TEDxNewYork
Ben Wellington: How we found the worst place to park in New York City -- using big data
本▪威靈頓: 如何找到紐約市最差的停車位？——用大數據說話
Filmed:
Readability: 3.7
1,055,247 views
市政機關擁有反映城市生活方方面面的龐大統計數據。但是，正如數據分析師本▪威靈頓在他有趣的演講中提到的，政府經常不知道如何使用這些數據。他展示了如何結合出乎意料的問題和機智的數據處理，從而產生奇特而有用的洞見，并分享了如何分享大規模數據庫供民眾使用的相關資訊。
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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六千英里公路，
00:15
600 miles of subway track,
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六百英里地鐵路線，
00:17
400 miles of bike lanes
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四百英里腳踏車專用道，
00:19
and a half a mile of tram track,
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半英里的有軌電車專用道
00:21
if you've ever been to Roosevelt Island.
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僅在羅斯福島。
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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這些數字構成了紐約市的基建。
這些基建的統計數字，
00:26
These are the statistics
of our infrastructure.
of our infrastructure.
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都可以在市政機關公佈的報告中找到。
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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譬如，交通部門可能會告訴你，
00:32
For example, the Department
of Transportation will probably tell you
of Transportation will probably tell you
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他們維護這多少英里的道路。
MTA（紐約交通運輸管理局）會自誇
他們掌管著多少英里捷運。
他們掌管著多少英里捷運。
00:36
how many miles of road they maintain.
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00:37
The MTA will boast how many miles
of subway track there are.
of subway track there are.
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多數的市政機關都在公佈統計數據。
00:40
Most city agencies give us statistics.
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這是今年計程車與轎車委員會發佈的報告，
00:42
This is from a report this year
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00:43
from the Taxi and Limousine Commission,
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我們從中知道紐約市運營著
大約一萬三千五百輛計程車。
大約一萬三千五百輛計程車。
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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很有趣，是嗎？
00:49
Pretty interesting, right?
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但你有否想過這些數據來自哪裡？
00:50
But did you ever think about
where these numbers came from?
where these numbers came from?
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既然有這些數字存在，
那肯定是因為在市政機關的某個人
那肯定是因為在市政機關的某個人
00:53
Because for these numbers to exist,
someone at the city agency
someone at the city agency
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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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想過：嗯......這個數字可能有人會想知道。
這個數字是市民們想知道的。
00:59
Here's a number
that our citizens want to know.
that our citizens want to know.
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所以他們找回那些原始數據，
01:02
So they go back to their raw data,
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他們計數、相加、計算，
01:04
they count, they add, they calculate,
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然後把得出的結果寫進報告中，
01:05
and then they put out reports,
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所以那些報告中會有這樣的數字。
01:07
and those reports
will have numbers like this.
will have numbers like this.
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那麼問題來了：他們怎麼會知道
我們的問題都是什麼？
我們的問題都是什麼？
01:09
The problem is, how do they know
all of our questions?
all of our questions?
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我們有很多問題。
01:11
We have lots of questions.
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01:13
In fact, in some ways there's literally
an infinite number of questions
an infinite number of questions
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事實上，可以說我們有無窮無盡的問題
有關我們這座城市。
01:16
that we can ask about our city.
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市政機關可無法跟得上（我們的節奏）。
01:18
The agencies can never keep up.
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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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現有模式並不具有實效，我覺得
我們的政策制定者也知道這點，
我們的政策制定者也知道這點，
01:23
because in 2012, Mayor Bloomberg
signed into law what he called
signed into law what he called
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因為在2012年，彭博市長
簽署了一個法令，他稱之為
簽署了一個法令，他稱之為
01:27
the most ambitious and comprehensive
open data legislation in the country.
open data legislation in the country.
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全美最具雄心和綜合性的
開放數據立法。
開放數據立法。
01:31
In a lot of ways, he's right.
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從各種意義上來說，他是對的。
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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在過去兩年中，市政有1000個數據庫
01:35
on our open data portal,
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放在我們的開放數據門戶網站上，
01:37
and it's pretty awesome.
