>> Michael Gamon: Okay. It's my great pleasure today to introduce Justin Cranshaw
from Carnegie Mellon. In the Mobile Commerce Lab. And he's done -- most people
actually know about this already, but he's done some pretty amazing work in this
livehoods project using social media to provide information about cities and how people
behave in cities. And I know that ties in very nicely with some of the research that's
going on here.
So it's really nice to see Justin here. And we also, as we saw before, they have some
opportunity to explore Seattle through the livehoods lens. Thanks.
>> Justin Cranshaw: Thank you. Thank you. So thank you, Michael for the invitation to
talk here. It's my pleasure to join you all.
So, yeah, I'm going to talk about this project, the livehoods project. But kind of the -- the
thrust of the talk is utilizing social media to understand the dynamics of the city. So this
is work, as Michael mentioned, done in the Mobile Commerce Lab.
And it's joint work with Raz Schwartz, Jason Hong, and my advisor Norman Sadeh.
Now, I understand Jason Hong spoke here a few weeks ago. Was anyone in that talk?
A couple.
So I don't think there will be a huge overlap. But I think it should complement it nicely.
So to give first -- to set some proper expectations I think this talk is going to have a
rather large and broad scope, perhaps more broad than you're used to. In addition to
covering our research on the livehoods project, since the general research area that
we're working in is quite new, I'm going to be successful motivating the work with
background from urban studies, including works from sociology, social psychology, and
urban design.
And the kinds of main contribution and the take away that I'd like you to get is that we're
kind of introducing this new methodology for studying the dynamics and the structure of
cities. And I'd like to -- for that to be the main take away is this methodology, this new
approach.
So that being said, this talk is about the convergence of three concepts, cities, social
media, or social computing and ubiquitous computing. In particular, I'll be talking about
how the latter two concepts, social media and ubiquitous computing are altering the
dynamics of cities but also allowing us to peer into the inner workings of cities at a level
of detail we haven't been able to do before.
So I tend to think of the synthesis of these three concepts as falling under the umbrella
of this new field called urban computing. This is a field that I guess like other new fields
has lots of different names. Some people call it urban analytics. Others call it urban
informatics. IBM loves the term smart cities for some reason. And architects tend to
love sentient cities, which is a little too sci-fi for me.
I don't know why a field with so few researchers needs so many names but such is life.
And but broadly speaking this field seeks to utilizing computing technology and typically
pervasive computing technology, to enhance the efficiency of cities and the general
overall well-being of the people that live in them.
So you might ask, okay, well, that sounds nice, but why cities, right? So smartphones
and ubiquitous computing, they're used everywhere. They're used in villages, they're
used in small towns, they're used in suburbs. And additionally social media is almost as
widespread as the Internet itself. So what's with this focus on cities?
So the question arises why this focus on urbanism. And to address that, I'm going to
talk about why cities are different from suburbs, from towns, and from villages.
So that's a really complex question. And I'd love to spend two hours talking to everyone
about that. But I did promise to get to some computer science as well. So I'll talk about
two reasons why cities are different.
And the first is in fact density. So density is actually the most critical reason why cities
are different. In particular, because density is sort of a defying property of cities. In
fact, if you were to define what a city is, probably density would come into that definition.
And so one of my -- my favorite quotes from Sherlock Holmes, homes says to Watson
the city is full of those whimsical little incidents which happen when you have four
million human beings jostling each other within the space of a few square miles.
He says amid the action and reaction of so dense a swarm of humanity, every possible
combination of events may be expected to take place. That's from the Blue Carbuncle if
you're a Holmes fan. And I think one of the truly great promises of urban computing is
in exposing, organizing and harnessing this dense swarm of humanity. And so I think
that's pretty exciting.
But this density, it creates a sort of nexus of resources, and of people and of ideas that
result in a sort of cosmopolitanism that was described by Lewis Mumford as the think in
essence multiplies man's power to think, to remember, to educate, and to communicate.
So it's this sheer density of people and ideas and interactions of the city that presents
an enormous array of opportunities for innovation through persuasive technologies.
Now, I want to stress that I'm not implying that density is always a good thing for cities.
In fact, it's not. So -- but such a kind of nuance discussion is beyond the scope of this
talk. I'm only implying the rather self-evident and noncontroversial claim that density is
a defining property of cities and it's why we're looking at cities through, in essence,
ubiquitous computing.
So a second reason why cities are different from suburbs, from towns, and from villages
is that cities exhibit an immense amount of what I call sociocultural spatial variability.
Now, another more common word for that is cities have neighborhoods. So I'm going to
-- this is a concept I'm going to get back to later in the talk. But something that I just
wanted to plant in your heads for a while to think about. But neighbors are quite
important for cities. And actually it's kind of an emergent property because of the
density and complexity of cities.
So traveling small distance in cities is quite difficult. And so distance is quite important.
And that's kind of why these -- this variability emerges.
So okay. I told you one of the take aways I want you to get is that we're introducing a
new methodology for studying cities. So if that's the case, it's important to understand
how researchers and scholars have traditionally studied the urban landscape. And so I
want to briefly overview, in essence some traditional methodologies for studying cities.
So one approach, and I mentioned him earlier, is one that was taken by Lewis Mumford.
So in order to better guide the potential forum of future cities, Mumford studied cities
through the history of how their form evolved.
So he wrote one of the most influential works of scholarship on this subject. This is a
book entitled The City in History. And he really looks at, like, architectural evidence of
how cities evolved and how the functions that they support evolve through time. And
his motivation essentially is that by studying the history of how these forms evolve we
can unlock greater potential for the future forms of cities.
Another approach was taken by a guy named Stanley Milgram. You probably heard of
this guy if you've taken like undergrad psychology course. But he's famous for things
quite different than this work that he did here. So Milgram -- does anyone raise their
hand? What's Milgram famous for?
>>: [inaudible] experiments.
>> Justin Cranshaw: Yeah. So the electric shock experiment is one. Another one is
the 6 degrees. He did that study. So those are the two main experiments that he's
famous for.
But he also when he was at CUNY in New York, he embarked on an amazing program
to study the psychology of what it's like to live in a city. And so he did these really
fascinating studies of how the urban environment affects the individual psychology.
So one example, a famous example, actually, he wanted to study how social norms
affect the process of riding the subway. And so he conducted this experiment where he
had able bodied graduate students enter the subway and ask people who were sitting to
give up their seat with no reason given whatsoever. Right? So they just repeatedly had
to ask these people, can I take your seat.
And so if you've ever lived in New York or spent any time in New York, this is a pretty
strong social norm. You just don't do that. And so the purpose of this experiment was
to kind of record these people's reactions to the question. But, in fact, the interesting
outcome turned out to be the effect on the experimenter of actually repeatedly having to
violate this norm over and over again. And so they actually experienced some physical
illness because of having to violate this norm. I mean, it's such a strong, powerful force
that violating it was very difficult.
So his work stands out as testament of actually the power of social norms in cities.
