If all goes according to plan, I should be receiving my second dose of Pfizer later this afternoon. Then it’s two more weeks until I’m fully vaccinated and ready to rejoin the world. But what kind of world will be rejoining? The allergy plagued one looking at the calendar. And that’s why this post from Indexed by Jessica Hagy made me laugh.
But it’s also true.
Credit for the piece goes to Jessica Hagy.
Two Fridays ago, I opened the door and found my copy of the New York Times with a nice graphic above the fold. This followed the announcement from the White House of aggressive targets to reduce greenhouse gas emissions
In general, I love seeing charts and graphics above the fold. As an added bonus, this set looked at climate data.
But there are a few things worth pointing out.
First from a data side, this chart is a little misleading. Without a doubt, carbon dioxide represents the greatest share of greenhouse gasses, according to the US Environmental Protection Agency (EPA) it was 76% in 2010. Methane contributes the next largest share at 16%. But the labelling should be a little clearer here. Or, perhaps lead with a small chart showing CO2’s share of greenhouse gasses and from there, take a look at the largest CO2 emitters per person.
Second, where are the axis labels?
I will probably have more on this at a later date, but neither the bar chart nor the line charts have axis labels. Now the designers did choose to label the beginning value for the lines and the bars, but this does not account for the minimums or maximums. (It also assumes that the bottom of the lines is zero.)
For example, we can see that China began 1990 with emissions at 3.4 billon metric tons. The annotation makes clear that China’s aggregate emissions surpassed those of the US in 2004. But where do they peak? What about developing countries?
If I pull out a ruler and draw some lines I can roughly make some height comparisons. But, an easier way would be simply to throw some dotted lines across the width of the page, or each line chart.
This piece takes a big swing at presenting the challenge of reducing emissions, but it fails to provide the reader with the proper—and I think necessary—context.
Credit for the piece goes to Nadja Popovich and Bill Marsh.
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.
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.
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.
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.
Not surprisingly, my neighbourhood in Lakeview was another political bubble, though this one even more Democratic than my current one.
But if I had wanted to move to an integrated political bubble, instead of Philadelphia, I could have moved to…Jefferson Park.
Credit for the piece goes to Gus Wezerek, Ryan D. Enos and Jacob Brown.
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.
I didn’t write a post last Monday, but this Monday I am. A few things may have changed in the Covid situation. The most important is that we may have finally seen the peak of this current wave’s surge of new cases.
For the last few weeks we’ve seen cases rising in the five states. Only New Jersey of late had shown a return to declining cases. About the middle of the week before last, we began to see those numbers decline. And so in this past week we did begin to see cases decline in all five states.
The thing to watch this week will be that at the very end of last week, new cases ticked up slightly for two or three days in a number of states. It could be an aberrant one-off, but with full vaccinations still well below herd immunity and cases still at high levels, it isn’t difficult to imagine a scenario where the virus begins to surge once again.
Deaths on the other hand, they continue to climb. We aren’t seeing massive increases, instead these are largely marginal. But they are increasing all the same.
Encouragingly, if cases can continue to decline, deaths will begin to fall. As a lagging indicator, they will be the last metric we see decline. Consequently, it’s a question of when, not if, deaths begin to decline. On Saturday, we did see a small decline in deaths, but one day before the weekend is insufficient to determine whether or not we’ve seen the inflection point, after which deaths would fall.
Vaccinations remain a broad set of positive news. All three states are now reporting just over 30% of their populations as fully vaccinated. However, the rate of vaccination has begun to slow.
And that worries me and the professionals, because we are still far from herd immunity. Until we reach that level, the virus can easily spread among unvaccinated populations. The charts above don’t show the decline, as they look only at the total, cumulative effect. But the charts that I see make it quite clear the decline over the last week or two.
Moral of that story is, if you haven’t been vaccinated yet, please register to do so or visit a location that allows walk-up vaccinations.
When I was in high school, the United States would regularly spend space shuttles into orbit to help build this new thing: the International Space Station (ISS). In the aftermath of the Cold War, the nations of the world joined together to commit to building an orbital space station.
There was of course a time before the ISS, and I can recall many jokes being made about Mir, the Soviet then Russian space station. And before Mir there were other, though none as long-lasting. But I digress, we’re here today because recently Canadian astronaut Chris Hadfield tweeted a graphic made by Peter Batenburg that visually captures the history of the International Space Station.
I think my favourite element is the graphical representation of the expansion of the ISS in terms of its volume. I’ve seen similar sort of graphics showing the addition of modules and new components, but I can’t recall seeing the amount of space where people can live and work being captured.
But really, the whole piece is worthy of sitting down and enjoying. After all the ISS is only about 22 years old. But there are questions of how much longer it will remain in orbit. I’m not aware of any concrete plans to fund it beyond 2030 nor any plans for an eventual replacement.
We can only hope that the ISS and its successor remain an area that fosters international cooperation for the next thirty years.
Credit for the piece goes to Peter Batenburg.
I was not planning on posting this today, because I was—am?—still working on it. But there was some baseball news last night that prompted me to export what I had to try and get this live.