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還是蠻驚人的。
01:39
So you go and look at data like this,
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我們來檢視這些數據，
01:41
and instead of just counting
the number of cabs,
the number of cabs,
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除了數數計程車的數量，
01:43
we can start to ask different questions.
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我們也能開始問不一樣的問題了。
01:45
So I had a question.
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我有一個問題：
01:46
When's rush hour in New York City?
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紐約市的交通高峰在什麼時候？
01:48
It can be pretty bothersome.
When is rush hour exactly?
When is rush hour exactly?
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這簡直煩人。高峰到底是什麼時候？
01:51
And I thought to myself,
these cabs aren't just numbers,
these cabs aren't just numbers,
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我想到，這些計程車可不僅僅是個數字，
01:53
these are GPS recorders
driving around in our city streets
driving around in our city streets
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它們可以是開遍全市道路的GPS記錄儀，
01:56
recording each and every ride they take.
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記錄著乘客的每一差車程。
01:58
There's data there,
and I looked at that data,
and I looked at that data,
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數據是現成的。我檢視它們，
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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並制出一張圖表，標出
一天中紐約市計程車的平均時速。
一天中紐約市計程車的平均時速。
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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大家可以看到，
從半夜到凌晨五點十八分，
從半夜到凌晨五點十八分，
02:07
speed increases, and at that point,
things turn around,
things turn around,
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時速一直在增加，然後到了拐點，
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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時速逐漸下降，在早間的八點三十五分，
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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時速降到十一英里半。
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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運營中計程車的平均時速
保持在十一英里半，
保持在十一英里半，
02:21
and it turns out it stays that way
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結果沒有變化，
02:23
for the entire day.
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整天都是如此。
02:27
(Laughter)
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（笑聲）
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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我告訴自己，紐約市並不存在高峰時段，
而是全天都高峰。
02:31
There's just a rush day.
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02:33
Makes sense. And this is important
for a couple of reasons.
for a couple of reasons.
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這是個有意義的結論，原因有幾點。
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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如果你是做交通規劃的，
知道這個結論會有意義。
知道這個結論會有意義。
02:39
But if you want to get somewhere quickly,
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如果你要快速到達某地，
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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只要把鬧鐘定在凌晨四點四十五分就行了。
紐約嘛！
02:45
New York, right?
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但這個數據背後還有故事。
02:46
But there's a story behind this data.
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這個數據並不真的是現成的。
02:47
This data wasn't
just available, it turns out.
just available, it turns out.
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你需要做一個「信息自由法案申請」，
02:50
It actually came from something called
a Freedom of Information Law Request,
a Freedom of Information Law Request,
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也叫「FOIL申請」。
02:53
or a FOIL Request.
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你可以在計程車和轎車委員會的網站上
找到相關申請表。
找到相關申請表。
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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如果要獲得這些數據，
你要弄到這張申請表，
你要弄到這張申請表，
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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填好上交，受理人員屆時會通知你。
03:01
fill it out, and they will notify you,
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一個叫克里斯▪旺的人就這樣做了。
03:02
and a guy named Chris Whong
did exactly that.
did exactly that.
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克里斯來到委員會，工作人員告訴他
03:05
Chris went down, and they told him,
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03:06
"Just bring a brand new hard drive
down to our office,
down to our office,
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「帶個全新的硬盤來辦公室，
我們會把相關數據拷貝給你，
過五小時來拿。」
過五小時來拿。」
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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03:13
And that's where this data came from.
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這就是拿到數據的經過。
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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克里斯想公開這些數據，
於是放到網路上供所有人使用，
所以我才能做出這張圖。
所以我才能做出這張圖。
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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這一切——這些GPS記錄儀真是酷。
03:22
And the fact that it exists is amazing.
These GPS recorders -- really cool.
These GPS recorders -- really cool.
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03:25
But the fact that we have citizens
walking around with hard drives
walking around with hard drives
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但是，市民要攜帶自己的移動硬盤
踏遍市政機關，
然後通過自己的努力公開，這件事——
然後通過自己的努力公開，這件事——
03:28
picking up data from city agencies
to make it public --
to make it public --
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03:31
it was already kind of public,
you could get to it,
you could get to it,
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政府數據可以說是公開的，
普通市民能得到它，
普通市民能得到它，
03:33
but it was "public," it wasn't public.