Another example was done by a guy named William Whyte. He did some amazing
work where he spent thousands of hours recording, taking pictures of, and observing
people in New York just interacting in public spaces. Nothing more. So just trying to
understand how the design of the environment and the various factors, for instance the
-- what time of day it is, where the light is shining, how these affect how people utilize
public spaces in the city. And there's some fascinating film clips associated with his
work, the life of small -- the social life of small urban spaces.
Whyte's work resulted in a better understanding of how people naturally utilize public
space in the city as a function of things like urban design, light, and time of day.
Another example is more of a sociological study. There's a sociologist from Chicago
named Gerald Suttles who actually conducted some embedded observations. So he
wanted to study the social order of life in Chicago slums. And so he spent three years
living in a tenement in Chicago and actually just documenting what life is like there. It's
a pretty amazing study to read actually.
And finally, Jane Jacobs -- I don't know if anyone has heard of Jane Jacobs. But she
wrote one of the most influential works in urban design, The Death and Life of Great
American Cities. She was a writer and an urbanist. And her work essentially was a
sharp critique of existing theories and planning. And she argued vehemently against
rigid planning movement that is were popular at the time.
So there was this great movement in the first half of the last century where urban
designers thought that they could harness and control the chaos of the city. And so you
had these really like spatially geometric plans that looked great on paper but in
implementation they hardly ever worked out right. And Jane Jacobs was a strong
opponent of such trends. And her -- her -- she preferred a more market driven
approach to urban design where the cities would grow organically based on the
demands of the people. Essentially her view was that the people can determine what
the best urban design is for them.
And why am I talking about all this? So why should you care? What I want to
emphasize is that studying cities is complex. All of these studies that I mentioned are
really intricate, long-term ambitious studies. And it's actually very difficult to actually
answer any questions which any rigor about a city.
So it requires historically immense hours of field work, interviews, surveys,
observations. And even though the works that I listed just now have been hugely
influential, such methodologies can only ever offer a small-scale view of the city. And
they're inherently partial, and they're biased, right? I mean, these are essentially one
individual studying one particular aspect of the city.
And by virtue of that alone, these are biased works. You know, they're powerful works,
they're good works, but they are biased. And we should recognize that. So can we do
better is the question. Can we come up with a large scale away of studying the city?
Can we come up with a less biased way of studying the city? And can we come up with
near realtime methodologies for studying the city?
So that's kind of the main goal for the rest of the talk is developing that.
So that leads into this next topic of computing in the city. Now, it should not be a
surprise that technological revolutions have profound effects on the urban form. To take
this point to a logical extreme, for example the technological development of farming an
animal husbandry led to the change of urban forum, if you want to call it that, from
paleolithic cave life to neolithic villages.
And then to take the next step, the development of masonry and bronze weaponry led
from us these neolithic villages to actually neolithic walled cities.
So, yeah, I mean, that's -- that's kind of a distant example. How can we actually look at
how computing currently is changing is urban forum? Now, I -- I say computing, but I
actually specifically mean ubiquitous computing because I think that's having the
greatest effect on the city and the urban forum.
So what is UbiComp? Ubiquitous computing aims to move computers off of desktops
and essentially into the environment to a point where they're actually invisible, they're so
ubiquitous, they're so blended into the environment that computing actually disappears.
So smartphones actually represent the first kind of wave in realization of this vision. I
wouldn't say that smartphones are ubiquitous computing, but it's kind of the first step to
getting there. And it has been -- they have been obviously amazingly successful. I
don't need to tell any of you that.
But you have these devices that are permeating our daily lives and permeating -- and
changing how we interact with each other and with the environment around us. And so
you also have these kind of new and rich sources of social data that are expanding our
understanding of the city and creating new possibilities for interactions.
So I have this diagram where you have these two directions, right? So you have on the
right hand these devices are changing how we interact with each other and the
environment, the city. And on the other hand, using these devices and using the
applications that are embedded on them it's actually creating data that helps us better
understand the city. It helps us better understand the environment. And so you have
these two forces that are kinds of converging. Yeah?
>>: So it's [inaudible] sometimes about ubiquitous social connection is that it's -- what's
the word? It's sort of metaspatial or something like that. Like we can connect with
people regardless of geography.
>> Justin Cranshaw: Yeah.
>>: Right? So I don't know. I mean, what are your thoughts?
>> Justin Cranshaw: I think that's definitely true. But I would argue that it's not -- you
can connect with people regardless of geography. But distance will always matter.
Right? And because distance will always matter, what's happening is you have a
change of what distance means. You're almost changing the topology of the city, right?
So things are still spatial. But spatial means something slightly different now. So if like,
if for instance I'm using foursquare and I'm watching my friends check into different
places in the city, that provides this kind of ambient update about places that I might
want to go to. Right? So it's changing my understanding of a spatial region. But it's still
spatial. All right? So distance still matters.
I think it's a function of the fact that -- I mean, let's foursquare for example, right? You
don't get updates from all of your friends, right? Because that's too noisy. You don't
care about all of that. You only get updates from people that are nearby you.
>>: [inaudible].
>> Justin Cranshaw: It's not entirely true, but it is by default true, right. So you get
these push updates, right, when people check in.
>>: Yeah. But it is sent to like who you define -- I mean, there's another type of
distance. But like the way foursquare updates [inaudible] on my phone are who are
closest to me in person space, not in physical space. So almost all the updates I get
while I'm here are from people home in New York.
>> Justin Cranshaw: Okay. So mine is set to physical space. Maybe it's a default or
something.
But in any case, I think my point is still valid that distance does still matter. And it
matters because we're still ultimately interacting with physical things, right?
>>: [inaudible].
>>: Yeah. That may be. But it may be ->> Justin Cranshaw: So what if -[brief talking over].
>>: Physical space relative to your hometown.
>>: Right.
>> Justin Cranshaw: So what it actually does for me is the last place that I checked in, I
will get updates from people nearby. So I still get updates from people in Pittsburgh
because last place I checked in is Pittsburgh.
>>: But you could also turn on updates from anyone.
>> Justin Cranshaw: Yeah.
>>: So like I get updates from all my friends.
>> Justin Cranshaw: But in any case, they have some default that takes distance into
account. And I think that's -- that's a critical realization that distance does matter and it
will always matter: And going back to what I said before, because of the complexity of
the city, going small distance in Seattle and any big city is difficult, right? And that will
never change. You know, we can get new methods of transportation, et cetera. But
that will never change.
>>: And I think changing the nature of what script geography means to people
[inaudible] is cool. I mean, it's interesting.
>> Justin Cranshaw: So, I mean, that leads us very nicely to these locations based
social applications. So thank you for that question.
So one example is foursquare. I don't really need to go into what foursquare is but
essentially you check into a venue and that venue is shared with some subset of your
friends or your social connections on foursquare. And a check-in is typically given -- or
it typically includes a wealth of information about that case. So you'll get the category of
the place. You'll get various tips about the particular venue. And you'll get things like
interesting things that you can do there.
