critique – 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.

Three-dimensional Charts Are Back, Baby

I thought three-dimensional charts died back in the 2010s. Alas, here we are in 2024 and I have to discuss one once again. have been following the Titan Inquiry this week and the opening presentation included this gem of data visualisation.

To be fair, I do not know how many designers, let alone specialist information designers, the US Coast Guard had or made available to create a clear and compelling chart and presentation, but…this is not it. First I will go through a number of points and then, when I had written about half of the post this morning, I decided it would simply be easier to put a white box over the main chart area and just recreate the graphic myself.

Unfortunately, after digging around, I could not find the actual dive depth data the Coast Guard used and so I essentially traced out the chart by hand. Not ideal, but for proof of concept as to how this chart could have been improved…I think my reinterpretation ssuffices.

To start, the chart sits on the slide with a drop shadow. Drop shadows are not all bad. They create perceived depth between an object and it’s background. The interwebs love them. I have used them. But I do not understand why here the chart needs a drop shadow to sit on the slide. Especially since the shadow pushes the chart “above” the deck, only for the three-dimensional bar chart to push the data “below” the chart’s surface, which means the chart data is being represented on the slide surface.

Deep breath.

The chart background features some kind of coloured gradient that became pixellated upon export and import into the PowerPoint deck.

The type was too small that it too became pixellated and grainy to the point that the dive labels are illegible. I would argue labelling each dive beyond its number is unnecessary in the context of Titan’s final dive, but without having listened to the presentation I cannot say for certain.

Next we have the third-dimension. It adds nothing and creates more coloured areas—because the dimension is fake, this a two-dimensional representation of three dimensions—that distract the eye from the important dimension, the length of the bar.

After that, we can look at the axis labels. First, there are far too many. Second, the maximum depth labelling makes no sense. Sometimes, if a line or a bar exceeds the chart maximum once or twice by a small amount, you can let it poke above the top—in this case bottom—line. If you know the rules, you know when you can break the rules. Here, however, the maximum label is 3800 metres.

But Titanic rests at 3840. Ergo 13 different measurements will need to sit below the chart’s maximum—minimum, technically—axis line.

Deep breath.

If she rests at 3840 metres, just add 60 to the chart minimum and you will ahve a final axis label of 3900 metres. Look carefully, however, and you will see in the bottom left how after the final white line, the chart keeps going. Clearly, the designers knew the chart needed more space. This unlabelled minimum is probably 3900 metres given the 100-metre increments used throughout.

But, however, if you add 160 metres to the chart you have a nice, round, divisible number of 4000, which means you do not need to mark the depths in 100-metre increments. It means all the bars sit within the chart. It means fewer pixels on the slide to distract the eyes. (Especially if you drop the background colour.)

Furthermore, if you look carefully at the green boxes, which represent successful dives to Titanic, you can see how the bars break the dimensional rules and are actually flat two-dimensional bars. Perhaps this was only noticeable to me as I worked off the downloaded file at a high-level of zoom to try and figure out the depths as precisely as possible. Or perhaps it is an artifact of the pixellated export of the graphic. If the latter, more of a reason not to make the thing a three-dimensional bar chart.

Then we can get to the colours.

Deep breath.

To start, red-green colour blindness is a thing. I harp on this often and so I will not rehash everything here. No, it does not mean all green and red combinations will not work, one just needs to be careful with them. This one comes pretty close to not working so I would have avoided it.

Secondly, just look at the red. I mean, how can you not. It is very bright and draws your eye almost immediately to all those red bars, particularly the one nearly a fifth of the way in from the right edge. That one is next to one of the successful Titanic dives. My first thought? Oh, that was the final dive. Wrong.

Red means non-Titanic dives. Again, I have not listened to the presentation, but these would presumably be dives of relatively less importance than the Titanic dives. I would not have made the less important dives the one colour that stands out the most.

If you want to go green represents successful Titanic dives and red represents unsuccessful Titanic dives, that makes sense. I can understand the design decision. (Though you would still need to ensure the shades work with each other.) In that case maybe the blue bars represent non-Titanic dives.

Instead, here blue represents unsuccessful dives to Titanic, which of course means the final dive, which of course includes the inquiry’s raison d’être. Not only that, the chart’s background is also blue, which makes visually separating the bars from the background more difficult. This is particularly true at the sides of the chart where the gradient leaves the darker blue.

Finally we have a little orange box with some tiny type pointing out the final dive’s depth. That bit, more visible than the green and orange bars, was still lost to me behind the red bars.

