TED2006
Hans Rosling: The best stats you've ever seen
Hans Rosling用前所未有的方法詮釋數字統計
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你肯定沒有看過這樣的數據演示。如解說體育比賽實況一般的生動與緊張，統計大師Hans Rosling將顛覆“發展中國家”這一理念。
Hans Rosling - Global health expert; data visionary
In Hans Rosling’s hands, data sings. Global trends in health and economics come to vivid life. And the big picture of global development—with some surprisingly good news—snaps into sharp focus. Full bio
In Hans Rosling’s hands, data sings. Global trends in health and economics come to vivid life. And the big picture of global development—with some surprisingly good news—snaps into sharp focus. Full bio
Double-click the English transcript below to play the video.
00:25
About 10 years ago, I took on the task to teach global development
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大約10年前，我開始
00:29
to Swedish undergraduate students. That was after having spent
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給瑞典大學生講授全球發展
00:33
about 20 years together with African institutions studying hunger in Africa,
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之前的20年我一直在非洲研究飢餓問題
00:37
so I was sort of expected to know a little about the world.
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所以大家以為我對世界有些了解
00:41
And I started in our medical university, Karolinska Institute,
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在我們的Karolinska醫學院
00:46
an undergraduate course called Global Health. But when you get
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我開設了一門本科生課程“全球健康”
00:50
that opportunity, you get a little nervous. I thought, these students
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剛開課的時候我還有些緊張
00:53
coming to us actually have the highest grade you can get
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因為來聽課的都是瑞典大學的優等生
00:56
in Swedish college systems -- so, I thought, maybe they know everything
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他們或許早已了解我準備教的內容
00:59
I'm going to teach them about. So I did a pre-test when they came.
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於是在第一堂課裡，我作了一個小測試
01:03
And one of the questions from which I learned a lot was this one:
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其中有一道題讓我受益匪淺
01:06
"Which country has the highest child mortality of these five pairs?"
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下列5對國家中，哪一個的兒童死亡率高於另一個？
01:10
And I put them together, so that in each pair of country,
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我所選擇的配對國家都是
01:14
one has twice the child mortality of the other. And this means that
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一個的兒童死亡率是另一個的兩倍
01:19
it's much bigger a difference than the uncertainty of the data.
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數據本身的不確定性可以忽略不計
01:24
I won't put you at a test here, but it's Turkey,
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今天我不會拿這來考大家
01:26
which is highest there, Poland, Russia, Pakistan and South Africa.
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土耳其，波蘭，俄羅斯，巴基斯坦和南非
01:31
And these were the results of the Swedish students. I did it so I got
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這是瑞典學生的測驗結果
01:34
the confidence interval, which is pretty narrow, and I got happy,
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讓我高興的是
01:37
of course: a 1.8 right answer out of five possible. That means that
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5道題平均答對的只有1.8題
01:41
there was a place for a professor of international health --
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我這個教授還有這門課
01:44
(Laughter) and for my course.
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因此都有了存在的必要
01:46
But one late night, when I was compiling the report
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但後來有天深夜，當我寫總結報告的時候
01:50
I really realized my discovery. I have shown
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我突然有了新的發現
01:54
that Swedish top students know statistically significantly less
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瑞典大學的優等生們對世界的了解
01:59
about the world than the chimpanzees.
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竟然還不如黑猩猩
02:01
(Laughter)
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（笑聲）
02:03
Because the chimpanzee would score half right if I gave them
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因為黑猩猩們至少能蒙對一半
02:07
two bananas with Sri Lanka and Turkey. They would be right half of the cases.
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在兩個選項旁邊各放一根香蕉，就有一半的機率答對。
02:10
But the students are not there. The problem for me was not ignorance;
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這些優等生們卻做不到。這不是由於知識缺乏
02:14
it was preconceived ideas.
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而是他們先入為主的錯誤理念
02:17
I did also an unethical study of the professors of the Karolinska Institute
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我還把這個測試拿去給卡羅林斯卡學院的教授們做
02:21
(Laughter)
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（笑聲）
02:22
-- that hands out the Nobel Prize in Medicine,
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他們每年負責頒發諾貝爾醫學獎
02:24
and they are on par with the chimpanzee there.
