TED2006
Hans Rosling: The best stats you've ever seen
Hans Rosling pokazuje najbolje statističke podatke koje ste ikad vidjeli
Filmed:
Readability: 3.6
14,386,844 views
Nikada niste vidjeli ovako predstavljene podatke. Sa dramatičnošću i žustrinom sportskog spikera, statistički guru Hans Rosling oduzima ugled mitovima o takozvanom ''svijetu u razvoju''.
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
0
0
4000
Prije nekih deset godina, uzeo sam si za zadatak da podučavam globalnom razvoju
00:29
to Swedish undergraduate students. That was after having spent
1
4000
4000
studente dodiplomskog studija u Švedskoj, i to nakon što sam proveo
00:33
about 20 years together with African institutions studying hunger in Africa,
2
8000
4000
oko dvadeset godina zajedno sa afričkim institucijama izučavajući problem gladi u Africi,
00:37
so I was sort of expected to know a little about the world.
3
12000
4000
tako da se od mene nekako očekuje da znam ponešto o svijetu.
00:41
And I started in our medical university, Karolinska Institute,
4
16000
5000
I počeo sam sa predavanjima, na našem Medicinskom fakultetu pri Karolinska Institutu, prvo
00:46
an undergraduate course called Global Health. But when you get
5
21000
4000
kursom za dodiplomce, nazvanim Globalno Zdravlje. Ali kada vam se
00:50
that opportunity, you get a little nervous. I thought, these students
6
25000
3000
pruži takva prilika, malo se unervozite. Pomislih, ovi studenti
00:53
coming to us actually have the highest grade you can get
7
28000
3000
koji nam pristižu ustvari imaju najveće ocjene koje se mogu dobiti
00:56
in Swedish college systems -- so, I thought, maybe they know everything
8
31000
3000
u švedskom sistemu višeg obrazovanja--tako da možda znaju sve
00:59
I'm going to teach them about. So I did a pre-test when they came.
9
34000
4000
čemu ću ih podučavati. Tako da sam uradio blic kviz prije no što su došli.
01:03
And one of the questions from which I learned a lot was this one:
10
38000
3000
I jedno od pitanja iz kojeg sam ponajviše naučio jeste ovo:
01:06
"Which country has the highest child mortality of these five pairs?"
11
41000
4000
''Koja zemlja ima najvišu stopu smrtnosti djece od ovih pet parova?''
01:10
And I put them together, so that in each pair of country,
12
45000
4000
A parove sam sastavio tako da u svakom paru zemalja,
01:14
one has twice the child mortality of the other. And this means that
13
49000
5000
jedna od njih ima dvostruko veću stopu smrtnosti djece nego druga zemlja što znači da
01:19
it's much bigger a difference than the uncertainty of the data.
14
54000
5000
je mnogo veća razlika nego neizvjesnost podataka.
01:24
I won't put you at a test here, but it's Turkey,
15
59000
2000
Vas neću testirati, ali to je Turska
01:26
which is highest there, Poland, Russia, Pakistan and South Africa.
16
61000
5000
koja ima najvišu stopu, Poljska, Rusija, Pakistan i Južnoafrička Republika.
01:31
And these were the results of the Swedish students. I did it so I got
17
66000
3000
I evo koje rezultate sam dobio od švedskih studenata. Rezultate sam uradio tako
01:34
the confidence interval, which is pretty narrow, and I got happy,
18
69000
3000
da sam dobio interval pouzdanosti, koji je poprilično ograničen, što je mene naravno
01:37
of course: a 1.8 right answer out of five possible. That means that
19
72000
4000
usrećilo: 1.8 tačnih odgovora od ukupnih pet. To znači da
01:41
there was a place for a professor of international health --
20
76000
3000
je tu bilo mjesta za rad za jednog profesora internacionalnog zdravlja--
01:44
(Laughter) and for my course.
21
79000
2000
(smijeh) i mjesta za moju obuku.
01:46
But one late night, when I was compiling the report
22
81000
4000
Ali jedne kasne večeri, dok sam sastavljao izvještaj
01:50
I really realized my discovery. I have shown
23
85000
4000
ustvari sam shvatio moje otkriće. Pokazao sam
01:54
that Swedish top students know statistically significantly less
24
89000
5000
da švedski vrhunski studenti prema statističkim podacima znaju značajno manje
01:59
about the world than the chimpanzees.
25
94000
2000
o svijetu nego čimpanze.
02:01
(Laughter)
26
96000
2000
Smijeh
02:03
Because the chimpanzee would score half right if I gave them
27
98000
4000
jer bi čimpanze dale pola tačnih odgovora da sam im dao
02:07
two bananas with Sri Lanka and Turkey. They would be right half of the cases.
28
102000
3000
dvije banane koje predstavljaju Šri Lanku i Tursku. Bili bi u pravu u barem pola slučajeva.
02:10
But the students are not there. The problem for me was not ignorance;
29
105000
4000
Ali za razliku od njih, studenti ne bi. Za mene problem ne predstavlja neznanje,
02:14
it was preconceived ideas.
30
109000
3000
nego već smišljene ideje.
02:17
I did also an unethical study of the professors of the Karolinska Institute
31
112000
4000
Takođe sam uradio isto, ali ne tako etično istraživanje sa profesorima na Karolinska Institutu
02:21
(Laughter)
32
116000
1000
Smijeh
02:22
-- that hands out the Nobel Prize in Medicine,
33
117000
2000
-- koji dodjeljuju Nobelovu nagradu za medicinska postignuća,
02:24
and they are on par with the chimpanzee there.