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但這只是名義上的「公開」，
並不是真正的公開。
並不是真正的公開。
我們的城市可以做得更好。
03:35
And we can do better than that as a city.
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我們不需要費力帶著移動硬盤到處跑。
03:37
We don't need our citizens
walking around with hard drives.
walking around with hard drives.
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並不是每一個數據庫都需要FOIL申請。
03:40
Now, not every dataset
is behind a FOIL Request.
is behind a FOIL Request.
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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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我做的這張地圖標出了紐約市最危險的路口，
03:46
based on cyclist accidents.
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來源是腳踏車騎行者的交通事故數據。
03:48
So the red areas are more dangerous.
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紅色區域更危險，
03:50
And what it shows is first
the East side of Manhattan,
the East side of Manhattan,
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圖上顯示，首先，曼哈頓的東側，
03:52
especially in the lower area of Manhattan,
has more cyclist accidents.
has more cyclist accidents.
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特別是曼哈頓的下城區域，
腳踏車事故更多。
腳踏車事故更多。
03:56
That might make sense
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這可能是因為，
03:57
because there are more cyclists
coming off the bridges there.
coming off the bridges there.
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在這裡有更多的騎行者從大橋下來。
04:00
But there's other hotspots worth studying.
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圖上還有其他的熱點區域值得研究。
威廉姆斯堡、皇后區的羅斯福大道，
04:02
There's Williamsburg.
There's Roosevelt Avenue in Queens.
There's Roosevelt Avenue in Queens.
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04:04
And this is exactly the kind of data
we need for Vision Zero.
we need for Vision Zero.
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這些咨詢才是Vision Zero項目所需要的。
這正是我們要找的東西。
04:07
This is exactly what we're looking for.
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04:09
But there's a story
behind this data as well.
behind this data as well.
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這個數據背後也有個故事。
04:11
This data didn't just appear.
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這個數據並不是現成的。
04:13
How many of you guys know this logo?
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有多少人知道這個符號？
04:16
Yeah, I see some shakes.
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我看到有人點頭了。
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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你們有沒有試過從PDF文檔中
拷貝和黏貼數據，
拷貝和黏貼數據，
04:20
and make sense of it?
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並據此作出結論呢？
04:21
I see more shakes.
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我看到更多人點頭了。
試圖拷貝粘貼的人
比認識這個標誌的人更多，真有趣。
比認識這個標誌的人更多，真有趣。
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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你們剛剛看到的數據是做在PDF裡的。
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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04:29
In fact, hundreds and hundreds
and hundreds of pages of PDF
and hundreds of pages of PDF
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事實上，是成千上萬頁的PDF文檔，
04:32
put out by our very own NYPD,
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由我們的紐約警署發佈。
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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如果你想享用這些數據，
你要不就持續
你要不就持續
做複製黏貼的動作，花掉成千上萬小時，
04:38
for hundreds and hundreds of hours,
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要不就像約翰▪克勞斯一樣。
04:39
or you could be John Krauss.
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04:41
John Krauss was like,
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約翰▪克勞斯
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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可不想重複地去複製黏貼，
他寫了一個程式。
他寫了一個程式。
這個程序叫做
「紐約警署交通事故數據OK蹦」，
「紐約警署交通事故數據OK蹦」，
04:45
It's called the NYPD Crash Data Band-Aid,
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04:47
and it goes to the NYPD's website
and it would download PDFs.
and it would download PDFs.
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它能到紐約警署的網站下載PDF文檔，
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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每天它都去搜索；
如果找到一個PDF文檔，就下載下來，
如果找到一個PDF文檔，就下載下來，
04:54
and then it would run
some PDF-scraping program,
some PDF-scraping program,
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然後運行某個PDF解碼的程式，
04:56
and out would come the text,
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把其中的文字信息提取出來，
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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其中的訊息會發佈在網路上，
人們就可以製作這些地圖。
人們就可以製作這些地圖。
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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這些數據就在那兒，我們都能得到——
05:04
Every accident, by the way,
is a row in this table.
is a row in this table.
