Temporal decomposition and semantic enrichment of mobility flows

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.