TED2010
Tim Berners-Lee: The year open data went worldwide
Tim Berners Lee: 公開資料遍布世界的一年
Filmed:
Readability: 4
771,473 views
在2009年的TED大會上， Tim Berners-Lee 呼籲政府， 科學家， 和科研部門在網上公開”即時的原始數據“。在2010年的TED大學演講中， 他展示了一些數據鏈接後的有趣結果。
Tim Berners-Lee - Inventor
Tim Berners-Lee invented the World Wide Web. He leads the World Wide Web Consortium (W3C), overseeing the Web's standards and development. Full bio
Tim Berners-Lee invented the World Wide Web. He leads the World Wide Web Consortium (W3C), overseeing the Web's standards and development. Full bio
Double-click the English transcript below to play the video.
00:15
Last year here at TED
0
0
2000
去年的TED我在這裡
00:17
I asked you to give me your data,
1
2000
2000
請求你們公開你們的數據和資料
00:19
to put your data on the web, on the basis
2
4000
2000
把數據資料放到網上，就跟老百姓
00:21
that if people put data onto the web --
3
6000
3000
把他們的數據資料放到網上一樣
00:24
government data, scientific data, community data,
4
9000
3000
政府數據，科學數據， 社區數據
00:27
whatever it is -- it will be used by other people
5
12000
2000
不管是什麼， 會被人們用來
00:29
to do wonderful things, in ways
6
14000
2000
做精彩的事情，他們使用這些數據的地方
00:31
that they never could have imagined.
7
16000
2000
是他們從來都根本無法想像的
00:33
So, today I'm back just to show you a few things,
8
18000
3000
所以， 今天我回來給你展示幾個事例
00:36
to show you, in fact, there is
9
21000
2000
向你展示， 事實上
00:38
an open data movement afoot,
10
23000
5000
公開數據資料的運動正在興起
00:43
now, around the world.
11
28000
2000
世界各地
00:45
The cry of "Raw data now!"
12
30000
2000
都渴望“即時的原始數據資料”
00:47
which I made people make in the auditorium,
13
32000
2000
我在禮堂裡的懇請大家做的事情
00:49
was heard around the world.
14
34000
2000
整個世界都聽到了
00:51
So, let's roll the video.
15
36000
3000
所以， 讓我們播放影像
00:54
A classic story, the first one which lots of people picked up,
16
39000
3000
經典的故事， 也是很多人首選的故事
00:57
was when in March -- on March 10th in fact, soon after TED --
17
42000
3000
是當3月10日， 事實上TED剛剛結束不久
01:00
Paul Clarke, in the U.K. government,
18
45000
3000
英國政府的Paul Clarke
01:03
blogged, "Oh, I've just got some raw data. Here it is,
19
48000
2000
在博客中說： “啊， 我才找到一些數據
01:05
it's about bicycle accidents."
20
50000
3000
是關於自行車事故的數據。 ”
01:08
Two days it took the Times Online
21
53000
3000
時代在線用了兩天時間
01:11
to make a map, a mashable map --
22
56000
2000
做了一個地圖， 一個合成的地圖
01:13
we call these things mash-ups --
23
58000
2000
我們稱之為：合成(把數據融合到地圖上)
01:15
a mashed-up user interface that allows you to go in there
24
60000
2000
經過這種合成，你可以通過用戶界面
01:17
and have a look and find out whether your bicycle
25
62000
2000
搜索和找到你的上班的自行車
01:19
route to work was affected.
26
64000
2000
路線是否受影響
01:21
Here's more data, traffic survey data,
27
66000
2000
除此， 網上還很多其他的數據， 交通調查數據等
01:23
again, put out by the U.K. government,
28
68000
2000
都是英國政府放上去的
01:25
and because they put it up using the Linked Data standards,
29
70000
3000
因為他們上傳的數據符合鏈接標準
01:28
then a user could just make a map,
30
73000
2000
所以用戶可以使用這些數據做地圖
01:30
just by clicking.
