Digital Footprinting: Uncovering Tourists with User-Generated Content
IEEE Pervasive Computing
Location Cheating: A Security Challenge to Location-Based Social Network Services
ICDCS '11 Proceedings of the 2011 31st International Conference on Distributed Computing Systems
Friendship and mobility: user movement in location-based social networks
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Human mobility, social ties, and link prediction
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
A Tale of One City: Using Cellular Network Data for Urban Planning
IEEE Pervasive Computing
Estimating Origin-Destination Flows Using Mobile Phone Location Data
IEEE Pervasive Computing
Real-Time Urban Monitoring Using Cell Phones: A Case Study in Rome
IEEE Transactions on Intelligent Transportation Systems
Human mobility modeling at metropolitan scales
Proceedings of the 10th international conference on Mobile systems, applications, and services
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
Exploiting Foursquare and Cellular Data to Infer User Activity in Urban Environments
MDM '13 Proceedings of the 2013 IEEE 14th International Conference on Mobile Data Management - Volume 01
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The vast amount of available spatio-temporal data of human activities and mobility has given raise to the rapidly emerging field of urban computing/informatics. Central to the latter is understanding the dynamics of the activities that take place in an urban area (e.g., a city). This can significantly enhance functionalities such as resource and service allocation within a city. Existing literature has paid a lot of attention on spatial dynamics, with the temporal ones often being neglected and left out. However, this can lead to non-negligible implications. For instance, while two areas can appear to exhibit similar activity when the latter is aggregated in time, they can be significantly different when introducing the temporal dimension. Furthermore, even when considering a specific area X alone, the transitions of the activity that takes place within X are important themselves. Using data from the most prevalent location-based social network (LBSN for short), Foursquare, we analyze the temporal dynamics of activities in New York City and San Francisco. Our results clearly show that considering the temporal dimension provides us with a different and more detailed description of urban dynamics. We envision this study to lead to more careful and detailed consideration of the temporal dynamics when analyzing urban activities.