Another week is now in the books. We can end it with an xkcd graphic looking at rings and a diagram identifying logos with that specified number.
It’s basically a Venn diagram with sets within sets.
But conceptually, I’d place it here.
Credit for the piece goes to Randall Munroe.
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.
Credit for the piece is mine.
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?
Credit for the piece is mine.
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.
Last week I wrote about some signals indicating a potential stagnation in terms of declining numbers of new cases. I also wrote about some potential signs of reversals, or increasing numbers of new cases.
This week, what we saw signs of came to pass.
At the tail ends of each chart, you can see that the last week was broadly stagnant. In Pennsylvania and Illinois the seven-day average was itself remarkably flat. Delaware is now where it was this time last week; a slight rise in new cases was met with an equal magnitude decline.
In reversals, we have New Jersey. New case numbers there increased throughout the week. With lower weekend data, those numbers have fallen slightly.
Only in Virginia did we see good numbers in new cases. Numbers there fell over the last week, though notably at a slower pace than in previous weeks.
Deaths presented broadly good news. Last week we had mixed signals with increasing numbers in Delaware and Virginia. We knew the increase in Virginia was due to the state processing a backlog of death certificates with Covid.
But in the last few days, those numbers have also fallen though the state reports it is still processing the backlog. And in Delaware, the daily number of deaths has also fallen again. I think it’s too early to say this peak has crested, but it could well be.
And in the other states, we continue to see slowly falling numbers of deaths. There are some potential signs of that bottoming or stalling out in Illinois, but we’ll have to see how this week pans out.
Finally, the best news we had over the course of last week was with vaccinations.
Last week I mentioned that we can see the lines moving upwards as we approach 10% fully vaccinated in Pennsylvania, Virginia, and Illinois.
This week, well let’s start here: as I’ve pointed out in the past, Pennsylvania does not have a centralised reporting system. Most notably the state reports figures for all but Philadelphia county (coterminus with the city). The city reports its own figures. I aggregate the two. But for the last several days, the Philadelphia data site has been broken, so we don’t know the progress of vaccinations in the city. And as the largest city/county in the state, Philadelphia is an enormous part of figuring out the statewide numbers.
So looking only at Virginia and Illinois, the numbers look good. Virginia is at nearly 9.5%. Illinois is on 8.92%.
But we really need Philadelphia to get its act together.
Credit for the piece is mine.
In 2020, baseball did not permit fans to attend regular season matches. (They changed this for the playoffs.) Instead, many stadiums opted for cardboard cutouts: fans often paid a fee and submitted a picture that the team printed on cardboard cutouts. Like so many things we will say about 2020, it was surreal.
But in Philadelphia at least, cardboard cutouts are out, and human fans are in. The state government in Harrisburg and the city government will allow 20% capacity at outdoor stadiums and 15% for indoor stadiums.
The Philadelphia Inquirer created a small graphic for its homepage to capture this news.
I intentionally included other site elements in the cropping to show how the graphic fits into the broader site. The extra white space around the image helps focus attention on the datagraphic over the numerous photographic elements for each article. Clicking on other tabs in the section brings up full-component-width graphics.
To the graphic itself.
My guess would be this was a quick turnaround piece. There are a few things going on here. The first and most obvious one, the squares as spectators. Now I confess this confused me at first. I was not entirely certain what the coloured squares meant; they mean in-person attendees. Was this supposed to be an overall stadium? Or was it a representative seating section?
The quick turnaround becomes important, because this is probably how I would have first conceptualised the graphic. But, with more time, I may have attempted to incorporate the shape of the playing field, be it a baseball diamond or basketball court, or hockey rink—I know all the sports terms!—and surrounded them with shapes representing a certain number of spectators. Squares might not work in that case because of the curves. Circles? Hexagons? Regardless of the shape, the filling of occupied seats would be the same as here, but it would perhaps be clearer to some readers, i.e. me.
Second, we get to the table below the graphics. Here we have a subtle design decision. Note that here the designer greyed out the normal capacity figures. The new figures at that 20% and 15% rates are what appear in black bold text. My usual instinct is to use typographic weight, regular vs. bold, in these situations. But the grey here works equally well.
Third, and this also involves the table, we have the first game data. We talked about the comparison of the capacity and permitted attendance. But I wonder, did the date of the first game with fans needed to be displayed in the same way as the permitted attendance? Because the news isn’t the dates of the first games—at least not as I read the news—but the numbers of attendees. And because of that, maybe I would have reduced the size of the type for the date of the first game. Or, conversely, set the type for the new attendance in a larger point size.
Overall, I enjoyed seeing this news presented visually, even if I was left confused.
