TED2006
Hans Rosling: The best stats you've ever seen
Hans Rosling viser den bedste statistik du nogensinde har set
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Statistik har aldrig før været fremlagt på denne måde. Med en sportskommentators indlevelse og intensitet punkterer statistikguruen Hans Roslink myterne om de såkaldte "udviklingslande".
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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For cirka 10 år siden, begyndte jeg på at undervise svenske studerende
00:29
to Swedish undergraduate students. That was after having spent
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i verdensomspændende udvikling. Det var efter jeg havde tilbragt
00:33
about 20 years together with African institutions studying hunger in Africa,
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omkring 20 år med at studere sult i Afrika i samarbejde med afrikanske institutioner,
00:37
so I was sort of expected to know a little about the world.
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så det var ligesom forventet af mig, at jeg vidste en smule om verdenen.
00:41
And I started in our medical university, Karolinska Institute,
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Og på vores lægeuniversitet, Karolingska Instituttet, startede jeg
00:46
an undergraduate course called Global Health. But when you get
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et kursus med navnet "Global sundhed". Men når man får
00:50
that opportunity, you get a little nervous. I thought, these students
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den mulighed, bliver man en smule nervøs. Jeg tænkte at disse studerende
00:53
coming to us actually have the highest grade you can get
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der kommer til os, faktisk har de højeste karakterer man kan få
00:56
in Swedish college systems -- so, I thought, maybe they know everything
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i de svenske gymnasier - så måske ved de allerede alt
00:59
I'm going to teach them about. So I did a pre-test when they came.
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det jeg vil lære dem. Så jeg lavede en forprøve da de ankom,
01:03
And one of the questions from which I learned a lot was this one:
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og et af de spørgsmål jeg lærte meget fra, var:
01:06
"Which country has the highest child mortality of these five pairs?"
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"Hvilket land har den højeste børnedødelighed af disse fem par?"
01:10
And I put them together, so that in each pair of country,
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Og jeg satte dem sammen, så for hvert landepar havde
01:14
one has twice the child mortality of the other. And this means that
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det ene land dobbelt så stor børnedødelighed som det andet. Og det betyder at
01:19
it's much bigger a difference than the uncertainty of the data.
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forskellen er meget større end usikkerheden på data.
01:24
I won't put you at a test here, but it's Turkey,
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Jeg vil ikke afprøve jer her, men det er Tyrkiet
01:26
which is highest there, Poland, Russia, Pakistan and South Africa.
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der er højest der, Polen, Rusland, Pakistan og Sydafrika.
01:31
And these were the results of the Swedish students. I did it so I got
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Og her er de svenske studerendes resultater. Jeg sørgede for at jeg
01:34
the confidence interval, which is pretty narrow, and I got happy,
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fik konfidensintervallet, som er ret snævert, og jeg blev naturligvis
01:37
of course: a 1.8 right answer out of five possible. That means that
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glad: 1.8 ud af 5 mulige. Det betyder at
01:41
there was a place for a professor of international health --
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der var en plads for en professor i international sundhed -
01:44
(Laughter) and for my course.
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(Latter) og for mit kursus.
01:46
But one late night, when I was compiling the report
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Men en sen nat, hvor jeg skrev rapporten, blev jeg
01:50
I really realized my discovery. I have shown
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virkelig klar over min opdagelse. Jeg har vist
01:54
that Swedish top students know statistically significantly less
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at svenske topstuderende ved statistisk signifikant mindre
01:59
about the world than the chimpanzees.
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om verdenen end chimpanser.
02:01
(Laughter)
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(Latter)
02:03
Because the chimpanzee would score half right if I gave them
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Fordi chimpanserne ville få halvdelen rigtige hvis jeg gav dem
02:07
two bananas with Sri Lanka and Turkey. They would be right half of the cases.
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to bananer med Sri Lanka og Tyrkiet. De ville have ret i halvdelen af tilfældene.
02:10
But the students are not there. The problem for me was not ignorance;
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Men det er ikke der de studerende er. Mit problem var ikke uvidenhed:
02:14
it was preconceived ideas.
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det var fordomme.
02:17
I did also an unethical study of the professors of the Karolinska Institute
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Jeg lavede også en uetisk undersøgelse af professorerne på det Karolinske Institut
02:21
(Laughter)
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(Latter)
02:22
-- that hands out the Nobel Prize in Medicine,
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- der uddeler Nobelprisen i medicin,
02:24
and they are on par with the chimpanzee there.
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og de står lige med chimpanserne der.
