Where will you be exactly 285 days from now at 2PM? Adam Sadilek and John Krumm, researchers at the University of Rochester, seek to answer this question through their work Far Out: Predicting Long-Term Human Mobility. Their model, a nonparametric method that extracts significant and robust patterns in location data through the framework of an eigendecomposition problem, is noted as the first to predict an individual’s future location with high accuracy, even years into the future.
Sadilek & Krumm evaluated a massive dataset, more than 32,000 days worth of GPS data across 703 diverse individuals, by creating a 56-element vector for each day a subject used their GPS device: 24 elements included for representation of median GPS latitude (for each hour of the day), 24 for median GPS longitude, 7 for binary representation of the days of the week, and the final for a binary indicator of a national holiday. By performing their analysis on these ‘eigendays’, Sadilek & Krumm were able to capture long-term correlations in the data, as well as joint correlations between their additional attributes (day of week, holiday) and GPS locations.
The data employed by Sadilek & Krumm is not inimitable; the GPS devices used to track individual location were near replicas of those most people already carry around in their phone. As such, implications for their model are numerous. When focused on an individual, ‘Far Out’ may promote better reminders, search results, and advertisements (e.g. “need a haircut? In 4 days, you will be within 100 meters of a salon that will have a $5 special at that time”). When focused on a societal scale, ‘Far Out’ may allow for the first comprehensive scientific approach to urban planning (traffic patterns, the spread of disease, demand for electricity, etc.), and facilitate previously unseen precision in both public and private investment decisions (where to build a fire station, new pizza shop, etc).
Additional implications may be drawn when long-term human mobility modeling is combined with broader personal information, such as real-time location data or demographic trends. To the former, one may compare recent location information with predicted long-term coordinates to detect unusual individual behavior; to the latter, one could combine long-term location predictions with age, gender, or ethnicity information to predict economic undulations, crime trends, or hyper-local political movements.
See their full methodology & results here!