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DAVID J. MALAN: All
right, welcome, everyone.

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Thank you so much for coming.

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So I'm really happy to have a classmate
of mine, Matt, from class of 1999,

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where both of us were
undergrads sometime ago.

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And we've been super excited to bring
Transit Screen-- and Matt especially--

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to campus, since you might have
seen here in CS50's office,

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or in Cabot House, or in Currier
House, and soon elsewhere,

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these so-called Transit Screens that
allows students to get up-to-the-minute

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information on when the next Harvard
shuttle is, when the next Uber is,

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when the next Hubway bikes
are available, and the like.

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So today Matt is here with us to talk
about smart cities more generally,

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and the power of harnessing
this kind of data.

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So over to you, Matt.

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MATT: Great.

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DAVID J. MALAN: Thank you, Matt.

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MATT: Thank you, David.

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And if you ever get
tired of teaching CS50,

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we might have a sales job for you.

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All right.

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DAVID J. MALAN: Welcome to campus.

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MATT: Thank you.

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Well great.

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So I'm going to talk a little bit about
what I do, and the kind of company

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that I and my colleagues and co-founders
have built at Transit Screen.

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But I also want to talk more
generally about urban data,

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about smart cities data,
and about how you too

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can get started building
interesting interventions that

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can help solve problems in cities.

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And I'm going to take a little bit of
a step back, sort of a 30,000 foot view

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at the beginning, and explain
why cities and why now.

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And so the most salient
thing you can take away here

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is that there are more people living
inside this circle on the globe than

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outside it.

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And we have a lot of
international students here.

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So of course, they appreciate that.

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Actually let me-- sorry, let me
just stop my slide show from going.

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I knew it was going to do that.

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OK, much better.

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OK, thank you.

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So there are more people living
inside this circle, China and India

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primarily, than outside of it.

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And the pace of urbanization has only
increased over the last 22 to 40 years.

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So just within India, for
instance, the urban population

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is this red bar continuing to increase.

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And the rural population is
sort of saturated, or declining.

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So what that means is tons of people
moving to cities, density increasing.

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And that means all of the issues
that come with that kind of density

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are also cropping up.

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And one of those major
issues is transportation.

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And the way we see
transportation at Transit Screen

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is that, in the words of Bogota
mayor, Enrique Penalosa-- who

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is an great documentary called
Urbanize that you might like--

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the sign of an advanced society is
not where the people who are poor

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drive cars, but where
the rich ride transit.

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And that's kind of inevitable.

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Because when you have enough
people and enough density,

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you just can't have everyone
driving a car and getting around.

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And you'll just have
gridlock, like you do

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in many cities in the developing world.

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And so what we have to do is we have
to find a way to shift some of that

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to two different modes in order
to make things achievable.

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So here's an example.

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I believe this is from South
America-- Brazil I think.

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And this is the kind of issue
with transportation supply

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that you see with cities
with all this urbanization.

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And then, back here in the
US, we have a great example

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from the last decade of Houston.

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Houston has the largest freeway
in the US, and possibly the world.

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It's 23 lanes wide.

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It's called the Katy Freeway.

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And it goes from Houston west.

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And they put $3 billion
into expanding it in 2011.

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And now traffic is 33% slower,
even though they increased tolls.

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What happened?

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Well, when they built
all those new lanes,

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and they allowed sprawling development
so that people who lived out

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in these suburbs who could only
get to the city, to their jobs,

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using this freeway, the result
was what's called induced demand.

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So you increase the supply, but
that creates its own demand.

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It's a feedback loop.

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And so the end result is actually
worse than when they started.

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And this is paradoxical.

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But this is the mode that
transportation solutions

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were for basically the last 50
years, was all this thinking that we

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could just fix it with supply.

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And that's not actually the case.

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Another manifestation
of this is parking lots.

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So in terms of cars, the number
of cars continues to increase.

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There are actually 1.2 billion cars on
the road today, and 2 billion by 2035.

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With the urbanization picking up as
well, the cars are sitting unused 96%

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of the time.

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And so this vast sea of
parking is made for the day

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after Thanksgiving, when
everyone goes shopping.

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Most of the time it's not used.

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Almost none of the time are
these cars actually used.

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So if you think, the amount of
this that is actually necessary,

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is just a fraction of this little car.

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So that's a way to think about
the scope of the problem,

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and how people are
looking for a solutions.

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So what are the solutions in
transportation going to look like?

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Well, I think one way to think about
it is that the transportation itself is

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changing, and it's becoming mobility.

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So car companies are saying,
we're now mobility companies.

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What does that mean?

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Well, mobility means, essentially, just
getting around in a variety of ways.

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And one of those ways, that's
very current and very relevant

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to CS50, I think, and what people
from computer science backgrounds

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are going to end up doing in the
future, is autonomous vehicles.

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So right now, there are 26
companies actively developing

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autonomous vehicle technologies--
ones you've heard of, of course,

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Google, Uber.

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But Tesla, General Motors, Ford, and
every other car company you could name

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has a research project in this area.

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One Google scientist,
Sebastian Thrun, said

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that the going rate for an
acquisition of one scientist who's

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working in autonomous vehicles
right now is $10 million.

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So if you have a active
team working on this,

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you could just get scooped
up by one of these people.

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And that's how the math works out.

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Nevertheless, autonomous
vehicles are here in some places.

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They're coming in some places.

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There are some autonomous taxis driving
around Pittsburgh-- autonomous Ubers.

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And Google cars driving around too.

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But it's not really here yet.

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And it's not clear how they're going
to work in an urban environment.

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That's still getting worked out.

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So in the meantime, let's focus
on some other technologies that

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are really here, and are really
growing very fast right now.

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One of those is, surprisingly
to some people, bikeshare.

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And so bikeshare, in the last year, 1.5
billion trips were taken on bikeshare.

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There is now a company in China
that's a bikesharing company that's

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valued at over a billion dollars,
so-called unicorn of bikesharing

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companies.

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So this is both a real commercial
marketplace, and a real transportation

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solution, with 1.5 billion trips.

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Carshare is also been
growing tremendously.

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And every single car share vehicle,
like a Zipcar, or a Car2go vehicle,

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has been studied and has been shown
to take up to about 8 to 12 vehicles

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off the road.

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So private cars, people who
have second private cars,

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will give up their car, because
it costs a lot to maintain.

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And they'll use a carsharer
instead as their second car

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for those rare occasions
when they actually need it.

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So this is still a pretty
significant number of trips.

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It's grown tremendously in Europe,
but it's still popular here

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in North America.

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Ridesharing, of course, everyone's
familiar with Uber, and Lyft, and DD,

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and the growth of all these services.

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It's still only four billion rides,
but it's increasing very rapidly.

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So it's about twice as big
as bikeshare right now.

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And then mass transit, I don't have
the number off hand for how many people

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it's carrying, but it's a lot more than
that, like by probably a factor of 10.

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And mass transit is still
continuing to grow tremendously.

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Sometimes people think subways are
old technology or something like that.

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But that's not actually true.

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And there have been 40
brand new metro systems

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built across the world in just the
last decade, which basically means

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a doubling of the number of cities
that have mass transit, so especially

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in countries like China, which
have been building a ton of them.

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So mobility has really changed.

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And the result of all this is
that now, more than ever before,

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it's more complicated to
try to get around cities.

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You have more choices, but you need
solutions for using those choices

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and for getting informed
about your different options.

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So this is just one example of how
the diversity of things that people

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are doing in this space has exploded.

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You can find this on our
website, but includes

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different on-demand mobility
options, and microtransit and stuff,

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as well as some of these
other self-driving cars,

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and other associated technologies.

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OK, so mobility has changed.

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Tremendous amount of activity there.

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What else is happening in cities?

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And one of the trends that's enabling
all these changes in mobility

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is what we call smart cities.

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And related to that is this concept
called the internet of things.

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So I'll talk a little bit
about the internet of things

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first, because it has a
more specific definition.

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What the internet of
things is really putting

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sensors and other
devices in the real world

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so that they are now
connected to the internet,

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and are now enabled for technology.

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The idea of a smart
city is a city in which

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you take all these
connected devices and you

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use that to get some
sort of intelligence

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or some sort of operation that makes
everything more efficient, often more

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sustainable, greener, less CO2.

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And so you see all of these
things, solar, and carsharing,

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and energy generation, and houses, and
everything are all connected together.

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And they all run efficiently
like a giant machine.

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The reality of that is that a lot
of this stuff is still emerging.

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Here are a few examples
of some smart city

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technologies that you might see today.

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And they're all
sensor-based, these ones.

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And they're all generating data that
often is being collected by cities,

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but sometimes is available
for you and I to use

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in different ways in our own projects.

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So here's an example of
smart parking systems.

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So in the old days, cities had no idea
who was parking in what space when.

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And so the reason that's a problem is
because, in many cities, 33% of traffic

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is people circling looking
for a parking spot.

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And so when they can't find
one, they keep circling.

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And they keep causing
mass amount of congestion.

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So the theory is, well, if we knew
exactly what parking spots were

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available, we could direct
people in their smartcars

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directly to those spaces, and
cut down on all the congestion,

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and all the wasted gasoline.

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So smart parking is being done
by both-- people put sensors

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in the parking spaces themselves.

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And that technology unfortunately
needs a lot of maintenance,

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and batteries need to
be replaced, and so on.

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So people are now looking at
sensors that are on light posts.

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You see all these light poles
around the urban environment.

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And those can be retrofitted
to put sensors on them

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for a variety of things.

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One of them is monitoring using either
video or some sort of laser sensor

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or something where the cars are parked.

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But you could also imagine
other uses for that space.

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And so people are coming up
with lots of neat proposals

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for how to use those sensor packs.

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Another technology, and this is
one that actually is, I believe,

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deployed in Cambridge,
Massachusetts here,

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is a network of microphones
called Shotspotter.

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And what Shotspotter is is, if
someone is shooting a gun in the city,

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these sensors, which are
basically just microphones,

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will triangulate the location.

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And in real time, that data then
gets fed to the police department,

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so that they know where to start
their search for this presumed gunman.

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You could imagine other
more peaceful applications

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for this kinds of technology.

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For instance, you've got
someone with a motorcycle that's

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generating sound at 120 decibels.