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結果教授們和黑猩猩半斤八兩
02:26
(Laughter)
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（笑聲）
02:29
This is where I realized that there was really a need to communicate,
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我意識到很有必要交流一下這個問題
02:33
because the data of what's happening in the world
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因為多數人並不知道
02:36
and the child health of every country is very well aware.
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世界各國的兒童健康的改善
02:39
We did this software which displays it like this: every bubble here is a country.
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我們作了一個軟件，每一個小球代表一個國家
02:44
This country over here is China. This is India.
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這個是中國，這個是印度
02:50
The size of the bubble is the population, and on this axis here I put fertility rate.
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小球的尺寸代表該國的人口，X軸是生育率
02:56
Because my students, what they said
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我曾問過學生們
02:59
when they looked upon the world, and I asked them,
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如果讓你們來審視這個世界
03:01
"What do you really think about the world?"
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你們的真實想法是什麼
03:03
Well, I first discovered that the textbook was Tintin, mainly.
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其實這些教科書上都是丁丁歷險記（帶有殖民主義思想的漫畫）的人物
03:07
(Laughter)
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（笑聲）
03:08
And they said, "The world is still 'we' and 'them.'
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學生們回答 世界是由“我們和他們”組成的
03:11
And we is Western world and them is Third World."
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“我們”指西方世界 “他們”指第三世界
03:14
"And what do you mean with Western world?" I said.
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我又問“什麼是西方世界？”
03:17
"Well, that's long life and small family, and Third World is short life and large family."
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“西方世界壽命長且家庭小；第三世界壽命短而家庭大。”
03:22
So this is what I could display here. I put fertility rate here: number of children per woman:
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那麼一起來看。X軸是生育率，每個婦女的育兒數
03:28
one, two, three, four, up to about eight children per woman.
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從每人1，2，3，4胎，到8胎
03:32
We have very good data since 1962 -- 1960 about -- on the size of families in all countries.
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我們有1962年之後的各國家庭大小的可靠數據
03:38
The error margin is narrow. Here I put life expectancy at birth,
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數據誤差相當小。 Y軸是平均壽命
03:41
from 30 years in some countries up to about 70 years.
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從30歲到70歲不等
03:45
And 1962, there was really a group of countries here
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1962年的時候的確有一群國家在上面
03:48
that was industrialized countries, and they had small families and long lives.
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這些是發達國家，他們家庭小，壽命長
03:53
And these were the developing countries:
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而這些則是發展中國家
03:55
they had large families and they had relatively short lives.
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他們家庭大，壽命也相對短些
03:58
Now what has happened since 1962? We want to see the change.
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從1962年到今天 世界有什麼變化嗎？
04:02
Are the students right? Is it still two types of countries?
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學生們正確嗎？國家還是分為2類嗎?
04:06
Or have these developing countries got smaller families and they live here?
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或者發展中國家的家庭變小 （這些小球）移動到了左邊?
04:09
Or have they got longer lives and live up there?
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或者發展中國家人們的壽命變長 （這些小球）移動到了上面?
04:11
Let's see. We stopped the world then. This is all U.N. statistics
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我們一起看看，這些數據都來自於聯合國
04:14
that have been available. Here we go. Can you see there?
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大家看到沒有？
04:17
It's China there, moving against better health there, improving there.
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這個是中國，他們在往上移動，健康狀況不斷改善
04:20
All the green Latin American countries are moving towards smaller families.
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這些綠色的拉丁美洲國家 正朝向小家庭的方向移動
04:23
Your yellow ones here are the Arabic countries,
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這些黃色的小球是阿拉伯國家
04:26
and they get larger families, but they -- no, longer life, but not larger families.
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壽命在變長但家庭規模不變
04:30
The Africans are the green down here. They still remain here.
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非洲國家是下面的綠球，他們一直在下面
04:33
This is India. Indonesia's moving on pretty fast.
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這個是印度，印度尼西亞的移動速度非常快
04:36
(Laughter)
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（笑聲）
04:37
And in the '80s here, you have Bangladesh still among the African countries there.
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80年代的時候，孟加拉國仍然和非洲國家在一起
04:40
But now, Bangladesh -- it's a miracle that happens in the '80s:
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但是80年代的奇蹟發生在孟加拉國
04:43
the imams start to promote family planning.