34
119000
2000
i oni su imali neriješen rezultat sa čimpanzama.
02:26
(Laughter)
35
121000
3000
Smijeh
02:29
This is where I realized that there was really a need to communicate,
36
124000
4000
Tada sam shvatio da stvarno postoji potreba za komunikacijom
02:33
because the data of what's happening in the world
37
128000
3000
jer podaci o tome šta se događa u svijetu
02:36
and the child health of every country is very well aware.
38
131000
3000
i podaci o zdravlju djece u svakoj pojedinoj zemlji su opštepoznati.
02:39
We did this software which displays it like this: every bubble here is a country.
39
134000
5000
Napravili smo ovaj program koji to predočava ovako: svaki krug predstavlja zemlju.
02:44
This country over here is China. This is India.
40
139000
6000
Ova zemlja ovdje je Kina. Ovo je Indija.
02:50
The size of the bubble is the population, and on this axis here I put fertility rate.
41
145000
6000
Veličina kruga predstavlja populaciju,i na ovu osu ovdje sam stavio stopu plodnosti.
02:56
Because my students, what they said
42
151000
3000
Jer studenti, kako oni kažu
02:59
when they looked upon the world, and I asked them,
43
154000
2000
kad gledaju na svijet, i kada sam ih ja upitao:
03:01
"What do you really think about the world?"
44
156000
2000
''Šta stvarno mislite o svijetu?''
03:03
Well, I first discovered that the textbook was Tintin, mainly.
45
158000
4000
Prvo sam otkrio da je udžbenik ustvari Tintin strip.
03:07
(Laughter)
46
162000
1000
Smijeh
03:08
And they said, "The world is still 'we' and 'them.'
47
163000
3000
Pa su oni odgovorili: ''Svijet je i dalje 'mi' i 'oni',
03:11
And we is Western world and them is Third World."
48
166000
3000
pod 'mi' misle na Zapadni svijet a 'oni' su Zemlje trećeg svijeta.
03:14
"And what do you mean with Western world?" I said.
49
169000
3000
''A šta mislite kad kažete zapadni svijet?'', rekoh.
03:17
"Well, that's long life and small family, and Third World is short life and large family."
50
172000
5000
''Pa, to je dug život i mala porodica, a Zemlje trećeg svijeta imaju kratke živote i velike porodice.''
03:22
So this is what I could display here. I put fertility rate here: number of children per woman:
51
177000
6000
I evo šta sam ja ovdje prikazao. Ovdje sam stavio stopu plodnosti: broj djece po ženi,
03:28
one, two, three, four, up to about eight children per woman.
52
183000
4000
jedno, dvoje,troje,četvero, pa sve do osmero djece po ženi.
03:32
We have very good data since 1962 -- 1960 about -- on the size of families in all countries.
53
187000
6000
Imamo vrlo dobre podatke od 1962., pardon, 1960-te, o veličini porodica u svim zemljama.
03:38
The error margin is narrow. Here I put life expectancy at birth,
54
193000
3000
Mogućnost za pogrešku je poprilično sužena. Ovdje sam stavio prosječni životni vijek pri rođenju,
03:41
from 30 years in some countries up to about 70 years.
55
196000
4000
od nekih 30 do 70 godina u nekim zemljama.
03:45
And 1962, there was really a group of countries here
56
200000
3000
A 1962, imamo grupu zemalja,
03:48
that was industrialized countries, and they had small families and long lives.
57
203000
5000
industrijaliziranih zemalja, i one su imale male porodice i duge živote.
03:53
And these were the developing countries:
58
208000
2000
A ovo ovdje su bile zemlje u razvoju:
03:55
they had large families and they had relatively short lives.
59
210000
3000
imale su velike porodice i relativno kratke živote.
03:58
Now what has happened since 1962? We want to see the change.
60
213000
4000
E, šta se dogodilo od 1962. godine pa nadalje? Želimo da vidimo promjenu.
04:02
Are the students right? Is it still two types of countries?
61
217000
3000
Jesu li studenti u pravu? Da li su to ustvari i dalje dva različita tipa zemalja?
04:06
Or have these developing countries got smaller families and they live here?
62
221000
3000
Ili jesu li ove zemlje u razvoju osnovale manje porodice i oni žive ovdje?
04:09
Or have they got longer lives and live up there?
63
224000
2000
Ili imaju dulje živote i žive ovdje gore?
04:11
Let's see. We stopped the world then. This is all U.N. statistics
64
226000
3000
Da vidimo. Zaustavili smo svijet. Ova statistika koja nam je dostupna je
04:14
that have been available. Here we go. Can you see there?
65
229000
3000
statistika UN-a. Idemo. Možete li vidjeti ono ondje?
04:17
It's China there, moving against better health there, improving there.
66
232000
3000
To je Kina tamo, kreće se prema boljem zdravlju tamo, tu se poboljšava.
04:20
All the green Latin American countries are moving towards smaller families.
67
235000
3000
Sve zemlje Latinske Amerike, obojene u zeleno se miču prema manjim porodicama.
04:23
Your yellow ones here are the Arabic countries,
68
238000
3000
Ove žute ovdje su arapske zemlje,
04:26
and they get larger families, but they -- no, longer life, but not larger families.
69
241000
4000
i one imaju veće porodice, ne, ne-- imaju dulje živote, ne veće porodice.
04:30
The Africans are the green down here. They still remain here.
70
245000
3000
Afrikanci su ovi zeleni ovdje dolje. I dalje ostaju tu.
