TED2006
Hans Rosling: The best stats you've ever seen
Hans Rosling aonyesha takwimu bora kuliko zote ulizoziona
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Hujapata kuona takwimu zikielelezwa namna hii. Kwa ufundi na umahiri wa kuhadhiri, gwiji wa takwimu Hans Rosling anachambua mtazamo potofu uitwao "nchi zinazoendelea."
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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Takriban miaka 10 iliyopita, nilianza kazi ya kufundisha maendeleo ya ulimwengu
00:29
to Swedish undergraduate students. That was after having spent
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kwa wanafunzi wa Kiswidishi wa shahada ya kwanza. Hii ilikuwa baada ya
00:33
about 20 years together with African institutions studying hunger in Africa,
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takriban miaka 20 ya kufanya kazi pamoja na taasisi mbalimbali za Afrika nikitafiti kuhusu njaa
00:37
so I was sort of expected to know a little about the world.
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katika Afrika, kwa hiyo nilikuwa natarajiwa niwe najua zaidi kuhusu dunia.
00:41
And I started in our medical university, Karolinska Institute,
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Na nilianzia kwenye chuo chetu cha utabibu, Taasisi ya Karolinska,
00:46
an undergraduate course called Global Health. But when you get
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kozi ya shahada ya kwanza iliyoitwa Afya ya Ulimwengu. Lakini ukipata
00:50
that opportunity, you get a little nervous. I thought, these students
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fursa hiyo, unapata mshawasha kidogo. Nilifikiri wanafunzi hawa
00:53
coming to us actually have the highest grade you can get
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kuja kwetu ni lazima wana maksi za juu unazoweza kupata
00:56
in Swedish college systems -- so, I thought, maybe they know everything
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kwenye mfumo wa vyuo vya Sweden -- kwahiyo labda wanajua kila kitu
00:59
I'm going to teach them about. So I did a pre-test when they came.
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kuhusu nitakachowafundisha. Kwa hiyo niliwapa mtihani mara tu walipokuja.
01:03
And one of the questions from which I learned a lot was this one:
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Na moja wapo kati ya maswali ambayo nilijifunza mengi ni hili hapa:
01:06
"Which country has the highest child mortality of these five pairs?"
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"Ni nchi gani kati ya hizi tano ina kiwango kikubwa cha vifo vya watoto kati ya jozi hizi tano?"
01:10
And I put them together, so that in each pair of country,
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Na niliziweka pamoja, ili katika kila kundi la nchi,
01:14
one has twice the child mortality of the other. And this means that
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moja ina kiwango kikubwa cha vifo vya watoto zaidi ya nyingine. Na hii inamaanisha kwamba
01:19
it's much bigger a difference than the uncertainty of the data.
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kuna tofauti kubwa sana kuliko uhakika wa takwimu.
01:24
I won't put you at a test here, but it's Turkey,
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Sitawapa mtihani hapa, lakini ni Uturuki,
01:26
which is highest there, Poland, Russia, Pakistan and South Africa.
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ambayo ina kiwango kikubwa pale, Poland, Urusi, Pakistani na Afrika Kusini.
01:31
And these were the results of the Swedish students. I did it so I got
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Na haya ndiyo majibu ya wanafunzi wa Kiswidishi. Nilifanya hivyo na nilipata
01:34
the confidence interval, which is pretty narrow, and I got happy,
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kiwango cha imani, ambacho kilikuwa kidogo, na nilifurahi,
01:37
of course: a 1.8 right answer out of five possible. That means that
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kwa hakika: 1.8 ya jibu sahihi kati ya matano inawezekana. Hii ina maana kwamba
01:41
there was a place for a professor of international health --
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kulikuwa kuna nafasi ya Profesa wa afya ya ulimwengu --
01:44
(Laughter) and for my course.
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(Kicheko) na kwa kozi yangu.
01:46
But one late night, when I was compiling the report
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Lakini usiku mmoja, wakati nilipokuwa natayarisha ripoti
01:50
I really realized my discovery. I have shown
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Niligundua uvumbuzi wangu. Nimeonyesha
01:54
that Swedish top students know statistically significantly less
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kuwa wanafunzi Waswidishi wenye alama za juu wanajua kidogo sana kuhusu takwimu
01:59
about the world than the chimpanzees.
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za ulimwengu kuliko hata sokwe.
02:01
(Laughter)
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(Kicheko)
02:03
Because the chimpanzee would score half right if I gave them
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Kwasababu sokwe wangepata nusu iwapo ningewapa
02:07
two bananas with Sri Lanka and Turkey. They would be right half of the cases.
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ndizi mbili zenye Sri Lanka na Uturuki. Wangekuwa sahihi kwa nusu yake.
02:10
But the students are not there. The problem for me was not ignorance;
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Lakini wanafunzi hawapo huko. Tatizo langu halikuwa kutokujua kwao:
02:14
it was preconceived ideas.
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ilikuwa ni mawazo waliyojijengea.
02:17
I did also an unethical study of the professors of the Karolinska Institute
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Pia nilifanya utafiti kinyume na maadili kwa maprofesa wa taasisi ya Karolinska
02:21
(Laughter)
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(Kicheko)
02:22
-- that hands out the Nobel Prize in Medicine,
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-- ambao wanatoa tuzo ya Nobel katika utabibu,
02:24
and they are on par with the chimpanzee there.
