BBC – Coffee Spoons

Imports, Tariffs, and Taxes, Oh My!

Apologies, all, for the lengthy delay in posting. I decided to take some time away from work-related things for a few months around the holidays and try to enjoy, well, the holidays. Moving forward, I intend to at least start posting about once per week. After all, the state of information design these days provides me a lot of potential critiques.

Let us start with the news du jour , the application of tariffs on China and the delayed imposition on both Canada and Mexico. Firstly, let us be very clear what a tariff is. A tariff is a tax paid by importers or consumers on goods sourced from outside the country. In this case, we are talking about Canadian, Mexican, and Chinese imports and the United States slapping tariffs on goods from those countries. Foreign governments do not pay money to the United States, neither Canada, nor Mexico, nor China will pay money to the United States.

You will.

You should expect your shopping costs to increase, whether that is on the price of gasoline (imported from Canada), fast fashion apparel (from China), or avocados (from Mexico). On the more durable goods side, homes are built with Canadian lumber and your automobiles with parts sourced from across North America—the reason why we negotiated NAFTA back in the 1990s.

Now that we have established what tariffs are, why is the Trump administration imposing them? Ostensibly because border security and fentanyl. What those two issues have to do with trade policy and economics…I have no idea. But a few news outlets created graphics showing US imports from our top-five trading partners.

First I saw this graphic from the New York Times. It is a variation of a streamgraph and it needs some work.

A streamgraph type chart from the *New York Times*

To start, at any point along the timeline, can you roughly get a sense of what the value for any country is? No. Because there is no y-axis to provide a sense of scale. Perhaps these are the top import sources and these are their share of the total imports? Read the fine print and…no. These are the countries with a minimum of 2% share in 2024, which is approximately 75% of US imports.

This graphic fails at clearly communicating the share of imports. You need to somehow extrapolate from the y-height in 2024 given the three direct labels for Canada, Mexico, and China what the values are at any other point in time or for any other country.

Nevertheless, the chart does a few things nicely. It does highlight the three countries of importance to the story, using colours instead of greys. That focuses your attention on the story, whilst leaving other countries of importance still available for your review. Secondly, the nature of this chart ranks the greatest share as opposed to a straight stacked area chart.

Overall, for me the chart fails on a number of fronts. You could argue it looks pretty, though.

The aforementioned stacked area charts—also not a favourite of mine for this sort of comparison—forces the designer to choose a starting country in this case and then stack other countries atop it.

What this chart does really well, especially well compared to the previous New York Times example is provide content for all countries across all time periods by the inclusion of the y-axis. Like the Times graphic it focuses attention on Canada, Mexico, and China with colour and uses grey to de-emphasise the other countries. You can see here how the Times’ decision to exclude all countries below 2% can skew the visual impact of the chart, though here all countries below Japan (everything but the top-five) are grouped as other.

Personally, the inclusion of the specific data labels for Canada, Mexico, and China distract from the visualisation and are redundant. The y-axis provides the necessary framework to visually estimate the share. If the reader needs a value to the precision level of tenths, a table may be a better option.

I could not find one of the charts I thought I had bookmarked and so in an image search I found a chart from one of my former employers on the same topic (though it uses value instead of share) and it is worth a quick critique.

A stacked area chart from Euromonitor International

Towards the end of my time there, I was creating templates for more wide-screen content. My fear from an information design and data visualisation standpoint, however, was the increased stretch in simple, low data-intensity graphics. This chart incorporates just 42 data points and yet it stretches across 1200 pixels on my screen with a height of 500.

Compare that to the previous BBC graphic, which is also 1200 pixels, but has a greater height of 825 pixels. Those two dimensions give ratios of 2.4 for Euromonitor International and 1.455 for the BBC. Neither is the naturally aesthetically pleasing golden ratio of 1.618, but at least the BBC version is close to Tufte’s recommended 1.5–1.6. The idea behind this is that the greater the ratio, the softer the slope of the line. This can make it more difficult to compare lines. A steeper slope can emphasise changes over time, especially in a line chart. You can roughly compare this by looking at the last few years of the longer time span in the BBC graphic to the entirety of this graphic. You can more easily see the change in the y-axis because you have more pixels in which to show the change.

Finally we get to another New York Times graphic. This one, however, is a more traditional line chart.