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It's a real nuisance.

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And in the past, the
police couldn't find them,

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because they just right away
as soon as you got there.

238
00:12:33,160 --> 00:12:35,320
But with a technology
like this, maybe you

239
00:12:35,320 --> 00:12:39,310
can actually find them and stop
them, confiscate their motorcycle,

240
00:12:39,310 --> 00:12:43,070
and restore peace and
quiet to the neighborhood.

241
00:12:43,070 --> 00:12:46,720
Another set of technologies
that's creating

242
00:12:46,720 --> 00:12:49,300
interesting sensor data--
and most of this data

243
00:12:49,300 --> 00:12:52,930
is private for privacy reasons,
but is widely available

244
00:12:52,930 --> 00:12:56,320
in the commercial sector--
is technology that

245
00:12:56,320 --> 00:13:01,150
monitors your location through
the use of your mobile phone,

246
00:13:01,150 --> 00:13:04,300
and your connection to the cellular
towers, and the cellular networks.

247
00:13:04,300 --> 00:13:06,460
So they know you're in this car.

248
00:13:06,460 --> 00:13:07,660
You're driving.

249
00:13:07,660 --> 00:13:09,120
The cellular network here.

250
00:13:09,120 --> 00:13:11,590
It knows where you are,
because it has to transfer you

251
00:13:11,590 --> 00:13:13,090
from one tower to another.

252
00:13:13,090 --> 00:13:15,760
And then that data says, you're moving.

253
00:13:15,760 --> 00:13:16,450
You're in a car.

254
00:13:16,450 --> 00:13:18,190
And you're over there.

255
00:13:18,190 --> 00:13:21,520
It says, there's a train going by,
and there's 200 people on the train,

256
00:13:21,520 --> 00:13:22,610
et cetera, et cetera.

257
00:13:22,610 --> 00:13:27,170
And so all of that location data can
be used for a variety of purposes,

258
00:13:27,170 --> 00:13:29,080
including transportation
planning, trying

259
00:13:29,080 --> 00:13:33,560
to figure out how congested things
are, and what we should do about it.

260
00:13:33,560 --> 00:13:37,390
So these are all examples
of smart city sensors.

261
00:13:37,390 --> 00:13:39,280
And so all of these
things are collecting

262
00:13:39,280 --> 00:13:45,910
data that could be used to make cities
run more efficiently and smarter.

263
00:13:45,910 --> 00:13:50,860
Now I'm a PhD in neuroscience.

264
00:13:50,860 --> 00:13:56,710
And so although I work mostly on
vision, which is one of the five senses,

265
00:13:56,710 --> 00:14:00,190
I actually learned, during
the course of my studies,

266
00:14:00,190 --> 00:14:02,690
that sensing is only sort
of half of the problem.

267
00:14:02,690 --> 00:14:05,230
And there's a benefit
from biology you can use,

268
00:14:05,230 --> 00:14:09,070
which says that a sensor is something
that transforms energy into data.

269
00:14:09,070 --> 00:14:11,680
So you're walking around
the world and your senses

270
00:14:11,680 --> 00:14:13,600
are acquiring data about the world.

271
00:14:13,600 --> 00:14:19,030
At the same time, you as a person,
as an animal or an organism,

272
00:14:19,030 --> 00:14:20,776
that's not the point.

273
00:14:20,776 --> 00:14:22,150
The point isn't just to get data.

274
00:14:22,150 --> 00:14:24,130
The point is actually a
do things in the world.

275
00:14:24,130 --> 00:14:27,209
And so what you need are
actuators or activators, things

276
00:14:27,209 --> 00:14:28,750
that transform that data into energy.

277
00:14:28,750 --> 00:14:30,220
For us, it's our muscles.

278
00:14:30,220 --> 00:14:31,600
We walk over there.

279
00:14:31,600 --> 00:14:35,590
We take a drink, et cetera.

280
00:14:35,590 --> 00:14:38,860
For cities, a lot of
the smart city stuff

281
00:14:38,860 --> 00:14:45,190
that you'll see in the media or just
around the world is focused on sensing.

282
00:14:45,190 --> 00:14:47,950
And it's not focused on
actually getting stuff done,

283
00:14:47,950 --> 00:14:51,920
making a change in the
world, activating things.

284
00:14:51,920 --> 00:14:56,290
And so this is actually the area that
we're working on with Transit Screen.

285
00:14:56,290 --> 00:15:00,730
And so our goal-- and you can
see the Transit Screens in CS50

286
00:15:00,730 --> 00:15:05,680
is one very small part of
that-- is to put real time

287
00:15:05,680 --> 00:15:09,370
information all around the
world in places where you live,

288
00:15:09,370 --> 00:15:13,960
places where you work, like right
here is CS50, places where you play,

289
00:15:13,960 --> 00:15:18,070
like these bar jukeboxes that we
just launched with touch tunes that

290
00:15:18,070 --> 00:15:20,450
now have transit information.

291
00:15:20,450 --> 00:15:24,410
You can have city halls, on the
streets, or when you're traveling,

292
00:15:24,410 --> 00:15:26,570
and vacationing or a hotel.

293
00:15:26,570 --> 00:15:32,750
So what's all this
information is provided to do

294
00:15:32,750 --> 00:15:35,900
is to make people make
different decisions,

295
00:15:35,900 --> 00:15:37,710
and make people make better decisions.

296
00:15:37,710 --> 00:15:39,380
They might be more efficient decisions.

297
00:15:39,380 --> 00:15:41,750
For them, they might be a
more sustainable decisions.

298
00:15:41,750 --> 00:15:44,242
Usually when you give
people more information,

299
00:15:44,242 --> 00:15:46,700
they'll shift away from whatever
their default behavior is.

300
00:15:46,700 --> 00:15:49,520
And right now, in transportation,
people's default behavior

301
00:15:49,520 --> 00:15:50,270
is pretty bad.

302
00:15:50,270 --> 00:15:52,160
It's usually, I get
in my car and I drive,

303
00:15:52,160 --> 00:15:54,330
or I take an Uber, or
something like that.

304
00:15:54,330 --> 00:15:57,280
So what you want is you want
people to see the choices.

305
00:15:57,280 --> 00:15:59,600
Most of the choices are
actually more sustainable.

306
00:15:59,600 --> 00:16:03,930
And so you end up with a better result
when you provide the information.

307
00:16:03,930 --> 00:16:09,094
The key is that, in a smart
city, people are the activators.

308
00:16:09,094 --> 00:16:10,010
They're the actuators.

309
00:16:10,010 --> 00:16:13,520
So you need to actually provide
people with the information.

310
00:16:13,520 --> 00:16:15,560
It's not enough just
to collect the data.

311
00:16:15,560 --> 00:16:17,300
You actually have to
turn it information,

312
00:16:17,300 --> 00:16:22,220
and then you have to get people to
use it to change their behavior.

313
00:16:22,220 --> 00:16:26,000
So here's one of our
transit screens close up

314
00:16:26,000 --> 00:16:28,850
here from Cabbot House, Harvard.

315
00:16:28,850 --> 00:16:31,760
Just got some very nice
feedback about this one.

316
00:16:31,760 --> 00:16:37,880
We had one of the CS50 TFs, who chooses
his breakfast location so that he

317
00:16:37,880 --> 00:16:42,990
can see this transit screen, so
then whether the shuttle is coming,

318
00:16:42,990 --> 00:16:44,840
or maybe he'll take a Hubway bikeshare.

319
00:16:44,840 --> 00:16:47,000
He has a choice based on that.

320
00:16:47,000 --> 00:16:49,840
And so that's a great
example of behavioral change.

321
00:16:49,840 --> 00:16:52,460
He's changing where he
sits in the dining hall.

322
00:16:52,460 --> 00:17:00,680
And then based on that, he's also
changing what kind of travel he uses.

323
00:17:00,680 --> 00:17:05,599
So what other examples are
there of behavioral change?

324
00:17:05,599 --> 00:17:10,700
Well, one very interesting
example is another company

325
00:17:10,700 --> 00:17:14,869
called Opower that's also
based in Washington, DC area.

326
00:17:14,869 --> 00:17:17,690
And what they did is they
took all this smart city

327
00:17:17,690 --> 00:17:20,750
data about people's energy usages.

328
00:17:20,750 --> 00:17:24,619
And you might have an
apartment or a home,

329
00:17:24,619 --> 00:17:27,020
and you use energy at a certain rate.

330
00:17:27,020 --> 00:17:29,510
This is how many
kilowatt hours you used.

331
00:17:29,510 --> 00:17:34,130
And then this is how much you paid
last month for your energy bills.

332
00:17:34,130 --> 00:17:39,810
Now this is the average, because they
have data from everyone in Cambridge,

333
00:17:39,810 --> 00:17:40,310
say.

334
00:17:40,310 --> 00:17:43,970
That they know what the average home
in Cambridge uses, in terms of energy.

335
00:17:43,970 --> 00:17:49,310
And then, in this case, this person
here, Garrett, he's using more.

336
00:17:49,310 --> 00:17:53,540
And so the objective
of Opower's software

337
00:17:53,540 --> 00:17:56,690
here is that they have
this dashboard here,

338
00:17:56,690 --> 00:17:59,000
so that he can see
that he is using more.

339
00:17:59,000 --> 00:18:03,110
And maybe there are some things he you
can do, specifically, turn off lights,

340
00:18:03,110 --> 00:18:05,955
or compete against other people
to reduce his energy usage.

341
00:18:05,955 --> 00:18:07,080
There are things he can do.

342
00:18:07,080 --> 00:18:11,060
They're kind of long term things, but
they can, ultimately, save him money,

343
00:18:11,060 --> 00:18:13,530
and improve the environment.

344
00:18:13,530 --> 00:18:16,340
So this is something
where they're taking

345
00:18:16,340 --> 00:18:20,762
data that was previously unavailable,
because meters didn't used to be smart.

346
00:18:20,762 --> 00:18:21,470
But now they are.

347
00:18:21,470 --> 00:18:23,390
They're feeding that
data into the internet.

348
00:18:23,390 --> 00:18:27,080
And Opower built a business using
combination of web technologies,

349
00:18:27,080 --> 00:18:30,440
and also they even mail this
information to you in a letter,

350
00:18:30,440 --> 00:18:32,780
because they know you're
more likely to look at it.