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每一個交通事故就是一行數據。
05:07
You can imagine how many PDFs that is.
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你們可以想像有多少PDF需要轉碼。
05:08
The fact that we
have access to that is great,
have access to that is great,
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——我們能看到這些數據固然好，
05:11
but let's not release it in PDF form,
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但能不能不要弄成PDF格式的，
05:13
because then we're having our citizens
write PDF scrapers.
write PDF scrapers.
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不然市民們就得去寫PDF解碼的程式，
05:15
It's not the best use
of our citizens' time,
of our citizens' time,
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這對市民的時間來說是一種浪費，
而我們的城市能做的更好。
05:18
and we as a city can do better than that.
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05:20
Now, the good news is that
the de Blasio administration
the de Blasio administration
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有個好消息，白思豪市長的班底
05:22
actually recently released this data
a few months ago,
a few months ago,
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在幾個月前公開了這份數據，
05:25
and so now we can
actually have access to it,
actually have access to it,
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所以我們能直接享用這些數據，
05:27
but there's a lot of data
still entombed in PDF.
still entombed in PDF.
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然而還有很多數據是PDF格式的。
05:29
For example, our crime data
is still only available in PDF.
is still only available in PDF.
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譬如，我們的罪案數據目前只有PDF格式的。
05:33
And not just our crime data,
our own city budget.
our own city budget.
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除了罪案數據，市政預算也是如此。
05:36
Our city budget is only readable
right now in PDF form.
right now in PDF form.
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目前我們的市政預算只有PDF格式的。
05:40
And it's not just us
that can't analyze it --
that can't analyze it --
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不僅是我們無法分析這些數字，
05:42
our own legislators
who vote for the budget
who vote for the budget
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那些為市政預算投票的立法委員們
05:45
also only get it in PDF.
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也只能拿到PDF版本的數字。
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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所以我們的立法委員是無法分析
他們要為之投票的市政預算的。
他們要為之投票的市政預算的。
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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我認為我們的城市還能做得更好。
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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很多數據已經不躲在PDF中了。
05:57
This is an example of a map I made,
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這裡有一幅地圖可以作為例證，
05:59
and this is the dirtiest waterways
in New York City.
in New York City.
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標示了紐約市最骯髒的水路。
06:02
Now, how do I measure dirty?
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我是如何衡量「骯髒」的呢?
06:03
Well, it's kind of a little weird,
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這裡有些奇怪，
06:05
but I looked at the level
of fecal coliform,
of fecal coliform,
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我衡量的是糞便大腸菌群的水平，
06:07
which is a measurement of fecal matter
in each of our waterways.
in each of our waterways.
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這是水路中糞便物質的一種衡量指標。
06:11
The larger the circle,
the dirtier the water,
the dirtier the water,
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圓圈越大，水就越髒，
06:14
so the large circles are dirty water,
the small circles are cleaner.
the small circles are cleaner.
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所以圖上的大圓圈代表髒水，
小圓圈代表乾淨的水。
小圓圈代表乾淨的水。
06:17
What you see is inland waterways.
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大家看到的是內河水道。
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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這裡有紐約市過去五年採樣的所有數據。
06:22
And inland waterways are,
in general, dirtier.
in general, dirtier.
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內河水道總的來說變髒了。
06:25
That makes sense, right?
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這個結論挺合理的，對嗎？
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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大圓圈代表髒水。
我從中學到了幾件事情。
我從中學到了幾件事情。
06:30
Number one: Never swim in anything
that ends in "creek" or "canal."
that ends in "creek" or "canal."
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第一：千萬別在任何叫做「xx溪」
或「xx運河」的地方游泳。
或「xx運河」的地方游泳。
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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但是第二：紐約市最髒的水路，
06:37
by this measure, one measure.
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只看（糞便大腸菌群）這個唯一的指標，
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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在康尼島溪，幸好不是你們游泳的康尼島。
06:43
It's on the other side.
