Foursquare and Gnip have entered into a partnership to fork over your check-in data to developers and big brands. Gnip will get access to Foursquare’s full firehose — every check-in, everywhere, from everyone, and in real time.
Of course, the companies tell us all the data will be totally anonymized. And we have no reason not to believe that’s the case. After all, from a business point of view, it’s very expensive and not too profitable to spy on inidividuals; it may be vastly profitable to be able to predict crowd behavior, define mass trends, and measure what kinds of location-based offers have historically performed well.
“Location is one of the most interesting ways to view data and no one understands the power of location like Foursquare,” writes Gnip product manager Steve Perella on the company blog.
“With more than 35 million registered users, nearly 4 billion total check-ins, and over 75 million API calls a day, Foursquare is the location layer for the Internet, helping to connect people with places around the world.”
Real-time firehose access will give Gnip data about every check-in that happens on Foursquare. Gnip, which bundles and resells the data (mostly to big brands), will get the check-in information only (time, date, location); no user data (username, name) will be provided.
So, for example, the restaurant chain Red Lobster could use Gnip’s Foursquare firehose to find out how many check-ins happen at its locations in San Francisco during Lobsterfest, but they couldn’t see who exactly was checking in. (This is so sadly hypothetical, if only because there are no Red Lobster locations in San Francisco. We oughta have a petition. Those biscuits, man.)
Foursquare data scientist Blake Shaw said in a statement, “We are capturing this amazing signal about what millions of people are doing in the real world at every moment of the day in cities all around the globe. We have seen that when we aggregate checkin patterns across many individuals, we can measure features of cities at a higher resolution than was ever possible before. I think this data can act almost like a microscope for cities.”
Image credit: jswaby/Flickr
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