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媽媽們開始宣傳和普及計劃生育
04:46
They move up into that corner. And in '90s, we have the terrible HIV epidemic
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他們向左上角移動。90年代恐怖的艾滋病流行
04:51
that takes down the life expectancy of the African countries
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導致非洲國家的平均壽命縮短
04:54
and all the rest of them move up into the corner,
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而其他國家都向左上角移動
04:58
where we have long lives and small family, and we have a completely new world.
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大家都有了長壽命和小家庭，而世界也煥然一新了
05:02
(Applause)
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（掌聲）
05:15
Let me make a comparison directly between the United States of America and Vietnam.
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現在我們對比一下美國和越南
05:20
1964: America had small families and long life;
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1964年的美國家庭小壽命長
05:25
Vietnam had large families and short lives. And this is what happens:
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越南的家庭大而壽命短。這是後來的變化
05:29
the data during the war indicate that even with all the death,
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越戰時期的數據顯示，儘管戰爭造成傷亡
05:35
there was an improvement of life expectancy. By the end of the year,
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越南人的平均壽命仍有提高
05:38
the family planning started in Vietnam and they went for smaller families.
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70年代末期，越南的計劃生育減小了家庭規模
05:41
And the United States up there is getting for longer life,
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美國人的平均壽命也在延長
05:44
keeping family size. And in the '80s now,
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而家庭規模不變
05:47
they give up communist planning and they go for market economy,
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到了90年代，越南由計劃經濟轉為市場經濟
05:50
and it moves faster even than social life. And today, we have
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其經濟發展的速度超過了社會的發展
05:54
in Vietnam the same life expectancy and the same family size
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今天（2003）越南人的平均壽命和家庭規模
05:59
here in Vietnam, 2003, as in United States, 1974, by the end of the war.
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已經和越戰結束時（1974）的美國一樣
06:06
I think we all -- if we don't look in the data --
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如果沒有看到這些數據的話
06:10
we underestimate the tremendous change in Asia, which was
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我們會低估了亞洲的巨大變化
06:14
in social change before we saw the economical change.
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這些超前於經濟發展的社會變革
06:18
Let's move over to another way here in which we could display
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下面我們換個視角
06:23
the distribution in the world of the income. This is the world distribution of income of people.
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X軸顯示了全世界的收入分佈
06:30
One dollar, 10 dollars or 100 dollars per day.
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每天收入1美元，10美元和100美元
06:35
There's no gap between rich and poor any longer. This is a myth.
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富與窮之間的鴻溝幾乎消失了，簡直是個奇蹟
06:39
There's a little hump here. But there are people all the way.
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這裡還有一個很小的峰，但總體上是均數分佈的
06:44
And if we look where the income ends up -- the income --
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我們看看收入的分配情況
06:48
this is 100 percent the world's annual income. And the richest 20 percent,
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這代表全世界人民每年的全部收入
06:54
they take out of that about 74 percent. And the poorest 20 percent,
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最富有的20%那部分人得到了全部收入的74%
07:01
they take about two percent. And this shows that the concept
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最貧窮的20%那部分人只得到2%
07:06
of developing countries is extremely doubtful. We think about aid, like
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可見發展中國家的理念極其的不確切
07:10
these people here giving aid to these people here. But in the middle,
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我們總以為最富的人應該給最窮的人提供援助
07:15
we have most the world population, and they have now 24 percent of the income.
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其實中間這部分才是世界人口的主體，而他們僅得到全部收入的24%
07:19
We heard it in other forms. And who are these?
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這是個老問題了，中間這些人是誰？
07:23
Where are the different countries? I can show you Africa.
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他們在哪些國家？先看非洲
07:27
This is Africa. 10 percent the world population, most in poverty.
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非洲佔世界人口的十分之一，多數是窮人
07:32
This is OECD. The rich country. The country club of the U.N.
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這個代表富裕的經合組織成員國，聯合國俱樂部的會員
07:37
And they are over here on this side. Quite an overlap between Africa and OECD.
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他們在這邊，很小一部分與非洲重疊
07:42
And this is Latin America. It has everything on this Earth,
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這是拉丁美洲，他們可以代表全世界
07:45
from the poorest to the richest, in Latin America.