And breathe.

All in all, a mess.

As I noted at the top, halfway through I decided this was such a mess I would prefer just to show how the chart could have been designed. It took a little over an hour to make the chart. Clearly I do not have the chart style guidelines for the Coast Guard, so I just chose a typeface I think worked and then picked some reasonable colours from the deck.

Call me biased, but my design substantially improves the chart. First, you can read the text. Second, the colours fit the brand, do not distract from and in fact highlight the final dive. If I started from scratch, I would prefer to use what looks like the full content area of the PowerPoint slide, but I simply traced over the existing chart. I.e., ideally the chart would have been a little bit taller. I did have to cut out the labels for each dive, but as I stated earlier, they were illegible.

Credit for the original piece goes to the US Coast Guard.

Credit for my reinterpretation goes to me.

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.

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.

Legendary Adjustments

The other day I was reading an article about the coming property tax rises in Philadelphia. After three years—has anything happened in those three years?—the city has reassessed properties and rates are scheduled to go up. In some neighbourhoods by significant amounts. I went down the related story link rabbit hole and wound up on a Philadelphia Inquirer article I had missed from early May that included a map of just where those increases were largest. The map itself was nothing crazy.

We have a choropleth with city zip codes coloured by the percentage increase. I was thrown for a bit of a loop as I immediately perceived the red representing lower values and green higher values, the standard green to red palette. But given that higher values are “bad”, I can live red representing bad and sitting at the top of the spectrum.

I filed it away to review later, but when I returned I visited on my mobile phone. And what I saw broadly looked the same, but there was a disconcerting difference. Take a look at the legend.

You can see that instead of running vertically like it did on the desktop, now the legend runs horizontally across the bottom. In and of itself, that’s not the issue. Though I do wonder if this particular legend could have still worked in roughly the same spot/alignment given the geographic shape of Philadelphia along the Delaware River.

Rather look at the order. We go from the higher, positive values on the left to the negative, lower values on the right. When you read the legend, this creates some odd jumps. For example, we move from “+32% to +49%” then to “+15% to +31%”. We would normally say something to the point of the increase bins moving from “+15% to +31%” then to “+32% to +49%”. In other words, the legend itself is a continuum.

The fix for this would be to simply flip the running order of the legend. Put the lower values on the left and then step up to the right. For a quick comparison, I visited the New York Times website and pulled up the first graphic I could find that looked like a choropleth. Here we have a map of the dangerous temperatures across the United States.

Note how here the New York Times also runs their legend horizontally below the graphic. But instead of running high-to-low like in the Inquirer, the Times runs low-to-high, making for a more natural and intuitive legend.

This kind of simple ordering change would make the Inquirer’s map that much better.

Credit for the Inquirer piece goes to Kasturi Pananjady and John Duchneskie.

Credit for the Times piece goes to Matthew Bloch, Lazaro Gamio, Zach Levitt, Eleanor Lutz, and John-Michael Murphy.

New Mexico Burns

Editor’s note: I was having some technical issues last week. This was supposed to post last week.

Editor’s note two: This was supposed to go up on Monday. Still didn’t. Third time’s the charm?

Yesterday I wrote about a piece from the New York Times that arrived on my doorstep Saturday morning. Well a few mornings earlier I opened the door and found this front page: a map of the western United States highlighting the state of New Mexico.

That doesn’t exactly look like a climate I’d enjoy.

Unlike the graphic we looked at yesterday, this graphic stretched down the page and below the fold, not by much, but still notably. The maps are good and the green–red spectrum passes the colour blind test. How the designer chose to highlight New Mexico is subtle, but well done. As the temperature and precipitation push towards the extreme, the colours intensify and call attention to those areas.

Also unlike the graphic we looked at yesterday, this piece contained some additional graphics on the inside pages.

Definitely not a place where I want to be.

These are also nicely done. Starting with the line chart at the bottom of the page, we can contrast this to some of the charts we looked at yesterday.

Here the designer used axis lines and scales to clearly indicate the scale of New Mexico’s wildfire problem. Not only can you see that the number of fires detected has spiked far above than the number in the previous years back to 2003. And not only is the number greater, the speed at which they’ve occurred is noticeably faster than most years. The designer also chose to highlight the year in question and then add secondary importance to two other bad years, 2011 and 2012.

The other graphics are also maps like on the front page. The first was a locator map that pointed out where the fires in question occurred. Including one isn’t much of a surprise, but what this does really nicely is show the scale of these fires. They are not an insignificant amount of area in the state.

