Collaborative location and activity recommendations with GPS history data
Proceedings of the 19th international conference on World wide web
Friendship and mobility: user movement in location-based social networks
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Constructing popular routes from uncertain trajectories
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Ads and the city: considering geographic distance goes a long way
Proceedings of the sixth ACM conference on Recommender systems
The hidden image of the city: sensing community well-being from urban mobility
Pervasive'12 Proceedings of the 10th international conference on Pervasive Computing
Location-based and preference-aware recommendation using sparse geo-social networking data
Proceedings of the 20th International Conference on Advances in Geographic Information Systems
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Location-based check-in services enable individuals to share their activity-related choices providing a new source of human activity data for researchers. In this paper urban human mobility and activity patterns are analyzed using location-based data collected from social media applications (e.g. Foursquare and Twitter). We first characterize aggregate activity patterns by finding the distributions of different activity categories over a city geography and thus determine the purpose-specific activity distribution maps. We then characterize individual activity patterns by finding the timing distribution of visiting different places depending on activity category. We also explore the frequency of visiting a place with respect to the rank of the place in individual's visitation records and show interesting match with the results from other studies based on mobile phone data.