And for my money, this is the best. The data is presented most clearly and the chart is the most legible and digestible. The colours clearly focus your attention on Canada, Mexico, and China. The use of lines instead of stacked area allow the top importer to “rise” to the top. You can track the rapid rise of Chinese imports from the late 1990s through to the first Trump administration and the imposition of tariffs in 2018—note the significant drop in the line. In fact you can see the impact in Mexico becoming the United States’ top trading partner in recent years.

Over the years, if I had a dollar for every time I was told someone wanted a graphic made “sexier” or with more “sizzle” or made “flashier”, I would have…a bigger bank account. The issue is that “cooler” graphics do not always lead to clearer graphics. Graphics that communicate the data better. And the guiding principle of information design and data visualisation should be to make your graphics clear rather than cool.

Credit for the New York Times streamgraph goes to Karl Russell.

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

Credit for the Euromonitor International graphic goes to Justinas Liuima.

Credit for the New York Times line chart goes to the New York Times.

Labelling Line Charts

Today I have a little post about something I noticed over the weekend: labelling line charts.

It begins with a BBC article I read about the ongoing return to office mandates some companies have been rolling out over the last few years. When I look for work these days, one important factor is the office work situation and so seeing an article about the tension in that issue, I had to read it.

The article includes this graphic of Office of National Statistics (ONS) data and BBC analysis.

Overall, the chart does a few things I like, most notably including the demarcation for the methodology change. The red–green here also works. Additionally the thesis expressed by the title, “Hybrid has overtaken WFH”, clearly evidences itself by the green line crossing the blue. (I would quibble and perhaps change the hybrid line to red as it is visually more impactful.)

I also like on the y-axis how we do not have a line connecting all the intervals. Such lines are often unnecessary and can often add visual clutter, see yesterday’s post for something similar. I quibble here with dropping the % symbol for the zero-line. Since the rest of the graphic uses it, I would have put the baseline as 0%. And that baseline is indeed represented by a darker, black line instead of the grey used for the other intervals.

Then we get to the labels on the right of the graphic. Firstly, I do not subscribe to the view charts and graphs need to label individual datapoints. If the designer created the chart correctly, the graph should be legible. Furthermore, charts show relationships, if one needs a specific value, I would opt for a table or a factette instead. These are not the most egregious labels, mind you, but here they label the datapoint, but not the line. Instead, for the line the reader needs to go back to the chart’s data definition and retrieve the information associated with the colour.

Now compare that to a chart representing Major League Baseball’s playoff odds from Fangraphs.

Here too we have mostly good things going on, but I want to highlight the labelling at the right. This chart also includes the precise value, which is fine, but here we also have the actual label for the lines. The user does not need to leave the experience of the chart to find the relevant information, although a secondary/redundant display or legend can be found at the bottom of the chart.

If you can take the time to label the end value, you may as well label the series.

Credit for the BBC graphic goes to the BBC’s graphics department.

Credit for the Fangraphs piece goes to Fangraphs’ design team.

Life Aboard the International Space Station

This weekend I read a neat little article from the BBC about astronauts’ lives aboard the International Space Station (ISS). This comes on the heels of two NASA astronauts being left on the station due to some uncertainty about their Boeing spacecraft’s safety. The article featured a number of annotated photographs and illustrations, but this was my favourite.

The designers do a nice job of highlighting the particular components/sections of the station. This was my favourite as they darkened the non-relevant sections—looking at you solar panels.

Credit for the piece goes to the BBC graphics department.

I Didn’t Predict a Riot

Yesterday I wrote about a BBC graphics locator map that was perhaps not as helpful as possible. Well today I want to talk about another BBC map, though not in as critical a fashion.

I landed upon this map whilst reading a series of updates about last month’s anti-immigrant riots throughout the United Kingdom—principally England.

The graphic uses small multiples of a cropping of the United Kingdom, excluding most of Northern Ireland and a good bit of Scotland. Red dots highlight where, on a particular date, far-right riots erupted. As the reader moves further into time, the red dots become a dark grey.

In general, I think this graphic works really well. The designer does not label every city and town as it’s not necessary so long as you hit the big and most notable ones. Nonetheless I have two peccadilloes with the graphic.

First, and the minor of the two, is the grey dots could perhaps be toned down a wee bit. Or fade as tints as they recede into the past.

Secondly, the note at the bottom of the graphic indicates “[t]here were no recorded incidents of unrest on Thursday 1 August”. Correspondingly, the graphic lacks a map for 1 August. If I had designed the graphic, I would have included a blank map for that date, because its emptiness could tell part of the story as sometimes nothing is something.