Credit for the piece goes to John Duchneskie.
This past weekend, I read an article in Politico discussing parents’ outrage over levels of lead and other toxic metals in baby food. The story focuses on a Congressional report into the matter, but that ties back into an EPA study from 2017 that investigated lead contamination. Specifically the article’s author notes “a chart that was buried in supplemental material”. Buried chart? Well I went off to investigate.
And I found all the charts. But I wanted to focus on one. I am not entirely clear what it means: Percent contribution by pathway adjusted for bioavailability of each media for NHEXAS Region 5 study. I get that it’s looking at channels of intake, but it’s unclear if this is lead or some other contaminant. Is this for all people? Or a sub-section of the population as other charts in that supplemental material pack are?
So I made a graphic where I compared the original to two alternate versions.
Now, the editorial focus of the article is on baby food, which is not the apparent focus of the study (unless it is couched in academic/technical terms). But what’s worth noting is that the pale yellow recedes into the background as the burgundy dominates the graphic.
If graphics are done well, they should show clear visual relationships, they do not need to label specific datapoints unless through a progressive disclosure of information. But if you are going to label everything, I would want to make certain that in the case of that same burgundy slice, we have sufficient contrast to read the 17% label.
Pie charts are not good at allowing people to compare slices. So the pie chart as the format here is not a great place to start, but as you can see in my Option 2, if you are going to choose a pie chart form, there are ways of making it more legible. Namely, do not make it three-dimensional.
Here the foreground receives prominence over the background, which may be receding and visually shrinking into the background. And as the point of a chart is to make visual comparisons, if we cannot compare like for like, it’s not ideal.
Also, we have the thickness of the pie chart. That vertical heights adds yellow to the slice of the pie we see in front. Casually, that makes the yellow slice appear even larger than it already is from the three-dimensional foreshortening.
Option 2 presents this as a stripped down pie chart. Make it flat. I used one colour with tints of one purple. I used the 100% to highlight the dietary intake channel, because of the Politico article’s focus.
But really, Option 1 is the improvement here. Comparing the smaller slices is easier here as the eye simply moves vertically down the graphic. We are also able to add axis lines that provide a context for where those values fall, between 0 and 10 for Water intake, and just over 10 for Air. Somewhere between 15 and 20 for Soil and dust ingestion.
Finally, that legend. We don’t want the reader to have to strain to identify what slice is what. Why is the legend in a box? Why is it so far away from the pie? In both my options I closely and visually link the labels to the slices/bars they represent. That makes it easier for the reader to know what they are looking at when they are looking at it.
The moral of the story, people, don’t use three-dimensional pie charts.
Credit for the original version goes to the EPA. Credit for the alternate versions is mine.
Last week we saw some positive trends with respect to new Covid-19 cases in the Pennsylvania, New Jersey, Delaware, Virginia, and Illinois area. What did we see this week? Curiously, we saw stagnating figures and, in some instances, slight reversals.
This stagnation can be seen by the small flattenings at the end of the lines for Pennsylvania, Illinois, and Virginia. And if you look at Delaware and New Jersey, you can see the reversals as little upward hooks.
I do not think this means we will be returning to the levels we saw earlier this winter. In fact, if you look a little ways back in Delaware and a bit further back in both Pennsylvania and Illinois you can see a similar pattern. Slight reversals appear as jagged little outcrops on the slope. New cases do indeed climb for a week or so—probably isolated to specific geographies within those states tied to outbreak clusters, but that’s pure speculation on my part.
These reversals, therefore, are something we should pay attention to this week when the weekday data resumes on Tuesday. But I am not worrying about this breaking the overall trend of falling numbers of new cases.
Deaths, on the other hand, while still a bit mixed, are broadly positive. Last week we were in a similar position as we are with new cases this week. In particular, we were looking at increasing numbers in both Delaware and Virginia while the other three states saw slowly falling numbers.
In Delaware we have the numbers down a bit, but the longer term trend remains generally up. I will be watching this closely this week. Virginia, however, is an easier, but maybe better explanation? During the course of this past week, Virginia stated that it’s processing death certificates from the post-holiday surge in deaths.
This means the state under-reported deaths earlier this year and so that the curve should have actually been significantly higher. But the positive news in that is that the deaths we are seeing now happened in the past so that deaths today are far lower than are being reported.
And with vaccinations we continue to have good news. The lines below are clearly off the baseline now as the three states we track move towards 10% fully vaccinated.
It’s not all perfect, as the rate in Pennsylvania appears to have slowed slightly. This after vaccine administrators mistakenly used second doses for first doses. Now the state has to play catch-up.