For a little while now I’ve been wondering why a number of baseball stars, albeit in their later years, are still looking for employment. Some are pretty obvious in that they are facing legal troubles. Some may have high demands that ball clubs are not willing to meet. Some may have reasonable demands but the clubs are just being incredibly cheap. Or it may be none of those. Or some combination of those. But when you see some of the players some teams put on the field each night, you can’t tell me some of these free agents wouldn’t be better options.
Separately, I also tend to think baseball needs to expand and add some new clubs. But they won’t until the Oakland Athletics and Tampa Bay Rays resolve their stadium issues.
But what if…
Well a normal expansion would include two teams to keep an even balance. The new teams would likely use some kind of draft to select players from the rosters of other teams, with a certain number of players almost certainly protected. But what if we just used those unsigned ball players?
Anibal Sanchez is the guy messing this up. He’s been a free agent for some time now but is reportedly going to sign by the end of this week, perhaps today. So with him and everyone else, could we field two expansion teams?
Kinda, yeah.
First up, the Charlotte Piedmonters.
Not a great team—nor would we expect it to be as all the really good free agents have already been signed. But these former stars, award winners, and fan favoutites may have just enough left in the tank to make for some competitive games if all goes well. My readers who happen to be fellow baseball fans will probably recognise most of these names, though I’ll admit a number of the relief pitchers are new to me. I can figure out basically everything but a centre fielder. But you could probably get somebody from an independent league or international league or just convert somebody.
I used projected Wins Above Replacement (WAR) to determine how good the players would be. For non-baseball fans, WAR is a value you can use to determine how good a player is relative to an average replacement player. Somebody with the value 0 to 1 is a scrub or bench player. Take any average ballplayer and sub them in and you wouldn’t know the difference. 2s and 3s are solid role playing guys, but not likely stars. Stars get into the picture around 4 and your best players are probably 5 to 6 or higher.
In Charlotte, nobody has a WAR higher than Rick Porcello’s 1.4. In other words, he’s a better than average pitcher, but not by much. Tyler Flowers: a better than average catcher, but not by much. Homer Bailey: barely better than average starting pitcher. Everyone else, generally you could sub them out and not know the difference. But, crucially for our purposes, they are not below average players. Some of those are still on the market, but I didn’t assign them to Charlotte.
Now if Charlotte gets a team, so does Portland, Oregon: the Portland Lumberjacks.
Here you can see Anibal Sanchez as the third man in the rotation. You can also see that the rotation here is the weakest part. For Charlotte you could get away with a bullpen game every five days. But two bullpen days? Well, take a look at the Boston Red Sox in 2020 and that pitching dumpster fire and you’ll see what having only two or three starters can do. (Though the relief starters they did use were all worse than the people on these lists, which just makes my point that there are talented if not star-level players available.)
Neither of these teams would be good. You can imagine a team like Charlotte getting beat almost every night in the AL East—except by Baltimore. The NL East might be a bit easier. And Portland in the NL West would be similarly a punching bag—except by Colorado probably. But dump either into the AL or NL Central and who knows.
Two teams is clearly a stretch. So what if we just made one? What if we brought back the Montreal Expos? Sure, it messes up the schedule, but we get to pick the best players from Charlotte and Portland.
The result is a team that is significantly improved. That doesn’t mean very good. These Expos wouldn’t make the playoffs. But the rotation is full of guys who could be, at best, solid middle- to, more likely, back-end starters. The lineup, well, the lineup would still be mostly replacement level players, a.k.a. scrubs, with two exceptions. But with past track records, it’s not impossible to imagine a few of these players having a better than projected year.
On paper, they still wouldn’t be as good as the worst team in baseball (by WAR), the Pirates. But Pittsburgh also doesn’t have a centre fielder, so…
Anyway, I was going to try and do some more analysis beyond using WAR, but I wanted to get this out before Sanchez signed this week.
I also got to add Oliver Perez, who despite having a good year was released by Cleveland today. Boston needs a solid lefty reliever for the middle innings, and I hope they pick up Perez and option Josh Taylor down to Worcester.
Credit for the piece is mine.
I don’t know if this is a trend, but I’ve now seen a few graphics appearing using arrows to show the direction or trend of the data. This graphic in an article by Bloomberg prompted me to talk about this piece.
I should add, after rereading my draft, that I’m not clear who made this graphic. I assume that it was the Bloomberg graphics team, because it appears in Bloomberg and all the data is presented to recreate the chart. But, it could also be a chart made by someone at Goldman Sachs that credits Bloomberg as a source and then someone at Bloomberg got hold of a copy. And a graphic made for a news/media outlet will typically be of a different quality or level of polish than one made perhaps by and for analysts. (Not that I think there should be said differences, as it does a disservice to internal users, but I digress from a digression.)
The arrow here appears above the peak quarter, i.e. the second of 2021, for both the Goldman Sachs Economics forecast and the consensus forecast. But what does it really add? First, it adds “ink”, in this case pixels. Here, every pixel consumes our attention and there is a finite number of available pixels within the space of this graphic.
