It’s time for another Friday just for fun posting. I once worked with a guy who could draw a map of the United States or the world on a whiteboard incredibly accurately. He then left it in the break room for the office to try and label correctly.
This is kind of that, but in reverse, from xkcd. Good luck.
Credit for the piece goes to Randall Munroe.
With the rollout of the first vaccination programme in the United Kingdom, the BBC had a helpful comparison table stating the differences between the four primary options. It’s a small piece, but as I often say, we don’t necessarily need large and complex graphics.
Since there are only four vaccines to compare and only a handful of metrics, a table makes a lot of sense.
But I wanted to take it a step further and so I threw together a quick piece that showed some of the key differences. In particular I wanted to focus on the effectiveness, storage temperatures (key to distribution in the developing world), and cost.
You can pretty quickly see why the United Kingdom’s vaccine developed by Oxford University and produced by AstraZeneca is so crucial to global efforts. The cost is a mere fraction of those of the other players and then for storage temperature, along with Russia’s Sputnik vaccine, it can be stored at common refrigerator temperatures. Both Pfizer’s and Moderna’s need to be kept chilled at temperatures beyond your common freezer.
And in terms of effectiveness, which is what we all really care about, they’re fairly similar, except for the Oxford University version. Oxford’s has an overall effectiveness of 70%. (In)famously, it exhibited a wide range of effectiveness during trials of between just over 60% and 90%.
The 60-odd% effectiveness was achieved when using the recommended dosage. However, in one small group of trial participants, they erroneously were given a half-dosage. And in that case, the dosage was found to be far more effective, approximately 90%. And this is why we would normally have longer, wider-ranging trials, to dial in things like doses. But, you know, pandemic and we’re trying to return to some sense of normalcy in a hurry.
All that said, Oxford’s will be crucial to the developing world, where incomes and government expenditures are lower and cold-storage infrastructure much less, well, developed. And we need to get this coronavirus under control globally, because if we don’t, the virus could persist in reservoirs, mutating for years until the right mutation comes along and the next pandemic sweeps across the globe.
I know we’re presently all fighting about wearing masks, but when we get to having vaccines available to the public, let’s really try to not make that a political issue.
Credit for the original piece goes to the BBC.
Credit for my piece goes to me.
I remember hearing and reading stories as a child about the Thames in London freezing over and hosting winter festivals. Of course most of that happened during what we call the Little Ice Age, a period of below average temperatures during the 15th through the early 19th century.
But those days are over.
The UK’s Meteorological Office, or the Met for short, released some analysis of the impacts of climate change to winter temperatures in the United Kingdom. And if, like me, you’re more partial to winter than summer, the news is…not great.
Broadly speaking, winters will become warmer and wetter, i.e. less snowy and more rainy. Meanwhile summers will become hotter and drier. Farewell, frost festivals.
But let’s talk about the graphic. Broadly, it works. We see two maps with a unidirectional stepped gradient of six bins. And most importantly those bins are consistent between the maps, allowing for the user to compare regions for the same temperatures: like for like.
But there are a couple of things I would probably do a bit differently. Let’s start with colour. And for once we’re not dealing with the colour of the BBC weather map. Instead, we have shades of blue for the data, but all sitting atop an even lighter blue that represents the waters around the UK and Ireland. I don’t think that blue is really necessary. A white background would allow for the warmest shade of blue, +4ºC, to be even lighter. That would allow greater contrast throughout the spectrum.
Secondly, note the use of think black lines to delineate the sub-national regions of the UK whilst the border of the Republic of Ireland is done in a light grey. What if that were reversed? If the political border between the UK and Ireland were black and the sub-national region borders were light grey—or white—we would see a greater contrast with less visual disruption. The use of lines lighter in intensity would allow the eye to better focus on the colours of the map.
Then we reach an interesting discussion about how to display the data. If the purpose of the map is to show “coldness”, this map does it just fine. For my American audience unfamiliar with Celsius, 4ºC is about 39ºF, many of you would definitely say that’s cold. (I wouldn’t, because like many of my readers, I spent eight winters in Chicago.)
The article touches upon the loss of snowy winters. And by and large, winters require temperatures below the freezing point, 0ºC. So what if the map used a bidirectional, divergent stepped gradient? Say temperatures above freezing were represented in shades of a different colour like red whilst below freezing remained in blue, what would happen? You could easily see which regions of the UK would have their lowest temperatures fail to fall below freezing.