02:26
(Laughter)
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(Latter)
02:29
This is where I realized that there was really a need to communicate,
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Her indså jeg at der virkelig var et behov for at kommunikere,
02:33
because the data of what's happening in the world
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fordi data for hvad der sker i verdenen
02:36
and the child health of every country is very well aware.
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og børnedødeligheden i hvert land er meget tydelige.
02:39
We did this software which displays it like this: every bubble here is a country.
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Vi lavede dette edb-program, som viser det således: Hver boble her er et land.
02:44
This country over here is China. This is India.
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Landet herovre er Kina. Dette er Indien.
02:50
The size of the bubble is the population, and on this axis here I put fertility rate.
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Boblens størrelse er befolkningstallet, og på den her akse sætter jeg fertilitetsraten.
02:56
Because my students, what they said
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Fordi mine studerende, det de sagde
02:59
when they looked upon the world, and I asked them,
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da de så på verden, og da jeg spurgte dem:
03:01
"What do you really think about the world?"
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"Hvad synes I virkelig om verdenen?"
03:03
Well, I first discovered that the textbook was Tintin, mainly.
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Nuvel, det første jeg opdagede var at lærebogen hovedsageligt var Tintin.
03:07
(Laughter)
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(Latter)
03:08
And they said, "The world is still 'we' and 'them.'
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Og de sagde, "Verdenen er stadig 'os' og 'dem',
03:11
And we is Western world and them is Third World."
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og vi er den vestlige verden, og dem er den tredie verden."
03:14
"And what do you mean with Western world?" I said.
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"Og hvad mener I med vestlige verden?" spurgte jeg.
03:17
"Well, that's long life and small family, and Third World is short life and large family."
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"Jo, det er langt liv, og en lille familie, og den tredie verden er kort liv og stor familie."
03:22
So this is what I could display here. I put fertility rate here: number of children per woman:
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Så det kan jeg vise her. Jeg placerer fertilitetsraten her: antal børn pr kvinde,
03:28
one, two, three, four, up to about eight children per woman.
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en, to, tre, fire, op til omkring otte børn pr kvinde.
03:32
We have very good data since 1962 -- 1960 about -- on the size of families in all countries.
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Vi har rigtigt gode data siden 1962 - 1960 cirka - på størrelsen af familier i alle lande.
03:38
The error margin is narrow. Here I put life expectancy at birth,
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Fejlmarginen er snæver. Her putter jeg forventet levetid ved fødslen
03:41
from 30 years in some countries up to about 70 years.
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fra 30 år i visse lande op til omkring 70 år.
03:45
And 1962, there was really a group of countries here
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Og i 1962 var der virkelig en gruppe lande her
03:48
that was industrialized countries, and they had small families and long lives.
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der var industrialiserede lande, og de havde små familier og lange levetider.
03:53
And these were the developing countries:
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Og disse var udviklingslandene:
03:55
they had large families and they had relatively short lives.
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De havde store familier og forholdsvist korte levetider.
03:58
Now what has happened since 1962? We want to see the change.
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Hvad er der sket siden 1962? Vi vil se ændringen.
04:02
Are the students right? Is it still two types of countries?
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Har de studerende ret? Er der stadig to slags lande?
04:06
Or have these developing countries got smaller families and they live here?
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Eller har disse udviklingslande fået mindre familier og de bor her?
04:09
Or have they got longer lives and live up there?
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Eller har de fået længere levetider og bor deroppe?
04:11
Let's see. We stopped the world then. This is all U.N. statistics
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Lad os se. Vi stoppede verden dengang. Dette er altsammen FN statistikdata
04:14
that have been available. Here we go. Can you see there?
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der er tilgængeligt. Så kører vi. Kan I se der?
04:17
It's China there, moving against better health there, improving there.
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Det er Kina her, der bevæger sig mod bedre helbred der, forbedrer der,
04:20
All the green Latin American countries are moving towards smaller families.
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Alle de grønne latinamerikanske lande bevæger sig mod mindre familier
04:23
Your yellow ones here are the Arabic countries,
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De gule herovre er de arabiske lande,
04:26
and they get larger families, but they -- no, longer life, but not larger families.
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og de får større familier, men de - nej, længere levetid, men ikke større familier.
04:30
The Africans are the green down here. They still remain here.
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De afrikanske er de grønne hernede. De forbliver her.
04:33
This is India. Indonesia's moving on pretty fast.
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Dette er Indien. Indonesien flytter sig ret hurtigt.
04:36
(Laughter)
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(Latter)
04:37
And in the '80s here, you have Bangladesh still among the African countries there.
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Og her i 80'erne, har vi Bangladesh stadig blandt de afrikanske lande der.
