TEDxNewYork
Ben Wellington: How we found the worst place to park in New York City -- using big data
Ben Wellington: Kako smo našli najslabši prostor za parkiranje v New Yorku — z uporabo masivnih podatkov
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Readability: 3.7
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Mestne agencije imajo dostop do bogastva podatkov in statistik, ki odsevajo vsakodnevno mestno življenje. A kot analitik podatkov Ben Wellington predlaga v tem govoru, včasih ne vedo, kaj z njimi početi. Pokaže, kako kombinacija nepričakovanih vprašanj in pametna obdelava podatkov lahko prineseta nenavadno uporabne rezultate, in deli nasvete, kako izdajati velike sete podatkov, da bi jih lahko uporabil vsak.
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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9656 kilometrov cest,
00:15
600 miles of subway track,
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965 kilometrov podzemne železnice,
00:17
400 miles of bike lanes
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643 kilometrov kolesarskih poti,
00:19
and a half a mile of tram track,
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in 804 metri tramvajske proge
00:21
if you've ever been to Roosevelt Island.
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če ste kdaj bili na Roosevelt Island-u.
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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To so številke, ki sestavljajo
infrastrukturo mesta New York.
infrastrukturo mesta New York.
00:26
These are the statistics
of our infrastructure.
of our infrastructure.
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To 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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To so številke, ki jih lahko najdete
v poročilih mestnih agencij.
v poročilih mestnih agencij.
00:32
For example, the Department
of Transportation will probably tell you
of Transportation will probably tell you
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Na primer, Oddelek za transport
vam bo najbrž povedal
vam bo najbrž povedal
00:36
how many miles of road they maintain.
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koliko kilometrov ceste nadzorujejo.
00:37
The MTA will boast how many miles
of subway track there are.
of subway track there are.
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MTA se hvali, koliko km
podzemne železnice je tu.
podzemne železnice je tu.
00:40
Most city agencies give us statistics.
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Večina mestnih agencij nam da statistiko.
00:42
This is from a report this year
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To je poročilo iz tega leta
00:43
from the Taxi and Limousine Commission,
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iz Komisije za taksije in 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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kjer izvemo da je v mestu New York
13,500 taksijev.
13,500 taksijev.
00:49
Pretty interesting, right?
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Precej zanimivo, kajne?
00:50
But did you ever think about
where these numbers came from?
where these numbers came from?
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Ste kdaj pomislili,
odkod vse te številke?
odkod vse te številke?
00:53
Because for these numbers to exist,
someone at the city agency
someone at the city agency
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Da te številke obstajajo,
se je moral nekdo v mestni agenciji
se je moral nekdo v mestni agenciji
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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ustaviti in reči, hmm, tu je številka,
ki bi jo nekdo morda hotel vedeti.
ki bi jo nekdo morda hotel vedeti.
00:59
Here's a number
that our citizens want to know.
that our citizens want to know.
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Tu je številka,
ki jo želijo vedeti meščani.
ki jo želijo vedeti meščani.
01:02
So they go back to their raw data,
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Vzamejo gole podatke,
01:04
they count, they add, they calculate,
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štejejo, dodajajo, računajo
01:05
and then they put out reports,
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in izdajo poročila,
01:07
and those reports
will have numbers like this.
will have numbers like this.
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in ta poročila bodo imela številke,
kot so te.
kot so te.
01:09
The problem is, how do they know
all of our questions?
all of our questions?
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Težava je, kako vedo
vsa naša vprašanja?
vsa naša vprašanja?
01:11
We have lots of questions.
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Imamo veliko vprašanj.
01:13
In fact, in some ways there's literally
an infinite number of questions
an infinite number of questions
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Pravzaprav je tu na nek način
dobesedno neskončno število vprašanj,
dobesedno neskončno število vprašanj,
01:16
that we can ask about our city.
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ki jih lahko vprašamo o mestu.
01:18
The agencies can never keep up.
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Agencije nas ne dohajajo.
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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Paradigma torej ne deluje in mislim,
da naši odločevalci to vedo,
da naši odločevalci to vedo,
01:23
because in 2012, Mayor Bloomberg
signed into law what he called
signed into law what he called
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ker je leta 2012 župan Bloomberg
podpisal zakon, ki ga je označil za
podpisal zakon, ki ga je označil za
01:27
the most ambitious and comprehensive
open data legislation in the country.
open data legislation in the country.
