TEDxNewYork
Ben Wellington: How we found the worst place to park in New York City -- using big data
本·威灵顿: 如何找到纽约最差停车点——使用大数据
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市政部门拥有大量反映城市生活各个领域的数据。但是作为数据分析师，本·威灵顿在这个愉悦的演讲中认为有时市政部门并不知道如何使用这些数据。他展示了 如何将未知问题和智能数据处理相结合，产生奇妙有效的见解，并且分享了关于如何发布大数据，方便每个人使用的方法。
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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六千英里的公路，
00:15
600 miles of subway track,
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六百英里的铁路，
00:17
400 miles of bike lanes
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四百英里的自行车道，
00:19
and a half a mile of tram track,
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以及半英里的电车轨道，
00:21
if you've ever been to Roosevelt Island.
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如果你曾经去过罗斯福岛就会知道。
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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上述数据构成了纽约的基础设施网络。
这些是我们基础设施的统计数据。
00:26
These are the statistics
of our infrastructure.
of our infrastructure.
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这些数据你们都可以在
市政机关的报告中找到。
市政机关的报告中找到。
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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比如，交通部可能会告诉你
00:32
For example, the Department
of Transportation will probably tell you
of Transportation will probably tell you
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他们维修了多少英里的路。
MTA(大纽约交通运输管理局)
会吹嘘纽约有多少英里的铁路。
会吹嘘纽约有多少英里的铁路。
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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多数的市政机关会给我们一些统计数据。
00:40
Most city agencies give us statistics.
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这是来自今年出租车与轿车
00:42
This is from a report this year
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委员会的一个报道，
00:43
from the Taxi and Limousine Commission,
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从中我们可以知道纽约一共有
13500辆出租车。
13500辆出租车。
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，对吧？
00:49
Pretty interesting, right?
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但是你们有没有想过这些数据从哪里来的？
00:50
But did you ever think about
where these numbers came from?
where these numbers came from?
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因为这些数据的存在，市政机关的某个人
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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可能会想：嗯，这个数据也许会有人想要知道。
这个数据我们的市民想要了解。
00:59
Here's a number
that our citizens want to know.
that our citizens want to know.
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于是他们找出原始数据
01:02
So they go back to their raw data,
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计数，做补充，再计算，
01:04
they count, they add, they calculate,
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并且将它们放在报告里。
01:05
and then they put out reports,
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于是这些报告就会包含这些数据。
01:07
and those reports
will have numbers like this.
will have numbers like this.
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不过问题是，
他们要怎么知道我们所有感兴趣的问题？
他们要怎么知道我们所有感兴趣的问题？
01:09
The problem is, how do they know
all of our questions?
all of our questions?
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我们有很多的疑问。
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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事实上，关于我们的城市，
我们有无数的问题要问。
01:16
that we can ask about our city.
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政府部门当然没办法全部回答。
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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所以现有模式不是十分有效，
并且我认为决策人也意识到了这个问题。
并且我认为决策人也意识到了这个问题。
01:23
because in 2012, Mayor Bloomberg
signed into law what he called
signed into law what he called
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因为在2012年，彭博市长签署了一项法令，
他称之为
他称之为
“全国最有雄心以及最综合性的
开放数据法案”。
开放数据法案”。
01:27
the most ambitious and comprehensive
open data legislation in the country.
open data legislation in the country.
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01:31
In a lot of ways, he's right.
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在很多方面，他的决定是对的。
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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在最近两年，
纽约已经在公开的数据门户上
纽约已经在公开的数据门户上
01:35
on our open data portal,
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发布了1000组数据库，
01:37
and it's pretty awesome.
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非常了不起。
01:39
So you go and look at data like this,
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因此你们可以直接得到这些数据，
01:41
and instead of just counting
the number of cabs,
the number of cabs,
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除了能够计算出租车的数量，
01:43
we can start to ask different questions.
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我们不妨问一些不一样的问题。
我有一个问题。
01:45
So I had a question.
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01:46
When's rush hour in New York City?
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纽约的交通高峰期是哪个时间段？
01:48
It can be pretty bothersome.