A blank map on Thursday could show that a brief flare up after the incident in Southport had, at best, burned out or, at worst, cooled to a simmer. Something then happened likely Friday night—after a day with only two reported incidents—or Saturday morning, which prompted a weekend of riots and destruction across the United Kingdom.

What could that be? Social media. Surprise, surprise. The BBC had a good article about the potential inflammatory aspect of social media posts on the reignition of the hatred the weekend of 3–4 August. Imagine a blank map for 1 August and a caption that notes a series of posts on, say, 2 or 3 August, followed by the red dots all across northern England.

As I said at the outset, however, I like the piece overall. Just a few small tweaks and the piece really could have hit home on just how bad things were in parts of the United Kingdom at the end of July and early August.

Credit for the piece goes to the BBC graphics department.

Where in the World Is Carmen Santiagova?

In the grand scheme of things, this graphic is not the end of the world. On the other hand, it is probably more than half of the world. In particular, I am talking about this graphic from a BBC article about a recent helicopter crash on the Kamchatka Peninsula in Russia’s Far East.

As you can see, Kamchatka extends from the eastern tip of Sibera at the Bering Strait southward towards Hokkaido, the northern-most large island of Japan.

But the thing is…this map is supposed to locate Kamchatka and the crash site of Vachkazhets, but if you look closely at the inset map of the world in the lower left, you can see that the audience is being zoomed into…more than half the world.

I am left to wonder about the efficacy of the map in clarifying the precise location of the crash site. To be fair, Kamchatka is very, very far away from Moscow, probably the city of reference most readers would recognise. But what if instead of a map including India and the Sahara Desert—not at all close to Russia—the map simply cropped in tighter on Russia? Yes, you lose the Kaliningrad Oblast, the little bit of Russia cut off from the rest of the country by the Baltic states, but contextually I think that acceptable.

Or, what if the map took a different approach and omitted Moscow as the point of reference and instead highlighted another global city, like Tokyo, Seoul, or Beijing? After all, those are also all far closer than Moscow.

Ultimately, however, the map irked me because of a glaring error. No, the map does not colour the Crimean Peninsula yellow despite its annexation by Russia. I am perfectly fine with that given the illegality of said annexation, however, after a decade of administration I think there is an argument to be made that Crimea is now administratively more Russian than Ukrainian.

No, all the way in the east, the very edge of the Eurasian continent is grey. But that is also part of Russia. I crudely coloured it—along with part of a larger island—in for you to help you see. There may be some smaller islands that are also grey—most certainly are—but the resolution of the map makes it too difficult to tell for certain.

All in all this just seems like a sloppy locator map. So sloppy I am not sure it even adds value to the article.

Credit for the piece goes to the BBC graphics team.

Cavalcante Captured

Well, I’ve had to update this since I first wrote, but had not yet published, this article. Because this morning police captured Danelo Cavalcante, the murderer on the lam after escaping from Chester County Prison, with details to follow later today.

This story fascinates me because it understandably made headlines in Philadelphia, from which the prison is only perhaps 30–40 miles, but the national and even international coverage astonished me. Maybe not the initial article, but the days-long coverage certainly seemed excessive when we had much larger problems or notable events occurring throughout the world.

That brings me to this quick comparison of these two maps. The first is from the local paper, the Philadelphia Inquirer. It is a screenshot in two parts, the first the actual map and the second the accompanying timeline.

Then we have the BBC and their map of the story:

Both maps use light greys and neutral colours to ground the reader’s experience, his or her welcome to the world of southeastern Pennsylvania. The Inquirer uses a beige and a white focus for Chester County and the BBC omits county distinctions and uses white for rural and grey for built-up areas around Philadelphia.

Both maps use red numbers in their timeline sections to sequence the events, though the Inquirer’s is more extensive in its details and links the red events to red map markers.

The Inquirer leans heavily on local roads and highways with lines of varying width in white with thin outlines. Whereas the BBC marks only significant roads as thin blue lines.

The Inquirer’s map adds a lot of geographical context, especially for an audience fastidiously following the situation. And the following makes sense given all the local closures and anxiety—though I’m of the opinion a significant bit of those closures and anxiety were unwarranted. But for a reader in London, Toronto, or Melbourne, does anyone really need to see Boot Road? Strasburg Road? Even Route 30? Or the Route 30 Bypass (at Route 100, hi, Mum)? Not really, and so the omission of many of the local roads makes sense.