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najbolj ambiciozen in izčrpen
zakon o dostopnih podatkih v državi.
zakon o dostopnih podatkih v državi.
01:31
In a lot of ways, he's right.
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V veliko stvareh, je imel prav.
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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V zadnjih dveh letih je mesto izdalo
1000 setov podatkov
1000 setov podatkov
01:35
on our open data portal,
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na našem portalu dostopnih podatkov
01:37
and it's pretty awesome.
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in to je precej neverjetno.
01:39
So you go and look at data like this,
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Greste in takole pogledate podatke
01:41
and instead of just counting
the number of cabs,
the number of cabs,
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in namesto da bi samo
šteli število taksijev,
šteli število taksijev,
01:43
we can start to ask different questions.
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lahko vprašate različna vprašanja.
01:45
So I had a question.
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Imel sem vprašanje.
01:46
When's rush hour in New York City?
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Kdaj je prometna konica
v New Yorku?
v New Yorku?
01:48
It can be pretty bothersome.
When is rush hour exactly?
When is rush hour exactly?
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Lahko je neprijetna.
Kdaj točno je prometna konica?
Kdaj točno je prometna konica?
01:51
And I thought to myself,
these cabs aren't just numbers,
these cabs aren't just numbers,
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In pomislil sem,
ti taksiji niso samo številke,
ti taksiji niso samo številke,
01:53
these are GPS recorders
driving around in our city streets
driving around in our city streets
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so GPS snemalniki, ki se vozijo
po naših mestnih cestah
po naših mestnih cestah
01:56
recording each and every ride they take.
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in posnamejo vsako vožnjo.
01:58
There's data there,
and I looked at that data,
and I looked at that data,
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Tu so podatki, in pogledal sem jih,
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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in grafično sem prikazal povprečno hitrost
taksijev v mestu New York čez dan.
taksijev v mestu New York čez dan.
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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Lahko vidite, da se od polnoči
do okrog 5:18 zjutraj
do okrog 5:18 zjutraj
02:07
speed increases, and at that point,
things turn around,
things turn around,
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hitrost poveča in ta tej točki
se stvari obrnejo
se stvari obrnejo
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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in postajajo počasnejše in počasnejše
in počasnejše do 8:35 zjutraj,
in počasnejše do 8:35 zjutraj,
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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ko končajo pri 18 kilometrih
in pol na uro.
in pol na uro.
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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Povprečen taksi gre 18 kilometrov
in pol na uro po naših cestah
in pol na uro po naših cestah
02:21
and it turns out it stays that way
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in izkaže se, da tako ostane
02:23
for the entire day.
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ves dan.
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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Pa sem si rekel, v mestu New York
prometna konica ne traja eno uro.
prometna konica ne traja eno uro.
02:31
There's just a rush day.
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Traja kar cel dan.
02:33
Makes sense. And this is important
for a couple of reasons.
for a couple of reasons.
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To ima smisel.
In je pomembno zaradi nekaj razlogov.
In je pomembno zaradi nekaj razlogov.
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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Če si načrtovalec prevoza,
je to zanimiv podatek.
je to zanimiv podatek.
02:39
But if you want to get somewhere quickly,
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A če bi rad nekam prišel hitro,
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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veš, da moraš nastaviti alarm
za 4:45 zjutraj in si pripravljen.
za 4:45 zjutraj in si pripravljen.
02:45
New York, right?
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New York, kajne?
02:46
But there's a story behind this data.
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A za temi podatki je zgodba.
02:47
This data wasn't
just available, it turns out.
just available, it turns out.
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Ti podatki, se izkaže,
niso bili kar na voljo.
niso bili kar na voljo.
02:50
It actually came from something called
a Freedom of Information Law Request,
a Freedom of Information Law Request,
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Prišli so iz obrazca po zakonu
o javnih informacijah,
o javnih informacijah,
02:53
or a FOIL Request.
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oziroma FOIL.
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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Na strani Komisije za taksije in limuzine
lahko najdete obrazec.
lahko najdete obrazec.
