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Can we use mobile data to understand people movement while fully protecting privacy? Yes we can.

Twitter: @nokianetworks


Mobile networks touch most people on the planet, every day. They allow people to stay in touch and benefit from advanced data applications and communication services. But I look at that interaction in a different way. What if we could use mobile networks to understand the movements of people, while respecting their anonymity and privacy?

What could we do with such insight that potentially cover 100% of the world's population? We could improve transportation routes and schedules to use limited transport resources more efficiently and get people more quickly to where they need to go. Or maybe we could help urban planners to organize metropolitan and public services better. Feasibly we would be able to use the movement patterns of people to show us the best places for medical centers or other service locations. Another possibility is to create a living map of people in a disaster zone to support relief efforts.

The potential is huge and goes far beyond what is possible with conventional, manually-gathered statistics from surveys and data collection at fixed points. These are costly, inaccurate and give only snapshots, with little insight into real world mobility.

To make all this possible we need to harness three technological advances of recent years.


Telecom big data

The first breakthrough is big data computing technology to collect and process colossal amounts of mobile data. Each time a subscriber logs onto a network, they leave transactional signaling at different network interfaces. Meaningful information is extracted by interpreting these transactions. With tens or hundreds of millions of subscribers active in a network, the aggregated amount of live raw data is indeed huge.

New technologies like in-memory parallel processing combined with scalable databases and cloud storage allow the acquisition and processing of massive amounts of data as they occur in a live network, and can put that information to use within a second.

We can also ensure that, while extracting the crowd information through aggregated big data computations, no individually identifiable records will ever be revealed.

Telecom data science


The raw data we extract from network interfaces rarely contains any positioning data and never contains GPS. Therefore, we need to apply mobile location intelligence.

Computed raw positioning data can vary in its precision depending on many factors in the field, so mobile network-based geo-positioning reference data cannot be used directly in the same way as GPS-based positioning data. We utilise the unique characteristics of mobile data to improve the precision of derived population location information. The overall scientific technique we have applied to create models, metrics, and methods that facilitate the usage of mobile data, is what we refer as “Telecom data science”.

Another important aspect of data science is information enrichment. For example, by applying transit labeling with road network information to a device population, we could discover the road segments most commonly used by a given group.

Cross-domain collaboration in an “out-of-the-box” solution

The third important breakthrough is the public acceptance of the idea of transforming our world through access to information. With data, companies can improve decision-making and optimize business, services and products.

Yet, more can be done, including using mobile technology outside telecommunications to deliver business growth. Clearly, this requires a comprehensive business understanding of the value proposition and a solid, long term commitment to fundamental physical research, software and product development.

Combining these capabilities has led to the creation of the Nokia Crowd Analytics solution, which brings together analysts and scientists from Nokia Bell Labs, location specialists and business consultants, partners and domain analytics experts to work with enterprises and public bodies to transform their business activities and community engagement.

Importantly, the solution is “out-of-the-box” to enable quicker use of the data and lower the entry barrier. This all translates into a new world of data anaytics that uses mobile networks to understand population movement, and importantly, makes it available to more organizations with truly innovative ideas for improving our world.

Let’s talk data! Contact us here at Crowd Analytics

Share your thoughts on this topic by replying below – or join the Twitter discussion with @nokianetworks using #analytics #dataservices #bigdata

Zhi-Chun Honkasalo

About Zhi-Chun Honkasalo

Zhi-Chun is a principal innovator for data services within Nokia’s Innovation Steering organization. With a passion for data intelligence and over 40 mobile technology patents to her name, Zhi-Chun is a dedicated pioneer and respected team-leader in the growing field of advanced mobile data analytics services.

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