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從最貧窮到最富有的人都在那裡
07:48
And on top of that, we can put East Europe, we can put East Asia,
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再往上是東歐，東亞還有南亞
07:53
and we put South Asia. And how did it look like if we go back in time,
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過去是什麼樣子的呢？
07:58
to about 1970? Then there was more of a hump.
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如果我們回到1970年，這裡有一個明顯的峰
08:03
And we have most who lived in absolute poverty were Asians.
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這些絕對貧困的人大多數在亞洲
08:07
The problem in the world was the poverty in Asia. And if I now let the world move forward,
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那時世界的問題就在於亞洲的貧窮
08:14
you will see that while population increase, there are
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後來隨著人口的增長
08:17
hundreds of millions in Asia getting out of poverty and some others
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數以億計的亞洲人擺脫了貧困
08:20
getting into poverty, and this is the pattern we have today.
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另外一些人卻陷入貧窮，這就是今天的世界
08:23
And the best projection from the World Bank is that this will happen,
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而這是世界銀行對未來最樂觀的預測
08:27
and we will not have a divided world. We'll have most people in the middle.
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世界再也不是貧富懸殊的，大多數人擁有中等的收入
08:31
Of course it's a logarithmic scale here,
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當然這是指數冪分佈的圖
08:33
but our concept of economy is growth with percent. We look upon it
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因為經濟的增長是用百分比來衡量的
08:38
as a possibility of percentile increase. If I change this, and I take
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我們用百分比的變化來評估經濟增長
08:44
GDP per capita instead of family income, and I turn these
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下面把X軸改為人均國內生產總值
08:48
individual data into regional data of gross domestic product,
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個人的數據轉為各大洲的數據
08:54
and I take the regions down here, the size of the bubble is still the population.
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球的大小代表人口的多少
08:58
And you have the OECD there, and you have sub-Saharan Africa there,
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這個是經合組織國家，這是撒哈拉以南非洲
09:01
and we take off the Arab states there,
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我們把阿拉伯國家
09:04
coming both from Africa and from Asia, and we put them separately,
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從非洲和亞洲單獨分出來
09:08
and we can expand this axis, and I can give it a new dimension here,
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然後把X軸延伸一下，再加上一個新的維度
09:13
by adding the social values there, child survival.
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一個有社會價值的參數：兒童生存率
09:16
Now I have money on that axis, and I have the possibility of children to survive there.
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X軸代表經濟，Y軸顯示兒童存活的比率
09:21
In some countries, 99.7 percent of children survive to five years of age;
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一些國家的99.7%的小孩可以活到5歲以上
09:25
others, only 70. And here it seems there is a gap
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另一些國家只有70%。很明顯可以看到
09:29
between OECD, Latin America, East Europe, East Asia,
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經合組織成員國和拉丁美洲，東歐，東亞
09:33
Arab states, South Asia and sub-Saharan Africa.
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阿拉伯國家，南亞和非洲撒哈拉以南地區
09:37
The linearity is very strong between child survival and money.
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兒童生存率和經濟之間聯繫非常緊密
09:42
But let me split sub-Saharan Africa. Health is there and better health is up there.
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下面把撒哈拉以南非洲地區分解成各個國家
09:50
I can go here and I can split sub-Saharan Africa into its countries.
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分佈靠上邊的國家擁有更高的健康水平
09:55
And when it burst, the size of its country bubble is the size of the population.
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撒哈拉以南的非洲各國是如此分佈的，球的尺寸代表該國人口
10:00
Sierra Leone down there. Mauritius is up there. Mauritius was the first country
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塞拉里昂在下邊，毛里求斯在上邊
10:04
to get away with trade barriers, and they could sell their sugar --
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毛里求斯是第一個消除了貿易壁壘的國家
10:08
they could sell their textiles -- on equal terms as the people in Europe and North America.
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他們的蔗糖和紡織品的貿易協定與歐洲和北美一樣
10:13
There's a huge difference between Africa. And Ghana is here in the middle.
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但是非洲內部的差異非常巨大。加納在中部
10:17
In Sierra Leone, humanitarian aid.
