In January, Ranganath took on the task of building a prototype for a new Foursquare app. By the spring, even he had to admit that the project was a mess. It caused batteries to drain after just a few hours. It gave bad directions. It sent alerts at the wrong times — tossing users recommendations for a nearby fashion boutique when they were comfortably seated at a bar around the corner.
The problem was the method the prototype was using to identify location — a straightforward combination of GPS, Wi-Fi signals, and cell towers. It couldn’t always find the right signals, and even if it did, it tended to seriously drain the battery as it searched.
But when Ranganath told Shaw about the problems, the data scientist had an idea. Why not take a shortcut? Foursquare already had a massive database of check-ins — location information about the places its users most liked to go. And this data didn’t just include the place where someone had checked in. It showed how strong the GPS signal was at the time, how strong each surrounding Wi-Fi hotspot signal was, what local cell towers were nearby, and so on. Leveraging this data meant that Foursquare could still grab a good current location even if users were underground, near a source of radio interference, or facing some other signal obstacle. Chances are, some prior Foursquare user had seen the world through the same flawed eyes and reported his or her location.
Read the full article on Wired.com.