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.
Last week the Philadelphia Inquirer published an investigation of the staggering number of horse deaths in Pennsylvania’s race track facilities. I found the article fascinating, but admittedly at a point or two a wee bit squeamish when the author described how horses essentially die. Then about halfway through the article I ran into the first of two graphics looking at the data.
The first is pretty simple, a timeline of deaths over the course of one year, 2019. Overall it works, you can clearly see clusters of racing deaths, but that those clusters spread across the year. When I sat with the graphic for a moment, however, a few things began to stick out at me. The first was a distracting vibration in the background. Not the alternating beige and blue of the months, but if you look closely you’ll see tightly spaced lines within the colour fields: presumably the days of the month for aligning the deaths.
On a large enough graphic it makes all the sense to tick off sub-monthly increments, but in this space I would have probably opted to show only the months. Maybe weeks could have worked, as that approach may have reinforced the statistic about a horse dying every six days on average.
The second point is the black stroke or outline of each dot. Here the designer faces a challenging constraint. Essentially, the smaller the dot (or the symbol) the brighter the colour. In a rich, blood red colour you have a dark heavier colour. Compare that to say a stop sign that is bright red. It has a lighter feel. The blood red colour, in a given space, has let’s say an amount of black ink or pixels—I’m simplifying here—mixed in with the red. But in a large area, there’s enough red ink or pixels to still be clearly blood red. The stop sign red has no other colours but red. And in large areas, it can be an eye-stabbing amount of red—precisely why it’s likely so useful for, you know, stop signs.
But at the small scale of these very small dots, you still proportionally have the same amount of red and black ink, but with fewer and fewer amounts, the eye can begin to experience difficulty in truly reading the colour for what it is. For example, in an area of say 49 pixels (7×7), while the ratio of red to black may be consistent, you still only have a total of 49 pixels with which to convey “red” to the reader. Consequently, in smaller spaces, you may find that designers sometimes opt for brighter colours, a la stop sign red, than they would in larger fields of colour.
Here we have a nice use of brighter red, green, and yellow. (I will quickly add that the choice of red and green can be problematic for colour blindness, but I don’t want to revisit that here.) But to provide better separation between those small, circle sized fields of colour a border probably helps. A thin black line, or stroke, makes sense. But the black is darker than the colours themselves, thus it can draw more attention than the colour fill. And that begins to happen here. I wonder if a thin white stroke may have been less distracting and placed more emphasis on the fill colours.
As I said, overall a really nice if not sobering graphic in an important but disturbing article. I think a few small tweaks could really bring the graphic over the finish line. Pun fully intended. Sorry, not sorry.
Two weeks ago I wrote about how new cases in the states of Pennsylvania, New Jersey, Delaware, Virginia, and Illinois were stalling out, i.e. no longer declining. Additionally, with the exception of Illinois, they were stalling at rates far higher than what we saw last summer. I wrote
This means that the environment is ripe for a new surge of cases if people stop following social distancing and begin resuming indoor activities with other people. Sadly, both those things appear to be occurring throughout the US.
Two weeks hence, one of one thing inevitably occurred.
New cases are now rising in all five states. I wrote about the flat tails of the curves for the seven-day averages. A quick look at the chart shows those have swung upwards, in some cases sharply.
Two weeks ago I referenced Europe as a cautionary tale. Governments there eased up on their restrictions, cases surged, and then as hospitalisations rose, governments had to reimpose restrictions and effect new lockdowns. Europe has typically been 3–4 weeks ahead of us throughout the pandemic. So that we are now at a point where we are seeing rising cases, absolutely none of this should be surprising.
The evidence has been in our faces for weeks, plus we have the European example to look at. Reopening makes no sense until we can get case numbers lower, especially with new more virulent and lethal strains of coronavirus now circulating.
Deaths too have been trending the wrong way over the last few weeks.
We have seen the curves largely bottom out. And if you look closely, these bottoms are higher than the rates we saw last summer, in some cases more than 3× as much. This flattening occurred just a few weeks after cases began to flatten. The question becomes, will they rise in a few weeks time? Or have we vaccinated enough of our most vulnerable populations?
That’s the real wildcard.
Right now, we have only fully vaccinated about 15% of the populations of Pennsylvania, Virginia, and Illinois.
Is that enough to prevent hospitalisations and deaths in what looks like will be a fourth wave?