04:40
But now, Bangladesh -- it's a miracle that happens in the '80s:
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Men se, Bangladesh - det er et mirakel der sker i 80'erne:
04:43
the imams start to promote family planning.
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imamerne begynder at støtte familieplanlægning.
04:46
They move up into that corner. And in '90s, we have the terrible HIV epidemic
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De bevæger sig op i det hjørne. Og i 90'erne har vi den forfærdelige HIV-epidemi
04:51
that takes down the life expectancy of the African countries
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der trækker den forventede levetid i de afrikanske lande ned,
04:54
and all the rest of them move up into the corner,
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og alle de andre bevæger sig op i hjørnet
04:58
where we have long lives and small family, and we have a completely new world.
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hvor vi har lang levetid og små familier, og vi har en fuldstændig ny verden.
05:02
(Applause)
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(Klapsalve)
05:15
Let me make a comparison directly between the United States of America and Vietnam.
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Lad mig lave en direkte sammenligning mellem USA og Vietnam.
05:20
1964: America had small families and long life;
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1964: USA havde små familier og lang levetid.
05:25
Vietnam had large families and short lives. And this is what happens:
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Vietnam havde store familier og kort levetid. Og dette er hvad der sker:
05:29
the data during the war indicate that even with all the death,
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Data fra krigen indikerer at på trods af alle de døde,
05:35
there was an improvement of life expectancy. By the end of the year,
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var der en levetidsforbedring. Ved slutningen af året
05:38
the family planning started in Vietnam and they went for smaller families.
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begyndte familieplanlægningen i Vietnam og de skiftede til mindre familier.
05:41
And the United States up there is getting for longer life,
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Og USA deroppe får længere levetid, og bevarer
05:44
keeping family size. And in the '80s now,
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familiens størrelse. Og i 80'erne nu,
05:47
they give up communist planning and they go for market economy,
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opgiver de kommunistisk planlægning, og skifter til markedsøkonomi,
05:50
and it moves faster even than social life. And today, we have
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og det går endnu hurtigere end det sociale liv. Og i dag, har vi i
05:54
in Vietnam the same life expectancy and the same family size
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Vietnam den samme forventede levetid og den samme familiestørrelse
05:59
here in Vietnam, 2003, as in United States, 1974, by the end of the war.
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her i Vietnam, 2003, som i USA, 1974, ved krigens slutning.
06:06
I think we all -- if we don't look in the data --
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Jeg tror at vi alle - hvis vi ikke kigger i data -
06:10
we underestimate the tremendous change in Asia, which was
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undervurderer den utrolige ændring i Asien som var
06:14
in social change before we saw the economical change.
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en social ændring, førend vi så den økonomiske ændring.
06:18
Let's move over to another way here in which we could display
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Lad os skifte til en anden måde her, hvor vi kan vise
06:23
the distribution in the world of the income. This is the world distribution of income of people.
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fordelingen af indtægt i verdenen. Dette er verdensfordelingen af folks indtægt.
06:30
One dollar, 10 dollars or 100 dollars per day.
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En dollar, 10 dollars eller 100 dollars pr dag.
06:35
There's no gap between rich and poor any longer. This is a myth.
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Der er ikke noget gab mellem rig og fattig længere. Det er en myte.
06:39
There's a little hump here. But there are people all the way.
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Der er et lille bump her. Men der er folk hele vejen.
06:44
And if we look where the income ends up -- the income --
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Og hvis vi ser på hvor indkomsten ender - indkomsten -
06:48
this is 100 percent the world's annual income. And the richest 20 percent,
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dette er 100 procent af verdens årlige indkomst. Og de rigeste 20 procent
06:54
they take out of that about 74 percent. And the poorest 20 percent,
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tager 74 procent heraf. Og de fattigste 20 procent,
07:01
they take about two percent. And this shows that the concept
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tager omkring to procent. Og dette viser at begrebet
07:06
of developing countries is extremely doubtful. We think about aid, like
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"udviklingslande" er ekstremt tvivlsomt. Vi tænker på hjælp, i form
07:10
these people here giving aid to these people here. But in the middle,
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af de her folk der giver hjælp til de her folk. Men i midten
07:15
we have most the world population, and they have now 24 percent of the income.
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har vi det meste af verdens befolkning, og de har nu 24 procent af indkomsten.
07:19
We heard it in other forms. And who are these?
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Vi har hørt det i andre former. Og hvem er de?
07:23
Where are the different countries? I can show you Africa.
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Hvor er de forskellige lande? Jeg kan vise jer Afrika.