04:33
This is India. Indonesia's moving on pretty fast.
71
248000
3000
Ovo je Indija. Indonezija se kreće veoma brzo.
04:36
(Laughter)
72
251000
1000
Smijeh
04:37
And in the '80s here, you have Bangladesh still among the African countries there.
73
252000
3000
A osamdesetih godina, imate Bangladeš i dalje među afričkim zemljama.
04:40
But now, Bangladesh -- it's a miracle that happens in the '80s:
74
255000
3000
Ali sada, Bangladeš - to je čudo šta se dogodilo osamdesetih,
04:43
the imams start to promote family planning.
75
258000
3000
imami promoviraju planiranje porodica.
04:46
They move up into that corner. And in '90s, we have the terrible HIV epidemic
76
261000
5000
Pomjeraju se u taj ćošak. A devedesetih godina, imamo užasnu epidemiju virusa HIV
04:51
that takes down the life expectancy of the African countries
77
266000
3000
koja znatno spušta životni vijek pojedinh afričkih zemalja
04:54
and all the rest of them move up into the corner,
78
269000
4000
a sve ostale zemlje se pomjeraju u taj ćošak,
04:58
where we have long lives and small family, and we have a completely new world.
79
273000
4000
gdje imamo dulje živote i male porodice, i imamo potpuno novi svijet.
05:02
(Applause)
80
277000
13000
Aplauz
05:15
Let me make a comparison directly between the United States of America and Vietnam.
81
290000
5000
Dopustite mi da napravim direktno poređenje između Sjedinjenih Američkih Država i Vijetnama.
05:20
1964: America had small families and long life;
82
295000
5000
1964.: SAD su imale manje porodice i dug život;
05:25
Vietnam had large families and short lives. And this is what happens:
83
300000
4000
Vijetnam je imao velike porodice i kratke živote. I evo šta se događa:
05:29
the data during the war indicate that even with all the death,
84
304000
6000
podaci tokom rata pokazuju da uprkos svim smrtnim gubicima,
05:35
there was an improvement of life expectancy. By the end of the year,
85
310000
3000
primjećujemo poboljšanje, odnosno povećanje životnog vijeka. Do kraja godine,
05:38
the family planning started in Vietnam and they went for smaller families.
86
313000
3000
planiranje porodica je krenulo u Vijetnamu i odlučili su se za manje porodice.
05:41
And the United States up there is getting for longer life,
87
316000
3000
A SAD,tu gore, su se odlučile za dulji život,
05:44
keeping family size. And in the '80s now,
88
319000
3000
održavajući veličinu porodice. A u osamdesetim,
05:47
they give up communist planning and they go for market economy,
89
322000
3000
odriču se komunističkih ideja i odlučuju se za tržišnu ekonomiju,
05:50
and it moves faster even than social life. And today, we have
90
325000
4000
koja se pomjera brže od društvenog života. Zato danas, u Vijetnamu
05:54
in Vietnam the same life expectancy and the same family size
91
329000
5000
imamo isti životni vijek i istu veličinu porodice 2003. godine,
05:59
here in Vietnam, 2003, as in United States, 1974, by the end of the war.
92
334000
7000
kao što je bila u SAD-u 1974., pri završetku rata.
06:06
I think we all -- if we don't look in the data --
93
341000
4000
Mislim da svi mi - ukoliko ne gledamo u podatke -
06:10
we underestimate the tremendous change in Asia, which was
94
345000
4000
potcjenjujemo ogromnu promjenu u Aziji, koja je
06:14
in social change before we saw the economical change.
95
349000
4000
ustvari prvo bila društvena, pa tek onda ekonomska promjena.
06:18
Let's move over to another way here in which we could display
96
353000
5000
Haj'd da pogledamo jedan drugi način na koji možemo predstaviti
06:23
the distribution in the world of the income. This is the world distribution of income of people.
97
358000
7000
raspodjelu u svijetu prihoda. Ovo je svjetska rapodjela prihoda ljudi.
06:30
One dollar, 10 dollars or 100 dollars per day.
98
365000
5000
Jedan dolar, deset dolara ili stotinu dolara na dan.
06:35
There's no gap between rich and poor any longer. This is a myth.
99
370000
4000
Ne postoji više praznina između bogatih i siromašnih. To je mit.
06:39
There's a little hump here. But there are people all the way.
100
374000
4000
Ovo je mala hrpa ovdje. Ali čitavim putem imamo ljude.
06:44
And if we look where the income ends up -- the income --
101
379000
4000
A ako pogledamo gdje završavaju prihodi --prihodi--
06:48
this is 100 percent the world's annual income. And the richest 20 percent,
102
383000
6000
ovo je 100 % od svjetskog godišnjeg prihoda. A najbogatijih 20 %,
06:54
they take out of that about 74 percent. And the poorest 20 percent,
103
389000
7000
od toga uzimaju nekih 74 %. A najsiromašnijih 20 %,
07:01
they take about two percent. And this shows that the concept
104
396000
5000
uzimaju oko 2 %. Ovo pokaziva da je koncept
07:06
of developing countries is extremely doubtful. We think about aid, like
105
401000
4000
zemalja u razvoju veoma sumnjiv. Razmišljamo o pomoći,
07:10
these people here giving aid to these people here. But in the middle,
106
405000
5000
kao da ovi ljudi pomažu ove ljude ovdje. Ali u sredini,
07:15
we have most the world population, and they have now 24 percent of the income.
107
410000
4000
imamo većinu svjetske populacije, i oni sada imaju 24 % prihoda.
07:19
We heard it in other forms. And who are these?
