The Journal of Machine Learning Research
Analysis of a Location-Based Social Network
CSE '09 Proceedings of the 2009 International Conference on Computational Science and Engineering - Volume 04
Discovering routines from large-scale human locations using probabilistic topic models
ACM Transactions on Intelligent Systems and Technology (TIST)
Extracting urban patterns from location-based social networks
Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Location-Based Social Networks
Discovering regions of different functions in a city using human mobility and POIs
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Towards understanding residential privacy by analyzing users' activities in foursquare
Proceedings of the 2012 ACM Workshop on Building analysis datasets and gathering experience returns for security
A comparison of Foursquare and Instagram to the study of city dynamics and urban social behavior
Proceedings of the 2nd ACM SIGKDD International Workshop on Urban Computing
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The location based social networking services (LBSNSs) are becoming very popular today. In LBSNSs, such as Foursquare, users can explore their places of interests around their current locations, check in at these places to share their locations with their friends, etc. These check-ins contain rich information and imply human mobility patterns; thus, they can greatly facilitate mining and analysis of local geographic topics driven by users' trajectories. The local geographic topics indicate the potential and intrinsic relations among the locations in accordance with users' trajectories. These relations are useful for users in both location and friend recommendations. In this paper, we focus on exploring the local geographic topics through check-ins in Pittsburgh area in Foursquare. We use the Latent Dirichlet Allocation (LDA) model to discover the local geographic topics from the checkins. We also compare the local geographic topics on weekdays with those at weekends. Our results show that LDA works well in finding the related places of interests.