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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Da bi lahko dostopali do teh informacij,
03:01
fill it out, and they will notify you,
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morate izpolniti obrazec
in obvestili vas bodo
in obvestili vas bodo
03:02
and a guy named Chris Whong
did exactly that.
did exactly that.
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in možakar Chris Whong
je naredil točno to.
je naredil točno to.
03:05
Chris went down, and they told him,
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Chris je šel tja in povedali so mu:
03:06
"Just bring a brand new hard drive
down to our office,
down to our office,
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"Prinesi
nov trdi disk v našo pisarno,
nov trdi disk v našo pisarno,
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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pusti ga tu 5 ur, prekopirali bomo
podatke in ti ga vzameš nazaj."
podatke in ti ga vzameš nazaj."
03:13
And that's where this data came from.
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Od tu so prišli ti podatki.
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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Chris je take vrste tip,
ki bi rad te podatke naredil javne,
ki bi rad te podatke naredil javne,
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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zato so končali na spletu.
Odtod tale graf.
Odtod tale graf.
03:22
And the fact that it exists is amazing.
These GPS recorders -- really cool.
These GPS recorders -- really cool.
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Dejstvo, da to obstaja, je neverjetno.
Ti GPS snemalniki--res so kul.
Ti GPS snemalniki--res so kul.
03:25
But the fact that we have citizens
walking around with hard drives
walking around with hard drives
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A dejstvo, da meščani
s trdimi diski hodijo okrog agencij
s trdimi diski hodijo okrog agencij
03:28
picking up data from city agencies
to make it public --
to make it public --
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in dobivajo podatke,
da bi jih objavili--
da bi jih objavili--
03:31
it was already kind of public,
you could get to it,
you could get to it,
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bili so že delno javni, dostopni,
03:33
but it was "public," it wasn't public.
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a bili so "javni", ne pa javni.
03:35
And we can do better than that as a city.
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Veliko bolje lahko to naredimo kot mesto.
03:37
We don't need our citizens
walking around with hard drives.
walking around with hard drives.
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Ni treba, da naši meščani
hodijo naokrog z diski.
hodijo naokrog z diski.
03:40
Now, not every dataset
is behind a FOIL Request.
is behind a FOIL Request.
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Ni vsak set podatkov
posledica obrazca FOIL.
posledica obrazca FOIL.
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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Tu je zemljevid, ki sem ga naredil,
z najbolj nevarnimi križišči v New Yorku,
z najbolj nevarnimi križišči v New Yorku,
03:46
based on cyclist accidents.
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glede na kolesarske nesreče.
03:48
So the red areas are more dangerous.
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Rdeča področja so bolj nevarna.
03:50
And what it shows is first
the East side of Manhattan,
the East side of Manhattan,
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Prvo pokaže vzhodni del Manhattna,
03:52
especially in the lower area of Manhattan,
has more cyclist accidents.
has more cyclist accidents.
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še posebej nižji predel Manhattna,
ima več kolesarskih nesreč.
ima več kolesarskih nesreč.
03:56
That might make sense
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To ima morda smisel,
03:57
because there are more cyclists
coming off the bridges there.
coming off the bridges there.
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ker tu pride več kolesarjev z mostov.
04:00
But there's other hotspots worth studying.
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A tudi druge vroče točke
velja preučiti.
velja preučiti.
04:02
There's Williamsburg.
There's Roosevelt Avenue in Queens.
There's Roosevelt Avenue in Queens.
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Tu je Williamsburg.
Pa Rooseveltova Avenija v Queensu.
Pa Rooseveltova Avenija v Queensu.
04:04
And this is exactly the kind of data
we need for Vision Zero.
we need for Vision Zero.
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Točno take podatke
potrebujemo za Vizijo nič.
potrebujemo za Vizijo nič.
04:07
This is exactly what we're looking for.
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Točno to iščemo.
04:09
But there's a story
behind this data as well.
behind this data as well.
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A tudi za temi podatki stoji zgodba.
04:11
This data didn't just appear.
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Ti podatki se niso kar pojavili.
04:13
How many of you guys know this logo?
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Koliko vas pozna tale logo?
04:16
Yeah, I see some shakes.
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Ja, vidim nekaj kimanja.
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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Ste kdaj poskušali kopirati
podatke iz PDF formata
podatke iz PDF formata
04:20
and make sense of it?