Another example is a status update like a tweet. Tweets can contain mentions of
various place entities. So in this tweet I'm mentioning Belltown, I'm mentioning Seattle,
and I'm mentioning a winery. These are entities. These are entities that are associated
with some location.
But in addition to that the tweet itself can be of course geotagged. And so I could have
the location that I am currently at.
Another example of such media is a photograph, like Instagram. Most Instagram
photos are geotagged. I think you can turn this off. But I think by default they're
geotagged. And the user can manually elect to tag that photo with an actual named
venue from the foursquare database. So you have both the latitude and longitude, but
also the venue of the place.
This is a new -- one that was new to me. Has anyone seen this before? Forkly? This
is great because it's an idea that I -- I wanted -- I thought would be a great idea. And it
turns out someone is doing it. But what it is, it's essentially foursquare or Instagram for
meals. So you -- if you're out at a restaurant and you're enjoying a greatly meal you
take a picture of it, you describe it, and you link it to the venue you're currently at.
But in essence, I mean, this is all -- some people like to use that term Internet of things.
I don't know if I like that term. But it's essentially putting a virtual entity about some
physical object, in some case a meal, in some database that's online and available for
other people to access.
So again I want to go back to these two points. I think these technologies are changing
how we interact with each other and the world around us. But they're also generating
massive amounts of data about ourselves, as well as how we interact with each other
and the world around us.
So that leads me to this idea of studying the city with smartphones and with sensors.
So studying the city through direct observation, as I mentioned, historically required an
immense amount of work, field work, interviews. And these are kind of small-scale
partial studies. You get really great insight from these. I don't want to discount these
methodologies. I think you get really amazing insights from them. But again it's a
partial view.
And so what we seek is computational ways of uncovering things like local cultural
knowledge by leveraging these rich new sources of social media.
That takes us to our work on the life -- yeah?
>>: [inaudible] you do recognize that you've been looking at social data [inaudible].
>> Justin Cranshaw: Yeah, I mean, there's so many biases in social data.
>>: I just -- I wasn't ->> Justin Cranshaw: And I'll get to that at the end. But it's an excellent point.
>>: So when you talk a lot about the cultural data, especially like in Chicago, there's
rich cultural neighborhood of inaccess to the Internet.
>> Justin Cranshaw: Absolutely, yes.
>>: You do lose that snapshot.
>> Justin Cranshaw: And that's a really big problem. And, I mean, I can talk a little bit
about it now because you brought it up, but, I mean, I think this type of analysis is
thinking forward, right? So, I mean, if you think of for example Facebook, right,
Facebook Five Years ago had really -- even then had pretty strong demographic biases,
whereas now these demographic biases are much less.
Still you get people using the service differently than others in different cultures. So
you'll always have the kind of variability but you lose some things like sample bias. I
think that gets less and less of a problem. Currently sample bias is a big problem in
these kind of mobile social data. You know, it's typically mostly wealthy people.
They're biases along almost any demographic you could mention. Yeah. Thanks.
So I want to return to this idea of neighborhoods. So neighborhoods play a number of
essential roles in the daily lives of city dwellers. So by virtue of our kind of everyone
day experience with neighborhoods, I think most people have an intuitive grasp on what
they are. But so you understand what the idea of a neighborhood is. But it can actually
be pretty difficult to describe concretely what a neighborhood is. And let alone it can be
-- if you wanted to actually with precision kind of draw the boundaries of what a
neighborhood is, that's actually difficult thing to do.
So, yeah, we can talk about a little bit about what roles neighborhoods play in a city.
Essentially I talked about cities being complex and dense. Neighborhoods provide a
sort of order to the chaos of the city. They help us determine things like where we
should live, where we should work, where we should play.
They provide things like a safe haven. Safety. They provide a sense of territory. They
also serve critical roles in many municipal governments. They're not necessarily a
political unit. In fact, they're rarely a political unit. But they do serve a sort of
organizational purpose in terms of resource allocation.
And that's typically because municipal governments interact with neighborhood groups
and so the neighborhood groups have to communicate back and forth with municipal
governments.
They're also centers of commerce and centers of economic development. And
interestingly enough, they're also brands, right? I mean, company -- businesses that
are located in a particular neighborhood, they like to advertise that they're in this
particular neighborhood, if it's a trendy place. And that's actually pretty important. And
-- yeah?
>>: [inaudible] neighborhoods, are they typically -- is there a pattern to how they
develop like is there a certain percentage of a city's population or sort of -- you know
what I mean? If you like city to city, would you see some sort of constant ->> Justin Cranshaw: I think you would see a pattern. But I think the pattern is -- it's a
cultural thing, right? I mean, the development of a neighborhood is a socially
constructed thing. It takes into account the history. It takes into account, you know,
things like property values, cultural. And so I -- I think that is the pattern, that they're
socially constructed. But can you specify that process? I don't know. That's hard.
So the final point is that neighborhoods actually because of this fact that they are
socially constructed, they provide a sense of cultural identity to the people that live
there. You like to define yourself by the neighborhood that you live in.
So I -- yeah, as I mentioned, neighborhoods are cultural entities. And because of that,
we actually each carry around our own biases of how we view the city and its
neighborhoods. So neighborhoods actually often evolve quite fast. And this can
typically be much faster than the actual physical boundaries that delineate them.
So one example in Seattle is South Lake Union, right? If you probably looked at a city
map, A, that neighborhood might not exist. I'm not sure like what -- what the boundaries
were 30, 40 years ago. But, these city maps, they tend to be drawn quiet irregularly.
They're based off things like census tracks, and they kind of aggregate census tracks in
a way that makes sense to whatever city planner is drawing the map.
And these are done like every 30, 40 years. They're not done at the speed in which a
neighborhood can actually come into existence. Even like thinking 30 years ago in
Seattle I'm not sure that the landscape would look anything like it does now.
And so these city boundaries, they can be misaligned with the cultural realities of the
neighborhood. And that's where we're going to be getting into in this work.
But to motivate the rest of the talk, what comes to mind when you picture your
neighborhood? Just take like 10 seconds. So I don't know what it is, but I know the
chances are you're probably not imagining this, which is a fixed set of boundaries on a
map. Right? Chances are when you're picturing your neighborhood you're going to
imagine something like this, right?
What you think of is you think of the shops and you think of the sights and you think of
the attractions and you think of the public art, and you think of the stores. Am I right or
am I wrong? Yeah. How many people actually thought of the map?
>>: Vaguely for a minute.
>> Justin Cranshaw: Vaguely. [laughter] It's always like one or two. I gave this talk to
a -- or a talk like this to a city -- a group of city planners and like half the room raised -yeah.
But, yeah, so you think of these kind of cultural entities that define the neighborhood.
And so one great quote that I think summarize this point is by Kevin Lynch, who is an
urban designer from MIT. Kevin Lynch said: Every citizen has had long association
was some part of his city, and his image is soaked in in memories and meaning. I think
that's quite apt.
