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.
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.
Dirty water
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.
Depending upon where you live, autumn presents us with a spectacular tapestry of colour with bright piercing yellows, soft warm oranges, and attention-grabbing reds all situated among still verdant green grasses and calming blue skies. But this technicolour dreamcoat that drapes the landscape disappears after only a few weeks. For those that chase the colour, the leaf peepers, they need to know the best time to travel.
For that we have this interactive timeline/map from SmokyMountains.com. It’s pretty simple as far as graphics go. We have a choropleth map coloured by a county’s status from no change to past peak, when the colours begin to dull.
All the colour
The map itself is not interactive, i.e. you cannot mouse over a county and get a label or some additional information. But the time slider at the bottom does allow you to see the progression of colour throughout the autumn.
Normally, as my longtime readers know, I am not a fan of the traffic light colour palette: green to red. Here, however, it makes sense in the context of changing colours of plant leaves. No, not all trees turn red, some stay yellow. Broadly speaking, though, the colours make sense.
And to that end, the designers of the map chose their colours well, because this map avoids the issues we often see—or don’t—when it comes to red-green colour blindness. This being the reason why a default of green-to-red is a poor choice. Their green is distinct from the red, as these two proof colour screenshots show (thanks to Photoshop’s Proof Colour option).
ProtanopiaDeuteranopia
The choice isn’t great, don’t get me wrong. You can see how the green still falls into the shades of red. A blue would be a better choice. (And that’s why I always counsel people to stick to a blue-to-red palette.) Compare, for example, what happens when I add a massive Borg cube of blue to the area of Texas and Oklahoma—not that you have a choice, because resistance is futile.
A bit of blue
Here the blue is very clearly different than the reds. That makes it very distinct, but again, I think in the context of a map about the changing of leaf colours from greens to reds, a green-to-red map is appropriate. But only if, as these designers have, the colours are chosen so that the green can be distinguished from the reds.
As I always say, know the rules—don’t use red-to-green as one—so that you know the few instances when and where it’s appropriate to break them. As this map is.
Credit for the piece goes to the SmokyMountains.com
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.
Death curves for PA, NJ, DE, VA, & IL.
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.
I will try to get to my weekly Covid-19 post tomorrow, but today I want to take a brief look at a graphic from the New York Times that sat above the fold outside my door yesterday morning. And those who have been following the blog know that I love print graphics above the fold.
On my proverbial stoop this morning.
Of the six-column layout, you can see that this graphic gets three, in other words half-a-page width, and the accompany column of text for the article brings this to nearly 2/3 the front page.
When we look more closely at the graphic, you can see it consists of two separate parts, a scatter plot and a line chart. And that’s where it begins to fall apart for me.
Pennsylvania is thankfully on the more vaccinated side of things
The scatter plot uses colour to indicate the vote share that went to Trump. My issue with this is that the colour isn’t necessary. If you look at the top for the x-axis labelling, you will see that the axis represents that same data. If, however, the designer chose to use colour to show the range of the state vote, well that’s what the axis labelling should be for…except there is none.
If the scatter plot used proper x-axis labels, you could easily read the range on either side of the political spectrum, and colour would no longer be necessary. I don’t entirely understand the lack of labelling here, because on the y-axis the scatter plot does use labelling.
On a side note, I would probably have added a US unvaccination rate for a benchmark, to see which states are above and below the US average.
Now if we look at the second part of the graphic, the line chart, we do see labelling for the axis here. But what I’m not fond of here is that the line for counties with large Trump shares, the line significantly exceeds the the maximum range of the chart. And then for the 0.5 deaths per 100,000 line, the dots mysteriously end short of the end of the chart. It’s not as if the line would have overlapped with the data series. And even if it did, that’s the point of an axis line, so the user can know when the data has exceeded an interval.
I really wanted to like this piece, because it is a graphic above the fold. But the more I looked at it in detail, the more issues I found with the graphic. A couple of tweaks, however, would quickly bring it up to speed.
I didn’t have the internet yesterday morning, so apologies for no posting. But at least it was back by the afternoon. Unlike utilities in La Palma, where a volcano has been erupting and lava flowing, covering parts of the island.
The BBC had a brief article last week detailing the spread of the lava, which has been devastating the town. And it was a neat little graphic that I really liked.
