data visualisation – Page 6

The Terrible No Good Chart About Gas Prices

Saw this graphic on the Twitter the other day from the Democratic Congressional Campaign Committee (DCCC), or the D Triple C or D Trip C. The context was that earlier in the day Matt Yglesias posted a clearly tongue-in-cheek chart about how after signing the infrastructure bill, President Biden had single-handedly fixed inflation and gas prices were heading down.

Of course, anyone with a brain knows this isn’t true. The President of the United States cannot control the price of petrol. Because, you know, market economy. The underlying problem of high demand and low supply was, of course, not solved by the infrastructure bill. But lots of people complain on the telly or the internets about Biden not doing more about inflation, but, you know, not really within the wheelhouse.

Anyway, this chart in particular does not bother me. Because Yglesias knows—and most of his audience knows—it is not meant to be taken seriously. It is really just a joke.

But emphasis most of his audience.

Because the DCCC later posted this graphic with the accompanying text “Thanks, Joe Biden”.

Oh boy.

Clearly they didn’t get the memo about the original being a joke.

The entire scale of the chart is 4¢. I cannot even recall the last time I had to use the glyph ¢ we’re talking so small a scale. The change in the the three week period amounts to a decline of 2¢.

And now you get the joke of the post. Ask me my 2¢ about the chart…

Now look closely at that y-axis. You’ll also note that we are carrying it all the way out to the third decimal point. Now, it’s true that some petrol stations will have a wee little nine trailing just after the two digits to the right of the decimal. Sometimes you might see a 9/10. As was explained to me in school that’s because people will buy something if it looks even a fraction of a cent cheaper. Thing 99¢—getting the use out of this glyph today—versus $1. Makes all the difference. So back when petrol was cheap (inflation stories come round and round), 0.899 looked better than 0.90. But now that it’s routinely well over a few dollars, that 9/10 is a laughable percentage of the total price.

So, yes, we do present petrol prices to three decimals in the environmental design space. But think to yourself, when have you ever aloud repeated a price to the third decimal point? You probably haven’t. And so this chart probably shouldn’t be using that granular a level of specificity.

The other underlying problem, jokes aside, is that the chart spends all that horizontal space looking at three data points. Three. If the data were showing the daily price, not the weekly average, we’d have 21 days worth of data, and that—scale notwithstanding—would be worthy of charting. My basic rule is that if it’s five or six data points, you can use a table unless there is a contextual or design reason for doing so. Say, for example, you’re doing a series of small multiples for a time series of objects in a category. For all but a few categories you have dozens of data points, but just a few have really spotty observations. In those cases, plot the three or four numbers. But in this case, just don’t.

Instead this kind of graphic is best presented as a factette, a big old number, preferably in a narrow or condensed width. Because a 2¢ decline over a three-week period is also not terribly newsworthy. (Unless your story is how prices haven’t changed much over the last three weeks.)

This also points to how the original chart misses the context of time. Granted, a lot can happen in three weeks, but a 2¢ shift is not massive. Give those three weeks their proper place in time, however, and you can see just how little movement that truly is. Cue my own quickly whipped up charts.

In the first chart you can begin to see how the change, during the course of the last nearly two years, is not significant. And in the second you can see that things really are not that bad compared to where they were back during the lead up to the Great Recession and then in the recovery that followed. (Aww, look at back in the early oughts when prices averaged just over a $1/gallon. I can still remember filling up my minivan for prices like 99¢.)

If the designer wants to make a point that perhaps we’re reaching the peak prices during this time period, sure. Because a two-week decline in prices could well be the beginning of that. But, to show that you also need to show the context of the time before that.

But once again, the President of the United States cannot much affect the price of petrol short of releasing the strategic reserve, which as its name implies, is meant for strategic purposes in case of national emergency. And high consumer prices are not a strategic national emergency on the scale of, say, a crippling storm impacting the refineries in the Gulf or an earthquake destroying pipelines in Alaska or an invasion or stifling blockade of overseas imports.

At the end of the day, this was just a terrible, terrible chart. And I think it speaks to a degree of chart illiteracy that I see creeping up in society at large. Not that it wasn’t there in the past—get off my lawn, kids—but seems more ever present these days. I don’t know if that’s because of the amplification effect of things like the Twitter or just a decline in education and critical thinking. But those are topics for another day.