So I mentioned Kevin Lynch because he did actually some amazing work to study this
effect. What he did is he wanted to study people's mental maps of the city. And his
kind of purpose was to try to understand the kind of legibility in the city as you said. He
wanted to understand how the built environment affects people's perception of the city.
So he wanted to understand things like basically knowing that each of us carries around
this biassed views, he wanted to understand what modifies, what affects this bias.
So what he did is he had people come in and draw their own mental map of the city. In
his case, he did Boston. I think he did LA. And -- and Jersey City. I don't know why
Jersey City, but he had people draw their mental maps of their city.
And then what he did was aggregate these mental maps into one composite map of the
city that showed basically the various kind of biases that people had, to have things like
cultural attractions would be more prominent than kind of neighborhoods that people
don't typically go to.
This approach and this methodology was, again, repeated by Stanley Milgram, who I
mentioned earlier. And Milgram wasn't necessarily interested in kind of this from an
urban design perspective. He was interested in the psychology. So what is the
underlying psychology that affects how people kind of navigate through an urban
environment.
But he did very much the same thing. He had people come in, draw maps of New York
and Paris. And he'd give very little kind of rules as to what these maps should look like.
And what they -- what these maps are, these cognitive maps, they provide a kind of
deep sense of the local knowledge of the city, granted, biased by these individuals that
are drawing them. But it provides a way of kind extracting what these biases are.
And so I think it's a pretty powerful technique. And so that kind of highlight these two
perspectives on the neighborhoods of the city. On the one hand you have what are
politically constructed neighborhoods. As I mentioned, there's this process that comes
in like 40-year cycles that defines neighborhoods.
But on the other hand, you have these socially constructed ideas of what a
neighborhood is, right? It's these cultural entities that define the neighborhood.
And so I think each is equally important. So the politically constructed view is important
from a municipal government to having something that's kind of static that they can kind
of from year to year to year go back to. But the kind of socially constructed view is
essentially how we every day interact with the city. Right?
And so I -- what's nice is that our mental maps can shift as the city shifts. So as there's,
you know, construction in South Lake Union I can kind of reconfigure my mental map to
understand where I should go and navigate to get to places I need to go.
So these cognitive maps, they can help shed light on this misalignment. And they can
help identify he's distinctly characterized areas of the city.
However, this technique of having people come in and draw their maps, again, it's one
that does not necessarily scale. This is one you can maybe have a couple dozen
people come in and draw a map for you, but then what do you do to it? Or do with it?
So our ultimate goal in this project in the livehoods project is to find automated ways of
discovering this local cultural knowledge and to actually be able to build these cognitive
maps from data. And so can we discover these sort of organic views of a
neighborhood? Can we extract local cultural knowledge from social media data? And
can we by aggregating the activities of thousands of people discover a sort of collective
cognitive map of the city?
And so these are our research questions in this project.
So the hypothesis here is that if we're talking about the character of a neighborhood or
of an urban area, this character is defined not just by the types of places that are found
there, but also by the people that make that area part of their daily routine.
So if we can -- we can essentially characterize a place, namely by observing the people
that are visiting it. And this should make sense, right? I mean, as you walk from one
neighborhood to the next, you'll notice that the buildings change. You'll notice that the
architecture change. You'll know that like the businesses change. But also the people
change, right? I mean, these are cultural entities, right? I mentioned that people are
defined -- their identity is linked in some way to a neighborhood that they live in.
So we should be able to look at actually where people are going to kind of define these
areas. And, again, there's another great Kevin Lynch quote: The moving elements of a
city, and in particular the people and their activities, are as important as the stationary
parts. I think that's great.
So to discover these areas of unique character, we should look for clusters of nearby
venues that are visited in essence by the same people.
So that brings us to this idea of trying to cluster the city. So if neighborhoods are a
manifestations of a sort of sociocultural spatial variability then we should be able to
detect this variability through clustering. So what properties of these clusters should we
be looking for?
Well, first they should be geographically contiguous, right? I mention -- or maybe I
didn't, but the data that we're working with is going to be foursquare data. So we'd like
to find clusters of geographically contiguous foursquare data.
Clusters each should have a distinction character from one another. And this should be
perceivable by city residents. The clusters should be such that venues within a cluster
are more likely to be visited by the same users than venues in different clusters.
So what's the intuition here? So if you look at foursquare venues, these are the dots.
And you look at people checking into them over time, you'll notice some patterns, right?
You'll notice that like the green and the orange guy tend to stay on the left-hand side of
the map and the purple and the pink guy tend to stay on the right-hand side of the map.
This is what I was describing before, is that people tend to stay in their neighborhood, in
their kind of cognitive map of what their neighborhood is.
And so we'd like to kind of look at these patterns and extract what these organic
neighborhoods are. And so if you watch these check-ins over time, you'll notice that
groups of like-minded people, they tend to stay in the same areas.
So we go aggregate these behaviors, these check-in activities, and determine
relationships between venues. And then we can use these relationships between
venues and apply some standard graph clustering techniques to discover these actual -these unique -- these uniquely characterized urban areas.
So that's -- that's the intuition of what we're doing. So we build some similarity and
matrix between venues and then we look for clusters. The clusters we're calling
livehoods. This is meant to kind of reflect the kind of dynamic nature of the way they're
constructed.
So what's the actual methodology that we're using? First what we need is a kind of
notion of venue-to-venue similarity. So we can get this actually by looking at the
check-ins to each venue. So in essence what we're doing is we're treating a venue -we're modeling it as sort of a bag of the people that have checked into it. So rather than
like a document as a bag-of-words, we think of a venue as a bag of check-ins. And so
given this kind of vector representation of a venue we can look at things like any sort of
vector similarity between venues. In this case, we're using cosine similarity.
And that's, in essence, how we measure the similarity between venues. But there are
some problems with this approach. First when you have check-ins and venues it turns
out that the resulting similarity graph is quite sparse. All right? So if you have
something like a hundred thousand venues in the city, there's like, you know, a billion
possible edges and you only have something like a million check-ins for this city, right?
So you're not going to get a dense observation of all kind of pair-wise relationships.
That's kind of natural.
The other problem with this approach is that in a city similarity tends to be dominated by
what I call hub venues. So like in New York, for example, Grand Central Terminal is a
hub venue because everybody's been there, as is kind of the airport and various kind of
venues that are kind of dominated by everyone in the city.
And so that really kind of skews the topology of the graph. Because these -- these
pair-wise venues will have high similarity. And they could be pretty far apart. And so
that's going to kind of collapse the graph towards these two venues. So we need to
kind of overcome these two problems.
So in addition to capturing the social similarity between places, we also want some
notion of geographic contiguity. And to do that, what we do is simply add a constraint to
the graph. And that has the effect of both adding this geographic contiguity, but it also
helps us with this kind of hub problem.
And so what we do is we connect each venue to its, say, M nearest neighbors and we
only look at similarities along these connections. So if it's not one of its M nearest
neighbors by geographic distance we kind of ignore any kind of social similarity that
could exist. So that solves this kind of hub problem because you're -- you're not going
to measure the similarity between Grand Central Terminal and JFK.
