Translator: Stephanie Brabant Reviewer: Claire Ghyselen
In the 90s, Internet changed our societies profoundly.
People all over the world became connected.
Frontiers opened up.
Then, in the early 2000s, our communications became mobile.
Just like the Internet, mobile phones changed our lives significantly.
We rely on our phones for almost everything:
scheduling our days, checking the news,
connecting to social media, listening to music.
Sometimes, we even use our phones to make a phone call.
Running out of battery has become a widespread concern.
(French) Sometimes, only one thing is missing,
and the whole world seems depopulated.
Mobile phones impacted our personal lives,
but also at a global scale.
Thanks to our mobile activity, we're leaving digital traces behind us.
Traces that are collected by telecom operators.
These traces have opened up a new way of analyzing how we move,
and how we create social ties.
We have become able to study entire populations simultaneously
through a new pervasive source of data.
Today, I want to show you how important telecom data is
for developing countries in particular.
Last year, the United Nations have set an ambition:
they aim to achieve a series of global goals,
such as putting an end to extreme poverty or achieving global health, by 2030.
This is just 15 years from now.
Achieving these goals is a very ambitious objective,
and to reach them, we will need to improve the way we currently do development work.
If you wonder what I mean by "development work",
it's anything that can help local populations
to improve their quality of life,
such as better road infrastructures, access to water or the Internet,
or financially supporting the poorest populations in the country.
While their jobs can be very diverse, development workers and policy makers
all rely on one key resource to function efficiently: information.
Let me show you two examples to illustrate this.
Like for many developing countries,
malaria is endemic in parts of Zambia.
It is needless to say how much malaria cripples all aspects of life.
It's affecting people's health and life expectancy.
It is also affecting the global economy of the country and its development.
This is why Zambia has a malaria control center,
which is responsible for monitoring the evolution of the disease
and planning the response at the level of the country,
such as distributing bednets, indoor spreading
or destroying mosquito larvae sites.
The malaria control center relies on two important pieces of information
to function efficiently:
good reporting of new cases when they occur
and a sound understanding of how those cases have occurred
to localize its root causes and act at their source.
Currently, the malaria control center gets the information
through a central reporting system
where practitioners encode new cases when they treat them.
They also encode other information
such as where the patients have traveled recently,
to understand where the disease is likely to be spread.
This last piece of information is very important
as human mobility is the main reason why diseases spread geographically.
However, the malaria control center has two problems with their data.
First, the data is often not of good quality:
patients might forget to name places they have traveled to,
because they don't seem important enough to be mentioned,
such as a city where they transited for a night.
Data is of low quality because city names can be misspelled
as data is entered manually.
Finally, the precision is not very accurate.
The second limitation
is that this data is actually not very relevant for the control center.
The patients might have spent a couple of hours in some places
and weeks in others,
which changes dramatically the risk of exposure,
but is not visible in the data.
First example.
I'll show you now a very different example
to show you why information is so important
across all facets of development, not only disease management.
This example comes from Kampala, the capital city of Uganda.
One of the key routes of achieving the productivity miracle
in developing countries is well-mastered urbanization.
For Uganda, urbanization is the single largest opportunity
in the coming decade.
Kampala is growing fast.
It's expected to triple in size in the next 35 years.
What does Kampala need to achieve well-mastered urbanization?
Roads that are large enough to sustain the traffic of the city.
Currently, Kampala is famous for its traffic jams:
a one-hour drive to the airport can quickly grow to 5 hours.
You believe Brussels has problems about congestion?
Try Kampala!
Systems for water, electricity and sewage
that are dimensioned to the size of the growing population.
A development plan to anticipate the future growth of the city.
Again, the city of Kampala will rely on information
to master its growth.
How many people are living in the neighborhoods of the city,
and in particular in slums, where the poorest populations are living?
What are the current traffic demands on the road infrastructure?
How are these numbers expected to grow in the near future?
But as for Zambia, we see the same problems emerging
when collecting data about it.
The crucial data about population and traffic
are collected manually,
by sending people on the streets to count how many cars are passing by
or estimate the density of a neighborhood.
What these two examples show us are common limitations
when working in developing countries:
dealing with scarce information,
often of poor quality
and not formatted to our needs.
But are these limitations we have to deal with?
Wouldn't there be a better way
to systematically collect rich and exhaustive data
to help development workers to be better in their jobs?
This is where mobile phones come into play.
Telecom data can be used to provide rich data
that can help development workers to change their jobs.
If you wonder how, let me show you
how telecom data can help Zambia's malaria control center.
This is the southern province of Zambia.
It's about 3 times the size of Belgium.
The colors on the map indicate how many cases of malaria
have been reported officially in each region.
The darker areas which indicate the most cases
are located in the south-east, next to Lake Kariba.