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na wao wako sawa tu na sokwe.
02:26
(Laughter)
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(Kicheko)
02:29
This is where I realized that there was really a need to communicate,
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Hapa ndipo nilipogundua kwamba kuna haja ya kuwasiliana,
02:33
because the data of what's happening in the world
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kwasababu ya takwimu za kinachotokea duniani
02:36
and the child health of every country is very well aware.
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na afya ya mtoto katika kila nchi inajulikana.
02:39
We did this software which displays it like this: every bubble here is a country.
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Tulitengeneza hii programu ya kompyuta ambayo inayoonyesha kama hivi: kila kiputo hapa ni nchi.
02:44
This country over here is China. This is India.
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Hii nchi hapa ni China. Hii ni India.
02:50
The size of the bubble is the population, and on this axis here I put fertility rate.
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Ukubwa wa kiputo ni idadi ya watu, na katika mhimili huu nimeweka kiwango cha uzazi.
02:56
Because my students, what they said
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Kwasababu wanafunzi wangu, walichosema
02:59
when they looked upon the world, and I asked them,
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wakati walipoangalia dunia, na nilipowauliza,
03:01
"What do you really think about the world?"
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"Nini mnafikiri kuhusu dunia?"
03:03
Well, I first discovered that the textbook was Tintin, mainly.
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Naam, kwanza niligundua kuwa kitabu cha kiada kilikuwa Tintin, angalau.
03:07
(Laughter)
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(Kicheko)
03:08
And they said, "The world is still 'we' and 'them.'
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Na walisema, "Dunia bado ni 'sisi' na 'wao.'
03:11
And we is Western world and them is Third World."
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Na sisi ni dunia ya Magharibi na wao ni Dunia ya Tatu."
03:14
"And what do you mean with Western world?" I said.
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"Na una maana gani kwa kusema dunia ya magharibi?" Niliuliza.
03:17
"Well, that's long life and small family, and Third World is short life and large family."
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"Naam, haya ni maisha marefu na familia ndogo, na dunia ya tatu ni maisha mafupi na familia kubwa."
03:22
So this is what I could display here. I put fertility rate here: number of children per woman:
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Kwa hiyo hii ndio ninayoweza kuonyesha hapa. Niliweka kiwango cha uzazi hapa: idadi ya watoto kwa mwanamke,
03:28
one, two, three, four, up to about eight children per woman.
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moja, mbili, tatu, nne, mpaka watoto nane kwa mwanamke mmoja.
03:32
We have very good data since 1962 -- 1960 about -- on the size of families in all countries.
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Tuna takwimu nzuri sana toka mwaka 1962 -- 1960 kuhusu -- ukubwa wa familia katika nchi zote.
03:38
The error margin is narrow. Here I put life expectancy at birth,
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Kiwango cha makosa ni kidogo sana. Hapa naweka umri wa kuishi wakati wa kuzaliwa,
03:41
from 30 years in some countries up to about 70 years.
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kuanzia miaka 30 katika nchi nyingine mpaka karibu miaka 70.
03:45
And 1962, there was really a group of countries here
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Na mwaka 1962 kulikuwa na kundi kubwa la nchi hapa,
03:48
that was industrialized countries, and they had small families and long lives.
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ambazo zilikuwa nchi zenye viwanda, na walikuwa na familia ndogo na maisha marefu.
03:53
And these were the developing countries:
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Na hizi zilikuwa nchi zinazoendelea:
03:55
they had large families and they had relatively short lives.
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walikuwa na familia kubwa na walikuwa na maisha mafupi.
03:58
Now what has happened since 1962? We want to see the change.
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Sasa nini kimetokea toka mwaka 1962? Tunataka kuona mabadiliko.
04:02
Are the students right? Is it still two types of countries?
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Je wanafunzi wako sahihi? Bado ni aina mbili za nchi?
04:06
Or have these developing countries got smaller families and they live here?
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Au hizi nchi zilizoendelea zina familia ndogo na wanaishi hapa?
04:09
Or have they got longer lives and live up there?
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Au wana maisha marefu na wanaishi hapo juu?
04:11
Let's see. We stopped the world then. This is all U.N. statistics
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Hebu tuone. Tulisimamisha dunia wakati ule. Hizi zote ni takwimu za Umoja wa Mataifa
04:14
that have been available. Here we go. Can you see there?
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ambazo zinapatikana. Hebu tuone. Unaweza kuona kule?
04:17
It's China there, moving against better health there, improving there.
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Ni China kule, ikiendelea dhidi ya afya bora hapa, inaboreka kule.
04:20
All the green Latin American countries are moving towards smaller families.
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Nchi zote za kijani za Amerika ya Kusini zimeanza kuelekea kuwa na familia ndogo.
04:23
Your yellow ones here are the Arabic countries,
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Hizi za njano hapa ni nchi za Kiarabu,
04:26
and they get larger families, but they -- no, longer life, but not larger families.
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na wana familia kubwa, lakini wao -- hawana maisha marefu, lakini si familia kubwa.
04:30
The Africans are the green down here. They still remain here.
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Waafrika ni kijani hapa chini. Bado wamebaki hapa.
04:33
This is India. Indonesia's moving on pretty fast.
