Perseverance landed on Mars on 18 February, almost a month ago. The video and photography the rover has already sent back has been stunning. We all know she is the most capable rover yet landed on the Red Planet, but what we all want to know is how cute is Perseverance compared to her predecessors?
Another week is over, and for the past few years I’ve often said we all made it to the end of the week. When in reality, for the last few months, thousands of people were not. We’ve started using Monday to sort of recap the state of the pandemic in a select region of the country. And then we moved straight into how the New York Times addressed the US reaching the grim milestone of 500,000 deaths.
So I want to end this week with a little story told over at xkcd that tries to explain these new mRNA vaccines. Who doesn’t love science, science fiction, and humour woven together into a narrative? True, this isn’t really data visualisation, but it dovetails nicely into the work we’ve been doing and reviewing of late. Plus, levity. We all need levity.
The middle third of the United States sits under some pretty cold Arctic air, helping to bring frozen precipitation, i.e. snow, to places unfamiliar with it, most notably Texas. I say unfamiliar, but Texas is also negligently unprepared. There are photos circulating the internet of Texarkana, a city straddling the Texas–Arkansas border, of the Arkansas side plowed and safe for travel whilst the Texas side…is not. You also have a deregulated and privatised energy grid, which has seen wholesale prices spike to $9,000 per megawatt hour. (Some companies in the Texan market charge wholesale rates to their customers, so I wouldn’t be surprised to see stories coming out in the next few weeks of excessively large electric bills.)
What’s driving this Texas-scale cold? Why is Arctic air over Dallas? In years past you’ve probably heard the term “polar vortex”. The super simple version is that really cold air spins in a tight upper-atmosphere vortex over the pole, hence the name. But when the vortex weakens, say due to warmer air, it becomes a bit more unstable. When it becomes unstable, a chunk of it might break off and descend south. It doesn’t always descend over continental North America, but when it does… well, since the mid 2010s we’ve been calling it the polar vortex. Keep in mind, that it’s not, it’s a part of an upper-level low whose cold air eventually falls to the surface, but despite my protestations, the name has stuck.
Anyway, it means we presently are witnessing some frigid temperatures across the US and this graphic from the National Weather Service (NWS) highlights just how extreme those temperatures are.
The distance is no surprise here, because in winter one would expect Minneapolis and Miami to exhibit extreme temperature differences. But it’s the scale of this difference that is so dramatic.
I could probably do a whole piece—or several—about the design of NWS graphics, but I’ll just point out that I’d probably lighten the black lines working as state/provincial borders. And while their colours are standardised, I wonder if making a clearer distinction between freezing and above freezing (32ºF in this map) would make some more sense.
No two rivers are the same, though they certainly can be similar. Rivers have their own ecosystems and when I was at school, I learned of the different classifications of rivers by the colour of their water: black, white, and clear. Broadly speaking, that just means the amount of sediment dissolved in the river’s water. Black colours appear when slow moving water has absorbed lots from its environment, think swamps. White waters resemble tea or coffee with added milk or cream. This happens when sediments enter and dissolved into the water. Clear water is that, relatively clear and free of sediment.
But a team of scientists at University of North Carolina at Chapel Hill (UNC Chapel Hill) recently released some work where they used shifts in blue to yellow and green to help classify rivers. Their classification differs, but broadly can point to a change from healthy (blue) to unhealthy (yellow and green). The novelty in their work, however, focuses on using satellite imagery to capture the colour of rivers and their evolution since the mid 1980s.
They published their findings as an interactive application driven primarily by a clickable map. Clearly not all rivers are available, but a large number are, and you can see some obvious patterns at a national scale—their work excludes Alaska and Hawaii. If blue represents healthy rivers, we see healthy rivers in New England and the Pacific Northwest with a host of green rivers in the Mid-Atlantic and Upper Midwest with yellow in the Mississippi basin and southeast.
I wanted to look at Pennsylvania a bit more specifically given my familiarity with the Commonwealth and zoomed in a bit on the map.
You can see that using that above scale, Pennsylvania’s rivers are in okay, not great state. Some of the upper stretches of the Delaware and Susquehanna Rivers are coloured blue, but we mostly see a lot of green.