This chart fails on so many levels. The concept is bad, i.e. neither Biden nor Trump nor their predecessors nor their successors—unless we adopt a planned economy, am I right, comrades?—can directly affect petrol prices. Prices are governed by larger market forces that boil down to supply and demand.

But also, the sheer design is bad. Don’t use a chart of three data points. Don’t stretch out the x-axis. Don’t use decimal points to a point where they’re unrecognisable.

In the meantime, charts like this? Don’t do them, kids.

Credit for the first original goes to FRED, whose chart Matt Yglesias used.

Credit for the second goes to the DCCC graphics department.

Oh, and because I used Federal Reserve data for the charts, and because I work there, I should add the views and opinions are my own and don’t represent those of my employer.

Those Are Some Heavy Balls

Unfortunately, I don’t subscribe to Business Insider, but I saw this graphic on the Twitter and felt the need to share it. Primarily because baseball will almost certainly stop at midnight when the owners of the teams will impose a lockout (as opposed to players going on strike). And with that baseball will be on hold until the two parties resolve their current labour issues.

And at present that seems like it could take quite some time.

So on the eve of the lockout Bradford William Davis tweeted a link to an article he wrote, alas no subscription as aforementioned, but he did share one of the graphics therein.

We have a basic dot plot charting the weight of the centre of baseballs, sorted by the month of game from which they were pulled.

The designer made a few interesting choices here. First, typographically, we have a few decisions around the type. I would have loved to have seen a bit of editing or design to eliminate the widow at the end of the graphic’s subtitle, that bit that just says “(blue)”. Do the descriptors in parentheses even need to be there when the designer included a legend immediately below? I find that one word incredibly distracting.

On the other hand, the designer chose to use a thin white outline around the text on the plot. Normally I’d really like this choice, because it can reduce some of the issues around legibility when lines intersect text, especially when they are the same colour. Here, however, the backgrounds are not white. I would have tried, for the top, using that light blue instead of white as the stroke for the outside of the letters. And on the bottom I would have tried the light pink. That would probably achieve the presumed desired effect of reducing the visual interference unintentionally created by the white. I also would have moved the top label up so it didn’t sit overlay the top dot.

As far as the dot plot itself goes, that works fine. I wonder if some transparency in the dots would have emphasised how many dots sit atop each other. Or maybe they could have clustered, but when overlapping moved horizontally off the vertical axis.

Overall this was a really nice graphic with which to end this half of the baseball off season. Hopefully the lockout doesn’t last too long.

Credit for the piece goes to Taylor Tyson.

There’s Water in the Basement

If you didn’t know, climate change is real and it threatens much of our current way of life. I don’t go so far as to say it threatens the extinction of mankind, because there are nearly seven billion of us and to wipe out every living soul would be a tall order. But, it could wipe out parts of our history.

If you didn’t know, the city of Washington in the District of Columbia was built on a swamp. Except, actually, it wasn’t. Most of the city was built on higher ground along the riverbank of the Potomac. True, there are low-lying areas affected by the tides and high water, such as the National Mall, but places like the Capitol were purposefully placed on high ground.

And that gets us to this article in the Washington Post. It takes a look at the impact of rising waters and flash flooding on the National Mall, home to some of the preeminent American museums. The article uses a map to show just how the museums are threatened by extreme weather events that will only increase in frequency as climate change ramps up.

Note the Capitol and the White House will both be fine.

The designer used colour to denote museums by their risk of flooding, and sadly there are several. But as the article describes, there are few short-term fixes that we can undertake to mitigate the risk of damage to the collections.

Credit for the piece goes to Taylor Johnston.

Hey Boo Boo

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.

How Far You Won’t Go

Housekeeping first, as you may have noticed, I haven’t been publishing as much lately. That’s because I’ve been on holiday. After a tremendously busy year, I need to use up all the time I didn’t spend on holiday. Consequently, I’m only going to be posting a handful or so more times before the end of the year. The plan is to return in early January to my regular posting schedule. For this week that means the next few days before I’m off for American Thanksgiving.

