TEDxNewYork
Ben Wellington: How we found the worst place to park in New York City -- using big data
Ben Velington (Ben Wellington): Kako smo pronašli najgore mesto za parkiranje u Njujorku - koristeći velike podatke
Filmed:
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Gradske agencije imaju pristup mnoštvu podataka i statistikama koje se tiču svakog dela urbanog života. Ali kako u ovom zanimljivom govoru navodi analitičar podataka Ben Velington, oni ponekad jednostavno ne znaju šta da rade sa ovim podacima. On pokazuje kako kombinacija neočekivanih pitanja i pregledanja pametnih podataka može da dovede do neverovatno korisnih informacija, i sa nama deli savete o tome kako da se objavi veliki broj skupova podataka tako da svako može da ih koristi.
Ben Wellington - Data scientist
Ben Wellington blends his love of statistics, the city, and comedy in his entertaining analysis of the story of New York City, told through data. Full bio
Ben Wellington blends his love of statistics, the city, and comedy in his entertaining analysis of the story of New York City, told through data. Full bio
Double-click the English transcript below to play the video.
00:12
Six thousand miles of road,
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9600 kilometara puteva,
00:15
600 miles of subway track,
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960 kilometara podzemne železnice,
00:17
400 miles of bike lanes
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640 kilometara staza za bicikl
00:19
and a half a mile of tram track,
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i 800 metara tramvajskih šina,
00:21
if you've ever been to Roosevelt Island.
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ako ste ikada bili na ostrvu Ruzvelt.
00:23
These are the numbers that make up
the infrastructure of New York City.
the infrastructure of New York City.
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Ovo su brojke koje čine
infrastrukturu Njujorka.
infrastrukturu Njujorka.
00:26
These are the statistics
of our infrastructure.
of our infrastructure.
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Ovo je statistika naše infrastrukture.
00:29
They're the kind of numbers you can find
released in reports by city agencies.
released in reports by city agencies.
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Ovakve brojke možete naći objavljene
u izveštajima gradskih agencija.
u izveštajima gradskih agencija.
00:32
For example, the Department
of Transportation will probably tell you
of Transportation will probably tell you
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Na primer, Odsek za transport
će vam verovatno reći
će vam verovatno reći
koliko kilometara puteva održavaju.
00:36
how many miles of road they maintain.
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Uprava transporta Metropolitena
pohvaliće se koliko pruge postoji.
pohvaliće se koliko pruge postoji.
00:37
The MTA will boast how many miles
of subway track there are.
of subway track there are.
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Većina gradskih agencija daje statistiku.
00:40
Most city agencies give us statistics.
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00:42
This is from a report this year
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Ovo je izveštaj za ovu godinu
00:43
from the Taxi and Limousine Commission,
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od Komisije za taksije i limuzine
00:45
where we learn that there's about
13,500 taxis here in New York City.
13,500 taxis here in New York City.
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iz kog vidimo da postoji
13 500 taksija u Njujorku.
13 500 taksija u Njujorku.
00:49
Pretty interesting, right?
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Interesantno, zar ne?
00:50
But did you ever think about
where these numbers came from?
where these numbers came from?
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Da li ste razmišljali odakle ove brojke?
00:53
Because for these numbers to exist,
someone at the city agency
someone at the city agency
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Kako bi ove brojke postojale,
neko iz gradske agencije
neko iz gradske agencije
00:56
had to stop and say, hmm, here's a number
that somebody might want want to know.
that somebody might want want to know.
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mora da zastane i kaže
da je ovo broj koji neko želi da zna.
da je ovo broj koji neko želi da zna.
00:59
Here's a number
that our citizens want to know.
that our citizens want to know.
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Ovo je broj koji naši građani žele.
01:02
So they go back to their raw data,
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Hajde da se vratimo sirovim podacima
01:04
they count, they add, they calculate,
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koje oni broje, dodaju, proračunavaju
01:05
and then they put out reports,
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i onda objave izveštaje,
01:07
and those reports
will have numbers like this.
will have numbers like this.
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a u tim izveštajima se nalaze
ovakve brojke.
ovakve brojke.
01:09
The problem is, how do they know
all of our questions?
all of our questions?
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Problem je u tome,
kako oni znaju sva naša pitanja?
kako oni znaju sva naša pitanja?
01:11
We have lots of questions.
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Imamo ih mnogo.
01:13
In fact, in some ways there's literally
an infinite number of questions
an infinite number of questions
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U stvari, na neki način postoji
neograničen broj pitanja
neograničen broj pitanja
01:16
that we can ask about our city.
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koja postavljamo o našem gradu.
01:18
The agencies can never keep up.
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Agencije nas ne mogu ispratiti.
01:19
So the paradigm isn't exactly working,
and I think our policymakers realize that,
and I think our policymakers realize that,
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Tako da paradigma ne radi kako treba
i zakonodavci shvataju to
i zakonodavci shvataju to
01:23
because in 2012, Mayor Bloomberg
signed into law what he called
signed into law what he called
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zato što je u 2012. gradonačelnik Blumberg
potpisao zakon koji on zove
potpisao zakon koji on zove
01:27
the most ambitious and comprehensive
open data legislation in the country.
open data legislation in the country.