31
75000
2000
只要點擊一下就做成了
01:32
Does this data affect things? Well, let's get back to 2008.
32
77000
2000
那麼這些數據是否會產生影響？讓我們回到2008
01:34
Look at Zanesville, Ohio.
33
79000
3000
看看曾斯維爾,俄亥俄州
01:37
Here's a map a lawyer made. He put on it the water plant,
34
82000
3000
這是一張律師製作的地圖，掛在自來水廠
01:40
and which houses are there,
35
85000
2000
你可以在圖上看見哪戶人家
01:42
which houses have been connected to the water.
36
87000
2000
誰家連著用水
01:44
And he got, from other data sources,
37
89000
2000
然後他得到一組其他數據
01:46
information to show
38
91000
3000
資料顯示了
01:49
which houses are occupied by white people.
39
94000
2000
哪個房子是白人擁有的
01:51
Well, there was too much of a correlation, he felt,
40
96000
3000
他感到， 兩者之間的連續太緊密了
01:54
between which houses were occupied by white people
41
99000
3000
哪些是白人擁有的房子
01:57
and which houses had water, and the judge was not impressed either.
42
102000
3000
哪些房子有自來水，法官看到以後對此也很不滿
02:00
The judge was not impressed to the tune of 10.9 million dollars.
43
105000
3000
法官因此罰了他們1090萬美元
02:03
That's the power of taking one piece of data,
44
108000
2000
這就是拿了一組數據
02:05
another piece of data, putting it together,
45
110000
3000
和另一組數據放在一起的力量
02:08
and showing the result.
46
113000
2000
結果是顯而易見的
02:10
Let's look at some data from the U.K. now.
47
115000
2000
我們看一組從英國來的數據
02:12
This is U.K. government data, a completely independent site,
48
117000
2000
這是英國政府的數據， 完全獨立的網站
02:14
Where Does My Money Go.
49
119000
2000
我的錢花到哪裡去了
02:16
It allows anybody to go there and burrow down.
50
121000
2000
這個網站讓任何人在網站上挖掘數據
02:18
You can burrow down by a particular type of spending,
51
123000
2000
你可以根據特別的消費種類來查找
02:20
or you can go through all the different regions and compare them.
52
125000
4000
也可以找出不同的地區的數據來做比較
02:24
So, that's happening in the U.K. with U.K. government data.
53
129000
3000
英國人正在用英國政府提供的數據資料做這些事情
02:27
Yes, certainly you can do it over here.
54
132000
2000
是的， 你在這裡也可以做
02:29
Here's a site which allows you to look at recovery spending
55
134000
3000
這個網站讓你查找復甦經費的使用情況
02:32
in California.
56
137000
2000
在加州
02:34
Take an arbitrary example, Long Beach, California,
57
139000
2000
隨便舉例，加州長灘
02:36
you can go and have a look at what recovery money they've been spending
58
141000
3000
你可以看見人們是怎樣使用政府發放的復甦經費的
02:39
on different things such as energy.
59
144000
3000
他們使用在能源上
02:42
In fact, this is the graph of the number of data sets
60
147000
3000
事實上， 這是幾組數據的圖表
02:45
in the repositories of data.gov,
61
150000
2000
他們存放在data.gov上
02:47
and data.gov.uk.
62
152000
2000
和data.gov.uk網上
02:49
And I'm delighted to see a great competition
63
154000
2000
我真高興看見這種積極的競爭
02:51
between the U.K. in blue, and the U.S. in red.
64
156000
2000
英國顯示的是藍色， 美國是紅色
02:53
How can you use this stuff?
