Data visualisation creates powerful, elegant images from complex information, but can also be potentially deceptive
Camouflage usually means blending in. That wasn’t an option for the submarine-dodging battleships of a century ago, which advertised their presence against an ever-changing sea and sky with bow waves and smokestacks. And so dazzle camouflage was born, an abstract riot of squiggles and harlequin patterns. It wasn’t hard to spot a dazzle ship but the challenge for the periscope operator was quickly to judge a ship’s speed and direction before firing a torpedo on a ponderous intercept. Dazzle camouflage was intended to provoke misjudgments, and there is some evidence that it worked.
Now let’s talk about data visualisation, the latest fashion in numerate journalism, albeit one that harks back to the likes of Florence Nightingale. She was not only the most famous nurse in history but the creator of a beautiful visualisation technique, the “Coxcomb diagram”, and the first woman to be elected as a member of the Royal Statistical Society.
Data visualisation creates powerful, elegant images from complex data. It’s like good prose: a pleasure to experience and a force for good in the right hands, but also seductive and potentially deceptive. Because we have less experience of data visualisation than of rhetoric, we are naive, and allow ourselves to be dazzled. Too much data visualisation is the statistical equivalent of dazzle camouflage: striking looks grab our attention but either fail to convey useful information or actively misdirect us.
For a relatively harmless example, consider The New Yorker’s recent online subway map of inequality. “New York has a problem with inequality,” we are told. Then we are invited to click on different subway maps to see a cross-sectional graph, showing us the peaks and troughs of median income along different subway lines. The result is gorgeous but far less informative than a map would have been. It is a piece of art pretending to be a piece of statistical analysis.
A more famous example is David McCandless’s unforgettable animation “Debtris”, in which large blocks fall slowly against an eight-bit soundtrack in homage to the addictive computer game Tetris. Their size indicates their dollar value. “$60bn: estimated cost of Iraq war in 2003” is followed by “$3000bn: estimated total cost of Iraq war”, and then Walmart’s revenue, the UN’s budget, the cost of the financial crisis, and much else.
The animation is pure dazzle camouflage. Statistical apples are compared with statistical oranges throughout. The Iraq comparison, for instance, is not one of “then versus now” as it first appears – but one of what the US Department of Defense once thought it would spend versus a broader estimate, including a financial value on the lives of dead soldiers, and over a trillion dollars of “macroeconomic costs”. The war was a disaster. No need for a statistical bait-and-switch to make that case.
Information can be beautiful, McCandless tells us. Unfortunately misinformation can be beautiful too. Or, as statistical guru Michael Blastland puts it, “We are in danger of making the same statistical mistakes that we’ve always made – only prettier.”
Those beautiful Coxcomb diagrams are no exception. They show the causes of mortality in the Crimean war, and make a powerful case that better hygiene saved lives. But Hugh Small, a biographer of Nightingale, argues that she chose the Coxcomb diagram in order to make exactly this case. A simple bar chart would have been clearer: too clear for Nightingale’s purposes, because it suggested that winter was as much of a killer as poor hygiene was. Nightingale’s presentation of data was masterful. It was also designed not to inform but to persuade. When we look at modern data visualisations, we should remember that.
Also published at ft.com.