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塞拉里昂需要人道主義援助
10:20
Here in Uganda, development aid. Here, time to invest; there,
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烏干達則需要發展援助，在加納可以進行投資了
10:25
you can go for a holiday. It's a tremendous variation
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毛里求斯則可以去度假。非洲內部的差異之大確實很驚人
10:28
within Africa which we rarely often make -- that it's equal everything.
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而我們卻總以為非洲國家都差不多
10:33
I can split South Asia here. India's the big bubble in the middle.
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下面分解南亞各國，印度是中間的藍色大球
10:37
But a huge difference between Afghanistan and Sri Lanka.
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而斯里蘭卡和阿富汗有著巨大差異
10:41
I can split Arab states. How are they? Same climate, same culture,
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把阿拉伯世界分解來看，儘管是相同的氣候，相同的文化
10:45
same religion -- huge difference. Even between neighbors.
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相同的宗教，卻有巨大的差異
10:49
Yemen, civil war. United Arab Emirate, money which was quite equally and well used.
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也門在打內戰，鄰國阿聯酋卻躺在錢堆裡
10:54
Not as the myth is. And that includes all the children of the foreign workers who are in the country.
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而且（阿聯酋的）兒童健康數據包含了所有的外籍勞工
11:01
Data is often better than you think. Many people say data is bad.
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大家總說數據不准確數據，其實比我們想像的好很多
11:06
There is an uncertainty margin, but we can see the difference here:
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數據是有誤差
11:08
Cambodia, Singapore. The differences are much bigger
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但柬埔寨和新加坡的差距肯定遠大於數據的誤差
11:11
than the weakness of the data. East Europe:
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再看東歐
11:14
Soviet economy for a long time, but they come out after 10 years
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在蘇聯經濟模式下發展了多年，但在過去10年
11:20
very, very differently. And there is Latin America.
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卻經歷了巨大的變化
11:23
Today, we don't have to go to Cuba to find a healthy country in Latin America.
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當今的拉丁美洲，古巴再也不是唯一的健康國家了
11:27
Chile will have a lower child mortality than Cuba within some few years from now.
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幾年後，智利的兒童死亡率將低於古巴
11:32
And here we have high-income countries in the OECD.
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這些是經合組織成員國
11:35
And we get the whole pattern here of the world,
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這裡顯示的就是我們的世界
11:39
which is more or less like this. And if we look at it,
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大概就是這樣的情形。如果我們回到過去
11:44
how it looks -- the world, in 1960, it starts to move. 1960.
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看看世界是怎樣的。從1960年開始
11:50
This is Mao Tse-tung. He brought health to China. And then he died.
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1960年（中國有）毛澤東，他給中國帶來了健康
11:53
And then Deng Xiaoping came and brought money to China, and brought them into the mainstream again.
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他去世後鄧小平給中國帶來了金錢，同時把中國帶回到世界的主流當中
11:58
And we have seen how countries move in different directions like this,
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其他國家的移動方向也不盡相同
12:02
so it's sort of difficult to get
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很難找出哪個國家
12:06
an example country which shows the pattern of the world.
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能代表全世界的發展模式
12:11
But I would like to bring you back to about here at 1960.
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我們回到1960年做個比較
12:17
I would like to compare South Korea, which is this one, with Brazil,
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先選中韓國（左邊的小黃球）；巴西（右邊的黃綠色大球）
12:27
which is this one. The label went away for me here. And I would like to compare Uganda,
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烏干達（Y軸上面的小紅球）
12:32
which is there. And I can run it forward, like this.
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隨著時間的推移，我們看到
12:37
And you can see how South Korea is making a very, very fast advancement,
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韓國的發展速度非常非常快
12:46
whereas Brazil is much slower.
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巴西就慢得多
12:49
And if we move back again, here, and we put on trails on them, like this,
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我們再回到過去，給每個球畫出運動的軌跡
12:55
you can see again that the speed of development
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可以看到，發展速度的差距非常大
12:59
is very, very different, and the countries are moving more or less
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雖然各國的經濟和健康發展的軌跡大同小異
13:05
in the same rate as money and health, but it seems you can move
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但是健康水平起點較高的國家
13:09
much faster if you are healthy first than if you are wealthy first.
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發展速度遠超過經濟水平起點高的
13:14
And to show that, you can put on the way of United Arab Emirate.
