Out with the New, In with the Old

After twenty years out of power, the Taliban in Afghanistan are back in power as the Afghan government collapsed spectacularly this past weekend. In most provinces and districts, government forces surrendered without firing a shot. And if you’re going to beat an army in the field, you generally need to, you know, fight if you expect to beat them.

I held off on posting anything about the Taliban takeover of Afghanistan simply because it happened so quick. It was not even two months ago when they began their offensive. But whenever I started to prepare a post, things would be drastically different by the next morning.

And so this timeline graphic from the BBC does a good job of capturing the rapid collapse of the Afghan state. It starts in early July with a mixture of blue, orange, and red—we’ll come back to the colours a bit later. Blue represents the Afghan government, red the Taliban, and orange contested areas.

The start of the summer offensive

The graphic includes some controls at the bottom, a play/pause and forward/backward skip buttons. The geographic units are districts, sub-provincial level units that I would imagine are roughly analogous to US counties, but that’s supposition on my part. Additionally the map includes little markers for some of the country’s key cities. Finally in the lower right we have a little scorecard of sorts, showing how many of the nearly 400 districts were in the control of which group.

Skip forward five weeks and the situation could not be more different.

So much for 20 years.

Almost all of Afghanistan is under the control of the Taliban. There’s not a whole lot else to say about that fact. The army largely surrendered without firing a shot. Though some special forces and commando units held out under siege, notably in Kandahar where a commando unit held the airport until after the government fell only to be evacuated to the still-US-held Hamid Karzai International Airport in Kabul.

My personal thoughts, well you can blame Biden and the US for a rushed US exodus that looks bad optically, but the American withdrawal plan, initiated by Trump let’s not forget, counted on the Afghan army actually fighting the Taliban and the government negotiating some kind of settlement with the Taliban. Neither happened. And so the end came far quicker than anyone thought possible.

But we’re here to talk graphics.

In general I like this. I prefer this district-level map to some of the similar province-level maps I have seen, because this gives a more granular view of the situation on the ground. Ideally I would have included a thicker line weight to denote the provinces, but again if it’s one or the other I’d opt for district-level data.

That said, I’d probably have used white lines instead of black. If you look in the east, especially south and east of Kabul, the geographically small areas begin to clump up into a mass of shapes made dark by the black outlines. That black is, of course, darker than the reds, blues, and yellows. If the designers had opted for white or even a light shade of grey, we would enhance the user’s ability to see the district-level data by dropping the borders to the back of the visual hierarchy.

Finally with colours, I’m not sure I understand the rationale behind the red, blue, yellow here. Let’s compare the BBC’s colour choice to that of the Economist. (Initially I was going to focus on the Economist’s graphics, but last minute change of plans.)

Another day, more losses for the government

Here we see a similar scheme: red for the Taliban, blue for the government. But notably the designers coloured the contested areas grey, not yellow. We also have more desaturated colours here, not the bright and vibrant reds, blues, and yellows of the BBC maps above.

First the grey vs. yellow. It depends on what the designers wanted to show. The grey moves the contested districts into the background, focusing the reader’s attention on the (dwindling) number of districts under government control. If the goal is to show where the fighting is occurring, i.e. the contest, the yellow works well as it draws the reader’s attention. But if the goal is to show which parts of the country the Taliban control and which parts the government, the grey works better. It’s a subtle difference, I know, but that’s why it would be important to know the designer’s goal.

I’ll also add that the Economist map here shows the provincial capitals and uses a darker, more saturated red dot to indicate if they’d fallen to the Taliban. Contrast that with the BBC’s simple black dots. We had a subtler story than “Taliban overruns country” in Afghanistan where the Taliban largely did hold the rural, lower populated districts outside the major cities, but that the cities like the aforementioned Kandahar, Herat, Mazar-i-Sharif held out a little bit longer, usually behind commando units or local militia. Personally I would have added a darker, more saturated blue dot for cities like Kabul, which at the time of the Economist’s map, was not under threat.

Then we have the saturation element of the red and blue.

Should the reds be brighter, vibrant and attention grabbing or ought they be lighter and restrained, more muted? It’s actually a fairly complex answer and the answer is ultimately “it depends”. I know that’s the cheap way out, but let me explain in the context of these maps.

Choropleth maps like this, i.e. maps where a geographical unit is coloured based on some kind of data point, in this instance political/military control, are, broadly speaking, comprised of large shapes or blocks of colour. In other words, they are not dot plots or line charts where we have small or thin instances of colour.