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najambicioznijim i najobuhvatnijim
zakonodavnim aktom o podacima u zemlji.
zakonodavnim aktom o podacima u zemlji.
01:31
In a lot of ways, he's right.
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On je u pravu na mnogo načina.
01:33
In the last two years,
the city has released 1,000 datasets
the city has released 1,000 datasets
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U poslednje dve godine,
grad je objavio 1000 skupova datoteka
grad je objavio 1000 skupova datoteka
na našem otvorenom portalu za podatke,
01:35
on our open data portal,
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01:37
and it's pretty awesome.
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što je zaista odlično.
01:39
So you go and look at data like this,
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Tako da možete otići na sajt
i pogledati ovakav podatak
i pogledati ovakav podatak
01:41
and instead of just counting
the number of cabs,
the number of cabs,
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i umesto brojanja taksija,
01:43
we can start to ask different questions.
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možemo postavljati različita pitanja.
01:45
So I had a question.
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Tako da ja imam pitanje.
01:46
When's rush hour in New York City?
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Kada je najveća gužva u Njujorku?
01:48
It can be pretty bothersome.
When is rush hour exactly?
When is rush hour exactly?
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Krajnje napadno pitanje.
Kada je tačno najveća gužva?
Kada je tačno najveća gužva?
01:51
And I thought to myself,
these cabs aren't just numbers,
these cabs aren't just numbers,
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I kažem sebi onda da
ovi taksiji nisu samo brojke,
ovi taksiji nisu samo brojke,
01:53
these are GPS recorders
driving around in our city streets
driving around in our city streets
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to su GPS odašiljači
koji voze našim ulicama
koji voze našim ulicama
01:56
recording each and every ride they take.
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snimajući svaku svoju vožnju.
01:58
There's data there,
and I looked at that data,
and I looked at that data,
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Postoje podaci
i ja sam pregledao te spise,
i ja sam pregledao te spise,
02:00
and I made a plot of the average speed of
taxis in New York City throughout the day.
taxis in New York City throughout the day.
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napravivši proračun prosečne brzine
taksista tokom dana u Njujorku.
taksista tokom dana u Njujorku.
02:04
You can see that from about midnight
to around 5:18 in the morning,
to around 5:18 in the morning,
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Možete videti da od ponoći
do oko 5:18 ujutru
do oko 5:18 ujutru
02:07
speed increases, and at that point,
things turn around,
things turn around,
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brzina raste i da se nakon toga
stvari menjaju
stvari menjaju
02:11
and they get slower and slower and slower
until about 8:35 in the morning,
until about 8:35 in the morning,
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i sve postaje sporije i sporije
do oko 8:35 ujutru
do oko 8:35 ujutru
02:15
when they end up at around
11 and a half miles per hour.
11 and a half miles per hour.
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kada voze oko 17 kilometara na čas.
02:18
The average taxi is going 11 and a half
miles per hour on our city streets,
miles per hour on our city streets,
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Prosečan taksi vozi tom brzinom
po gradskim ulicama.
po gradskim ulicama.
02:21
and it turns out it stays that way
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Ispadne da ostane pri toj brzini
02:23
for the entire day.
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tokom celog dana.
02:27
(Laughter)
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(Smeh)
02:28
So I said to myself, I guess
there's no rush hour in New York City.
there's no rush hour in New York City.
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Kako sam kazao, izgleda da nema
saobraćajnog špica u Njujorku.
saobraćajnog špica u Njujorku.
02:31
There's just a rush day.
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Postoji samo dnevni špic.
02:33
Makes sense. And this is important
for a couple of reasons.
for a couple of reasons.
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To ima smisla i to je važno
iz više razloga.
iz više razloga.
02:36
If you're a transportation planner,
this might be pretty interesting to know.
this might be pretty interesting to know.
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Ukoliko ste organizator transporta,
važno je da ovo znate.
važno je da ovo znate.
02:39
But if you want to get somewhere quickly,
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Ali ako treba negde brzo da stignete,
02:41
you now know to set your alarm for
4:45 in the morning and you're all set.
4:45 in the morning and you're all set.
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sada znate da treba da namestite
alarm na 4:45 ujutru i na konju ste.
alarm na 4:45 ujutru i na konju ste.
02:45
New York, right?
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Njujork, zar ne?
02:46
But there's a story behind this data.
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Ali postoji priča iza toga.
02:47
This data wasn't
just available, it turns out.
just available, it turns out.
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Ispostavlja se da podaci
nisu bili prosto dostupni.
nisu bili prosto dostupni.
02:50
It actually came from something called
a Freedom of Information Law Request,
a Freedom of Information Law Request,
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To je pošlo od nečega što se zove
Zahtev za zakon o slobodi informacija,
Zahtev za zakon o slobodi informacija,
02:53
or a FOIL Request.
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ili FOIL zahtev.
02:54
This is a form you can find on the
Taxi and Limousine Commission website.
Taxi and Limousine Commission website.
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Ovaj formular možete naći na stranici
Komisije za taksije i limuzine.
Komisije za taksije i limuzine.
02:58
In order to access this data,
you need to go get this form,
you need to go get this form,
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Kako biste dobili pristup podacima,
potrebno vam je ovo.
potrebno vam je ovo.