351
00:18:32,780 --> 00:18:38,270
They were able to find ways to reduce
energy usage in different cities

352
00:18:38,270 --> 00:18:40,250
around the country.

353
00:18:40,250 --> 00:18:44,030
And coincidentally enough,
Opower was actually

354
00:18:44,030 --> 00:18:46,340
founded by a couple of
our other classmates, mine

355
00:18:46,340 --> 00:18:50,840
and David's, these guys, Alex and Dan.

356
00:18:50,840 --> 00:18:54,050
And they actually went
public a couple of years ago.

357
00:18:54,050 --> 00:18:56,420
And this is them on the New
York Stock Exchange floor.

358
00:18:56,420 --> 00:19:00,620
So there's a lot of real
impact and also real potential

359
00:19:00,620 --> 00:19:02,360
in some of these kinds
of businesses that

360
00:19:02,360 --> 00:19:06,290
are based on the idea of smart cities.

361
00:19:06,290 --> 00:19:13,030
All right, so that's kind of the
background for smart cities and data,

362
00:19:13,030 --> 00:19:17,450
and both the sensor
and the activator side.

363
00:19:17,450 --> 00:19:21,200
So what I want to talk
about next is a little bit

364
00:19:21,200 --> 00:19:25,440
of how you can get started by
yourself in working in this area.

365
00:19:25,440 --> 00:19:30,680
And really the easiest way to
get started is with open data.

366
00:19:30,680 --> 00:19:36,340
And in case you haven't heard of open
data, the idea of open data as this.

367
00:19:36,340 --> 00:19:40,070
It's data that can be freely used,
reused, or redistributed by any one,

368
00:19:40,070 --> 00:19:44,090
subject only to, at most, you
say it's from a certain source.

369
00:19:44,090 --> 00:19:49,670
And so it's often required to be
available in machine-readable formats.

370
00:19:49,670 --> 00:19:53,690
So if I give you a stack of
paper, that's not open data.

371
00:19:53,690 --> 00:19:56,690
It's got to be in data format.

372
00:19:56,690 --> 00:20:00,100
And then it's also ought to
be available in its entirety.

373
00:20:00,100 --> 00:20:04,910
So if I have to log into some
website, and take a screenshot,

374
00:20:04,910 --> 00:20:09,350
or just scrape the web or something
like that, that's not open data either.

375
00:20:09,350 --> 00:20:13,250
I have to be able to get it like
a table, like an Excel file.

376
00:20:13,250 --> 00:20:18,280
Or I have to be able to get some
sort of interchange format like XML,

377
00:20:18,280 --> 00:20:20,350
or JSON, or these other kinds of things.

378
00:20:20,350 --> 00:20:25,000
So open data is often generated
by the government, but not always.

379
00:20:25,000 --> 00:20:28,760
Because governments have to be public
and transparent about their data.

380
00:20:28,760 --> 00:20:30,730
So they have to share it with citizens.

381
00:20:30,730 --> 00:20:34,570
And the result of this-- and there's
been a lot of open data momentum

382
00:20:34,570 --> 00:20:39,130
recently-- the result of this is that
it's a really good way to get started.

383
00:20:39,130 --> 00:20:43,900
Because with some of this
other stuff like this data,

384
00:20:43,900 --> 00:20:47,240
few years ago, you weren't able to get
this data from the energy companies.

385
00:20:47,240 --> 00:20:50,290
Your utility, you'd say, well,
I have a really great idea

386
00:20:50,290 --> 00:20:53,760
and I'd like to be able to do something
to make my home more efficient.

387
00:20:53,760 --> 00:20:54,760
And they'd say, too bad.

388
00:20:54,760 --> 00:20:56,830
You can't have the data.

389
00:20:56,830 --> 00:20:57,580
You're a customer.

390
00:20:57,580 --> 00:20:59,350
You're not our partner.

391
00:20:59,350 --> 00:21:02,260
But with the government involved
and generating this data,

392
00:21:02,260 --> 00:21:06,910
for instance, the Boston T, the
MBTA, is a government agency.

393
00:21:06,910 --> 00:21:10,450
And so they provide open data
that's available to a lot of people,

394
00:21:10,450 --> 00:21:14,170
and without very many
restrictions on it.

395
00:21:14,170 --> 00:21:17,500
So one of the impacts of this is
that you can develop your app,

396
00:21:17,500 --> 00:21:20,450
or you can even develop a
business based on open data.

397
00:21:20,450 --> 00:21:24,820
And so you're no longer
required, say, for the T--

398
00:21:24,820 --> 00:21:27,900
I'm not even sure if they have
their own MBTA app or not.

399
00:21:27,900 --> 00:21:29,740
But there are a lot
of other apps you can

400
00:21:29,740 --> 00:21:34,000
use on your mobile phone that allow
you to get information about them.

401
00:21:34,000 --> 00:21:36,430
Transit Screen is another
example of something

402
00:21:36,430 --> 00:21:42,640
that uses open data from the T, as well
as dozens and dozens, if not hundreds,

403
00:21:42,640 --> 00:21:46,060
of other agencies around the world.

404
00:21:46,060 --> 00:21:48,506
And then another nice
thing about this is

405
00:21:48,506 --> 00:21:51,130
that there are many different
people contributing to open data.

406
00:21:51,130 --> 00:21:53,590
So it doesn't just have to be
sourced from the government.

407
00:21:53,590 --> 00:21:55,660
Someone can come along
and say, well, I think

408
00:21:55,660 --> 00:21:57,649
this data set would be
better in this format,

409
00:21:57,649 --> 00:21:59,440
or it would be easier
to use, or something.

410
00:21:59,440 --> 00:22:01,990
And you can clean it up,
and then redistribute it.

411
00:22:01,990 --> 00:22:05,499
And then the results of
all this kind of activity

412
00:22:05,499 --> 00:22:07,540
is that people can build
little businesses on it.

413
00:22:07,540 --> 00:22:10,810
And so I'm just going to plug
the startup incubator that we

414
00:22:10,810 --> 00:22:13,840
come from, which is in Washington, DC.

415
00:22:13,840 --> 00:22:15,400
And it's called 1776.

416
00:22:15,400 --> 00:22:17,800
And their mission is
to solve hard problems

417
00:22:17,800 --> 00:22:21,340
in the areas of cities, and government,
and health care, and education,

418
00:22:21,340 --> 00:22:24,220
and transportation,
a lot of which can be

419
00:22:24,220 --> 00:22:26,664
addressed using some form of open data.

420
00:22:26,664 --> 00:22:29,980


421
00:22:29,980 --> 00:22:32,620
Like I said, the US
federal government has

422
00:22:32,620 --> 00:22:36,430
gotten behind this crusade
of openness and transparency.

423
00:22:36,430 --> 00:22:39,530
And so the Obama Administration
had a pronouncement.

424
00:22:39,530 --> 00:22:41,650
Then they created an
independent authority

425
00:22:41,650 --> 00:22:45,430
under the US CTO's office
to promote open data.

426
00:22:45,430 --> 00:22:49,210
And this was me meeting
the President at 1776.

427
00:22:49,210 --> 00:22:52,330
I just wanted to show this slide.

428
00:22:52,330 --> 00:22:56,740
All right, so I'm going to
walk you through a couple

429
00:22:56,740 --> 00:23:01,180
of different examples of open data,
and things you can actually use.

430
00:23:01,180 --> 00:23:03,790
These ones are ones that I
know well, because they're

431
00:23:03,790 --> 00:23:06,340
relevant to transportation
for the most part.

432
00:23:06,340 --> 00:23:10,360
But there's a whole variety of different
sources out there that you can go to.

433
00:23:10,360 --> 00:23:13,510
So I'm going to talk about four
different areas very quickly.

434
00:23:13,510 --> 00:23:19,540
One of them is open geographic data,
so maps, OpenStreetMap specifically,

435
00:23:19,540 --> 00:23:23,680
which is like the
Wikipedia of maps, GTFS,

436
00:23:23,680 --> 00:23:26,500
which is how transit
schedules are represented,

437
00:23:26,500 --> 00:23:31,270
and it's been a very successful
open data standard, Realtime APIs--

438
00:23:31,270 --> 00:23:33,170
so this is just transit
schedules, but if you

439
00:23:33,170 --> 00:23:36,010
want to know where a
train is right now, you

440
00:23:36,010 --> 00:23:40,870
need to use a Realtime
API-- and then energy data.

441
00:23:40,870 --> 00:23:45,250
So there's some new standards
there that we can talk about.

442
00:23:45,250 --> 00:23:48,160
So OpenStreetMap, a lot of people
have heard about Google Maps.

443
00:23:48,160 --> 00:23:49,480
They're kind of the standard.

444
00:23:49,480 --> 00:23:52,300
Some people have even
heard of Apple Maps.

445
00:23:52,300 --> 00:23:58,450
And OpenStreetMap is less well-known,
but it underlies a tremendous amount

446
00:23:58,450 --> 00:23:59,980
of activity on the internet.

447
00:23:59,980 --> 00:24:03,230
And it's really at sort of
world-class standards right now.

448
00:24:03,230 --> 00:24:07,990
So this was Sochi Olympics, the
Winter Olympics before the last summer

449
00:24:07,990 --> 00:24:08,830
Olympics.

450
00:24:08,830 --> 00:24:12,430
And during that time-- Sochi is a
Russian city that not a lot of people

451
00:24:12,430 --> 00:24:13,750
are familiar with.

452
00:24:13,750 --> 00:24:15,880
They kind of built it
up for the Olympics.

453
00:24:15,880 --> 00:24:18,700
This is what OpenStreetMap
looked like during that Olympics.

454
00:24:18,700 --> 00:24:22,551
So if you were a visitor, and you
were trying to find your way around,

455
00:24:22,551 --> 00:24:23,800
you can see all the buildings.

456
00:24:23,800 --> 00:24:26,900
You can see all the new paths,
the Olympic Park, et cetera.

457
00:24:26,900 --> 00:24:30,580
And this is what it looked
like in Google Maps.

