It feels like we're all suffering from information overload or data glut. And the good news is there might be an easy solution to that, and that's using our eyes more. So, visualizing information, so that we can see the patterns and connections that matter and then designing that information so it makes more sense, or it tells a story, or allows us to focus only on the information that's important. Failing that, visualized information can just look really cool.
我们好像总是被超负荷的信息 和过多的数据折磨. 好消息是也许我们能有种办法解决这个问题, 那就是更多的用我们的眼睛. 把信息视觉化,我们就能看出 其中的规律和联系 然后再设计这些信息,让它更能有意义, 或者让它来说明一个故事, 或者让我们能只关注那些重要的信息. 如果不能做到这一点,至少视觉化的信息看起来特别酷.
So, let's see. This is the $Billion Dollar o-Gram, and this image arose out of frustration I had with the reporting of billion-dollar amounts in the press. That is, they're meaningless without context: 500 billion for this pipeline, 20 billion for this war. It doesn't make any sense, so the only way to understand it is visually and relatively. So I scraped a load of reported figures from various news outlets and then scaled the boxes according to those amounts. And the colors here represent the motivation behind the money. So purple is "fighting," and red is "giving money away," and green is "profiteering." And what you can see straight away is you start to have a different relationship to the numbers. You can literally see them. But more importantly, you start to see patterns and connections between numbers that would otherwise be scattered across multiple news reports.
让我们来看看. 这是十亿美元图, 我在看到媒体的十亿美元的报道时 很沮丧, 于是我就画了这幅图. 如果没有文字辅助,它是没意义的. 五千亿用于运输管道的建设 二百亿用于战争. 这样并不能说明问题,所以唯一能理解它的方法 就是视觉化,相关化. 所以我从不同的信息渠道 收集了很多报道的数据 然后根据数量画出了这些方框. 颜色代表了这部分钱的用途. 紫色代表战乱 红色是把钱捐赠出去,绿色代表牟取暴利. 你能直接看出来的是 你开始和这些数字有了不同的关系. 你能真正的看见它们了. 但更重要的是,你能开始看到 这些数字中的规律和联系了 如果不通过画图,这些信息都将只能散落在不同的新闻报道中.
Let me point out some that I really like. This is OPEC's revenue, this green box here -- 780 billion a year. And this little pixel in the corner -- three billion -- that's their climate change fund. Americans, incredibly generous people -- over 300 billion a year, donated to charity every year, compared with the amount of foreign aid given by the top 17 industrialized nations at 120 billion. Then of course, the Iraq War, predicted to cost just 60 billion back in 2003. And it mushroomed slightly. Afghanistan and Iraq mushroomed now to 3,000 billion. So now it's great because now we have this texture, and we can add numbers to it as well. So we could say, well, a new figure comes out ... let's see African debt. How much of this diagram do you think might be taken up by the debt that Africa owes to the West? Let's take a look. So there it is: 227 billion is what Africa owes. And the recent financial crisis, how much of this diagram might that figure take up? What has that cost the world? Let's take a look at that. Dooosh -- Which I think is the appropriate sound effect for that much money: 11,900 billion. So, by visualizing this information, we turned it into a landscape that you can explore with your eyes, a kind of map really, a sort of information map. And when you're lost in information, an information map is kind of useful.
让我来讲讲我很喜欢的一个例子. 这是石油输出国组织的财政收益,绿色的方格这里 是7800亿每年. 这个角落里的小像素是三十个亿 这是他们的气候变化基金. 美国,这个特别慷慨的国家 每年捐赠多于3000个亿 相比于其它17个工业化最发达的国家 一共对外捐出的 1200亿. 当然, 在2003年的预算中 伊拉克战争只要花掉600亿 这项实际的花销比预算稍多了一点, 但阿富汗战争的投入 迅速增加到了3万亿. 现在就很方便了, 因为我们有了这个结构,我们还能往里面加数字. 我们能说一个新的图形出现了.....让我们来看下非洲的债务. 你们认为在这张图里非洲 欠西方国家的债务能占多大比例呢? 我们来看一看. 这就是了. 非洲的债务一共是二千二百七十亿. 最近的这场金融危机-- 在这张图里能占多大的比例呢? 这场危机又给世界带来了多大损失呢?让我们来看这里. Doosh(拟声词,相似于"哇"),我想应该是配合这么多钱 的恰当的声音效果. 十一万九千个亿. 所以,通过视觉化这些信息, 我们把它们转化成了一个直观景象 这样你就能只通过眼睛探索, 真的类似于一个地图,一种信息的地图. 当你在信息里迷失的时候, 这样的地图是很有帮助的.
