the Boston Globe that mapped out all of David Ortiz’s home runs. We did that because he has just been voted into baseball’s Hall of Fame. But to be voted in means there must be votes and a few weeks after the deadline, the Globe posted an article about how that publication’s eligible voters, well, voted.
The graphic here was a simple table. But as I’ll always say, tables aren’t an inherently bad or easy-way-out form of data visualisation. They are great at organising information in such a way that you can quickly find or reference specific data points. For example, let’s say you wanted to find out whether or not a specific writer voted for a specific ballplayer.
Simple red check marks represent those players for whom the Globe’s eligible staff voted. I really like some of the columns on the left that provide context on the vote. For the unfamiliar, players can only remain on the list for up to ten years. And so for the first four, this was their last year of eligibility. None made the cut. Then there’s a column for the total number of votes made by the Globe’s staff. Following that is more context, the share of votes received in 2021. Here the magic number if 75% to be elected. Conversely, if you do not make 5% you drop off the following year. Almost all of those on their first year ballot failed to reach that threshold.
The only potential drawback to this table is that by the time you reach the end of the table, there are few check marks to create implicit rules or lines that guide you from writer to player. David Ortiz’s placement helps because six—remarkably not all Globe writers voted for him—it grounds you for the only person below him (alphabetically) to receive a vote. And we need that because otherwise quickly linking Alex Rodriguez to Alex Speier would be difficult.
Finally below the table we have jump links to each writer’s writings about their selections. And if you’ll allow a brief screenshot of that…
We have a nicely designed section here. Designers delineated each author’s section with red arrows that evoke the red stitching on a baseball. It’s a nice design tough. Then each author receives a headline and a small call out box inside which are the players—and their headshots—for whom the author voted. An initial dropped capital (drop cap), here a big red M, grabs the reader’s attention and draws them into the author’s own words.
Overall this was a solidly designed piece. I really enjoyed it. And for those who don’t follow the sport, the table is also an indicator of how divisive the voting can be. Even the Globe’s writers couldn’t unanimously agree on voting for David Ortiz.
Credit for the piece goes to Daigo Fujiwara and Ryan Huddle.
Yesterday baseball writers elected David Ortiz of the Boston Red Sox, better known as Big Papi, to the Baseball Hall of Fame. I was trying to work on a thing for yesterday, but ran out of time. While I will attempt to return to that later, for now I want to share a simple interactive graphic from the Boston Globe. As the blog title suggests, it’s about the 558 career home runs Ortiz hit between his time with the Twins and the Red Sox. He hit 541 of those during the regular season, tacking on 17 more in the post season including his famous 2013 ALCS grand slam against the Detroit Tigers. (The one where the cop’s arms are in the air alongside Torii Hunter’s legs.)
Now you can see that Ortiz was a left-handed pull hitter with that home run concentration to right field, especially those wrapped around Fenway’s (in)famous Pesky Pole.
But with the number of dots you see inside the grounds at Fenway, you can also see the one downside of a chart like this. The graphic maps home runs at all Major League ballparks to that of Fenway. Not to mention the role that the Green Monster plays in turning a lot of those line drive home runs that when hit to right field leave the yard, but to left simply bounce off the Monster for doubles or the dreaded long single. But in part that’s why Ortiz also had ridiculous season numbers for extra base hits because of all those Green Monster doubles. (Conversely, how many popups a mile in the sky came down into the Green Monster seats?)
You access this interactive piece by scrolling through the experience as the Globe chose 12 home runs to represent Ortiz’s entire career. I’m fortunate enough to remember watching several of them on the television.
Big Papi was a force to be reckoned with and watching him hit was entertainment. I’m very excited to see him enter the Hall of Fame.
Many of us know the debt that comes along with undergraduate degrees. Some of you may still be paying yours down. But what about graduate degrees? A recent article from the Wall Street Journal examined the discrepancies between debt incurred in 2015–16 and the income earned two years later.
The designers used dot plots for their comparisons, which narratively reveal themselves through a scrolling story. The author focuses on the differences between the University of Southern California and California State University, Long Beach. This screenshot captures the differences between the two in both debt and income.
Some simple colour choices guide the reader through the article and their consistent use makes it easy for the reader to visually compare the schools.
From a content standpoint, these two series, income and debt, can be combined to create an income to debt ratio. Simply put, does the degree pay for itself?
What’s really nice from a personal standpoint is that the end of the article features an exploratory tool that allows the user to search the data set for schools of interest. More than just that, they don’t limit that tool to just graduate degrees. You can search for undergraduate degrees.
Below the dot plot you also have a table that provides the exact data points, instead of cluttering up the visual design with that level of information. And when you search for a specific school through the filtering mechanism, you can see that school highlighted in the dot plot and brought to the top of the table.
Fortunately my alma mater is included in the data set.
Unfortunately you can see that the data suggests that graduates with design and applied arts degrees earn less (as a median) than they spend to obtain the degree. That’s not ideal.
Overall this was a really nice, solid piece. And probably speaks to the discussions we need to have more broadly about post-secondary education in the United States. But that’s for another post.
Credit for the piece goes to James Benedict, Andrea Fuller, and Lindsay Huth.
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.
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).
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.
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 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.