When is rush hour exactly?
When is rush hour exactly?
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这个问题可能令人厌烦。
交通高峰期具体指哪个时间段？
交通高峰期具体指哪个时间段？
01:51
And I thought to myself,
these cabs aren't just numbers,
these cabs aren't just numbers,
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我在想，
这些出租车不仅仅是一些数据，
这些出租车不仅仅是一些数据，
它们是行驶在城市街道的
GPS（全球定位系统）记录器，
GPS（全球定位系统）记录器，
01:53
these are GPS recorders
driving around in our city streets
driving around in our city streets
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记录下每一段它们行驶的路线。
01:56
recording each and every ride they take.
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01:58
There's data there,
and I looked at that data,
and I looked at that data,
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这里有一些数据，我处理了一下，
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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将纽约出租车全天的
平均速度做了一个图表。
平均速度做了一个图表。
你会发现从午夜到凌晨5点18分，
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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02:07
speed increases, and at that point,
things turn around,
things turn around,
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速度在增加，到了这个点后，
接下来就不一样了，
接下来就不一样了，
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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速度越来越慢直到早上8点35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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这时候出租车的时速
只有每小时11.5英里。
只有每小时11.5英里。
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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我们城市街道上的出租车
平均时速是每小时11.5英里，
平均时速是每小时11.5英里，
02:21
and it turns out it stays that way
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并且最终保持这个速度
02:23
for the entire day.
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一整天。
02:27
(Laughter)
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（笑）
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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所以我心说，
纽约并没有什么交通高峰时间段。
纽约并没有什么交通高峰时间段。
只有“高峰日”。
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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这个结论是有意义的，
因为以下几个原因：
因为以下几个原因：
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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如果你是交通运输系统的规划人，
你可能有兴趣知道这些。
你可能有兴趣知道这些。
02:39
But if you want to get somewhere quickly,
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如果你想要快速到达某个地方，
你现在就知道只要将闹钟设在早上4点45分，
估计就没问题了。
估计就没问题了。
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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在纽约嘛，这样做很合理是吧？
02:45
New York, right?
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但是在这些数据背后有一个故事，
02:46
But there's a story behind this data.
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这些数据不是现成的。
02:47
This data wasn't
just available, it turns out.
just available, it turns out.
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它实际上来自一个叫
”信息自由法案申请“的东西，
”信息自由法案申请“的东西，
02:50
It actually came from something called
a Freedom of Information Law Request,
a Freedom of Information Law Request,
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或者称为”FOIL 申请“。
02:53
or a FOIL Request.
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这个表格你可以在出租车
与轿车管理委员会的网站上找到。
与轿车管理委员会的网站上找到。
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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要拿到这些数据，你必须拿到这个表格，
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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填写表格，然后他们会通知你，
03:01
fill it out, and they will notify you,
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有一个叫克里斯·王的人就是这样做的。
03:02
and a guy named Chris Whong
did exactly that.
did exactly that.
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03:05
Chris went down, and they told him,
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克里斯去了那里，他们告诉他，
03:06
"Just bring a brand new hard drive
down to our office,
down to our office,
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“只要带一个新的硬盘到我们办公室，
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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我们会将数据拷贝到里面，
过5个小时你就可以拿回去了。”
过5个小时你就可以拿回去了。”
03:13
And that's where this data came from.
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这就是这些数据怎么得来的。
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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现在，克里斯想将这些数据公开，
这些数据最终被上传到网络上供大家使用，
这就是这个图表背后的故事。
这就是这个图表背后的故事。
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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03:22
And the fact that it exists is amazing.
These GPS recorders -- really cool.
These GPS recorders -- really cool.
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这一切真的很不可思议，
这些GPS记录器实在太酷了。
这些GPS记录器实在太酷了。
03:25
But the fact that we have citizens
walking around with hard drives
walking around with hard drives
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但是实际上市民为了使数据公开，
需要带着装有市政部门数据的
硬盘走来走去——
硬盘走来走去——
03:28
picking up data from city agencies
to make it public --
to make it public --
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这已经是一种公开性质的，
你们都可以得到，
你们都可以得到，
03:31
it was already kind of public,
you could get to it,
you could get to it,
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不过它虽说是“公开”的，
但并不是真正意义上的“公开”。
但并不是真正意义上的“公开”。
03:33
but it was "public," it wasn't public.