108
414000
4000
O tome smo čuli i na drukčije načine. A ko su ovi?
07:23
Where are the different countries? I can show you Africa.
109
418000
4000
Gdje su različite zemlje? Mogu vam pokazati Afriku.
07:27
This is Africa. 10 percent the world population, most in poverty.
110
422000
5000
Ovo je Afrika. 10 % svjetske populacije, većina u siromaštvu.
07:32
This is OECD. The rich country. The country club of the U.N.
111
427000
5000
Ovo je OECD (Organizacija zemalja za ekonomsku saradnju i razvoj). Bogate Zemlje. Klub zemalja UN-a.
07:37
And they are over here on this side. Quite an overlap between Africa and OECD.
112
432000
5000
I one su ovdje na ovoj strani. Poprilično podudaranje između Afrike i OECD-a.
07:42
And this is Latin America. It has everything on this Earth,
113
437000
3000
Ovo ovdje je Latinska Amerika. Ima sve na planeti Zemlji,
07:45
from the poorest to the richest, in Latin America.
114
440000
3000
od najsiromašnijih do najbogatijih.
07:48
And on top of that, we can put East Europe, we can put East Asia,
115
443000
5000
Povrh svega ovoga, možemo staviti Istočnu Evropu, zatim istočnu Aziju,
07:53
and we put South Asia. And how did it look like if we go back in time,
116
448000
5000
te Južnu Aziju. Kakvo je stanje bilo ako bismo se vratili kroz vrijeme,
07:58
to about 1970? Then there was more of a hump.
117
453000
5000
do neke 1970.? Tada je bila veća ova hrpa.
08:03
And we have most who lived in absolute poverty were Asians.
118
458000
4000
Ovdje vidimo da su većina onih koji su živjeli u siromaštvu Azijci.
08:07
The problem in the world was the poverty in Asia. And if I now let the world move forward,
119
462000
7000
Problem svijeta je tada bio siromaštvo Azije. Ali ako sada svijet malo naprijed pomjerimo,
08:14
you will see that while population increase, there are
120
469000
3000
vidjet ćete da porastom stanovništva, stotine
08:17
hundreds of millions in Asia getting out of poverty and some others
121
472000
3000
miliona Azijaca izlazi iz okvira siromaštva, a neki drugi
08:20
getting into poverty, and this is the pattern we have today.
122
475000
3000
ulaze u taj isti okvir, i evo koji pater dobijamo danas.
08:23
And the best projection from the World Bank is that this will happen,
123
478000
4000
Najbolje razmatranje iz Svjetske Banke je da će se ovo dogoditi,
08:27
and we will not have a divided world. We'll have most people in the middle.
124
482000
4000
te da više nećemo imati podjeljen svijet. Imat ćemo većinu ljudi u sredini.
08:31
Of course it's a logarithmic scale here,
125
486000
2000
Naravno, ovo ovdje je logaritamska skala,
08:33
but our concept of economy is growth with percent. We look upon it
126
488000
5000
ali naš koncept ekonomije je rast uz postotak. Na to gledamo kao
08:38
as a possibility of percentile increase. If I change this, and I take
127
493000
6000
na mogućnost povećanja procenta. Ako ovo promijenim, i uzmem
08:44
GDP per capita instead of family income, and I turn these
128
499000
4000
(BDP) bruto domaći proizvod po osobi umjesto porodičnih prihoda, i ove
08:48
individual data into regional data of gross domestic product,
129
503000
6000
individualne podatke promijenim u regionalni bruto domaći proizvod,
08:54
and I take the regions down here, the size of the bubble is still the population.
130
509000
4000
a regije spustim ovdje dolje gdje je veličina kruga i dalje stanovništvo.
08:58
And you have the OECD there, and you have sub-Saharan Africa there,
131
513000
3000
Ovdje imate OECD (Organizacija zemalja za ekonomsku saradnju i razvoj), i imate subsaharsku Afriku,
09:01
and we take off the Arab states there,
132
516000
3000
a ovdje skinemo arapske zemlje,
09:04
coming both from Africa and from Asia, and we put them separately,
133
519000
4000
bilo afričke ili azijske, i stavimo ih odvojeno,
09:08
and we can expand this axis, and I can give it a new dimension here,
134
523000
5000
zatim proširimo ovu osu, dodamo novu dimenziju,
09:13
by adding the social values there, child survival.
135
528000
3000
dodavajući društvene vrijednosti i opstanak djece.
09:16
Now I have money on that axis, and I have the possibility of children to survive there.
136
531000
5000
Sada, imamo novac na toj osi, i vjerovatnoću da djeca tu prežive.
09:21
In some countries, 99.7 percent of children survive to five years of age;
137
536000
4000
U nekim zemljama, 99.7 % djece do pet godina preživi;
09:25
others, only 70. And here it seems there is a gap
138
540000
4000
u nekim drugim, samo 70%. A ovdje se čini da imamo jaz
09:29
between OECD, Latin America, East Europe, East Asia,
139
544000
4000
između OECD-a, Latinske Amerike, istočne Evrope, istočne Azije,
09:33
Arab states, South Asia and sub-Saharan Africa.
140
548000
4000
arapskih zemalja, južne Azije i subsaharske Afrike.
09:37
The linearity is very strong between child survival and money.
141
552000
5000
Ravnomjernost je veoma jaka između opstanka djece i novca.
09:42
But let me split sub-Saharan Africa. Health is there and better health is up there.
142
557000
8000
Ali hajd' da podijelim subsaharsku Afriku. Zdravlje je tu, a bolje zdravlje tu gore.