Or another way of considering looking at the data is through the lens of absolute vs. change. This graphic compares the lowest annual temperature. But what if we instead had only one map? What if it coloured the UK by the change in temperature? Then you could see which regions are being the most (or least) impacted.
If the data were isolated to specific and discrete geographic units, you could take it a step further and then compare temperature change to the baseline temperatures and create a simple scatterplot for the various regions. You could create a plot showing cold areas getting warmer, and those remaining stable.
That said, this is still a really nice piece. Just a couple little tweaks could really improve it.
Credit for the piece goes to the UK Met Office.
Once more we look at the Covid-19 outbreak in Pennsylvania, New Jersey, Delaware, Virginia, and Illinois. And things are bad getting worse. I skipped last week because I was on holiday for Thanksgiving, but the data was perhaps not the most indicative of the current state of affairs at the time. But we now have a full week’s worth of data since the holiday, and like I said at the top, things are bad. Especially when we compare the charts below to where we were two weeks ago.
We look at new cases and we can see the impact of Thanksgiving on the shape of the curves. Note how in Pennsylvania, New Jersey, and to a lesser extent Delaware, we see a sharp plateau with the average before a sudden resumption of positive results. That’s Thanksgiving for you. You can see a similar, though perhaps more pronounced pattern in Illinois and Virginia where both states actually saw their average rate of new cases per day fall over the holiday.
Illinois, however, had been trending downward before Thanksgiving, and it might be due in part to the lockdown imposed by the city of Chicago. Whilst unpopular, lockdowns are an effective way of tamping down rising rates of new cases—vital to maintain capacity at hospitals and intensive care units (ICUs).
Of course cities and states are slowly implementing their own new lockdowns now. Philadelphia has been in one for two weeks now. Of course, we would want to wait 2–4 weeks to see if the numbers of new cases begin to fall, but the big intervening factor here is that very same Thanksgiving holiday. Did people travel? Anecdotally, I can say that the rooftop deck of my building’s parking garage, which I can see from my flat, was empty but for about five cars including my own. So people definitely travelled and likely visited other households. Not great.
And that could set us back, because new cases lead to new hospitalisations lead to new deaths. I would say that we probably should not expect as many deaths as we saw in the spring, because we are no longer dealing with a new virus. We know how to treat it far more effectively. But we also see that people aren’t taking the most basic preventative measures: wear a mask, and stay isolated.
In Illinois we are seeing death rates in excess of what we saw in the spring. And Pennsylvania isn’t far behind what we saw in March and April.
And so I am increasingly worried that we will see more death in the winter than we did in the spring. And it’s depressing because so much of it could be avoided. Wearing a mask isn’t 100% effective. And there’s no guarantee that the few people an isolated household interact with, e.g. the delivery guy, aren’t themselves vectors. But both measures are far more effective than only occasionally wearing a mask at a house party to celebrate the holidays because we won’t let the virus beat us and interfere with our way of life.
The virus doesn’t care and you and I are tired of it. Tired of isolation. Of wearing masks. The virus is out there, spreading, and making people sick. And a fraction of those people are becoming ill enough to warrant hospitalisation. And a fraction of those are dying.
The next several weeks are going to be awful.
But you know that now. And you can brace yourself.
Credit for the graphics is mine.
And by world, I mean one-bedroom apartment. It’s another Friday, and so here’s a nice piece from Indexed and Jessica Hagy. It sums up rather well our worlds these days.
Credit for the piece goes to Jessica Hagy.
Yesterday we looked at an article from the Inquirer about the 2020 election and how Biden won because of increased margins in the suburbs. Specifically we looked at an interactive scatter plot.
Today I want to talk a bit about another interactive graphic from the same article. This one is a map, but instead of the usual choropleth—a form the article uses in a few other graphics—here we’re looking at three-dimensional pyramids.
Yesterday we talked about the explorative vs. narrative concept. Here we can see something a bit more narrative in the annotations included in the graphic. These, however, are only a partial win, though. They call out the greatest shifts, which are indeed mentioned in the text. But then in another paragraph the author writes about Bensalem and its rightward swing. But there’s no callout of Bensalem on the map.
But the biggest things here, pun intended, are those pyramids. Unlike the choropleth maps used elsewhere in the article, the first thing this map fails to communicate is scale. We know the colour means a county’s net shift was either Democratic or Republican. But what about the magnitude? A big pyramid likely means a big shift, but is that big shift hundreds of votes? Thousands of votes? How many thousands? There’s no way to tell.
