[Music]
good morning everyone and thank you for
the introduction as a woman working in
technology I was hoping to share this
morning with you the news that for the
first time in US history a woman was the
next president but I guess this hasn't
happened and as a scientist working for
the past 20 years of my career on
building data-driven machine learning
based of human behavior the results are
obviously extremely interesting to me
because I get I'm sure that there is
more research that we can do and better
models that we can build because if we
look at one of the latest polls from
yesterday the chances were clearly for
Clinton and in fact Newsweek even dare
to issue number with Clinton as the next
president and as we all know everything
was wrong right so I think this is very
interesting to me because Annie's been
mentioned in the panel we live in a
world where increasingly we are modeling
humans from data and we are making
decisions based on the results of those
analyses and what is clear is that
humans we are still unpredictable in a
sense and we are way more complex than
we think we are and also that we need to
be very cautious about the data that we
are using and understanding the
limitations of that data but I didn't
come here to talk about data-driven
models for predicting elections I came
here to talk to you about a topic that
might be not as known to you which is a
lot of the work that we have been doing
and other teams have been doing on
understanding the value of big data to
have positive social impact I will
describe work that we've done while I
was a telefónica I'm no longer a
telefónica but all the projects that I
will present were projects that within
my research team while I was a
telefónica
and in particular when we talk about big
data there are many sources of data a
lot of them have nothing to do with
humans there is a lot of data about the
universe about particles colliding in an
accelerator etc but a lot of the data is
human behavioral data and part of this
data comes from a device that we all
have in our pockets which is our mobile
phone because the reality is that the
mobile phone is the most pervasive piece
of technology in human history the
mobile phone penetration radians between
90% and 120% worldwide
we love our phones we spend more time
with our phones than with anyone in our
lives this is important to think about
and the phone is connected and it's
really much more than a phone in fact
many people don't even realize it's a
phone because they don't talk with it
anymore
and what is really relevant for this
talk this is a global phenomenon this is
a phenomenon happening everywhere both
in developing economies and in developed
economies and in the context of social
good most of the projects are done in
developing economies and the phone has
become an incredible tool to be able to
help you know those societies in fact in
2013 MIT Technology Review declared as
one of the breakthrough technologies of
2013 the idea of using the data coming
from cheap phones so coming from all
phones not just from smart phones as one
breakthrough technology that was going
to enable us to really solve important
problems or make better decisions and I
will illustrate this today during my
talk and as many of you might be aware
of this in the past couple years United
Nations has been really paying attention
to the potential value of data in the
context of the sustainable development
goals so when they were trying to define
the new 17 sustainable development goals
they called for a data revolution and
they commissioned a panel of experts and
one of them was actually my PhD advisor
at MIT to issue a white paper that will
help them understand the potential of
data and big data to
both reach the 17 Development Goals and
also measure whether we have achieved
them or not because one of the
challenges is not going to just
achieving them is that when you say we
want to eradicate poverty how do you
measure poverty how do you know that you
have a radical Authority so both for
measuring the goals and for achieving
the goals there is an opportunity to use
large-scale data to help so in my
research team at telefónica for the past
eight years we've been trying to answer
the question on the slide could we use
mobile digital footprints to help the
world both individually as individuals
and I'm going to talk about this today
but also the world at large and this is
a very abstract question so I'll try to
sort of like bring it down to earth in
my presentation as I mentioned we are
not the only people there is an entire
new field in computer science called
computational social sciences which has
emerged as the combination of
large-scale human behavioral data and
social sciences and sociology as well as
the new way of doing social sciences
where for the first time in human
history we are able to quantify
large-scale human behavior and this is
something that we've never been able to
do before so a lot of social science
theories were based on anecdotal
evidence or our intuitions but today
thanks to the existence of technology we
can actually verify or not those social
theories I'm gonna very quickly give you
an overview of how phone works just to
make it more clear the type of data that
I'm talking about but I imagine all of
you know this so this is a
simplification of how a phone would see
the world so the phone is to be
connected to a cell tower it's just
usually called a BTS and the cell towers
are all over the space you can see them
outside when you go to the cities you
see their their little antennas so
usually and this is a simplification is
more complex than this but in most
projects this is how we do it we divide
the space we do what is called a board
or a tessellation of the space where we
divide the space in these irregular
cells such that at the center of itself
there is a cell tower and all the phones
that are connected to the cell tower
here
are assumed to be here but there is a
fair amount of spatial uncertainty
because the size of this cell tower this
of the cells is proportional to the
density of cell towers which is
proportional to the density of
population so in urban areas it could be
a hundred meter by 100 meter in in rural
areas could be a few kilometers by few
kilometers so we are not talking about
GPS precision or all but still is some
graph sense of location temporally the
data that is typically used for these
projects is also very imprecise because
it's not an event-driven data so it's
not data that is being collected
continuously it's only recorded when the