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Hii ni India. Indonesia inaenda kwa kasi sana.
04:36
(Laughter)
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(Kicheko)
04:37
And in the '80s here, you have Bangladesh still among the African countries there.
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Na miaka ya 80 hapa, kuna Bangladesh bado iko miongoni mwa nchi za Afrika kule.
04:40
But now, Bangladesh -- it's a miracle that happens in the '80s:
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Lakini sasa, Bangladesh -- ni miujiza iliyotokea miaka ya 80:
04:43
the imams start to promote family planning.
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Maimamu walianza kuhamasisha uzazi wa mpango.
04:46
They move up into that corner. And in '90s, we have the terrible HIV epidemic
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Walisogea juu kwenye ile kona. Na katika miaka ya 90, tulikuwa na janga la Ukimwi
04:51
that takes down the life expectancy of the African countries
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ambalo lilishusha umri wa kuishi wa nchi za Afrika
04:54
and all the rest of them move up into the corner,
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na nyingine zote zilipanda kwenye ile kona,
04:58
where we have long lives and small family, and we have a completely new world.
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ambako tuna maisha marefu na familia ndogo, na tuna ulimwengu mpya kabisa.
05:02
(Applause)
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(Makofi)
05:15
Let me make a comparison directly between the United States of America and Vietnam.
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Ngoja nifananishe kati ya Marekani na Vietnam.
05:20
1964: America had small families and long life;
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1964: Marekani ilikuwa na familia ndogo na maisha marefu;
05:25
Vietnam had large families and short lives. And this is what happens:
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Vietnam ilikuwa na familia kubwa na maisha mafupi. Na hiki ndicho kilichotokea:
05:29
the data during the war indicate that even with all the death,
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takwimu wakati wa vita zilionyesha kuwa pamoja na vifo vyote,
05:35
there was an improvement of life expectancy. By the end of the year,
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kulikuwa kuna mabadiliko katika umri wa kuishi. Mwisho wa mwaka,
05:38
the family planning started in Vietnam and they went for smaller families.
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uzazi wa mpango ulianza Vietnam na waliamua kuwa na familia ndogo.
05:41
And the United States up there is getting for longer life,
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Na Marekani pale juu wanakuwa na maisha marefu,
05:44
keeping family size. And in the '80s now,
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wanabaki na ukubwa wa familia. Na miaka ya 80 sasa,
05:47
they give up communist planning and they go for market economy,
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waliacha mpango wa kikomunisti na wakaingia kwenye uchumi wa soko huria,
05:50
and it moves faster even than social life. And today, we have
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na inaenda haraka hata zaidi ya maisha ya jamii. Na leo,
05:54
in Vietnam the same life expectancy and the same family size
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Vietnam ina umri wa kuishi na ukubwa wa familia sawa
05:59
here in Vietnam, 2003, as in United States, 1974, by the end of the war.
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hapa Vietnam, 2003, kama ilivyokuwa Marekani, 1974, mwishoni mwa vita.
06:06
I think we all -- if we don't look in the data --
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Nafikiri sote -- kama hatutaangalia vielelezo --
06:10
we underestimate the tremendous change in Asia, which was
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tutapuuza mabadiliko makubwa huko Asia, ambayo yalikuwa
06:14
in social change before we saw the economical change.
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mabadiliko ya kijamii kabla hatujaona mabadiliko ya kiuchumi.
06:18
Let's move over to another way here in which we could display
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Hebu tuendelee kwingine hapa ambako tunaweza kuonyesha
06:23
the distribution in the world of the income. This is the world distribution of income of people.
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mgawanyo wa kipato duniani. Hii ni mgao wa kipato cha watu.
06:30
One dollar, 10 dollars or 100 dollars per day.
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Dola moja, dola 10 au dola 100 kwa siku.
06:35
There's no gap between rich and poor any longer. This is a myth.
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Hakuna pengo tena kati ya matajiri na maskini. Hii ni hali ya kufikirika
06:39
There's a little hump here. But there are people all the way.
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Kuna kituta kidogo hapa. Lakini kuna watu kila sehemu.
06:44
And if we look where the income ends up -- the income --
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Na tukiangalia kipato kinapoishia -- kipato hicho --
06:48
this is 100 percent the world's annual income. And the richest 20 percent,
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hii ni asilimia 100 ya kipato cha dunia kwa mwaka. Na asilimia 20 ya matajiri wakubwa kabisa,
06:54
they take out of that about 74 percent. And the poorest 20 percent,
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wanachukua karibu asilimia 74. Na asilimia 20 ya masikini zaidi,
07:01
they take about two percent. And this shows that the concept
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wanachukua karibu asilimia mbili. Na hii inaonyesha kwamba dhana
07:06
of developing countries is extremely doubtful. We think about aid, like
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ya nchi zinazoendelea ni ya mashaka. Tunafikiria kuhusu misaada, kama
07:10
these people here giving aid to these people here. But in the middle,
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watu hawa wanatoa misaada kwa watu wale pale. Lakini katikati,
07:15
we have most the world population, and they have now 24 percent of the income.
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tuna idadi kubwa ya watu duniani, wenye asilimia 24 ya kipato.
07:19
We heard it in other forms. And who are these?
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Tuliyasikia haya kwa namna nyingine. Na hawa ni akina nani?