07:27
This is Africa. 10 percent the world population, most in poverty.
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Dette er Afrika. 10 procent af verdens befolkning, de fleste er fattige.
07:32
This is OECD. The rich country. The country club of the U.N.
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Dette er OECD. De rige lande. FN's golfklub.
07:37
And they are over here on this side. Quite an overlap between Africa and OECD.
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Og de er herovre på denne her side. Et ret stort overlap mellem Afrika og OECD.
07:42
And this is Latin America. It has everything on this Earth,
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Og dette er Latinamerika. Det har alt på denne jord
07:45
from the poorest to the richest, in Latin America.
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fra de fattigste til de rigeste, i Latinamerika.
07:48
And on top of that, we can put East Europe, we can put East Asia,
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Og oveni det, kan vi putte Østeuropa, Østasien
07:53
and we put South Asia. And how did it look like if we go back in time,
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og Sydasien. Og hvordan ser det ud hvis vi går tilbage i tid
07:58
to about 1970? Then there was more of a hump.
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til omkring 1970? Dengang var der mere en pukkel.
08:03
And we have most who lived in absolute poverty were Asians.
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Og vi kan se at de fleste der levede i den yderste fattigdom var asiater.
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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Verdens problem var fattigdommen i Asien. Og hvis
08:14
you will see that while population increase, there are
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kan I se at mens befolkningen øges, er der
08:17
hundreds of millions in Asia getting out of poverty and some others
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hundredvis af millioner i Asien der kommer ud af fattigdommen og andre
08:20
getting into poverty, and this is the pattern we have today.
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der kommer ind i fattigdom, og dette er mønsteret vi har i dag.
08:23
And the best projection from the World Bank is that this will happen,
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Og den bedste forudsigelse fra Verdensbanken er at dette vil ske,
08:27
and we will not have a divided world. We'll have most people in the middle.
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og vi vil ikke have en delt verden. Vi vil have flest mennesker i midten.
08:31
Of course it's a logarithmic scale here,
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Selvfølgelig er det en logaritmisk skala her,
08:33
but our concept of economy is growth with percent. We look upon it
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men vores økonomiske koncept er vækst i procent. Vi ser på det
08:38
as a possibility of percentile increase. If I change this, and I take
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som en mulighed for procentvis vækst. Hvis jeg ændrer dette og jeg tager
08:44
GDP per capita instead of family income, and I turn these
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bruttonationalindkomsten pr indbygger i stedet for familieindkomst, og jeg ænder disse
08:48
individual data into regional data of gross domestic product,
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individuelle data til områdedata for bruttonationalproduktet
08:54
and I take the regions down here, the size of the bubble is still the population.
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og jeg tager områderne hernede, er boblens størrelse stadig befolkningen.
08:58
And you have the OECD there, and you have sub-Saharan Africa there,
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Og vi har OECD der, og Afrika syd for Sahara der,
09:01
and we take off the Arab states there,
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og vi tager de arabiske stater der
09:04
coming both from Africa and from Asia, and we put them separately,
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der kommer både fra Afrika og Asien og placerer den separat,
09:08
and we can expand this axis, and I can give it a new dimension here,
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og vi kan udvide denne akse, og jeg kan give den en ny dimension her,
09:13
by adding the social values there, child survival.
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ved at tilføje de sociale værdier der, børnedødelighed.
09:16
Now I have money on that axis, and I have the possibility of children to survive there.
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Nu har jeg penge på den akse, og jeg har sandsynligheden for børns overlevelse der.
09:21
In some countries, 99.7 percent of children survive to five years of age;
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I nogen lande, overlever 99,7 procent af børnene til femårsalderen;
09:25
others, only 70. And here it seems there is a gap
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i andre, kun 70. Og her ser det ud til der er et gab
09:29
between OECD, Latin America, East Europe, East Asia,
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mellem OECD, Latinamerika, Østeuropa, Østasien,
09:33
Arab states, South Asia and sub-Saharan Africa.
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Arabiske stater, Sydasien og Afrika syd for Sahara.
09:37
The linearity is very strong between child survival and money.
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Der er en meget stærk sammenhæng mellem børnedødelighed og penge.
09:42
But let me split sub-Saharan Africa. Health is there and better health is up there.
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Men lad mig opdele Afrika syd for Sahara. Sundhed er der og bedre helbred er deroppe.
09:50
I can go here and I can split sub-Saharan Africa into its countries.
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Jeg kan gå hertil og jeg kan opdele Afrika syd for Sahara i dets lande.
09:55
And when it burst, the size of its country bubble is the size of the population.