03:01
fill it out, and they will notify you,
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Popunite i obavestiće vas.
03:02
and a guy named Chris Whong
did exactly that.
did exactly that.
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Tip po imenu Kris Vong je to uradio.
03:05
Chris went down, and they told him,
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Kris je tamo otišao i rekli su mu:
03:06
"Just bring a brand new hard drive
down to our office,
down to our office,
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"Donesite nov hard disk u kancelariju,
03:09
leave it here for five hours,
we'll copy the data and you take it back."
we'll copy the data and you take it back."
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ostavite ga na pet sati
i prebacićemo vam te podatke."
i prebacićemo vam te podatke."
03:13
And that's where this data came from.
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Eto odakle ovi podaci.
03:15
Now, Chris is the kind of guy
who wants to make the data public,
who wants to make the data public,
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Sad, Kris je osoba
koja je htela da ih objavi
koja je htela da ih objavi
03:18
and so it ended up online for all to use,
and that's where this graph came from.
and that's where this graph came from.
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i tako su oni osvanuli na internetu
i eto odakle je ovaj grafikon.
i eto odakle je ovaj grafikon.
03:22
And the fact that it exists is amazing.
These GPS recorders -- really cool.
These GPS recorders -- really cool.
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Činjenica da postoji je neverovatna.
Ovi GPS prijemnici - stvarno sjajno.
Ovi GPS prijemnici - stvarno sjajno.
03:25
But the fact that we have citizens
walking around with hard drives
walking around with hard drives
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Ali sama činjenica da građani nose diskove
03:28
picking up data from city agencies
to make it public --
to make it public --
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i grabe podatke od agencija da
bi ih objavili -
bi ih objavili -
03:31
it was already kind of public,
you could get to it,
you could get to it,
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već su nekako ti podaci javni,
možete do njih doći,
možete do njih doći,
03:33
but it was "public," it wasn't public.
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ali te nisu javni, te jesu.
03:35
And we can do better than that as a city.
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Ali možemo više učiniti kao grad.
03:37
We don't need our citizens
walking around with hard drives.
walking around with hard drives.
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Ne moraju naši građani
da šetaju okolo sa hard diskovima.
da šetaju okolo sa hard diskovima.
03:40
Now, not every dataset
is behind a FOIL Request.
is behind a FOIL Request.
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Sad, nije sve obuhvaćeno
FOIL inicijativom.
FOIL inicijativom.
03:42
Here is a map I made with the most
dangerous intersections in New York City
dangerous intersections in New York City
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Evo mape najopasnijih
njujorških raskrsnica
njujorških raskrsnica
03:46
based on cyclist accidents.
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na osnovu nesreća biciklista.
03:48
So the red areas are more dangerous.
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Crvene zone su najopasnije.
03:50
And what it shows is first
the East side of Manhattan,
the East side of Manhattan,
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Mapa pokazuje istočnu stranu Menhetna,
03:52
especially in the lower area of Manhattan,
has more cyclist accidents.
has more cyclist accidents.
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posebno donje delove u kojima su
biciklističke nesreće češće.
biciklističke nesreće češće.
03:56
That might make sense
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To ima smisla
03:57
because there are more cyclists
coming off the bridges there.
coming off the bridges there.
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zato što ima više biciklista
koji prelaze mostove tamo.
koji prelaze mostove tamo.
04:00
But there's other hotspots worth studying.
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Ali vredi proučiti ključne tačke.
04:02
There's Williamsburg.
There's Roosevelt Avenue in Queens.
There's Roosevelt Avenue in Queens.
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To je Vilijamzburg,
to je Ruzveltova avenija u Kvinsu.
to je Ruzveltova avenija u Kvinsu.
04:04
And this is exactly the kind of data
we need for Vision Zero.
we need for Vision Zero.
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To je vrsta podataka
potrebna za Nultu viziju.
potrebna za Nultu viziju.
04:07
This is exactly what we're looking for.
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To je ono što upravo tražimo.
04:09
But there's a story
behind this data as well.
behind this data as well.
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Ali postoji priča iza tih podataka.
04:11
This data didn't just appear.
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Ti se podaci nisu sami pojavili.
04:13
How many of you guys know this logo?
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Koliko vas poznaje ovaj logo?
Da, vidim neke podignute ruke.
04:16
Yeah, I see some shakes.
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04:17
Have you ever tried to copy
and paste data out of a PDF
and paste data out of a PDF
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Da li ste ikada pokušali da iskopirate
nešto iz PDF-a
nešto iz PDF-a
04:20
and make sense of it?
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i dešifrujete to?
04:21
I see more shakes.
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Sad vidim više ruku.
04:22
More of you tried copying and pasting
than knew the logo. I like that.
than knew the logo. I like that.
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Više vas je nešto kopiralo nego što
prepoznaje logo. To mi se sviđa.
prepoznaje logo. To mi se sviđa.
04:26
So what happened is, the data
that you just saw was actually on a PDF.
that you just saw was actually on a PDF.
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Podaci koje ste upravo videli su
smešteni u PDF-u.
smešteni u PDF-u.