Secondly, when we are looking at rural parts of Bucks, Chester, and Montgomery Counties, the pyramids are fine. They remain small and contained within their municipality boundaries. Intuitively this makes sense. Broadly speaking, population decreases the further you move from the urban core. (Unless there’s a secondary city, e.g. Minneapolis has St. Paul.) But nearer the city, we have more population, and we have geographically smaller municipalities. Compare Colwyn, Delaware County to Springfield, Bucks County. Tiny vs. huge.
In choropleth maps we face this problem all the time. Look at a classic election map at the county level from 2016.
You can see that there is a lot more red on that map. But Hillary Clinton won the popular vote by more then 3,000,000 votes. (No, I won’t rehash the Electoral College here and now.) More people are crowded into smaller counties than there are in those big, expansive red counties with far, far fewer people.
And that pattern holds true in the Philadelphia region. But instead of using the colour fill of an area as above, this map from the Inquirer uses pyramids. But we face the same problem, we see lots of pyramids in a small space. And the problem with the pyramids is that they overlap each other.
At a glance, you cannot see one pyramid beind another. At least in the choropleth, we see a tiny field of colour, but that colour is not hidden behind another.
Additionally, the way this is constructed, what happens if in a municipality there was a small net shift? The pyramid’s height will be minimal. But to determine the direction of the shift we need to see the colour, and if the area under the line creating the pyramid is small, we may be unable to see the colour. Again, compare that to a choropleth where there would at least be a difference between, say, a light blue and light red. (Though you could also bin the small differences into a single neutral bin collecting all small shifts be them one way or the other.)
I really think that a more straight forward choropleth would more clearly show the net shifts here. And even then, we would still need a legend.
The article overall, though, is quite strong and a great read on the electoral dynamics of the Philadelphia region a month ago.
Credit for the piece goes to John Duchneskie.
The thing with election results is that we don’t have the final numbers for a little while after Election Day. And that’s normal.
There are a few things I want to look at in the coming weeks and months once my schedule eases up a bit. But for now, we can use this nice piece from the Philadelphia Inquirer to look at a story close to home: the vote in the Philadelphia suburbs.
I’ve already looked at some analysis like this for Wisconsin and I shared it on my social. But there I looked at the easy, county-level results. What the Inquirer did above is break down the Pennsylvania collar counties of Philadelphia, i.e. the suburbs, into municipality level results. It then plotted them 2020 vs. 2016 and the results were—as you can guess since we know the result—Biden beat Trump.
What this chart does well is colours the municipalities that Biden flipped yellow. It’s a great choice from a colour standpoint. As the third of the primaries, with both blue and red well represented, it easily contrasts with the Biden- and Trump-won towns and cities of the region. The colour is a bit “darker” than a full-on, bright yellow, but that’s because the designers recognised it needs to stand out on a white field.
Let’s face it, yellow is a great colour to use, but it’s difficult because it’s so light and sometimes difficult to see. Add just the faintest bit of black to your mix, especially if you’re using paints, and voila, it works pretty well. So here the designer did a great job recognising that issue with using yellow. Though you can still see the challenge, because even though it is a bit darker, look at how easy it is to read the text in the blue and the red. Now compare that to the yellow. So if you’re going to use yellow, you want to be careful how and when you do.
The other design decision here comes down to what I call the explorative vs. the narrative. Now, I don’t think explorative is a word—and the red squiggle agrees—but it pairs nicely with narrative. And I’ve been talking about this a lot in my field the last several works, especially offline. (In the non-blog sense, because obviously all my work is done online these days. Oh, how I miss my old office.)
Explorative works present the user with a data set and then allow them to, in this case, mouse over or tap on dots and reveal additional layers of information, i.e. names and specific percentages. The idea is not to tell a specific story, but show an overall pattern. And if the piece is interactive, as this is, potentially allow the user to drill down and tease out their own stories.
Compare that to the narrative, my Wisconsin piece I referenced above is more in this category. Here the work takes you through a guided tour of the data. It labels specific data points, be them on trend or outliers and is sometimes more explicit in its analysis. These can also be interactive—though my static image is not—and allow users to drill down, and critically away, from the story to see dots of interest, for example.
This piece is more explorative. The scatter plot naturally divides the municipalities into those that voted for Biden, Trump, and then more or less than they voted for Trump in 2016. The labels here are actually redundant, but certainly helpful. I used the same approach in my Wisconsin graphic.
