But when Ranganth 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.
“It’s one thing for us to match one point to another point, but we have a lot more options when we can match a cloud of points to another cloud of points,” Ranganth says. “It was very much an ah-ha moment for everybody.”