04:29
In fact, hundreds and hundreds
and hundreds of pages of PDF
and hundreds of pages of PDF
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U stvari, stotine, stotine i stotine
stranica u PDF-u
stranica u PDF-u
04:32
put out by our very own NYPD,
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koje je naša policija objavila
04:34
and in order to access it,
you would either have to copy and paste
you would either have to copy and paste
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a kako bi im pristupili,
ili morate da kopirate i lepite
ili morate da kopirate i lepite
04:38
for hundreds and hundreds of hours,
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stotinama sati
ili biste mogli biti Džon Kraus.
04:39
or you could be John Krauss.
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04:41
John Krauss was like,
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Džon Kraus nije hteo
04:42
I'm not going to copy and paste this data.
I'm going to write a program.
I'm going to write a program.
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da kopira podatke.
On je napisao program.
On je napisao program.
04:45
It's called the NYPD Crash Data Band-Aid,
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Zove se NYPD Crash Data Band-Aid
04:47
and it goes to the NYPD's website
and it would download PDFs.
and it would download PDFs.
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i odlazi na sajt policije
kako bi preuzeo PDF datoteke.
kako bi preuzeo PDF datoteke.
04:50
Every day it would search;
if it found a PDF, it would download it
if it found a PDF, it would download it
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Svakog dana bi pretraživao,
našao PDF fajl i preuzeo ga.
našao PDF fajl i preuzeo ga.
04:54
and then it would run
some PDF-scraping program,
some PDF-scraping program,
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Zatim bi pokrenuo neki program
koji otvara PDF
koji otvara PDF
04:56
and out would come the text,
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i ostavlja čisti tekst.
04:57
and it would go on the Internet,
and then people could make maps like that.
and then people could make maps like that.
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Potom bi podatke objavio na internetu
da bi ljudi mogli praviti ovakve mape.
da bi ljudi mogli praviti ovakve mape.
05:01
And the fact that the data's here,
the fact that we have access to it --
the fact that we have access to it --
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Činjenica da imamo podatke ovde
i da im možemo pristupiti -
i da im možemo pristupiti -
05:04
Every accident, by the way,
is a row in this table.
is a row in this table.
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usput, svaka nesreća je kolona
u ovoj tabeli.
u ovoj tabeli.
05:07
You can imagine how many PDFs that is.
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Možete zamisliti koliko je to fajlova.
05:08
The fact that we
have access to that is great,
have access to that is great,
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Sjajno je što možemo da im pristupimo,
05:11
but let's not release it in PDF form,
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ali hajde da ne budu u PDF-formatu,
05:13
because then we're having our citizens
write PDF scrapers.
write PDF scrapers.
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zato što onda građani moraju
da programiraju čitače.
da programiraju čitače.
05:15
It's not the best use
of our citizens' time,
of our citizens' time,
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Nije dobro da građani gube vreme,
05:18
and we as a city can do better than that.
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a mi kao grad možemo bolje od toga.
05:20
Now, the good news is that
the de Blasio administration
the de Blasio administration
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Sad, dobre su vesti da je
administracija De Blazija
administracija De Blazija
05:22
actually recently released this data
a few months ago,
a few months ago,
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objavila podatke pre nekoliko meseci,
05:25
and so now we can
actually have access to it,
actually have access to it,
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tako da im sad možemo pristupiti,
mada je velika količina podataka
i dalje zaglavljena u PDF-u.
i dalje zaglavljena u PDF-u.
05:27
but there's a lot of data
still entombed in PDF.
still entombed in PDF.
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05:29
For example, our crime data
is still only available in PDF.
is still only available in PDF.
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Na primer, podaci o stopi kriminala
su i dalje u tom formatu.
su i dalje u tom formatu.
05:33
And not just our crime data,
our own city budget.
our own city budget.
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Ne samo ti podaci, već i naš budžet.
05:36
Our city budget is only readable
right now in PDF form.
right now in PDF form.
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Podaci o budžetu su dostupni
samo u PDF formatu.
samo u PDF formatu.
05:40
And it's not just us
that can't analyze it --
that can't analyze it --
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Nismo samo mi ti koji ga
ne mogu analizirati,
ne mogu analizirati,
05:42
our own legislators
who vote for the budget
who vote for the budget
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već i naši zakonodavci koji su
glasali za njega,
glasali za njega,
05:45
also only get it in PDF.
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mogu ga dobiti samo u PDF-u.
05:47
So our legislators cannot analyze
the budget that they are voting for.
the budget that they are voting for.
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Tako naši zakonodavci ne mogu
analizirati budžet koji treba da donesu.
analizirati budžet koji treba da donesu.
05:51
And I think as a city we can do
a little better than that as well.
a little better than that as well.
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Smatram da mi kao grad možemo učiniti
nešto bolje od toga.
nešto bolje od toga.
Sad, postoje brojni podaci koji nisu
skriveni u PDF-u.
skriveni u PDF-u.
05:55
Now, there's a lot of data
that's not hidden in PDFs.
that's not hidden in PDFs.
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Primer toga je mapa koju sam izradio.
05:57
This is an example of a map I made,
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05:59
and this is the dirtiest waterways
in New York City.
in New York City.
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Ovo je jedan od najprljavijih
njujorških kanala.
njujorških kanala.
06:02
Now, how do I measure dirty?
