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.
迷彩通常意味着与周围环境协调一致。在一个世纪之前，战舰想躲避潜艇时不会用这一招，因为在不断变幻的海天背景之下，船首波与主烟囱会时刻暴露出战舰的位置。于是炫幻迷彩应运而生，这种迷彩由弯弯曲曲的线条和五颜六色的花纹，以很抽象的方式显示出来。采用炫幻迷彩的战舰不难定位，难的是在程序繁琐的拦截中，发射鱼雷之前需要潜望镜操作手迅速判断出战舰的航速与航向。也就是说，设计炫幻迷彩的目的就是造成误判，有证据表明这种策略确实有效。
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.
现在让我们讨论一下数据可视化，这是数字导向新闻报道中近来最炙手可热的操作手法，不过这也让我们想起弗洛伦斯•南丁格尔(Florence Nightingale)这样的人物。她不仅是史上最著名的护士，而且是最漂亮的可视化技术“鸡冠花图”(Coxcomb diagram)的创始人。另外，她还是首位当选英国皇家统计学会(Royal Statistical Society)的女性会员。
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.
数据可视化用复杂数据生成强大而精美的图像。它就像行文技巧一样：它能带来舒适的体验，如果应用得当，能成为一种发挥积极作用的武器；但另一面，它充满诱惑，并可能具有欺骗性。我们对于数据可视化的经验少于在修辞方面的经验，在这方面还处于懵懂状态，很容易可视化冲昏头脑。数据可视化被等同于统计版炫幻迷彩的情形太多了——富有冲击力的形象吸引了我们的注意力，而这要么不能传递有用信息，要么会在很大程度上误导我们。
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.
举一个危害相对不大的例子，比如《纽约客》(The New Yorker)最近上线的地铁不平等在线地图。文中说：“纽约有不平等问题。”接着该文请我们点击不同的地铁地图，这会让我们看到一幅截面图，截面图显示出沿着不同地铁线路乘客收入中位数分布的波峰和波谷。这一结果看上去十分漂亮，但是它所提供的信息远远比不上一幅地图本来能提供的信息量。这其实是一件打着统计分析幌子的艺术品。
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.
更有名的例子，是戴维•麦坎德利斯(David McCandless)令人印象深刻的动画“债务方块(Detris)”。在这个动画中，巨大的方块缓缓落下。为了向那个令人上瘾的电脑游戏“俄罗斯方块”(Tetris)致敬，背景音乐采用了8位音轨。这些方块的大小表示相应的美元金额。在“600亿美元：2003年伊拉克战争耗费成本估值”的方块之后，随之而来的是“3万亿美元：伊拉克战争总成本估值”。在这之后则是沃尔玛(Walmart)营收、联合国(UN)预算、金融危机成本以及许多其他项目。
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.
这一动画纯粹是一种炫幻迷彩。这完全把统计学的苹果和统计学的桔子放在一起进行了比较。例如，与伊拉克战争有关的比较并不是第一眼看上去的那种“过去和现在的比较”，而是美国国防部(DoD)曾经估计的可能花销与一种涵盖因素更多的宏观估值的比较，后者包括了死亡战士生命的在财务估值，另外还包括了一万亿美元的“宏观经济损失”。伊拉克战争确实是一场灾难，但完全没必要为了证明这一观点而在统计上采取调包手法。
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.”
麦坎德利斯告诉我们，信息可以是十分美妙的。不幸的是，误导性信息也可能同样美妙。或者，正如统计大师麦克•布拉斯特兰德(Michael Blastland)所说：“我们很有可能会犯下与过去常犯的统计错误一样的错误，只是表现形式更漂亮一些。”
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.
那些漂亮的鸡冠花图也不例外。它们确实展示了克里米亚战争(Crimean war)中人员大量致死的原因，并且无可辩驳地证明了改善卫生条件能拯救人们的生命。但是南丁格尔传记作家休•斯莫尔(Hugh Small)声称，南丁格尔选择鸡冠花图的目的是为了专门证明以上观点。实际上，简单的直方图原本能呈现得更清楚一些，但是就南丁格尔的目的而言，直方图的问题在于因果关系表现得太过清晰，因为读者会从直方图得出结论，冬天来临与糟糕的卫生条件导致的死亡人数同样多。南丁格尔展示数据的方式是极有控制力的。这样的呈现方式不是为了传递信息，而是为了说服读者。我们观赏当代数据可视化图案之时，应该铭记这一点。
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