The other thing that we do is that we add some small constant among these nearest
neighbors. So we'll actually -- the way then the similarity graph will be the social
similarity that we measure between venues plus some small constant.
And basically what this does is it -- if the similarity is zero, meaning you've never
observed any sort of co-occurrence of users at these two places, then you're still going
to take into account some of the geographic similarity between them. And so what you
have is sort of you're linking nearby places but then you're kind of pulling them together
based on the data that you do have.
So that's our basic approach. And then we kind of -- you can think of this all as a big
graph, we're connecting each venue to its M nearest neighbors and weighing it by some
social similarity plus a small constant.
Once we have this built, we conduct some very standard spectral clustering. We use
the version of spectral clustering by Ng, Jordon, and Weiss. This works pretty well for
our purposes, and it's nice in that it's well studied and easy to implement.
We -- so, again, this is a parametric model. We kind of use the popular heuristics that
kind of everyone else uses to pick the number of clusters. So we'll have some
maximum allowable number of clusters and some minimum allowable number of
clusters. And we'll select the K based on the largest kind of eigengap in that range.
So I'm not going to go into much more detail around the methodology because we're
using fairly standard approaches. Instead I want to talk about once we have these
clusters from the spectral algorithms, we do some post processing to clean up the
resulting clusters. So one thing if you've ever -- if you've done these kind of image
segmentation before or other -- if you've used spectral clustering before, you'll notice
sometimes you get kind of background clusters. So in image segmentation you'll get
like a background noise cluster. And that's -- we get that as well. And so what we do to
overcome that, is we -- if any cluster spends too large of a geographic distance, we'll
actually split it up and we'll divide the venues to their nearest geographic distance.
And then we'll also do things like split clusters into connected components. These are
standard techniques as well. Finally once we have the clusters we do kind of a neat
thing to measure the relationships between each cluster. And so what we do is we
begin apply a sort of bag of check-ins similarity approach. But now rather than looking
at the check-ins between any two venues, we look at all of the check-ins to each cluster,
to each livehood. And so we'll compute a pair-wise similarity between all of the clusters
that we've discovered.
And the reason for doing this is that you can kind of review this related livehood of a
way to sort of hierarchically group clusters. So once we divided and segmented the
city, we can then see for a given livehood what are the other neighborhoods that people
go to. I think this is actually one of the coolest things in the website which I will demo to
you.
Let me first talk about our data. So, again, we're using foursquare. But foursquare
check-ins are by default private. But what we can do is gather check-ins that have been
shared by people on Twitter publicly. And so we've done this. We're actually combining
a dataset from other researchers of 11 million foursquare check-ins with our own
dataset of approximately 7 million check-ins.
And, yeah, so once we have these kind of Tweets of the check-in we then align the data
with the underlying foursquare venue.
Let me talk about the website a little. You guys all saw this when I came in, but
essentially these dots are venues. And nearby dots of the same color form a livehood.
So, again, the colors don't have any meaning. So if you have two clusters that are kind
of far apart, they're not related in any way, think of it as more of a map coloring problem.
So here is the livehood SoHo in New York. We also add things like various
descriptives, statistics about the check-in of the venue. Here's an illustration of this
related livehoods feature. So for here I'm settling SoHo, and I'm showing what
livehoods are related to it. And I think this is kind of neat because it highlight various
transportation corridors in the city and various patterns of behavior.
So one cool example in Seattle is that if you click on Bellevue -- or Belltown, Belltown is
related to a few nearby neighbors downtown, et cetera. But it's also related to these
kind of wineries that are kind of near Redmond, which I find very unusual, but it also
makes sense to my model of the type of person that lives in Belltown.
So the question is we have these clusters, right? How are we actually going to evaluate
them? So we started by clustering various cities. Since we lived in Pittsburgh, we
naturally clustered Pittsburgh. So here's an example of the Pittsburgh clusters
downtown. But once we have them, what do we do with them?
I mean, you can try to come up with some sort of quantitative evaluation method but
really what we're after here is we know that livehoods are different from neighborhoods,
but we want to understand how they're different.
In our evaluation we want to be able to characterize what livehoods are. For instance,
do residence derive social meaning from the livehood mappings? Can livehoods help
elucidate the various forces that shape and define the city? And so if this is our
objective, any sort of quantitative measure that we can come up with to evaluate the
quality of our clusters does not really capture that objective. At least none that I could
come up with.
I'd be open to any discussion about ideas that people had. But so in this case,
quantitative methods are going to fall short. And so we default to a qualitative approach
of evaluating this quantitative model.
So that required us doing some field work. So to uncover what insights our model tells
us about the social section texture of city life, the only way to effectively explore the
relationships between our model and the social realities of citizens is by going out and
talking to people, by learning about their lives and the way they perceive the urban
landscape.
And so our goal was to see how well our clustering actually maps on to their cognitive
maps of the city.
And so we conducted 27 interviews. Each interview was about an hour long of
residents of the city of Pittsburgh. These residents were recruited through various
social media campaigns. We kind of had neighborhood groups post our flier on their
Facebook page, things like that. And we conducted a semi-structured interview protocol
that explored the relationship between livehoods, municipal borders, and the
participants' own perceptions of the city.
The one requirement we had is the participants must have lived in the neighborhood for
at least one year.
The interview protocol as I mentioned, it was sort of semi-structured. But in essence we
began with a discussion of their backgrounds and their relationship with their
neighborhood. We asked them to draw their own sort of cognitive map of their
neighborhood. And given this map, we asked them is there any shift in feel in the
neighborhood? Are there any places where the character changes or there's a shift?
Are there any places in the city where they can think of where the municipal borders are
changing? And finally, after all of this, after we gathered all this data, we showed them
the livehood clusters and asked for their feedback. And that's an portion thing in
mentioning. So we're not top loading the interview in showing them the maps, we're
gathering data first, then showing them the maps.
And then, finally, we showed them the related livehoods feature and asked for feedback
as well.
So I'm going to skip a few slides and go to actually some interview results. The slides I
skipped basically gives some intuition of how we structured the interview. But I don't
want to bore you with that.
So the interview results are actually kind of fascinating. So they paint a picture of how
people view these different neighborhoods in the city. Now, first I want to ask -- I know
a couple of you are from Pittsburgh, but is anyone like familiar with Pittsburgh? Yeah,
just the few that I know. So that's okay. I mean, I think that you'll be able to recognize
the patterns that are going on here in your own cities. So the first example I want to
describe is of Shadyside and East Liberty. Now, Shadyside and East Liberty are
actually two contiguous neighborhoods in Pittsburgh. Both of them are kind of east of
downtown. But despite being contiguous, they're quite socioeconomically different.
They're separated, for example, by train tracks. And the tale of these two
neighborhoods is that Shadyside is thought of sort of the rich neighborhood in the city,
with some upscale shopping. East Liberty is often characterized as sort of a poor part
of town. It once -- used to be a major commercial area of the state, but it was sort of
destroyed by de-urbanization and failed renewal projects.
