Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Journal of Machine Learning Research
PLDA+: Parallel latent dirichlet allocation with data placement and pipeline processing
ACM Transactions on Intelligent Systems and Technology (TIST)
Tracking trends: incorporating term volume into temporal topic models
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Space-time dynamics of topics in streaming text
Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Location-Based Social Networks
Extracting urban patterns from location-based social networks
Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Location-Based Social Networks
Temporal decomposition and semantic enrichment of mobility flows
Proceedings of the 6th ACM SIGSPATIAL International Workshop on Location-Based Social Networks
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This paper explores the use of textual and event-based citizen-generated data from services such as Twitter and Foursquare to study urban dynamics. It applies a probabilistic topic model to obtain a decomposition of the stream of digital traces into a set of urban topics related to various activities of the citizens in the course of a week. Due to the combined use of implicit textual and movement data, we obtain semantically rich modalities of the urban dynamics and overcome the drawbacks of several previous attempts. Other important advantages of our method include its flexibility and robustness with respect to the varying quality and volume of the incoming data. We describe an implementation architecture of the system, the main outputs of the analysis, and the derived exploratory visualisations. Finally, we discuss the implications of our methodology for enriching location-based services with real-time context.