On Mon, Mar 16, 2009 at 9:50 AM, Carol Upadhya <carol.upad...@gmail.com> wrote: > This is interesting, especially in view of the ongoing project in Bangalore > to mine cell phone data to help the traffic police predict traffic jams: > > http://www.mapunity.in/ > > I don't know whether this initiative has been discussed on silklist or not. > > Carol > > On Mon, Mar 16, 2009 at 9:40 AM, Udhay Shankar N <ud...@pobox.com> wrote: > >> This is rather cool, though there are obvious implications for privacy >> here. >> >> Two things come to mind: >> >> * The "mapping a city using the sound of footsteps" bit from >> _Cryptonomicon_ >> * This is "traffic analysis" in more than one sense. :-)
More err imagery that comes to mind : http://en.wikipedia.org/wiki/Swarm_intelligence You're now a worker-ant, effusing pheromone all over the city, for the smart algorithms to sniff out and optimize. Welcome to the ant colony you've always lived in. -Jai http://iyermatter.wordpress.com >> >> Udhay >> >> http://www.technologyreview.com/communications/22286/?a=f >> >> Friday, March 13, 2009 >> >> Mapping a City's Rhythm >> A phone application highlights hot spots and will soon show where >> different urban "tribes" gather. >> >> By Kate Greene >> >> Over the course of any day, people congregate around different parts of >> a city. In the morning hours, workers commute downtown, while at >> lunchtime and in the evening, people disperse to eateries and bars. >> >> While this sort of behavior is common knowledge, it hasn't been visible >> to the average person. Sense Networks, a startup based in New York, is >> now trying to bring this side of a city to life. Using cell-phone and >> taxi GPS data, the startup's software produces a heat map that shows >> activity at hot spots across a city. Currently, the service, called >> Citysense, only works in San Francisco, but it will launch in New York >> in the next few months. >> >> On Wednesday, at the O'Reilly Emerging Technologies conference in San >> Jose, CA, Tony Jebara, chief scientist for Sense Networks and a >> professor at Columbia University, detailed plans of a forthcoming update >> to Citysense that shows not only where people are gathering in real >> time, but where people with similar behavioral patterns--students, >> tourists, or businesspeople, for instance--are congregating. A user >> downloads Citysense to her phone to view the map and can choose whether >> or not to allow the application to track her own location. >> >> The idea, says Jebara, is that a person could travel to a new city, >> launch Citysense on her phone, and instantly get a feel for which >> neighborhoods she might want to spend the evening visiting. This >> information could also help her filter restaurant or bar suggestions >> from online recommendation services like Yelp. Equally important, from >> the company's business perspective, advertisers would have a better idea >> of where and when to advertise to certain groups of people. >> >> Citysense, which has access to four million GPS sensors, currently >> offers simple statistics about a city, says Jebara. It shows, for >> instance, whether the overall activity in the city is above or below >> normal (Sense Networks' GPS data indicates that activity in San >> Francisco is down 34 percent since October) or whether a particular part >> of town has more or less activity than usual. But the next version of >> the software, due out in a couple of months, will help users dig more >> deeply into this data. It will reveal the movement of people with >> certain behavior patterns. >> >> "It's like Facebook, but without the self-reporting," Jebara says, >> meaning that a user doesn't need to actively update her profile. "We >> want an honest social network where you're connected to someone because >> you colocate." >> >> In other words, if you live in San Francisco and go to Starbucks at 4 >> P.M. a couple of times a week, you probably have some similarities with >> someone in New York who also visits Starbucks at around the same time. >> Knowing where a person in New York goes to dinner on a Friday night >> could help a visitor to the city make a better restaurant choice, Jebara >> says. >> >> As smart phones with GPS sensors become more popular, companies and >> researchers have clamored to make sense of all the data that this can >> reveal. Sense Networks is a part of a research trend known as reality >> mining, pioneered by Alex Pentland of MIT, who is a cofounder of Sense >> Networks. Another example of reality mining is a research project at >> Intel that uses cell phones to determine whether a person is the hub of >> a social network or at the periphery, based on her tone of voice and the >> amount of time she talks. >> >> Jebara is aware that the idea of tracking people's movements makes some >> people uncomfortable, but he insists that the data used is stripped of >> all identifying information. In addition, anyone who uses Citysense must >> first agree to let the system log her position. A user can also, at any >> time, delete her data from the Sense Networks database, Jebara says. >> >> Part of Sense Networks' business plan involves providing GPS data about >> city activity to advertisers, Jebara says. But again, this does not mean >> revealing an individual's whereabouts--just where certain types of >> people congregate and when. For instance, Sense Networks' data-analysis >> algorithms may show that a particular demographic heads to bars downtown >> between 6 and 9 P.M. on weekdays. Advertisers could then tailor ads on a >> billboard screen to that specific crowd. >> >> So far, Jebara says, Sense Networks has categorized 20 types, or >> "tribes," of people in cities, including "young and edgy," "business >> traveler," "weekend mole," and "homebody." These tribes are determined >> using three types of data: a person's "flow," or movements around a >> city; publicly available data concerning the company addresses in a >> city; and demographic data collected by the U.S. Census Bureau. If a >> person spends the evening in a certain neighborhood, it's more likely >> that she lives in that neighborhood and shares some of its demographic >> traits. >> >> By analyzing these types of data, engineers at Sense Networks can >> determine the probability that a user will visit a certain type of >> location, like a coffee shop, at any time. Within a couple of weeks, >> says Jebara, the matrix provides a reliable probability of the type of >> place--not the exact place or location--that a person will be at any >> given hour in a week. The probability is constantly updated, but in >> general, says Jebara, most people's behavior does not vary dramatically >> from day to day. >> >> Sense Networks is exploring what GPS data can reveal about behavior, >> says Eric Paulos, a professor of computer science at Carnegie Mellon. >> "It's interesting to see things like this, [something] that was just >> research a few years ago, coming to the market," he adds. Paulos says it >> will be important to make sure that people are aware of what data is >> being used and how, but he predicts that more and more companies are >> going to find ways to make use of the digital bread crumbs we leave >> behind. "It's going to happen," he says. >> >> >> -- >> ((Udhay Shankar N)) ((udhay @ pobox.com)) ((www.digeratus.com)) >> >> > > > -- > Dr. Carol Upadhya > Fellow, School of Social Sciences > National Institute of Advanced Studies > Indian Institute of Science Campus > Bangalore 560012 > India > > office: +91 80 2218 5000/ 5141 (ext) > cell: +91(0) 97408 50141 > > ca...@nias.iisc.ernet.in > carol.upad...@gmail.com >