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Og når det springer, er størrelsen af landets boble størrelsen af dens befolkning.
10:00
Sierra Leone down there. Mauritius is up there. Mauritius was the first country
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Sierra Leone dernede. Mauritius er deroppe. Mauritius var det første land
10:04
to get away with trade barriers, and they could sell their sugar --
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der slap for handelshindringer, og de kunne sælge deres sukker.
10:08
they could sell their textiles -- on equal terms as the people in Europe and North America.
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De kune sælge deres tekstiler på lige vilkår med befolkningen i Europa og Nordamerika.
10:13
There's a huge difference between Africa. And Ghana is here in the middle.
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Der er en stor forskel i Afrika. Og Ghana er her i midten.
10:17
In Sierra Leone, humanitarian aid.
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I Sierra Leone, humanitær hjælp.
10:20
Here in Uganda, development aid. Here, time to invest; there,
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Her i Uganda, udviklingshjælp. Her, investeringstid, der,
10:25
you can go for a holiday. It's a tremendous variation
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kan man tage på ferie. Det er en kæmpe variation
10:28
within Africa which we rarely often make -- that it's equal everything.
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i Afrika som vi sjældent tager højde for - at det er ens over det hele.
10:33
I can split South Asia here. India's the big bubble in the middle.
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Jeg kan opdele Sydasien her. Indien er den store boble i midten.
10:37
But a huge difference between Afghanistan and Sri Lanka.
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Men der er en stor forskel mellem Afghanistan og Sri Lanka.
10:41
I can split Arab states. How are they? Same climate, same culture,
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Jeg kan opdele de arabiske stater. Hvordan er de? Samme klima, samme kultur,
10:45
same religion -- huge difference. Even between neighbors.
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samme religion. Stor forskel. Selv mellem naboer.
10:49
Yemen, civil war. United Arab Emirate, money which was quite equally and well used.
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Yemen, borgerkrig. Forenede Arabiske Emirater, penge som var ligeligt og godt brugt.
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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Ikke som myten er. Og det indbefatter alle børnene af fremmedarbejderne der er i landet.
11:01
Data is often better than you think. Many people say data is bad.
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Data er ofte bedre end I tror. Mange siger data er dårlige.
11:06
There is an uncertainty margin, but we can see the difference here:
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Der er en usikkerhedsmargin, men vi kan se forskellen her:
11:08
Cambodia, Singapore. The differences are much bigger
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Cambodia, Singapore. Forskellene er meget større,
11:11
than the weakness of the data. East Europe:
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end svaghederne i data. Østeuropa:
11:14
Soviet economy for a long time, but they come out after 10 years
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Sovjettisk økonomi i lang tid, men efter 10 år har de klaret sig
11:20
very, very differently. And there is Latin America.
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meget, meget anderledes. Og der er Latinamerika.
11:23
Today, we don't have to go to Cuba to find a healthy country in Latin America.
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I dag behøver vi ikke tage til Cuba, for at finde et sundt land i Latinamerika.
11:27
Chile will have a lower child mortality than Cuba within some few years from now.
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Chile vil have en lavere børnedødelighed end Cuba om ganske få år.
11:32
And here we have high-income countries in the OECD.
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Og her har vi højindtægtslande i OECD.
11:35
And we get the whole pattern here of the world,
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Og vi ser hele mønsteret for verden,
11:39
which is more or less like this. And if we look at it,
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som er mere eller mindre som det her. Og hvis vi ser på det,
11:44
how it looks -- the world, in 1960, it starts to move. 1960.
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hvordan det ser ud - verden, i 1960, begynder det at bevæge sig. 1960.
11:50
This is Mao Tse-tung. He brought health to China. And then he died.
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Dette er Mao Zedong. Han bragte sundhed til Kina. Og så døde han.
11:53
And then Deng Xiaoping came and brought money to China, and brought them into the mainstream again.
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Og så kom Deng Xiaoping og bragte penge til Kina, og bragte dem tilbage i hovedstrømmen igen.
11:58
And we have seen how countries move in different directions like this,
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Og vi har set hvordan lande bevæger sig i forskellige retninger som dette,
12:02
so it's sort of difficult to get
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så det er lidt svært at få
12:06
an example country which shows the pattern of the world.
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et land som eksempel der viser mønsteret for verden.
12:11
But I would like to bring you back to about here at 1960.
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Jeg vil gerne bringe jer tilbage til omkring her i 1960.