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Kako da izmerim prljavštinu?
06:03
Well, it's kind of a little weird,
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Pomalo je čudno,
06:05
but I looked at the level
of fecal coliform,
of fecal coliform,
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ali kada pogledam nivo
fekalnog koliforma
fekalnog koliforma
06:07
which is a measurement of fecal matter
in each of our waterways.
in each of our waterways.
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koji je mera fekalnog otpada
u svakom našem kanalu.
u svakom našem kanalu.
06:11
The larger the circle,
the dirtier the water,
the dirtier the water,
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Što je veći krug, to je prljavija voda.
06:14
so the large circles are dirty water,
the small circles are cleaner.
the small circles are cleaner.
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Tako da je u velikim krugovima
prljava voda, dok su mali čistiji.
prljava voda, dok su mali čistiji.
Ovo što vidite su kanali u unutrašnjosti.
06:17
What you see is inland waterways.
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06:19
This is all data that was sampled
by the city over the last five years.
by the city over the last five years.
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Grad je ove podatke pet godina prikupljao.
06:22
And inland waterways are,
in general, dirtier.
in general, dirtier.
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Kanali u unutrašnjosti su
uglavnom prljaviji.
uglavnom prljaviji.
06:25
That makes sense, right?
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Ima smisla, zar ne?
06:26
And the bigger circles are dirty.
And I learned a few things from this.
And I learned a few things from this.
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Veći su prljaviji. Iz toga sam naučio
nekoliko stvari.
nekoliko stvari.
06:30
Number one: Never swim in anything
that ends in "creek" or "canal."
that ends in "creek" or "canal."
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Prvo, nikad ne plivajte u onom što
se naziva potokom ili kanalom.
se naziva potokom ili kanalom.
06:33
But number two: I also found
the dirtiest waterway in New York City,
the dirtiest waterway in New York City,
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Drugo, pronašao sam
najprljaviji njujorški kanal
najprljaviji njujorški kanal
06:37
by this measure, one measure.
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ovim merenjem, samo jednim merenjem.
06:39
In Coney Island Creek, which is not
the Coney Island you swim in, luckily.
the Coney Island you swim in, luckily.
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Radi se o Koni Ajlend Kriku, što nije
srećom Koni Ajlend u kom plivate.
srećom Koni Ajlend u kom plivate.
Ovaj je na drugoj strani.
06:43
It's on the other side.
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06:44
But Coney Island Creek, 94 percent
of samples taken over the last five years
of samples taken over the last five years
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Ali na prvom je 94% uzoraka
uzetih tokom pet godina
uzetih tokom pet godina
06:48
have had fecal levels so high
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imalo tako visok nivo fekalija
06:50
that it would be against state law
to swim in the water.
to swim in the water.
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da bi po državnom zakonu bilo
ilegalno plivati u toj vodi.
ilegalno plivati u toj vodi.
06:53
And this is not the kind of fact
that you're going to see
that you're going to see
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Ovakav podatak neće biti
naduvan u gradskom izveštaju, tačno?
06:56
boasted in a city report, right?
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06:57
It's not going to be
the front page on nyc.gov.
the front page on nyc.gov.
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Neće biti na prvoj stranici nyc.gov.
06:59
You're not going to see it there,
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Nećete ih tamo videti,
ali činjenica da možemo da dođemo
do tih podataka je sjajna.
do tih podataka je sjajna.
07:01
but the fact that we can get
to that data is awesome.
to that data is awesome.
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07:04
But once again, it wasn't super easy,
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Ali da kažem ponovo, to nije prelako
07:05
because this data was not
on the open data portal.
on the open data portal.
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zato što ovi podaci nisu
u javnoj bazi podataka.
u javnoj bazi podataka.
07:08
If you were to go to the open data portal,
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2013
Kada biste otvorili javnu bazu podataka,
07:10
you'd see just a snippet of it,
a year or a few months.
a year or a few months.
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to je samo mali deo njih,
godina ili par meseci.
godina ili par meseci.
07:12
It was actually on the Department
of Environmental Protection's website.
of Environmental Protection's website.
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Bili su u stvari na sajtu
Odseka za zaštitu okoline.
Odseka za zaštitu okoline.
07:16
And each one of these links is an Excel
sheet, and each Excel sheet is different.
sheet, and each Excel sheet is different.
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Svaki od ovih linkova je tabela u Ekselu,
a svaka Eksel tabela je drugačija.
a svaka Eksel tabela je drugačija.
Svaki naslov je različit: kopirate ga,
prenosite, reorganizujete.
prenosite, reorganizujete.
07:20
Every heading is different:
you copy, paste, reorganize.
you copy, paste, reorganize.
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07:22
When you do you can make maps
and that's great, but once again,
and that's great, but once again,
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Super je kada to radite i pravite mape,
ali još jednom,
ali još jednom,
07:25
we can do better than that
as a city, we can normalize things.
as a city, we can normalize things.
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mi to možemo bolje kao grad,
mi možemo normalizovati stvari.
mi možemo normalizovati stvari.
07:28
And we're getting there, because
there's this website that Socrata makes
there's this website that Socrata makes
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Mi dostižemo to zato što postoji sajt
koji je Sokrata napravila
koji je Sokrata napravila
i koji se zove Otvoreni portal Njujorka.