At least it doesn’t move super quickly?
This graphic does a couple of things that I really like. First, context. Yes, the main graphic is the actual spread over four days (the fifth layer is almost half-a-day later). But in the upper-right corner, we have the same graphic layered over a satellite image of the region. I’m not sure how I feel about the satellite image, but overall it does provide a sense of scale.
Because the second thing I like is how the larger map shows not a satellite view, but rather a topographic or terrain view. The lines represent points of continuous height and help explain why the lava flow looks the way it does. The drawback here is that you don’t get any sense of urban development, like streets or neighbourhoods impacted. For that you could often use a satellite image, but then the colours and their saturation could detract from the importance of the graphical element, the lava flow layers.
Finally for the layers, I like the stepped gradients of the dark reds. This makes the sequential flow very clear. My only quibble might be the stroke or border on the shape. You can see that for all but the final shape, the stroke is a thin white line. But because those layers are stacked in reverse order—or else you would only be able to see the last layer, the most distant spread—the white stroke often overlaps and hides the black stroke for the final day.
Here I would recommend taking the five layers, duplicating them then merging them into a single sixth shape that sits atop the original five layers. I would eliminate the fill colour from the shape and then put the outline to black, that way the final borders of the lava flow in the graphic could be seen for the entirety of the flow.
But overall, this was a really nice piece that provides a lot of context to the lava flow.
Credit for the piece goes to the BBC graphics department.
It’s been a little over a week now since my last update on Covid-19 in Pennsylvania, New Jersey, Delaware, Virginia, and Illinois. So where do we stand now, especially since last week we had seen a split with some good news and some not so good news?
Well let’s start with where we had good news last week: Illinois and New Jersey. In those two states we had the clearest evidence of the fourth wave peaking and beginning a slow descent.
New case curves for PA, NJ, DE, VA, & IL.
This week we can see that in Illinois the peak really does appear to have been reached as the seven-day average for new cases has been heading down slowly over the last week or so. In New Jersey we saw a sort of false peak, because new cases began to rise again not long after I posted. And with it the seven-day average did as well. However, in the last few days, the seven-day average has flattened ever so slightly, though it is still increasing.
Delaware is a bit harder to judge. When I last posted the seven-day average sat at 457 new cases per day. Yesterday? 454 new cases per day. If you look at the chart, you can see there was a brief spike that I had noted as a potential indicator of a peak for Delaware. After that brief decline however, you can see how the curve shot back up again, exceeding the earlier peak with an average of 470 new cases per day before cooling off slightly. New cases have been increasing for the last four days, but they are still below that 470 new cases number.
Virginia’s fourth wave long looked the worst. You can see some aberrant declines and spikes due to the extra day holiday in reporting—recall Virginia does not publish its weekend data. Since then however, there are some initial indications that Old Dominion may have peaked. Consider that when I last posted, the seven-day average sat at 4700 new cases per day. But over the last nine days, the average dropped to the 3600s for six days, then the 3500s for two days, and yesterday the average fell into the 3400s. That is the kind of flattening we want to see if there is a real peak.
Finally we have Pennsylvania. Right before Labour Day we had evidence of a slowing outbreak. But then after the holiday, new cases began to climb sharply. There was then a quick slowdown, but ever since we’ve continued to see rising numbers of new cases in the Commonwealth. At the time of my last post we had an average of 4100 new cases per day. Yesterday that was at 4700.
Pennsylvania looks like the only state we cover here that is clearly moving in the wrong direction.
But what about deaths?
Death curves for PA, NJ, DE, VA, & IL.
Well, here it’s almost all bad news. Before we can reasonably expect deaths to begin to slowdown, we need to see the spread of new cases slowdown. Remember that deaths are a lagging indicator as it can take weeks from infection to hospitalisation to death. And if most of our states have not yet clearly peaked, we shouldn’t really expect deaths to have peaked yet.
Here the only good news is Illinois where deaths peaked at 41 per day, but have since fallen to 31. Compare that to the shape of the curve in the new cases chart. We can clearly see the peak in new cases being followed by sometime by the peak in deaths.