When I work with authors or subject matter experts, I often find myself asking them “what’s the most important thing to communicate?” or something along those lines. If the person answers with a long laundry list, I remind them that if everything is important, nothing is important. If everything is set in bold, all caps text, what will look most important is the rare bit of text set in regular, lower-case letters.
In the above graphic, there are so many things screaming for my attention, it’s difficult to say which is the most important. First, I’m fairly certain that “US QoQ annualised GDP growth” could move to the graphic subhead or data definition. Allow the graphic’s data container to contain, well, data. Second, the data series labels can be moved outside the data container. The labels here have an inherent problem is that the Goldman Sachs Economics numbers are in blue, and that blue text has less visual weight than the black text of the Consensus label. Consequently, the Goldman Sachs Economics label recedes into the background and becomes lost, not what you want from your legend.
Third, I don’t believe the data labels here add anything to the chart. They function as sparkly distractions from the visual trend, which should be the most important aspect of a visual chart.
Finally, we get to the arrow, the impetus for this post. First, I should note that it is not clear what growth it shows. The fact the line is black makes me think it reflects the Consensus forecast whereas a blue line would represent the Goldman Sachs forecast. But it could also be the average of the two or even a more general “here’s the general shape”. The problem is that the shape matters. If you look at the slope of the actual forecasts, you see a sharp increase to the peak followed by a slower, more gradual taper. The arrow in the original graphic shows a decelerating curve that is shallower in the lead up to the peak and that is not what is forecast to happen.
Now we get to the issue I mentioned at the top, the extraneous labelling and data ink wasted. If we look at the chart as is, but remove the arrow, we see this.
Immediately to the right of the peak, we have have some blue data labels and then just a bit to the right of that, but sitting vertically above the label we have the bold blue text labelling the data series. But further to the upper right we have a dark and bold block of text that draws the eye away from the peak and into the corner. It draws the eye away from the very element of the shape the peak needs to be a peak, the trough in the wave. Consequently, it makes sense with the eye being drawn up and to the right that the designers threw an arrow in above the peak to show how, no, actually your eye needs to go down and to the right.
But what happens if we then strip out the data series labelling? Do we still need the arrow? Let’s take a look.
I would argue that no, we do not. And so let’s strip the arrow out of the picture and take a look.
Here the shape of the curve is clear, a sharp rise and then a gradual taper to the right. No arrow needed to show the contour. In other words, the additional labelling wastes our attention, which then forces us to add an arrow to see what we needed to see in the first place, but then further wasting our attention.
There are a number of other things I take issue with in this chart: the black outlines of the blue rectangles, the tick marks on the x-axis, the solid border of the container, the lack of axis lines. But the arrow points to this graphic’s central problem, a poorly thought out labelling structure.
So because the chart provides all the data, I took a quick stab at how I would chart it using my own styles. I gave myself a 3:2 ratio, less space than the original graphic had. This is where I landed. I would prefer the legend below the chart labelling, but it felt cramped in the space. And with so few data points along the x-axis, the chart doesn’t need a ton of horizontal space and so I repurposed some of it to create a vertical legend space.
I mixed typefaces only because my default does not have a proper small capitals and I wanted to use small capitals to reduce and balance out the weight of the exhibit label in the graphic title.
I could still tweak the spacing between the bars and perhaps the treatment of the years below the quarters could use some additional work, but the main point here is that the shape of the curve is clear. I need no arrow to tell the user that there is a peak and that after the peak the line goes down. The white space around the bars and the line does that for me.
Credit for the piece goes to either the Bloomberg graphics department or the Goldman Sachs graphics department. Not sure.
A few years ago, I created a piece about the missing Argentine submarine ARA San Juan. For those that do not remember, back in 2017, the Argentine Navy Type 1700 submarine ARA San Juan disappeared on a voyage from Ushuaia to Mar del Plata. At the time, people thought it may have sunk over the continental shelf where the seafloor was shallow enough the boat could have survived and not imploded. A year later, surveyors discovered the wreck at a much deeper depth east of the continental shelf. But at the time of the loss I made a graphic trying to show how the submarine was much smaller than the standard American submarine, the Los Angeles class, and how much deeper the Los Angeles class can dive.
So when the news broke this week that an Indonesian submarine, KRI Nanggala disappeared north of Bali, Indonesia, I decided to update the graphic. Since I finished the piece, however, the Indonesian Navy discovered the wreck in three pieces at 850 metres, far below even the presumed crush depth of the Los Angeles class. In other words, at that depth there was never any hope to find survivors.
The Indonesia submarine is an even smaller than the Argentine boat, both having an operating depth of about 250 metres—actual depths are generally not disclosed because, well, military secrets. But the graphic shows just how far below that depth the sub’s wreckage rests.
Credit for the piece is mine.
Last Friday I received my first dose of the vaccine, and I’m not counting the time until my second and then the two weeks after that to let it take effect. It also means that the repetition can begin to end.
Over at Indexed, Jessica Hagy sort of captured that idea in a single Venn diagram.
Credit for the piece goes to Jessica Hagy.