07:32
called the Open Data Portal NYC.
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07:33
This is where 1,100 data sets
that don't suffer
that don't suffer
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Ovo je bilo 1100 datoteka koje ne pate
07:35
from the things I just told you live,
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od stvari koje sam ovde naveo,
07:37
and that number is growing,
and that's great.
and that's great.
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a taj broj raste, što je odlično.
Možete preuzeti podatke u bilo kom formatu
poput CSV-a, PDF-a ili Eksela.
poput CSV-a, PDF-a ili Eksela.
07:39
You can download data in any format,
be it CSV or PDF or Excel document.
be it CSV or PDF or Excel document.
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07:43
Whatever you want,
you can download the data that way.
you can download the data that way.
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Šta god želeli, možete
preuzeti podatke na taj način.
preuzeti podatke na taj način.
Problem je što kada to uradite,
07:45
The problem is, once you do,
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07:47
you will find that each agency
codes their addresses differently.
codes their addresses differently.
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otkrićete da svaka agencija drukčije
kodira svoje adrese.
kodira svoje adrese.
07:50
So one is street name,
intersection street,
intersection street,
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2141
Tako da je jedno naziv ulice, raskrsnice,
07:52
street, borough, address, building,
building address.
building address.
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2491
opštine, adrese zgrade.
07:55
So once again, you're spending time,
even when we have this portal,
even when we have this portal,
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Još jednom trošite vreme
čak i kada imate ovaj portal,
čak i kada imate ovaj portal,
07:58
you're spending time
normalizing our address fields.
normalizing our address fields.
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trošite vreme sređujući adrese.
08:01
And that's not the best use
of our citizens' time.
of our citizens' time.
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A to nije najprikladnije trošenje
vremena građana.
vremena građana.
08:03
We can do better than that as a city.
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Možemo to bolje uraditi kao grad.
08:05
We can standardize our addresses,
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1645
Možemo standardizovati adrese.
08:07
and if we do,
we can get more maps like this.
we can get more maps like this.
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2185
Ako to uradimo, dobićemo ovakve mape.
08:09
This is a map of fire hydrants
in New York City,
in New York City,
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Ovo je mapa njujorških
nadzemnih hidranata,
nadzemnih hidranata,
ali ne bilo kakvih nadzemnih hidranata.
08:11
but not just any fire hydrants.
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08:13
These are the top 250 grossing fire
hydrants in terms of parking tickets.
hydrants in terms of parking tickets.
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4726
Ovo su 250 najboljih hidranata
što se tiče plaćanja kazni za parking.
što se tiče plaćanja kazni za parking.
08:17
(Laughter)
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1986
(Smeh)
08:19
So I learned a few things from this map,
and I really like this map.
and I really like this map.
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Naučio sam nekoliko stvari sa ove mape
i zaista je volim.
i zaista je volim.
08:23
Number one, just don't park
on the Upper East Side.
on the Upper East Side.
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2402
Pod jedan: ne parkirajte se na Aper Istu.
08:25
Just don't. It doesn't matter where
you park, you will get a hydrant ticket.
you park, you will get a hydrant ticket.
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Jednostavno nemojte. Gde god se
vi parkirali, dobićete kartu za hidrante.
vi parkirali, dobićete kartu za hidrante.
08:29
Number two, I found the two highest
grossing hydrants in all of New York City,
grossing hydrants in all of New York City,
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Pod dva: pronašao sam dva
najprofitnija njujorška hidranta,
najprofitnija njujorška hidranta,
08:33
and they're on the Lower East Side,
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1886
a oni su na nižem Ist Sajdu.
08:35
and they were bringing in over
55,000 dollars a year in parking tickets.
55,000 dollars a year in parking tickets.
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Donose više od 55 000 dolara godišnje
naplatom parkinga.
naplatom parkinga.
08:40
And that seemed a little strange
to me when I noticed it,
to me when I noticed it,
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Delovalo mi je čudno
kada sam to primetio,
kada sam to primetio,
08:42
so I did a little digging and it turns out
what you had is a hydrant
what you had is a hydrant
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tako da sam malo kopao
i ispalo je da postoji hidrant
i ispalo je da postoji hidrant
08:46
and then something called
a curb extension,
a curb extension,
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514244
1996
i proširenje za parkiranje
08:48
which is like a seven-foot
space to walk on,
space to walk on,
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2059
što je oko 2 metra prostora za hodanje,
08:50
and then a parking spot.
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1156
i mesto za parkiranje.
08:51
And so these cars came along,
and the hydrant --
and the hydrant --
205
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2254
Tako su se pojavili ovi automobili,
a hidrant -
a hidrant -
"Ima mesta ovde koliko hoćete,
meni je ok",
meni je ok",
08:53
"It's all the way over there, I'm fine,"
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1911
08:55
and there was actually a parking spot
painted there beautifully for them.
painted there beautifully for them.
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3474
dok u stvari za automobile postoji
obeleženo mesto za parkiranje.
obeleženo mesto za parkiranje.
08:59
They would park there, and the NYPD
disagreed with this designation
disagreed with this designation
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Tamo bi parkirali, dok se policija
sa tim ne bi složila
sa tim ne bi složila
09:02
and would ticket them.
