ABOUT THE SPEAKER
Jer Thorp - Data artist
Jer Thorp’s work focuses on adding meaning and narrative to huge amounts of data as a way to help people take control of the information that surrounds them.

Why you should listen

Currently the data artist in residence at the New York Times, Jer’s software-based art has been featured all over the world. His former career as a data artist explains why his art often brings big data sets to life and is deeply influenced by science. Originally from Vancouver, he lives in New York City, where, along with his work at the New York Times, he teaches in NYU’s ITP program.

More profile about the speaker
Jer Thorp | Speaker | TED.com
TEDxVancouver

Jer Thorp: Make data more human

Filmed:
300,699 views

Jer Thorp creates beautiful data visualizations to put abstract data into a human context. At TEDxVancouver, he shares his moving projects, from graphing an entire year's news cycle, to mapping the way people share articles across the internet.
- Data artist
Jer Thorp’s work focuses on adding meaning and narrative to huge amounts of data as a way to help people take control of the information that surrounds them. Full bio

Double-click the English transcript below to play the video.

00:10
I want to talk to you about two
of the most exciting possible things.
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You've probably guessed what they are --
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data and history.
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Right?
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So, I'm not a historian.
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I'm not going to give you
a definition of history.
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But let's think instead
of history within a framework.
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So, when we're making history,
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or when we're creating
historical documents,
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we're taking things
that have happened in the past,
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and we're stitching them
together into a story.
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So let me start with a little bit
of my own story.
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Like anybody my age
who works creatively with computers,
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I was a popular, socially
well-adjusted young man --
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(Laughter)
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And sporty!
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Sporty young man.
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And like a lot of people my age
in the type of business that I'm in,
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I was influenced tremendously by Apple.
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But notice my choice of logo here, right?
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The Apple on the left,
not the Apple on the right.
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I'm influenced as much
by the Apple on the right
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as the next person,
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but the Apple on the left --
I mean, look at that logo!
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It's a rainbow.
It's not even in the right order!
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(Laughter)
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That's how crazy Apple was.
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(Laughter)
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But I don't want to talk too much
about the company.
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I'll start talking about
a machine, though.
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How amazing it is to think about this.
I go back and I think about this.
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Wednesday -- one Wednesday,
when I was about 12 years old,
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I didn't have a computer.
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On Thursday, I had a computer.
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Can you imagine that change?
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It's so drastic.
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I can't even think about anything
that could change our lives that way.
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But I'm actually not even going
to talk about the computer.
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I'm going to talk about a program
that came loaded on that computer.
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And it was build by,
not the guy on the left,
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but the guy on the right.
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Does anybody know
who the guy on the right is?
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Nobody ever knows the answer
to this question.
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This is Bill Atkinson.
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And Bill Atkinson was responsible
for tons of things
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that you see on your computer every day.
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But I want to talk about one program
that Bill Atkinson wrote,
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called HyperCard.
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Someone's cheering over there.
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(Laughter)
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HyperCard was a program
that shipped with the Mac,
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and it was designed
for users of the computer
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to make programs on their computers.
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Crazy idea today.
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And these programs were not the apps
that we think about today,
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with their large budgets
and their big distribution.
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These were small things,
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people making applications to keep track
of their local basketball team scores
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or to organize their research
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or to teach people about classical music
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or to calculate weird astronomical dates.
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And then, of course,
there were some art projects.
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This is my favorite one.
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It's called "If Monks Had Macs,"
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and it's a nonlinear
kind of exploratory environment.
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I thank the stars for HyperCard
all of the time.
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And I thank the stars
for putting me in this era
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where I got to use HyperCard.
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HyperCard was the last program to ship
on a public computer
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that was designed for the users
of the computer to make programs with it.
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If you talked to the people
who invented the computer
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and you told them there would be
a day, a magical day,
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when everybody had a computer
but none of them knew how to program,
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they would think you were crazy.
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So let's skip forward a few years.
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I'm starting my career as an artist,
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and I'm building things
with my computer, small-scale things,
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investigating things like
the growth systems of plants.
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Or, in this example, I'm building
a simulated economy
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in which pixels are trading color
with one another,
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trying to investigate how
these types of systems work,
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and just kind of having fun.
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And then this project led me
to start working with data.
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So I'm building graphics like this,