But on with the show.

One of the things I haven’t been doing too much of is travelling. There are many reasons for this, but one is that air travel in the United States has, of late, been, shall we say unreliable. Hundreds if not thousands of flight cancellations, sometimes with no obvious cause. And in one notorious case, Southwest claimed inclement weather cancelled flights in Florida, but it was the only airline to cancel significant numbers of flights. In other words, it wasn’t the weather.

The Wall Street Journal recently posted an article that explored the issue, doing so via a great example. It followed the literal path of one Southwest aircraft over one long holiday weekend. The screenshot below captures two of the graphics with a wee bit of text between.

What’s nice about the graphics’ design is how they use small multiples and consistent colours. The intended route is always on the left and what actually happened is on the right. Red and blue colours depict those throughout.

The only thing I quibble with is the embedded HTML text. Sometimes the page loads fine for me, other times it looks like it did this morning for this screenshot. Note how for some city labels the final letters get dropped to a second line, e.g. the “o” in Chicago or the “e” in Baltimore.

This is far from a deal breaker on this being a good graphic, but I find it mildly annoying, especially when in situations like the bottom left Orlando, there’s no obvious reason as to why, because the little airplane departure icon sits atop the final letter.

I understand the idea behind using native HTML text in graphics, but when things like this happen, I wonder if it wouldn’t simply be better to include the text as part of the graphic and avoid these potential mishaps altogether.

Credit for the piece goes to Emma Brown.

Data Analysis and Baseball

First, a brief housekeeping thing for my regular readers. It is that time of year, as I alluded to last week, where I’ll be taking quite a bit of holiday. This week that includes yesterday and Friday, so no posts. After that, unless I have the entire week off—and I do on a few occasions—it’s looking like three days’ worth of posts, Monday through Wednesday. Then I’m enjoying a number of four day weekends.

But to start this week, we have Game 6 of the World Series tonight between the Atlanta Braves and the Houston Astros. That should the Braves vs. the Red Sox, but whatever. If you want your bats to fall asleep, you deserve to lose. Anyways, rest in peace, RemDawg.

Yesterday the BBC posted an article about baseball, which is first weird because baseball is far more an American sport that’s played in relatively few countries. Here’s looking at you Japanese gold medal for the sport earlier this year. Nevertheless I fully enjoyed having a baseball article on the BBC homepage. But beyond that, it also combined baseball with history and with data and its visualisation.

You might say they hit the sweet spot of the bat.

There really isn’t much in the way of graphics, because we’re talking about work from the 1910s. So I recommend reading the piece, it’s fascinating. Overall it describes how Hugh Fullerton, a sportswriter, determined that the 1919 White Sox had thrown the World Series.

Fullerton, long story short, loved baseball and he loved data. He went to games well before the era of Statcast and recorded everything from pitches to hits and locations of batted balls. He used this to create mathematical models that helped him forecast winners and losers. And he was often right.

For the purposes of our blog post, he explained in 1910 how his system of notations worked and what it allowed him to see in terms of how games were won and lost. Below we have this screen capture of the only relevant graphic for our purposes.

In it we see the areas where the batter is like safe or out depending upon where the ball is hit. Along the first and third base foul lines we thin strips of what all baseball fans fear: doubles or triples down the line. If you look closely you can see the dark lines become small blobs near home plate. We’ve all seen those little tappers off the end of the bat that die, effectively a bunt.

Then in the outfield we have the two power alleys in right- and left-centre. When your favourite power hitter hits a blast deep to the outfield for a home run, it’s usually in one of those two areas.

We also have some light grey lines, which are more where batted balls are going to get through the infielders. We are talking ground balls up the middle and between the middle infielders and the corners. Of course this was baseball in the early 20th century. And while, yes, shifting was a thing, it was nowhere near as prevalent. Consequently defenders were usually lined up in regular positions. These correspond to those defensive alignments.

Finally the vast majority of the infield is coloured another dark grey, representing how infielders can usually soak up any groundball and make the play.

The whole article is well worth the read, but I loved this graphic from 1910 that explains (unshifted) baseball in the 21st century.

Credit for the piece goes to Hugh Fullerton.