I would keep the roads relevant to the story of the search or the capture, for example Routes 23 and 1, and places relevant, for example Longwood Gardens and South Coventry. Here the BBC perhaps goes too far in omitting any place labels aside from Philadelphia, which is itself borderline out of place.

What I like about the BBC’s map, however, is the use of the white vs. grey to denote rural vs. built-up areas, a contextual element the Inquirer lacks. Over the last two weeks I have heard from city folks here in Philadelphia, why can’t the cops capture Cavalcante in Chester County? Well, if you’ve ever driven around the area where he initially roamed, it’s an area replete with wooded hills and creeks and lots of not-so-dense rich people homes. We don’t yet know where he was finally captured, but in Phoenixville he was spotted on camera because it’s an actual borough (I’m pretty certain it’s incorporated) with a walkable downtown. It’s dense with people. And not surprisingly the number of spottings increased as he moved into a denser area.

The Inquirer’s map, however, doesn’t really capture that. It’s just some lines moving around a map with some labels. The BBC’s map, though imperfect because the giant red box obscures a lot of the initial search area, at least shows us how Cavalcante evaded capture in a white thus rural, less-dense area before being seen in a grey thus built-up dense area.

All-in-all, both are good enough. But I wish somebody had managed to combine both into one. Less road map than the Inquirer’s, but more context and grounding than the BBC.

Credit for the Inquirer piece goes to John Duchneskie.

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

The Great British Baking

Recently the United Kingdom baked in a significant heatwave. With climate change being a real thing, an extreme heat event in the summer is not terribly surprising. Also not surprisingly, the BBC posted an article about the impact of climate change.

The article itself was not about the heatwave, but rather the increasing rate of sea level rise in response to climate change. But about halfway down the article the author included this graphic.

As graphics go, it is not particularly fancy—a dot plot with ten points labelled. But what this piece does well is using a dot plot instead of the more common bar chart. I most typically see two types of charts when plotting “hottest days” or something similar. The first is usually a simple timeline with a dot or tick indicating when the event occurred. Second, I will sometimes see a bar chart with the hottest days presented all as bars, usually not in the proper time sequence, i.e. clustered bar next to bar next to bar.

My issue with the the latter is always where is the designer placing the bottom of the bar? When we look at the best temperature graphics, we usually refer to box plots wherein the bar is aligned to the day and then top of the bar is the daily high and the bottom of the bar the daily low. It does not make sense to plot temperatures starting at, say 0º.

In this particular case, however, the dates would appear to overlap too closely to allow a proper box plot. Though I suspect—and would be curious to see—if the daily minimum temperatures on each of those ten hottest days have also increased in temperature.

As to the timeline option, this does a better job of showing not just the increasing frequency of the hottest days, but also the rising maximum value. In the early 20th century the hottest day was 36.7ºC, and you can see a definite trend towards the hottest days nearing and finally surpassing 40ºC.

I do wonder if a benchmark line could have been added to the chart, e.g. the summertime average daily high or something similar. Or perhaps a line showing each day’s temperature faintly in the background.

Finally, I want to point out the labelling. Here the designers do a nice job of adding a white stroke or outline to the outside of the text labels. This allows the text to sit atop the y-axis lines and not have the lines interfere with the text’s legibility. That’s always a nice feature to see.

Credit for the piece goes to the BBC graphics department.

Warming Towards Women Leaders

We are going to start this week off with a nice small multiple graphic that explores the reducing resistance to women in positions of leadership in Arab countries. The graphic comes from a BBC article published last week.

These kinds of graphics allow a reader to quickly compare the trajectory of a thing between a start and an endpoint. The drawback is it can obscure any curious or interesting trends in the midpoints. For example, with Libya, is its flat trajectory always been flat? You could imagine a steep fall off but then rapid climb back up. That would be a story worth telling, but a story obscured by this type of graphic.

I do think the graphic could use a few tweaks to help improve the data clarity. The biggest change? I would work to improve the vertical scale, i.e. stretch each chart taller. Since we care about the drop in opposition to women leaders, let’s emphasise that part of the graphic. There could be space constraints for the graphic, but that said, it looks like some of the spacing between chart header and chart could be reduced. And I think for most of the charts except for the first, the year range could be added as a data definition to the graphic and removed from each chart. Similar to how every row only once uses the vertical axis labels.

