A gentle reminder: if you haven't looked at, or maybe even filled out, my survey about failure, I'd really appreciate it if you did. It only takes a few minutes and I'd love to get some different stories and perspectives for something I'm working on: https://t.co/p7Fim8ysB7
Aren't you darling? Just as sweet as peach pie in a cotton candy basket. Now let's get on with the show.
Although I am not a child of the 90s, I nevertheless developed an incredible fondness for one particular Nickelodeon show of that era: “The Adventures of Pete and Pete.” For those unfamiliar with its particular tone, I can best describe it as an absurdist take on teen- and tween-focused sitcoms; it centered around two brothers, both named Pete Wrigley, and their adventures in the small town of Wellsville. “Big Pete” (Michael Maronna) was high school age (9th or 10th grade), and “Little Pete” (Danny Tamberelli) was four years younger.
As a single-camera, filmed show, it was ahead of its time; various adults in their world were played by indie darlings and musicians, like R.E.M.’s Michael Stipe, Janeane Garofalo, Adam West, Steve Buscemi, Chris Elliott, Selma Blair, Iggy Pop, LL Cool J, Debbie Harry, and a host of others.
Here are a couple of episode summaries, to give you an idea of the kind of show we’re talking about. These are courtesy of the world’s foremost irrefutable authority, Wikipedia.
“The Nightcrawlers: Little Pete and his friends aim to overthrow the International Adult Conspiracy's reign over bedtimes by staying up for eleven nights and thus breaking the world record.”
“Inspector 34: Little Pete finds his guardian angel, Inspector 34, who inspects the Kreb of the Loom underwear worn by the Wrigleys and their friends. While Inspector 34 recruits Little Pete to be an inspector, Pete shows him how to interact with normal people and have fun. Little Pete struggles with perfection while Inspector 34 lets his tendency toward perfection and his hormones go to his head. The newfound perfection of Inspector 34 begins to infect the neighborhood, and people begin behaving erratically. Little Pete must help everyone find a way to live in moderation between perfection and abnormality.”
“Hard Day’s Pete (my favorite episode of all): On his way to school, Little Pete happens upon a garage band playing his favorite song: "Summerbaby" by Polaris. When he returns later that day, all traces of the band have vanished; Pete starts his own band to try to find them—and his song.”
Notably for our purposes, the younger brother—Little Pete (Danny Tamberelli)—was in elementary school, and had an adult friend-slash-superhero, played by Toby Huss, named “Artie, the Strongest Man in the World.” Artie is the man pictured at the very top of this article, in the striped shirt and pre-Lululemon stretch pants, so you can understand how absurd his superhero-ness is in the context of the show.
Other than the 34 episodes of Pete and Pete that were aired, Nickelodeon also produced a number of shorts and videos to include on VHS and DVD releases. One of those, “The Artie Workout,” is exactly what you think it is: Toby Huss being delightfully goofy and sharing workout programs like running crazily around the living room (“Aerobics”) and lifting your house (“Lifting”). At one point, Artie flexes for the camera and says:
“Look at the work, puny viewer! LOOK. AT. THE. WORK!”
And now that you’ve suffered through the preamble, I can explain what it has to do with anything.
See, I have this theory about public (and Public) visualizations. And that is this: the more hard work you’re willing to do before you get to the visualization part of the process, the better your end product will be. And, along with that: the best visualizations make the hard work that went into creating them completely invisible.
When Artie says “LOOK AT THE WORK!” he is talking about his (supposed) muscles—he’s not saying “watch how much effort I am currently expending,” but rather, “look at what the results of hard work are.” (Why am I using a fictional character from a 90s children’s show to make this point, when it’s a very, very common phrase in the workout community? Because I hate working out and I love Pete and Pete. That’s why.)
When the Makeover Monday dataset came out this week, about water footprints—similar to carbon footprints—my first thought was that it might be a good opportunity to do a visual metaphor, using a bubble chart with specified XY coordinates for the 16 provided data points to create the appearance of a literal footprint. That evolved into the idea of doing separate footprints for green, blue, and gray water data, and one larger footprint for total water and all the other metrics.
