A4 For Ever (and Ever)

Most of my readers know that I am a designer who works in all formats. But, I really love working in print. Colours, textures, and the physicality of it all. Give me a foil stamp or metallic ink any day.

Any American designer who’s ever worked for an overseas client or overseas designer who’s ever worked for an American client knows all about the US Letter vs A4 debate.

For those that don’t, the US (along with Canada, Mexico, and a very few other countries) use what we call letter size paper. The rest of the world uses A4, part of the ISO 216 international standard. A4 has some special properties that make it the superior choice in my opinion.

But this is a Friday, so we’re here for the lighter take. And for that we have a video by CCP Grey, who explains some of the properties of A4 and then provides a fascinating perspective on it all. It’s about nine minutes long for what it’s worth.

A4 is in the middle.

Credit for the piece goes to CCP Grey.

Viral Mutations

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.

Dove vs. Hawk

Earlier, I saw these two graphics floating around the Twitter. They each come from a major financial institution and attempt to place the voting (and non-voting members) of the Federal Open Market Committee (FOMC) on a spectrum of doves to hawks or slightly less dovish. The FOMC, part of the Federal Reserve system, sets interest rates for the US economy. Now, I’m being super simplistic here, but it’s broadly true. I should add, full disclosure, I presently work for the Federal Reserve Bank of Philadelphia.

The first graphic is from JPMorgan and plots in one-colour all the voting and non-voting members on a single axis from very dovish to somewhat less dovish. Thin black lines point to evenly spaced points on the axis and people are listed at each interval.

It’s a fairly simple approach, but effective. Nothing revolutionary here. What I find a bit odd is the line underneath the centre tick. What prompts that group to have what I’ll call a summary bar? Is it because Jay Powell, the chair of the Federal Reserve, is placed within that group? It’s a bit unclear.

Now keep in mind the classifications here, very dovish and somewhat less dovish, as we compare JPMorgan’s graphic to that of Bank of America.

The first thing that strikes me is the use of colour. Here we have a fairly straightforward divergent spectrum of red to blue. Along with other design elements, like typographic scale and contrast for the header, subhead, and labels, this piece strikes me as better designed and more polished.

But I still have questions.

Here we have dovish to hawkish. At the hawkish extreme, we have Esther George of Kansas City and Robert Kaplan of Dallas. In JPMorgan’s chart, both are grouped together as somewhat less dovish. But with Bank of America, they are decidedly hawkish. (Although with nine intervals, the Bank of America graphic has a bit more granularity than JPMorgan’s.)

So the biggest question, unfortunately left unanswered by each graphic, is what defines hawkish and somewhat less dovish? Just by words, they sound not at all alike. But both companies clearly place both individuals at the same end of the spectrum.

Part of the issue stems from the divergence point between red and blue. For most spectra of this type, that would be the demarcation between a committee member who is a dove or a hawk. But we have no similar separation for JPMorgan.

There is, however, one design element for Bank of America’s piece that I really like. My explanation of the FOMC at the top was a bit simplistic. Not every regional Federal Reserve president gets to vote every year. They rotate each year except for New York. These presidents get to vote alongside those on the Board of Governors.

In the graphic, note that everybody above the axis label is a member of the Board, i.e. they get to vote every year until their term expires. Below the axis we have the rotation schedule. Each line represents a bank president who can vote in a particular year. For example, the Philadelphia president, Patrick Harker, was a voting member on the committee in 2020, but falls off in 2021 and will not return to 2023. The Bank of America graphic captures this for each president very well.

I am a bit confused as to why some members, i.e. Kaplan and John Williams of New York, appear to sit between lines. I am unaware of any reasons why they would be between years.

Overall, I prefer the Bank of America piece. It more clearly presents the rotation element of the voting members of the FOMC. Yes, it has colours, but I’m confused as to why the demarcation between doves and hawks happens where it does. And why JPMorgan doesn’t describe anyone as a hawk. So while I prefer it, I think it could still use some additional information or context to make it clearer to readers.

Credit for the JPMorgan piece goes to a designer at JPMorgan.

Credit for the Bank of America piece goes to a Bank of American Global Research designer.

Difficult Descendancy Charts

The holiday break is over as your author has burned up all his remaining time for 2020 and so now we’re back to work. And that means attempting to return to a more frequent and regular posting schedule for Coffeespoons.

I wanted to start with the death of Diego Maradona, a legendary Argentinian footballer. He died in December of a heart attack and left behind a complicated inheritance situation. To help explain the situation, the BBC created what in genealogy we call a descendancy chart. You typically use a descendancy chart to show the children, and sometimes grandchildren, of a person. (You can also attach people above the person of interest and show the person’s ancestral families.)

This is an example of a descendancy chart from my research into an unrelated family.

The descendants of Samuel Miller

You can see Samuel Miller married Sabra Clark and had at least nine children with her. And I followed one of them, another Samuel, who married Elizabeth Woodruff and they had four children. In this version, you can also see Samuel the elder’s parents and siblings.

