Thoroughbred racing is big business. And Philadelphia’s Parx Casino owns a racing track that, in a recent article in the Philadelphia Inquirer, has seen a number of horse deaths. The article includes a single graphic worth noting, a bar chart showing the thoroughbred death rate. The graphic contrasts rising deaths at Parx with a national trend of declining deaths.
Traditionally rate statistics are shown using dots or line. The idea is that a bar represents counting stats, i.e. how many total horses died. I understand the coloured bars present a more visually compelling graphic on the page, and so I can buy that reason if you are selling it.
Labelling each datapoint, however, with a grey text label above the bar remains unnecessary. They create sparkling, distracting grey baubles above the important blue bars. If you need the specificity to the hundredths degree, use a table. This graphic is also interactive. The mouseover state is where a specific number can be provided, adding an additional layer or level of depth in a progressive disclosure of information.
The Teamsters Union decided to officially endorse neither candidate in the 2024 US presidential election. Prior to their non-announcement announcement, however, the union surveyed its members and then released the polling data ahead of the announcement.
Of course, the teamsters represent but a single union in a large and diverse country. More importantly, the survey results reported only the share of responses for either candidate—and “Other”—so we have no idea how many of what number opted for whom. But hey, it’s another talking point in the final six weeks of the campaign.
Naturally, I decided to visualise the data.
The trend is pretty, pretty clear. The union’s rank-and-file clearly support Trump for president, with the exception of the teamsters in the District of Columbia. (Note, no survey was taken in Wyoming.) In fact, in only eight states plus DC did Harris’ support top 40%.
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.
As it happens, the Latino culture largely remains x’ed out on using the term Latinx, according to a new survey from Pew Research.
The issue of supplanting Latino/Latina with Latinx as a gender neutral replacement—or as a complementary alternative—emerged in the general discourse in that oh-so-fun year of 2020 when everything went well.
One common argument I have heard is the inherent gender within the Spanish language. Broadly you use -o for singular masculine endings and -a for singular feminine forms and -os for plural masculine and mixed gender forms and -as for plural feminine forms.
Perhaps my biggest issue is that -x does not linguistically make sense. X is typically pronounced like a j or sometimes an s. Consider how Mexicans pronounce Mexico, May-hie-co. Latinx becomes La-teen-h, an almost silent ending that does not fit, at least to my ears. Pero, hablo solo un pocito Español. Aprendí a hablar por cuatro años en la escuela, dos años de niño, y trabaja en una cocina del restaurante. Thus Latinx, pronounced Lat-in-ecks, always seemed, daresay, a gringo solution to a problem that earlier polling of Latino communities did not indicate was a problem. With the potential exception of the young, but even then not terribly so.
Four years later, however, and not much has changed according to Pew. Their graphic shows as much.
Significantly more people are aware of Latinx as a term. Fewer people use the term, though not significantly. Although a shift from four to three percent can be seen as significant given its low adoption. Moreover, as a second graphic shows, more people who are aware of the term think it should not be used.
The article continues with a discussion of a new new alternative, Latine, which to my ears makes more sense. But is largely yet unheard of in the community—20%—and of those who have heard it, almost nobody uses it.
As far as the graphics go, I am not a huge fan.
For the first, we have two lines showing the movement between two datapoints. At the most basic level, the use of a line chart makes sense to depict two series moving between two points in time. But without any axis labelling one can only trust the lines begin and end at the correct position. Furthermore people need to read the specific labels to get a sense of the line charts’ magnitude. More of a tell, don’t show approach. If the chart had even a simple 0% line and 50% line, one need not label all four datapoints to convey the scale of the graphic.
Ultimately, though, does a chart with four datapoints even need to be graphed? Some would argue in most instances a dataset with fewer than five or six numbers need not be visualised; a table should suffice. Broadly I agree. This chart does show a particularly striking trend of increasing awareness of the term, but largely static to declining usage.