07:23
Where are the different countries? I can show you Africa.
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Nchi mbalimbali ziko wapi? Naweza kukuonyesha Afrika.
07:27
This is Africa. 10 percent the world population, most in poverty.
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Hii ni Afrika. Asilimia 10 ya idadi ya watu duniani, wengi wao wako kwenye umaskini.
07:32
This is OECD. The rich country. The country club of the U.N.
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Hii ni OECD. Nchi tajiri. Nchi za kundi la Umoja wa Mataifa.
07:37
And they are over here on this side. Quite an overlap between Africa and OECD.
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Na wapo huku upande huu. Kuna mwingiliano kati ya Afrika na OECD
07:42
And this is Latin America. It has everything on this Earth,
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Hii hapa ni Amerika Kusini. Ni kila kitu katika dunia hii,
07:45
from the poorest to the richest, in Latin America.
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kuanzia maskini zaidi mpaka matajiri, huko Amerika Kusini.
07:48
And on top of that, we can put East Europe, we can put East Asia,
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Zaidi ya hayo, tunaweza kuiweka Ulaya Mashariki, Asia Mashariki,
07:53
and we put South Asia. And how did it look like if we go back in time,
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na Asia Kusini. Na ingekuwaje iwapo tungerejea nyuma,
07:58
to about 1970? Then there was more of a hump.
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mpaka mwaka 1970? Wakati huo kulikuwa na nundu kubwa zaidi.
08:03
And we have most who lived in absolute poverty were Asians.
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Na waliokuwa kwenye umaskini mkubwa zaidi ni Waasia.
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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Tatizo la dunia lilikuwa umaskini huko Asia. Na sasa kama nitaicha dunia isogee mbele,
08:14
you will see that while population increase, there are
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utaona kwamba wakati idadi ya watu inaongezeka, kuna
08:17
hundreds of millions in Asia getting out of poverty and some others
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mamia ya milioni huko Asia wanajikwamua kutoka umaskini na wengine
08:20
getting into poverty, and this is the pattern we have today.
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wanaingia katika umaskini, na hii ndiyo hali tuliyonayo leo hii.
08:23
And the best projection from the World Bank is that this will happen,
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Na makadirio mazuri kutoka Benki ya Dunia, ni kwamba haya yatatokea,
08:27
and we will not have a divided world. We'll have most people in the middle.
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na hatutakuwa na dunia iliyogawanyika. Tutakuwa na watu wengi katikati.
08:31
Of course it's a logarithmic scale here,
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Naam, hiki ni kipimo cha logarithm,
08:33
but our concept of economy is growth with percent. We look upon it
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lakini dhana yetu ya uchumi ni kukua kwa asilimia. Tunaiangalia
08:38
as a possibility of percentile increase. If I change this, and I take
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kama ni uwezekano wa kuongezeka kwa asilimia. Kama nitabadili hii, na kuchukua
08:44
GDP per capita instead of family income, and I turn these
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GDP kwa taifa badala ya kipato cha familia, na ninabadili hivi
08:48
individual data into regional data of gross domestic product,
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takwimu moja moja kwenye takwimu za kanda za GDP,
08:54
and I take the regions down here, the size of the bubble is still the population.
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na ninazileta kanda hapa chini, ukubwa wa kiputo bado ni idadi ya watu.
08:58
And you have the OECD there, and you have sub-Saharan Africa there,
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Na una OECD pale, na una Afrika Kusini mwa Jangwa la Sahara hapo,
09:01
and we take off the Arab states there,
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na tunatoa nchi za Kiarabu pale,
09:04
coming both from Africa and from Asia, and we put them separately,
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zinatoka Afrika na Asia, na tunaziweka tofauti,
09:08
and we can expand this axis, and I can give it a new dimension here,
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na tunaweza kuukuza muhimili huu, na ninaipa vipimo vipya hapa,
09:13
by adding the social values there, child survival.
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kwa kuongeza thamani ya ustawi wa jamii pale, uwezekano wa kusalimika mtoto.
09:16
Now I have money on that axis, and I have the possibility of children to survive there.
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Sasa nimeweka pesa pale kwenye mhimili, na nina uwezekano wa watoto kusalimika pale.
09:21
In some countries, 99.7 percent of children survive to five years of age;
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Katika baadhi ya nchi, asilimia 99.7 ya watoto wanaishi mpaka miaka mitano;
09:25
others, only 70. And here it seems there is a gap
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wengine, mika 70 tu. Na hapa inaonekana kuna pengo
09:29
between OECD, Latin America, East Europe, East Asia,
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kati ya OECD, Amerika Kusini, Ulaya Mashariki, Asia Mashariki,
09:33
Arab states, South Asia and sub-Saharan Africa.
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Nchi za kiarabu, Asia Kusini na Afrika Kusini mwa jangwa la Sahara.
09:37
The linearity is very strong between child survival and money.
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Uwiano baina ya maisha ya watoto na pesa ni wa karibu sana.
09:42
But let me split sub-Saharan Africa. Health is there and better health is up there.
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Lakini hebu niigawanye Afrika Kusini mwa jangwa la Sahara. Afya iko hapa na afya bora iko kule.
09:50
I can go here and I can split sub-Saharan Africa into its countries.