09:50
I can go here and I can split sub-Saharan Africa into its countries.
143
565000
5000
Mogu ići ovdje i podijeliti subsaharsku Afriku na sve njene zemlje.
09:55
And when it burst, the size of its country bubble is the size of the population.
144
570000
5000
Kada se raspukne, veličina kruga koji predstavlja zemlju predstavlja stanovništvo.
10:00
Sierra Leone down there. Mauritius is up there. Mauritius was the first country
145
575000
4000
Sijera Leone je tu dolje. Mauricijus je gore. Mauricijus je prva zemlja
10:04
to get away with trade barriers, and they could sell their sugar --
146
579000
3000
koja je ukinula granice trgovanja, te tako mogla prodavati svoj šećer.
10:08
they could sell their textiles -- on equal terms as the people in Europe and North America.
147
583000
5000
Pod istim uslovima kao ljudi Evrope i Sjeverne Amerike su mogli prodavati svoj tekstil.
10:13
There's a huge difference between Africa. And Ghana is here in the middle.
148
588000
4000
Postoji velika razlika među zemljema Afrike. Gana je ovdje u sredini.
10:17
In Sierra Leone, humanitarian aid.
149
592000
3000
U Sijera Leoneu, humanitarna pomoć.
10:20
Here in Uganda, development aid. Here, time to invest; there,
150
595000
5000
Ovdje u Ugandi, pomoć pri razvoju. Ovdje--možete ulagati, tamo -
10:25
you can go for a holiday. It's a tremendous variation
151
600000
3000
možete otići na odmor. Ogromne varijacije postoje
10:28
within Africa which we rarely often make -- that it's equal everything.
152
603000
5000
u samoj Africi kojih često nismo ni svjesni.
10:33
I can split South Asia here. India's the big bubble in the middle.
153
608000
4000
Ovdje mogu podijeliti Južnu Aziju. Indija je veliki krug u sredini.
10:37
But a huge difference between Afghanistan and Sri Lanka.
154
612000
4000
Ali ogromna je razlika između Šri Lanke i Afganistana.
10:41
I can split Arab states. How are they? Same climate, same culture,
155
616000
4000
Mogu podijeltiti arapske zemlje. Kakvo je stanje kod njih? Ista klima, ista kultura,
10:45
same religion -- huge difference. Even between neighbors.
156
620000
4000
ista religija. Ogromna razlika. Čak i između komšija.
10:49
Yemen, civil war. United Arab Emirate, money which was quite equally and well used.
157
624000
5000
U Jmenu građanski rat. Ujedinjeni Arapski Emirati, novac koji je jednako i dobro potrošen,
10:54
Not as the myth is. And that includes all the children of the foreign workers who are in the country.
158
629000
7000
ali mit ostaje. Mit koji uključuje svu djecu stranih radnika koji su u zemlji.
11:01
Data is often better than you think. Many people say data is bad.
159
636000
4000
Podaci su ponekad bolji nego što mislite. Mnogi ljudi se žale na podatke.
11:06
There is an uncertainty margin, but we can see the difference here:
160
641000
2000
Postoji moguća nesigurnost u podatke, ali ipak možemo vidjeti razliku:
11:08
Cambodia, Singapore. The differences are much bigger
161
643000
3000
Kambodža, Singapur. Razlike su mnogo veće
11:11
than the weakness of the data. East Europe:
162
646000
3000
nego što je slabost podataka. Istočna Evropa:
11:14
Soviet economy for a long time, but they come out after 10 years
163
649000
6000
Sovjetska ekonomija dugo vremena, ali nakon deset godina
11:20
very, very differently. And there is Latin America.
164
655000
3000
ishod je puno, puno drukčiji. A ovdje je Latinska Amerika.
11:23
Today, we don't have to go to Cuba to find a healthy country in Latin America.
165
658000
4000
Danas, ne morate ići na Kubu da biste našli zdravu državu u Latinskoj Americi,
11:27
Chile will have a lower child mortality than Cuba within some few years from now.
166
662000
5000
Čile će imati nižu stopu smrtnosti djece nego Kuba u samo nekoliko sljedećih godina.
11:32
And here we have high-income countries in the OECD.
167
667000
3000
A ovdje imamo zemlje sa visokim prihodom u OECD-u.
11:35
And we get the whole pattern here of the world,
168
670000
4000
A ovdje dobijemo čitavu shemu svijeta,
11:39
which is more or less like this. And if we look at it,
169
674000
5000
koja je manje-više ovakva. Pa ako pogledamo kako
11:44
how it looks -- the world, in 1960, it starts to move. 1960.
170
679000
6000
svijet izgleda-- svijet se šezdesetih počinje pomjerati.
11:50
This is Mao Tse-tung. He brought health to China. And then he died.
171
685000
3000
Ovo je Mao Cetung. On je donio zdravlje u Kinu. I onda je umro,
11:53
And then Deng Xiaoping came and brought money to China, and brought them into the mainstream again.
172
688000
5000
A zatim Deng Xiaoping stiže i donosi novac u Kinu, i ponovo ih vraća u kolosijek.
11:58
And we have seen how countries move in different directions like this,
173
693000
4000
Vidjeli smo kako se zemlje pomjeraju u različitim pravcima kao ova,
12:02
so it's sort of difficult to get
174
697000
4000
tako da je veoma teško naći zemlju koja bi poslužila kao primjer
12:06
an example country which shows the pattern of the world.
175
701000
5000
koji pokazuje cjelokupnu shemu svijeta.