Pointing out where I really don’t want to be in New Mexico.

Finally we have the main graphic of the piece, which is a map of the spread of the Calf Canyon and Hermits Peak fire, which was two separate fires until they merged into one. The article does a good job explaining how part of the fire was actually intentionally set as part of a controlled burn. It just became a bit uncontrolled shortly thereafter.

This reminded me of a piece I wrote about last autumn when the volcano erupted on La Palma. In that I looked at an article from the BBC covering the spread of the lava as it headed towards the coast. In that case darker colours indicated the earlier time periods. Here the Times reversed that and used the darker reds to indicate more recent fire activity.

Overall the article does a really nice job showing just what kind of problems New Mexico faces not just now from today’s environmental conditions, but also in the future from the effects of climate change.

Credit for the piece goes to Guilbert Gates, Nadja Popovich, and Tim Wallace.

May Jobs Report

Friday the Bureau of Labour Statistics published the data on the jobs facet of the American economy. Saturday morning I woke up and found the latest New York Times visualisation of said jobs report waiting for me at my door. The graphic sat\s above the fold and visually led the morning paper.

We have a fairly simple piece here, in a good way. Two sections comprise the graphic. The first uses a stacked bar chart to detail the months wherein the US economy lost jobs during the previous two and a half years. We can take a closer look in this second photo that I took.

But the recovery hasn’t been uniformly good for all.

Here we can see the stacked bars pile up with the most recent bars to the right. Some of the larger bars have labels stating the number of jobs either lost (top) or gained (bottom). I’m not normally a fan of stacked bar charts, because they don’t allow a reader to easily discern like-for-like changes. In this instance, the goal is to show how close all the little bits have come towards making up the three negative bars. Where I take issue is that I would prefer the designers used some sort of scale to indicate even a rough sense of how many jobs the various bars represent.

That issue crops up again to a slightly lesser degree with the bottom set of graphics. These compare the growth of hourly earnings and inflation both from February 2020. During the first few months of the pandemic and its recession, you can see earnings for those most directly impacted by shutdowns drop. But there is no negative scale accompanying the positive scale and that makes it difficult to determine just how far earnings fell for those in, say, leisure and hospitality.

The second part of the graphic works overall, however it’s just some of the finer design details that are missing and take away from the graphic’s overall effectiveness.

This all fits part of a larger trend in data visualisation that I’ve been noticing the last few months. Fewer charts seem to be using axes and scales. It’s not a good thing for the field. Maybe some other day I’ll write some things about it.

For this piece, though, we have an overall solid effort. Some different design decisions could have made the piece clearer and more effective, but it still does the job.

Credit for the piece goes to Ben Casselman, Ella Koeze, and Bill Marsh.

Into the Memory Hole

I noticed an interesting thing this morning. Over the holiday weekend I bookmarked a BBC News article about new airlines because it included a small graphic showing the number of airlines started during the pandemic (32) and the number of new airlines lost during the pandemic (55). The graphic used a stock three-dimensional illustration of a passenger airlines with a blank white body. From the top of the body rose two white bars, next to the left was the shorter of the two with a 32. The right was taller and had a 55. Above each was a header saying something to the effect of “Airlines started in 2020” and “Airlines lost in 2020”, respectively. Funny thing this morning that when I returned to the bookmark with this post in mind, the article’s graphic had disappeared.

This weekend I happened to start re-reading 1984, George Orwell’s classic dystopian novel about a man named Winston Smith. He works in the Records Department and is tasked with “rectifying” misstatements. I had just finished reading the section where Orwell describes Smith’s work wherein he takes previously published newspaper articles about statistics and figures and then edits them to include new numbers aligned with the actual outputs. This way should anyone read the old article for evidence of a previous past, they find the output forecasts have always been correct. He then destroys the written record of the old past by dumping it into a memory hole, a pneumatic tube that delivers it straight to a furnace where the old past is incinerated and thus replaced with Smith’s new version.

When I read the article again, because the graphic was gone, I read a paragraph that had figures for 2021. I cannot recall those numbers being present earlier this weekend. But they are roughly where I remember the old graphic being. Yet the article includes no note about any edits to a previous version let alone what those edits may have been. And so now I am left wondering if I really saw what I think I remember that I saw. How very Orwellian.

But let’s assume I did see what I thought I saw, the graphic was actually unnecessary. It presented two figures, 32 and 55. The bar chart itself had no axis labels and that made it a bit difficult to believe the numbers themselves. It did not help that the white bars blended almost seamlessly into the white body of the airliner. Moreover, the graphic was large and fit the full width of the text column. For two figures.