But Diego presents a complicated situation. He was married and had two children, then divorced. That’s not terribly uncommon. But he then went on to have potentially eight children with potentially five different women. (I say potentially because some of the claims are still working their way through the courts via paternity tests.)

The above type of chart works well with one couple. In my own family, I have at least one ancestor who had potentially two husbands (the second marriage has not yet been confirmed, but she definitely had children with two different men). And when we use this chart type to look at my ancestor’s descendants, you can see it becomes tricky.

Mary Remington’s descendants

Her children’s fathers can be placed to either side and then the children flow out from that. But whereas in the first chart we could see all nine children in one glance, Mary Remington had four and we only see two in this same view.

So how do you deal with one person who has six total relationships that have offspring?

The BBC opted for a vertical chart that uses colour to link the couples. Diego and his ex-wife receive a red line, and that link moves vertically down from Diego with the two daughters shown as descendants on the right.

Diego Maradona’s descendants

Each subsequent relationship with offspring receives its own colour and continues to move vertically down the page, linking the mother on the left to the children on the right.

What I find interesting is the inconsistency within the chart, however. At the end, with the unidentified women, we have two instances of multiple children. Santiago Lara and Magali Gil, for example, descend from one stem. But note at the top how Diego’s two daughters Gianinna and Dalma each receive their own stem. Is there a reason for combining the two children from one unidentified mother into one branch?

And why the vertical format? You can see in my two examples, we are looking at a horizontal format. It works well when I am working on my desktop. The format is less useful on a mobile. I wonder if the BBC knows from their analytics that most people access their content like this via mobile phone and created a graphic that best uses that tall but narrow proportion. Because the proportions do not work well when the article is viewed on a desktop.

The vertical descendancy chart here is an intriguing solution to show descendants from multiple partners in a single mobile screen display. I am not sure how useful it would be as a new form, because I am not certain of how many times we would run into issues of children from six partners, but it could be worth exploring.

Credit for the images from my examples goes to the designers at Ancestry.com.

Credit for the BBC graphic goes to the graphics department of the BBC.

How Would the Covid-19 Vaccines Work

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.

The first steps in the process.

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.

Red Shift, Blue Shift

Last night I published a graphic on Instagram that I think people may find helpful if they try to follow Election Day results on Tuesday. I wanted to explain the concept of a red shift or blue shift. (I’ve also seen it described as states having a red mirage or a blue mirage.)

For my non-American readers, it’s important to understand that while this is a national election, the United States’ federal system means that each state runs its own election with its own rules and they can vary some state to state. For example, early or mail-in voting can vary significantly from state to state with some states allowing it only in emergencies (and some of those this cycle will not allow people to cite COVID-19 as an emergency).

Another factor for everyone to consider is that polling indicates President Trump’s fraudulent messaging about, well, voting fraud has shifted a normally split use of early/mail-in voting to a Democratic advantage. In other words, Democrats are far more likely to vote early, either in person or by post. Republicans are far more likely to vote on Election Day.

Combine those two factors and we get Red Shift vs. Blue Shift.

Some states allow election officials to begin counting their early votes prior to Election Day. Other states forbid counting until Election Day morning, or in some cases until after the polls close.

In states where early votes can be counted—the swing states Arizona, Florida, and North Carolina are among this group—it is possible that when the polls close, or shortly thereafter, we will see an instant and enormous lead for Joe Biden. But, as the states begin to count in-person day-of votes, which again favour Republicans, Trump may begin to eat into those margins. The question will be, can Trump’s numbers eat in so much that when the final counts are complete, he can overtake those Biden numbers? This is the Red Shift.

Conversely we have the Blue Shift. In these states—swing states like Georgia, Michigan, Pennsylvania, Texas, and Wisconsin are in this group—election officials cannot begin to count early votes either until the morning or when the polls close. In these states we may see the in-person day-of votes, largely expected to be for Republicans, run up to high totals fairly quickly. At that time, Trump may have a significant lead. Then when officials pivot to counting the early votes, Biden will begin to eat into those margins. And again, the question will be, can Biden eat into those margins sufficiently to shift the outcome after all the votes are counted?

Be prepared to hear about these scenarios Tuesday night.

Credit for the piece is mine.

Super Spreading Garden Parties

If you were unaware, in the wee hours of Friday, President Trump announced that he had tested positive for the coronavirus that causes Covid-19. It should be stated in the just three days hence, there is an enormous amount of confusion about the timeline as the White House is not commenting. From the prepared statement initially released it seems Trump first tested positive Wednesday. But that statement was then changed to fit the diagnosis in the wee hours of Friday morning. But just last night I saw reporting saying that test was actually a second, confirmatory test and the president first tested positive earlier Thursday.

The timeline is also important because it would allow us to more definitively determine when the president was infected. The reporting indicates that he caught the virus at a Rose Garden ceremony at the White House to introduce his Supreme Court nominee, Amy Coney Barrett. This BBC graphic does a great job showing who from that ceremony has tested positive with the virus.