So I want to show you another landscape now. We need to imagine what a landscape of the world's fears might look like. Let's take a look. This is Mountains Out of Molehills, a timeline of global media panic. (Laughter) So, I'll label this for you in a second. But the height here, I want to point out, is the intensity of certain fears as reported in the media. Let me point them out. So this, swine flu -- pink. Bird flu. SARS -- brownish here. Remember that one? The millennium bug, terrible disaster. These little green peaks are asteroid collisions. (Laughter) And in summer, here, killer wasps.
我现在想展示另一番景象. 我们来想象下世界的恐惧会是 什么样的一番景象. 让我们来看一下. 这是摩尔山之外的一座"山", 是全球医疗恐惧的时间线. (笑声) 我等下会把这些标记出来. 但我想指出这里的高度 是媒体报道里 人们的恐惧程度. 让我来指出来. 这里,猪流感,标记成粉色. 禽流感. 非典--棕色一样的颜色.记住这一条. 千禧虫-- 可怕的灾难. 这些小的绿色的峰 是对小行星碰撞的恐惧. (笑声) 夏天的时候,这点是对杀人蜂的恐惧.
(Laughter)
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So these are what our fears look like over time in our media. But what I love -- and I'm a journalist -- and what I love is finding hidden patterns; I love being a data detective. And there's a very interesting and odd pattern hidden in this data that you can only see when you visualize it. Let me highlight it for you. See this line, this is a landscape for violent video games. As you can see, there's a kind of odd, regular pattern in the data, twin peaks every year. If we look closer, we see those peaks occur at the same month every year. Why? Well, November, Christmas video games come out, and there may well be an upsurge in the concern about their content. But April isn't a particularly massive month for video games. Why April? Well, in April 1999 was the Columbine shooting, and since then, that fear has been remembered by the media and echoes through the group mind gradually through the year. You have retrospectives, anniversaries, court cases, even copy-cat shootings, all pushing that fear into the agenda. And there's another pattern here as well. Can you spot it? See that gap there? There's a gap, and it affects all the other stories. Why is there a gap there? You see where it starts? September 2001, when we had something very real to be scared about.
所以这就是媒体描述里 我们的恐惧"长的模样", 但我最喜欢的是--我是个记者-- 我最喜欢的是找出隐藏着的规律. 我喜欢做个数据侦探. 而且这些数据里有很有趣又很奇怪的规律 你只能通过视觉化数据才能看出这些规律. 我来强调一下. 看这条线.这是暴力视频游戏的"景象". 正如你能看到的,是个有点奇怪的长方形的样子, 每年都有两个差不多大小的峰. 如果我们仔细看看,我们能看出这些峰 每年出现的时间是一样的. 为什么呢? 十一月的时候,是圣诞视频游戏发放的日子, 就很可能引起关于其内容的恐慌. 但四月对游戏而言 并不是个很特别的月份 为什么会是四月呢? 其实,1999年的四月发生了哥伦拜恩枪击案, 从那以后, 人们总是能通过媒体想起这份恐惧 在这一年里,这份恐惧也总是渐渐环绕在这个群体里. 你总会回顾,纪念, 也总有诉讼甚至是只单纯模仿别人的枪击案, 所有这些都能引起人们的恐惧,而体现在这日历里. 而且这里还有另一个规律.你看出来了吗? 看见这个间断了吗?这里有一个间断. 这一点影响了其它所有部分. 为什么这里会有一个间断呢? 看到它是从什么时候开始的吗?2001年9月(911发生的时间) 那个月里发了一件真的 很令人恐惧的事情.