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1058
i naplatila im kaznu.
09:03
And it wasn't just me
who found a parking ticket.
who found a parking ticket.
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Nisam samo ja bio taj
koji je pronašao kaznu za parking.
koji je pronašao kaznu za parking.
09:05
This is the Google
Street View car driving by
Street View car driving by
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Ovde je Guglov automobil koji prolazi
09:07
finding the same parking ticket.
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i nalazi istu kaznu za parking.
09:09
So I wrote about this on my blog,
on I Quant NY, and the DOT responded,
on I Quant NY, and the DOT responded,
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O ovome sam pisao na svom blogu,
I Quant NY, i Odeljenje za saobraćaj
I Quant NY, i Odeljenje za saobraćaj
je odgovorilo i rekli su:
09:13
and they said,
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541918
1020
"Iako Odeljenje za saobraćaj nije dobilo
nikakve zvanične žalbe za ovu lokaciju,
nikakve zvanične žalbe za ovu lokaciju,
09:14
"While the DOT has not received
any complaints about this location,
any complaints about this location,
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542938
3410
09:18
we will review the roadway markings
and make any appropriate alterations."
and make any appropriate alterations."
216
546348
4542
razmotrićemo oznake na putu
i napraviti neophodne izmene."
i napraviti neophodne izmene."
09:22
And I thought to myself,
typical government response,
typical government response,
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2959
Pomislio sam da je to
tipičan odgovor vlasti,
tipičan odgovor vlasti,
09:25
all right, moved on with my life.
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1881
i nastavio sam sa svojim životom.
09:27
But then, a few weeks later,
something incredible happened.
something incredible happened.
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Ali nekoliko nedelja kasnije,
desilo se nešto neverovatno.
desilo se nešto neverovatno.
09:31
They repainted the spot,
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2520
Prefarbali su to mesto
i na sekund sam pomislio da sam video
budućnost otvorenih podataka,
budućnost otvorenih podataka,
09:34
and for a second I thought I saw
the future of open data,
the future of open data,
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09:36
because think about what happened here.
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2000
jer razmislite o tome šta se ovde desilo.
09:38
For five years, this spot was being
ticketed, and it was confusing,
ticketed, and it was confusing,
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566910
5100
Godinama su se na ovom mestu
naplaćivale kazne i bilo je zbunjujuće,
naplaćivale kazne i bilo je zbunjujuće,
09:44
and then a citizen found something,
they told the city, and within a few weeks
they told the city, and within a few weeks
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a onda je građanin otkrio nešto,
to rekao gradu i za nekoliko nedelja
to rekao gradu i za nekoliko nedelja
09:48
the problem was fixed.
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problem je bio rešen.
To je neverovatno. Dosta ljudi vidi
otvorene podatke kao vrstu čuvara.
otvorene podatke kao vrstu čuvara.
09:49
It's amazing. And a lot of people
see open data as being a watchdog.
see open data as being a watchdog.
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3200
09:52
It's not, it's about being a partner.
227
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1772
Ne radi se o tome, već o partnerstvu.
09:54
We can empower our citizens
to be better partners for government,
to be better partners for government,
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Možemo osnažiti svoje građane
da budu bolji partneri vladi
da budu bolji partneri vladi
09:57
and it's not that hard.
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1881
i to nije tako teško.
Samo je potrebno nekoliko promena.
09:59
All we need are a few changes.
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10:01
If you're FOILing data,
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1107
Ukoliko zahtevate podatke,
10:02
if you're seeing your data
being FOILed over and over again,
being FOILed over and over again,
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2867
ukoliko vidite da ljudi stalno
od vas zahtevaju podatke,
od vas zahtevaju podatke,
10:05
let's release it to the public, that's
a sign that it should be made public.
a sign that it should be made public.
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3574
to je znak da treba da budu javni.
10:08
And if you're a government agency
releasing a PDF,
releasing a PDF,
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2482
A ako ste javna agencija
koja postavlja PDF dokument,
koja postavlja PDF dokument,
10:11
let's pass legislation that requires you
to post it with the underlying data,
to post it with the underlying data,
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599090
3649
donesimo zakon koji zahteva od vas
da ga postavite sa pratećim podacima
da ga postavite sa pratećim podacima
10:14
because that data
is coming from somewhere.
is coming from somewhere.
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602739
2028
zato što ti podaci dolaze odnekud.
10:16
I don't know where, but it's
coming from somewhere,
coming from somewhere,
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604767
2482
Ne znam odakle, ali odnekud
10:19
and you can release it with the PDF.
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607249
1725
i možete ih postaviti sa PDF-om.
I hajde da usvojimo i delimo
neke standarde o otvorenim podacima.
neke standarde o otvorenim podacima.
10:20
And let's adopt and share
some open data standards.
some open data standards.
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608974
2411
10:23
Let's start with our addresses
here in New York City.
here in New York City.
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2481
Počnimo sa adresama ovde u Njujorku.
10:25
Let's just start
normalizing our addresses.
normalizing our addresses.
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2074
Počnimo samo da normalizujemo
naše adrese.
naše adrese.
10:27
Because New York is a leader in open data.