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那在島的另一面。
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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但在康尼島溪中，
過去五年的採樣中有94%
過去五年的採樣中有94%
06:48
have had fecal levels so high
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含有超標的糞便含量，
06:50
that it would be against state law
to swim in the water.
to swim in the water.
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以至於達到州法律禁止游泳的水平。
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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這種類型的事實
你可不會在市政報告中看到，不是嗎？
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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這也不會登上紐約市政府網站的頭條。
06:59
You're not going to see it there,
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我們肯定不會看到的，
但能看到這些數據真實不錯。
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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同樣，拿到這些數據並不容易，
07:05
because this data was not
on the open data portal.
on the open data portal.
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因為它們並不在公開數據門戶網站上。
07:08
If you were to go to the open data portal,
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如果你看公開數據的門戶網站，
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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你只能看到其中一些片段，
只有一年內或幾個月的數據。
只有一年內或幾個月的數據。
07:12
It was actually on the Department
of Environmental Protection's website.
of Environmental Protection's website.
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這些數據其實是在環境保護部門的網站上。
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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每一個鏈接都是一個Excel文件，
而每個Excel文件都是不一樣的。
而每個Excel文件都是不一樣的。
07:20
Every heading is different:
you copy, paste, reorganize.
you copy, paste, reorganize.
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每一個表頭都不同：
需要複製、黏貼、還有重新整理。
需要複製、黏貼、還有重新整理。
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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一旦完成你就能做出這些地圖，
但我要再次重申，
但我要再次重申，
我們的城市能做的更好，
我們可以標準化。
我們可以標準化。
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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我們正在改善這裡有個
索克拉塔公司建立的網站
索克拉塔公司建立的網站
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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叫做「紐約市公開數據門戶」。
07:32
called the Open Data Portal NYC.
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這裡，1100個數據庫
都不存在標準化的問題，
07:33
This is where 1,100 data sets
that don't suffer
that don't suffer
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07:35
from the things I just told you live,
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而且（這些無縫連接的數據庫）數字還在增加。
你可以下載任一格式的數據：
CSV、PDF或Excel文件都可以。
CSV、PDF或Excel文件都可以。
07:37
and that number is growing,
and that's great.
and that's great.
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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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按你自己的需求來下載。
07:43
Whatever you want,
you can download the data that way.
you can download the data that way.
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但問題又來了，
07:45
The problem is, once you do,
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你會發現不同的機構
用不同的代碼來表示地址。
用不同的代碼來表示地址。
07:47
you will find that each agency
codes their addresses differently.
codes their addresses differently.
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07:50
So one is street name,
intersection street,
intersection street,
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有街道名、有路口名、
07:52
street, borough, address, building,
building address.
building address.
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行政區、地址、建築物、建築物地址等等。
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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所以，即使有這個門戶網站的幫助，
07:58
you're spending time
normalizing our address fields.
normalizing our address fields.
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你還得花時間來標準化地址這塊的數據。
08:01
And that's not the best use
of our citizens' time.
of our citizens' time.
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這也不是有效利用市民時間的方法。
08:03
We can do better than that as a city.
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我們的城市能做得更好。
08:05
We can standardize our addresses,
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我們可以對地址進行標準化，
08:07
and if we do,
we can get more maps like this.
we can get more maps like this.
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如果做到了，
我們就能做出更多這樣的地圖。
我們就能做出更多這樣的地圖。
08:09
This is a map of fire hydrants
in New York City,
in New York City,
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這是紐約市消防龍頭的地圖，
08:11
but not just any fire hydrants.
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但不僅於此。
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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這些是前250個吃到最多違章停車罰單的
消防栓位置圖。
消防栓位置圖。
08:17
(Laughter)
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1986
（笑聲）
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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我從圖中學到了幾件事，
我也真的喜歡這張圖。
我也真的喜歡這張圖。
08:23
Number one, just don't park
on the Upper East Side.
on the Upper East Side.