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Ninaweza kwenda hapa na kuigawa Afrika Kusini mwa jangwa la Sahara katika nchi tofauti.
09:55
And when it burst, the size of its country bubble is the size of the population.
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Na ikipasuka, ukubwa wa puto la nchi ni sawa na idadi ya watu.
10:00
Sierra Leone down there. Mauritius is up there. Mauritius was the first country
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Hapa chini ni Siera Leone. Mauritus iko pale juu. Mauritius ilikuwa nchi ya kwanza
10:04
to get away with trade barriers, and they could sell their sugar --
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kuondokana na vikwazo vya biashara, na waliweza kuuza sukari yao.
10:08
they could sell their textiles -- on equal terms as the people in Europe and North America.
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Waliweza kuuza nguo kwa taratibu sawa na watu wa Ulaya na Amerika Kaskazini.
10:13
There's a huge difference between Africa. And Ghana is here in the middle.
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Kuna tofauti kubwa sana ndani ya Afrika. Na Ghana iko hapa katikati.
10:17
In Sierra Leone, humanitarian aid.
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Huko Siera Leone, misaada ya kibinadamu.
10:20
Here in Uganda, development aid. Here, time to invest; there,
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Hapa Uganda, misaada ya maendeleo. Hapa, muda wa kuwekeza, kule,
10:25
you can go for a holiday. It's a tremendous variation
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unaweza kwenda kwa mapumziko. Ni tofauti kubwa sana
10:28
within Africa which we rarely often make -- that it's equal everything.
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katika Afrika ambayo mara nyingi tunaitambua -- kuwa iko sawa kwa kila kitu.
10:33
I can split South Asia here. India's the big bubble in the middle.
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Naweza kuigawa Asia Kusini hapa. India ni kiputo kikubwa cha katikati.
10:37
But a huge difference between Afghanistan and Sri Lanka.
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Lakini kuna tofauti kubwa kati ya Afghanistani na Sri Lanka.
10:41
I can split Arab states. How are they? Same climate, same culture,
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Naweza kugawa nchi za Kiarabu. Wakoje? Hali ya hewa sawa, utamaduni sawa,
10:45
same religion -- huge difference. Even between neighbors.
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dini sawa. Tofauti kubwa. Hata kati ya majirani.
10:49
Yemen, civil war. United Arab Emirate, money which was quite equally and well used.
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Yemen, vita vya wao kwa wao. Umoja wa Falme za Kiarabu, pesa ya kutosha ni sawa na ikatumiwa vizuri.
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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Sio kama tunavyofikiria. Na hii inajumuisha watoto wa raia wa kigeni ambao wapo nchini.
11:01
Data is often better than you think. Many people say data is bad.
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Takwimu ni bora zaidi ya unavyofikiria. Watu wengi wanasema takwimu ni mbaya.
11:06
There is an uncertainty margin, but we can see the difference here:
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Kuna nafasi ya mashaka, lakini tunaweza kuona tofauti hapa:
11:08
Cambodia, Singapore. The differences are much bigger
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Cambodia, Singapore. Tofauti ni kubwa
11:11
than the weakness of the data. East Europe:
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zaidi ya udhaifu wa takwimu. Ulaya Mashariki:
11:14
Soviet economy for a long time, but they come out after 10 years
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Uchumi wa Kisovieti muda mrefu, lakini waliweza kujikwamua baada ya miaka kumi
11:20
very, very differently. And there is Latin America.
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kwa utofauti sana. Na kuna Amerika Kusini.
11:23
Today, we don't have to go to Cuba to find a healthy country in Latin America.
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Leo, hatuna haja ya kwenda Cuba kutafuta nchi yenye afya bora Amerika Kusini.
11:27
Chile will have a lower child mortality than Cuba within some few years from now.
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Chile itakuwa na idadi ndogo ya vifo vya watoto zaidi ya Cuba miaka michache ijayo kuanzia sasa.
11:32
And here we have high-income countries in the OECD.
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Na hapa tuna nchi zenye kipato kikubwa katika OECD.
11:35
And we get the whole pattern here of the world,
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Na hapa tunapata mwelekeo wote wa ulimwengu,
11:39
which is more or less like this. And if we look at it,
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ambao ni karibu ni sawa na hali hii. Na tukiiangalia,
11:44
how it looks -- the world, in 1960, it starts to move. 1960.
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inavyoonekana -- dunia, mwaka 1960, inaanza kusogea. 1960.
11:50
This is Mao Tse-tung. He brought health to China. And then he died.
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Huyu ni Mao Tse-tung. Alileta afya China. Halafu akafariki.
11:53
And then Deng Xiaoping came and brought money to China, and brought them into the mainstream again.
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Halafu akaja Deng Xiaoping na akaleta pesa kwa China, na kuwapandisha chati tena.
11:58
And we have seen how countries move in different directions like this,
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Na tumeona jinsi nchi zinavyosogea katika mwenendo tofauti kama hivi,
12:02
so it's sort of difficult to get
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kwa hiyo inakuwa vigumu kupata
12:06
an example country which shows the pattern of the world.
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mfano wa nchi ambayo inaonyesha mwelekeo wa ulimwengu.
12:11
But I would like to bring you back to about here at 1960.
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Ningependa kuwarudisha nyuma mpaka karibu na mwaka 1960.