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2062
Njujork vodi
što se tiče otvorenih podataka.
što se tiče otvorenih podataka.
10:30
Despite all this, we are absolutely
a leader in open data,
a leader in open data,
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Uprkos svemu, mi smo na vrhu
što se tiče otvorenih podataka
što se tiče otvorenih podataka
10:32
and if we start normalizing things,
and set an open data standard,
and set an open data standard,
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3121
i ako počnemo da normalizujemo stvari
i odredimo standard za otvorene podatke,
i odredimo standard za otvorene podatke,
10:35
others will follow. The state will follow,
and maybe the federal government,
and maybe the federal government,
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3634
drugi će nas pratiti - država,
a možda i savezna vlada.
a možda i savezna vlada.
10:39
Other countries could follow,
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Mogle bi da nas prate i druge države
10:40
and we're not that far off from a time
where you could write one program
where you could write one program
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3411
i nismo tako daleko od vremena
gde biste mogli da napišete program
gde biste mogli da napišete program
10:44
and map information from 100 countries.
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i mapirate informacije iz 100 država.
10:46
It's not science fiction.
We're actually quite close.
We're actually quite close.
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To nije naučna fantastika.
Zapravo smo veoma blizu.
Zapravo smo veoma blizu.
10:48
And by the way, who are we
empowering with this?
empowering with this?
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Usput, koga osnažujemo ovim?
10:51
Because it's not just John Krauss
and it's not just Chris Whong.
and it's not just Chris Whong.
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Nisu to samo Džon Kraus i Kris Vong.
10:54
There are hundreds of meetups
going on in New York City right now,
going on in New York City right now,
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Postoji stotine sastanaka
koji se sada dešavaju u Njujorku,
koji se sada dešavaju u Njujorku,
10:57
active meetups.
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aktivnih sastanaka.
10:58
There are thousands of people
attending these meetups.
attending these meetups.
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Postoji hiljade ljudi koji posećuju
ove sastanke.
ove sastanke.
11:00
These people are going after work
and on weekends,
and on weekends,
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Ti ljudi dolaze nakon posla
i tokom vikenda
i tokom vikenda
11:03
and they're attending these meetups
to look at open data
to look at open data
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i posećuju ove sastanke
da bi videli velike podatke
da bi videli velike podatke
11:05
and make our city a better place.
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i načinili naš grad boljim mestom.
11:07
Groups like BetaNYC, who just last week
released something called citygram.nyc
released something called citygram.nyc
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Grupe poput BetaNYC, koja je
prošle nedelje objavila citygram.nyc
prošle nedelje objavila citygram.nyc
koji vam omogućava da se pretplatite
na servis za žalbe
na servis za žalbe
11:11
that allows you to subscribe
to 311 complaints
to 311 complaints
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11:13
around your own home,
or around your office.
or around your office.
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oko vašeg doma ili kancelarije.
11:15
You put in your address,
you get local complaints.
you get local complaints.
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Ukucate svoju adresu,
dobijete lokalne žalbe.
dobijete lokalne žalbe.
11:18
And it's not just the tech community
that are after these things.
that are after these things.
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Nije samo zajednica informatičara
koja je uključena ovde.
koja je uključena ovde.
Tu su i arhitekte, poput studenata
kojima predajem na Pretu.
kojima predajem na Pretu.
11:21
It's urban planners like
the students I teach at Pratt.
the students I teach at Pratt.
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11:24
It's policy advocates, it's everyone,
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To su pravni zastupnici, to su svi,
11:25
it's citizens from a diverse
set of backgrounds.
set of backgrounds.
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građani iz raznih pozadina.
11:28
And with some small, incremental changes,
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I uz male, postepene promene,
11:31
we can unlock the passion
and the ability of our citizens
and the ability of our citizens
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možemo osloboditi strast
i mogućnosti naših građana
i mogućnosti naših građana
11:34
to harness open data
and make our city even better,
and make our city even better,
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da iskoriste otvorene podatke
i načine naš grad još boljim,
i načine naš grad još boljim,
bez obzira da li je to jedan po jedan
komplet podataka ili po parking mesto.
komplet podataka ili po parking mesto.
11:37
whether it's one dataset,
or one parking spot at a time.
or one parking spot at a time.
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11:41
Thank you.
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Hvala vam.
11:43
(Applause)
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(Aplauz)
ABOUT THE SPEAKER
Ben Wellington - Data scientistBen Wellington blends his love of statistics, the city, and comedy in his entertaining analysis of the story of New York City, told through data.
Why you should listen
Ben Wellington runs the I Quant NY blog, in which he crunches city-released data to find out what's really going on in the Big Apple. To date he has tackled topics such as measles outbreaks in New York City schools, analyzed how companies like Airbnb are really doing in NYC, and asked questions such as "does gentrification cause a reduction in laundromats?" (Answer: inconclusive.)
Ben is a visiting assistant professor in the City & Regional Planning program at the Pratt Institute in Brooklyn; his day job involves working as a quantitative analyst at the investment management firm, Two Sigma. A budding comedian and performer, he also teaches team building workshops through Cherub Improv, a non-profit that uses improv comedy for social good.
Ben Wellington | Speaker | TED.com