12:11
But I would like to bring you back to about here at 1960.
176
706000
6000
Volio bih vas dovesti do ove tačke ovdje, 1960. godine.
12:17
I would like to compare South Korea, which is this one, with Brazil,
177
712000
10000
Volio bih da uporedim Južnu Koreju, ova ovdje, sa Brazilom,
12:27
which is this one. The label went away for me here. And I would like to compare Uganda,
178
722000
5000
ovo ovdje. Ovdje nemam oznake. i volio bih još porediti Ugandu,
12:32
which is there. And I can run it forward, like this.
179
727000
5000
koja je ovdje. I mogu to ubrzati, ovako.
12:37
And you can see how South Korea is making a very, very fast advancement,
180
732000
9000
Možete vidjeti kako Južna Koreja veoma, veoma brzo napreduje,
12:46
whereas Brazil is much slower.
181
741000
3000
dok je Brazil znatno sporiji.
12:49
And if we move back again, here, and we put on trails on them, like this,
182
744000
6000
Ako se vratimo nazad, ovdje, te ako im pokažemo putanju,
12:55
you can see again that the speed of development
183
750000
4000
opet možete vidjeti da je brzina razvoja
12:59
is very, very different, and the countries are moving more or less
184
754000
6000
veoma, veoma različita, i države se manje-više kreću
13:05
in the same rate as money and health, but it seems you can move
185
760000
4000
u istom omjeru kao novac i zdravlje, ali čini se da se mnogo brže pomjerate
13:09
much faster if you are healthy first than if you are wealthy first.
186
764000
4000
ako ste zdravi, a ne ako ste bogati.
13:14
And to show that, you can put on the way of United Arab Emirate.
187
769000
4000
Kako bismo vam to pokazali, pogledajmo putanju Ujedinjenih Arapskih Emirata.
13:18
They came from here, a mineral country. They cached all the oil;
188
773000
3000
Došli su odavde, iz zemlje minerala. Imaju velike rezerve nafte,
13:21
they got all the money; but health cannot be bought at the supermarket.
189
776000
4000
imaju mnoštvo novca, ali zdravlje ne može biti kupljeno u supermarketu.
13:25
You have to invest in health. You have to get kids into schooling.
190
780000
4000
U zdravlje morate ulagati. Morate školovati djecu.
13:29
You have to train health staff. You have to educate the population.
191
784000
3000
Morate obučavati zdravstveno osoblje. Morate educirati stanovništvo.
13:32
And Sheikh Sayed did that in a fairly good way.
192
787000
3000
Šeik Sajed je to poprilično dobro odradio.
13:35
In spite of falling oil prices, he brought this country up here.
193
790000
4000
Uprkos padu cijena nafte, doveo je zemlju do ove tačke gore.
13:39
So we've got a much more mainstream appearance of the world,
194
794000
4000
Zbog svega ovoga imamo svijet koji ide jednakim kolosijekom,
13:43
where all countries tend to use their money
195
798000
2000
gdje sve zemlje teže da iskoriste novac
13:45
better than they used in the past. Now, this is, more or less,
196
800000
5000
bolje no što su ga koristile u prošlosti. Ovakva je situacija
13:50
if you look at the average data of the countries -- they are like this.
197
805000
7000
ukoliko se gledaju prosječni podaci jedne zemlje. To izgleda ovako.
13:57
Now that's dangerous, to use average data, because there is such a lot
198
812000
5000
Ali, veoma je opasno koristiti prosječne podatke, jer postoji ogromna razlika
14:02
of difference within countries. So if I go and look here, we can see
199
817000
6000
unutar samih granica države. Pa ako ovdje pogledate, možete vidjeti
14:08
that Uganda today is where South Korea was 1960. If I split Uganda,
200
823000
6000
da je Uganda danas na onom mjestu gdje je bila Južna Koreja 1960. Ako podijelim Ugandu,
14:14
there's quite a difference within Uganda. These are the quintiles of Uganda.
201
829000
5000
primjetit ćemo velike razliku. Ovo su raspodijeljene petine u Ugandi.
14:19
The richest 20 percent of Ugandans are there.
202
834000
3000
Najbogatijih 20 % u Ugandi su ovdje.
14:22
The poorest are down there. If I split South Africa, it's like this.
203
837000
4000
Najsiromašniji su ovdje. Ako podijelim Južnu Afriku, ovako izgleda.
14:26
And if I go down and look at Niger, where there was such a terrible famine,
204
841000
5000
Ako se osvrenemo na NIger, gdje je bila užasna glad,
14:31
lastly, it's like this. The 20 percent poorest of Niger is out here,
205
846000
5000
to izgleda ovako. 20% majsiromašnijih u Nigeru su ovdje,
14:36
and the 20 percent richest of South Africa is there,
206
851000
3000
a 20 % najbogatijih u Južnoj Africi su ovdje,
14:39
and yet we tend to discuss on what solutions there should be in Africa.
207
854000
5000
a mi se još uvijek raspravljamo o rješenjima potrebnim za Afriku.
14:44
Everything in this world exists in Africa. And you can't
208
859000
3000
Afrika ima sve što i ostali svijet. I ne možete
14:47
discuss universal access to HIV [medicine] for that quintile up here
209
862000
4000
raspravljati o univerzalnim rješenjima i strategijama za HIV virus za ovu petinu ovdje
14:51
with the same strategy as down here. The improvement of the world
210
866000
4000
u poređenju sa ovom petinom dolje. Poboljšanje svijeta se mora staviti
14:55
must be highly contextualized, and it's not relevant to have it
211
870000
5000
u isti referentni okvir, i nije toliko važno da ga imamo
15:00
on regional level. We must be much more detailed.