12:17
I would like to compare South Korea, which is this one, with Brazil,
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Ningependa kulinganisha Korea Kusini ambayo ni hii hapa, na Brazil,
12:27
which is this one. The label went away for me here. And I would like to compare Uganda,
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ambayo ni hii hapa. Kibandiko kimetoka hapa. Na ningependa kufananisha Uganda,
12:32
which is there. And I can run it forward, like this.
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ambayo iko kule. Na ninaweza kuileta mbele, kama hivi.
12:37
And you can see how South Korea is making a very, very fast advancement,
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Na unaweza kuona jinsi Korea Kusini wanavyosonga mbele kwa kasi sana,
12:46
whereas Brazil is much slower.
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wakati Brazil inakwenda polepole.
12:49
And if we move back again, here, and we put on trails on them, like this,
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Na kama tukirudi nyuma tena, hapa, na tukiweka alama juu yao, kama hivi,
12:55
you can see again that the speed of development
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unaweza kuona tena kuwa kasi ya maendeleo
12:59
is very, very different, and the countries are moving more or less
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ni tofauti sana, na nchi zinasogea sana au kidogo
13:05
in the same rate as money and health, but it seems you can move
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katika kiwango sawa na kukua kwa pesa na afya, lakini inaonekana unaweza kusogea
13:09
much faster if you are healthy first than if you are wealthy first.
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haraka sana iwapo una afya kwanza kuliko ukiwa na pesa kwanza.
13:14
And to show that, you can put on the way of United Arab Emirate.
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na kuonyesha hii, unaweza kuweka Umoja wa Falme za Kiarabu.
13:18
They came from here, a mineral country. They cached all the oil;
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Walitokea hapa, nchi ya madini. Walivuna mafuta yote,
13:21
they got all the money; but health cannot be bought at the supermarket.
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walipata pesa zote, lakini afya haiwezi kununuliwa dukani.
13:25
You have to invest in health. You have to get kids into schooling.
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Inabidi uwekeze kwenye afya. Inabidi uwapeleke watoto shule.
13:29
You have to train health staff. You have to educate the population.
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Inabidi kuwafunza wafanyakazi wa afya. Inabidi kuwaelimisha watu.
13:32
And Sheikh Sayed did that in a fairly good way.
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Na Sheikh Sayed alifanya hivyo kwa namna nzuri.
13:35
In spite of falling oil prices, he brought this country up here.
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Pamoja na kuanguka kwa bei ya mafuta, aliipandisha nchi yake hapa juu.
13:39
So we've got a much more mainstream appearance of the world,
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Kwa hiyo tumepata muelekeo wa ulimwengu,
13:43
where all countries tend to use their money
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ambapo nchi zote zinatabia ya kutumia pesa zao
13:45
better than they used in the past. Now, this is, more or less,
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vizuri zaidi ya walivyokuwa wakitumia huko nyuma. Naam, hivi ndivyo, zaidi au pungufu kidogo,
13:50
if you look at the average data of the countries -- they are like this.
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ukiangalia wastani wa takwimu za nchi. Ziko kama hivi.
13:57
Now that's dangerous, to use average data, because there is such a lot
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Sasa hii ni hatari, kutumia wastani wa takwimu, kwasababu kuna
14:02
of difference within countries. So if I go and look here, we can see
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tofauti kubwa kati ya nchi. Kwa hiyo nikienda kuangalia hapa, tunaona
14:08
that Uganda today is where South Korea was 1960. If I split Uganda,
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kuwa Uganda ya leo ni mahali ambapo Korea ya Kusnini ilikuwa mwaka 1960. Na kama nikiigawa Uganda,
14:14
there's quite a difference within Uganda. These are the quintiles of Uganda.
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kuna tofauti ndani ya Uganda. Hii ni moja ya tano ya takwimu ndani ya Uganda.
14:19
The richest 20 percent of Ugandans are there.
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Asilimia 20 ya matajiri zaidi wa Uganda wako pale.
14:22
The poorest are down there. If I split South Africa, it's like this.
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Masikini zaidi wako hapa chini. Iwapo nikiigawa Afrika Kusini, iko kama hivi.
14:26
And if I go down and look at Niger, where there was such a terrible famine,
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Na iwapo nikiangalia Niger, ambako kulikuwa na ukame mbaya sana,
14:31
lastly, it's like this. The 20 percent poorest of Niger is out here,
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mwishoni, iko kama hivi. Asilimia 20 ya masikini zaidi huko Niger wako hapa,
14:36
and the 20 percent richest of South Africa is there,
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na asilimia 20 ya matajiri zaidi wa Afrika Kusini wako kule,
14:39
and yet we tend to discuss on what solutions there should be in Africa.
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na bado tunatabia ya kuzungumzia kuhusu utatuzi upi unafaa kwa matatizo ya Afrika.
14:44
Everything in this world exists in Africa. And you can't
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Kila kitu kilichopo hapa duniani kinapatikana Afrika. Na hamuwezi
14:47
discuss universal access to HIV [medicine] for that quintile up here
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kuongelea upatikanaji wa dawa za VVU [madawa] kwa moja ya tano hapa juu
14:51
with the same strategy as down here. The improvement of the world
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kwa mkakati sawa kama hapa chini. Maendeleo ya ulimwengu
14:55
must be highly contextualized, and it's not relevant to have it
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ni lazima yawekwe kwa makundi tofauti, na si lazima kuwa nayo
15:00
on regional level. We must be much more detailed.