phone is being used either when the
phone is making a phone call receiving a
phone call or sending or receiving an
SMS so the most common data that is used
for a lot of these projects is
anonymized an aggregated CDRs which you
might have heard of which are these
records that are generated when the
phone makes a phone call or receives a
phone call or sends an SMS and citizen
SMS and the fields that are usually
employed are the encrypted originating
phone destination phone and then the
location of the tower if it's known many
in many cases is not known for example
if it's from a different operator and
for phone calls also the duration of the
phone call so it's fairly coarse data
and is always done in an aggregated
manner I'm just going to show you two
videos so you get a sense of how this
data looks like the first video shows
the every bubble here is proportional
the size of the bubble is proportional
to the number of phone calls that a cell
tower is handling in that moment in time
and this is the state of Oaxaca Mexico
right before during and after an
earthquake takes place and the epicenter
of the earthquake will be here so I so
pay attention to what happens when the
earthquake takes place so here's the
epicenter and then there's this big
surge of phone calls so the fact that
that is this big surge of phone calls is
extremely valuable because when the
government needs to send
help or when an NGO needs to send help
thanks to the fact that that is the
search we can have an estimation of
roughly how many people are in the
affected areas so they can dimension
they help appropriately and this is
something that we have never been able
to do before if we look at the mobility
of phones then we get a sense of
mobility of large-scale mobility between
cities which is also very vulnerable in
some scenarios for example for Public
Health
so using this type of data which is very
course very aggregated and seemingly
useless
we actually are finding that it brings a
lot of value in a number of scenarios
and the four scenarios that we have
explored a telefónica where the first
one is how can we use this data for
helping design the cities better or
understand how cities are working the
second scenario is how can we help in
the case of official statistics and this
is an active discussion that is
happening right now in most official
statistics offices in a worldwide how
can we use new data sources to have more
up-to-date censuses that we have now
because usually they are 10 years apart
and as you all know society's very
dynamic and information from 10 years
ago or even from two years ago is
probably not applicable today the third
area is crisis management tools as an
example that I showed for the earthquake
and the fourth area is Public Health
tools and particularly in the context of
pandemics where human mobility is
critical and for the first time we can
model human mobility thanks to this type
of data so now I'm I don't have the time
to give you an example of four different
projects on these categories so to keep
with the spirit of the election I'm
going to ask for a vote
so I'm gonna give you four options and
then organize you to raise your hand and
whichever winner it is I'll explain that
project like in five or ten minutes so
the first project that I could talk
about is a project of how we use these
data to understand how effective the
Mexican government measures were during
the h1n1 flu pandemic the second project
is a parade that we did to predict crime
hotspots in London's Metropolitan Area
third project is a project that we did
within two projects but I can
top-1 we did one on inferring
socioeconomic status in a developing
country and another one on inferring
credit score to help the unbanked access
credit in a developing country and the
fourth one is similar to the earthquake
one but it's in the context of floodings
in Mexico in collaboration with the
governor of Mexico and United Nations so
votes for the pandemic one Wow no that
many votes for the crime one votes for
the credit one little bit less and votes
for the natural disaster okay so crime
it is so it's the only project that is
not in a developing economy here we go
so this project was done in the context
of data phone for social good the
telefónica organized and it's a project
in collaboration with MIT with fpk and
with the University of Trento and with a
top alliance where I'm actually chief
data scientist right now which is a
nonprofit focused on how to use big data
to help development and to help the
world as you all know crime affects the
quality of life of the area where the
crime is happening there have been a lot
of studies that have tried to find
relationships between different
socio-economic indicators or census
indicators and crime like an employment
and ethnicity and education levels and
so forth and in the past years you know
I also know there has been movement
towards focusing on place centric models
of crime as opposed to people centric
models of crime so there's been the
realization and I think you all can
empirically prove to that that crime
tends to be concentrated in certain
areas in space and most cities have safe
neighborhoods and also safe
neighborhoods so there is this concept
of a crime hot spot which is location in
space an area in space that tends to
have higher crimes than in other areas
in terms of theories to explain crime
and the relationship between crime and
the
environment I'm going to describe two
theories because they seem to be opposed
and we actually tried to figure out who
was right in this project so the first
theory are the theories of Jane Jacobs I
don't know if you've heard of Jane
Jacobs
anyone so she was a social activist in
the 60s and she did a lot of work on
trying to understand what made a city
liveable and what were the
characteristics that made a city be a
good place for people to be and one of
the topics that she discussed was crying
so she wrote a book called eyes on the
street and she talked about the fact
that areas that have a lot of diversity
and have a lot of people moving are
safer areas because we all act as sort
of like policemen of each other that's
what she talks about eyes on the street
so we all protect each other by looking
and seeing what's happening but our ten
years later Newman proposed the opposite
theory in a sense called the defensible
space theory where he said well places
that have a lot of movement and a lot of
people are less safe because they are
more anonymous so then people are less
protected so you we should have this