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in jih nato urediti?
04:21
I see more shakes.
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Še več kimanja.
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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Več vas je kopiralo, kot pa
vas je poznalo logo. To mi je všeč.
vas je poznalo logo. To mi je všeč.
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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Zgodilo se je, da so bili ti podatki
pravzaprav v PDF formatu.
pravzaprav v PDF formatu.
04:29
In fact, hundreds and hundreds
and hundreds of pages of PDF
and hundreds of pages of PDF
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Pravzaprav na stotine
in stotine strani PDF,
in stotine strani PDF,
04:32
put out by our very own NYPD,
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ki jih je izdala naša NYPD,
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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in da bi prišli do njih,
bi morali kopirati in lepiti
bi morali kopirati in lepiti
04:38
for hundreds and hundreds of hours,
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na stotine in stotine ur,
04:39
or you could be John Krauss.
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ali pa bi bili John Krauss.
04:41
John Krauss was like,
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John Krauss je menil,
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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ne bom kopiral in lepil teh podatkov.
Program bom napisal.
Program bom napisal.
04:45
It's called the NYPD Crash Data Band-Aid,
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Imenuje se NYPD obliž za zlomljene podatke
04:47
and it goes to the NYPD's website
and it would download PDFs.
and it would download PDFs.
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in gre na stran NYPD in naloži PDF-je.
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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Vsak dan išče; če bi našel PDF,
bi ga naložil
bi ga naložil
04:54
and then it would run
some PDF-scraping program,
some PDF-scraping program,
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in potem bi zagnal nek PDF pretvornik
04:56
and out would come the text,
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in ven bi prišlo besedilo
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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in potem bi ga dal na internet,
da bi lahko ljudje delali take zemljevide.
da bi lahko ljudje delali take zemljevide.
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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Dejstvo, da so ti podatki tu,
dejstvo, da imamo do njih dostop--
dejstvo, da imamo do njih dostop--
05:04
Every accident, by the way,
is a row in this table.
is a row in this table.
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Vsaka nesreča, mimogrede,
je ena vrstica.
je ena vrstica.
05:07
You can imagine how many PDFs that is.
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Predstavljajte si količino PDF-jev.
05:08
The fact that we
have access to that is great,
have access to that is great,
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Dejstvo, da imamo
do tega dostop, je super,
do tega dostop, je super,
05:11
but let's not release it in PDF form,
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a ne izdajmo jih v PDF obliki,
05:13
because then we're having our citizens
write PDF scrapers.
write PDF scrapers.
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ker morajo potem naši meščani
napisati PDF pretvornike.
napisati PDF pretvornike.
05:15
It's not the best use
of our citizens' time,
of our citizens' time,
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To ni najboljša raba časa naših meščanov,
05:18
and we as a city can do better than that.
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in mi kot mesto lahko to storimo bolje.
05:20
Now, the good news is that
the de Blasio administration
the de Blasio administration
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Dobra novica je,
da je administracija župana De Blasia
da je administracija župana De Blasia
05:22
actually recently released this data
a few months ago,
a few months ago,
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pravzaprav izdala podatke
pred nekaj meseci,
pred nekaj meseci,
05:25
and so now we can
actually have access to it,
actually have access to it,
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tako da imamo sedaj
dostop do njih,
dostop do njih,
05:27
but there's a lot of data
still entombed in PDF.
still entombed in PDF.
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a še vedno je veliko podatkov
v formatu PDF.
v formatu PDF.
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, podatki o kriminalu
so še vedno dostopni samo v PDF.
so še vedno dostopni samo v PDF.
05:33
And not just our crime data,
our own city budget.
our own city budget.
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Ne samo podatki o kriminalu,
proračun našega mesta.
proračun našega mesta.
05:36
Our city budget is only readable
right now in PDF form.
right now in PDF form.
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Proračun našega mesta
lahko preberete samo v PDF obliki.
lahko preberete samo v PDF obliki.
05:40
And it's not just us
that can't analyze it --
that can't analyze it --
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Nismo edini,
ki ga ne moremo analizirati--
ki ga ne moremo analizirati--
05:42
our own legislators
who vote for the budget
who vote for the budget
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naši zakonodajalci,
ki glasujejo o proračunu,
ki glasujejo o proračunu,
05:45
also only get it in PDF.