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我们作为一个城市可以做得更好。
03:35
And we can do better than that as a city.
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我们不需要市民带着硬盘走来走去。
03:37
We don't need our citizens
walking around with hard drives.
walking around with hard drives.
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另外，不是每一组数据都需要
填写信息自由法案的申请。
填写信息自由法案的申请。
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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这是一张我根据自行车事故数据绘制出的
纽约最危险十字路口的地图。
03:46
based on cyclist accidents.
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03:48
So the red areas are more dangerous.
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红色区域是最危险的。
03:50
And what it shows is first
the East side of Manhattan,
the East side of Manhattan,
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首先显示的是曼哈顿的东区，
03:52
especially in the lower area of Manhattan,
has more cyclist accidents.
has more cyclist accidents.
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特别曼哈顿的下城区，
发生过很多自行车车祸。
发生过很多自行车车祸。
03:56
That might make sense
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这可能说得通，
03:57
because there are more cyclists
coming off the bridges there.
coming off the bridges there.
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因为很多自行车都是从桥上过来的。
04:00
But there's other hotspots worth studying.
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不过还有其他值得研究的热点区域。
这里是威廉斯堡。
这里是皇后区的罗福斯大道。
这里是皇后区的罗福斯大道。
04:02
There's Williamsburg.
There's Roosevelt Avenue in Queens.
There's Roosevelt Avenue in Queens.
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这些正是Vision Zero
（跨国道路交通安全项目）所需要的数据。
（跨国道路交通安全项目）所需要的数据。
04:04
And this is exactly the kind of data
we need for Vision Zero.
we need for Vision Zero.
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这正是我们所要找的。
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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在这些数据的背后也有一个故事。
04:11
This data didn't just appear.
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它们也不是现成的。
04:13
How many of you guys know this logo?
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有多少人知道这个标志？
04:16
Yeah, I see some shakes.
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我看到有些人点头。
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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你们是否曾要复制和黏贴
PDF里的内容，
PDF里的内容，
04:20
and make sense of it?
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并且搞清楚这些数据的意义？
我看到更多的人在点头。
04:21
I see more shakes.
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比起知道这个标志，有更多的人
试过复制和黏贴。很不错。
试过复制和黏贴。很不错。
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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所以你们刚才看到的
数据确实是来自一个PDF。
数据确实是来自一个PDF。
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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事实上，有不计其数的PDF文档
04:29
In fact, hundreds and hundreds
and hundreds of pages of PDF
and hundreds of pages of PDF
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04:32
put out by our very own NYPD,
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都是通过纽约警察局公布的，
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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为了得到这些数据，
你要么需要花几百个小时
你要么需要花几百个小时
复制和黏贴，
04:38
for hundreds and hundreds of hours,
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或者像约翰·克劳斯那样做。
04:39
or you could be John Krauss.
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04:41
John Krauss was like,
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他是这样做的：
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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我不想复制黏贴这些数据，
我要写一个程序。
我要写一个程序。
这个程序被称为：”纽约警察局事故数据创可贴“
（NYPD Crash Data Band－Aid），
（NYPD Crash Data Band－Aid），
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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这个程序先进入纽约警察局的网站，
然后下载PDF文件。
然后下载PDF文件。
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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每天进行搜索，如果发现PDF文件，
就会下载下来，
就会下载下来，
04:54
and then it would run
some PDF-scraping program,
some PDF-scraping program,
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然后它会运行PDF解码程序，
04:56
and out would come the text,
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就可以将文本拷出来，
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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然后上传到网络上，
大家就可以做出这样的地图了。
大家就可以做出这样的地图了。
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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只要数据在这里，
我们就可以使用这些数据——
我们就可以使用这些数据——
05:04
Every accident, by the way,
is a row in this table.
is a row in this table.
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每个事故都是这个
表格里的一行数据。
表格里的一行数据。
05:07
You can imagine how many PDFs that is.