65
158000
2000
你怎麼用這些東西呢
02:55
Well, for example, if you have lots of data about places
66
160000
3000
舉個例子吧， 你有一個地區的大量數據
02:58
you can take, from a postcode --
67
163000
2000
你可以從一個郵政編碼
03:00
which is like a zip plus four --
68
165000
2000
就是區碼加4
03:02
for a specific group of houses, you can make paper,
69
167000
3000
對一組特別的人家， 你可以製作報紙
03:05
print off a paper which has got very, very
70
170000
2000
打印的報紙會有特別具體
03:07
specific things about the bus stops,
71
172000
2000
的信息， 比如公車站
03:09
the things specifically near you.
72
174000
2000
包括那些離你很近的事情
03:11
On a larger scale, this is a mash-up
73
176000
3000
從大面相看說， 這是一個
03:14
of the data which was released about the Afghan elections.
74
179000
3000
阿富汗選舉數據的與地圖的合成
03:17
It allows you to set your own criteria
75
182000
2000
它允許你設立你自己的標準
03:19
for what sort of things you want to look at.
76
184000
2000
你可以查找你想要找的資料
03:21
The red circles are polling stations,
77
186000
2000
這些紅圈是投票點
03:23
selected by your criteria.
78
188000
2000
是根據你的標準選擇的
03:25
And then you can select also other things on the map
79
190000
2000
你也可以選擇地圖上的其他內容
03:27
to see what other factors, like the threat level.
80
192000
2000
你可以看見其他數據，比如威脅的程度
03:29
So, that was government data.
81
194000
3000
那也是政府的數據
03:32
I also talked about community-generated data -- in fact I edited some.
82
197000
2000
我也談過社區製作的， 事實上我也參與編輯的
03:34
This is the wiki map, this is the Open Street Map.
83
199000
2000
這是wiki地圖， 這是公開的街道地圖
03:36
"Terrace Theater" I actually put
84
201000
2000
Terrace 劇院， 是我放到
03:38
on the map because it wasn't on the map before TED last year.
85
203000
3000
地圖上去的， 因為去年TED召開的時候地圖上沒有它
03:41
I was not the only person editing the open street map.
86
206000
3000
我不是唯一編輯公開地圖的人
03:44
Each flash on this visualization --
87
209000
2000
這裡每個可視的閃亮點
03:46
put together by ITO World --
88
211000
2000
是ITO World放在一起的
03:48
shows an edit in 2009
89
213000
2000
顯示了2009年人們每次對
03:50
made to the Open Street Map.
90
215000
2000
開發地圖做的編輯
03:52
Let's now spin the world during the same year.
91
217000
3000
讓我們繞著整個世界看一下這一年發生的情況
03:55
Every flash is an edit. Somebody somewhere
92
220000
2000
每個亮點都是一次編輯的記錄。有人在某些地方
03:57
looking at the Open Street Map, and realizing it could be better.
93
222000
3000
看著公開的街道地圖， 發現它可以改進得更好
04:00
You can see Europe is ablaze with updates.
94
225000
3000
你看見歐洲的更新非常頻繁
04:03
Some places, perhaps not as much as they should be.
95
228000
3000
有些地方， 也許還沒什麼其他地方多
04:06
Here focusing in on Haiti.
96
231000
2000
這裡聚焦的是海地
04:08
The map of Port au-Prince at the end
97
233000
2000
太子港在地圖在
04:10
of 2009 was not all it could be,
98
235000
2000
2009年年底還不是很完整
04:12
not as good as the map of California.
99
237000
2000
不如加州的地圖好
04:14
Fortunately, just after the earthquake,
100
239000
3000
幸運的是， 地震以後
04:17
GeoEye, a commercial company,
101
242000
2000
GeoEye， 一間商業公司
04:19
released satellite imagery
102
244000
2000
發布了衛星圖像
04:21
with a license, which allowed
103
246000
2000
它發放授權允許
04:23
the open-source community to use it.
104
248000
2000
公開資源的社區使用這些圖像
04:25
This is January, in time lapse,
105
250000
2000
這是一月的
04:27
of people editing ... that's the earthquake.