But in Virginia and Illinois, we continue to see increasing rates. You can see this as the curve is beginning to gradually slope more and more upward instead of the shallow angle we saw for the last few weeks.
Like with new cases, which, while positive, still have a ways to go before we get to summer-like levels that would allow us to head out and socialise, vaccinations have a long way to go.
And importantly, just because someone is vaccinated doesn’t mean society should reopen just for those lucky to get their doses early. We need to wait—or should wait—for higher levels of vaccination before reopening.
Credit for the piece is mine.
Yesterday we looked at how the New York Times covered the deaths of 500,000 Americans due to Covid-19. But I also read another article, this by the BBC, that attempted to capture the scale of the tragedy.
Instead of looking at the deaths in a timeline, the BBC approached it from a cumulative impact, i.e. 500,000 dead all in one go. To do this, they started with an illustration of 1,000 people. Then they zoomed out and showed how that group of 1,000 fit into a broader picture of 500,000.
We’re going to take a look at this in reverse, starting with the 500,000.
I think this part of the graphic works well. There’s just enough resolution to see individual pixels in the smaller squares, connecting us to the people. And of course the number 500 stacks nicely.
My quibble here might be whether the text overlay masks 8,000 people. Initially, I thought the design was akin to hollow square, but when I looked closer I could see the faint grey shapes of the boxes behind a white overlay. Perhaps it could be a bit clearer if the text fell at the end of all the boxes?
But overall, this part works well. So now let’s look at the top.
This is where I have some issues.
When I first saw this, my eyes immediately went to the visual patterns. On the left and right there are rivers or columns of what look like guys in white t-shirts. Of course, once I focused on those, I saw other repeated patterns, the guy in the black jacket with his arms bent out, hands on his hips. The person in the wheelchair occupies a different amount of area and has a distinct shape and so that stood out too.
Upon even closer inspection, I noticed the pattern began to repeat itself. Every other line repeated itself and with the wheelchair person it was easy to see the images were sometimes just flipped to look different.
Now, allow me to let you in on a secret, unless you gave a designer a budget of infinite time, they wouldn’t illustrate 1,000 actual people to fill this box. We don’t have time for that. And I’ll also admit that not all designers are good illustrators—myself first and foremost. A good design team for an organisation that uses illustration should have either a full-time illustrator, or a designer who can capably illustrate things.
But this gets to my problem with the graphic. I normally can distance myself from reading a piece to critiquing it. But here, I immediately fixated on the illustrations, which is not a good sign.
There are three things I think that could have been done. The first two are relatively simple fixes whilst the third is a bit grander in scope.
First, I wonder if a little more time could have been spent with the illustrations. For one, white t-shirt guy, I don’t see his illustration reused, so why not change the colour of his t-shirt. Maybe in some instances make it purple, or orange, or some other colour. I think re-colouring the outfits of the people could actually solve this problem a good bit.
But second, if the patterns still appear visible to readers, mix it up a bit. I understand the lack of desire to spend time creating an individualised row for each row. Crafting each row person by person probably is out of the time requirements—though maybe the people above the designer(s) should know that content takes time to create. So what about repeating smaller blocks? I counted 20 rows, which means there should be 50 people per row. Make each row about ten blocks, and have several different blocks from which you can choose. Ideally, you have more blocks than you need per row, so not all figures are repeated, but if constrained, just make sure that no two rows have the same alignment of blocks.
Thirdly, and here’s the one that would really have required more time for the designer to do their job, make the illustrations meaningful. In a broad sense, we do have some statistics on the deaths in the United States. According to the CDC, 63% of deaths have been by white non-Hispanics, 15% by Black non-Hispanics, and 12% by Hispanic/Latino, 4% by Asian Americans, 1% by Native Americans, 0.3% by Hawaiian and Pacific Islander, and 4% by multiple non-Hispanic. Using those numbers, we would need 630 obviously white illustrations, 150 obviously Black, and so on.
If the designer had infinite time, the illustrations could also be made to try and capture age as well. Older people have been hit harder by this pandemic, and the illustrations could skew to cover that cohort. In other words, few young people. According to the CDC, fewer than 5% of deaths have been by people aged under 40. In other words, no baby illustrations needed.
That’s not to say babies haven’t died—87 deaths of people between 0 and 4 have been reported—but that when creating a representative average, they can be omitted, because that’s less than 0.1%, or not even 1 out of 1000.
To reiterate though, that third concept would take time to properly execute. And it would also require the skills to execute it properly. And I am no illustrator, so could I draw enough representative people to fake 1,000? Sure, but time and money.
The first two options are probably the most effective given I’d bet this was a piece thought up with little time to spare.
Credit for the piece goes to the BBC graphics team.