In order to do this, though, I needed to figure out the correct XY coordinates for the 16 different items (which I had further grouped into 6 types of food). There’s no online resource for this; no web from which I could scrape the data; no framework to draw on; no calculations to leverage. It would come down to manually determining the correct coordinates—in other words, the hard work.
Although, to be honest, with only 16 data points, the work wasn’t especially hard. I’ve tried to build hex-based shapes for subdivisions within a stylized country map, maintaining a balance between population ratios and pure geography, by eyeballing it the whole way. THAT is hard work, and boring, and frustrating. This was finding 16 XY points that would be flexible enough to support a footprint-like bubble chart where each point would resize based on one of seven different metrics.
The specifics of how I did this aren’t very interesting but here it is:
Create an Illustrator canvas 500px wide by 1000px tall.
Put 16 identical circles on the canvas in the approximate shape of a footprint.
Grow or shrink the circles individually until the overall canvas looks as much like a footprint as possible.
Create a new CSV and list each of the 16 food items in it, sorted descending by total water per metric ton of food.
Type in the XY coordinates of the center of each Illustrator circle, from largest circle to smallest, next to the corresponding food item. Switch the Y value to [1000-Y], since Illustrator’s coordinate system uses the top left corner as the origin, not the bottom left.
Join this CSV with the existing Water Footprint data as it is brought into Tableau.
Color the circles in the bubble chart by Type of Food.
Swap some circles’ coordinates around so that the sizes stay pretty close to the ideal size for that part of the foot, but that the food groupings are as close together as possible (like, all in the toes, or all in the arch, for instance).
But for the sake of this discussion let’s call that, the identifying of the correct XY coordinates, the “hard work,” simply because they didn’t exist on a shelf or in a box ready to be deployed on command. That “hard work” is what made the creation of this visual metaphor possible. In this particular product, the visual is pretty much the whole thing.
What do I mean by that? I mean it is clearly not meant to be an especially usable dashboard, nor is it meant to generate complex insights. Its flaws in those areas are multifold:
It’s hard to compare one food item across multiple metrics.
You can’t see how certain foods rank.
The values are compared by area rather than by length—a notoriously substandard way to provide comparative data.
The circles aren’t labeled with the food or the measurement until you highlight them (with a single exception).
It is not optimized for mobile viewing.
It makes no assertions about the water footprint of any specific food or any type of food.
It neither asks nor answers any specific question about water footprints.
If the “Type of Food” is highlighted, the labels overlap and clarity breaks down.
If the “Selected Metric” is changed, the large footprint loses its footprinty-ness relatively quickly.
I could go on, of course. However, all of these flaws—or qualities of the final product—were things I considered acceptable tradeoffs, in exchange for the visual impact of the footprint metaphor. It does attract the attention of the viewer; it does encourage engagement and interaction with the visualization; and (hopefully) it does place a definite marker in the audience’s mind, that there is such a thing as a water footprint, and that meats (and nuts) require the most water to cultivate.
At first, it just looks like a colorful footprint. Then they look closer and see it’s a bubble chart. Then they start processing the data. The little endorphin hit from experiencing these revelations is a positive experience, and from this pleasure, their memory becomes engaged, linking what they saw (the footprint) to how they felt (positive) and what it means (it takes lots of water to make certain foods, particularly meats).
The imagery gets the audience in the front door and gives the brain something tangible to recall. For a public audience that might not be predisposed to look at a visualization on this particular topic, a strong visual component, at the expense of more sophisticated analytic components, seemed to be the right direction. In order to achieve that, “hard work” behind the scenes was required.
For practitioners, there might be a bit of how-did-you-do-that curiosity, but for the audience in general, the mechanics of creating the viz are immaterial. It is in this way that I consider the “hard work” behind the viz to be invisible—but essential.
Whether it's tracking down hundreds of URLs, merging and cleaning dozens of files, building shapefiles from multiple sources, or hand-coding coordinates, it's often the drudgery and hard work behind the scenes that are key to making the final product different from what's out there, more representative of your own personal vision, and appealing to your target audience. While you're doing the crap work it pretty much blows, but at the end, you'll be able to point to your visualization, flex your striped-shirt-covered arms like Artie, the Strongest Man in the World, and shout, "Look at the work, puny viewer! LOOK AT THE WORK!"