Germany’s Political Coalitions

Two weekends ago, Germany went to the polls for their federal election in which they chose their representatives in the Bundestag, or legislature. Germany, however, is not a two-party system and no single party won a majority of seats. Consequently, the parties need to negotiate and form a coalition government. That could take a number of different forms given the number of different parties and their number of seats.

Thankfully the BBC produced a small graphic in an article that detailed how Angela Merkel’s political heir likely won’t take charge of the new government.

Here in the States we can only dream of coalition governments…

It’s a simple graphic, but given the terms Traffic Light coalition, Jamaica coalition, and Kenya coalition I think it’s a necessary graphic to help explain the makeup of these potential coalition arrangements. This falls into the category of small but exceptionally clear graphics. More proof that not all useful graphics need to be flashy.

Credit for the piece goes to the BBC graphics department.

Low Expectations

Today the 2021 Major League Baseball season begins its playoffs. Tomorrow we get the Los Angeles Dodgers and the St. Louis Cardinals. Why the Dodgers, the team with the second-best record in all of baseball, need to play a one-game play-in is dumb, but a subject for perhaps another post. Tonight, however, is the American League (AL) Wildcard game and it features one of the best rivalries in baseball if not American sports: the Boston Red Sox vs. the New York Yankees.

Full disclosure, as many of you know, I’m a Sox fan and consider the Yankees the Evil Empire. But at the beginning of the year, the consensus around the sport was that the Yankees would win first place in their division and be followed by the Tampa Bay Rays or the Toronto Blue Jays. The Red Sox would place fourth and the lowly Baltimore Orioles fifth. The Red Sox, as the consensus went, were, after gutting their team of top-flight talent and a no-good, rotten, despicable 2020 showing, nowhere near ready to reach the playoffs. The Yankees were an unstoppable offensive juggernaut.

When the 2021 season ended Sunday night, as the dust around home plate settled, the Rays dominated the AL East to take first. But it was the Red Sox that finished second and the Yankees who took third. Whilst the two teams had the same record, in head-t0-head match-ups the Red Sox won more games than the Yankees, 10–9. Not bad for a team that everyone thought couldn’t make the playoffs and would be in fourth place.

That got me thinking though, how wrong were our expectations? After doing some Googling to find individual reports and finding a Red Sox twitter account (@RedSoxStats) that captured as many preseason forecasts as he could, I was ready to make a chart. The caveat here is that we don’t have data for all beat writers, who cover the Red Sox exclusively or almost exclusively on a daily basis, or even national media writers, who cover the Red Sox along with the rest of the sport and its teams. For example, ESPN polled 37 of its writers, but all we know is that 0 of 37 expected the Red Sox to make the playoffs. I don’t have a single estimate for the number of wins, which obviously determines who gets into said playoffs, for those 37 forecasts. Others, like CBS Sports, broke down each of their five writers’ rankings for the division and all five had the Red Sox finishing fourth. But again, we don’t have numbers of wins. So in a sense, if we could get numbers from back in the winter and early spring, this chart would look even crazier with the Red Sox being even more outperform-ier than they do here.

We should also remember that during September, in the lead-up to the playoffs, the Red Sox were struggling with a Covid-19 outbreak that put nearly half their starting roster on the Injured List (IL). The Sox had the backups to the backups starting alongside the backups, some of whom then also went on the IL with Covid-19 leading to signings of players who, despite being integral to the September success, are not eligible to play in the playoffs due to when they signed. José Iglesias brought some 2013 magic to be sure. Earlier in the year, MLB would postpone games when significant numbers of players were unavailable, but the Red Sox, for whatever reason, had to play every game. And there were instances where players started the game, but in the middle of the game their tests came back positive and they had to be removed from the field in the middle of the game.

I’m not certain where I stand on how much managers influence the win-loss record in baseball. But if the Sox manager, Alex Cora, doesn’t at least get some nods for being manager of the year, I’ll be truly shocked.