Now, I’m certain that in the past you’ve seen a wall or a painting or an advert for something where the artist or designer used a large, vast area of a bright colour, so bright that it hurt your eyes to look at the area. I mean imagine if the walls in your room were painted that bright yellow colour of warning signs or taxis.

That same concept also applies to maps, data visualisation, and design. We use bright colours to draw attention, but ideally do so sparingly. Larger areas or fields of colours often warrant more muted colours, leaving any bright uses to highlight particular areas of attention or concern.

Imagine that the designers wanted to highlight a particular district in the maps above. The Economist’s map is better designed to handle that need, a district could have its red turned to 11, so to speak, to visually separate it from the other red districts. But with the BBC map, that option is largely off the table because the colours are already at 11.

Why do we have bright colours? Well over the years I’ve heard a number of reasons. Clients ask for graphics to be “exciting”, “flashy”, “make it sizzle” because colours like the Economist’s are “boring”, “not sexy”.

The point of good data visualisation, however, is not to make things sexy, exciting, or flashy. Rather the goal is clear communication. And a more restrained palette leaves more options for further clarification. The architect Mies van der Rohe famously said “less is more”. Just as there are different styles of architecture we have different styles of design. And personally my style is of the more restrained variety. Using less leaves room for more.

Note how the Economist’s map is able to layer labels and annotations atop the map. The more muted and desaturated reds, blues, and greys also allow for text and other artwork to layer atop the map but, crucially, still be legible. Imagine trying to read the same sorts of labels on the BBC map. It’s difficult to do, and you know that it is because the BBC designers needed to move the city labels off the map itself in order to make them legible.

Both sets of maps are strong in their own right. But the ultimate loser here is going to be the Afghan people. Though it is pretty clear that this was the ultimate result. There just wasn’t enough support in the broader country to prop up a Western style liberal democracy. Or else somebody would have fought for it.

Credit for the BBC piece goes to the BBC graphics department.

Credit for the Economist piece goes to the Economist graphics department.

You Thought That Was All China Was Doing in Its Western Deserts?

Yesterday I wrote about some new ICBM silos China is building in its western desert. These things clearly interest me and so I was doing a little more digging when I found this even more recent article, this one from the BBC about an entirely different ICBM silo field that China is building in another western desert.

In terms of data visualisation and information design, we are looking at the same kind of graphic: an annotated satellite photograph. But the story it paints is the same: China is rapidly expanding its nuclear missile arsenal.

Similar to the earlier piece we see dots to indicate missile silo construction sites. But the Federation of American Scientists noted these silos appear to be at earlier phase of the construction process given that sites were still being cleared and prepared for construction activity.

You get a silo, and you get a silo, and you get a silo…

But put it together with the publicly available information from yesterday and, again, we can only draw the conclusion that China wants to greatly increase its nuclear arsenal. And like yesterday we’re left with the same question:

How will the United States and her allies respond?

Credit for the piece goes to the Federation of American Scientists.

Biden’s English Ancestry Revisited

Last week I posted about an article in the BBC on the English ancestry of American president Joe Biden. And these types of article are a bit pro forma, famous person has an article about their personal ancestry with a family tree attached. Interestingly, this article did not, just the timeline I mentioned and a graphic as part of an aside on the declining self-identification as English-American.

And that, normally is it. Perhaps the article comes out with a few revisions upon the famous person’s marriage, birth of children, and more rarely death, but that is it. Yesterday, however, the BBC posted a follow-up article about an English family claiming kinship with Joe Biden. This article, however, included a family tree of sorts.

With some interesting spacing here…

This isn’t a family tree in the traditional sense, I would argue it’s the sort of chart genealogists would use to highlight two parties’ relationship to their most recent common ancestor (MCRA). But this chart does something odd, it spaces out the generations inconsistently and so Joe Biden appears at the bottom, aligned with the grandchildren of Paul Harris, the man at the centre of the story.

If you compare the height/length of the lines linking the different generations you can see the lines on Biden’s side of the graphic are very long compared to those on the Harris’ side. This isn’t technically incorrect, but it muddies the water when it comes to understanding the generational differences. So I revisited the design below.

Now with more even spacing…

Here I dropped the photographs because, primarily, I don’t have access to them. But they also eat up valuable real estate and aren’t necessary to communicate the relationships. I kept the same distance between generations, which does a better job showing the relationship between Joe Biden and Paul Harris, who appear to be actual fifth cousins. Joe is clearly at a different level than that of Paul’s grandchildren.