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第一：別在上東區停車。
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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千萬別停。因為不管停哪裡都會吃罰單。
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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第二：我找出了全紐約市最最容易
吃到違章停車罰單的兩個消防栓的位置，
吃到違章停車罰單的兩個消防栓的位置，
08:33
and they're on the Lower East Side,
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兩個都在下東區，
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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每年能在罰單上創收五萬五千多美金。
08:40
And that seemed a little strange
to me when I noticed it,
to me when I noticed it,
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我注意到這點，覺得有些奇怪，
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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於是深入挖掘了一下原因，
結果發現消防栓
結果發現消防栓
08:46
and then something called
a curb extension,
a curb extension,
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1996
都有一個叫做控制擴展的區域，
08:48
which is like a seven-foot
space to walk on,
space to walk on,
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2059
是約有七英呎的一塊地方，可以走路，
08:50
and then a parking spot.
204
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1156
然後是一個停車位。
08:51
And so these cars came along,
and the hydrant --
and the hydrant --
205
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2254
所以車開過來，司機發現消防栓，
08:53
"It's all the way over there, I'm fine,"
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1911
想“還有一段距離，這裡沒問題的”，
08:55
and there was actually a parking spot
painted there beautifully for them.
painted there beautifully for them.
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何況地上還有一個畫得美美的停車位，
司機停好車，但紐約警署不同意這種配置，
08:59
They would park there, and the NYPD
disagreed with this designation
disagreed with this designation
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開出了罰單。
09:02
and would ticket them.
209
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可不只是我本人吃了罰單，
09:03
And it wasn't just me
who found a parking ticket.
who found a parking ticket.
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2344
這是谷歌街景拍到的一輛過路車，
09:05
This is the Google
Street View car driving by
Street View car driving by
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也吃了同樣的一張罰單。
09:07
finding the same parking ticket.
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1617
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
於是我把這件事發到自己的部落格上
以及“I Quant NY”上，
以及“I Quant NY”上，
結果交通部門回復如下：
09:13
and they said,
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“交通部並未就此地點收到相關投訴，
09:14
"While the DOT has not received
any complaints about this location,
any complaints about this location,
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我們會重新檢視道路標誌，
並做出適當的改善措施。”
並做出適當的改善措施。”
09:18
we will review the roadway markings
and make any appropriate alterations."
and make any appropriate alterations."
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4542
09:22
And I thought to myself,
typical government response,
typical government response,
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2959
我暗自想：多麼官腔！
09:25
all right, moved on with my life.
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1881
好吧，我該幹嘛幹嘛去了。
09:27
But then, a few weeks later,
something incredible happened.
something incredible happened.
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3970
然而，幾週時間過去，
發生了意料之外的事情。
發生了意料之外的事情。
09:31
They repainted the spot,
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2520
停車位重新畫了，
09:34
and for a second I thought I saw
the future of open data,
the future of open data,
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2690
那一瞬間我覺得能看到公開數據的未來。
09:36
because think about what happened here.
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2000
大家想想這件事，
09:38
For five years, this spot was being
ticketed, and it was confusing,
ticketed, and it was confusing,
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5100
過去五年，這個讓人困惑的停車位
一直讓人吃罰單，
一直讓人吃罰單，
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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但某一天，一位市民發現了問題
報告市政機關，又過了幾週時間，
報告市政機關，又過了幾週時間，
09:48
the problem was fixed.
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1294
問題車位被修正了。
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
太不可思議了。很多人認為
公開數據讓市民變成政府的監視者，
公開數據讓市民變成政府的監視者，
09:52
It's not, it's about being a partner.
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並非如此，它實則讓人們成為了合作夥伴。
09:54
We can empower our citizens
to be better partners for government,
to be better partners for government,
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3138
市民能夠有底氣成為政府更好的合作夥伴，
09:57
and it's not that hard.
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1881
這並不難。
09:59
All we need are a few changes.
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1459
我們只需要作出一些改變。
10:01
If you're FOILing data,
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1107
如果我們在申請FOIL信息自由法案數據，
10:02
if you're seeing your data
being FOILed over and over again,
being FOILed over and over again,
232
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2867
如果你看到自己申請的數據已經被反覆申請，
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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讓我們直接向公眾公開，
因為反覆申請就是需要公開的一种信號。
因為反覆申請就是需要公開的一种信號。
10:08
And if you're a government agency
releasing a PDF,
releasing a PDF,
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2482
如果某個政府機關正在發佈PDF數據，
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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讓我們通過法案
要求他們發佈隱藏的數據，
要求他們發佈隱藏的數據，
10:14
because that data
is coming from somewhere.
is coming from somewhere.