12:17
I would like to compare South Korea, which is this one, with Brazil,
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Jeg vil gerne sammenligne Sydkorea, som er den her, med Brasillien,
12:27
which is this one. The label went away for me here. And I would like to compare Uganda,
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der er den her. Mærkaten forsvandt for mig her. Og jeg vil gerne sammenligne Uganda,
12:32
which is there. And I can run it forward, like this.
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som er der. Og jeg kan føre det fremad, således.
12:37
And you can see how South Korea is making a very, very fast advancement,
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Og I kan se hvordan Sydkorea laver meget, meget hurtigt fremskridt,
12:46
whereas Brazil is much slower.
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hvor Brasillien er meget langsommere.
12:49
And if we move back again, here, and we put on trails on them, like this,
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Og hvis vi bevæger os tilbage igen, her, og vi putter sporing på dem, således.
12:55
you can see again that the speed of development
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kan I se igen at udviklingshastigheden
12:59
is very, very different, and the countries are moving more or less
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er meget, meget forskellig og at landene bevæger sig mere eller mindre
13:05
in the same rate as money and health, but it seems you can move
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i den samme hastighed som penge og sundhed, men det ser ud til man kan bevæge sig
13:09
much faster if you are healthy first than if you are wealthy first.
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meget hurtigere hvis man er sund først end hvis man er rig først.
13:14
And to show that, you can put on the way of United Arab Emirate.
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Og for at vise det, kan vi tilføje de Forenede Arabiske Emirater.
13:18
They came from here, a mineral country. They cached all the oil;
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De kom herfra, et mineralland. De havde al olien,
13:21
they got all the money; but health cannot be bought at the supermarket.
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de fik alle pengene, men sundhed kan ikke købes i et supermarkede.
13:25
You have to invest in health. You have to get kids into schooling.
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Man skal investere i sundhed. Man skal have børnene ind i skolesystemet.
13:29
You have to train health staff. You have to educate the population.
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Man skal oplære sundhedspersonale. Man skal uddanne befolkningen.
13:32
And Sheikh Sayed did that in a fairly good way.
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Og Sheik Sayed gjorde det på en ret god måde.
13:35
In spite of falling oil prices, he brought this country up here.
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Og på trods af faldende oliepriser, bragte han det land herop.
13:39
So we've got a much more mainstream appearance of the world,
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Så vi har en meget mere strømlinjet udgave af verden,
13:43
where all countries tend to use their money
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hvor alle lande hælder til at bruge deres penge
13:45
better than they used in the past. Now, this is, more or less,
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bedre end de gjorde tidligere. Nuvel, det er, mere eller mindre,
13:50
if you look at the average data of the countries -- they are like this.
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hvis man ser på gennemsnitsdata for landene. De ser sådan ud.
13:57
Now that's dangerous, to use average data, because there is such a lot
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Men det er farligt at bruge gennemsnitsdata fordi der er megen forskel
14:02
of difference within countries. So if I go and look here, we can see
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i lande. Så hvis jeg kigger her, kan vi se at
14:08
that Uganda today is where South Korea was 1960. If I split Uganda,
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Uganda i dag er hvor Sydkorea var i 1960. Hvis jeg opdeler Uganda
14:14
there's quite a difference within Uganda. These are the quintiles of Uganda.
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er der en ret stor forskel internt i Uganda. Dette er femdelingen af Uganda
14:19
The richest 20 percent of Ugandans are there.
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De rigeste 20 procent af folk i Uganda er der.
14:22
The poorest are down there. If I split South Africa, it's like this.
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De fattigste er dernede. Hvis jeg opdeler Sydafrika, er det sådan.
14:26
And if I go down and look at Niger, where there was such a terrible famine,
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Hvis jeg går ned og ser på Republikken Niger, hvor der har været en forfærdelig hungersnød
14:31
lastly, it's like this. The 20 percent poorest of Niger is out here,
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på det sidste, ser det sådan ud. De fattigste 20 procent i Niger er herude.
14:36
and the 20 percent richest of South Africa is there,
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og de rigeste 20 procent i Sydafrika er der,
14:39
and yet we tend to discuss on what solutions there should be in Africa.
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og alligevel hælder vi mod at diskutere hvilke løsninger der skal være i Afrika.
14:44
Everything in this world exists in Africa. And you can't
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Alt i denne verden findes i Afrika. Og man kan ikke
14:47
discuss universal access to HIV [medicine] for that quintile up here
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diskutere HIV-medicin til alle for den femtedel heroppe
14:51
with the same strategy as down here. The improvement of the world
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med samme strategi som hernede. Forbedringen af verden må
14:55
must be highly contextualized, and it's not relevant to have it
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gøres i den enkelte sammenhæng, og det er ikke relevant at have det
15:00
on regional level. We must be much more detailed.