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你门可以想象得有多少个PDF文件。
05:08
The fact that we
have access to that is great,
have access to that is great,
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我们可以使用这些数据当然非常棒，
05:11
but let's not release it in PDF form,
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但是不要用PDF的格式公开这些数据，
05:13
because then we're having our citizens
write PDF scrapers.
write PDF scrapers.
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因为还需要我们的市民写PDF解码程序。
05:15
It's not the best use
of our citizens' time,
of our citizens' time,
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这是在浪费市民的时间，
我们作为一个城市可以做得更好。
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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有一个好消息，
白思豪（纽约市长）的行政办公室
白思豪（纽约市长）的行政办公室
05:22
actually recently released this data
a few months ago,
a few months ago,
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几个月前公开了这些数据。
05:25
and so now we can
actually have access to it,
actually have access to it,
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现在我们可以直接使用这些数据了，
05:27
but there's a lot of data
still entombed in PDF.
still entombed in PDF.
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但是依然有很多数据隐藏在PDF里。
05:29
For example, our crime data
is still only available in PDF.
is still only available in PDF.
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比如，与犯罪相关的数据
依然只能在PDF里才能看到。
依然只能在PDF里才能看到。
05:33
And not just our crime data,
our own city budget.
our own city budget.
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不仅仅是与犯罪相关的数据，
还有我们城市的预算。
还有我们城市的预算。
05:36
Our city budget is only readable
right now in PDF form.
right now in PDF form.
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我们城市的预算只能
在PDF格式的文件里才能看到。
在PDF格式的文件里才能看到。
05:40
And it's not just us
that can't analyze it --
that can't analyze it --
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不仅仅是我们无法分析这些数据——
05:42
our own legislators
who vote for the budget
who vote for the budget
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我们为预算投票的立法者
05:45
also only get it in PDF.
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也只能通过PDF得到这些数据。
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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因此我们的立法者也无法
分析他们要投票的预算方案。
分析他们要投票的预算方案。
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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我认为我们作为一个
城市同样可以做得更好。
城市同样可以做得更好。
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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有许多没有被隐藏在PDF文件里的数据。
05:57
This is an example of a map I made,
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我做的地图就是一个例子，
05:59
and this is the dirtiest waterways
in New York City.
in New York City.
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这是纽约最脏的水沟。
06:02
Now, how do I measure dirty?
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我怎么判断肮脏的程度呢?
06:03
Well, it's kind of a little weird,
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可能有点奇怪，
06:05
but I looked at the level
of fecal coliform,
of fecal coliform,
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我是根据粪便菌群数的水平来判断的，
06:07
which is a measurement of fecal matter
in each of our waterways.
in each of our waterways.
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就是测量每一个水沟粪便物的指标。
06:11
The larger the circle,
the dirtier the water,
the dirtier the water,
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圆圈越大，表示水越脏。
06:14
so the large circles are dirty water,
the small circles are cleaner.
the small circles are cleaner.
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所以大圆圈代表脏水，
小圆圈代表较干净的水。
小圆圈代表较干净的水。
06:17
What you see is inland waterways.
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你们看到的是内陆地区的水沟。
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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这些是过去五年城市取样的所有数据。
06:22
And inland waterways are,
in general, dirtier.
in general, dirtier.
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内陆地区的水沟普遍更脏。
06:25
That makes sense, right?
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有道理，是吧？
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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圆圈越大表示越脏。
我从中了解到几件事。
我从中了解到几件事。
第一：不要在任何名字末尾有
“小溪”、“运河”的地方游泳。
“小溪”、“运河”的地方游泳。
06:30
Number one: Never swim in anything
that ends in "creek" or "canal."
that ends in "creek" or "canal."
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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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第二：经过一次测量我就发现了
06:37
by this measure, one measure.
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纽约最脏的水沟。
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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就是康尼岛小溪，
幸运的是这并不是你们去游泳的康尼岛，
幸运的是这并不是你们去游泳的康尼岛，
是在岛的另一面。
06:43
It's on the other side.