defensive or spaces what everyone knows
each other like gated communities where
only neighbors know each other and that
way would be safer so one of the
questions is so who was right because
they seem to be saying different things
and as I said all these theories until
now have been very difficult to prove or
disprove because we hadn't had access to
large-scale human behavioral data but
today we are able to start shedding some
light on this so I will tell you at the
end of the this project we could shed
some light on who was right but I'll
just do a quick poll so who things
change Jacobs was right
just raise your hand know that many
people so who thinks Newman was right
more people and I guess the rest of the
people you have your own theory which is
not here so anyways so as I also mention
you can have a people centric approach
to crime where you're trying to predict
if an individual is going to commit a
crime this is not what we're doing or
you can have a place centric perspective
or you say well I want to know whether
this neighborhood is gonna be safe or
not and that's what we are doing and
this is a very important distinction
because when we made this project
he made it to the press and most of the
headlines were Minority Report this and
that and he has nothing to do with like
them I know the report perspective so it
is a data-driven approach we're gonna be
using data it's a play centric approach
we're gonna be using multiple sources of
data so it's a multi-modal approach and
the goal is to predict crime for spots
in the metropolitan area of London so
the reason why is in London is because
this project was part of a data phone
the telefónica organized it was a data
from for sure ship good where
large-scale anonymized and aggregated
data was shared with research teams from
all over the world that could explore
how this data could be used for social
good and there was not only their mobile
data that was shared
there was also data shared because it
was in collaboration with the open data
Institute there was also data that was
shared from coming from the open data
Institute a lot of census data and
London data that open data for this
particular project we use three data
sets we used a mobile data set that we
call the smart stuff data set because it
comes from a product the telefónica had
in in the UK we had the criminal cases
data set which was a data set shared by
the London Police where it had the
number of crimes in the metropolitan
area of London for two months and then
the London borough profiles data set
which is the Census sort of like data
set has 64 indicators of census type of
information so quickly explain each data
set so the smart stuffs data set was the
data set coming from this product where
instead of dividing the space as I
showed before in this regular grid now
it's a regular grid but it's the same
idea you have squares the size of the
square is proportional is inversely
proportional to the density so the
higher the density the smaller the
square and then for each square and for
every hour what he was shared was the an
estimation of how many people were in
that square in that hour and estimation
in percentages of what percentage of
those people this was their home their
work or they were visiting and then a
market research company also provided
estimations on the gender splits and the
HS plates because that data is not
available just by looking at the
activity in their cell towers of
the crank data as I mentioned was shared
by the London police and it had two
months of data so we use one month for
training the models and one month for
testing the models so there is an
assumption that past behavior can
predict future behavior which as I share
with elections you might not always
apply but I hear it seems to work better
we need to define where our crime hot
spot is so we use the median value
across the entire metropolitan area and
areas that had higher than the median
where crime hotspots and lower the equal
to the median they were not crime
hotspots and then I'm not gonna go into
the details but this is I mention it
because all these things they seem very
simple and it's extremely hard so their
representation the spatial
representation is different between the
census data and the crime data and the
mobile data so the mobile data has
agreed but the official data the open
data has what is called the L is a way
that for you in the UK you probably
familiar with this which are the census
regions that have a mean population of
1500 and they are this very regular kind
of zip code kind of regions so one
important step is how you match both
datasets to have the same representation
so for each of these LS aways we had
access to the sixty-eight matrix there
are sort of like coming from the census
on which in the UK are very rich
we even have estimation of the happiness
levels of the people in those allosaurus
but there is more typical demographic
information like you know age and income
and there are house prices and education
levels and immigration levels and
unemployment levels etc so the challenge
is we built three different models so
the challenge is we either have the
mobile data or we have the census data
or we combine both we have all the data
and what we want to do is we use this
data for the month of December we train
the model with the month of December and
we want to predict which of these will
be a crime hot spot in the month of
January and see if we are able to do
this task which is not even sure whether
we can actually do it so to do this we
need to first compute some features from
the mobile data so usually in these
types of projects
you have a spatial-temporal series and
one of the questions is you don't know
what is the right temporal granularity
for the problem at hand so is the number
of people every hour the most important
variable or is it the number of people
in the mornings versus the evening's or
is it the number of people every day or
is it every week or is it every month we
don't know so typically what you do is
you compute very huge amount of features
thousands of features all the features
that you can imagine that could be
meaningful for this problem
and then you apply feature selection
techniques to identify which ones are
the features that seem to matter the
most for this problem so in this problem
we computed up to 6,000 features
originally and then we applied a feature
selection algorithm in this case was the
mean decrease of the Gini coefficient to
go down to 68 features coming from the
mobile data and we chose 68 to have the