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på regionsniveau. Vi må være meget mere detaljerede.
15:03
We find that students get very excited when they can use this.
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Vi har fundet at studererende bliver meget begejstrede når de kan bruge det her.
15:07
And even more policy makers and the corporate sectors would like to see
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Og endnu flere politiske beslutningstagere og forretningsfolk vil gerne se
15:12
how the world is changing. Now, why doesn't this take place?
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hvordan verden ændrer sig. Nuvel, hvorfor sker dette ikke?
15:16
Why are we not using the data we have? We have data in the United Nations,
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Hvorfor bruger vi ikke de data vi har? Vi har data i FN,
15:20
in the national statistical agencies
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i de enkelte landes statistiske afdelinger,
15:22
and in universities and other non-governmental organizations.
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og på universiteter og andre organisationer udenfor regeringsregi.
15:26
Because the data is hidden down in the databases.
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Fordi data er gemt dybt i databaserne.
15:28
And the public is there, and the Internet is there, but we have still not used it effectively.
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Og offentligheden er der, Internettet er der, men vi har stadig ikke brugt det effektivt.
15:33
All that information we saw changing in the world
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Al den information vi så forandre sig i verden
15:36
does not include publicly-funded statistics. There are some web pages
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indbefatter ikke offentligt finansieret statistik. Der er nogen websider
15:40
like this, you know, but they take some nourishment down from the databases,
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som denne, men de får nogen næring fra databaserne
15:46
but people put prices on them, stupid passwords and boring statistics.
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men folk sætter prisskilte på dem, dumme adgangskoder og kedelige statistikker.
15:51
(Laughter) (Applause)
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(Latter)(Klapsalve)
15:54
And this won't work. So what is needed? We have the databases.
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Og det vil ikke fungere. Så hvad er der brug for? Vi har databaserne.
15:58
It's not the new database you need. We have wonderful design tools,
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Det er ikke en ny database der er brug fro. Vi har fantastiske designværktøjer,
16:02
and more and more are added up here. So we started
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og der bliver tilføjet mere og mere heroppe. Så vi begyndte
16:05
a nonprofit venture which we called -- linking data to design --
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en almennyttig organisation som vi kaldte - sammenkædning af data med design -
16:10
we call it Gapminder, from the London underground, where they warn you,
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vi kaldte det "Gapminder", fra undergrundsbanen i London hvor de advarer en
16:13
"mind the gap." So we thought Gapminder was appropriate.
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"pas på gabet mellem tog og perron". Så vi tænkte at Gapminder var passende
16:16
And we started to write software which could link the data like this.
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Og vi begyndte at skrive programmer som kunne sammenkæde data således.
16:20
And it wasn't that difficult. It took some person years, and we have produced animations.
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Og det var ikke så svært igen. Det tog nogen mandeår, og vi har lavet bevægende fremstillinger.
16:26
You can take a data set and put it there.
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Man kan tage et datasæt og placere det der.
16:28
We are liberating U.N. data, some few U.N. organization.
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Vi frigør FN-data, nogen få FN-organisationer.
16:33
Some countries accept that their databases can go out on the world,
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Nogen lande accepterer at deres databaser kan komme ud i verden,
16:37
but what we really need is, of course, a search function.
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men hvad vi virkelig har brug for er, naturligvis, en søgefunktion.
16:40
A search function where we can copy the data up to a searchable format
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En søgefunktion hvor vi kan kopiere data op i et søgbart format
16:45
and get it out in the world. And what do we hear when we go around?
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og få det ud i verden. Og hvad hører vi når vi kommer rundt?
16:48
I've done anthropology on the main statistical units. Everyone says,
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Jeg har lavet antropologi på de statistiske hovedenheder. Alle siger,
16:53
"It's impossible. This can't be done. Our information is so peculiar
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"Det er umuligt. Det kan ikke gøres. Vores information er så særegen
16:57
in detail, so that cannot be searched as others can be searched.
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i detaljen, at den ikke kan søges i som andres kan blive søgt i.
17:00
We cannot give the data free to the students, free to the entrepreneurs of the world."
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Vi kan ikke give data frit til de studerende, frit til verdens entreprenører."
17:05
But this is what we would like to see, isn't it?
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Men dette er hvad vi gerne vil se, er det ikke?
17:08
The publicly-funded data is down here.
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De offentligt financierede data er hernede.
17:11
And we would like flowers to grow out on the Net.
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Og vil vil gerne se blomster vokse frem på Nettet.