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ga dobijo samo v PDF obliki.
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 zakonodajalec ne more
analizirati proračuna, o katerem glasuje.
analizirati proračuna, o katerem glasuje.
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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Mislim, da bi kot mesto
lahko izvedli tudi to malo bolje.
lahko izvedli tudi to malo bolje.
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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Veliko je podatkov,
ki niso skriti v PDF-jih.
ki niso skriti v PDF-jih.
05:57
This is an example of a map I made,
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To je primer zemljevida
05:59
and this is the dirtiest waterways
in New York City.
in New York City.
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in na njem so najbolj umazane
vodne poti v New Yorku.
vodne poti v New Yorku.
06:02
Now, how do I measure dirty?
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Kako merimo umazanost?
06:03
Well, it's kind of a little weird,
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No, malo je čudno,
06:05
but I looked at the level
of fecal coliform,
of fecal coliform,
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a pogledal sem raven fekalne bakterije,
06:07
which is a measurement of fecal matter
in each of our waterways.
in each of our waterways.
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s čimer merimo fekalije
v vsaki vodni poti.
v vsaki vodni poti.
06:11
The larger the circle,
the dirtier the water,
the dirtier the water,
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Večji ko je krog, bolj umazana je 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 so veliki krogi umazana voda,
manjši krogi so čistejši.
manjši krogi so čistejši.
06:17
What you see is inland waterways.
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To so kopenske vodne poti.
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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To so vsi podatki, ki bili pridobljeni
v mestu v zadnjih petih letih.
v mestu v zadnjih petih letih.
06:22
And inland waterways are,
in general, dirtier.
in general, dirtier.
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Kopenske vodne poti so
v večini bolj umazane.
v večini bolj umazane.
06:25
That makes sense, right?
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To je logično, kajne?
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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Večji krogi so bolj umazani.
Iz tega sem se naučil nekaj stvari.
Iz tega sem se naučil nekaj 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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Prvič: Nikoli ne plavaj v nečem
kar se konča s "potok" ali "kanal".
kar se konča s "potok" ali "kanal".
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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Drugič: prav tako sem našel
najbolj umazano vodno pot v New Yorku
najbolj umazano vodno pot v New Yorku
06:37
by this measure, one measure.
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s to meritvijo, eno meritvijo.
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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V potoku Coney Island, na srečo ne tistem,
v katerem plavate.
v katerem plavate.
06:43
It's on the other side.
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Je na drugi strani.
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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V potoku Coney Island ima 94 % vzorcev,
vzetih v zadnjih petih letih,
vzetih v zadnjih petih letih,
06:48
have had fecal levels so high
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tako visoko raven fekalij,
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 bilo protizakonito
plavati v tej vodi.
plavati v tej 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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In to ni ravno dejstvo,
s katerim bi se hvalili
s katerim bi se hvalili
06:56
boasted in a city report, right?
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v mestnem poročilu, kajne?
06:57
It's not going to be
the front page on nyc.gov.
the front page on nyc.gov.
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Ne bo na prvi strani nyc.gov.
06:59
You're not going to see it there,
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Tam ga ne boste videli,
07:01
but the fact that we can get
to that data is awesome.
to that data is awesome.
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a dejstvo, da lahko dobimo te podatke,
je neverjetno.
je neverjetno.
07:04
But once again, it wasn't super easy,
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A še enkrat, ni bilo zelo lahko,
07:05
because this data was not
on the open data portal.
on the open data portal.
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ker ti podatki niso bili
na prostem portalu.
na prostem portalu.
07:08
If you were to go to the open data portal,
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2013
Če bi odprli prost portal podatkov,
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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bi videli samo delček,
leto ali pa nekaj mesecev.
leto ali pa nekaj mesecev.
07:12
It was actually on the Department
of Environmental Protection's website.
of Environmental Protection's website.
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Bilo je na strani
Oddelka za okolje.
Oddelka za okolje.
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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Vsaka izmed teh povezav je Excelova tabela
in vsaka je drugačna.
in vsaka je drugačna.
07:20
Every heading is different:
you copy, paste, reorganize.
you copy, paste, reorganize.
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Vsak vrh strani je drugačen:
kopiraš, lepiš, preurejaš.
kopiraš, lepiš, preurejaš.