From this perspective, all places close to the lake
seem equally important to have interventions, right?
Well, watch this:
with access to mobility information in the country,
we know how many people move between the different regions.
We represent this by a network.
We represent the mobility corridors by links:
green links represent low mobility,
so few people traveling between the regions,
and red links represent high mobility, so many people.
If we input this information into an epidemiological model,
we can predict how many cases of malaria
can be transmitted to the rest of the country.
By adding mobility, we can change the importance of the regions
by understanding how much they spread around them.
Let's see how the picture changes for these two regions on the map
when we add the information about mobility.
As you can see, they have about the same malaria incidence
and they are close geographically.
On each map, we see the blue area,
which is the place where malaria has been cured,
and the rest of the map is colored to show the impact it has on other regions
by reducing the risk of exposure to malaria.
Clearly, there is one region that has a much broader impact
because of its central position on the network.
Don't take me wrong:
I'm not saying that other places should not get an intervenion,
but this place needs to receive special attention
given its particular position.
What a change for the malaria control center!
Where before we relied on scarce information of poor quality,
today we can understand how a disease spreads across an entire country
and localize its root causes thanks to the crucial information
of human mobility.
This mobility map has been built using telecom data.
Every time we make a phone call or send a text message,
operators store some meta-data about our activity:
the phone number we have contacted, the place we are located,
the date, the time, but not the content of the conversations.
It's as if we all had a little sensor in our pockets,
collecting some pieces of information about our daily activities
and these sensors are distributed across entire populations:
half of the population in the developing world owns a mobile phone.
What happens when we aggregate the information
of these millions of sensors together?
We can build mobility maps across entire countries.
We can build revenue maps and localize where the poorest populations are living.
We can estimate population densities in cities
and track the evolution of slums.
We can map population migrations and predict food crises.
We can create relevant information
for development workers and policy makers.
Right now, many of you might wonder
if I'm not asking local populations to trade their privacy for a better life.
These mobile data logs represent very personal and private data.
These are the phone numbers we have been calling,
the places we have been, how much we spent.
But none of the solutions presented here require any personal information.
They all have in common to exhibit common dynamics
of entire populations, not of individuals.
By law, mobile operators are required
to collect this personal information for billing purposes
and to protect the privacy of their users.
Once these pieces of information are aggregated over entire geographical areas,
they only represent average behaviors of entire populations,
protecting the privacy of individuals in the crowd.
At that time, everyone agrees, both technical experts and legislators,
that this information is safe to be shared,
as are census data, which are freely available in many countries.
Over the last 10 years, dozens of research groups have studied
how to improve emergency response or development work
with the help of telecom data.
Studies have been conducted in Haiti, Rwanda, Nepal, Mexico,
Pakistan or Ivory Coast, to only name a few.
But the next steps, going from research to operational tools, are still missing.
Large institutions, telecom operators, local regulators, donors,
all agree on the need.
But a common dynamic to move forward and unified regulations are still missing.
It is very difficult to make the first successful case.
We need everyone on board to make this happen.
We need to convince regulators that a new legal framework is necessary
to allow such analyses and protect the privacy of users.
Operators need to understand
that it is in their own interest to share their data for a better society,
because a better society will guarantee them
more and better customers tomorrow.
We need strong bonds with local development workers
to develop tools that are fully adapted to their needs.
We need large institutions to oversee the system.
Finally, we need donor funds to finance the costs
of developing and implementing these solutions.
Yet, things are changing step by step
and stakeholders align in the effort.
Three years ago, my colleagues and I realized
we were in a unique position,
close to operators in developing countries and with strong analytical expertise.
As we wanted to contribute to the system,
we launched a project called "Data for Good",
with the objective to create operational tools for development workers
with the help of telecom data.
And the results are already visible.
We signed partnerships with local authorities,
such as the city of Kampala, or Zambia's malaria control center.
We have signed a global agreement
with one of Africa's largest telecom operators
to share their data for Data for Good actions.
We are partnering with UN agencies
on topics such as food security, or national statistics.
We are getting financial support
from the Belgian ministry of development and large donor funds
to develop these tools.
And last but not least,
we are not the only ones to believe that this is more than a utopia.
Large telecom operators, UN agencies, researchers across the world -
our initiative is not unique,
and many others are also sharing how convinced they are
about the concept of telecom data for social good.
In the 90s, the Internet.
Early 2000s, mobile phones.
Over the last few years,
a new technological revolution has emerged,
where analytics are put at the service of humans
for an ever more optimized and efficient society.
Together, these technological revolutions give us today a unique opportunity.
It is that tomorrow, with the help of telecom data,
development workers fighting poverty or combating diseases
will become as efficient as our businesses have become
when it comes to selling us new products.
And that day, achieving the global goals
will be within the reach of our hands.
Thank you.
(Applause)