Coffee Spoons – Page 30 – Examining How We Measure Our Lives

Lead Pie

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.

Covid Update: 28 February

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.

New case curves in PA, NJ, DE, VA, & IL.

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.

No, Your Vaccine Is Now Fully Operational

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.

These are not the cells you’re looking for.

You’ll want to click through to read it all.

Credit for the piece goes to Randall Munroe.

Another Look at 500,000

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.

500,000 Deaths

The United States surpassed 500,000 deaths from Covid-19. On Sunday, in advance of that sobering statistic, the New York Times published a front-page graphic that dominated the layout.

Sunday front page for the New York Times

Usually a front-page graphic will make use of the four-colour process and present richly coloured graphics. This, however, starkly lays out the timeline of deaths in the United States in black and white.

Meaningful graphics do not need to reinvent the wheel. This takes each life lost as a black dot and then, starting at the top in February, plots each day.

The colour here serves as the annotation. The red circle drawing attention to the first reported death. And down the side the tick marks for days. Red lines indicate 50,000 death increments. The labels tell the story, we’ve needed fewer and fewer days to reach each subsequent 50,000 milestone.

As the first wave intensifies in March and April, the space fills with black dots. But as we enter summer and deaths fell, the space lightens. Late autumn and winter bring more death and you can see clearly towards the bottom of the chart, as we approach today, the graphic is nearly solid black.

If we want to look towards a hopeful point in the content, we can see first that it took 17 days then 15 to reach 400,00 deaths and 450,000 deaths, respectively. But it took 19 days to reach 500,000. As a nation we appear to finally be on the downward slope of this wave.

Returning to the piece, it’s a gut punch of simplicity in design.

Credit for the piece goes to Lazaro Gambio, Lauren Leatherby, Bill Marsh, and Andrew Sondern.

Covid Update: 20 February

Another week, another snowstorm in the Northeast. This winter has been far busier than last, when Philadelphia saw no snow. Unfortunately, whilst people like me enjoy seeing the snow, it’s hampering with testing and vaccination.

Last week we saw some middling signs of improvement, but perhaps partially exaggerated by the closures caused by the storm. When we look back at the last week, despite the impact of a storm later in the week, it’s been a categorically positive week with respect to new cases.

New case curves for PA, NJ, DE, VA, & IL.

After the plateaus of the week before, most notably in the straight line in Pennsylvania, this week we saw the line for the seven-day average resume a sharp trajectory down. That isn’t to say we are seeing a slowdown in that reduction of new cases. Illinois best fits that, but we can see slight flattening of the downward curve also in Delaware and New Jersey. In Illinois’ case, that is still welcome as the state approaches early autumn levels of new case rates. In the remaining states, we still have a little ways to go before we reach those levels.

Deaths, on the other hand, remain a mixed bag of results. Last week we talked about a much improved picture from the week before with Delaware and Virginia in particular exhibiting significantly decreased rates.

This week we saw some reversal of fortune in those two same states. In Delaware, the numbers of deaths have ticked back upwards and the seven-day average has made up about a third of the gains we saw. In Virginia, the upward swing can be largely—though not entirely—attributed to a one-day spike in numbers.

Whilst the other three states continued to see gradual improvements, the question over the coming week will be what trends emerge within Delaware and Virginia. Do the deaths increase and the situation worsen? Or will the increases prove a temporary aberration followed by a return to decreasing numbers of new deaths.

Finally with vaccines

The vaccination curves for PA, NJ, DE, VA, & IL.

The story to follow in Pennsylvania will be how distribution sites mistakenly administered second doses as first. 60,000 people awaiting their second dose will now have to wait—though still within the recommended window—for their second dose whilst 50,000 people will now have to wait for their first dose.

Otherwise, we continue to see an uptick in vaccinations. Last week we saw states make significant gains in their fully vaccinated populations. Virginia had passed 4% and Pennsylvania was about to hit the same milestone. This week begins with Virginia at nearly 5.5% and Pennsylvania almost at 5%, sitting on 4.77%. We need to keep in mind that this excludes any new vaccinations from the city, which doesn’t report vaccination data at the weekend. Illinois is now the lagging state at 4.29%.

Credit for the piece is mine.

Texas-scale Cold

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.

Credit for the piece goes to the NWS.

Covid Update: 15 February

Last week we discussed the potential impact of a major nor’easter that struck the East Coast and interrupted testing and vaccination operations in the states we cover: Pennsylvania, New Jersey, Delaware, Virginia, and Illinois (affected by the storm as one of the components moved east across the Midwest).