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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Nato lahko ustvariš zemljevid,
kar je super, a spet
kar je super, a spet
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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bi lahko kot mesto bolje storili,
lahko bi standardizirali stvari.
lahko bi standardizirali 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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Kmalu bomo tam, ker je tu stran,
ki jo izdeluje Socrata,
ki jo izdeluje Socrata,
07:32
called the Open Data Portal NYC.
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imenovana Portal prostih podatkov NYC.
07:33
This is where 1,100 data sets
that don't suffer
that don't suffer
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Tu je 1100 setov podatkov, ki ne trpijo
07:35
from the things I just told you live,
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za omenjenimi slabostmi,
07:37
and that number is growing,
and that's great.
and that's great.
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in ta številka raste, kar je enkratno.
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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Lahko si naložite podatke v kateremkoli
formatu, naj bo CSV ali PDF ali Excel.
formatu, naj bo CSV ali PDF ali Excel.
07:43
Whatever you want,
you can download the data that way.
you can download the data that way.
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Karkoli želite, lahko si
jih naložite na tak način.
jih naložite na tak način.
07:45
The problem is, once you do,
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Težava je, da ko to storite,
07:47
you will find that each agency
codes their addresses differently.
codes their addresses differently.
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boste videli, da vsaka agencija
kodira svoj naslov drugače.
kodira svoj naslov drugače.
07:50
So one is street name,
intersection street,
intersection street,
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Eno je ime ulice, ime križišča,
07:52
street, borough, address, building,
building address.
building address.
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2491
cesta, mestni okraj,
naslov, stavba, naslov stavbe.
naslov, stavba, naslov stavbe.
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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Tako da spet zapravljaš čas kljub portalu,
07:58
you're spending time
normalizing our address fields.
normalizing our address fields.
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zapravljaš čas z urejanjem
polj z naslovi.
polj z naslovi.
08:01
And that's not the best use
of our citizens' time.
of our citizens' time.
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To ni najboljša raba časa
naših državljanov.
naših državljanov.
08:03
We can do better than that as a city.
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1796
Kot mesto lahko to naredimo bolje.
08:05
We can standardize our addresses,
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1645
Lahko standardiziramo
naše naslove
naše naslove
08:07
and if we do,
we can get more maps like this.
we can get more maps like this.
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in če jih bomo,
dobimo več takih zemljevidov.
dobimo več takih zemljevidov.
08:09
This is a map of fire hydrants
in New York City,
in New York City,
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To je zemljevid gasilskih hidrantov
v New Yorku,
v New Yorku,
08:11
but not just any fire hydrants.
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a ne katerihkoli.
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
To je top 250 gasilskih hidrantov,
kar se tiče kazni za parkiranje.
kar se tiče kazni za parkiranje.
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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Nekaj stvari sem se naučil
iz tega zemljevida in zelo mi je všeč.
iz tega zemljevida in zelo mi je všeč.
08:23
Number one, just don't park
on the Upper East Side.
on the Upper East Side.
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Prvič, ne parkirajte
na zgornjem vzhodnem delu.
na zgornjem vzhodnem delu.
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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Enostavno ne. Ni važno, kje parkirate,
dobili boste kazen zaradi hidranta.
dobili boste kazen zaradi hidranta.
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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Drugič, našel sem dva največja
zaslužkarja med hidranti v New Yorku,
zaslužkarja med hidranti v New Yorku,
08:33
and they're on the Lower East Side,
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sta na nižjem delu vzhodne strani
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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in prinašata več kot 55 000 dolarjev letno
v kaznih za parkiranje.
v kaznih za parkiranje.
08:40
And that seemed a little strange
to me when I noticed it,
to me when I noticed it,
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Zdelo se mi je malo čudno,
ko sem to opazil,
ko sem to opazil,
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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zato sem malo pobrskal in izkaže se,
da je tam hidrant
da je tam hidrant
08:46
and then something called
a curb extension,
a curb extension,
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nato nekaj, imenovano
podaljšek robnika,
podaljšek robnika,
08:48
which is like a seven-foot
space to walk on,
space to walk on,
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kar je dvometrski prostor za hojo,
08:50
and then a parking spot.
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1156
in potem parkirno mesto.