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為了說明這一點，我們看看阿聯酋
13:18
They came from here, a mineral country. They cached all the oil;
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他們從這裡出發，一個資源型國家
13:21
they got all the money; but health cannot be bought at the supermarket.
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他們靠石油大把賺錢，但健康絕不是超市裡的貨物
13:25
You have to invest in health. You have to get kids into schooling.
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需要衛生方面的投資，需要提高兒童的教育水平
13:29
You have to train health staff. You have to educate the population.
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需要培訓衛生工作者，還要教育民眾
13:32
And Sheikh Sayed did that in a fairly good way.
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Sheikh Sayed 幹的非常漂亮
13:35
In spite of falling oil prices, he brought this country up here.
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儘管油價下跌了，他仍改善了阿聯酋的健康
13:39
So we've got a much more mainstream appearance of the world,
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這裡我們可以看到世界發展的主流
13:43
where all countries tend to use their money
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各國對資金的分配和使用
13:45
better than they used in the past. Now, this is, more or less,
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都比過去合理的多
13:50
if you look at the average data of the countries -- they are like this.
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這里大家看到各國的數據基本上都是平均數
13:57
Now that's dangerous, to use average data, because there is such a lot
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但是用平均數可能會很危險
因為國家內部也存在很大的差異
因為國家內部也存在很大的差異
14:02
of difference within countries. So if I go and look here, we can see
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我們看這裡
14:08
that Uganda today is where South Korea was 1960. If I split Uganda,
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今天的烏干達和1960年的韓國差不多
14:14
there's quite a difference within Uganda. These are the quintiles of Uganda.
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如果把烏干達分解開，可以看到內部的明顯差異
14:19
The richest 20 percent of Ugandans are there.
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烏干達最富有的20%在右邊
14:22
The poorest are down there. If I split South Africa, it's like this.
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最貧窮的在左下邊。如果把南非分解開
14:26
And if I go down and look at Niger, where there was such a terrible famine,
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尼日在下邊，他們剛遭受一場恐怖的飢荒
14:31
lastly, it's like this. The 20 percent poorest of Niger is out here,
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最貧窮的20%的尼日人在最左邊
14:36
and the 20 percent richest of South Africa is there,
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而最富有的20%的南非人在最右邊
14:39
and yet we tend to discuss on what solutions there should be in Africa.
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今天我們仍然在討論什麼方案能解決非洲的問題
14:44
Everything in this world exists in Africa. And you can't
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世界上所有的問題非洲都有
14:47
discuss universal access to HIV [medicine] for that quintile up here
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我們不可能討論出一套通用方案，既能解決這些地方的艾滋病問題
14:51
with the same strategy as down here. The improvement of the world
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同時也適用於這些地方
14:55
must be highly contextualized, and it's not relevant to have it
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世界的發展一定要因地制宜來分析
15:00
on regional level. We must be much more detailed.
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僅從各大洲的水平上來分析是不夠的
15:03
We find that students get very excited when they can use this.
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當學生們接觸到這個軟件的時候他們都非常興奮
15:07
And even more policy makers and the corporate sectors would like to see
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此外，政策制定者，各企業部門都會想知道世界的變化
15:12
how the world is changing. Now, why doesn't this take place?
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但為什麼大家仍然不知道（世界的變化）
15:16
Why are we not using the data we have? We have data in the United Nations,
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為什麼我們無法使用已知的數據呢
15:20
in the national statistical agencies
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我們的聯合國，國家統計部門
15:22
and in universities and other non-governmental organizations.
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學院還有非政府組織都擁有數據
15:26
Because the data is hidden down in the databases.
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但數據被隱藏在底層的數據庫裡
15:28
And the public is there, and the Internet is there, but we have still not used it effectively.
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而公眾在上面（太陽），互聯網（地平線）並未得到有效的使用
15:33
All that information we saw changing in the world
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之前我們看到的關於世界變化的信息
15:36
does not include publicly-funded statistics. There are some web pages
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並不包括公眾資助的統計數據
15:40
like this, you know, but they take some nourishment down from the databases,
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的確有一些網站依靠數據庫的營養而存在著
15:46
but people put prices on them, stupid passwords and boring statistics.
