GeoLife2.0: A Location-Based Social Networking Service
- Yu Zheng ,
- Xing Xie ,
- Wei-Ying Ma
Proceedings of the 10th International Conference on Mobile Data Management (MDM 2009) |
GeoLife2.0 is a GPS-data-driven social networking service where people can share life experiences and connect to each other with their location histories. By mining people’s location history, GeoLife can measure the similarity between users and perform personalized friend recommendation for an individual. Later, we can predict the individual’s interest level in the locations visited by their friends while have not been found by them. The locations with relatively high interesting level can be recommended. Therefore, GeoLife2.0 can expand a user’s social network, provide them with a trustworthy resource matching their interests and help them sponsor geo-related activities like cycling with minimal effort.
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GeoLife GPS Trajectories
August 9, 2012
This is a GPS trajectory dataset collected in (Microsoft Research Asia) GeoLife project by 182 users in a period of over three years (from April 2007 to August 2012). A GPS trajectory of this dataset is represented by a sequence of time-stamped points, each of which contains the information of latitude, longitude and altitude. This dataset contains 17,621 trajectories with a total distance of about 1.2 million kilometers and a total duration of 48,000+ hours. These trajectories were recorded by different GPS loggers and GPS-phones, and have a variety of sampling rates. 91 percent of the trajectories are logged in a dense representation, e.g. every 1~5 seconds or every 5~10 meters per point. This dataset recoded a broad range of users' outdoor movements, including not only life routines like go home and go to work but also some entertainments and sports activities, such as shopping, sightseeing, dining, hiking, and cycling. This trajectory dataset can be used in many research fields, such as mobility pattern mining, user activity recognition, location-based social networks, location privacy, and location recommendation.