Using cellphone data to study human behaviors

I was listening to the Technology Podcast from the NPR that relates results of a study from Laszlo Barabasi, a human behavior researcher from Northeastern University. Laszlo negotiated access to full blind data from 50,000 cellphones subscribers to study their travelings and movements throughout a defined period (all cellphone signals transit through nearby cellphone towers, enabling the tracking).

The key finding is the extremely high percentage of predictability in day-to-day patterns. On average, he was able to see a 93% rate of predictability. That means that in 93% of the cases, you could in theory predict where that specific user would be. A lot of us might tend to believe that we’re fairly diversified creatures but when it comes to daily patterns, you’re pretty much the same as the one sitting next to you in the subway.

But what caught my attention was that phrase from Laszlo:

“We were seeing an average of 93 percent predictability across the user base. What does it mean? That means that for the vast majority of the people, you could, in principle, write an algorithm that could predict 93 percent of the time, correctly, their present location.

Now imagine what you could do with that, once your algorithm is build and you don’t have to rely anymore on actual data (hence getting rid of the immediate issues of privacy, data collection and storage and other Big Brothers driftings). The services you can bring to any organization managing large infrastructures, being it roads, trains, subways, local development etc. If somebody could convince carriers to open all anonymous and blind data through an APIs and let the hordes of developers coming up with applications on top of it, it would probably spur a great deal of innovative services.

The full 4 minutes of the interview are there.

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