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katika ngazi ya kanda. Ni lazima tuingie ndani zaidi.
15:03
We find that students get very excited when they can use this.
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Tumetambua kuwa wanafunzi wanapatwa na mshawasha wakiweza kutumia hii.
15:07
And even more policy makers and the corporate sectors would like to see
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Na wapanga sera na sekta binafsi zingependa kuona
15:12
how the world is changing. Now, why doesn't this take place?
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namna gani dunia inabadilika. Sasa, kwanini hii haitokei?
15:16
Why are we not using the data we have? We have data in the United Nations,
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Kwanini hatutumii takwimu tulizonazo? Tuna takwimu katika Umoja wa Mataifa,
15:20
in the national statistical agencies
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na katika taasisi za takwimu za nchi
15:22
and in universities and other non-governmental organizations.
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na katika vyuo vikuu na mashirika yasiyo ya kiserikali.
15:26
Because the data is hidden down in the databases.
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Kwasababu takwimu zimefichwa kwenye masijala.
15:28
And the public is there, and the Internet is there, but we have still not used it effectively.
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Na umma uko pale, na mtandao wa Intaneti uko, lakini bado hatujautumia ipasavyo.
15:33
All that information we saw changing in the world
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Taarifa zote tunazoziona zikibadilika duniani
15:36
does not include publicly-funded statistics. There are some web pages
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hazihusishi takwimu zinazogharimiwa na umma. Kuna baadhi ya kurasa za tovuti
15:40
like this, you know, but they take some nourishment down from the databases,
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mfano hii, kama ujuavyo, lakini zinachukua kutoka kwenye masijala ya takwimu,
15:46
but people put prices on them, stupid passwords and boring statistics.
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lakini watu wanaziwekea bei, funguo za siri na takwimu za kuchosha.
15:51
(Laughter) (Applause)
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(Kicheko). (Makofi).
15:54
And this won't work. So what is needed? We have the databases.
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Na hii haitatusaidia. Sasa nini kinatakiwa? Tuna masijala za takwimu.
15:58
It's not the new database you need. We have wonderful design tools,
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Si masijala mpya ya takwimu unayoihitaji. Tuna vifaa vizuri vya ubunifu,
16:02
and more and more are added up here. So we started
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na vingi vinaongezewa hapa. Kwa hiyo tulianzisha
16:05
a nonprofit venture which we called -- linking data to design --
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shirika lisilo la kibiashara ambalo tukaliita -- kuunganisha takwimu kwa ubunifu --
16:10
we call it Gapminder, from the London underground, where they warn you,
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tunaiita Gapminder, kutoka London chini ya ardhi, ambako wanakuonya,
16:13
"mind the gap." So we thought Gapminder was appropriate.
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"angalia upenyo" Kwa hiyo tulifikiri Gapminder ilikuwa ni sahihi.
16:16
And we started to write software which could link the data like this.
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Na tulianza kuandika programu ya kompyuta ambayo ingeweza kuunganisha takwimu kama hivi.
16:20
And it wasn't that difficult. It took some person years, and we have produced animations.
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Na haikuwa vigumu sana. Iliwachukua watu wengine miaka kadhaa, na tumetengeneza vielelezo.
16:26
You can take a data set and put it there.
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Unaweza kuchukua seti ya takwimu na kuiweka hapa.
16:28
We are liberating U.N. data, some few U.N. organization.
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Tunakomboa takwimu za Umoja wa Mataifa, mashirika machache ya Umoja wa Mataifa.
16:33
Some countries accept that their databases can go out on the world,
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Nchi nyingine zinakubali takwimu zao ziwe wazi duniani,
16:37
but what we really need is, of course, a search function.
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lakini tunachohitaji zaidi ni, kwa hakika, namna ya kuzichambua.
16:40
A search function where we can copy the data up to a searchable format
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Programu ya kutafuta ambayo itakuwezesha kunakili takwimu katika muundo wa kutafutika
16:45
and get it out in the world. And what do we hear when we go around?
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na kuiweka wazi duniani. Na nini tunasikia tuzungukapo?
16:48
I've done anthropology on the main statistical units. Everyone says,
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Nimefanya anthopolojia katika sehemu kubwa za takwimu. Kila mtu anasema,
16:53
"It's impossible. This can't be done. Our information is so peculiar
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"Haiwezekani. Hii haiwezi kufanyika. Taarifa zetu ni za ovyoovyo
16:57
in detail, so that cannot be searched as others can be searched.
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kwa ndani, na kwahiyo haziwezi kupangiliwa zitafutike kama nyingine zinavyoweza kutafutwa.
17:00
We cannot give the data free to the students, free to the entrepreneurs of the world."
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Hatuwezi kutoa takwimu bure kwa wanafunzi, bure kwa wajasiliamali wa dunia."
17:05
But this is what we would like to see, isn't it?
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Lakini hivi ndivyo tungependa tuone, au sio?
17:08
The publicly-funded data is down here.
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Takwimu zilizogharimiwa na umma zipo hapa chini.
17:11
And we would like flowers to grow out on the Net.