So, I've been working as a data journalist for about a year, and I keep hearing a phrase all the time, which is this: "Data is the new oil." Data is the kind of ubiquitous resource that we can shape to provide new innovations and new insights, and it's all around us, and it can be mined very easily. It's not a particularly great metaphor in these times, especially if you live around the Gulf of Mexico, but I would, perhaps, adapt this metaphor slightly, and I would say that data is the new soil. Because for me, it feels like a fertile, creative medium. Over the years, online, we've laid down a huge amount of information and data, and we irrigate it with networks and connectivity, and it's been worked and tilled by unpaid workers and governments. And, all right, I'm kind of milking the metaphor a little bit. But it's a really fertile medium, and it feels like visualizations, infographics, data visualizations, they feel like flowers blooming from this medium. But if you look at it directly, it's just a lot of numbers and disconnected facts. But if you start working with it and playing with it in a certain way, interesting things can appear and different patterns can be revealed.
我做数据统计记者已经有一年了, 我总是能听到这样一个说法 说: "数据是新的石油." 而且数据是一种无法不在的资源 我们可以用它来开创新的革命和用新眼光来洞察世界, 而且它就在我们身边,很容易被挖掘. 而且在这个时代这也不是什么很特别的比喻 尤其是如果你生活在墨西哥湾, 但我想,或者可以对这个比喻稍加改动, 我要说数据是新土壤. 因为对我而言,它更像个可以施肥,能创造出东西来的媒体. 经过这么多年,在网上 我们已经积攒下 巨大数量的信息和数据, 而且我们用网络和连接来浇灌它们, 不收取工资的人们和政府一直在努力耕作这片"土壤". 好吧,我有点夸大这个比喻了. 但它真的是一个可以繁殖的媒体, 它想让被视觉化,把咨询图像化,数据可视化, 它们就像是从土壤里长出的花朵. 但如果你直接看这些数据, 它们就只是一堆数字和没有关联的事件. 但如果你开始分析它们,并用一种方式处理它们, 有趣的事就会发生,不同的规律也会显现出来.
Let me show you this. Can you guess what this data set is? What rises twice a year, once in Easter and then two weeks before Christmas, has a mini peak every Monday, and then flattens out over the summer? I'll take answers. (Audience: Chocolate.) David McCandless: Chocolate. You might want to get some chocolate in. Any other guesses? (Audience: Shopping.) DM: Shopping. Yeah, retail therapy might help. (Audience: Sick leave.) DM: Sick leave. Yeah, you'll definitely want to take some time off. Shall we see?
让我来展示下这个. 你能猜出这是关于什么的数据吗? 有什么东西每年要升两次? 一次在复活节 另一次在圣诞前的两周, 每个周一都有一个小峰 并在夏天的时候一直保持平坦. 我来听听你们的想法. (观众:巧克力).David:巧克力. 你或许会想加一点巧克力进去. 还有别的猜想呢? (观众:购物).David:购物. 嗯,购物疗法也许会有帮助. (观众:病假) David:病假.嗯,你一定想休息几天. 我们来看答案吧?
(Laughter)
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(Applause)
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So, the information guru Lee Byron and myself, we scraped 10,000 status Facebook updates for the phrase "break-up" and "broken-up" and this is the pattern we found -- people clearing out for Spring Break, (Laughter) coming out of very bad weekends on a Monday, being single over the summer, and then the lowest day of the year, of course: Christmas Day. Who would do that? So there's a titanic amount of data out there now, unprecedented. But if you ask the right kind of question, or you work it in the right kind of way, interesting things can emerge.
这里的信息是,Lee Byron和我自己 搜集了一万个Facebook的状态 寻找包括"分手"和"分手了"的字样的更新 这就是我们收集的结果-- 人们在春假期间"清理"关系. (笑声) 接着的周一从过得很糟的周末中走出来 一个暑假里都保持着单身. 当然一年中分手最少的一天是:圣诞. 谁会这么做呢? 现在外面有极多的数据, 史无前例的多. 但如果你问正确的问题, 或者你用正确的方式处理它, 有趣的事就会发生.
So information is beautiful. Data is beautiful. I wonder if I could make my life beautiful. And here's my visual C.V. I'm not quite sure I've succeeded. Pretty blocky, the colors aren't that great. But I wanted to convey something to you. I started as a programmer, and then I worked as a writer for many years, about 20 years, in print, online and then in advertising, and only recently have I started designing. And I've never been to design school. I've never studied art or anything. I just kind of learned through doing. And when I started designing, I discovered an odd thing about myself. I already knew how to design, but it wasn't like I was amazingly brilliant at it, but more like I was sensitive to the ideas of grids and space and alignment and typography. It's almost like being exposed to all this media over the years had instilled a kind of dormant design literacy in me. And I don't feel like I'm unique.