I added some context with labelling the generational relationship. At the top we have William and James Biden, assuming they are brothers, listed as siblings. The next level down are first cousins, then second, &c. Beyond Paul, however, we have two additional generations that are removed from the same relationship level. This is where the confusing “once-removed” or “twice-removed” comes into play. One way to think of it is as the number of steps you need to take from, say, Paul’s grandchildren, to get to a common generational level. In their case two levels, hence the grandchildren are fifth cousins to Joe Biden, twice removed.

These types of charts are great to show narrow relationships. Because, if we assume that up until recently each of the generations depicted above had four or five children, that tree would be unwieldy at best to show the relationship between Paul’s family and Joe Biden. If you ever find yourself working on your family ancestry or history and need to show someone how you are related, this type of chart is a great tool.

Credit for the original goes to the BBC graphics department

Credit for my remake is mine.

Biden’s English Ancestry

We all know Joe Biden as the Irish American president. And that’s no malarkey. But, go back far enough in your family tree and you may find some interesting ancestry and ethnic origins and that’s no different with Joe Biden. Keep in mind that our number of ancestors doubles every generation. You have four grandparents, and many of us met most of them. But you had eight great-grandparents. How many of those did you know? And you had 16 great-great-grandparents, you likely didn’t know any of them personally. It becomes pretty easy for an ethnic line to sneak into your ancestry.

And in Biden’s case it may well be English. Although sneaking in is probably a stretch, as this BBC article points out, because his patrilineal line, i.e. his father’s father’s father’s, &c., is likely English. Of course back in the day the Irish and the English mixing would have been unconscionable, at least as my grandmother would have described it. And so it’s easy to see how the exact origins of family lines are quietly forgotten. But that’s why we have genealogists.

The article eschews the traditional family tree graphic and instead uses only two charts. The first is a simple timeline of Biden’s direct ancestors.

Biden’s patrilineal timeline

No, it’s no family tree, but timelines are a critical tool used by genealogists because at its core, genealogy is all about time and place. And a timeline has got one of those two facets covered.

Timelines help visualise stories in chronological order. I cannot tell you the number of family trees I have seen where people who create trees casually simply copy and paste data without scrutiny. Children born well after the deaths of parents are common. Or children born to parents in their 50s or 60s—perhaps not strictly impossible, but certainly highly irregular. And so to see Biden’s ancestors plotted out chronologically is a common graphic for those who do any work in genealogy, which my regular readers know is my hobby.

That alone would make the article worth sharing. Because, I enjoyed that graphic. I probably would have created a separate line for the birthplace of each individual, but I quibble.

However, we have another graphic that’s not so great. And once again with the BBC I’m talking about axis lines.

American ethnic origins

Here we have a chart looking at US ancestry as claimed in the US censuses of 1980 and 2000. But we do not have any vertical lines making it easy for readers to accurately compare the lengths of the various bars. Twice lately I’ve posted about axis lines and the BBC. Third time’s the charm?

We can also look at using these not as bars, but as line charts as I did in this re-imagining to the right.

First, we no longer need two distinct colours, though you could argue the English line should be a highlight or call out colour given its role in the article. Instead each line receives a label at the right and only the English line crosses any other, but given their point-to-point slope, it’s not confusing like a line chart with all years between 1980 and 2000 could be.

Secondly, the slope here of the line reinforces the idea of falling population numbers. The bar chart also shows this, but through a leftward movement in bars. The bar option certainly works and there’s nothing wrong with it, but these lines offer a more intuitive concept of falling numbers.

I also added some clarification to the data definition. These lines represent the number of people who reported at least one ethnic ancestry—at the time US census respondents could enter upwards of two. For myself, as an example, I could have entered Irish and Carpatho-Rusyn. But my own small sliver of English ancestry would have been left off the list.

Ultimately, the declining numbers of responses along with some reporting on self-identification points to the disappearing concepts of “Irish American” or “English American” as many increasingly see themselves as simply White Americans. But that’s a story for another day.

In the meantime, we have Joe Biden, the Irish American president, with a small bit of English ancestry. Those interested in the genealogy, the article also includes some nice photos of baptismal records and marriage records. It’s an interesting read, though I’m hungry for more as it’s a very light duty pass.

Credit for the BBC pieces goes to the BBC graphics department.

Credit for my reimagination is mine.

Back to the Office, Back to Basics

Two weeks ago I posted about an article from the BBC that used graphics about which I was less than thrilled. Inconsistent use of axis lines, centring the graphic were two of the things that irked me. Two weeks hence, I do want to draw some positive attention to another article in the BBC. This one discusses the, for many of us, impending return to the office. (I’ve also heard the phrase “return to work”, although a coworker of mine pointed out that’s not a great phrase because many of us never stopped working when we decamped for our flats and houses.)

