Temporal decomposition and semantic enrichment of mobility flows

Mobility data has increasingly grown in volume over the past decade as localisation technologies for capturing mo-bility flows have become ubiquitous. Novel analytical ap-proaches 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 appli-cations. 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 ques-tions 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