所以信息是漂亮的,数据也是漂亮的. 我想我能不能把我的生活也弄得很漂亮. 这是我的视觉化的简历. 我还不太确定这份简历是不是成功. 有很多的小块,颜色也不是特别的好. 但我想传递些东西给你们. 我从一个程序员开始做起, 之后我做了很多年作家,大约有二十年, 在杂志上,网上之后还有广告中, 只在最近这一段我才开始进行设计. 而且我从来没去过设计学校, 我从没学校艺术或这类的东西. 我就仅仅是从做的过程中来学. 当我开始设计的时候, 我发现了关于我自己的一件奇怪的事. 我已经知道怎么去设计了, 不是说我奇迹般的对设计特别在行, 而更像是我对关于网格,空间 以及排列和版面设计之类的 特别的敏感. 就好像是暴露在媒体 这么多年之后, 我被潜移默化地注入了设计的能力. 而且我不觉得自己是唯一一个.
I feel that everyday, all of us now are being blasted by information design. It's being poured into our eyes through the Web, and we're all visualizers now; we're all demanding a visual aspect to our information. There's something almost quite magical about visual information. It's effortless, it literally pours in. And if you're navigating a dense information jungle, coming across a beautiful graphic or a lovely data visualization, it's a relief, it's like coming across a clearing in the jungle. I was curious about this, so it led me to the work of a Danish physicist called Tor Norretranders, and he converted the bandwidth of the senses into computer terms.
我感觉每一天,我们所有人 都对信息设计特别的不满. 它们从网上一股脑灌入我们眼里 而且我们都是视觉型的人, 我们都想从视觉的角度 来看我们的信息. 这就是关于视觉化信息的神奇的地方. 不需要努力的,它就自己进来了. 如果你在密集的信息丛林里搜寻, 突然看到一个漂亮的图画, 或者是一个可爱的视觉化的数据, 那将会是怎样的释然啊! 就好像是在森林里碰到了开阔的地方. 我对这很好奇,所以它引导我 关注一个丹麦的物理学家 名叫Tor Norretranders, 他把感观的带宽转化成了电脑术语.
So here we go. This is your senses, pouring into your senses every second. Your sense of sight is the fastest. It has the same bandwidth as a computer network. Then you have touch, which is about the speed of a USB key. And then you have hearing and smell, which has the throughput of a hard disk. And then you have poor old taste, which is like barely the throughput of a pocket calculator. And that little square in the corner, a naught .7 percent, that's the amount we're actually aware of. So a lot of your vision -- the bulk of it is visual, and it's pouring in. It's unconscious. The eye is exquisitely sensitive to patterns in variations in color, shape and pattern. It loves them, and it calls them beautiful. It's the language of the eye. If you combine the language of the eye with the language of the mind, which is about words and numbers and concepts, you start speaking two languages simultaneously, each enhancing the other. So, you have the eye, and then you drop in the concepts. And that whole thing -- it's two languages both working at the same time.
过程是这样的.这是你的感观, 周围世界每一秒都往你的感观里注入东西. 你的视觉是最快的. 它有和电脑网络一样的带宽. 你还有触觉,它有和USB接口一样的速度. 你的听觉和嗅觉, 有和硬盘一样的处理速度. 你还有可怜的,古老的味觉, 它仅仅只有口袋计算器那样的工作效率. 那角落里小小的正方形,占百分之0.7的比例, 这就是我们真正能感知的部分. 所以你很多的想像-- 大部分是视觉的,而且它是自己大批进入的. 潜意识里进行的. 眼睛对颜色变化中的规律, 形状和模式是特别敏感的. 如果眼睛喜欢它们,它就把它们叫作漂亮. 这就是眼睛的语言. 而且你把眼睛的语言和头脑的语言联系在一起, 关于词语,数字和概念这些, 你将能同时讲两种语言, 每一种都能加强另一种. 所以你有眼睛,然后你想到了概念, 这个过程,其实是两种语言, 它们同时在一起工作.