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Na tungependa maua yaote nje kwenye mtandao.
17:14
And one of the crucial points is to make them searchable, and then people can use
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Na jambo la muhimu zaidi ni kuzipangilia ili ziweze kutafutika, na watu waweze kuzitumia
17:19
the different design tool to animate it there.
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vifaa tofauti vya ubunifu kueleleza pale.
17:21
And I have a pretty good news for you. I have a good news that the present,
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Nina habari nzuri kwenu. Nina habari nzuri kwamba,
17:26
new Head of U.N. Statistics, he doesn't say it's impossible.
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Mkuu wa Kitengo cha Takwimu cha Umoja wa Mataifa, hasemi haiwezekani.
17:30
He only says, "We can't do it."
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Anasema, "Hatuwezi kufanya."
17:32
(Laughter)
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(Kicheko)
17:36
And that's a quite clever guy, huh?
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Huyu ni mtu mwenye akili, eeeh?
17:38
(Laughter)
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(Kicheko)
17:40
So we can see a lot happening in data in the coming years.
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Kwa hiyo tunaona mambo mengi yakitokea kwenye takwimu katika miaka ijayo.
17:44
We will be able to look at income distributions in completely new ways.
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Tutaweza kuangalia mgawanyo wa kipato katika namna mpya kabisa.
17:48
This is the income distribution of China, 1970.
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Huu ni mgao wa kipato huko China, 1970,
17:54
the income distribution of the United States, 1970.
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mgao wa kipato wa Marekani, 1970.
17:59
Almost no overlap. Almost no overlap. And what has happened?
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Karibu hakuna mwingiliano, karibu hakuna mwingiliano. Na nini kimetokea?
18:03
What has happened is this: that China is growing, it's not so equal any longer,
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Kilichotokea ni hiki: China inakua, haiko sawa tena,
18:08
and it's appearing here, overlooking the United States.
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na inatokea hapa, ikiingalia Marekani.
18:12
Almost like a ghost, isn't it, huh?
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Kama vile mzuka, au sio, eeeh?
18:14
(Laughter)
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(Kicheko)
18:16
It's pretty scary. But I think it's very important to have all this information.
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Inatisha. Lakini nadhani ni muhimu sana kuwa na taarifa hizi.
18:26
We need really to see it. And instead of looking at this,
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Tunahitaji sana kuziona. Badala ya kuangalia hii,
18:32
I would like to end up by showing the Internet users per 1,000.
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ningependa kumalizia kwa kuwaonyesha watumiaji wa mtandao kwa kila 1,000.
18:37
In this software, we access about 500 variables from all the countries quite easily.
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Katika programu hii ya kompyuta, tunaweza kupata karibu alama 500 kutoka katika nchi zote kwa urahisi.
18:42
It takes some time to change for this,
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Inachukua muda kubadilika kwa hii,
18:46
but on the axises, you can quite easily get any variable you would like to have.
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lakini katika mihimili, unaweza kupata alama yeyote utakayopenda kupata.
18:51
And the thing would be to get up the databases free,
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Na kitu kizuri itakuwa ni kuziweka masijala za takwimu bure,
18:56
to get them searchable, and with a second click, to get them
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kuweza kuzifanya ziweze kutafutika, na kuzipata kwa kubonyeza kwa nukta moja
18:59
into the graphic formats, where you can instantly understand them.
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kwenye mfumo wa michoro majira ya nukta, ambapo utazielewa kwa urahisi.
19:04
Now, statisticians doesn't like it, because they say that this
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Naam, wanatakwimu hawazipendi, kwasababu wanasema kuwa hii
19:07
will not show the reality; we have to have statistical, analytical methods.
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haitaonyesha hali halisi; inabidi tuwe na mbinu za kuchambua takwimu.
19:16
But this is hypothesis-generating.
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Lakini hii inajenga nadharia.
19:19
I end now with the world. There, the Internet is coming.
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Ninamalizia sasa na dunia. Pale, mtandao wa intaneti unakuja.
19:23
The number of Internet users are going up like this. This is the GDP per capita.
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Idadi ya wanaotumia mtandao inaongezeka kama hivi. Hii ni GDP per capita
19:27
And it's a new technology coming in, but then amazingly, how well
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Na ni teknolojia mpya inayokuja, lakini cha kushangaza, ni namna ambavyo
19:32
it fits to the economy of the countries. That's why the 100 dollar
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inashabihiana na hali ya uchumi wa nchi. Ndio maana
19:37
computer will be so important. But it's a nice tendency.
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kompyuta ya dola 100 itakuwa ya muhimu sana. Lakini ni muelekeo mzuri.
19:40
It's as if the world is flattening off, isn't it? These countries
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Ni kama vile dunia inakuwa bapa. Au sio? Nchi hizi
19:43
are lifting more than the economy and will be very interesting
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zinanyanyuka zaidi ya uchumi na itakuwa ni ya kufurahisha
19:46
to follow this over the year, as I would like you to be able to do
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kufuatilia hii kwa miaka ijayo, na kama ambavyo ningependa muweze kufanya
19:50
with all the publicly funded data. Thank you very much.
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kwa kutumia takwimu zilizogharamiwa na umma. Asanteni sana.
19:53
(Applause)
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(Makofi)
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