458
00:24:30,580 --> 00:24:36,430
So Google, which, reminder, is a
company that has hundreds of billions

459
00:24:36,430 --> 00:24:39,730
of dollars sitting around, didn't
really prepare for the Olympics,

460
00:24:39,730 --> 00:24:41,920
or even get the data together.

461
00:24:41,920 --> 00:24:45,220
Whereas a community of people,
just like Wikipedia editors,

462
00:24:45,220 --> 00:24:48,010
people generating their
own open data, managed

463
00:24:48,010 --> 00:24:49,780
to achieve this in OpenStreetMap.

464
00:24:49,780 --> 00:24:54,010
So last Olympics, it
wasn't quite as clear

465
00:24:54,010 --> 00:24:56,860
a distinction, because Google
actually put a lot of money

466
00:24:56,860 --> 00:25:01,250
into making sure that the last summer
Olympics wasn't like this for them.

467
00:25:01,250 --> 00:25:03,850


468
00:25:03,850 --> 00:25:07,341
So OpenStreetMap is
one of these open data

469
00:25:07,341 --> 00:25:10,090
sets that's not generated by a
government, although in many cases,

470
00:25:10,090 --> 00:25:11,530
it uses government data.

471
00:25:11,530 --> 00:25:13,900
It's generated by
crowdsourcing, like Wikipedia.

472
00:25:13,900 --> 00:25:16,960
So here's an interesting map
showing the number of edits

473
00:25:16,960 --> 00:25:19,990
in each of these geographic areas.

474
00:25:19,990 --> 00:25:23,190
One thing to notice is that, in
many of these areas in Europe,

475
00:25:23,190 --> 00:25:27,400
there's-- I think this is per
square kilometer or something--

476
00:25:27,400 --> 00:25:33,040
it's like over 500,000 edits per square
kilometer, which is just amazing.

477
00:25:33,040 --> 00:25:37,420
You're really converging very
quickly on sort of ground truth

478
00:25:37,420 --> 00:25:39,340
when you have that kind of activity.

479
00:25:39,340 --> 00:25:41,700
And the US is not too far behind.

480
00:25:41,700 --> 00:25:44,560


481
00:25:44,560 --> 00:25:47,340
So you, yourself, can
actually edit OpenStreetMap.

482
00:25:47,340 --> 00:25:48,130
It's very easy.

483
00:25:48,130 --> 00:25:52,159
You just go OpenStreetMap.org and
start editing stuff that you know.

484
00:25:52,159 --> 00:25:55,200
One thing you can do with it-- and I
think this is kind of a neat thing--

485
00:25:55,200 --> 00:25:59,872
is in some cities, the
sidewalks and pedestrian paths

486
00:25:59,872 --> 00:26:01,080
aren't very well represented.

487
00:26:01,080 --> 00:26:07,710
So let's say that you have a elderly
relative who uses like a wheelchair,

488
00:26:07,710 --> 00:26:09,930
or a scooter, or something
like that, they're not

489
00:26:09,930 --> 00:26:12,030
going to be able to get
from point A to point B,

490
00:26:12,030 --> 00:26:18,082
or to have an app tell them how they can
safely get from one point to another.

491
00:26:18,082 --> 00:26:20,040
They'll just have to
discover it by themselves.

492
00:26:20,040 --> 00:26:23,320
But you can actually go into
OpenStreetMap and edit these paths.

493
00:26:23,320 --> 00:26:25,530
Say, here's an accessible path.

494
00:26:25,530 --> 00:26:27,930
There's a curb here, so if
you're using a wheelchair,

495
00:26:27,930 --> 00:26:30,090
you can't jump over that, and so on.

496
00:26:30,090 --> 00:26:34,410
And then this all gets put
into trip planning apps,

497
00:26:34,410 --> 00:26:40,390
so that, when you're using one of
these apps like a city navigation app,

498
00:26:40,390 --> 00:26:42,360
it will actually now
give you these paths.

499
00:26:42,360 --> 00:26:44,850
And you could build an
app for someone who's

500
00:26:44,850 --> 00:26:48,270
using a wheelchair that says, how
can I actually get from point A

501
00:26:48,270 --> 00:26:50,430
to point B in a safe way?

502
00:26:50,430 --> 00:26:53,100
And so you can build that
for your local community,

503
00:26:53,100 --> 00:26:56,190
and then extend that out as you grow.

504
00:26:56,190 --> 00:27:00,330
So I think this is really an
interesting use of open data

505
00:27:00,330 --> 00:27:02,530
where you can actually
solve a real problem.

506
00:27:02,530 --> 00:27:05,940
And then you can also maybe
build an app, or even a company

507
00:27:05,940 --> 00:27:08,010
on something like that.

508
00:27:08,010 --> 00:27:09,750
Here's another interesting use of data.

509
00:27:09,750 --> 00:27:18,540
You can use this open satellite
data to measure the solar potential

510
00:27:18,540 --> 00:27:20,310
of different buildings in the city.

511
00:27:20,310 --> 00:27:22,560
So this is a solar map of
Cambridge, Massachusetts.

512
00:27:22,560 --> 00:27:26,280
And this is showing that
some parts of the roof

513
00:27:26,280 --> 00:27:29,700
here could actually have solar
installations on here that

514
00:27:29,700 --> 00:27:34,230
could theoretically generate a
lot of power for this building.

515
00:27:34,230 --> 00:27:38,730
And then you could even measure, using
open data from the electric utility

516
00:27:38,730 --> 00:27:40,710
about how much they
charge and so on, you

517
00:27:40,710 --> 00:27:43,626
could say, well, how long will it
take me to pay off the solar panels,

518
00:27:43,626 --> 00:27:45,090
and so on?

519
00:27:45,090 --> 00:27:47,070
Does CS50 have solar panels?

520
00:27:47,070 --> 00:27:48,250
Not yet.

521
00:27:48,250 --> 00:27:51,600
All right, working on it.

522
00:27:51,600 --> 00:27:54,950
So another interesting
application of all this Geodata

523
00:27:54,950 --> 00:27:56,580
is you can do custom styles.

524
00:27:56,580 --> 00:27:59,100
And there's a great
company we work with called

525
00:27:59,100 --> 00:28:01,800
Mapbox that's based in Washington, DC.

526
00:28:01,800 --> 00:28:04,530
And they've built a whole
business on open data

527
00:28:04,530 --> 00:28:07,800
and on using different
ways to interact with it.

528
00:28:07,800 --> 00:28:10,980
So they have a styling
language that's kind

529
00:28:10,980 --> 00:28:15,510
of like how you use HTML to make
the web pages look different.

530
00:28:15,510 --> 00:28:18,700
They've created language for
making maps look different.

531
00:28:18,700 --> 00:28:22,920
And so these all they
look completely different.

532
00:28:22,920 --> 00:28:25,650
And yet, they're all generated
with the same open data,

533
00:28:25,650 --> 00:28:29,640
with the same OpenStreetMap data, just
with different styling rules applied.

534
00:28:29,640 --> 00:28:32,400
So you guys could actually
see these examples online.

535
00:28:32,400 --> 00:28:36,120
You could edit them, and change
them yourself, and come up

536
00:28:36,120 --> 00:28:39,570
with maps that look ranging from,
this is very similar to Google Maps,

537
00:28:39,570 --> 00:28:45,570
to something that's more like a hiking
map, or sort of a '50s style map,

538
00:28:45,570 --> 00:28:48,690
or even a sort of
comic book kind of map.

539
00:28:48,690 --> 00:28:54,690
So all these different styles are now
available because the data exists.

540
00:28:54,690 --> 00:28:56,820
So that's geographical data.

541
00:28:56,820 --> 00:29:00,870
And that's a really important
source of knowledge about the world.

542
00:29:00,870 --> 00:29:05,830
Lots of great solutions to urban
problems can be found with Geodata.

543
00:29:05,830 --> 00:29:08,370
Another kind of open data
that I know very well,

544
00:29:08,370 --> 00:29:11,820
and that I think has been a real
success story, is called GTFS.

545
00:29:11,820 --> 00:29:14,744
And this is the general
transit feed specification.

546
00:29:14,744 --> 00:29:15,660
You need to know that.

547
00:29:15,660 --> 00:29:18,790
Basically, all you need to know
is that it's transit schedules.

548
00:29:18,790 --> 00:29:22,770
So when is the bus running
from A to B, and where?

549
00:29:22,770 --> 00:29:26,190
So there are a couple of places where
you can explore this kind of data

550
00:29:26,190 --> 00:29:27,870
and get started.

551
00:29:27,870 --> 00:29:31,330
One great one is called transit feeds.

552
00:29:31,330 --> 00:29:37,074
And just search for transit
feeds and you can find it.

553
00:29:37,074 --> 00:29:38,490
You can actually click through it.

554
00:29:38,490 --> 00:29:39,960
This is San Francisco.

555
00:29:39,960 --> 00:29:41,310
You can find the latest file.

556
00:29:41,310 --> 00:29:43,050
You can look for a certain route.

557
00:29:43,050 --> 00:29:46,740
And then the data is all
available to download that powers

558
00:29:46,740 --> 00:29:48,540
this particular visualization.

559
00:29:48,540 --> 00:29:52,110
Another really nice tool that's been
developing very quickly from a company

560
00:29:52,110 --> 00:29:56,220
called Mapzen that we work
with is called Transitland.

561
00:29:56,220 --> 00:30:02,160
And so Transitland is what's
called a data commons, which

562
00:30:02,160 --> 00:30:05,100
is that they're taking all open
data from all over the world, all

563
00:30:05,100 --> 00:30:07,650
over the planet, and bring
it together in one place,

564
00:30:07,650 --> 00:30:10,530
so that you can see everything
sort of unequal footing.

565
00:30:10,530 --> 00:30:15,090
You have data from Sydney,
Australia, and then you

566
00:30:15,090 --> 00:30:16,410
have data from San Francisco.

567
00:30:16,410 --> 00:30:18,900
And it's all in the same
standard and the same format.

568
00:30:18,900 --> 00:30:22,290
So you can take a solution that you
develop that works for San Francisco,

569
00:30:22,290 --> 00:30:25,654
and you can immediately make
work for Sydney, Australia.