My initial goal was to show this graphic I made to show just how little space truly needs to be used to show an effective graphic. I also changed the direction of the bars. Instead of making one bar about the positive change and the other the negative change, I made both bars about the change. Therefore the one bar moved upwards with the positive (32) and the other downwards with the negative (55). I then plotted a dot to show the net change between the two. Yes, 32 airlines were created in 2020. But that still made for a net loss of 23 that year.

But because the graphic was missing and there was some new text for 2021 figures, I decided to incorporate them as well to show how the trend basically continued year over year.

I left the white space to the right to illustrate how you really do not need a full-width graphic to display only six data points, itself a three-fold increase on the original graphic’s data content. The original graphic contained more illustrated plane than it did data content.

Graphics should be about the data, not about the splashy, flashy, whizbang background content that ultimately distracts our attention away from what should be the focal point of the piece: the data. The article still contains photos of planes with the livery of the new airlines, of empty terminals to represent the pandemic losses, and portraits of executives. This graphic did not need an illustrated plane taking over the graphic. It needed to only show those two numbers.

I would even contend that the article could have made do with a simple factette, two big numbers. Airlines closed in 2020 and the airlines opened. It need not be fancy, but it quickly delivers the big numbers with which the reader should be concerned. You don’t need to see an aircraft or a terminal. You could add some colour to the numbers or even a minus sign as there is a significant difference between a 55 and a -55. But all in all, the graphic need not be full width like it was originally.

But I think we should all keep in mind the value of transparency. The graphic did exist, of that I am certain. But future readers or even my sanity cannot be sure that it did. And in an era where “fake news” and fact-checking are important, I wonder if we need to be including corrections notes in more of our news articles. Because if we lose faith in our news, we have little left to lean upon in our societal discourse about the events of our time.

Credit for the piece is mine.

More on Those Million Covid-19 Deaths

Yesterday I focused on the big graphic from the New York Times that crossed the full spread of the front/back page. But the graphic was merely the lead graphic for a larger piece. I linked to the online version of the article, but for this post I’m going to stick with the print edition. The article consists of a full-page open then an entire interior spread, all in limited colour. The remainder of the extensive coverage consists of photo essays and interviews that understandably attempt to humanise the data points, after all, each dot from yesterday represented one individual, solitary, human being. That is an important element of a story like this and other national and international tragedies, but we also need to focus on the data and not let the emotion of the story overwhelm our rational and logical analysis.

Sometimes it’s hard to realise we’re in the third year of this pandemic.

From a data visualisation standpoint the first page begins simply enough with a long timeline of the Covid-19 pandemic charting the number of absolute deaths each day. As we looked at yesterday, the absolute deaths tell part of the story. But if we were to have looked at the number of absolute cases in conjunction with the deaths, we could also see how the virus has thus far evolved to be more transmissible but less lethal. Here the number of daily deaths from Omicron surpassed Delta, but fell short of the winter peak in early 2021. But the number of cases exploded with Omicron, making its mortality rate lower. In other words, far more people were getting sick, but as far fewer were dying.

An interesting note is that if you take a look at the online version, there the designers chose a more stylised approach to presenting the data.

Here they kept the dot approach and simply stacked and reordered the dots. However, I presume for aesthetic reasons, they kept the stacking loose dots and dropped all the axis lines because it does make for a nice transition from the map to this chart. But they also dropped all headings and descriptors that tell the reader just what they are looking at. These decisions make the chart far less useful as a tool to tell the data-driven element of the story.

There are three annotations that label the number of deaths in New York, the Northeast, and the rest of the United States. But what does the chart say? When are the endpoints for those annotations? And then you can compare the scale of the y-axis of this chart and compare it to the printed version above. A more dramatic scale leads to a more dramatic narrative.

This sort of visual style of flash and fancy transitions over the clear communication of the data is why I find the print piece more compelling and more trustworthy. I find the online version, still useful, but far more lacking and wanting in terms of information design.

The interior spread is where this article shines.

From an editorial design standpoint, the symmetry works very well here. It’s a clear presentation and the white space around the graphic blocks lets that content shine as it should in this type of story. Collectively these pieces do a great job telling the story of the pandemic thus far across the nation. The graphics do not need a lot of colour and make do with sparse flash. Annotations call the reader’s attention to salient points and outliers.