So we can use this new kind of language, if you like, to alter our perspective or change our views. Let me ask you a simple question with a really simple answer: Who has the biggest military budget? It's got to be America, right? Massive. 609 billion in 2008 -- 607, rather. So massive, in fact, that it can contain all the other military budgets in the world inside itself. Gobble, gobble, gobble, gobble, gobble. Now, you can see Africa's total debt there and the U.K. budget deficit for reference. So that might well chime with your view that America is a sort of warmongering military machine, out to overpower the world with its huge industrial-military complex. But is it true that America has the biggest military budget? Because America is an incredibly rich country. In fact, it's so massively rich that it can contain the four other top industrialized nations' economies inside itself, it's so vastly rich. So its military budget is bound to be enormous. So, to be fair and to alter our perspective, we have to bring in another data set, and that data set is GDP, or the country's earnings. Who has the biggest budget as a proportion of GDP? Let's have a look. That changes the picture considerably. Other countries pop into view that you, perhaps, weren't considering, and American drops into eighth.
所以我们能用这种新的语言,如果你喜欢的话, 来改变我们的视角或者我们的观点. 让我来问一个简单的问题 它有个特别简单的答案. 谁拥有最多的军队预算? 这一定是美国,对吧? 非常多的.在2008年有六千零九十亿 六千零七十亿实际上是. 这么多,事实上,它包含了 所有其它国家的军队预算在里面. 格格,格格,格格,格格,格格(火鸡的叫声) 现在你能拿整个非洲的债务 和英国的预算赤字来比较一下. 这很可能 和你印象中的美国形象一致, 那个好战的,有军队机器, 强大到要用它的巨大的工业军事合成体 来压倒世界的国家. 但美国真的有最多的军事预算吗? 因为它是个特别富有的国家. 事实上,它是这么有富有 它能把四个在它之后的 工业国家的经济收入 全包含在它之内.如此的富有. 它的军队预算是和经济联系在一起的 所以,为了公平为了改变我们的视角, 我们得引入另一组数据, 这就是国民生产总值,或者是一个国家的收入. 哪个国家的军队预算是占国民生产总值最多的呢? 让我们来看一看. 这把格局改变了很多. 其它你很可能没想到的国家一下子出现了, 美国跌入了第八位.
Now you can also do this with soldiers. Who has the most soldiers? It's got to be China. Of course, 2.1 million. Again, chiming with your view that China has a militarized regime ready to, you know, mobilize its enormous forces. But of course, China has an enormous population. So if we do the same, we see a radically different picture. China drops to 124th. It actually has a tiny army when you take other data into consideration. So, absolute figures, like the military budget, in a connected world, don't give you the whole picture. They're not as true as they could be.
你也同样可以对士兵进行这样的运算. 哪个国家有最多的士兵?一定是中国. 当然,二百一十万. 同样的,和你对中国, 那个军事化的政权,准备着 要发挥其强大的武装力量的印象一致. 但不可否认,中国有巨大的人口, 如果我们做同样的处理, 我们看到一个彻底不同的景象. 中国落到了124位. 它其实只有很小的一支部队, 如果你把人口考虑进去的话. 所以,绝对的数字,就像是军队预算, 在一个紧密联系的世界里 好像不能给你一个完整的格局. 它们并不是它们能达到的最真实的状态.
We need relative figures that are connected to other data so that we can see a fuller picture, and then that can lead to us changing our perspective. As Hans Rosling, the master, my master, said, "Let the dataset change your mindset." And if it can do that, maybe it can also change your behavior.
我们需要相对的数据,这些数据和其它数据相联系 我们才能看到一个更全面的世界. 而这一点能改变我们的视角. 就像Hans Rosling, 校长 我的校长说的那样, "让数据模式改变你的心理模式." 如果它能做到这一点,也许它能同时改变你的行为.
Take a look at this one. I'm a bit of a health nut. I love taking supplements and being fit, but I can never understand what's going on in terms of evidence. There's always conflicting evidence. Should I take vitamin C? Should I be taking wheatgrass? This is a visualization of all the evidence for nutritional supplements. This kind of diagram is called a balloon race. So the higher up the image, the more evidence there is for each supplement. And the bubbles correspond to popularity as regards to Google hits. So you can immediately apprehend the relationship between efficacy and popularity, but you can also, if you grade the evidence, do a "worth it" line. So supplements above this line are worth investigating, but only for the conditions listed below, and then the supplements below the line are perhaps not worth investigating.