570
00:30:25,654 --> 00:30:28,880


571
00:30:28,880 --> 00:30:32,500
So GTFS, one of the reasons I
like it, and one the reasons

572
00:30:32,500 --> 00:30:35,350
I'm talking about it, is that
it's actually really simple.

573
00:30:35,350 --> 00:30:39,500
And so it's plain text files,
and it's written in tables.

574
00:30:39,500 --> 00:30:43,310
So if you download a GTFS
file, it's like a zip file.

575
00:30:43,310 --> 00:30:45,760
And it's got this stuff in it.

576
00:30:45,760 --> 00:30:47,170
These things all have formats.

577
00:30:47,170 --> 00:30:52,309
But if you just double click on one
of them, stops.txt, open that up,

578
00:30:52,309 --> 00:30:53,600
and this is what it looks like.

579
00:30:53,600 --> 00:30:58,390
It's got a list of stops, the names of
those stops, latitudes and longitude,

580
00:30:58,390 --> 00:31:00,730
so you can plot it on
your OpenStreetMap,

581
00:31:00,730 --> 00:31:02,450
and then some other information here.

582
00:31:02,450 --> 00:31:05,890
So it's the kind of data that
you can really start exploring

583
00:31:05,890 --> 00:31:10,060
and start building on very readily.

584
00:31:10,060 --> 00:31:12,190
Of course, it's a little
bit more complicated.

585
00:31:12,190 --> 00:31:14,770
And if you really want to
do some complicated things,

586
00:31:14,770 --> 00:31:18,460
you'll have to start working a little
bit with databases, or at least

587
00:31:18,460 --> 00:31:20,410
matching data from one thing to another.

588
00:31:20,410 --> 00:31:23,170
But it's not it's not terribly complex.

589
00:31:23,170 --> 00:31:25,180
And there are tools
that are available that

590
00:31:25,180 --> 00:31:27,280
are mostly open source,
easy to download,

591
00:31:27,280 --> 00:31:30,140
that can be used for this kind of stuff.

592
00:31:30,140 --> 00:31:32,650
So for instance, those
stops I showed you

593
00:31:32,650 --> 00:31:38,080
are linked to particular stop times,
and to certain bus trips, and so on.

594
00:31:38,080 --> 00:31:40,780
So you can actually go
from one thing to another,

595
00:31:40,780 --> 00:31:45,100
and match all these
things up in the database.

596
00:31:45,100 --> 00:31:48,940
Another interesting source of data
from around here is bikeshare data.

597
00:31:48,940 --> 00:31:51,640
And a lot of bikeshare data is open.

598
00:31:51,640 --> 00:31:54,070
In DC, I helped open that up.

599
00:31:54,070 --> 00:31:59,020
But then other cities, like Boston,
have just done that from the beginning.

600
00:31:59,020 --> 00:32:01,810
And so here's an interesting
example of someone

601
00:32:01,810 --> 00:32:06,590
who-- they had a contest
for visualizing Hubway data.

602
00:32:06,590 --> 00:32:12,390
And so that resulted in a really nice
visualization of trips versus, I think,

603
00:32:12,390 --> 00:32:13,960
distance.

604
00:32:13,960 --> 00:32:18,555
And they have actual trip history here,
so you can see not just your trips,

605
00:32:18,555 --> 00:32:20,680
or just what the stations
are, but you can actually

606
00:32:20,680 --> 00:32:25,070
see how many people have traveled
from what part to what other part.

607
00:32:25,070 --> 00:32:30,100
And so that's the kind of more
the sort of monitoring data,

608
00:32:30,100 --> 00:32:32,940
or the real historical data
that we're talking about.

609
00:32:32,940 --> 00:32:34,690
A lot of that is
available with bikeshare.

610
00:32:34,690 --> 00:32:36,700
So if you're interested
in what you could

611
00:32:36,700 --> 00:32:39,100
do if you had data from
everyone's cellular records

612
00:32:39,100 --> 00:32:41,502
of how they got from point
A to point B, then bikeshare

613
00:32:41,502 --> 00:32:44,710
is an interesting way you might want to
get started with something like that,

614
00:32:44,710 --> 00:32:45,701
because it is open.

615
00:32:45,701 --> 00:32:48,790


616
00:32:48,790 --> 00:32:51,340
So all of that data is static data.

617
00:32:51,340 --> 00:32:53,050
The maps are just static.

618
00:32:53,050 --> 00:32:54,820
The schedule never changes.

619
00:32:54,820 --> 00:32:58,742
And the bikeshare data is the
history of the bikeshare system.

620
00:32:58,742 --> 00:33:00,700
When you're talking about
real time data, stuff

621
00:33:00,700 --> 00:33:04,690
that's actually happening
right now in real time,

622
00:33:04,690 --> 00:33:06,670
you're not talking
about data sets anymore.

623
00:33:06,670 --> 00:33:08,860
You're really talking about APIs.

624
00:33:08,860 --> 00:33:13,490
And I think APIs are covered later in
the course, sort of towards the end.

625
00:33:13,490 --> 00:33:20,050
But basically, all an API is is the
way you talk to another program,

626
00:33:20,050 --> 00:33:22,180
or the way you talk to a
database about something.

627
00:33:22,180 --> 00:33:25,900
And so you can get information from
that program, or from that database,

628
00:33:25,900 --> 00:33:27,280
that you can then use.

629
00:33:27,280 --> 00:33:29,890
And if that database is
changing, is updating

630
00:33:29,890 --> 00:33:33,730
in real time, then you have that
information and it's up to the minute,

631
00:33:33,730 --> 00:33:34,600
or up to the second.

632
00:33:34,600 --> 00:33:35,620
And you can use it.

633
00:33:35,620 --> 00:33:41,000
So a lot of APIs can actually be
accessed just through your web browser.

634
00:33:41,000 --> 00:33:43,690
So in many cases, if
you have an API, you

635
00:33:43,690 --> 00:33:50,440
can just type the name of the so-called
endpoint into your web browser.

636
00:33:50,440 --> 00:33:54,750
And then you'll get out, in a readable
format-- this one is called JSON,

637
00:33:54,750 --> 00:33:57,220
J-S-O-N-- you can get information.

638
00:33:57,220 --> 00:33:59,830
So here's one from a transit service.

639
00:33:59,830 --> 00:34:02,282
And it says, well, here
are the stop times.

640
00:34:02,282 --> 00:34:03,490
There's an arrival coming up.

641
00:34:03,490 --> 00:34:05,050
Don't worry about the numbers.

642
00:34:05,050 --> 00:34:06,880
This is in a particular format.

643
00:34:06,880 --> 00:34:10,719
But the point is, this one is
just 0 seconds from the stop.

644
00:34:10,719 --> 00:34:13,210
So the train has just
gotten to the stop.

645
00:34:13,210 --> 00:34:17,409
And now you know that because you called
that API, and you got information back.

646
00:34:17,409 --> 00:34:19,179
And now your program can use that too.

647
00:34:19,179 --> 00:34:21,159
You can put it up on
a screen, like we do.

648
00:34:21,159 --> 00:34:24,699
Or you can do other things with it.

649
00:34:24,699 --> 00:34:27,060
APIs are readable by code.

650
00:34:27,060 --> 00:34:28,870
This is Twitter.

651
00:34:28,870 --> 00:34:30,520
And this is someone's tweet.

652
00:34:30,520 --> 00:34:32,560
This is not what I would
consider to be an API.

653
00:34:32,560 --> 00:34:38,199
You can get tweets from an
API, but it's not the same,

654
00:34:38,199 --> 00:34:41,170
because this information
isn't structured.

655
00:34:41,170 --> 00:34:42,820
It's just someone's typing.

656
00:34:42,820 --> 00:34:46,139
And it's very hard to know what to
do with that to write a program that

657
00:34:46,139 --> 00:34:47,889
can use all of this text.

658
00:34:47,889 --> 00:34:50,949
Whereas with this, I can write a
program that says, I want this,

659
00:34:50,949 --> 00:34:53,020
then I want that, then I want this part.

660
00:34:53,020 --> 00:34:54,699
So there's a big difference.

661
00:34:54,699 --> 00:34:58,680
And it's a lot easier to work with APIs
that generate machine-readable data

662
00:34:58,680 --> 00:35:00,910
than other data.

663
00:35:00,910 --> 00:35:06,520
Another key property of APIs is that
some of them are giving you bulk data,

664
00:35:06,520 --> 00:35:11,560
and some of them are like family style,
and some are giving you single serving.

665
00:35:11,560 --> 00:35:14,020
So we just went to a Chinese restaurant.

666
00:35:14,020 --> 00:35:15,160
There was a buffet.

667
00:35:15,160 --> 00:35:16,770
You could take as much as you want.

668
00:35:16,770 --> 00:35:20,230
If you're a particularly rude, you
could just grab everything and walk out

669
00:35:20,230 --> 00:35:21,770
the door with it.

670
00:35:21,770 --> 00:35:25,660
APIs are like that, except of
course, you can get to the food,

671
00:35:25,660 --> 00:35:27,670
and someone else can also get the food.

672
00:35:27,670 --> 00:35:31,420
And so some of them will tell you about,
for instance, where every train is

673
00:35:31,420 --> 00:35:34,810
in the entire metro system in DC here.

674
00:35:34,810 --> 00:35:37,560
And so one API call will
tell you all of that.

675
00:35:37,560 --> 00:35:42,102
There are also some APIs for metro that
will tell you about a particular stop.

676
00:35:42,102 --> 00:35:45,060
And so you just ask about one stop,
but that's your single-serving API.

677
00:35:45,060 --> 00:35:53,200
And that can say, there's a train
right now going to 16th and Colorado.

678
00:35:53,200 --> 00:36:01,720
So want to show you another example
just related to the energy data stuff

679
00:36:01,720 --> 00:36:03,640
that I was talking about.

680
00:36:03,640 --> 00:36:09,191
So there's also a
website for energy data,

681
00:36:09,191 --> 00:36:10,690
like I showed in the Opower example.

682
00:36:10,690 --> 00:36:13,090
And it's called it's
called Green Button.

683
00:36:13,090 --> 00:36:18,220
And what it is USdata.gov
office and the EPA

684
00:36:18,220 --> 00:36:20,960
created a standard for
interchanging energy data.