From a content standpoint, I would be particularly curious if we have robust data for deaths by education level. Earlier this year I recall reading news about a study that said education best correlated to Covid cases, and I would be curious to see if that held true for deaths. Of course these charts do a great job of showing just how effective the vaccines were and remain. They are the best preventative measure we have available to us.

Here I disagree with the design decision of how to break down the states into regions. The Census Bureau breaks down the United States into four regions using the same names as in the graphic above. However, if you look closely at the inset map, you will see that Delaware, Maryland, and West Virginia in particular are included as part of the Northeast. (I cannot tell if the District of Columbia is included as part of the Northeast or South.)

Now compare that to the Census Bureau’s definition:

If you ask me to include Delaware and Maryland as part of the Northeast, well, if you’re selling it, I’ll buy it. After all, just because the Census Bureau defines the United States this way does not mean the New York Times has to. Both are connected to the Northeast Corridor via Amtrak and I-95 and are plugged into the Megalopolis economy. Maybe the Potomac should be the demarcation between Northeast and South. But I struggle to understand West Virginia. Before you go and connect it to the Northeast, I would argue that West Virginia has far more in common with the Midwest geographically, economically, and culturally.

More critically, given this issue, it strikes me as a serious problem when the online version of the chart—with the aforementioned issues—does not even include the little inset to highlight this at best unusual regional definition.

And so while I have reservations about the data—how would the data have looked if the states were realigned?—the design of the line charts overall is good.

Again, I am talking about the print version, not that online graphic. I would argue that the above screenshot is barely even a chart and more “data art” or an illustration of data. Consider here, for example, that for the South we have that muted slate blue for the dots, but the spacing and density of the dots leads to areas of lighter slate and darker slate. But a lighter slate means more space between stacked dots and darker slate means a more compact design. A lighter colour therefore pushes the “edge” of the line further up the y-axis and artificially inflates its value, not that we can understand what that value is as the “chart” lacks any sort of y-axis.

Finally the print piece has a set of small multiples breaking down deaths by income in the three largest American cities: New York, Los Angeles, and Chicago. These are just great little charts showing the correlation between income and death from Covid, organised by Zip code.

But this also serves as a stark reminder of just how much better the print piece is over the online version. Because if we take a look at a screenshot from the online article, we have a graphic that addresses all the issues I pointed out earlier.

Why couldn’t the online article kept to this style?

I am left to wonder why the reader of the online version does not have access to this clearer and more accurate representation of the data throughout the piece?

To me this article is a great example of when the print piece far exceeds that of the online version. Content-wise this is a great story that needed to be told this weekend, but design wise we see a significant gap in quality from print to online. Suffice it to say that on Sunday I was very glad I received the print version.

Credit for the piece goes to Sarah Almukhtar, Amy Harmon, Danielle Ivory, Lauren Leatherby, Albert Sun, and Jeremy White.

Political Hatch Jobs

Earlier this week I read an article in the Philadelphia Inquirer about the political prospects of some of the candidates for the open US Senate seat for Pennsylvania, for which I and many others will be voting come November. But before I get to vote on a candidate, members of the political parties first get to choose whom they want on the ballot. (In Pennsylvania, independent voters like myself are ineligible to vote in party primaries.)

This year the Republican Party has several candidates running and one of them you may have heard of: Dr. Oz. Yeah, the one from television. And while he is indeed the front runner, he is not in front by much as the article explains. Indeed, the race largely had been a two-person contest between Oz and David McCormick until recently when Kathy Barnette pulled just about even with the two.

In fact, according to a recent poll the three candidates are all statistically tied in that they all fall within the margin of error for victory. And that brings us to the graphic from the article.

It would be funny to see a candidate finish with negative vote share.

Conceptually this is a pretty simple bar chart with the bar representing the share of the support of those polled. But I wanted to point out how the designer chose to represent the margin of error via hatched shading to both sides of the ends of the red bar.

In some cases the hatch job does not work for me, particularly with those smaller candidates where the bar goes negative. I would have grave reservations about the vote should any candidate win a negative share of the vote. 0% perhaps, but negative? No. I also don’t think the grey hatching works as well over the grey bar in particular and to a lesser degree the red.

I have often thought that these sorts of charts should use some kind of box plot approach. So this morning I took the chart above and reworked it.

Overall, however, I really like this designer’s approach. We should not fear subtlety and nuance, and margins of error are just that. After all, we need not go back too far in time to remember a certain candidate who thought she had a presidential election locked up when really her opponent was within the margin of error.

Credit for the piece goes to John Duchneskie.