In all the other states, however, we continue to see climbing numbers of deaths. In Pennsylvania over the last nine days we’ve seen the average climb from 24 deaths per day to 43. New Jersey increased a bit more slowly, from 13 to 19. And Delaware, again due to its small size, climbed, but only from 1.1 to 2.6. And in Virginia, we’ve seen the average number of deaths climb from 20 to 34.
If we are nearing peaks in New Jersey and Virginia, we should begin to see deaths cool down in the near future. The same holds true for Delaware, but there we have less evidence of a peaking outbreak.
Last week I wrote about how CBS News’ coverage of the California recall election featured a misleading graphic. In particular, the graphic created the appearance that the results were closer than they really were.
This week we had another election and, sadly, I find that I have to write the same sort of piece again. Except this time we are headed north of the border to Canada.
I was watching CBC coverage last night and I noticed early on that the vote share bar chart looked off given the data points. Next time it popped up I took a screenshot.
Look at the bars
First we need to note these are three-dimensional and the camera angle kept swinging around—not ideal for a fair comparison. This was the most straight-on angle I captured.
Second, at first glance, we have the Conservative share at a little more than 3/4 the Liberal vote share. That looks to be about right. Then you have the New Democratic Party (NDP) at roughly half the vote of the Conservatives. And the bar looks about half the height of the blue Conservative bar. Checks out. Then you have the People’s Party of Canada at roughly 1/4 the amount of NDP votes. But now look at the bar’s height. The purple bar is nearly the same height as the orange bar.
Clearly that is wrong and misleading.
The problem, I think, is that the designers artificially inflated the height of the bars to include the labels and data points for the bars. The designers should have dropped the labelling below the bars and let the bars only represent the data.
I created the following graphic to show how the chart should have looked.
And my take…
Here you can more clearly see how much greater the NDP victory was over the People’s Party. The labelling falls below the charts and doesn’t distort the height comparison between the bars. In some respects, it wasn’t even close. But the original graphic made it look else wise.
I just wish I knew what the designers were thinking. Why did they inflate the bars? Like with the CBS News graphic, I hope it wasn’t intentional. Rather, I hope it was some kind of mistake or even ignorance.
Credit for the original piece goes to the CBC graphics department.
Last month on another Friday I shared some graphics from a video by CCP Grey that looked at the origin and history of the name Tiffany. It’s a great video and I highly recommend it. But last week he published…an addendum I guess you could call it.
The piece takes a look at a research path he took for the video. It happened to involve some history and genealogy, two things I personally enjoy, and found it to be a fascinating insight into his research process.
All the paths don’t lead to Rome
The screenshot above hints at the idea that sometimes work is not linear and, especially when I’m doing genealogy work, there are often tangents and dead ends. In other words, to an extent, I can relate.
One of the long-running critiques of Fox News Channel’s on air graphics is that they often distort the truth. They choose questionable if not flat-out misleading baselines, scales, and adjust other elements to create differences where they don’t exist or smooth out problematic issues.
But yesterday a friend sent me a graphic that shows Fox News isn’t alone. This graphic came from CBS News and looked at the California recall election vote totals.
If you just look at the numbers, 66% and 34%, well we can see that 34 is almost half of 66. So why does the top bar look more like 2/3 of the length of the bottom? I don’t actually know the animus of the designer who created the graphic, but I hope it’s more ignorance or sloppiness than malice. I wonder if the designer simply said, 66%, well that means the top bar should be, like, two-thirds the length of the bottom.
The effect, however, makes the election seem far closer than it really was. For every yes vote, there were almost two no votes. And the above graphic does not capture that fact. And so my friend asked if I could make a graphic with the correct scale. And so I did.
One really doesn’t need a chart to compare the two numbers. And I touch on that with the last point, using two factettes to simply state the results. But let’s assume we need to make it sexy, sizzle, or flashy. Because I think every designer has heard that request.
A simple scale of 0 to 66 could work and we can see how that would differ from the original graphic. Or, if we use a scale of 0 to 100, we can see how the two bars relate to each other and to the scale of the total vote. That approach would also have allowed for a stacked bar chart as I made in the third option. The advantage there is that you can easily see the victor by who crosses the 50% line at the centre of the graphic.
Basically doing anything but what we saw in the original.
Credit for the original goes to the CBS News graphics department.