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过去五年，康尼岛小溪94%的取样
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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06:48
have had fecal levels so high
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所含排泄物含量水平如此高，
06:50
that it would be against state law
to swim in the water.
to swim in the water.
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以至于州立法已经
禁止人们在此游泳。
禁止人们在此游泳。
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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这不是你所看到的市政报告中
06:56
boasted in a city report, right?
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吹嘘的那一类数据，对吧？
06:57
It's not going to be
the front page on nyc.gov.
the front page on nyc.gov.
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也不会出现在纽约政府官网的首页上。
06:59
You're not going to see it there,
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你们不会在那里看到，
但是我们能得到这些数据是非常棒的。
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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但是再说一次，
这些数据并不是轻易得到的，
这些数据并不是轻易得到的，
07:05
because this data was not
on the open data portal.
on the open data portal.
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因为这些数据并不是公开的数据库。
如果你们查看公开的数据，
07:08
If you were to go to the open data portal,
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只会看到其中的部分数据，
一年或几个月的数据。
一年或几个月的数据。
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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环境保护部门网站上
提供的数据就是这样的。
提供的数据就是这样的。
07:12
It was actually on the Department
of Environmental Protection's website.
of Environmental Protection's website.
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每一个链接都是一个Excel表格，
每一个表格都是不一样的。
每一个表格都是不一样的。
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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标题也是不一样的：
你复制，黏贴，重新整理。
你复制，黏贴，重新整理。
07:20
Every heading is different:
you copy, paste, reorganize.
you copy, paste, reorganize.
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然后你就可以做一份地图，
那是很棒的，但是再说一次，
那是很棒的，但是再说一次，
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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作为一个城市我们可以做得更好，
我们可以使数据标准化。
我们可以使数据标准化。
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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我们正在慢慢改进，
Socrata做了一个网站
Socrata做了一个网站
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“纽约市公开数据门户网站”。
这里有1100组数据，
这些数据不会遭遇
这些数据不会遭遇
07:32
called the Open Data Portal NYC.
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07:33
This is where 1,100 data sets
that don't suffer
that don't suffer
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我刚才跟你们说的那种情况，
并且数量还在增长，
这个网站真的很不错。
这个网站真的很不错。
07:35
from the things I just told you live,
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07:37
and that number is growing,
and that's great.
and that's great.
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你们可以下载任何格式的数据，
CSV或者PDF 或者Excel。
CSV或者PDF 或者Excel。
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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你想要什么格式，就以那种格式下载。
07:43
Whatever you want,
you can download the data that way.
you can download the data that way.
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问题是，一旦你开始下载数据，
07:45
The problem is, once you do,
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07:47
you will find that each agency
codes their addresses differently.
codes their addresses differently.
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就会发现不同的部门会
用不同方式编码他们的地址。
用不同方式编码他们的地址。
07:50
So one is street name,
intersection street,
intersection street,
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有的用街道名，十字路口，
07:52
street, borough, address, building,
building address.
building address.
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街道，区，地址，大厦，大厦地址。
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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所以你还是需要花时间，
即使我们有这个网站，
即使我们有这个网站，
07:58
you're spending time
normalizing our address fields.
normalizing our address fields.
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你还是需要花时间使地址格式规范化。
08:01
And that's not the best use
of our citizens' time.
of our citizens' time.
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这在浪费市民时间。
我们作为一个城市完全可以做得更好。
08:03
We can do better than that as a city.
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08:05
We can standardize our addresses,
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我们可以将我们的地址格式标准化，
08:07
and if we do,
we can get more maps like this.
we can get more maps like this.
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如果我们做到了，
就可以做更多类似这样的地图，
就可以做更多类似这样的地图，
08:09
This is a map of fire hydrants
in New York City,
in New York City,
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这是纽约市防火栓的地图，
08:11
but not just any fire hydrants.
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但并不是全部的。
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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这些是最容易被开罚单的前250个防火栓。
08:17
(Laughter)
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(笑声）
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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我从这张地图学到了一些东西，
我非常喜欢这张地图。
我非常喜欢这张地图。
08:23
Number one, just don't park
on the Upper East Side.
on the Upper East Side.