As many of my long-term readers know, I am really only a one sport kind of guy. And that sport is baseball. American football, well, I’ve seen one match live and in person and it was…boring. But it’s a big deal in America. And this is the time of the year when teams begin signing free agents.
I happened to be reading the Boston Globe for news on the Red Sox, my team, when I saw a link to this interactive tool allowing users to build their own roster with free agent signings.
Conceptually, the piece is fairly simple. There is a filterable list of free agents, broken out by whether their forecast signing values falls into the high-, middle-, or low-end of the range. Plus a draft pick.
I root for the Patriots. However, if you asked me to name a single player on last season’s roster, I could only name Cam Newton. Apparently he wasn’t great. I really and truly don’t follow the sport.
The piece displays the available free agents, along with those no longer available. (Though, the piece does offer you the option to go back to the beginning of free agent season and pretend reality didn’t happen.)
I went through and began semi-randomly picking names. I’d heard of some of them, and others were blind choices. Once you’ve selected within the budget, you can choose a draft pick. They all appear in list format to the right with the ability to remove them via a small X button.
Once you’ve confirmed your choices you’re taken to a screen that reviews your selection. You are able to either tweet it to the world—which I did not do—or start over again. I would do that, but I wouldn’t do any better than how I just did.
Overall, the piece felt intuitive and I never had any issues selecting my free agents. Of course, it would help if I knew anything about the sport. But that’s a user problem.
Or just shake my hand, because today marks the second St. Patrick’s Day spent in isolation. I am lucky, of course, because two years ago I spent the holiday in Dublin. One of those bucket list kind of things. There I ran into a(n American) friend who was coincidentally in town. Then the next day I took the train to Cork to visit another friend. If you don’t count weddings, I think that was the last big trip I took.
Two years hence, I am here in my flat alone on a holiday meant to be spent with family and friends. But in the last year, I made significant progress on my Irish genealogy. For part of that progress I took two additional DNA tests. So this St. Patrick’s Day seems like a good time to reflect on those tests.
For those that don’t know, I do a lot of genealogy work as a hobby. Primarily I focus on paper records, but DNA is an important piece of the puzzle. In a sense, it is the only record that cannot lie. It will reveal your biological connections to family that may have been otherwise lost. And it cannot be faked.
But that’s only true for your genetic matches. Those are the real power of taking a DNA test. I would bet, however, that most people initially take the tests for the ethnicity estimates. On a day like today, how Irish are you? How Irish am I?
Not surprisingly, I’m pretty Irish.
Of course, if you look at me, those Irish values do not quite equal each other. So what’s the deal? After all, the underlying DNA does not change from spit tube to cheek swab.
The first thing to know is that in one sense, ethnicity is, like so many things, a social construct. Super broadly, every individual is unique—except twins. Of course humans have spread across the globe and in that spread, certain regions have evolved incredibly slight differences between the populations. In addition to those genetic differences, the populations created civilisations and cultures. An ethnicity, in a sense, is a group of people who share that culture, civilisation, and genetic similarities vis-a-vis genetic differences across the world.
Importantly, within those groups, we still have differences. The Irish, for example, are known for freckles and red hair. But not all Irish have those traits. Instead, again super broadly, we say that for a group of people, a certain percentage will share a certain set of features. Consequently, within an ethnic group, you will still have variations and outliers. In some cases because generations ago a traveller from a different group entered the gene pool for some reason or another. And while the offspring might identify entirely with their new civilisation and culture, their genes don’t lie and a DNA test would reveal their traits from their ancestor’s foreign gene pool.
The second point to make is that Ireland is a fairly modern creation. Ireland did not exist as a sovereign state until 1922. Before then, the idea of Ireland existed. The country, however, did not. A better example would be German or Italian. Neither Germany nor Italy existed until the 1870s and 1860s, respectively. If you have “German” ancestors who arrived in Philadelphia in 1848, you don’t have German ancestors. You have ancestors from one of the various principalities or bishoprics comprising the German Confederation. Italy had the Venetian Republic, the Kingdom of the Two Sicilies, and many others. Being Irish, German, or Italian is thus a modern construct.
The third point is that identifying anyone as any of these ethnic groups requires a baseline for a comparison. To do that, you need a reference population in the area you are going to define as Ireland, Germany, or Italy. But humans have migrated throughout history. Ireland was conquered by the English. Germans…well, let’s just say Germans have a history with conquering parts of Europe. And so you can see exchanges of genetic information among populations pretty easily. And over time, those genetic populations evolve.