08:51
And so these cars came along,
and the hydrant --
and the hydrant --
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2254
Potem pridejo ti avti, in hidrant--
08:53
"It's all the way over there, I'm fine,"
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1911
"Saj je tam daleč, v redu je."
08:55
and there was actually a parking spot
painted there beautifully for them.
painted there beautifully for them.
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Tam je bilo parkirno mesto zanje
lepo narisano.
lepo narisano.
08:59
They would park there, and the NYPD
disagreed with this designation
disagreed with this designation
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Parkirali so tam in NYPD
se ni strinjal z njimi
se ni strinjal z njimi
09:02
and would ticket them.
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in dobili so kazen.
09:03
And it wasn't just me
who found a parking ticket.
who found a parking ticket.
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2344
Nisem samo jaz dobil kazni.
09:05
This is the Google
Street View car driving by
Street View car driving by
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To je Googlov avto Street view,
09:07
finding the same parking ticket.
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1617
ki je dobil isto kazen.
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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4504
O tem sem pisal na blogu
I Quant NY. Urad za transport se je odzval.
I Quant NY. Urad za transport se je odzval.
09:13
and they said,
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1020
Dejali so:
09:14
"While the DOT has not received
any complaints about this location,
any complaints about this location,
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3410
Čeprav nismo dobili nobene pritožbe
o tej lokaciji,
o tej lokaciji,
09:18
we will review the roadway markings
and make any appropriate alterations."
and make any appropriate alterations."
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bomo preverili označbe na cestah
in naredili potrebne popravke.
in naredili potrebne popravke.
09:22
And I thought to myself,
typical government response,
typical government response,
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2959
Mislil sem si, tipičen vladni odziv,
09:25
all right, moved on with my life.
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1881
v redu, in nadaljeval z življenjem.
09:27
But then, a few weeks later,
something incredible happened.
something incredible happened.
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A čez nekaj tednov
se je zgodilo nekaj neverjetnega.
se je zgodilo nekaj neverjetnega.
09:31
They repainted the spot,
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To mesto so prebarvali
09:34
and for a second I thought I saw
the future of open data,
the future of open data,
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in za sekundo sem videl prihodnost
prostih podatkov,
prostih podatkov,
09:36
because think about what happened here.
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2000
ker pomislite, kaj se je zgodilo tu.
09:38
For five years, this spot was being
ticketed, and it was confusing,
ticketed, and it was confusing,
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5100
Pet let so delili kazni na tem mestu
in bilo je nejasno,
in bilo je nejasno,
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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potem je meščan našel nekaj, to povedal
in čez nekaj tednov
in čez nekaj tednov
09:48
the problem was fixed.
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1294
so problem rešili.
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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Neverjetno je. Veliko ljudi vidi
odprte podatke kot psa čuvaja.
odprte podatke kot psa čuvaja.
09:52
It's not, it's about being a partner.
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Niso, tu gre za partnerstvo.
09:54
We can empower our citizens
to be better partners for government,
to be better partners for government,
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3138
Lahko damo moč državljanom,
da bodo boljši partnerji vladi,
da bodo boljši partnerji vladi,
09:57
and it's not that hard.
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1881
in to ni tako težko.
09:59
All we need are a few changes.
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1459
Potrebnih je le malo sprememb.
10:01
If you're FOILing data,
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1107
Ko "FOILate" 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
če vidite da so podatki
"FOILani" spet in spet,
"FOILani" spet in spet,
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
jih naredite javne, to je znak,
da jih naredite javne.
da jih naredite javne.
10:08
And if you're a government agency
releasing a PDF,
releasing a PDF,
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2482
Če ste vladna agencija, ki izda PDF,
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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3649
sprejmimo zakon, da ga morate izdati
skupaj z osnovnimi podatki,
skupaj z osnovnimi podatki,
10:14
because that data
is coming from somewhere.
is coming from somewhere.
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2028
ker so ti podatki morali od nekje priti,
10:16
I don't know where, but it's
coming from somewhere,
coming from somewhere,
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2482
ne vem, od kje, a prišli so od nekod,
10:19
and you can release it with the PDF.
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1725
in to lahko izdate poleg PDF.
10:20
And let's adopt and share
some open data standards.
some open data standards.