685
00:36:20,960 --> 00:36:27,370
So if you have an apartment,
or a condo, or whatever house,

686
00:36:27,370 --> 00:36:32,290
and you're paying energy bills
to a certain set of utilities,

687
00:36:32,290 --> 00:36:37,810
they are required to give you access to
your own historical energy usage data.

688
00:36:37,810 --> 00:36:42,640
So there should be, on the
Customer Portal website,

689
00:36:42,640 --> 00:36:44,740
a log in that you can get.

690
00:36:44,740 --> 00:36:47,910
And then there should be one
of these green buttons that

691
00:36:47,910 --> 00:36:50,684
allows you to access your own
data, and pull that data down.

692
00:36:50,684 --> 00:36:52,600
And then you can do
whatever you want with it.

693
00:36:52,600 --> 00:36:54,430
You can plot your own historical use.

694
00:36:54,430 --> 00:36:55,930
You can try to optimize it.

695
00:36:55,930 --> 00:36:57,760
You can compare it to other things.

696
00:36:57,760 --> 00:37:00,810
And so this is also now
an open data ecosystem.

697
00:37:00,810 --> 00:37:03,190
And last time I looked,
there were about 250 apps

698
00:37:03,190 --> 00:37:05,440
people had built using this
kind of green button data.

699
00:37:05,440 --> 00:37:08,150
So this is another interesting
place to get started

700
00:37:08,150 --> 00:37:11,020
if you have paying a utility bill.

701
00:37:11,020 --> 00:37:14,160
Not sure if Harvard is doing
this right now for students.

702
00:37:14,160 --> 00:37:19,270
But if you're living off campus,
you can probably get this.

703
00:37:19,270 --> 00:37:23,470
So some places you can
get inspired to come up

704
00:37:23,470 --> 00:37:28,470
with different project ideas for smart
cities using open data, one of them

705
00:37:28,470 --> 00:37:30,640
is, like I said, data.gov.

706
00:37:30,640 --> 00:37:35,380
And data.gov is a place where you
can find almost an infinite number

707
00:37:35,380 --> 00:37:36,810
of open data sets.

708
00:37:36,810 --> 00:37:39,330
It can be a little bit
overwhelming, just trying

709
00:37:39,330 --> 00:37:41,180
to figure out what's available there.

710
00:37:41,180 --> 00:37:44,347
But they let you sort them
by different types of things

711
00:37:44,347 --> 00:37:45,430
that you're interested in.

712
00:37:45,430 --> 00:37:47,180
You might be interested
in transportation.

713
00:37:47,180 --> 00:37:48,720
You might be interested in energy.

714
00:37:48,720 --> 00:37:50,560
You might be interested in agriculture.

715
00:37:50,560 --> 00:37:52,740
All of those data sets
are available there.

716
00:37:52,740 --> 00:37:57,000
And you can find a catalog of them,
and then start generating ideas

717
00:37:57,000 --> 00:38:00,510
for how you might use some of
that data to get some insight,

718
00:38:00,510 --> 00:38:05,100
or maybe build an app that
would be useful to people.

719
00:38:05,100 --> 00:38:09,370
There's another class of kind of places
like that called developer portals.

720
00:38:09,370 --> 00:38:13,060
And so, for instance, for
MBTA, for the Boston T,

721
00:38:13,060 --> 00:38:15,810
they have a developer
portal that you can use.

722
00:38:15,810 --> 00:38:18,590
And you get log in for that.

723
00:38:18,590 --> 00:38:20,830
And then you get access
to their open data.

724
00:38:20,830 --> 00:38:25,520
You get access to their APIs as
well, and a variety of other things.

725
00:38:25,520 --> 00:38:30,970
So often, if you're looking
to find some open data,

726
00:38:30,970 --> 00:38:35,540
you might be able to look for it
by looking for a developer site.

727
00:38:35,540 --> 00:38:37,570
There are some other
catalogs of other APIs.

728
00:38:37,570 --> 00:38:40,890
There are a lot of private
APIs from other services,

729
00:38:40,890 --> 00:38:44,080
so things you might
use-- Yelp, if you use

730
00:38:44,080 --> 00:38:47,100
that to search for restaurants
or local businesses,

731
00:38:47,100 --> 00:38:48,340
they have an API you can use.

732
00:38:48,340 --> 00:38:50,310
Foursquare has an API.

733
00:38:50,310 --> 00:38:53,520
A variety of other different
commercial APIs exist.

734
00:38:53,520 --> 00:38:56,760
Google Maps, you can use
their data to some extent.

735
00:38:56,760 --> 00:39:00,540
And all these things have
some way to access them.

736
00:39:00,540 --> 00:39:01,770
And it's often free.

737
00:39:01,770 --> 00:39:06,120
So you can find some of them just
by searching for developer portals,

738
00:39:06,120 --> 00:39:07,680
like Yelp developer portal.

739
00:39:07,680 --> 00:39:10,390
But there are also other
catalogs you can use too,

740
00:39:10,390 --> 00:39:15,850
like publicapis.com is one that seems
fairly recent that I came across.

741
00:39:15,850 --> 00:39:19,000
There's an old one called
programmableweb.com

742
00:39:19,000 --> 00:39:24,640
that's been around for a long time,
and has a lot of APIs, some of which

743
00:39:24,640 --> 00:39:28,660
go back almost 10 or even 15 years.

744
00:39:28,660 --> 00:39:31,960
So all of these places are good ways to
sort of discover the richness of what's

745
00:39:31,960 --> 00:39:35,310
available, in terms of APIs.

746
00:39:35,310 --> 00:39:35,950
and open data.

747
00:39:35,950 --> 00:39:39,180
And then green button data, that's the
one that I mentioned for their energy

748
00:39:39,180 --> 00:39:40,200
data as well.

749
00:39:40,200 --> 00:39:43,352
And even if you don't have
a apartment, or house,

750
00:39:43,352 --> 00:39:45,310
and you don't pay a
utility bill, you can still

751
00:39:45,310 --> 00:39:47,700
get test data from these
kinds of sites that

752
00:39:47,700 --> 00:39:52,300
would allow you to build an app that
would be based on that test data.

753
00:39:52,300 --> 00:39:58,060
And many of these will also allow you
to test certain things against them.

754
00:39:58,060 --> 00:40:02,680
So I hope that's been sort
of a rapid-fire introduction

755
00:40:02,680 --> 00:40:06,750
to some of the different
aspects of data and APIs,

756
00:40:06,750 --> 00:40:10,770
and especially where it relates to
cities, and the kinds of data sources

757
00:40:10,770 --> 00:40:15,390
that you have in cities, and
for solving urban problems.

758
00:40:15,390 --> 00:40:19,570
So I just want to wrap up
a little bit by showing

759
00:40:19,570 --> 00:40:21,450
some more slides of our team.

760
00:40:21,450 --> 00:40:26,580
This is our DC team for
Transit Screen, and of course,

761
00:40:26,580 --> 00:40:31,180
mention that we're very interested
in having any of the CS50 students,

762
00:40:31,180 --> 00:40:34,870
or anyone else who's interested,
please drop us a line.

763
00:40:34,870 --> 00:40:38,430
If you're interested in a summer
internship, or a job, or whatever,

764
00:40:38,430 --> 00:40:41,760
please let us know.

765
00:40:41,760 --> 00:40:43,270
We tend to be very mission driven.

766
00:40:43,270 --> 00:40:46,420
And we're focused on making
cities more sustainable,

767
00:40:46,420 --> 00:40:48,750
promoting walkability,
urbanization, and public health.

768
00:40:48,750 --> 00:40:51,929
And our own operations are
actually carbon neutral.

769
00:40:51,929 --> 00:40:54,970
And I wish I had a better way to do
that and to show that with open data.

770
00:40:54,970 --> 00:41:00,330
But we've managed to audit our
own energy usage and prove that.

771
00:41:00,330 --> 00:41:03,240
And then also, zero of our
employees commute by car.

772
00:41:03,240 --> 00:41:05,580
So we get around a lot
of fun ways in the city.

773
00:41:05,580 --> 00:41:09,430
And so I just wanted to
say, sort of in conclusion,

774
00:41:09,430 --> 00:41:14,010
the way that my personal
opinion about this goes,

775
00:41:14,010 --> 00:41:18,600
is that in a truly smart city is
not one just where all the data gets

776
00:41:18,600 --> 00:41:22,140
brought together and kind of
funneled into some sort of silo

777
00:41:22,140 --> 00:41:24,060
to be used by people in the government.

778
00:41:24,060 --> 00:41:27,180
It's one where data is
open and data shared,

779
00:41:27,180 --> 00:41:29,700
so that that data and
the technology can be

780
00:41:29,700 --> 00:41:36,780
used to improve the lives of everyone,
from the janitor to the CEO, people

781
00:41:36,780 --> 00:41:42,570
who are old, people who are young, and
just the whole variety of residents

782
00:41:42,570 --> 00:41:43,870
and citizens of the city.

783
00:41:43,870 --> 00:41:46,830
So I really tend to think
about smart citizens

784
00:41:46,830 --> 00:41:49,210
as being the goal,
rather than smart cities.

785
00:41:49,210 --> 00:41:55,930
And I think that's a good way to
keep in mind the real problems that

786
00:41:55,930 --> 00:42:00,120
need to be addressed, and the
real problems that can be solved.

787
00:42:00,120 --> 00:42:01,680
So this in my email.

788
00:42:01,680 --> 00:42:02,850
It's matt@transitscreen.com.

789
00:42:02,850 --> 00:42:05,200
Feel free to drop me a line.

790
00:42:05,200 --> 00:42:10,250
This is a picture from 1999 of me
in Leverette House Dining Hall,

791
00:42:10,250 --> 00:42:12,030
which is, I believe, closed right now.

792
00:42:12,030 --> 00:42:15,780
This painting was hung upside
down for half the year that year,

793
00:42:15,780 --> 00:42:16,612
and no one noticed.

794
00:42:16,612 --> 00:42:19,660


795
00:42:19,660 --> 00:42:24,000
And these are a couple shots of me on
the computer programming competition.

796
00:42:24,000 --> 00:42:27,490
Especially if you're just starting
CS50 and you're interested,

797
00:42:27,490 --> 00:42:29,820
I think there are tryout
processes for these.