Take those three points and add them together in admixture test and your results are really only good back to about 500 years. And even then, you may find yourself belonging to something incredibly vague and all-encompassing because, especially as with France and Germany, there’s been too much mixture to get so granular as to fit ourselves within the borders of modern political states.
In the above results, you can see my “Irishness” varies from 63% to 75%. Though, as far as I know 21/32 (66%) of my 3xgreat-grandparents arrived from Ireland. That’s why I say I’m 2/3 Irish. But, genetically, I may be more or less because those 21 might have English or Scottish ancestors. Ancestry says I may be 18% Scottish, but whilst I have ancestors who lived in Scotland, I’m not aware of any ancestors born and raised for multiple generations in Scotland.
And then that’s just how Ancestry defines it. Compare that to my results from My Heritage. Because of the aforementioned difficulty in separating out certain population groups, they lump the Irish, Scottish, and Welsh together. Add my Ancestry Irish and Scottish together and I have 81%, not far from My Heritage’s 85% estimate. Then look at my results from Family Tree. They estimate me as 75% Irish, but add in the 10% Scandinavia and I’m up to 85%.
That brings me to my last point about DNA tests. It’s probably fair to say that I’m something like 80–85% genetically from the British Isles/North Sea region. What about the other 15–20%?
You will often hear you receive half your DNA from each of your parents. And they get half from each of theirs and so on and so forth. I’ve had conversations with folks who take that to mean they get 25% from each grandparent and 12.5% from each great-grandparent et cetera. But that’s not quite true.
You do receive 50% of your DNA from your father and the other 50% from your mother. But that 50%, well that’s a sort of random sample from the share your parents received from their parents.
My maternal grandfather was 100% Carpatho-Rusyn. For generations, his ancestors lived, reproduced, and died in the Carpathian Mountains. If we received exactly half from each previous generation, I should expect 25% of my DNA from my grandfather. But Ancestry, which has the best representation of this small ethnic group, says it’s 17% (though they give it as a range of being between 2 and 27%). In other words, I’m missing seven percentage points.
And so if you take a DNA test and you know you have a great-great grandparents of Irish descent, you may only see a small fraction in your results. If your connection to Ireland (or anywhere else) is even further back, the result becomes smaller still. In fact, beyond 5–7 generations back, you may not even inherit any genetic material from a specific ancestor in your family tree.
But ultimately, for today, as I wrote in one of my very first posts here on Coffeespoons, back in 2010, on St. Patrick’s Day, we’re all at least a little bit Irish.
Hopefully next year we’ll be able to celebrate in person.
Yesterday I wrote about Covid-19 here in five states of the US. I mentioned how I am concerned about the levelling out of new cases in certain states, notably Pennsylvania and New Jersey. In Italy, the government issued a new round of lockdowns in an attempt to contain a new wave before it swamps their healthcare system.
At the end of that BBC article, they used a small multiples graphic showing the seven-day average in several European countries. Today is the 16th, and so the data is now a few days old, but the concept remains important.
From a design standpoint, we are seeing a few things here. First, each country’s line chart exists with its own scale. Unfortunately this makes comparing country-to-country nigh impossible. We know from the title that in the present these are the countries with the highest new case rates in Europe. But, how do these rates today compare to earlier peaks? Without axis lines or a baseline, it’s difficult to say.
Of course, the point could well be just to show how in places like Italy, France, Poland, &c. we are seeing an emergent surge of new cases since the holiday peak.
If that is the goal, I think this chart works well. However, if the goal is to provide more context of the state of the pandemic in these select countries, we need some additional context and information.
Credit for the piece goes to the BBC graphics department.
Last week I wrote about how our progress in dealing with Covid-19 was stagnating. To put it simply, this past week did not get any better on that front.
In Pennsylvania, Delaware, and Illinois we see that the flattened tail I described last week, well remained a flattened tail. In Delaware, we see more movement, but the average of the average, if you will, is flat over the last two weeks. And in New Jersey, where I mentioned some signs of rising numbers, we see a clearly rising number of new cases over the last week. Only in Virginia are numbers heading down, and those are shallowing out.