The article discusses why some think the return to a five-day office week will occur within the next few years. There is some sound logic to the idea and for those like your author who are closely following the issue, I recommend the article.

But that’s not why we’re here, instead I wanted to focus on the one data visualisation graphic in the piece. It displays the amount of office space used in the city centres of six different UK cities outside London.

But what about Slough?

Here we have small multiples with the same fixed y-axis display. Axis lines are present and consistent and the baseline is distinct from the other lines. Solid improvement over what we discussed two weeks ago.

My only quibble? The colours here are not necessary. A single colour would work because each city’s graphic exists apart from the rest. The charts also all represent the same type of data, occupied office space. If the chart were doubling or tripling up cities somehow—though I wouldn’t want to see this as a stacked area chart—I would buy the need for colours to differentiate the cities. This, however, represents an opportunity to use a single, BBC-branded colour to define the experience whilst not negatively impacting the communication from the data visualisation standpoint.

Again, though, that’s a minor quibble. Of course, the BBC puts out copious amounts of content daily and I see only a fraction, but it is nice to see an improvement. Furthermore, at the end of the article I also spotted a graphic credit, which I don’t often see—and honestly cannot recall when I last saw period—from the BBC.

I wonder if moving forward the BBC intends to highlight the contributors to articles who are not solely the writers, i.e. the people creating the graphics? Of course, if we did that, we should also probably take a look at the copy editors who also play a role. Especially for an online article as opposed to say a print newspaper or magazine where space is money.

Credit for the piece goes to Daniele Palumbo.

On a Line. Or Not.

Two weeks ago I was reading an article in the BBC that fact checked some of President Biden’s claims about the economy. Now I noted the other day in a post about axis lines and their use in graphics. Axis lines help ground the user in making comparisons between bars, lines, or whatever, and the minimum/maximum/intervals of the data set.

I was reading the article and first came upon this graphic. It’s nothing crazy and shows job growth in the aggregate for the first three months of a presidential administration. A pretty neat comparison in the combination of the data. I like.

Pay attention to what you see here. There will be a quiz.

I don’t like the lack of grid lines for the axis, however. But, okay, none to be found.

I keep reading the article. And then a couple of paragraphs later I come upon this graphic. It looks at the monthly figures and uses a benchmark line, the red dotted one, to break out those after January 2021 when Biden took office.

Spot the differences.

But do you notice anything?

The lines for the y-axis are back!

The article had a third graphic that also included axis lines.

I don’t have a lot to say about these graphics in particular, but the most important thing is to try and be consistent. I understand the need to experiment with styles as a brand evolves. Swap out the colours, change the styles of the lines, try a new typeface. (Except for the blue, we are seeing different colours and typefaces here, but that’s not what I want to write about.)

First, I don’t know if these are necessarily style experiments. I suspect not, but let’s be charitable for the sake of argument. I would refrain from experimenting within a single article. In other words, use the lines or don’t, but be consistent within the article.

For the record, I think they should use the lines.

Another point I want to make is with the third graphic. You’ll note that, like I said above, it does use axis lines. But that’s not what I want to mention.

At least we have lines.

Instead I want to look at the labelling on the axes. Let’s start with the y-axis, the percentage change in GDP on the previous quarter. The top of the chart we have 30%. As I’ve said before, you can see in the Trump administration, the bar for the initial Covid-19 rebound rises above the 30% line. It’s not excessive, I can buy it if you’re selling it.

But let’s go down below the 0-line. Just prior to the rebound we had the crash. Similarly, this extends just below the -30% line. But here we have a big space and then a heavy black line below that -30% line. It looks like the bottom line should be -40%, but scanning over to the left and there is no label. So what’s going on?

First, that heavy black line, why does it appear the same as the baseline or zero-growth line? The axis lines, by comparison, are thin and grey. You use a heavier, darker line to signify the breaking point or division between, in this case, positive and negative growth. Theoretically, you don’t need the two different colours for positive and negative growth, because the direction of the bar above/below that black line encodes that value. By making the bottom line the same style as the baseline, you conflate the meaning of the two lines, especially since there is no labelling for the bottom line to tell you what the line means.

Second, the heaviness of the line draws visual attention to it and away from the baseline, especially since the bottom line has the white space above it from the -30% line. Consider here the necessity of this line. For the 30% line that sets the maximum value of the y-axis, we have the blue bar rising above the line and the administration labels sit nicely above that line. There is no reason the x-axis labels could not exist in a similar fashion below the -30% line. If anything, this is an inconsistency within the one chart, let alone the one graphic.

Third, is it -40%? I contend the line isn’t necessary and that if the blue bar pokes above the 30% line, the orange bar should poke below the -30% line. But, if the designer wants to use a line below the -30% line, it should be labelled.