798
00:42:29,820 --> 00:42:35,640
So you can go and try to get yourself
on the international programming team.

799
00:42:35,640 --> 00:42:39,492
I got to go to exciting
San Jose this year.

800
00:42:39,492 --> 00:42:41,200
And this year was a
little more exciting,

801
00:42:41,200 --> 00:42:45,990
we got to go to Amsterdam
for the international finals.

802
00:42:45,990 --> 00:42:51,030
Look at these computers,
just going to say.

803
00:42:51,030 --> 00:42:54,380
And then a bit later, I
got to-- see, the computer

804
00:42:54,380 --> 00:42:56,120
is a lot newer in this one.

805
00:42:56,120 --> 00:42:59,340
That's all I'm going
to say about that one.

806
00:42:59,340 --> 00:43:01,230
All right, so thanks very much.

807
00:43:01,230 --> 00:43:03,510
And I appreciate your attention.

808
00:43:03,510 --> 00:43:05,621
And again, please drop
me a line if you'd like.

809
00:43:05,621 --> 00:43:08,412
And I'd love to take any questions
from the audience at this point.

810
00:43:08,412 --> 00:43:11,010


811
00:43:11,010 --> 00:43:11,902
Yeah.

812
00:43:11,902 --> 00:43:13,240
AUDIENCE: Thanks so much, Matt.

813
00:43:13,240 --> 00:43:14,198
This is really helpful.

814
00:43:14,198 --> 00:43:15,527
My name's Amira.

815
00:43:15,527 --> 00:43:18,020
I'm a CS50 student.

816
00:43:18,020 --> 00:43:21,232
I feel like, when I hear
about smart cities, the topics

817
00:43:21,232 --> 00:43:22,940
that you brought up
come into play a lot.

818
00:43:22,940 --> 00:43:25,700
A lot of it is about transportation,
and a bit about energy.

819
00:43:25,700 --> 00:43:27,080
Where aren't people looking?

820
00:43:27,080 --> 00:43:29,260
And where do you think should the
next wave of progress in smart cities

821
00:43:29,260 --> 00:43:30,120
could be?

822
00:43:30,120 --> 00:43:36,539
MATT: Yeah, well, back when
we started Transit Screen,

823
00:43:36,539 --> 00:43:38,830
people weren't really working
in transportation enough.

824
00:43:38,830 --> 00:43:43,560
So that was one area that we thought
was really open for exploration.

825
00:43:43,560 --> 00:43:47,910
I think that's less true
now than it was at the time.

826
00:43:47,910 --> 00:43:49,740
I think I'd sort of
turn that back to you

827
00:43:49,740 --> 00:43:52,770
and say, what are you
really interested in?

828
00:43:52,770 --> 00:43:57,190
And where do you see data
that's kind of being underused?

829
00:43:57,190 --> 00:44:01,260
Because there's definitely--
if you think of,

830
00:44:01,260 --> 00:44:06,220
what are the different sectors that
make up a city, there's housing.

831
00:44:06,220 --> 00:44:08,680
There's transportation.

832
00:44:08,680 --> 00:44:09,450
There's retail.

833
00:44:09,450 --> 00:44:10,320
There's energy.

834
00:44:10,320 --> 00:44:14,790
There's all these different
inputs and outputs to the cities.

835
00:44:14,790 --> 00:44:18,560
And some of those are going to be on
the verge of opening up more data,

836
00:44:18,560 --> 00:44:22,570
or where you can put
together the data you

837
00:44:22,570 --> 00:44:26,010
need from interesting sources,
kind of combine things in ways

838
00:44:26,010 --> 00:44:28,740
that people haven't looked at before.

839
00:44:28,740 --> 00:44:33,490
So for instance, I'll just give
an example of the top of a head.

840
00:44:33,490 --> 00:44:35,950
I talked to a guy who is in DC.

841
00:44:35,950 --> 00:44:40,590
And he was in a sort of software
development training program.

842
00:44:40,590 --> 00:44:43,872
And he was a labor inspector.

843
00:44:43,872 --> 00:44:46,830
So he was looking for workers who
were being cheated out of their wages

844
00:44:46,830 --> 00:44:50,850
by businesses that were unethical.

845
00:44:50,850 --> 00:44:55,320
And he thought, well,
there's no data set

846
00:44:55,320 --> 00:44:58,554
I can use that will tell me
whether a business is shady or not.

847
00:44:58,554 --> 00:45:01,470
I mean, maybe you can find bad reviews
on Yelp or something like that,

848
00:45:01,470 --> 00:45:03,600
but maybe not.

849
00:45:03,600 --> 00:45:09,490
So he thought, well, what else would
be a sign that the business is either

850
00:45:09,490 --> 00:45:14,220
shady, or may be falling apart in
unable to manage itself properly,

851
00:45:14,220 --> 00:45:17,520
which makes that likely
to underpay its workers?

852
00:45:17,520 --> 00:45:21,490
And health inspection
data for restaurants

853
00:45:21,490 --> 00:45:25,200
turns out to be an open data sets that's
available in a lot of cities, including

854
00:45:25,200 --> 00:45:28,450
New York City, and I think
maybe even Cambridge.

855
00:45:28,450 --> 00:45:33,190
So he took that data set and
combined it with some internal data

856
00:45:33,190 --> 00:45:37,620
sets that he had access to, and
showed that actually, it was true

857
00:45:37,620 --> 00:45:41,920
If you just went to inspect restaurants
that were already failing their health

858
00:45:41,920 --> 00:45:47,369
exams, you could probably also
predict, with some accuracy,

859
00:45:47,369 --> 00:45:49,660
which ones were likely to be
underpaying their workers,

860
00:45:49,660 --> 00:45:52,826
because those are probably restaurants
that are falling apart, or something.

861
00:45:52,826 --> 00:45:58,190
So I thought that was a really
interesting example of using a data

862
00:45:58,190 --> 00:46:00,700
set that was maybe not intuitive.

863
00:46:00,700 --> 00:46:02,820
And so I think there's a
little bit of creativity

864
00:46:02,820 --> 00:46:05,250
that you have to pull in
when it comes to this.

865
00:46:05,250 --> 00:46:07,521
And it really should be
kind of problem-focused.

866
00:46:07,521 --> 00:46:10,818


867
00:46:10,818 --> 00:46:15,150
AUDIENCE: I was wondering,
in terms of Transit Screen,

868
00:46:15,150 --> 00:46:19,132
how is the data for people who drive,
and if they see your transit screen,

869
00:46:19,132 --> 00:46:22,265
they would-- because
what I think is people

870
00:46:22,265 --> 00:46:24,916
who use a transit screen are
people who use transit anyway.

871
00:46:24,916 --> 00:46:28,030
So you're not really taking
away from people actually drive.

872
00:46:28,030 --> 00:46:29,010
MATT: Yeah.

873
00:46:29,010 --> 00:46:30,140
AUDIENCE: How does-- yeah.

874
00:46:30,140 --> 00:46:34,640
MATT: Yeah, so this gets into sort of
interesting areas of something called

875
00:46:34,640 --> 00:46:37,640
mobility management or transportation
demand management, which is,

876
00:46:37,640 --> 00:46:41,870
instead of paying a ton of
money to build bigger roads,

877
00:46:41,870 --> 00:46:47,810
how can we be more efficient and
change people's demand behavior

878
00:46:47,810 --> 00:46:50,990
for those roads, or for
transit, or something?

879
00:46:50,990 --> 00:46:54,210
And there are a lot of
incentives you can use.

880
00:46:54,210 --> 00:46:56,330
Information is sort of fundamental.

881
00:46:56,330 --> 00:46:59,470
Because if people don't have information
about a bus, that they don't know

882
00:46:59,470 --> 00:47:02,510
it runs by them, they're
never going to use it.

883
00:47:02,510 --> 00:47:07,340
So someone who might actually
have a very convenient trip by bus

884
00:47:07,340 --> 00:47:09,980
might just drive because they've
never heard of the bus before.

885
00:47:09,980 --> 00:47:10,896
They've never seen it.

886
00:47:10,896 --> 00:47:13,780
So there's a real education
component we try to address.

887
00:47:13,780 --> 00:47:15,530
And the other thing
we try to do is we try

888
00:47:15,530 --> 00:47:19,990
to be in places where
people have just moved.

889
00:47:19,990 --> 00:47:23,420
So you just move into
a new condo building,

890
00:47:23,420 --> 00:47:26,720
and so you could do a variety of things.

891
00:47:26,720 --> 00:47:27,950
You might have a car.

892
00:47:27,950 --> 00:47:29,930
You might bring the car
and pay for parking.

893
00:47:29,930 --> 00:47:33,370
Or you might decide to leave the car
and just use transit, and bikeshare,

894
00:47:33,370 --> 00:47:34,370
and Uber.

895
00:47:34,370 --> 00:47:36,470
And so if we can get
you at that point when

896
00:47:36,470 --> 00:47:39,950
you're making that kind of decision to
keep the car, to get rid of the car,

897
00:47:39,950 --> 00:47:44,120
to start driving to work, or to
start learning how to use transit

898
00:47:44,120 --> 00:47:46,550
to get to work-- if we
can get you then, then we

899
00:47:46,550 --> 00:47:48,620
have the ability to
change your behavior.

900
00:47:48,620 --> 00:47:50,360
And we have a special
amount of leverage.

901
00:47:50,360 --> 00:47:53,871
It's when people are new to a place,
or people's habits are already

902
00:47:53,871 --> 00:47:55,370
changing, that you have the ability.

903
00:47:55,370 --> 00:48:00,010
And there's some good research in this
area in sort of behavioral change.

904
00:48:00,010 --> 00:48:05,010
There are a few other books I'd
like to mention in that area.

905
00:48:05,010 --> 00:48:07,220
There's one called
Nudge, which is really

906
00:48:07,220 --> 00:48:10,550
sort of interesting popular
read about how you can influence

907
00:48:10,550 --> 00:48:14,180
people's behavior using
economic principles,

908
00:48:14,180 --> 00:48:16,730
written by guy who taught here.

909
00:48:16,730 --> 00:48:24,050
And there's a couple other books
in the area called action design.

910
00:48:24,050 --> 00:48:27,560
And so this is about
design as it applies

911
00:48:27,560 --> 00:48:31,460
to getting people to change
their behavior for the better.

