sampling urban mobility through on-line repositories of gps tracks
We analyze urban mobility by relying on the short-term mobility traces gathered from a publicly available web-based repository of GPS tracks - the Nokia Sports Tracker service. All mobility traces are obtained from a set of kml files. We show how the data collected voluntarily by individuals, equipped with GPS-enabled mobile phones, can be used to infer accurate, large-scale footprint of urban mobility. This method, unlike others - for example, personal interviewing, is more scalable and less time consuming. It exploits the fact that the on-line masses are willing to share their experience with others. We present a set of heuristics used to filter out bogus tracks from the dataset. We show that the mobility patterns, inferred from the remaining, credible, short-term mobility traces have macroscopic characteristics similar to the characteristics of mobility patterns retrieved from the long-term mobility traces, gathered in different urban environments. The results of our analysis lead to a proposal for creating city-specific mobility profiles. We discuss how such profiles could help improve location privacy and help develop new context-aware applications and services for mobile users.
privacy context-awareness modeling mobility modeling