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但這是要收費的，還有愚蠢的密碼和討厭的統計表格
15:51
(Laughter) (Applause)
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（笑聲，掌聲）
15:54
And this won't work. So what is needed? We have the databases.
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這個是行不通的。我們需要什麼？
15:58
It's not the new database you need. We have wonderful design tools,
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數據庫是現成的，不需要新的數據庫
16:02
and more and more are added up here. So we started
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我們有很好的視覺軟件，還將有更多的問世
16:05
a nonprofit venture which we called -- linking data to design --
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於是我們成立了一個非營利機構
16:10
we call it Gapminder, from the London underground, where they warn you,
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我們稱之為“數據與圖樣的聯結” - Gapminder
16:13
"mind the gap." So we thought Gapminder was appropriate.
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靈感來自倫敦地鐵（他們提醒乘客“小心列車與站台間的縫隙”）
16:16
And we started to write software which could link the data like this.
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而且我們製作了一個軟件，把數據和圖樣聯結起來
16:20
And it wasn't that difficult. It took some person years, and we have produced animations.
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這個並不難，需要幾個人花幾年時間
16:26
You can take a data set and put it there.
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建立數據庫後大家就能看到動畫
16:28
We are liberating U.N. data, some few U.N. organization.
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我們正嘗試解放聯合國的數據庫
16:33
Some countries accept that their databases can go out on the world,
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少數聯合國機構和幾個國家已經開放了數據庫
16:37
but what we really need is, of course, a search function.
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但我們最需要的是數據搜索引擎
16:40
A search function where we can copy the data up to a searchable format
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依靠搜索引擎，我們先把原始數據複製成可搜索的格式
16:45
and get it out in the world. And what do we hear when we go around?
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再把數據發佈到全世界。外界對這個設想的反應如何呢？
16:48
I've done anthropology on the main statistical units. Everyone says,
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我嘗試跟幾個大型統計機構交涉
16:53
"It's impossible. This can't be done. Our information is so peculiar
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所有人都說，這是不可能的，“這行不通，我們的信息很獨特，
16:57
in detail, so that cannot be searched as others can be searched.
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不可能像其它數據那樣檢索的出來
17:00
We cannot give the data free to the students, free to the entrepreneurs of the world."
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我們也不能免費把數據開放，給全世界的學生們和企業部門使用。 ”
17:05
But this is what we would like to see, isn't it?
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但這正是我們期望看到的，不是嗎？
17:08
The publicly-funded data is down here.
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下邊是公眾資助採集的數據
17:11
And we would like flowers to grow out on the Net.
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我們希望互聯網上長出美麗的花朵
17:14
And one of the crucial points is to make them searchable, and then people can use
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關鍵的一步，是讓這些數據可被搜索到
17:19
the different design tool to animate it there.
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並藉助軟件實現動畫的演示
17:21
And I have a pretty good news for you. I have a good news that the present,
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我有個很好的消息要告訴大家
17:26
new Head of U.N. Statistics, he doesn't say it's impossible.
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新上任的聯合國統計部門的領導並沒有說這是不可能的
17:30
He only says, "We can't do it."
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他只說“我們不能這麼做。”
17:32
(Laughter)
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（笑聲）
17:36
And that's a quite clever guy, huh?
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他很聰明吧
17:38
(Laughter)
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（笑聲）
17:40
So we can see a lot happening in data in the coming years.
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未來幾年中我們將會看到數據庫的變化
17:44
We will be able to look at income distributions in completely new ways.
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我們會用全新的視角來看收入的分配
17:48
This is the income distribution of China, 1970.
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這是1970年中國的收入分配
17:54
the income distribution of the United States, 1970.
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這是1970年美國的收入分配
17:59
Almost no overlap. Almost no overlap. And what has happened?
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幾乎沒有重疊，後來呢？
18:03
What has happened is this: that China is growing, it's not so equal any longer,
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中國在增長，再也不像以前那樣平等了
18:08
and it's appearing here, overlooking the United States.
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它出現在右邊，俯視著美國
18:12
Almost like a ghost, isn't it, huh?
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是不是像個鬼一樣
18:14
(Laughter)
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（笑聲）
18:16
It's pretty scary. But I think it's very important to have all this information.