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which compare "communism" --
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the frequency of usage of the word
"communism" in the New York Times --
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to "terrorism," at the top.
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You see "terrorism" kind of appears
as "communism" is going away.
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And with these graphics, I was really
interested in the aesthetic of the graphs.
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This is Iran and Iraq.
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It reads like a clock. It's called
a "timepiece graph."
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This is another timepiece graph,
overlaying "despair" over "hope."
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And there's only three times -- actually,
it's "crisis" over "hope" --
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there's only three times
when "crisis" eclipses "hope."
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We're in the middle
of one of them right now.
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But don't think about that too much.
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(Laughter)
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And finally, the culmination of this work
with the New York Times data
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a few years ago
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was the attempt to combine
an entire year's news cycle
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into a single graphic.
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So these graphics actually show us
a full year of news, all the people,
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and how they're connected
into a single graphic.
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And from there, I started to be
interested again in more active systems.
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Here's a project called "Just Landed,"
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where I'm looking at people
tweeting on Twitter.
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"Hey! I just landed
in Hawaii!" -- you know,
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how people just casually try to sneak
that into their Twitter conversation.
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"I'm not showing off. Really.
But I did just land in Hawaii."
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And then I'm plotting
those people's trips,
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in the hopes that maybe
we can use social network
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and the data that it leaves behind
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to provide a model of how people move,
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which would be valuable
to epidemiologists, among other people.
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And, more fun -- this
is a similar project,
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looking at people
saying "Good morning" to each other
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all around the world.
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Which taught me, by the way,
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that it is true that people in Vancouver
on the West Coast wake up much later
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and say "Good morning" much later
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than the people on the East Coast,
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who are more adventurous.
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Here's a more useful -- maybe -- project,
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where I took all the information
from the Kepler Project
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and tried to put it into some visual form
that made sense to me.
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And I should say that everything
I've shown you up to now --
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these are all things
that I just did for fun.
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It may seem weird,
but this comes back from HyperCard.
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I'm building tools for myself.
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I may share them with a few other people,
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but they're for fun, they're for me.
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So, all these tools I show you
kind of occupy this weird space
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somewhere between science, art and design.
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That's where my practice lies.
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And still today,
from my experience with HyperCard,
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what I'm doing is building visual tools
to help me understand systems.
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So today, I work at the New York Times.
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I'm the data artist in residence
at the New York Times.
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And I've had an opportunity at the Times
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to work on a variety
of really interesting projects,
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two of which I'm going
to share with you today.
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The first one, I've been working on
in conjunction with Mark Hansen.
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Mark Hansen is a professor of statistics
at UCLA. He's also a media artist.
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And Mark came to the Times
with a very interesting question
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to what may seem like an obvious problem:
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When people share content on the internet,
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how does that content get
from person A to person B?
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Or maybe, person A to person B
to person C to person D?
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We know that people share content
in the internet,
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but what we don't know
is what happens in that gap
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between one person to the other.
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So we decided to build
the tool to explore that,
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and this tool is called Cascade.
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If we look at these systems
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that start with one event
that leads to other events,
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we call that structure a cascade.
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And these cascades
actually happen over time.
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So we can model these things over time.
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Now, the New York Times has
a lot of people who share our content,
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so the cascades do not look like that one,
they look more like this.
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Here's a typical cascade.
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At the bottom left, the very first event.
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And then as people are sharing
the content from one person to another,
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we go up in the Y axis,
degrees of separation,
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and over on the X axis, for time.
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So we're able to look at that conversation
in a couple of different views:
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this one, which shows us
the threads of conversation,
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and this one, which combines
that stacked view
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with a view that lets us see the threads.
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Now, the Times publishes
about 7,000 pieces of content
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every month.
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So it was important for us,
when we were building this tool,
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to make it an exploratory one,
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so that people could dig through
this vast terrain of data.
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I think of it as a vehicle
that we're giving people