There's these train tracks that separate them. There's a new Whole Foods that was
built about 10 years ago on East Liberty right on the border of the two and some recent
development that includes the building of a pedestrian bridge that actually spans the
train tracks.
So let me describe this in detail. So Shadyside, again, it's described as upscale. It's -again, it's not a sort of homogenous neighborhood. On its western end, there are some
large houses with high property values. And on the right, on the western -- on the
eastern end, they're kind of more apartments for students and young professionals.
There are within this neighborhood three commercial districts. There's this area on the
western owned called Walnut Street which has lots of national brands, upscale
shopping, upscale boutiques, some restaurants.
It's thought of generally as conservative. Not politically, but the restaurants there are
not necessarily experimental restaurants. It's very traditional.
Ellsworth and Highland Ave are the other two commercial districts in the neighborhood.
And they have very few, if any, nationally branded stores. They're much more
independent, locally owned boutiques. Local eateries. And they generally attract a
much younger crowd.
Again, I mentioned the train tracks between them. There's also a kind of about us way
that goes along the train tracks.
Now, across these train tracks is East Liberty. As I mentioned, East Liberty was once a
vibrant shopping area. This vibrancy was destroyed by some pretty bad planning
decisions as well as some trends of de-urbanization in the '60s and '70s.
Currently it's typically characterized as poor. There's a lot of vacant store fronts. The
store fronts that the -- the nonvacant store fronts are the ones that typically cater to
lower income neighborhoods -- or lower income clientele. But recently, and following
the construction of this Whole Foods that I mentioned, there has been efforts to
revitalize the neighborhood.
Now, I say revitalize rather than gentrify because the forces that planned Pittsburgh are
kind of different. Gentrification forces aren't necessarily -- they don't necessarily push
people out of the city. And that's a function of the low property values.
So the historic shopping areas are along Penn Ave, which is this kind of central one,
and on Highland Ave and on Centre Ave.
And again, most of the recent development activity is long Centre and typically the
intersection of Highland and Centre. So I -- again, the development has followed the
construction of this Whole Foods. So the Whole Foods was part of this large vision that
was started by this company called Mosites Company in Pittsburgh. And they had a
whole regional vision for this neighborhood. I mean, basically it was their plan to
revitalize it.
So in the words of Steve Mosites talking about -- oh, I'm sorry. This green part here is
actually the pedestrian bridge. So the pedestrian bridge is connecting the Whole Foods
to this kind of independent shopping area on Ellsworth and Highland.
And so in the words of the planner, Steve Mosites, he said the purpose was to connect
the three shopping districts. We wanted a pedestrian bridge because we wanted as
many connections to East Side itself. So East Side itself is a shopping district. South
Highland 10 years ago was just starting to come around, and now it has all these great
things. And also you have Ellsworth that's right there.
And so the vision for this region was to connect, to bridge these two shopping districts.
So there is kind of my mental map of Shadyside.
Now, here's actually what our livehoods clusters did to that region. So you see, if you
remember the kind of borders that we drew, it's actually splitting Shadyside into two.
And it's combining the eastern end of Shadyside with this area of East Liberty. So the
clustering, again, it splits Shadyside into two. On the western end it combines the high
end national brand shopping area with the kind of high end property valued housing.
And then it combines the kind of student apartment area on the eastern end of
Shadyside with this new revitalization area of East Liberty.
>>: So [inaudible].
>> Justin Cranshaw: Yeah. That's exactly where the Whole Foods is. It's below the V
in revitalization. Area of East Liberty.
>>: So the Whole Food is like [inaudible].
>> Justin Cranshaw: Yeah. That's exactly where the Whole Foods is. It's below the V
in revitalization.
>>: [inaudible].
>> Justin Cranshaw: Google?
>>: Yeah.
>> Justin Cranshaw: No, Google is ->>: [inaudible].
>> Justin Cranshaw: So there's a Google Pittsburgh. That's kind of off the map to the
east. That's a different neighborhood.
So here's some labels to help you. But so you have the independent shops on the east,
the kind of upscale boutiques, kind of stead shopping on the west and then this
revitalization emanating from the big orange circle.
So let me talk about our interview results. So here's Kelly, who is a 29 year old resident
of Shadyside again describing this neighborhood. She says when you go to Walnut
Street, that's where I often see an older demographic. You'll see women and men
above the age of 50 walking around with shopping bags.
I don't see that demographic on Ellsworth ever shopping around. So I would say that's
a big difference. You are going to see an older, straight, richer people on Walnut, and
you're going to see much more younger, indie looking people on Ellsworth. So that's -we saw that this is one example. We saw this over and over again in our interviews.
And that's kind of validating this split between the western end of Shadyside and the
eastern end of Shadyside.
And it's essentially validating that there's different demographics that operate in these
neighborhoods. So validation of the kind of combination of the eastern end of
Shadyside with East Liberty, this is Allison, a 26 year old resident of Lawrenceville.
She said: If I didn't know anything about East Liberty and Shadyside, I would say this
area here is the same neighborhood, centered around Highland. So that's centered
around in essence where that orange dot is. They just seem to be blending the two
neighborhoods. They seem to be blending. And in just as far as the businesses that
are there go.
And another resident, Erin, said -- and this was after she had seen the map. So after
she had seen our clustering. She said: That makes sense to me. I think at one point it
was more walled off, talking about East Liberty. East Liberty was thought of -Shadyside was wealthy, East Liberty was poor. But now there's all these nice places in
East Liberty, and there's some more diversity in this area, so it's becoming more of the
same. And I think Shadyside is almost shrinking and you only have a few streets that
are really only wealthy.
And so all of this highlight that our clusters are good at identifying these kind of areas
that are in flux. So we actually asked all of our participants what areas in the city are
kind of in flux or blurry to you. And I think 85 percent of them said that this area was the
one that was in flux. And this is one of the few areas where you can see actually the
livehood spilling across the neighborhood border. So it's pretty interesting to see that.
The take aways here are that you can actually visually observe the concerted effort of
the Mosites companies and others to blend East Liberty with Shadyside in the creation
of this new neighborhood that they call East Side, which is actually a blend of the two
words.
And you can actually observe that through the activity patterns of residents. And so the
new emerging developments in East Liberty are developing a character that is more
similar to the younger, more independent, eastern end of Shadyside than it is to the
more older, wealthier, staid western end of Shadyside. And so livehoods can help
identify these emerging trends.
So I'm actually -- I have another case study that I'm going to kind of skip because my
talk's running over, and I would like to kind of play with the maps and take questions
and stuff. But I will hop over to some conclusions.
So, I mean, we found these -- so I give you one example. We found these over and
over again, these confirmations of these patterns, making sense to people, in our
interviews. So there's strong support for the clusterings. So the interviews showed that
residence found strong social meaning behind the livehoods. And we found that
livehoods can help shed light on the various forces that actually shape people's
behavior in the city. And these include things like demographics, economic factors and
cultural perceptions.