212
875000
3000
samo na regionalnom nivou. Moramo biti mnogo detaljniji.
15:03
We find that students get very excited when they can use this.
213
878000
4000
Uvidjeli smo da se studenti veoma uzbude kada mogu koristiti ovo.
15:07
And even more policy makers and the corporate sectors would like to see
214
882000
5000
Te čak kako mnogi političari i korporacijski sektor žele vidjeti
15:12
how the world is changing. Now, why doesn't this take place?
215
887000
4000
kako se svijet mijenja. Ali, zašto ovo ne uzima maha?
15:16
Why are we not using the data we have? We have data in the United Nations,
216
891000
4000
Zašto ne koristimo podatke koje imamo? Imamo podatke Ujedinjenih Nacija,
15:20
in the national statistical agencies
217
895000
2000
u nacionalnim statističkim agencijama
15:22
and in universities and other non-governmental organizations.
218
897000
4000
i na univerzitetima i nevladinim organizacijama.
15:26
Because the data is hidden down in the databases.
219
901000
2000
Ne uzima maha jer se podaci sakrivaju u bazama podataka.
15:28
And the public is there, and the Internet is there, but we have still not used it effectively.
220
903000
5000
Imamo i javnost, imamo i internet, ali još ih nismo efektivno iskoristili.
15:33
All that information we saw changing in the world
221
908000
3000
Sve te informacije o tome kako se mijenja svijet
15:36
does not include publicly-funded statistics. There are some web pages
222
911000
4000
ne uključuju statistike sponzorirane od strane javnosti. Postoje neke web stranice
15:40
like this, you know, but they take some nourishment down from the databases,
223
915000
6000
kao ova, znate, ali one dobiju prihoda od baze podataka,
15:46
but people put prices on them, stupid passwords and boring statistics.
224
921000
5000
pa na podatke stavljaju cijene i glupe šifre i imaju glupu statistiku.
15:51
(Laughter) (Applause)
225
926000
3000
Smijeh & Aplauz
15:54
And this won't work. So what is needed? We have the databases.
226
929000
4000
Ali ne ide to tako. Šta nam je potrebno? Imamo baze podataka.
15:58
It's not the new database you need. We have wonderful design tools,
227
933000
4000
Nije nova baza podataka to što nam treba. Imamo sve više i više
16:02
and more and more are added up here. So we started
228
937000
3000
novih alata za dizajn. Tako da smo mi započeli
16:05
a nonprofit venture which we called -- linking data to design --
229
940000
5000
sa neprofitnim poduhvatom koji povezuje podatke sa dizajnom,
16:10
we call it Gapminder, from the London underground, where they warn you,
230
945000
3000
koji smo nazvali Gapminder, naziv smo uzeli iz londonskog podzemlja, u kojem koriste sličan izraz.
16:13
"mind the gap." So we thought Gapminder was appropriate.
231
948000
3000
Tako da smo mislili da je Gapminder prihvatljiv naziv.
16:16
And we started to write software which could link the data like this.
232
951000
4000
Tako smo počeli sa izradnjom programa koji bi mogao povezivati podatke na ovaj način.
16:20
And it wasn't that difficult. It took some person years, and we have produced animations.
233
955000
6000
I nije uopšte bilo tako teško. Nekim ljudima su trebale godine, a mi smo odradili animacije.
16:26
You can take a data set and put it there.
234
961000
2000
Možete uzeti čitav set podatak i staviti ih ovdje.
16:28
We are liberating U.N. data, some few U.N. organization.
235
963000
5000
Iznosimo podatke pojedinih oraganizacija UN-a.
16:33
Some countries accept that their databases can go out on the world,
236
968000
4000
Neke zemlje pristaju na to da njihove baze podataka mogu putovati svijetom,
16:37
but what we really need is, of course, a search function.
237
972000
3000
ali što nam uistinu treba je, naravno, opcija pretrage,
16:40
A search function where we can copy the data up to a searchable format
238
975000
5000
u kojoj možemo podatke kopirati tako da su lahki za pretragu
16:45
and get it out in the world. And what do we hear when we go around?
239
980000
3000
i da ih takve predstavimo svijetu. I šta čujemo kao reakciju?
16:48
I've done anthropology on the main statistical units. Everyone says,
240
983000
4000
Uradio sam antropolšku analizu glavnih statističkih jedinica. Svi kažu:
16:53
"It's impossible. This can't be done. Our information is so peculiar
241
988000
4000
''To je nemoguće. To se ne može uraditi. Naše informacije su tako svojstveno
16:57
in detail, so that cannot be searched as others can be searched.
242
992000
3000
detaljne, tako da se ne mogu pretraživati kao što se druge mogu pretraživati.
17:00
We cannot give the data free to the students, free to the entrepreneurs of the world."
243
995000
5000
Ne možemo tek tako dati naše podatke studentima, niti svjetskim poduzetnicima.''
17:05
But this is what we would like to see, isn't it?
244
1000000
3000
Ali je ovo upravo ono što bismo voljeli vidjeti, zar ne?
17:08
The publicly-funded data is down here.
245
1003000
3000
Podaci finansirani od strane javnosti su ovdje dolje
17:11
And we would like flowers to grow out on the Net.
246
1006000
3000
A mi bismo voljeli da ove informacije isplivaju na Internet.