The second graphic, however, falls more squarely into that argument’s camp of “Why bother?” It shows simply two numbers. Numbers placed atop purple rectangles. Without any axis labelling, we presume these bars represent columns encoding the percent—at least the lines in the first chart were clearer to their meaning. Then we still have the issue of telling and not showing. Perhaps labelling to the left from 0% to 75% or 80% would help. Then you need not even add additional “ink” with the four digits sitting atop the bars and sparkling for unnecessary reader attention.
This falls into a broader trends I have witnessed over the last few years in the information design and data visualisation field of labelling individual datapoints within a chart. It is a trend with which I strongly disagree, but perhaps is best left for another post another day. Suffice it to say, if knowing the precise measurement is important, a chart is not the best form. For that use case I would opt for a table, best used to organise and find specific datapoints.
Overall, Pew shows that within the Latino community, very few use the term Latinx. Consequently, perhaps this entire post is, to use a Spanish-language expression, a tempest in a teapot.
Last night I went to see Top Gun: Maverick, the sequel to the 1986 film Top Gun. Don’t worry, no spoilers here. But for those that don’t know, the first film starred Tom Cruise as a naval aviator, pilot, who flew around in F-14 Tomcats learning to become an expert dogfighter. Top Gun is the name of an actual school that instructs US Navy pilots.
Back in the 1980s, the F-14 was the premiere fighter jet used by the Navy. But the Navy retired the aircraft in 2006 and it’s been replaced by the F/A-18E/F Super Hornet, a larger and more powerful version of the F/A-18 Hornet. So no surprise that the new film features Super Hornets instead of Tomcats.
And so I wanted to compare the two.
The important thing to note is that the Tomcat flies farther and faster than the Hornet. The F-14 was designed to intercept Soviet bombers that were equipped with long-range missiles that could sink US carriers. The Hornet was designed more of an all-purpose aircraft. It can shoot down enemy planes, but it can also bomb targets on the ground. That’s the “/A” in the designation F/A-18. In the role of intercepting enemy aircraft, the F-14 was superior. It could fly well past two-times the speed of sound and it could fly combat missions over 500 miles away from its carrier.
In the interception role, however, the F-14 had another crucial advantage: the AIM-54 Phoenix missile. It was a long-range air-t0-air missile designed for the Tomcat. It does not work with any other US aircraft and so the Hornet uses the newer AIM-120 AMRAAM, a medium-range air-to-air missile.
There are plans to design a long-range version of the AIM-120, but it doesn’t exist yet and so the Hornet ultimately flies slower, less distance, and cannot engage targets at longer ranges.
However, dogfighting isn’t about long-range engagements with missiles. It’s about close-up twisting and turning to evade short-range missiles and gunfire. And even in that, the F-14 could use four AIM-9 Sidewinder missiles whereas the F/A-18 carries only two on its wingtips.
By the 2000s F-14 was an older aircraft and while the moving, sweeping wings look cool, they cause maintenance problems. They were expensive to maintain and troublesome to keep in the air. But they are arguably superior to what the Navy flies today.
Moving forward, the Navy is beginning to introduce the F-35 Lightning II to the carrier fleets. Maybe I’ll need to a comparison between those three.
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.
Almost out of the hole.
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.
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.
Finally, a graphic
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.
The Wall Street Journal put together a nice piece about the uptick in elementary school shootings, both in the number of shootings and the number of deaths. It used two bar charts, regular and stacked, and a heat map to tell the story. The screenshot below is from a graphic that looks at the proportion of school shootings that occur at elementary schools. They are not as common, but as other graphics in the article show, they can be quite deadly.
Not a great trend…
The graphic above does a nice job of distilling the horror of a tragedy into a single rectangle. That is an important task because it allows us to detach ourselves and more rationally analyse the situation. Unfortunately the analysis is that yes, Virginia, things really have been getting worse.
Overall the article is simple but soberingly effective. School shootings are a problem with which American society has not dealt and my cynical side believes with which we will continue to not deal.
Credit for the piece goes to James Benedict and Danny Dougherty.
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.
All the dots
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.
Just a fantastic spread.
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.
Very nice work here.
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.
More really nice graphics
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:
How the government defines US geography
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.
Where would you place West Virginia?
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.
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.
Now with box plots.
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.