But in my Wisconsin graphic, I labelled specific counties of interest. If I had written an accompanying article, they would have been cited in the textual analysis so that the graphic and text complemented each other. But here in the Inquirer, it’s a bit of a missed opportunity in a sense.
The author mentions places like Upper Darby and Lower Merion and how they performed in 2020 vis-a-vis 2016. But it’s incumbent on the user to find those individual municipalities on the scatter plot. What if the designer had created a version where the towns of interest were labelled from the start? The narrative would have been buttressed by great visualisations that explicitly made the same point the author wrote about in the text. And that is a highly effective form of communication when you’re not just telling, but also showing your story or argument.
Overall it’s a great article with a lot to talk about. Because, spoiler, I’m going to be talking about it again tomorrow.
Credit for the piece goes to Jonathan Lai.
Who remembers when AOL used to announce that to you? Old millennials, am I right?
Anyway, your humble author is using up some more holiday time the next several days and will be on holiday for Thanksgiving. Not that I will be travelling anywhere to see anybody. And for my American audience, you really shouldn’t be travelling either.
But that’s for a Covid-19 post. This is about e-mail. Because even though today is a Wednesday, it’s more like a Friday. So thanks to xkcd we have this post on how everything eventually becomes like e-mail.
For the record, I’m at 999+ on my personal account and at 1200+ at work. So yeah, one of these days maybe I’ll clean it out.
And if you have a thought about this, just send me an e-mail. I’ll read it eventually.
Credit for the piece goes to Randall Munroe.
Over the last week or so, we have been receiving some encouraging news from the makers of three viable Covid-19 vaccines: Pfizer, Moderna, and AstraZeneca. All three have reported their vaccines as at least 90% effective. This doesn’t mean the relevant regulatory agencies have verified that data, but it’s better than injecting ourselves with bleach.
Keep this in mind, though, a full vaccination roll out will take months. Having 20–40 million doses is great, but the population of the United States is 330 million. The expectation is a return to normalcy will not really begin until the end of Q3 or beginning of Q4 2021.
This article from the Washington Post does a good job of explaining some of the next steps—and some of the significant logistical hurdles. They illustrate part of the process of shipping the Pfizer vaccine, which needs to remain cooled -70ºC. That’s -94ºF. A wee bit colder than most normal freezers operate.
The Post article also illustrates how the Pfizer/Moderna type of vaccine works—the Pfizer and Moderna tackle it one way whilst AstraZeneca tackles it via a second method.
There’s a lot going on here, but I like the simplified approach the designers took. This whole situation is complicated, but here we see the process distilled to its most essential elements. And the restrained use of colour helps tremendously.
The vial and then needle are filled red, and that red colour carries through into the messenger RNA (mRNA) that is absorbed by the cells and ultimately creates the spike proteins used by the virus (not the virus itself).
Credit for the piece goes to Carolyn Y. Johnson and Aaron Steckelberg.
I have been taking and have yet to take a lot of holiday time this year. So apologies for the sporadic posting. But we’re working this week, because travelling to see family this year is a bad idea.
So the last Covid-19 update I posted was about a month ago. A lot has happened in the last month, like an entire election. But you really should go back a month and look at the charts for the five states I cover. At the time I said things were
Bad and getting worse.
I added that
while we are seeing dramatic rises in new cases, we are not yet seeing the rises in deaths that accompanied similar rises in March and April
And so let’s take a look at where we are now. First with cases.
And now with those deaths.
I mean…
This has all been so obvious for so long. And yet, I had to run two errands yesterday—timed so that I’d be running them whilst most of Philadelphia watched their football team and they must have played poorly from all the people yelling “c’mon” out their windows—and whilst the streets were fairly empty, about half of the people I passed either had their masks down, were doing that idiotic cover-the-mouth-but-not-the-nose thing, or—and this is the kicker—flat out had no mask on at all.
I’ll repeat what I said a month ago, things are bad and getting worse. But, maybe unlike a month ago, people will start taking this seriously. Because a month ago I wrote about how new deaths were not yet at the levels of the spring.
Well take a look at Illinois. They got there in just four weeks.
Pennsylvania? Halfway there.
New Jersey? Starting to rise a little bit faster now.
Virginia? Well Virginia has one of the odder death patterns I’ve seen—partly by their repeated cycling through backlogged data—but it’s clearly on the upswing now.
Delaware? It’s hard to see because the numbers are so small, but it’s also on the rise.
So please, just wear a bloody mask.
Credit for the piece is mine.