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2028
因為這些數據必定有來源。
10:16
I don't know where, but it's
coming from somewhere,
coming from somewhere,
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2482
我不知道從哪兒，但肯定有來源，
10:19
and you can release it with the PDF.
238
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1725
可以發佈PDF之外的信息。
10:20
And let's adopt and share
some open data standards.
some open data standards.
239
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2411
讓我們運用并分享一些公開數據的標準。
10:23
Let's start with our addresses
here in New York City.
here in New York City.
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2481
讓我們從紐約本市的地址開始，
10:25
Let's just start
normalizing our addresses.
normalizing our addresses.
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2074
把地址標準化。
10:27
Because New York is a leader in open data.
242
615940
2062
因為紐約是公開數據的領導者。
10:30
Despite all this, we are absolutely
a leader in open data,
a leader in open data,
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儘管如此，我們絕對是公開數據的領導者，
10:32
and if we start normalizing things,
and set an open data standard,
and set an open data standard,
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如果我們開始做標準化的工作，
建立公開數據的標準，
建立公開數據的標準，
其他人都會追隨的。州里會、聯邦政府也可能，
10:35
others will follow. The state will follow,
and maybe the federal government,
and maybe the federal government,
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我知道這或許聽上去有些瘋狂，
但別的國家也未嘗不會追隨。
但別的國家也未嘗不會追隨。
10:39
Other countries could follow,
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我們不久後也許能開發出
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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可以涵蓋100個國家地圖信息的程式。
10:44
and map information from 100 countries.
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這可不是科幻小說，
而是指日可待的事實。
而是指日可待的事實。
10:46
It's not science fiction.
We're actually quite close.
We're actually quite close.
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這能幫助誰？
10:48
And by the way, who are we
empowering with this?
empowering with this?
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可不單單是約翰▪克勞斯和克里斯▪旺。
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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紐約城現在正有幾百個聚會在進行，
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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10:57
active meetups.
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都是活躍的聚會。
這些聚會讓幾千人參與其中。
10:58
There are thousands of people
attending these meetups.
attending these meetups.
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他們下班後或在週末會面，
11:00
These people are going after work
and on weekends,
and on weekends,
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共同研究空開數據，
11:03
and they're attending these meetups
to look at open data
to look at open data
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幫助我們的城市變得更好，
11:05
and make our city a better place.
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BetaNYC這樣的團體，上週剛剛發佈了
citygram.nyc
citygram.nyc
11:07
Groups like BetaNYC, who just last week
released something called citygram.nyc
released something called citygram.nyc
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11:11
that allows you to subscribe
to 311 complaints
to 311 complaints
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讓你能夠訂閱311個
11:13
around your own home,
or around your office.
or around your office.
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自己住家或辦公地周圍的投訴。
11:15
You put in your address,
you get local complaints.
you get local complaints.
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你輸入地址，就能看到附近的投訴。
11:18
And it's not just the tech community
that are after these things.
that are after these things.
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而且，做這些事情的並不限於技術社團。
11:21
It's urban planners like
the students I teach at Pratt.
the students I teach at Pratt.
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我在Pratt學院教的城市規劃學生
也在做同樣的事。
也在做同樣的事。
11:24
It's policy advocates, it's everyone,
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還有政策提倡者、以至每個人，
11:25
it's citizens from a diverse
set of backgrounds.
set of backgrounds.
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是擁有不同領域背景的市民們。
11:28
And with some small, incremental changes,
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隨著一個個小的改變，
11:31
we can unlock the passion
and the ability of our citizens
and the ability of our citizens
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我們能解開市民們激情和能力的封印，
11:34
to harness open data
and make our city even better,
and make our city even better,
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好好利用空開數據，建設更好的城市，
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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就算每次只有一個數據庫，或只是一個停車位。
11:41
Thank you.
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謝謝。
11:43
(Applause)
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（掌聲）
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