The photo also does a great job showing how the seven people there were situated. Six of the seven did not wear masks, only North Carolina Senator Thom Tillis did. There is no social distancing whatsoever. And not shown in this photo are the indoor pre- and post-ceremony festivities where people are in close quarters, mingling, talking, hugging, shaking hands, all also without masks.

It should be noted others not in the photograph, e.g. campaign manager Bill Stepien, communications advisor Hope Hicks, and body man Nicholas Luna, have also now been confirmed positive.

The final point is that this goes to show how much the administration does not take the pandemic seriously. Right now the Covid data for some states indicates that the virus is beginning to spread once again. And so maybe this serves as a good reminder to the general public.

Just because you are socialising outdoors does not make you safe. Outdoors is better than indoors. No gatherings is better than small gatherings is better than large, well attended garden parties. Masks are better than no masks. Rapid result test screening is better than no test screening. Temperature checks are better than no temperature checks.

But the White House only did that last one, temperature checks, in order to protect the president before admitting people to the Rose Garden. Compare that to how they protect the president from other physical threats. He has Secret Service agents standing near him (or riding with him in hermetically sealed SUVs for joyrides whilst he is infected and contagious); he has checkpoints and armed fences further out to secure the perimeter. Scouts and snipers are on the White House roof for longer range threats. And there is a command centre coordinating this with I presume CCTV and aerial surveillance to monitor things even further out. In short, a multi-layered defence keeps the president safe.

If you just take temperatures; if you just hang out outside; if you just wear masks; if you just do one of those things without doing the others I mentioned above, you are putting yourself—and through both pre-diagnostic/pre-symptomatic and asymptomatic spreading, others—at risk.

But on Sunday night, Trump campaign strategist went on television said that now that President Trump has been infected, been hospitalised, he is ready to lead the fight on coronavirus. Great. We need leadership.

But where was that leadership seven months ago when your advisors told you in January about the impact this pandemic would likely have on the United States? Where was the leadership in February saying the coverage was a hoax? Where was it in March when he said the virus would go away in April with the warmer weather? Where was it in April when it didn’t go away, when things continued to get worse? Where was it in May when thousands of Americans were dying? Where was it in June when states began to reopen even though the virus was still out-of-control and testing and contact tracing was less available than necessary to contain outbreaks? Where was it in July? And August? And September? Where was the leadership at a Rose Garden party celebrating the nomination of a Supreme Court justice, a party where at least seven people have been infected and one of them, the president of the United States, has been hospitalised with moderate to severe symptoms?

Credit for the piece goes to the BBC.

Wear a Mask

It sounds so easy, but too many don’t do it.

Yesterday, Agence France-Presse published an article about a recent study in the Journal of the American Medical Association that examined the efficacy of the coronavirus’ airborne spread potential.

The study centred on a bus trip in eastern China from January, before the widespread adoption of masks as common courtesy let alone mandated safety equipment. Nobody on the bus of 68 travellers wore a mask and the bus’ air conditioning system recycled the air inside the vehicle. (Remember the importance of cycling and filtering air inside subway cars?)

Researchers then mapped the location of patient zero, conveniently from my point of view in the centre of the bus. It should also be noted that patient zero was also asymptomatic at the time of the bus trip. Then researchers mapped the seats of those infected on the bus and this is what they found.

One of the key findings is these conditions, recirculated air amongst people not wearing masks, the virus was able to infect people outside the 2-metre safety radius (6-feet in the non-metric States).

Now from a design standpoint, I really like this graphic. It shows people’s seats and their condition to show the physical spread of the virus from patient zero. (Eerily, people far away were infected whilst one person sitting next to patient zero remained uninfected.) Not only that, but from a chain of transmission standpoint, the designer also included how many people these newly infected victims infected. Some infected nobody further whilst others infected up to four additional people.

My only real quibble here is with the colours used for the status of the infected. I think the light grey works well for those who were not diagnosed with Covid-19. But the green, yellow, blue, and red don’t quite work for me here. The value of the yellow is too close to the grey and consequently almost the mildly symptomatic people fall almost into the background. Compare that to the asymptomatic victims in green, who appear far more prominently.

I understand the desire to progress from mild to moderate vs. asymptomatic. So I wonder if those with mild symptoms were given a light blue and those with moderate a dark blue to contrast with the asymptomatic green. Of course, we still run into the red-green issue, but the dotted circle around patient zero mitigates that concern.

Remember, this was all among people not wearing masks. This piece strongly shows how important it is to wear a mask—not just to reduce the risk of receiving the virus, but to reduce your risk of spreading the virus if you are an asymptomatic case. (To be fair to the people on the bus, we knew very little about the virus in January and who knows what they knew as China was still attempting to downplay the virus.)

The point? Wear a mask.

Credit for the piece goes to John Saeki.