Finally, look at the x-axis. This is more of a minor quibble, but while we’re here…. Look at the intervals of the years. 2012, 2014, 2016, every two years. Good, make sense. 2018. 20…21? Suddenly we jump from every two years to a three-year interval. I understand it to a point, after all, who doesn’t want to forget 2020. But in all seriousness, the chart ends at 2021 and you cannot divide that evenly. So what is a designer to do? If this chart had less space on the x-axis and the years were more compressed in terms of their spacing, I probably wouldn’t bring this up.

However, we have space here. If we kept to a two-year interval system, I would introduce the labels as 2012, but then contract them with an apostrophe after that point. For example, 2014 becomes ’14. By doing that, you should be able to fit the two-year intervals in the space as well as the ending year of the data set.

Overall, I have to say that this piece shocked me. The lack of attention to detail, the inconsistency, the clumsiness of the design and presentation. I would expect this from a lesser oganisation than the BBC, which for years had been doing solid, quality work.

The first chart is conceptually solid. If Biden spoke about job creation in the first three months of the administration vs. his predecessor, aggregate the data and show it that way. But the presentation throughout this piece does that story a disservice. I wish I knew what was going on.

Credit for the piece goes to the BBC graphics department.

The Super Short European Super League

Sunday night, news broke that a number of European football clubs were creating a rogue league, the European Super League. My British and European readers—and Americans who follow football—will know the names of Manchester United, Liverpool, AC Milan, Juventus, Real Madrid, and the others.

To put this in perspective for my American readers, imagine the Yankees, Dodgers, Red Sox, Astros, Padres, Mets, Cardinals, Phillies, Angels, and Nationals saying that they were leaving Major League Baseball to go and form their own new baseball league. That they were doing so to “save the sport”. But in so doing, they also guarantee they all make the playoffs every year.

My frequent readers and those who know me will know I’m a fan of the Boston Red Sox. I should point out that the owner of the Red Sox, John Henry, owns both the Red Sox and Liverpool through his company Fenway Sports Group.

Of course, the analogy doesn’t quite hold up, because there are some significant differences between American sports and European football. Relegation is a big one. Personally, I wish American sports had some way of using relegation to incentivise teams to not intentionally suck.

The basic premise of relegation. Take English football. You have four levels of play and in theory any team can exist in any level. Each year, the worst teams move from their current level down one whilst the best teams move up. And for the top level, the top teams get to compete in lucrative European-wide matches. That is a bit simplistic, but imagine that at the end of last year, the Pirates, Rangers, Tigers, and Red Sox became AAA minor league teams and the four best AAA minor league teams became MLB teams. MLB teams would theoretically try to do everything they could to stay in the MLB and not drop to AAA, because that would mean a loss of money. After all, the Yankees would no longer be heading to Fenway nor the White Sox to Detroit. Would seeing the Detroit Tigers play the Woo Sox really be worth the ticket prices you pay at Comerica Park?

But that’s not how American sports work. And so a few American owners, namely those of Manchester United, Arsenal, and Liverpool, want to ensure a steady stream of money. By creating their own league where their teams cannot be relegated, they guarantee that revenue stream.

In other words, this is all about the owners of these Super League teams making even more money.

Because, during the last year, teams have been hurting without fans in attendance. And that gets us to why I can write this up. Because the BBC in an article about this new league addressed the fact that most of these teams are heavily in debt.

This graphic, however, is a bit misleading. Look at Liverpool. There is no available data for how much financial debt the club holds. So why is it placed between Chelsea and Manchester City? It could well have more debt than Tottenham. Liverpool should really be left off this chart and included in the note, because its placement suggests that it has little debt, when that may well not be the case. This is a really misleading graphic when it comes to how Liverpool fits with the other 11 clubs.

From a design standpoint, I’m also not clear on why the x-axis line extends beyond the labels for £-200m and £600m.

I’m not going to touch all the data labels. That’s for another piece I’ve been working on off and on for a little while now.

At this point I should point out that I was going to post this article later, but in the last 18 hours or so the whole thing has fallen apart as the English teams, followed by the others, have been dropping out under immense pressure from the sport and their fans. To bring back my analogy above, imagine MLB retaliating and saying that if those teams created their own league, the players would not be allowed to play in any other matches and the teams would be locked out from all other competitive baseball games. It’s a mess.

Credit for the piece goes to the BBC graphics department.

And Up We Go Again

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.

New cases curves for several European countries.

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.

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.

Half a statistic.

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.

1,000 tragedies

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.

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.