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第一，不要在上东区停车。
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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不论你停在哪里，
你都会收到一张罚单。
你都会收到一张罚单。
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，我发现全纽约市消防栓
最多的两个区域，
最多的两个区域，
08:33
and they're on the Lower East Side,
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都在下东区，
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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并且一年可以带来
55000美元的罚单收入。
55000美元的罚单收入。
08:40
And that seemed a little strange
to me when I noticed it,
to me when I noticed it,
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我发现这点的时候，感觉有点奇怪，
我做了一些研究，比如有一个消防栓，
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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08:46
and then something called
a curb extension,
a curb extension,
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旁边有横向拓宽的人行道，
就像一个可以行走的七英尺的空间，
08:48
which is like a seven-foot
space to walk on,
space to walk on,
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然后旁边是停车位置。
08:50
and then a parking spot.
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08:51
And so these cars came along,
and the hydrant --
and the hydrant --
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司机将车开过来，心想
“ 还离得挺远，没问题的，“
08:53
"It's all the way over there, I'm fine,"
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何况地上还有一个画得美美的停车位。
08:55
and there was actually a parking spot
painted there beautifully for them.
painted there beautifully for them.
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于是他们将车停在这里，
但纽约警察局不允许这样，
但纽约警察局不允许这样，
08:59
They would park there, and the NYPD
disagreed with this designation
disagreed with this designation
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然后他们就会被开罚单。
09:02
and would ticket them.
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不仅仅是我收到罚单。
09:03
And it wasn't just me
who found a parking ticket.
who found a parking ticket.
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谷歌街景车开过的时候
09:05
This is the Google
Street View car driving by
Street View car driving by
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也看到了同样的罚单。
09:07
finding the same parking ticket.
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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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我将这件事情写在我的博客和
“I Quant NY ”上，交通部回复了我，
“I Quant NY ”上，交通部回复了我，
09:13
and they said,
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他们说，
09:14
"While the DOT has not received
any complaints about this location,
any complaints about this location,
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3410
”虽然交通部没有收到关于这个地点的投诉，
09:18
we will review the roadway markings
and make any appropriate alterations."
and make any appropriate alterations."
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我们还是会评估道路标志，
做出合适的调整。
做出合适的调整。
09:22
And I thought to myself,
typical government response,
typical government response,
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我认为这是典型的政府答复，
09:25
all right, moved on with my life.
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好吧，继续过我的日子。
09:27
But then, a few weeks later,
something incredible happened.
something incredible happened.
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但是几个礼拜后，
难以置信的事情发生了。
难以置信的事情发生了。
09:31
They repainted the spot,
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他们重新粉刷了停车位置，
09:34
and for a second I thought I saw
the future of open data,
the future of open data,
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这让我立刻看到了公开数据的未来，
09:36
because think about what happened here.
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想想这里发生的事情吧。
五年来，这个位置的车常被开罚单，
令人很困惑。
令人很困惑。
09:38
For five years, this spot was being
ticketed, and it was confusing,
ticketed, and it was confusing,
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5100
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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然后有人发现了一些事情，
并且告诉了城市的管理者，在几周内
并且告诉了城市的管理者，在几周内
09:48
the problem was fixed.
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这个问题就得到了解决。
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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这太不可思议了。
很多人把公开数据当做监督者。
很多人把公开数据当做监督者。
09:52
It's not, it's about being a partner.
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不是的，它更像是一个合作伙伴。
09:54
We can empower our citizens
to be better partners for government,
to be better partners for government,
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我们可以赋予市民权利
成为政府的优秀合作伙伴，
成为政府的优秀合作伙伴，
09:57
and it's not that hard.
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这不是很难。
09:59
All we need are a few changes.
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只需要一些改变。
10:01
If you're FOILing data,
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1107
如果你正在申请FOIL数据，
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
如果你看到你要的数据被反复的请求获得，
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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让我们直接将这些数据向大众公开吧，
这是一个数据需要公开的信号。
这是一个数据需要公开的信号。
如果你是一个政府部门，
要公开一个PDF，
要公开一个PDF，
10:08
And if you're a government agency
releasing a PDF,
releasing a PDF,
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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
让我们通过立法，发布被要求的源数据，
10:14
because that data
is coming from somewhere.
is coming from somewhere.