912
00:48:31,460 --> 00:48:36,760
And so there's a whole interesting
area of exploration there.

913
00:48:36,760 --> 00:48:37,260
Yeah.

914
00:48:37,260 --> 00:48:38,760
AUDIENCE: Oh, hi.

915
00:48:38,760 --> 00:48:41,260
My name is [INAUDIBLE] and I
work at the Planning Office

916
00:48:41,260 --> 00:48:43,233
at Harvard, the campus planning.

917
00:48:43,233 --> 00:48:47,070
So you're talking about the
smart cities, so in your eyes,

918
00:48:47,070 --> 00:48:50,070
[INAUDIBLE], what is a smart campus?

919
00:48:50,070 --> 00:48:58,233
And what kind of data is smart
data that students want to see?

920
00:48:58,233 --> 00:49:01,937
But considering that a lot
of data is actually private,

921
00:49:01,937 --> 00:49:06,070
so you cannot expose it, so [INAUDIBLE].

922
00:49:06,070 --> 00:49:09,080
MATT: Yeah, so the
question is bringing it

923
00:49:09,080 --> 00:49:13,640
close to home, what makes
a smart campus, as opposed

924
00:49:13,640 --> 00:49:15,800
to sort of a broader smart city?

925
00:49:15,800 --> 00:49:20,360
And I think the fact that you
are from the Office of Planning,

926
00:49:20,360 --> 00:49:22,730
and the University has
an office planning,

927
00:49:22,730 --> 00:49:26,450
sort of underscores the
idea that a campus is kind

928
00:49:26,450 --> 00:49:28,130
of like a miniature city in many ways.

929
00:49:28,130 --> 00:49:31,940
I'm sure you look at it
under a similar lens.

930
00:49:31,940 --> 00:49:38,210
And so I would say that the
areas for a smart campus,

931
00:49:38,210 --> 00:49:42,050
obviously, there are some standards
that everyone on the campus

932
00:49:42,050 --> 00:49:43,850
can agree on, similar to the city.

933
00:49:43,850 --> 00:49:46,260
You want it to be a sustainable campus.

934
00:49:46,260 --> 00:49:49,510
You want it to be an efficient campus.

935
00:49:49,510 --> 00:49:54,680
And you want people to feel
safe on campus, and so on.

936
00:49:54,680 --> 00:49:58,820
So I think really, you look
at all the different areas,

937
00:49:58,820 --> 00:50:02,480
whether it's law enforcement,
or it's sustainability,

938
00:50:02,480 --> 00:50:07,160
or transportation, everything where
there's a similarity between a campus

939
00:50:07,160 --> 00:50:13,730
and the city, all of those
areas are areas where you

940
00:50:13,730 --> 00:50:17,260
can help make the student body smarter.

941
00:50:17,260 --> 00:50:21,350
At the same time, there's also a
workforce who has to get to the campus.

942
00:50:21,350 --> 00:50:23,690
So you're dealing with those
transportation problems

943
00:50:23,690 --> 00:50:24,960
at the same time.

944
00:50:24,960 --> 00:50:29,530
And so how do you get people
from the suburbs, or from Boston?

945
00:50:29,530 --> 00:50:31,760
And how do you get them
in here in a way that

946
00:50:31,760 --> 00:50:34,740
doesn't create problems for everyone?

947
00:50:34,740 --> 00:50:39,740
And so I think, for a campus, you
need to think a little bit more

948
00:50:39,740 --> 00:50:42,350
expansively, not just about
the footprint of the campus,

949
00:50:42,350 --> 00:50:45,050
but about the whole
network of how everyone

950
00:50:45,050 --> 00:50:49,990
is coming to and from the campus.

951
00:50:49,990 --> 00:50:54,530
Transit Screen, just to
tell us another short story,

952
00:50:54,530 --> 00:50:57,200
comes from a city called
Arlington, Virginia.

953
00:50:57,200 --> 00:51:01,250
We actually did it within the
city government as a pilot project

954
00:51:01,250 --> 00:51:02,930
before we launched the startup.

955
00:51:02,930 --> 00:51:05,810
And Arlington is a sort
of interesting, almost

956
00:51:05,810 --> 00:51:10,100
like a campus, in the way that it
has only 150,000 people, but almost

957
00:51:10,100 --> 00:51:11,960
250,000 people working in it.

958
00:51:11,960 --> 00:51:14,840
So there's a flood of
people coming in every day,

959
00:51:14,840 --> 00:51:19,010
because the Pentagon is there, and
a variety of other major employers.

960
00:51:19,010 --> 00:51:23,260
So there's a real need for
solutions that take into account

961
00:51:23,260 --> 00:51:25,259
the whole footprint of the campus.

962
00:51:25,259 --> 00:51:27,050
That's kind of the
direction I would go in.

963
00:51:27,050 --> 00:51:29,547
But love to talk with
you about that more.

964
00:51:29,547 --> 00:51:33,800


965
00:51:33,800 --> 00:51:34,300
[INAUDIBLE]

966
00:51:34,300 --> 00:51:37,868
AUDIENCE: [INAUDIBLE] sensors, who has
accessed information to the sensors?

967
00:51:37,868 --> 00:51:40,010
Can anyone request access?

968
00:51:40,010 --> 00:51:44,185
MATT: Yeah, so that's the
challenge, is that a lot-- like,

969
00:51:44,185 --> 00:51:46,750
for instance, the gunshot detectors.

970
00:51:46,750 --> 00:51:47,920
That's the police's thing.

971
00:51:47,920 --> 00:51:50,110
They're not going to share that.

972
00:51:50,110 --> 00:51:53,620
But sometimes with sensors,
you can get a certain data set,

973
00:51:53,620 --> 00:51:58,750
or you can get historical data set
that preserves some kind of privacy.

974
00:51:58,750 --> 00:52:02,950
So for instance, with
New York City, you can

975
00:52:02,950 --> 00:52:07,900
get a data set that has all the taxi
trips that were taken in the city.

976
00:52:07,900 --> 00:52:11,020
Or with bikeshare, you've got all
the history of the bikeshare trips.

977
00:52:11,020 --> 00:52:18,280
And so some of those were
collected with sensors.

978
00:52:18,280 --> 00:52:21,460
But you can't get them in real time,
but you can get them historically.

979
00:52:21,460 --> 00:52:24,910
And so I would focus
on that kind of data.

980
00:52:24,910 --> 00:52:30,590
Or of course, might even try to
put your own sensor out somewhere.

981
00:52:30,590 --> 00:52:34,270
You interested in video,
put a camera up somewhere,

982
00:52:34,270 --> 00:52:36,723
and start collecting that data.

983
00:52:36,723 --> 00:52:39,889
AUDIENCE: So what would incentify those
companies to release their real time

984
00:52:39,889 --> 00:52:42,234
data for everyone?

985
00:52:42,234 --> 00:52:44,110
Why would companies do that?

986
00:52:44,110 --> 00:52:45,410
MATT: Generally they don't.

987
00:52:45,410 --> 00:52:47,662
They don't share that kind
of sensor data because--

988
00:52:47,662 --> 00:52:52,105
AUDIENCE: But we are able to access
API, like the transit information.

989
00:52:52,105 --> 00:52:54,400
Is that just like the
government wants to?

990
00:52:54,400 --> 00:52:57,910
MATT: So there are two things that
make companies often share their data.

991
00:52:57,910 --> 00:53:01,240
One is if there's a public policy
reason, like the government says,

992
00:53:01,240 --> 00:53:05,230
you should have open data
because of transparency.

993
00:53:05,230 --> 00:53:10,960
Let's say the subway isn't running
very well, or it's not running on time,

994
00:53:10,960 --> 00:53:13,450
maybe people should have the
right to look at the data

995
00:53:13,450 --> 00:53:17,229
and say whether it's
running on time or not.

996
00:53:17,229 --> 00:53:18,770
Subways don't always see it that way.

997
00:53:18,770 --> 00:53:21,070
But that might be one
reason to release the data.

998
00:53:21,070 --> 00:53:24,550
The other reason people release
data is often just related

999
00:53:24,550 --> 00:53:26,650
to marketing and promotion.

1000
00:53:26,650 --> 00:53:30,430
So why do we get data from so many
public and private transportation

1001
00:53:30,430 --> 00:53:31,840
providers at Transit Screen?

1002
00:53:31,840 --> 00:53:35,740
Because we promote their
services on public screens.

1003
00:53:35,740 --> 00:53:39,460
And so we're free advertising
for these kinds of agencies.

1004
00:53:39,460 --> 00:53:43,750
And so if you can
identify a common interest

1005
00:53:43,750 --> 00:53:46,960
where-- another thing is,
maybe a company doesn't

1006
00:53:46,960 --> 00:53:49,840
have enough people to do all
the analysis they'd like.

1007
00:53:49,840 --> 00:53:52,720
Maybe they don't have data
scientists, but they have data.

1008
00:53:52,720 --> 00:53:56,350
So you might be able to say, well,
if you share this data with me,

1009
00:53:56,350 --> 00:54:01,240
I can do some things with it that might
provide insight or information to you

1010
00:54:01,240 --> 00:54:04,910
that make you run your business better.

1011
00:54:04,910 --> 00:54:08,172
So there are a variety of different
pitches from the regulation.

1012
00:54:08,172 --> 00:54:09,880
There's even something
called the Freedom

1013
00:54:09,880 --> 00:54:12,130
of Information Act, where
you can request data

1014
00:54:12,130 --> 00:54:14,770
from a lot of public agencies.

1015
00:54:14,770 --> 00:54:16,530
It's pretty easy to use.

1016
00:54:16,530 --> 00:54:20,570
There's a site called muckrock.com
that's really good for it.

1017
00:54:20,570 --> 00:54:23,740
But then, there's often some
kind of common interest.

1018
00:54:23,740 --> 00:54:26,779
And I think that's maybe, in many
cases with private companies,

1019
00:54:26,779 --> 00:54:27,695
is a better way to go.

1020
00:54:27,695 --> 00:54:30,220


1021
00:54:30,220 --> 00:54:32,970
All right, thank you very much.

1022
00:54:32,970 --> 00:54:34,593