来看一下这个. 我有一点健康迷. 我喜欢吃一些保养品来保持健康 但我从来不能用证据证明这些东西是不是真的有用. 总是会有相当矛盾的证据. 我是应该吃维他命C?还是吃小麦草素? 这里把所有这些关于 营养补品的证据都视觉化了. 这样的图表叫作热气球竞赛. 图上越高的地方, 有越多的关于这些补品的证据. 这些气泡的大小对应于GOOGLE上这样补品的受欢迎程度. 这样你能马上就领会效力 和受欢迎程度间的关系. 而且同时,如果你把证据分级别的话, 画出一条"值得"的线. 所以在这条线以上的补品是那些值得投资的, 但只是针对下面列出的这些症状. 对于这条线以下的补品, 就很可能不太值得投资了.
Now this image constitutes a huge amount of work. We scraped like 1,000 studies from PubMed, the biomedical database, and we compiled them and graded them all. And it was incredibly frustrating for me because I had a book of 250 visualizations to do for my book, and I spent a month doing this, and I only filled two pages. But what it points to is that visualizing information like this is a form of knowledge compression. It's a way of squeezing an enormous amount of information and understanding into a small space. And once you've curated that data, and once you've cleaned that data, and once it's there, you can do cool stuff like this.
这幅图凝结了我们大量的工作. 我们挖掘了Pubmed上一千个左右的研究, Pubmed是一个生物医学的数据库, 我们收集了这些研究并把他们分了级别. 这让我相当沮丧 因为我的书需要250个可视化图像 我用了一个月做这幅图, 却只能填充两页纸. 但重点是 像这样把信息视觉化的过程 是把知识压缩的过程. 这是一种从巨大数量的信息 中压榨出精华 并理解的过程. 一旦你整理了这些数据,或者说清理了这些数据, 一旦它在那了 你就能做像这样很酷的事了.
So I converted this into an interactive app, so I can now generate this application online -- this is the visualization online -- and I can say, "Yeah, brilliant." So it spawns itself. And then I can say, "Well, just show me the stuff that affects heart health." So let's filter that out. So heart is filtered out, so I can see if I'm curious about that. I think, "No, no. I don't want to take any synthetics, I just want to see plants and -- just show me herbs and plants. I've got all the natural ingredients." Now this app is spawning itself from the data. The data is all stored in a Google Doc, and it's literally generating itself from that data. So the data is now alive; this is a living image, and I can update it in a second. New evidence comes out. I just change a row on a spreadsheet. Doosh! Again, the image recreates itself. So it's cool. It's kind of living.
所以我用这个原理开发出了一个互动的应用程序, 我现在能在网上生成这个程序-- 并在网上进行视觉化-- 我能称赞它"真好,太棒了" 它自动开始工作. 然后我就能说"嗯,给我看下那些 影响心脏健康的东西" 它就开始筛选 心脏被选出去了,我对这很好奇. 我想"不,不,我不想吃任何合成的药品 我只想用植物-- 就只显示那些草本和植物.我有所有自然的营养成分." 现在这个程序又开始从数据 里自动生成. 所有数据都保存在Google doc里面, 它实际上是在自己利用数据完成视觉化. 现在数据变成活的了,它现在是一个动态的图像了, 而且我能一下就完成更新. 如果新的证据出现了--我只需要在表格里改一行. 哇!又一次的,图像自己重新生成了自己. 这很酷. 有点有生命的感觉.
But it can go beyond data, and it can go beyond numbers. I like to apply information visualization to ideas and concepts. This is a visualization of the political spectrum, an attempt for me to try and understand how it works and how the ideas percolate down from government into society and culture, into families, into individuals, into their beliefs and back around again in a cycle. What I love about this image is it's made up of concepts, it explores our worldviews and it helps us -- it helps me anyway -- to see what others think, to see where they're coming from. And it feels just incredibly cool to do that.
