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)
Space-time dynamics of topics in streaming text
Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Location-Based Social Networks
Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Learning location naming from user check-in histories
Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
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
Inferring land use from mobile phone activity
Proceedings of the ACM SIGKDD International Workshop on Urban Computing
When a city tells a story: urban topic analysis
Proceedings of the 20th International Conference on Advances in Geographic Information Systems
| Hi-index | 0.00 |
Mobility data has increasingly grown in volume over the past decade as localisation technologies for capturing mobility flows have become ubiquitous. Novel analytical approaches for understanding and structuring mobility data are now required to support the backend of a new generation of space-time GIS systems. It is increasingly important as GIS is becoming a decision support platform for operations in fleet management, urban data analysis and related applications. This paper applies the machine learning method of probabilistic topic modelling for semantic enrichment of mobility data recorded in terms of trip counts by using geo-referenced social media data. It further explores the questions of causality and correlation, as well as predictability of the obtained semantic decompositions of mobility flows on a real dataset from a bike sharing network.