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2411
Osvojimo in delimo nekaj standardov.
10:23
Let's start with our addresses
here in New York City.
here in New York City.
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2481
Začnimo z naslovi v New Yorku.
10:25
Let's just start
normalizing our addresses.
normalizing our addresses.
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2074
Začnimo normalizirati naše naslove.
10:27
Because New York is a leader in open data.
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2062
New York je prvi v prostih podatkih.
10:30
Despite all this, we are absolutely
a leader in open data,
a leader in open data,
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Kljub vsemu smo absolutni prvaki
v prostih podatkih.
v prostih podatkih.
10:32
and if we start normalizing things,
and set an open data standard,
and set an open data standard,
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Če začnemo normalizirati stvari
in postavimo standarde,
in postavimo standarde,
10:35
others will follow. The state will follow,
and maybe the federal government,
and maybe the federal government,
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3634
bodo drugi sledili.
Država, morda federalna vlada.
Država, morda federalna vlada.
10:39
Other countries could follow,
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1445
Druge države bodo sledile,
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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628991
3411
in nismo daleč od časa,
ko bomo lahko napisali program
ko bomo lahko napisali program
10:44
and map information from 100 countries.
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1890
in načrtali informacije 100 držav.
10:46
It's not science fiction.
We're actually quite close.
We're actually quite close.
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Ni znanstvena fantastika.
Zelo blizu smo.
Zelo blizu smo.
10:48
And by the way, who are we
empowering with this?
empowering with this?
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Komu smo s tem dali moč?
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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Ker ni samo John Krauss
in ni samo Chris Whong.
in ni samo Chris Whong.
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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Na stotine srečanj se dogaja
v New Yorku,
v New Yorku,
10:57
active meetups.
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aktivnih srečanj.
10:58
There are thousands of people
attending these meetups.
attending these meetups.
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Na tisoče ljudi se udeležuje teh srečanj.
11:00
These people are going after work
and on weekends,
and on weekends,
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Ti ljudje pridejo po službi in ob vikendih
11:03
and they're attending these meetups
to look at open data
to look at open data
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in se udeležujejo srečanj,
in gledajo proste podatke
in gledajo proste podatke
11:05
and make our city a better place.
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in izboljšali naše mesto.
11:07
Groups like BetaNYC, who just last week
released something called citygram.nyc
released something called citygram.nyc
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Skupine kot je BetaNYC. Ta je ravno
prejšnji teden izdala citygram.nyc.
prejšnji teden izdala citygram.nyc.
11:11
that allows you to subscribe
to 311 complaints
to 311 complaints
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ki vam dovoli, da se naročite
na pritožbe 311
na pritožbe 311
11:13
around your own home,
or around your office.
or around your office.
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okrog svojega doma
ali okoli svoje pisarne.
ali okoli svoje pisarne.
11:15
You put in your address,
you get local complaints.
you get local complaints.
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Vpišeš svoj naslov
in dobiš lokalne pritožbe.
in dobiš lokalne pritožbe.
11:18
And it's not just the tech community
that are after these things.
that are after these things.
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In ni samo tehnološka skupnost
navdušena nad tem.
navdušena nad tem.
11:21
It's urban planners like
the students I teach at Pratt.
the students I teach at Pratt.
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Krajinski arhitekti, kot tisti,
ki jih učim na Prattu.
ki jih učim na Prattu.
11:24
It's policy advocates, it's everyone,
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Podporniki zakonov, vsi.
11:25
it's citizens from a diverse
set of backgrounds.
set of backgrounds.
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Meščani iz različnih okolij.
11:28
And with some small, incremental changes,
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In z nekaj majhnimi,
postopnimi spremembami
postopnimi spremembami
11:31
we can unlock the passion
and the ability of our citizens
and the ability of our citizens
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lahko odklenemo strast
in sposobnost naših državljanov,
in sposobnost naših državljanov,
11:34
to harness open data
and make our city even better,
and make our city even better,
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da izkoristimo proste podatke,
da izboljšamo naše mesto,
da izboljšamo naše 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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naj gre za en set podatkov
ali eno parkirno mesto naenkrat.
ali eno parkirno mesto naenkrat.
11:41
Thank you.
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Hvala.
11:43
(Applause)
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(Aplavz)
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