The problem here is that in Pennsylvania and Delaware, the new case rate, whilst flat, is well above the summer rate of low transmission. This means that the environment is ripe for a new surge of cases if people stop following social distancing and begin resuming indoor activities with other people. Sadly, both those things appear to be occurring throughout the US.
In Europe we see a cautionary tale. They too saw their holidays peaks decline and the national governments began easing restrictions on their populations. Within the last several days, however, new cases have begun to surge. Italy has gone so far as to announce a new lockdown. Other governments are considering the same.
If the United States cannot resume pushing its numbers of new cases down, it could well follow Europe into a new wave of outbreaks that would threaten lockdowns and push back our eventual return of normalcy.
None of this would be an issue if vaccinations were nearing herd immunity levels. However, in the states we cover, nowhere is above 12% fully vaccinated.
Pennsylvania now lags behind the other two states. But at least the Commonwealth is over 10% fully vaccinated.
And of course, the problem under this dire scenario is that deaths could rise once again, though at this point the most vulnerable are in the middle of being vaccinated. Indeed, if we look at the last week, we see the good news for the week, that deaths are headed down in all five states.
Previously, Virginia had been working through a backlog of death records, but those appear now cleared. We are not quite back to summer-level lows, but we are steadily approaching them.
The big question this week will be what happens to those new cases numbers. Today’s data, Monday, will likely show lower numbers because of lower testing on the weekend. But starting Tuesday, what do we see over the course of the next five days?
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?
For years, one issue with the American economy had been that we did not save enough. It’s understandable, as it’s hard to keep up with the image of the carefree American without profligate spending. But that’s also not great long-term. But thanks to Covid-19, we’ve now swung to the other side of the spectrum: Americans may be saving too much.
Saying that sounds callous to the devastation the pandemic has wrought upon large swathes of the economy. But it’s true in the aggregate as this New York Times piece explains. In particular, the authors highlight one example. Consider a corporate CEO who earned a $100,000 bonus for keeping the company he runs afloat during the recession. He adds $100k to the aggregate American income. But at a restaurant shuttered by the pandemic, owners lay off a hostess, a server, a bartender, and a dishwasher, each earning $25,000. Their collective lost income is $100,000 and so balances out that one CEO. And as CEOs are more able to work remotely than servers, it’s not hard to see how the upper-income earning cohorts of the economy have done well. In human-terms, four unemployed service industry people is terrible. But statistically, it’s a wash. Once we understand that, it makes the piece sensible.
It uses decomposition charts, basically stacked bar charts broken apart, to show what constitutes the two sides of the American household budget: earning and spending. I’ve taken a screenshot of the spending side of the ledger.
We see that starting from the baseline, the solid line, American households spent more money this year on durable goods. A dotted line then carries that adjusted baseline to the right for the next component of the ledger: nondurable goods. We spent more on those too, so the baseline moves up. The designers annotated the graphic, adding descriptions of what each bar represents in a casual, lighthearted tone. I’ve definitely been cooking for myself a lot more.
Here I wish we had some more traditional charting elements, e.g. axis lines and labels. Now this piece is published under the Upshot, a more conversational and less formal brand than the Times as a whole. That probably explains the casual annotations. But I think some basic axis labels, e.g. spending more vs. spending less, could add some context without the need for the annotations.
Where the piece might lose people is what happens after durable goods. Americans stopped spending on services, a decline of over half a trillion dollars. That’s a lot of money. And so the adjusted baseline shifts to well below where we started. Add on savings from things like interest rates (Jay Powell is the chair of the Federal Reserve, for whose Philadelphia bank I work in full disclosure) and Americans have spent more than half a trillion dollars less. And as the article explains, we’ve also saved an enormous amount, to the tune of $1 trillion. Add it together and you’ve got America saving $1.5 trillion in 2020.
That money has to go somewhere. And you can see where some of it went when you look at surging prices in GameStop. Longer term, when the pandemic begins to end, we are going to have a pent up demand from people who have had their lives on hold for a year or more. And if there is insufficient supply for whatever’s in demand, prices will rise and we could see a sharp jump in inflation. But that’s a post for another day.
Back to this graphic, as a statistical graphic, it works. But without axis labels and data definitions, barely so. However, I think it’s meant to be more casual and illustrative than data-driven. If I look at this piece through that lens, I do think it works.
Credit for the piece goes to Neil Irwin and Weiyi Cai.