To the right of the map, the designers placed three smaller charts driven by the user’s selection of river. Let’s take a look at the Juniata River as an example—my grandfather grew up living alongside a tributary that emptied into the Frankstown Branch just a short walk from his house.
We can see that the chart on the upper right shows the colour shift over the decades for that observed section of the river. The legend provides the information that the section of the river has shifting blue—gotten healthier—and then below it looks for any seasonal changes. Here the chart is grey, indicating the system lacks enough data for a clear trend. This examines the short changes we might see in a river based on seasonal effects like rainy season, dry season, and human-driven effects—perhaps we pollute more in the spring and then use rivers recreationally in the summer.
Finally a distribution of the river section’s colour, all in wavelengths of light.
My biggest critique here would be the wavelengths. Users likely will not the colour spectrum by wavelength, and adding some labels like blue, yellow, and green could go a long ways to help users understand at what they are looking.
Overall, though, this is a really fascinating project.
Today’s post is not about data visualisation per se, but rather an element of it: colour. Two weeks ago, the Times reported on the creation of a new artificially made pigment of the colour blue.
You can read the article for the full details, but the new pigment contains yttrium, indium, and manganese. Combine the symbols for those elements, Y, In, and Mn, and you have YInMn Blue. In particular, the colour exhibits permanence and thus does not fade, say when mixed with water.
And it’s non-toxic, because for those who don’t know, some of the most popular paint colours in history have turned out to be toxic. White paint? Made with lead. Some of your bright, rich reds? Turns out cadmium can kill. And with blues we often see cobalt or chromium as part of the mixture and, guess what, they’re both toxic. But not YInMn.
Last summer, the Environmental Protection Agency (EPA) approved the pigment for commercial use. And so we can begin to use it in oils and watercolour paints. (The EPA had approved its use for industrial purposes back in 2017. Check out this article for an image of the blue used to make an electric guitar.)
For data visualisation and design purposes, for web stuff, colours work differently. The blue in the screenshot above from the Times article, that is made by photons emitted by your computer or mobile phone. Whereas, when you view that pile of pigment in person, or a guitar body, or a painting—all in person—what you are seeing is the absorption and reflection of light waves striking the objects. What you see is the portion of the light wave that is reflected, i.e. not absorbed, by the object.
So it’s possible that we could see YInMn Blue as the basis for a paint used in printing and therefore tints of it used to make a choropleth map of freshwater availability. But if your work is strictly digital/web based, this probably won’t make too much of an immediate impact.
With Covid-19, one of the big challenges we face is the rapid mutations in the viral genetic code that have produced several beneficial—from the virus’ standpoint—adaptations. Several days ago the New York Times published a nice, illustrated piece that showed just what these mutations look like.
Of course, these were not just nice illustrations of protein molecules, but the screenshot below is of the code itself and you can see how just a few alterations can produce subtle, but impactful, effects.
In a biological sense, these mutations are nothing new. In fact, humanity wouldn’t be humanity but for mutations. Rather we are seeing evolution play out in front of our eyes—albeit eyes locked in the same household for nearly a year now—as the virus evolves adaptations better suited to spreading and surviving in a host population.
The piece includes several illustrations, but begins with an overall, simplified diagram of the virus and where its genetic code lies. And then breaks that code down similar to a stacked bar chart.
Designers identify where in the code the different mutations occur and the type of mutation. Later on in the piece we see a map of where this particular variant can be found.
I might come back to that map later, so I won’t comment too much on it here.
But I think this piece does a great job of showcasing just what we mean when we talk about virus mutations. It’s really just a beneficial slip up in the genetic alphabet.
Credit for the piece goes to Jonathan Corum and Carl Zimmer.
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.
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.
If you didn’t hear the news, scientists have discovered a compound in the atmosphere of Venus. They’ve also ruled out a number of the normal ways the compound is created, and we’re left with two possibilities: some kind of unknown chemistry/chemical process or…aliens.
It’s got to be aliens. Because it’s Friday.
And because it’s Friday, we can turn to xkcd, who covered this news brilliantly.