106
252000
2000
人們參與了編輯， 然後地震發生了
04:29
After the earthquake, immediately,
107
254000
2000
地震發生後不久
04:31
people all over the world, mappers
108
256000
2000
全世界的人們， 地圖製作者們
04:33
who wanted to help, and could,
109
258000
2000
都想要提供幫助，可以
04:35
looked at that imagery, built the map, quickly building it up.
110
260000
3000
看著這些圖像， 迅速地建立地圖
04:38
We're focusing now on Port-au-Prince.
111
263000
1000
我們現在看到的是太子港
04:39
The light blue is refugee camps these volunteers had spotted from the [satellite images].
112
264000
4000
藍色的是哪些自願者在空中發現的難民營
04:43
So, now we have, immediately, a real-time map
113
268000
2000
所以現在， 我們可以立刻製作一個實時地圖
04:45
showing where there are refugee camps --
114
270000
2000
顯示難民營在什麼地方
04:47
rapidly became the best map
115
272000
2000
如果你在太子港從事援助工作
04:49
to use if you're doing relief work in Port-au-Prince.
116
274000
3000
那這就是最好的地圖
04:52
Witness the fact that it's here on this Garmin device
117
277000
2000
目睹這一切在Garmin儀器上發生
04:54
being used by rescue team in Haiti.
118
279000
2000
被救援隊伍運用
04:56
There's the map showing,
119
281000
3000
海地， 這張地圖
04:59
on the left-hand side,
120
284000
2000
左邊的這張
05:01
that hospital -- actually that's a hospital ship.
121
286000
2000
那個醫院，事實上是一個建在船上的醫院
05:03
This is a real-time map that shows blocked roads,
122
288000
3000
這是實時地圖現實路障
05:06
damaged buildings, refugee camps --
123
291000
2000
背毀的建築， 難民營
05:08
it shows things that are needed [for rescue and relief work].
124
293000
2000
現實世界哪裡需要東西
05:10
So, if you've been involved in that at all,
125
295000
2000
所以， 如果你參與了
05:12
I just wanted to say: Whatever you've been doing,
126
297000
2000
我只想說不管你做了什麼
05:14
whether you've just been chanting, "Raw data now!"
127
299000
2000
無論是你在為原始資料製圖
05:16
or you've been putting government or scientific data online,
128
301000
3000
還是將政府或者科學研究的數據放到網上
05:19
I just wanted to take this opportunity to say: Thank you very much,
129
304000
2000
我只想藉此機會說聲感謝
05:21
and we have only just started!
130
306000
3000
我們才剛剛開始
05:24
(Applause)
131
309000
2000
謝謝
ABOUT THE SPEAKER
Tim Berners-Lee - InventorTim Berners-Lee invented the World Wide Web. He leads the World Wide Web Consortium (W3C), overseeing the Web's standards and development.
Why you should listen
In the 1980s, scientists at CERN were asking themselves how massive, complex, collaborative projects -- like the fledgling LHC -- could be orchestrated and tracked. Tim Berners-Lee, then a contractor, answered by inventing the World Wide Web. This global system of hypertext documents, linked through the Internet, brought about a massive cultural shift ushered in by the new tech and content it made possible: AOL, eBay, Wikipedia, TED.com...
Berners-Lee is now director of the World Wide Web Consortium (W3C), which maintains standards for the Web and continues to refine its design. Recently he has envisioned a "Semantic Web" -- an evolved version of the same system that recognizes the meaning of the information it carries. He's the 3Com Founders Professor of Engineering in the School of Engineering with a joint appointment in the Department of Electrical Engineering and Computer Science at the Laboratory for Computer Science and Artificial Intelligence (CSAIL) at the MIT, where he also heads the Decentralized Information Group (DIG). He is also a Professor in the Electronics and Computer Science Department at the University of Southampton, UK.
Tim Berners-Lee | Speaker | TED.com