Another way this could be done is by reducing the horizontal width of each chart in an attempt to squeeze the nine from three rows down to two. That would mean two additional chart positions per row. Tight fit? Probably, but there is also some extraneous space to the right and left of each chart and a large gap between the charts themselves. This all appears to be due to those aforementioned x-axis labels. An additional benefit to reducing the horizontal dimensions of each chart is it increases the vertical depth of the chart as each line’s slope, its rise over run, sees its horizontal distance shrink.

Overall this is a really smart graphic that works well, but with a few extra tweaks could take it to the next level.

Credit for the piece goes to the BBC graphics department.

It’s a Little Steamy Out There

And by out there I mean 1150 light years away. One of the five amazing images out of the first day’s announcement by the James Webb Space Telescope (JWST) team was not a sexy photo of a nebula or a look back 13.5 billion years in time. Instead it was a plot of the amount of infrared light was blocked as exoplanet WASP-96b, a hot Jupiter, transited in front of its sun. A hot Jupiter is a gas giant roughly the size of Jupiter that orbits its sun so closely—often closer than Mercury does our Sun—its year takes mere days. WASP-96b is about half the mass of Jupiter and a year takes a little over three Earth days. Hot indeed.

The JWST means not just to take those images we saw, but to also capture data about the light passing through planetary atmospheres, just like WASP-96b. And showing the world Tuesday just how that works was a brilliant idea. What they shared was this graphic.

The original post explains the science behind it, but in short we see telltale signs of water vapor in the atmosphere. Remember that the planet is far too hit for liquid water to exist. But because the peaks and troughs were not as pronounced as expected, scientists can conclude that there are clouds and haze in the atmosphere. It did not detect any significant signs of oxygen, carbon dioxide, or methane, all of which would be noticeable if present as we expect in future exoplanets to be studied.

But later that day, the BBC published an article summarising the releases, but included a different version of the above graphic. Though the other four photos were unchanged. The BBC presented us with this.

The most notable difference is the background. What was a giant illustration of a planet and then a semi-transparent chart background atop that on which the graphic sat is here replaced by a simple white background. Off the bat this chart is easier to read.

But then here we also lose some data clarity. Note on the original how we have axis markers for the wavelengths of light and the parts per million of light blocked. All are absent here. Instead the BBC opted to only put “Shorter” and “Longer” on the wavelength axis. I would submit that there was no real need to remove those labels, but that they could have been added to with these new ones. The new labels certainly explain the numbers to an audience that may not be as scientifically literate as perhaps the JWST’s audience was or was thought to be. There is certainly a value to simplifying and distilling things to a level at which your audience can understand. But there’s also a value in presenting more complex data, issues, and ideas in an attempt to educate and elevate your audience. In other words, instead of always trying to play to the lowest common denominator, it sometimes is worth it to lose a few in the audience if you ultimately increase the level of said denominator overall.

The other notable difference is that the data is presented without what I assume to be plots of the range of observations with their respective medians. You can see this in the original by how every wavelength has a line and a dot sitting in the middle of that line. In other words, over the 6+ hours the planet was observed, at each wavelength a certain amount of light was blocked. The average middle point over that whole time period is the dot. Then a line of best fit “connects” the dots to show the composition of the light streaming though that steamy atmosphere.

Again, I can understand the desire to remove the ranges and keep the median, but I also think that there is little harm in showing both. Though, the first graphic could like have used an explanation of what was shown, as I’m only assuming what we have and I could be way off. You can show more things and raise the level of the denominator, but you can only do so if you explain what your audience is looking at.

Overall both graphics are nice and capture not just the particular makeup of this one exoplanet’s atmosphere, but more broadly the potential power of the JWST and its impact on astronomy.

Credit for the original goes to the NASA, ESA, CSA, and STScI graphics teams.

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

Those Quirky Quarks

Last week scientists working at the Large Hadron Collider in Switzerland announced the discovery of new sub-atomic particles: a pentaquark and tetraquarks. This BBC article does a really good job of explaining the role of quarks in the composition of our universe, so I encourage you to read the article.

But they also included a graphic to show how quarks relate to atoms. It’s a simple illustration, but it does a great job.

Sometimes great and informative graphics can be simple. They needn’t be flashy or over-designed. I could quibble about the depiction of the electron cloud around the nucleus, but it’s not terrible.

Credit for the piece goes to the BBC graphics department.