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▣ People are Predicable?

posted by Steven Listino on February 20th, 2010 at 6:28 AM

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Here's and interesting article:

BOSTON (Feb. 18) -- Physicist Albert-László Barabási can guess where you will be tomorrow at 3 p.m. And where you'll be Saturday night at 8. In fact, given enough data, he can predict your location at any time, with an average 93 percent accuracy. But don't worry. He's not watching you. In fact, his work shouldn't be cause for alarm so much as existential distress.

In a new paper published in the Feb. 19 issue of Science, the Northeastern University physicist and his colleagues describe how they used data from 50,000 anonymous cell phone users to study human mobility, or where we are and when. Their work reveals that our movements follow a pattern, whether we are homebodies or frequent fliers.


Diagrams represent the movements of two mobile phone users
Chaoming Song
These diagrams represent the movements of two mobile phone users. The one on the left shows that the person moved between 22 different cell towers during a three-month period, and placed 52 percent of his calls from one area; the other subject hit 76 spots, and was much less rooted.
"The surprise was that we couldn't find unpredictable people," Barabási says. "We are all boring."

Barabási, who also explores these ideas in his forthcoming book, "Bursts," is a pioneer in the field of network science, which is effectively the academic version of the Kevin Bacon game -- it describes how seemingly disparate systems or entities are connected.

Naturally, then, his latest paper isn't merely concerned with illuminating the mundane nature of modern life. Studying human mobility on a large scale could improve urban planning and traffic engineering while also enabling scientists to predict the spread of viruses and disease down to the neighborhood level. (The link to this last example is simple, he explains. "How we move affects how diseases spread.") Though scientists have tried to apply similar ideas in the past, Barabási and his team say their techniques are far more accurate.

But let's get back to the tedium. Barabási first used himself as a test subject: He wore a GPS-enabled watch that repeatedly recorded his position from July 2007 to August 2008. As a popular academic, he attended conferences and meetings all over the world, yet most of the time he merely hopped between his home and his office. Even those long trips fell into a groove. "I was terribly boring," he says.

The real surprises came when Barabási and his group began applying data-mining algorithms and advanced mathematics to a much larger set of people. A European mobile phone carrier -- they won't disclose which one -- granted the scientists access to portions of the anonymous records of 50,000 mobile phone users, each of whom made, on average, at least one call every two hours. Chaoming Song, a co-author of the paper, says that because the data are made anonymous, the people the study tracked are effectively like particles in a gas that move and interact.

The carrier filed the location of the closest mobile tower whenever one of the individuals used the phone. From this, Barabási and his group could extract roughly -- within a square mile or so -- where a given phone and, by extension, its associated user were at a given time.

They found that most people stay close to home and, more intriguingly, that even the frequent travelers were no less predictable than the homebodies. Furthermore, they discovered that this phenomenon didn't merely stem from the workweek -- the fact that so many of us spend Monday to Friday in the same office. Weekend movements were no more random.

In effect, we are predictable even when we don't have to be. A summary of the findings puts it this way: "Spontaneous individuals are largely absent from the population."

This doesn't bother Barabási, who says he hasn't tried to spice up his own life with more peregrination. "Some people may worry," he says. "Not me. If somebody wants me, they know where to find me."

last edited on February 20th, 2010 at 6:29 AM


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