There are a bunch of limitations that I want to talk about. First, although most
neighborhoods had real social meaning to the participants, no algorithm is perfect.
There are certainly livehoods that didn't make sense.
And so we document these in our paper. I didn't talk about them in the talk here. There
are obvious biases using this kind of data, foursquare data. And we view this as a kind
of limitation of the data, not necessarily our methodology.
So we feel that as you get better and better and more representative data, you'll be able
to produce more accurate images of the city.
There is definitely experimenter bias associated with tuning the clustering. So by and
large the clustering tended to be pretty stable to parameter selection. Generally the
location of the clusters was always the same. But you'd have -- particularly depending
on the number of clusters that you'd select, you'd have various clusters combining or
splitting. So the sizes of them changed. But the low indications were pretty consistent.
But this is an anecdotal evidence. I don't have a rigorous exploration of the parameter
sensitivity.
Again, because of the biases in the data you have some populations that are entirely
left out. The ones that don't use foursquare or ones that don't have smartphones.
These tend to be kind of older and much more wealthy people that are too busy for
these kind of technologies and poor people that can't afford these technologies.
And so you have these two end of the spectrum that you're not capturing.
I also don't want to overemphasize the kind of sharp division that is are being drawn on
the map. I think neighborhoods tend to blend into one another. And, in fact, you could
come up with model that is are kind of more soft in their clustering. But unfortunately
the visualization needs to be kind of hard. It's hard to come up with a soft transition
without doing like gradients or something like that.
And, again, this is not a comparative work. This is a qualitative work. So we're not
claiming that our model is the best, just that it does a good job, and it produces things
that are meaningful for people.
And finally, thanks. I'll take as many questions as you have. And I'm happy to also play
with the maps, if you want to look at them.
[applause].
>> Justin Cranshaw: Yes?
>>: So I actually have had a lot of fun drawing the mental models with people when I
was a Peace Corps volunteer in Africa. And I only worked with women. But I'm
absolutely convinced that the maps they drew were completely different than what
would happen if we even included men in those groups.
So we would see like where you would go to get firewood or where the water was, or
where they would even go to like sell or buy food, which I don't think would happen.
>> Justin Cranshaw: Yeah.
>>: As much if men were in the group.
>> Justin Cranshaw: Yeah.
>>: So, I mean, this is a bias, but I think it's a pretty strong one. Do you have any
sense of -- are there any groups that if you split them with this data you would get totally
different maps?
>> Justin Cranshaw: I think that's -- that's absolutely true. And it's not something I had
thought about too much before. But in particular you could imagine with regard to time,
right? If we had time elements to our clusters you would definitely see a different
between men and women, I think. Especially at night. So I think there's much more
concern about safety for instance about the places that you're willing to travel to. And
so I think you would certainly see that. Yeah. I think probably with regard to any
demographic if you were to take one demographic and exclude others you would see a
different map.
>>: So you mentioned that you didn't find quantitative evaluation very convincing. What
have you tried?
>> Justin Cranshaw: So I spent some time trying to link it to property values. And I
think it's a natural thing to try. And you do see some evidence. But it's just not the kind
of evidence that I -- that I felt was capturing the spirit of the work, let's say. So you'll find
some correlations between like variability of property values within a clusters and
outside of a cluster.
>>: [inaudible] just internal consistency checks like cross-validation? Because you're
doing these parameter checks but they're basically like [inaudible].
>> Justin Cranshaw: No.
>>: So they're not automated?
>> Justin Cranshaw: No, I haven't done that. It's a natural thing to try. I haven't done it
yet.
>>: So beyond property values where you try to see some socioeconomic indicators ->> Justin Cranshaw: Not yet. I'm sure you would see some. I think it's a fascinating ->>: Right.
>> Justin Cranshaw: But it's not something I've gotten to yet. I'm sure you would see
like demographic differences, income differences.
>>: Yeah. I mean, I don't know. I mean, census data ->> Justin Cranshaw: Yeah. Absolutely.
>>: Go ahead.
>>: I [inaudible] I'm wondering if down the road similar to the way that things like
[inaudible] you can [inaudible] a restaurant or different attributes if you're planning on
like filter out the livehoods.
>> Justin Cranshaw: Yeah.
>>: [inaudible].
>> Justin Cranshaw: Yeah. I think that's -- I mean, the next step I think is -- so this is
kind of an exhibition. I'd like to turn this into a tool, right? And there are various
different tools that you could imagine hanging off of a following like this. You could
design them for various stakeholders of the city.
So, for instance, like real estate development. So you're mentioning a more consumer
oriented tool, like it's searching in the city. Yeah. Absolutely. That would be cool.
>>: And -- oh.
>>: Yeah.
>>: Okay. Sure. I have two questions that I think are both about how the technology
impacts our mental models of the cities. The first one is whether [inaudible] direct map
run comparison between people who use like foursquare, people who don't.
>> Justin Cranshaw: Yeah.
>>: It's kind ever like what you did, and I just want -- I can see if you used foursquare
maybe you sort of shrink distances, like things feel closer than they are.
>> Justin Cranshaw: So there was a short paper in CHI last year ->>: Oh, really?
>> Justin Cranshaw: -- that did this. It's Henriette Cramer and Frank Bentley from
Motorola.
>>: [inaudible].
>> Justin Cranshaw: And they did -- actually they replicated the kind of Milgram
studies. But they looked at people that used foursquare versus people that didn't. And
their conclusions are basically that they have different kind of views of the city. It's an
interesting work.
>>: I'll take a look. And then the second part of it is you were talking about how
neighborhood had sort of shifted and changed. So I'm just wondering about shifting and
changing beyond -- so for example this friend of mine over the weekend said that
Ballard is the new Belltown, which if you live in Ballard is probably horrifying to you.
[laughter]. But his explanation was now that you can go to Ballard and they have valet
parking now a sufficient number of places so that people who would go to Belltown are
now feeling comfortable to go to Ballard.
But anyway, but those are not contiguous. But that's a perfect example of how you can
-- you can detect that type of ->> Justin Cranshaw: So I mean I think I'll go back to this. I'll go back to the Seattle
example. Only because I finds it so fascinating.
If you look at Belltown and you use this -- I think it's this one. And you use this kind of
related feature which hides everything else that's not like the top 5 most related of the
livehoods, you see way out here -- did I take the wrong one? Yeah. Is this area where
there's the wineries. And it's basically highlighting the -- it could be a bias of like a
super user, right? But I think it's probably not the case. I think I have enough data that
it's not biased by a single super user.
I haven't looked at it yet. This was just kind of released. But here you're seeing that like
ideally these should be like all continuous contiguous regions, right? I mean, if distance
mattered so much, all of the related livehoods would be nearby. Which by and large
they are. Except for this one that jumps way over here.
And you see that in various other cases of the city too.