17:14
And one of the crucial points is to make them searchable, and then people can use
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Og et af de afgørende punkter er at gøre dem søgbare, og så kan folk bruge
17:19
the different design tool to animate it there.
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de forskellige designværkøjer til at animere det der.
17:21
And I have a pretty good news for you. I have a good news that the present,
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Og jeg har en ret god nyhed til jer. Jeg har den gode nyhed at lederen
17:26
new Head of U.N. Statistics, he doesn't say it's impossible.
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af FN's statistikafdeling, siger ikke det er umuligt.
17:30
He only says, "We can't do it."
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Han siger bare "Vi kan ikke gøre det."
17:32
(Laughter)
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(Latter)
17:36
And that's a quite clever guy, huh?
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Og det er en ret klog fyr, ikke?
17:38
(Laughter)
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(Latter)
17:40
So we can see a lot happening in data in the coming years.
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Så vi kan se der sker en masse med data i de kommende år.
17:44
We will be able to look at income distributions in completely new ways.
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Vi vil være i stand til at se på indkomstfordelinger på helt nye måder.
17:48
This is the income distribution of China, 1970.
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Dette er indkomstfordelingen i Kina, 1970.
17:54
the income distribution of the United States, 1970.
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indkomstfordelingen for USA, 1970.
17:59
Almost no overlap. Almost no overlap. And what has happened?
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Næsten intet overlap. Næsten intet overlap. Og hvad er der sket?
18:03
What has happened is this: that China is growing, it's not so equal any longer,
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Hvad der er sket er følgende: at Kina vokser, det er ikke så lige længere,
18:08
and it's appearing here, overlooking the United States.
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og det dukker op her, med udsigt til USA.
18:12
Almost like a ghost, isn't it, huh?
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Næsten som et spøgelse, er det ikke, hva'?
18:14
(Laughter)
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(Latter)
18:16
It's pretty scary. But I think it's very important to have all this information.
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Det er ret skræmmende. Men jeg mener det er vigtigt at have al denne information.
18:26
We need really to see it. And instead of looking at this,
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Vi er virkelig nødt til at kunne se det. Og i stedet for at se på dette,
18:32
I would like to end up by showing the Internet users per 1,000.
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vil jeg gerne slutte med at vise antallet af internetbrugere pr 1000.
18:37
In this software, we access about 500 variables from all the countries quite easily.
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I dette program, har vi let adgang til omkring 500 variable fra alle landene.
18:42
It takes some time to change for this,
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Det tager nogen tid at ændre
18:46
but on the axises, you can quite easily get any variable you would like to have.
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men på akserne kan man ret nemt få en vilkårlig variabel man gerne vil have.
18:51
And the thing would be to get up the databases free,
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Og det vigtige vil være at få frigivet databaserne,
18:56
to get them searchable, and with a second click, to get them
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få dem gjort søgbare, og med et klik mere, få dem
18:59
into the graphic formats, where you can instantly understand them.
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på grafisk form, hvor man umiddelbart kan forstå dem.
19:04
Now, statisticians doesn't like it, because they say that this
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Nuvel, statistikere kan ikke lide det, fordi de siger at dette
19:07
will not show the reality; we have to have statistical, analytical methods.
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ikke vil vise virkeligheden - vi er nødt til at have statistiske, analytiske metoder.
19:16
But this is hypothesis-generating.
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Men denne måde genererer hypoteser.
19:19
I end now with the world. There, the Internet is coming.
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Jeg afslutter nu med verden. Der er det Internettet der kommer.
19:23
The number of Internet users are going up like this. This is the GDP per capita.
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Antallet af internetbrugere vokser således. Dette er bruttonationalindkomsten pr indbygger.
19:27
And it's a new technology coming in, but then amazingly, how well
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Og det er en ny teknologi der kommer ind, men på utroligste vis passer det
19:32
it fits to the economy of the countries. That's why the 100 dollar
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godt med landenes økonomi. Det er grunden til at 100 dollar
19:37
computer will be so important. But it's a nice tendency.
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computeren vil være så vigtig. Men det er en god tendens.
19:40
It's as if the world is flattening off, isn't it? These countries
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Det er ligesom at verden flader ud, ikke? Disse lande
19:43
are lifting more than the economy and will be very interesting
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rejser sig mere end økonomen og vil være særdeles interessant
19:46
to follow this over the year, as I would like you to be able to do
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at følge dette over året, som jeg gerne vil have at I skal kunne
19:50
with all the publicly funded data. Thank you very much.
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med alle de offentligt finansierede data. Mange tak.
19:53
(Applause)
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(Klapsalve)
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