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2028
因为数据肯定来自某个地方。
虽然我不知道是哪里，
10:16
I don't know where, but it's
coming from somewhere,
coming from somewhere,
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但你们可用PDF的形式将这些数据公开。
10:19
and you can release it with the PDF.
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1725
让我们采用和共享公开数据的标准。
10:20
And let's adopt and share
some open data standards.
some open data standards.
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我们先从纽约的地址开始。
10:23
Let's start with our addresses
here in New York City.
here in New York City.
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从规范地址格式开始。
10:25
Let's just start
normalizing our addresses.
normalizing our addresses.
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纽约在公开数据上属于引领者。
10:27
Because New York is a leader in open data.
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抛开这些问题，
我们在公开数据方面绝对是领先者，
我们在公开数据方面绝对是领先者，
10:30
Despite all this, we are absolutely
a leader in open data,
a leader in open data,
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如果我们开始规范化一些事情，
并且建立一个公开数据的标准，
并且建立一个公开数据的标准，
10:32
and if we start normalizing things,
and set an open data standard,
and set an open data standard,
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其他人会照着标准做，
纽约州也会照着做，甚至是联邦政府。
纽约州也会照着做，甚至是联邦政府。
10:35
others will follow. The state will follow,
and maybe the federal government,
and maybe the federal government,
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其他国家也可能参照这套标准，
不久的将来，我们可以编写一个程序，
10:39
Other countries could follow,
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1445
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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并整理出100个国家的数据信息。
10:44
and map information from 100 countries.
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这不是科幻小说。
我们已经很接近这个目标了。
我们已经很接近这个目标了。
10:46
It's not science fiction.
We're actually quite close.
We're actually quite close.
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顺便说一下，我们的授权对象是谁？
10:48
And by the way, who are we
empowering with this?
empowering with this?
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不仅仅是约翰·克劳斯和克里斯·王。
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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现在纽约有上百个聚会小组，
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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活跃的聚会小组。
10:57
active meetups.
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成千上万的人参加了这些小组。
10:58
There are thousands of people
attending these meetups.
attending these meetups.
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人们在下班后或周末去参加这些活动，
11:00
These people are going after work
and on weekends,
and on weekends,
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在活动上，人们会处理这些公开数据，
11:03
and they're attending these meetups
to look at open data
to look at open data
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想办法让我们的城市越来越好。
11:05
and make our city a better place.
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像BetaNYC这样的组织，
上周刚公开了一个叫citygram.nyc的网站,
上周刚公开了一个叫citygram.nyc的网站,
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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允许你订阅你家附近
11:13
around your own home,
or around your office.
or around your office.
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或者你公司附近的311投诉。
11:15
You put in your address,
you get local complaints.
you get local complaints.
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输入地址，你就能看到本地的投诉。
11:18
And it's not just the tech community
that are after these things.
that are after these things.
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这背后不仅仅有个技术团体，
还有许多城市规划者,
比如我在普瑞特艺术学院教过的学生。
比如我在普瑞特艺术学院教过的学生。
11:21
It's urban planners like
the students I teach at Pratt.
the students I teach at Pratt.
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不仅仅是政策的提倡者，是每一个人，
11:24
It's policy advocates, it's everyone,
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11:25
it's citizens from a diverse
set of backgrounds.
set of backgrounds.
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是具有不同背景的城市市民。
11:28
And with some small, incremental changes,
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通过一些微小但与日俱增的变化，
11:31
we can unlock the passion
and the ability of our citizens
and the ability of our citizens
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我们就能释放市民的热情和能力，
11:34
to harness open data
and make our city even better,
and make our city even better,
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去利用公开数据
使我们的城市变得更美好，
使我们的城市变得更美好，
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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无论是每次一组数据，
还是一个停车点。
还是一个停车点。
11:41
Thank you.
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谢谢。
11:43
(Applause)
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（掌声}
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