>>: Would you call that a neighborhood, quote/unquote, including the ->> Justin Cranshaw: I'm not sure I would be comfortable calling any of these
neighborhoods. I mean, that's why we used a different name. I mean, so I mean it's
representing kind of commercial activities we're not really capturing -- neighborhoods
are kind of residential in my mind. So there's some quality of neighborhood to them.
But it's not quite a neighborhood. Right?
I mean, you'll see that malls sometimes exit at livehoods. But malls aren't
neighborhoods, right, they're just -- an airport is the livehood because it's a functional
unit of innocence.
>>: So, you know, regarding your -- the interviews that you did, the examples that you
showed, it seemed like the demographic was mostly like under 30 people who use
social media because that's probably how they saw your ad. Were there other
demographic people ->> Justin Cranshaw: Of our interview participants?
>>: Sorry?
>> Justin Cranshaw: You mean the interviewed participants?
>>: Yeah.
>> Justin Cranshaw: Yeah. So I can go back. I think I have a demography slide.
Yeah. So I mean we had people in like 40s -- 40s to 60s actually.
>>: Right. All of them essentially are like kind of like [inaudible] in some sense.
>> Justin Cranshaw: Yeah. I think by and large with -- yeah, that's true.
>>: Did you try showing the maps to someone who doesn't use -- I mean, were these
foursquare ->> Justin Cranshaw: Yes. So, I mean, we had I think two participants that were -could be categorized as not tech savvy. And hardly any of them actually were
foursquare users. So that's one nice thing about the study. And it would be good to
compare.
>>: Right.
>> Justin Cranshaw: But I think that the density of foursquare users in a random
sample is not very high. So it's kind of hard -- a hard study to actually construct. But,
yeah, so we did interview some non-tech savvy people and actually have some
interesting perspectives.
One example is -- I'll go to Pittsburgh. So in Pittsburgh you have this big donut hole
right here. There's nothing in the middle. That's not true. This is like -- this is a left-out
population from our sample. So this is very low income in the center here. But we
interviewed actually residents of this neighborhood. And so one of them said wow that's
a visualization of the digital divide. And they're right. That's exactly what it is.
>>: Could I ask one more question? Are we -- do we have time? What's your sense of
the relationship between -- talking about density at the start, between density and
degree of change in neighborhoods? Or is there one?
>> Justin Cranshaw: I'd hesitate to answer. But, I mean, my instinct says that densities
change faster. Right? That's my instincts. I could be wrong. What's your motivation
for asking? What's your thought?
>>: I don't know. I'm not sure. In some of the example -- I was just trying to sort of
debating in my mind as you were giving some of the examples, I can kind of see both. I
could see things that are [inaudible] have more opportunity to change.
>> Justin Cranshaw: Yeah.
>>: Right? Like once things get sort of settled with density, it's almost harder to
change.
>> Justin Cranshaw: I could see.
>>: Or I could see the other way around too. So I just didn't -- I wasn't sure.
>> Justin Cranshaw: It's a function -- yeah, there's different forces, right? Like in a
density you typically have larger financial forces at play that have the power to make
changes fast if they want to.
But you're right. And then there are all these historical considerations in a city that there
is a -- I went to a talk where someone showed the layout -- a historical layout of Boston
overlaid like on a modern layout. And although the buildings are different, the things
that don't change are the actual layouts. Layouts is kind of like an API and, you know,
once you set it out, things can change. But the API never changes.
>>: Now, it seems -- just one quick comment. It seems to me that, you know, I mean,
the main [inaudible] of these changes that certainly has an impact on how
neighborhoods change. I mean, I don't know if that's -- [inaudible] big cities in the U.S.
But I see in a lot of like big cities in Indian which are expanding very fast because of the
growing population that businesses are actually convenient entering out to like other
suburbanian areas and growing very fast and redefining the neighborhoods. So that is
probably another way.
And these changes are really fast, like five years, 10 years neighborhoods change. So
[inaudible].
>> Justin Cranshaw: I would say less than areas -- it's rare to see like the construction
of a neighborhood. Because the idea is a little less well defined in a less dense area.
And it has to do with the variability issue. You don't have the same level of variability in
less dense areas.
So you will see like new businesses come up, maybe new roads. But you won't see the
construction of like South Lake Union. Rebecca?
>>: When you're using something besides foursquare as your source? Like what could
you use? What are the features ->> Justin Cranshaw: So, I mean, I'm actively in the process of trying to expand my
data. So I mean the next things to go for are Instagram is a natural next things. I think
it's a different sample. It's also a bias simple. But it's biased in a different way. It might
be interesting to compare.
But also Twitter, right? I mean, so I -- I don't necessarily like using like -- I find using
geotag Tweets is kind of a pain. But what I could do is download all of the Tweets from
users that I currently have check-ins for and use the Tweets as sort of a kind of model
for their model for the user and have some sort of user to user similarity to incorporate
into sort of a -- almost a demography score. Right? So there's different ways of
thinking about it.
But the point is, I won't make a prediction that foursquare will exist in 10 years. But I
think this kind of this kind of data will continues to exist in various forms.
>>: It's important that they're actually checking in and choosing to [inaudible] the public
that they're there. Or if you had like phone -- and ->> Justin Cranshaw: So, I mean, that's actually another bias that I didn't mention. The
fact that these are public Tweets and are public check-ins and not their everyday
check-ins is a bias. Because the things that people share publicly on Twitter are
different from their kind of everyday activities.
>>: [inaudible] foursquare.
>> Justin Cranshaw: Yeah. So there have been discussions but never moving beyond
discussions. Yeah.
>>: [inaudible] doesn't scale very well. Historical atlas.
>> Justin Cranshaw: Yeah.
>>: Sherlock Holmes, his livehood or Leopold Bloom and gather a few books written
about New York and compared the livehoods of their [inaudible].
>> Justin Cranshaw: I like that. It would be fun to see like Sherlock Holmes livehood.
>>: Compared to a person living Baker Street 221 doesn't really exist. But in that area.
>> Justin Cranshaw: Yeah. I like that.
>>: [inaudible] interesting in places like New York or LA comparing tourist maps and
sort of the way that those maps are presided by hubs. And if you could remove some of
those and compare those path ways compared to regular commercial path ways and
see if there's like the citizen livehoods versus the commercial livehoods.
>> Justin Cranshaw: Yeah. So even looking at like the difference between tourists and
locals is fascinating. There's that fascinating Eric Fisher map. Have you seen that?
Has anyone not seen that? I actually have a slide of it. I've just got to find my cursor.
Yeah. So this is the -- this is San Francisco. The red are colored as -- this is from Flickr
data. The red are tourists. The blue are locals. The yellow are kind of indeterminant.
But it's ->>: [inaudible].
>> Justin Cranshaw: It's based on profile information and I guess other data of that
person. So where else have they taken photos?
>>: [inaudible].
>> Justin Cranshaw: Yeah. He does some fascinating work.
But this -- this idea being a tourist, right, you almost have a different persona, right?
You're a different person. You have different objectives. You have different -- you're
serving some different functional role in the city. So I think that distinction is fascinating.
Well, thank you.
[applause].