The Red Sox are not a great team. This is not the 2018 behemoth, but rather an early rebuild for a hopefully competitive team in 2023. Their defence is not great. They lack depth in the rotation and the bullpen. I, for one, never doubted their offence—2020 surely had to have been a pandemic fluke. But I had serious questions about their starting rotation. Ultimately the rotation proved itself to be…adequate. And while they played through Covid-19 and kept their heads above water in September, the last few weeks were, at times, hard to watch. The Yankees swept them at Fenway, site of tonight’s game, just last weekend. Of late, the Yankees have been the better team. And all year long, the Red Sox played less competitively than I’d like against the other teams that made the playoffs.

I don’t expect them to win let alone make the World Series, but nobody expected them to be here anyway. Maybe they still have a few more surprises in them. After all, anything can happen in October baseball.

Credit for the piece is mine.

Peeping Map

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

Covid Update: 29 September

Last week when I wrote my update on Covid-19, we had seen a few signs for optimism, but in other states the news was hard to interpret or, in the case of Pennsylvania, not going the right way at all. So where are we this week? In some ways, not a lot has changed over the last seven days.

New case curves for PA, NJ, DE, VA, & IL.

Last week, we had positive developments in both New Jersey and Illinois. There cases had begun to noticeably and consistently fall with clear peaks in this fourth wave of infections. Their seven-day averages were decidedly below their recent peaks. That trend continued last week. In fact, in Illinois the seven-day average is now also below the peak from not just this fourth wave, but also the third wave. That’s good.

New Jersey’s fourth wave was nowhere near as impactful as its first three. It helps to have one of the highest vaccination rates in the United States. But the Garden State’s seven-day average is also falling, though not as quickly as in Illinois. You could even make the argument that over the last week cases have really remained flat, though the last few days I would contend are evidence of a slow slowdown.

Delaware had been a tricky state to judge given some recent volatility in its average. But as we can see over the last week the new case curve clearly has flattened. The flat line, however, remains just that, a flat line. This is more of a plateau shape than a descending hill shape. That means that cases are continuing to spread, but at a steady rate of about 450 new cases per day. This isn’t uncommon, but hopefully it precedes a fall in new cases rather than serving as a respite on an ever upward climb.

In Virginia I had mentioned some early indications of a potential flattening, the first step towards a decline in the average. That flattening appears to be taking hold. In the chart above you can clearly see a sharp decline beginning to take root in Old Dominion. The curve here most closely resembles Illinois in what, at least for now, is a fairly symmetrical increase and decrease.

Finally we have Pennsylvania. I was pretty short in my analysis last week, the state was headed in the wrong direction. The latest data shows that the Commonwealth may just be beginning to turn the corner and flatten the curve. However, after the pre-Labour Day slowdown that then erupted into a full-blown outbreak, I’m wary of saying anything definitive about Pennsylvania. All we can do is hope that these early trends hold true.

So what about deaths? Are we seeing any progress on that front? Last week I noted that it was almost all bad news. In all but Illinois we had death rates continuing to climb.

That story, sadly, remains largely the same. Illinois, unfortunately has actually seen its seven-day average resume ticking upwards, although not by a significant degree. It’s enough that I think it fair to say deaths have largely plateaued and not necessarily begun to climb. And as I keep saying, that would track for a state where we have seen new cases falling for the last few weeks now.

Unfortunately, that’s about it. Deaths in New Jersey have remained fairly stable, though the average has moved from 19.3 to 17.4 as of yesterday. Perhaps that could be an indication of a falling death rate. But just a few days ago it was still nearer 19 than 18. I would want to see more data showing a consistent and persistent decline before saying New Jersey is headed the right way.

And in Pennsylvania, Delaware, and Virginia, deaths are headed the wrong way, plain and simple. At the beginning of the sample set, Delaware reported 14 deaths in one day, the most in a month. Consequently the average has jumped from 2.6 last week to 3.4 today. In Virginia we had seen deaths jump from 20 to 34. Well this week they jumped again, though by half the amount, to 41 deaths per day. Pennsylvania performed the worst, however. Deaths here climbed from 43 to 57 per day.

While we have seen new cases plateau in Delaware and begin to fall in Virginia, which should mean declining death rates in a few weeks, in Pennsylvania the numbers of new cases may only be beginning to flatten. Consequently, unless we begin to see a sharp decline in new cases, we will likely continue to see rising deaths in the Commonwealth. At least for a little while longer.

Credit for the piece is mine.