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Network Data with a View on Society

by: Staff, with Alexandre Gerber, May 19, 2010

In the Museum of Modern Art's New York Talk Exchange visualization project, AT&T call data from New York City was used to help answer the following questions: How does the city of New York connect to other cities around the world? With which cities does New York have the strongest ties? How do these relationships shift with time? And, how does the rest of the world reach into the neighborhoods of New York?

This video shows a time series of snapshots taken at 3-hour intervals on January 11, 2008. The video was exhibited at the Museum of Modern Art (MoMA) as part of the Design and the Elastic Mind exhibit (Feb. 24, 2008 through May 12, 2008).

The glowing arcs represent the combined flow of IP data (such as email, peer-to-peer, and web browsing) between New York and other cities. The height of an arc is proportional to the amount of IP traffic. The aggregated data consisted of time of day, call origin, destination, and size (in megabytes). No personal information was captured.

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Credit: New York Talk Exchange video created by the SENSEable City Laboratory at the Massachusetts Institute of Technology (MIT) in collaboration with AT&T.
 
Because cell phones have become so ubiquitous, mining the data they generate can really revolutionize the study of human behaviorRamón Cáceres, "Mobile Data: A Gold Mine for Telcos", MIT Technology Review, May 27, 2010

Large public events upend the everyday patterns of people within cities. But until today, there was little information about the social dynamics of large events. Traditional methods for obtaining such data—head counts, surveys, and aerial observations—were difficult and expensive.

Variations in mobile call activity can show the movement of people as they travel to cities or tourist areas and then return to their home states and countries. This image depicts mobile phone activity during a public event, superimposed on a city grid. Distribution of home states of people at event are depicted on left.

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Credit: SENSEable City Laboratory at the Massachusetts Institute of Technology

 

 

Packets move to and from a city of interest (here, Washington, DC) depending on whether call activity increased or decreased relative to the previous hour. The transfer of identical packets creates denser or thinner flows.

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Credit: SENSEable City Laboratory at the Massachusetts Institute of Technology

 

 

Cell phone data from summer 2008 was used to analyze the economic impact of visitors at the NYC Waterfalls project (by Danish/Icelandic artist, Olafur Eliasson), which was intended to raise the awareness of New York’s waterfront. The presence and movement of visitors was inferred from both cellular network activity (over AT&T networks) and photo activity via Flickr. The project provided quantifiable data about the increased activity generated by the waterfalls (39.1% increase over other nearby points of interest, based on historical cellphone data).  All data was aggregated and included no personal information.

For one week, phone registration information established the distribution of tourists vs resident New Yorkers. Images on right below show non-residents (with phone registrations outside the city) visited Lower Manhattan much more than they visited Brooklyn. The picture is different for residents, who were more distributed throughout the city and had a stronger presence in Brooklyn during weekends.

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Spatial distribution of resident New Yorkers, US visitors, and foreign visitors in the proximity of the Waterfalls. Red areas indicate high density of phone calls.
Credit: SENSEable City Laboratory at the Massachusetts Institute of Technology

 

The collaboration between AT&T Labs Research and MIT SENSEable City Lab brings a whole new dimension to data mining. Historically, AT&T mined data to better engineer its network, and universities and cities studied our society with on-the-ground measurements or census data. By bringing together scientists from various disciplines, we can now leverage digital footprints to better understand our society and better plan cities.Alexandre Gerber