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to traverse this really big
terrain of data.
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So here's what it really looks like,
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and here's the cascade
playing in real time.
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I have to say, this was
a tremendous moment.
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We had been working with canned
data, fake data, for so long,
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that when we saw this
for the first moment,
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it was like an archaeologist who had
just dusted off these dinosaur bones.
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We discovered this thing,
and we were seeing it for the first time,
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these sharing structures
that underlie the internet.
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And maybe the dinosaur
analogy is a good one,
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because we're actually making
some probabilistic guesses
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about how these things link.
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We're looking at some of these
pieces and making some guesses,
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but we try to make sure that those
are as statistically rigorous as possible.
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Now tweets, in this case,
they become parts of stories.
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They become parts of narratives.
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So we are building histories here,
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but they're very short-term histories.
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And sometimes these very large cascades
are the most interesting ones,
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but sometimes the small ones
are also interesting.
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This is one of my favorites.
We call this the "Rabbi Cascade."
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It's a conversation amongst rabbis
about this article in the New York Times,
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about the fact that religious workers
don't get a lot of time off.
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I guess Saturdays and Sundays are bad days
for them to take off.
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So, in this cascade, there's a group
of rabbis having a conversation
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about a New York Times story.
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One of them has the best
Twitter name ever --
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he's called "The Velveteen Rabbi."
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(Laughter)
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But we would have never found this
if it weren't for this exploratory tool.
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This would just be sitting somewhere,
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and we would have never
been able to see that.
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But this exercise of taking
single pieces of information
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and building narrative structures,
building histories out of them,
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I find tremendously interesting.
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You know, I moved to New York
about two years ago.
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And in New York, everybody has a story
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that surrounds this
tremendously impactful event
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that happened on September 11 of 2001.
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And my own story with September 11
has really become a more intricate one,
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because I spent a great deal of time
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working on a piece
of the 9/11 Memorial in Manhattan.
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The central idea about the 9/11 Memorial
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is that the names in the memorial
are not laid out in alphabetical order
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or chronological order,
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but instead, they're laid out in a way
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in which the relationships
between the people who were killed
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are embodied in the memorial.
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Brothers are placed next to brothers,
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coworkers are placed together.
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So this memorial actually considers
all of these myriad connections
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that were part of these people's lives.
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I worked with a company
called Local Projects
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to work on an algorithm
and a software tool
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to help the architects build
the layout for the memorial:
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almost 3,000 names
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and almost 1,500 of these
adjacency requests,
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these requests for connection --
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so a very dense story,
a very dense narrative,
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that becomes an embodied part
of this memorial.
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Working with Jake Barton,
we produce the software tool,
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which allows the architects to,
first of all, generate a layout
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that satisfied all of those
adjacency requests,
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but then second, make little adjustments
where they needed to
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to tell the stories
that they wanted to tell.
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So this memorial, I think,
has an incredibly timely concept
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in our era of social networks,
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because these networks -- these real-life
networks that make up people's lives --
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are actually embodied
inside of the memorial.
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And one of the most tremendously
moving experiences
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is to go to the memorial
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and see how these people
are placed next to each other,
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so that this memorial
is representing their own lives.
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How does this affect our lives?
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Well, I don't know if you remember,
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but in the spring,
there was a controversy,
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because it was discovered
that on the iPhone
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and, actually, on your computer,
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we were storing a tremendous amount
of the location data.
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So Apple responded, saying,
this was not location data about you,
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it was location data
about wireless networks
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that were in the area where you are.
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So it's not about you,
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but it's about where you are.
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(Laughter)
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This is very valuable data.
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It's like gold to researchers,
this human-mobility data.
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So we thought, "Man!
How many people have iPhones?"
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How many of you have iPhones?
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So in this room, we have this tremendous
database of location data
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13:14
that researchers
would really, really like.
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13:18
So we built this system called Open Paths,