same complexity in the model as the
census data because the census data were
sixty eight variables so at the end of
this process we had 68 features coming
from the mobile data 68 features coming
from the census data and then the ground
truth for the crimes and we built a
model only using the mobile data to
predict crimes a model only using the
census data to predict crimes and a
model that combined both to predict the
crimes and then the question is you know
how well can we do it and which one is
doing better so this is a binary
classification approach we use random
forests is a state of the art machine
learning algorithm and that was
performing better than other algorithms
and we got some surprising results so I
think the first surprising result is
that the mobile phone data model was
doing significantly better than the
model using only the census data which
had about a 62 percent accuracy versus
68 percent accuracy which was very
surprising because the census data is
very rich and very detailed data
compared to the other data and then when
we combined both models we reach almost
70% accuracy which is very clearly
illustrated in these two slides which
compare the ground truth versus the
predictions I'm joking I don't think you
can really tell you know who is which
one is knowing better but I think the
most interesting element beyond that you
can do it with 70% accuracy is when just
your start looking at which variables
matter
because they start shedding light on
what what is really correlated you know
with crimes so when I talked about the
fact that we don't know which temporal
granularity matters so we found that the
features about the daily dynamics we're
the ones that were being selected above
features of hourly dynamics or weekly
dynamics of monthly dynamics so it seems
that the daily dynamics was important to
predict the crime another observation
and this is like to reveal who was right
whether Jane Jacobs or Newman so we
found two evidences that support
actually Jane Jacobs so there are a
couple people that thought she was right
we found some evidence for that so the
first one was that we found that the
higher the number of residents the
higher the probability that there would
be a crime hot spot and this is a
contrast in Newman's theory that was
saying the opposite and we also found
that entropy related features were
actually correlated with lower crime an
entropy measures how predictable a
variable is which is somehow related to
Jane Jacobs theories of diversity and
having a lot of mix of people so these
two findings seem to point in the
direction that Jane Jacobs might have
been right
we also looked into the joint model that
has 16 features that were coming both
from the mobile data and the census data
and one of the surprises there was that
only six out of the 16 features were
coming from the Census and the other 62
features were about human dynamics so it
seems that for this particular problem
human dynamics is very important and
then when we looked at with census
variables we're important we found what
may be intuitively people could think is
correlated like unemployment levels or
immigration levels so this was one of
the first projects has been able to show
the value of human dynamic data coming
from in this case the passive mobile
network to predict crime so with this I
think I'm going to I don't know if I
have five more minutes so I should
finish organizers should I finish okay
so I'll just quickly finish they're like
I just wanted to probably just leave you
so if you're interested we have a lot of
scientific publications about this and
it was been many of the projects of a
PLL or in the press and I'll just
mention in two seconds some of the
barriers because this seems you know
that as I said this is very easy but
it's actually very hard and there are a
lot of barriers why we don't actually
are using big data to have real positive
impact you know right now this was a
research project it's not a real play
that is being used there are internal
barriers I don't know right now of any
company that has a big data for social
good Department
so there are various usually when this
happens there is an evangelist in the
company like myself or Nicoletta carved
in orange that are really advocating for
this there are a lot of barriers related
to the risk and benefit analysis and
potential and intended consequences of
some of these projects for example if
your modern migrations in a non
democratic country where the government
happens to you know be persecuting a
certain group then you know you could
have an internet consequence and in fact
one of the important things is the
creation of special panels and ethics
panels to make sure that you can
anticipate the maximum number of this
and intended consequences there are a
lot of barriers related to social
challenges as well and related to making
sure that this is a tool that will
benefit you know everyone and that we
don't create a gap you know between the
ones that have access to the data and
the technology and the ones that don't
because this is precisely the opposite
of overtime to do there are many
technical challenges that as a as an
expert in this topic I'm very excited
about there is loss of technical
challenges that you know we still need
to solve there are privacy and security
challenges as well even if the data is
fully aggregated fully anonymize is
fundamental to have a very clear code of
conduct on how to handle this data clear
ethics guidelines on how to handle the
data obviously protect the data make
sure that is you know securely stored
and so forth and finally there are a lot
of challenges in the way of working
because these projects are by nature
multi disciplinary and multi team so we
are collaborating with United Nations or
with the government or with the police
and we expect different languages
and we are computer scientists and they
are whatever lawyers or whatever they
are so it's very important to to make
sure that you know we understand each
other and we say the right expectations
and we actually have our agendas somehow
be aligned and that's not easy but I
think it's a fundamental and a
completely necessary condition for these
projects to become a reality I'm not
going to explain this if you're accuse
you can talk to me later but I think the
question that I leave you with is you
know how can we all responsibly make use
of this opportunity because the
opportunity is huge and I think it would
be a shame if we don't liberate it to
actually have positive impact in the
world thank you