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很嚇人吧，我認為這些信息很重要
18:26
We need really to see it. And instead of looking at this,
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大家很有必要看到這些
18:32
I would like to end up by showing the Internet users per 1,000.
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另外我最後要給大家展示，每千人中的網民數量
18:37
In this software, we access about 500 variables from all the countries quite easily.
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這個軟件能讓我們很容易的看到全球各國的近500個參數
18:42
It takes some time to change for this,
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通過點擊坐標軸
18:46
but on the axises, you can quite easily get any variable you would like to have.
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你能輕易改變參數的設定
18:51
And the thing would be to get up the databases free,
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我們的初衷是，數據免費下載且易於查找
18:56
to get them searchable, and with a second click, to get them
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然後再點一下鼠標，數據就成為圖表的形式
18:59
into the graphic formats, where you can instantly understand them.
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那樣大家就可以立刻看明白這些數據了
19:04
Now, statisticians doesn't like it, because they say that this
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統計學家們不喜歡這樣子
19:07
will not show the reality; we have to have statistical, analytical methods.
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他們認為這不能準確地反映事實，傳統的統計和分析方法是不能取代的
19:16
But this is hypothesis-generating.
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但數據動畫可以幫助提出假說
19:19
I end now with the world. There, the Internet is coming.
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最後我們看一下當今的互聯網世界
19:23
The number of Internet users are going up like this. This is the GDP per capita.
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網民數量不斷向上攀升，（X軸是）人均國民生產總值
19:27
And it's a new technology coming in, but then amazingly, how well
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互聯網是一項新技術，但令人驚訝的是
19:32
it fits to the economy of the countries. That's why the 100 dollar
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它的普及和國家的經濟水平極其一致
19:37
computer will be so important. But it's a nice tendency.
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這也解釋了100美元電腦的重要性，但這是很好的趨勢
19:40
It's as if the world is flattening off, isn't it? These countries
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世界各國的差距將會縮小，不是嗎
19:43
are lifting more than the economy and will be very interesting
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這些國家的互聯網普及速度超過了經濟的發展速度
19:46
to follow this over the year, as I would like you to be able to do
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我也希望大家都可以自由使用公眾資助採集的數據
19:50
with all the publicly funded data. Thank you very much.
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非常感謝！
19:53
(Applause)
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www.gapminder.org
ABOUT THE SPEAKER
Hans Rosling - Global health expert; data visionaryIn Hans Rosling’s hands, data sings. Global trends in health and economics come to vivid life. And the big picture of global development—with some surprisingly good news—snaps into sharp focus.
Why you should listen
Even the most worldly and well-traveled among us have had their perspectives shifted by Hans Rosling. A professor of global health at Sweden's Karolinska Institute, his work focused on dispelling common myths about the so-called developing world, which (as he pointed out) is no longer worlds away from the West. In fact, most of the Third World is on the same trajectory toward health and prosperity, and many countries are moving twice as fast as the west did.
What set Rosling apart wasn't just his apt observations of broad social and economic trends, but the stunning way he presented them. Guaranteed: You've never seen data presented like this. A presentation that tracks global health and poverty trends should be, in a word: boring. But in Rosling's hands, data sings. Trends come to life. And the big picture — usually hazy at best — snaps into sharp focus.
Rosling's presentations were grounded in solid statistics (often drawn from United Nations and World Bank data), illustrated by the visualization software he developed. The animations transform development statistics into moving bubbles and flowing curves that make global trends clear, intuitive and even playful. During his legendary presentations, Rosling took this one step farther, narrating the animations with a sportscaster's flair.
Rosling developed the breakthrough software behind his visualizations through his nonprofit Gapminder, founded with his son and daughter-in-law. The free software — which can be loaded with any data — was purchased by Google in March 2007. (Rosling met the Google founders at TED.)
Rosling began his wide-ranging career as a physician, spending many years in rural Africa tracking a rare paralytic disease (which he named konzo) and discovering its cause: hunger and badly processed cassava. He co-founded Médecins sans Frontièrs (Doctors without Borders) Sweden, wrote a textbook on global health, and as a professor at the Karolinska Institut in Stockholm initiated key international research collaborations. He's also personally argued with many heads of state, including Fidel Castro.
Hans Rosling passed away in February 2017. He is greatly missed.
Hans Rosling | Speaker | TED.com