17:14
And one of the crucial points is to make them searchable, and then people can use
247
1009000
5000
Glavna prekretnica je da ove podatke učinimo pretraživim, a onda bi ljudi
17:19
the different design tool to animate it there.
248
1014000
2000
koristili različite dizajnerske alate kako bi ih animirali.
17:21
And I have a pretty good news for you. I have a good news that the present,
249
1016000
5000
A ja za sve vas imam dobre vijesti. Vijesti da sadašnji upravnik
17:26
new Head of U.N. Statistics, he doesn't say it's impossible.
250
1021000
4000
UN-ovog odjela za statistiku ne smatra to nemogućim.
17:30
He only says, "We can't do it."
251
1025000
2000
On samo kaže: ''Mi to možemo uraditi.''
17:32
(Laughter)
252
1027000
4000
Smijeh
17:36
And that's a quite clever guy, huh?
253
1031000
2000
Poprilično pametan lik, zar ne?
17:38
(Laughter)
254
1033000
2000
Smijeh
17:40
So we can see a lot happening in data in the coming years.
255
1035000
4000
Možemo vidjeti dosta dešavanja na polju podataka u nadolazećim godinama.
17:44
We will be able to look at income distributions in completely new ways.
256
1039000
4000
Distribuciju prihoda ćemo moći posmatrati na jedan sasvim drugačiji način.
17:48
This is the income distribution of China, 1970.
257
1043000
5000
Ovo je distribucija prihoda Kine, 1970. godine,
17:54
the income distribution of the United States, 1970.
258
1049000
5000
zatim distribucija prihoda SAD-a, iste godine.
17:59
Almost no overlap. Almost no overlap. And what has happened?
259
1054000
4000
Gotovo da nema preklapanja. Gotovo da nema preklapanja. I šta se dogodilo?
18:03
What has happened is this: that China is growing, it's not so equal any longer,
260
1058000
5000
Ono što se desilo je činjenica da Kina raste, nije više isto,
18:08
and it's appearing here, overlooking the United States.
261
1063000
4000
i to se pojavljuje ovdje, izdižući se iznad SAD-a.
18:12
Almost like a ghost, isn't it, huh?
262
1067000
2000
Skoro kao duh, zar ne?
18:14
(Laughter)
263
1069000
2000
Smijeh
18:16
It's pretty scary. But I think it's very important to have all this information.
264
1071000
10000
To je poprilično zastrašujuće. Ali ja mislim da je veoma važno imati sve ove informacije.
18:26
We need really to see it. And instead of looking at this,
265
1081000
6000
Zaista moramo to da vidimo. I umjesto da gledamo u ovo,
18:32
I would like to end up by showing the Internet users per 1,000.
266
1087000
5000
volio bi da sumiram sve posmatrajući korisnike interneta u odnosu na 1000 stanovnika.
18:37
In this software, we access about 500 variables from all the countries quite easily.
267
1092000
5000
Pomoću ovog programa, imamo pristup za 500 varijabli iz različitih zemalja, veoma lagano.
18:42
It takes some time to change for this,
268
1097000
4000
Treba malo vremena da se ovo promjeni,
18:46
but on the axises, you can quite easily get any variable you would like to have.
269
1101000
5000
ali na ovim osama ovdje, možete, i to veoma lagano, dobiti koju god varijablu želite.
18:51
And the thing would be to get up the databases free,
270
1106000
5000
A mi želimo da dobijemo pristup bazama podataka,
18:56
to get them searchable, and with a second click, to get them
271
1111000
3000
da ih učinimo pretraživim, i poslije toga ih pretvorimo u
18:59
into the graphic formats, where you can instantly understand them.
272
1114000
5000
grafički format kako bi ih svi momentalno razumjeli.
19:04
Now, statisticians doesn't like it, because they say that this
273
1119000
3000
Ali statističarima se to ne sviđa, jer kako kažu
19:07
will not show the reality; we have to have statistical, analytical methods.
274
1122000
9000
ovo neće odslikavati realnost; moramo imati statističke, analitičke metode.
19:16
But this is hypothesis-generating.
275
1131000
3000
Ali ovo je samo proizvođenje hipoteza.
19:19
I end now with the world. There, the Internet is coming.
276
1134000
4000
Završavam sa svijetom. Internet dolazi.
19:23
The number of Internet users are going up like this. This is the GDP per capita.
277
1138000
4000
Broj internet korisnika raste ovako. Ovo je Bruto domaći proizvod po osobi.
19:27
And it's a new technology coming in, but then amazingly, how well
278
1142000
5000
A ovo su nove tehnologije što nam dolaze, ali ipak je čudesno kako se odlično
19:32
it fits to the economy of the countries. That's why the 100 dollar
279
1147000
5000
uklapaju sa privredom zemalja. Zbog toga kompjuter od 100 dolara
19:37
computer will be so important. But it's a nice tendency.
280
1152000
3000
će biti važna stvar. Sve u svemu, to je dobra namjera.
19:40
It's as if the world is flattening off, isn't it? These countries
281
1155000
3000
Čini se kako se svijet dovodi u jednu ravan, zar ne? Ove zemlje
19:43
are lifting more than the economy and will be very interesting
282
1158000
3000
pomjeraju standarde ne samo u privredi i bit će veoma interesantno
19:46
to follow this over the year, as I would like you to be able to do
283
1161000
4000
pratiti ovo kroz godine, što bi ja želio da vi svi možete
19:50
with all the publicly funded data. Thank you very much.
284
1165000
2000
sa javno sponzorisanim podacima. Hvala vam velika.
19:53
(Applause)
285
1168000
3000
Aplauz
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