The possibility of an exaggerated downward trajectory concerned me and that it could be followed with an uptick in new cases and deaths. So a week later, where are we?

We can see something in the middle. With the exception of Illinois, which has continued its downward trend for new cases, we saw a brief interruption last week. In some cases, like Pennsylvania, that emerged as a rolling seven-day average that began and ended the workweek with the same exact number. And without a lot of variation during the week, you can see that pattern as the flat line towards the end of the chart. As numbers resumed heading down, you can see that beginning of a downward direction at the line’s very end.

In the remaining states of New Jersey, Delaware, and Virginia we saw brief upticks in the seven-day averages with daily spikes of new cases. None of these upticks came anywhere close enough to be threatening—though any upward tick should be monitored—but they were all significant enough to be seen as the quick, upward pointing jogs in the lines. But as we entered the weekend, those numbers also began to drop again.

Next we look at deaths. Last week I described a muddled picture. Delaware and Virginia had begun to rebound and reach or approach new peaks whilst Pennsylvania and Illinois continued to see steady but significant declines. New Jersey fell somewhere between the two. What about this week?

This week is an improved picture. We did see the potential interruption from the storm—Pennsylvania’s death trend evinces the disruption with the same straight line pattern we saw with new cases. But, overall, numbers continue to trend down. Delaware and Virginia show dramatic improvement with steep drops over the last week. And whilst Illinois continues to show steadily declining numbers, New Jersey now falls somewhere near the top of the pile. Its death rate continues to decline very slowly, relative to the other states. But it is heading down.

Finally, a look at vaccinations for Pennsylvania, Virginia, and Illinois.

Last week we talked about how the states all reached at least 2% over the course of the week. Even better news this week.

Last week we needed approximately one week to climb one percentage point from 1% to 2%. This week in the same one week time period we saw Virginia climb two percentage points from 2% to 4%. Illinois has slowed its vaccination efforts as it’s still in the mid 3% range. And Pennsylvania is tricky. Because the city of Philadelphia does not report its data on the weekend, we have an incomplete picture until after I post this on Mondays. Even though today is Tuesday, yesterday was a holiday so the same pattern holds true. I would suspect, however, the Commonwealth surpasses 4% later today when the new numbers are released or it comes near to reaching that level.

Credit for the piece is mine.

Appliance Matrix

Well, it’s Friday. And in the Northeast that means another snowstorm. In normal times, that would mean a nice half-hour walk to the office wherein my overcoat would likely become covered in snow and my trousers soaked in disgusting, salty, slush water. In other words, I’d need to wash and dry my clothes. But what household appliances should I use?

Thankfully, over at xkcd, Randall Munroe tackled that very problem with this helpful matrix.

Of course my aforementioned scenario is entirely moot, because like so many of you, I haven’t seen my office nor really left my flat in 11 months now. But here’s looking at you vaccines.

Credit for the piece goes to Randall Munroe.

Trading Andrew Benintendi

Yesterday, one year to the day the Boston Red Sox traded Mookie Betts to the Los Angeles Dodgers, the Red Sox made another big trade, sending Andrew Benintendi, their starting left fielder, to the Kansas City Royals as part of another three-team trade—last year’s three-team part fell apart, but initially involved Boston receiving a quality reliever from the Minnesota Twins.

In this year’s trade, the Red Sox receive an outfielder, Franchy Cordero, from the Royals and a pitcher, Josh Winckowski, from the New York Mets. Boston is sending $2.8 million to Kansas City to help defray the costs. Using data from Baseball Trade Value, we can make a quick graphic to show how this trade shakes out for the teams involved.

How the trade looks, with incomplete data

At first glance, we see that the Red Sox and the Royals are giving up more than they are receiving in value. The Mets look like the clear winner here, by a long shot.

And it could end up that way this time next year.

But, there is one enormous question mark—or maybe three. The Red Sox are also acquiring one player to be named later from the Mets and two from the Royals. Players to be named later are usually not the high end of prospects, but instead of low to middle value. And what appears likely in this case is that the Red Sox will be presented lists of players from both teams and Boston can choose which ones they like. The key here is that this could take a few months to sort out, because Boston wants to see how these players perform in the minor leagues. In 2020, there was no minor league season and so teams have very little to no information on players, which makes it nigh impossible to accurately assess their skill sets.

And so yes, we can make graphics like this and talk about how the Red Sox lost this trade. But in reality, we’ll need to wait a few months to see the last three players of the deal to see how badly—or how well—Boston does in the end.

Credit for the piece is mine.