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13:20
which lets people upload their iPhone data
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13:23
and broker relationships
with researchers to share that data,
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13:26
to donate that data to people
that can actually put it to use.
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13:30
Open Paths was a great
success as a prototype.
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13:33
We received thousands of data sets,
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13:36
and we built this interface
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13:37
which allows people to actually
see their lives unfolding
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13:41
from these traces
that are left behind on your devices.
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13:45
Now, what we didn't expect
was how moving this experience would be.
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13:50
When I uploaded my data,
I thought, "Big deal.
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13:52
I know where I live. I know where I work.
What am I going to see here?"
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13:56
Well, it turns out, what I saw
was that moment I got off the plane
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13:59
to start my new life in New York;
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831100
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14:02
the restaurant where I had Thai food
that first night,
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2606
14:04
thinking about this new experience
of being in New York;
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2953
14:07
the day that I met my girlfriend.
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1623
14:11
This is LaGuardia airport.
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2275
14:13
(Laughter)
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14:14
This is this Thai restaurant
on Amsterdam Avenue.
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14:19
This is the moment I met my girlfriend.
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14:22
See how that changes the first time
I told you about those stories
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3451
14:26
and the second time I told
you about those stories?
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2468
14:28
Because what we do
in the tool, inadvertently,
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3207
14:31
is we put these pieces of data
into a human context.
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3115
14:35
And by placing data into a human context,
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2498
14:37
it gains meaning.
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1474
14:39
And I think this is tremendously,
tremendously important,
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14:42
because these are our histories
that are being stored on these devices.
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4918
14:49
And by thinking about them that way,
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1994
14:52
putting them in a human context --
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1902
14:53
first of all, what we do with our own data
is get a better understanding
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3662
14:57
of the type of information
that we're sharing.
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2479
15:00
But if we can do this with other data,
if we can put data into a human context,
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15:04
I think we can change a lot of things,
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2918
15:07
because it builds, automatically, empathy
for the people involved in these systems.
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6385
15:14
And that, in turn, results
in a fundamental respect,
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2953
15:17
which, I believe, is missing
in a large part of technology,
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3163
15:20
when we start to deal
with issues like privacy,
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2938
15:25
by understanding that these numbers
are not just numbers,
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2717
15:28
but instead they're attached, tethered to,
pieces of the real world.
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3619
15:31
They carry weight.
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1506
15:33
By understanding that,
the dialog becomes a lot different.
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3332
15:38
How many of you have ever clicked a button
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2331
15:40
that enables a third party to access
your location data on your phone?
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4987
15:46
Lots of you.
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1555
15:47
So the third party is the developer,
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2245
15:49
the second party is Apple.
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1801
15:52
The only party that never gets access
to this information is the first party!
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4823
15:58
And I think that's because we think
about these pieces of data
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3135
16:01
in this stranded, abstract way.
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2055
16:03
We don't put them into a context
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1897
16:05
which, I think, makes them
a lot more important.
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2309
16:08
So what I'm asking you
to do is really simple:
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2166
16:10
start to think about data
in a human context.
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2323
16:13
It doesn't really take anything.
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1657
16:15
When you read stock prices,
think about them in a human context.
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3359
16:18
When you think about mortgage reports,
think about them in a human context.
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3542
16:22
There's no doubt that big data
is big business.
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3930
16:26
There's an industry being developed here.
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3018
16:30
Think about how well we've done
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1501
16:31
in previous industries
that we've developed involving resources.
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3369
16:34
Not very well at all.
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1300
16:36
I think part of that problem is, we've had
a lack of participation in these dialogues
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4522
16:40
from multiple pieces of human society.
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4428
16:45
So the other thing that I'm asking for
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1992
16:48
is an inclusion in this dialogue
from artists, from poets, from writers --
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4378
16:52
from people who can bring a human element
into this discussion.
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4013
16:57
Because I believe that this world of data
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2356
16:59
is going to be transformative for us.
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3025
17:03
And unlike our attempts
with the resource industry
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3169
17:06
and our attempts
with the financial industry,
337
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2153
17:08
by bringing the human
element into this story,
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2931
17:11
I think we can take it
to tremendous places.
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2178
17:14
Thank you.
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1155
17:15
(Applause)
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4052
Translated by Camille Martínez
Reviewed by Brian Greene

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ABOUT THE SPEAKER
Jer Thorp - Data artist
Jer Thorp’s work focuses on adding meaning and narrative to huge amounts of data as a way to help people take control of the information that surrounds them.

Why you should listen

Currently the data artist in residence at the New York Times, Jer’s software-based art has been featured all over the world. His former career as a data artist explains why his art often brings big data sets to life and is deeply influenced by science. Originally from Vancouver, he lives in New York City, where, along with his work at the New York Times, he teaches in NYU’s ITP program.

More profile about the speaker
Jer Thorp | Speaker | TED.com