但它其实能比数据走得更远, 而且也不仅仅限于数字. 我喜欢用信息视觉化 的方法来处理思想和概念. 这是一个对政治观点 视觉化的图像, 是我在试图理解 政治是如果工作的. 理解想法是怎么向下渗透的 从政府开始到社会和文化, 再传递到家庭,各人以及他们的信念中 而且又如何再反过来影响上层,形成一个循环的. 关于这幅图我喜欢的是 它是由概念组成的, 它开拓了我们的世界观 并且帮助我们--至少是帮助我-- 看清别人是怎么想的, 了解他们的想法是从哪里来的. 做这件事感觉真是太棒了.
What was most exciting for me designing this was that, when I was designing this image, I desperately wanted this side, the left side, to be better than the right side -- being a journalist, a Left-leaning person -- but I couldn't, because I would have created a lopsided, biased diagram. So, in order to really create a full image, I had to honor the perspectives on the right-hand side and at the same time, uncomfortably recognize how many of those qualities were actually in me, which was very, very annoying and uncomfortable. (Laughter) But not too uncomfortable, because there's something unthreatening about seeing a political perspective, versus being told or forced to listen to one. You're capable of holding conflicting viewpoints joyously when you can see them. It's even fun to engage with them because it's visual. So that's what's exciting to me, seeing how data can change my perspective and change my mind midstream -- beautiful, lovely data.
最让我兴奋的是 设计 当我在设计这些图的时候, 我极其希望这一边,左边 能比右边好-- 作为一个记者,我是个有点左倾的人-- 但我不能,因为如果这样我全造出一个 不平衡的,带着偏见的图. 所以,为了真正的创造一个完整的图, 我得尊重右手边的这些观点 同时,有点不太情愿地认识到 我其实也有这其中很多的品质, 对我而言,这是特别特别地讨厌和不舒服的. (笑声) 但也不是特别不舒服, 因为看到一些政治观点, 总比被告知或者被强迫听着这些观点, 不让人那么有压迫和威胁感. 事实上,在你看到它们的时候 你能快乐的承受矛盾的观点. 甚至和它们打交道是有趣的 因为这是视觉的. 这就是它让我着迷的地方, 看数据是如何让我改变观点 并且改变我思维的主流-- 漂亮的,可爱的数据.
So, just to wrap up, I wanted to say that it feels to me that design is about solving problems and providing elegant solutions, and information design is about solving information problems. It feels like we have a lot of information problems in our society at the moment, from the overload and the saturation to the breakdown of trust and reliability and runaway skepticism and lack of transparency, or even just interestingness. I mean, I find information just too interesting. It has a magnetic quality that draws me in.
简单总结一下, 我想说 我感觉设计其实是在解决问题 并且给出一个精美的解决办法. 信息设计是在 解决信息上的问题. 现在我们社会里,好像有很多 信息方面的问题, 来自于过多的饱和的数据, 到信任和可靠性的缺失 以及逃避的怀疑和缺乏透明度, 甚至是失去兴趣. 我想说,我觉得信息真是特别有趣. 它有一种磁力吸引着我.
So, visualizing information can give us a very quick solution to those kinds of problems. Even when the information is terrible, the visual can be quite beautiful. Often we can get clarity or the answer to a simple question very quickly, like this one, the recent Icelandic volcano. Which was emitting the most CO2? Was it the planes or the volcano, the grounded planes or the volcano? So we can have a look. We look at the data and we see: Yep, the volcano emitted 150,000 tons; the grounded planes would have emitted 345,000 if they were in the sky. So essentially, we had our first carbon-neutral volcano.
所以,视觉化信息 能给我们一种快速的方法来解决这些问题. 即使是在信息很糟的情况下, 视觉上仍会很漂亮. 经常地我们能很快的 理清条理,或者找到一个简单问题的答案. 就像这个, 关于冰岛的火山的最近的讨论. 哪一个喷发出了最多的二氧化碳? 是那些飞机还是火山, 是那些严禁起飞的飞机还是火山? 让我们来看一下. 看着这些数据 我们发现,火山喷发出了十五万吨; 那些禁止起飞的飞机如果飞着的话 将喷发出三十四万五千吨. 所以实际上,我们有了第一个碳中性的火山.
(Laughter)
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And that is beautiful. Thank you.
而且这很漂亮!谢谢
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