A few days ago I released a new project. I used a pen plotter to explain the history of oil prices and production over the last half-century. I’ve had a great response to it, and so I wanted to talk a little about plotters and why I think this particular format has great potential for data visualization.
First, it’s probably worth watching the video if you haven’t already:
A quick history of plotters
Pen plotters are basically just a robot arm with a pen on the end. They differ from printers in that they draw lines, rather than laying down dots. That makes them vector-, rather than raster-based, with all the advantages that brings in terms of resolution and speed.
They’re not a new technology at all — the earliest reference to something similar that I can find is from Italian teacher Andrea Bina, who in 1751 used a pendulum with a pointer attached to the bottom to trace lines in sand during earthquakes. Over time, these kinds of analogue recording devices attached to measuring tools became XY writers, and these eventually became digitally controlled. The plotter was born.
The heyday of plotters was in the 1960s and 70s, when they were used in much the same way that printers are today. In computer-aided design, in particular, they could produce line drawings very quickly at a quality much higher than the printers of the time. As printer technology improved, though, they became defunct for most purposes.
Over the last decade or so, however, artists and makers have begun to rediscover pen plotters. The cheap availability of “vintage” plotters on auction websites, and the ability to accurately draw large, complex designs made them perfect for generative artists and other creative coders.
Some young robotics firms, like Evil Mad Scientist Laboratories, began making new plotters that can read modern formats like SVG. Today, there’s a thriving community of pen plotter enthusiasts on the web, which can be found at the #plottertwitter hashtag and through sites like Drawing Bots.
Why plotters are great for visualization
I picked up a plotter at the start of April, and I noticed that lots and lots of people are making geometric art with them. That art is beautiful, but I like to try and do what other people aren’t doing, so I’ve been trying to find different things to plot. I’ve experimented with plotting maps, architecture, more abstract compositions, and even a few science-related SVGs grabbed from Wikipedia. But I knew even before I got the plotter that it would be an interesting tool for data visualization.
One of my favourite things about the plotter is that it sits right on the boundary of the physical and the digital. You’re using pens and paper, but they’re controlled with digital precision. Want a big chunky pink marker? You got it. Want a fine, smooth black line? Also cool. Want to write in sparkly gold ink on black paper? Not a problem. That fact alone immediately elevates the plotter over a printer for me.
You can also combine digital drawing with hand drawing. I love that I can get the plotter to draw part of the graphic — the axes and the data points, for example. But I can annotate them by hand, draw circles around ones that are particularly interesting, and even fill them in by hand if I want to (plotters are good at lines but bad at fills). It combines the humanity and tactility of hand-drawn visualisations with the precision of digital control.
Finally, plotters are tremendously satisfying to watch. There’s something about watching perfectly-placed lines being laid down with a physical pen that makes the experience extremely compelling.
Telling data stories with a plotter
The combination of accurate representation while still preserving something human about the whole thing makes a plotter a wonderful tool for data storytelling. You can talk while the plotter does its thing, and even pause during the process to draw the viewer’s attention to something particularly interesting. There’s also the interesting creative constraint, compared to traditional animation, that you can draw things but not erase them.
To tell this story in this way, I drew on a couple of key inspirations. The first is, of course, the wonderful work of late statistician Hans Rosling. Rosling was a master data storyteller, able to walk almost any audience through even the most complex and unintuitive visualization. Watch him talking through 200 years of history in just four minutes, and you’ll see what I mean.
The other is more recent — a video about Brexit by David McCandless (who I did a lot of work with in 2019) that explains the network of country groupings that make up Europe. It makes a dull and complex subject exciting, clear and understandable with the addition of simple PowerPoint animation and minimal voiceover.
In both cases, the gradual reveal of a data visualization accompanied by careful narration helps make a complex chart easily understandable. In fact, I deliberately chose an unusual chart form for my video — a connected scatterplot — because I wanted to see if I could make it sufficiently accessible to a general audience.
This video is just the first in what I intend to be an ongoing series of data stories told as a collaboration between myself and my trusty plotter.
If you’d like to follow along and see more, then you should subscribe to my YouTube channel, Plottervision. As well as visualizations, I also post other plotter experiments and learning materials — as well as live broadcasts of large, complex plots accompanied by some chill music. They’re the perfect thing to put on in the background while you’re working.
I should mention that the connected scatterplot I used here was originally created by Hannah Ritchie for Our World In Data, and that my remake of it was only possible because they license their work under a Creative Commons license.
Finally, I’ve written a post on my blog about the cobbled-together technology that I used to film and edit this, so if that interests you then definitely go and check that out. Oh, and if you’d like to hear more from me about my work then you should sign up to my newsletter.