Mobility data has increasingly grown in volume over the past decade as loc-
alisation technologies for capturing mobility
ows have become ubiquitous.
Novel analytical approaches for understanding and structuring mobility data
are now required to support the back end of a new generation of space-time GIS
systems. This data has become increasingly important as GIS is now an essen-
tial decision support platform in many domains that use mobility data, such
as
eet management, accessibility analysis and urban transportation planning.
This thesis applies the machine learning method of probabilistic topic mod-
elling to decompose and semantically enrich mobility
ow data. This process
annotates mobility
ows with semantic meaning by fusing them with geograph-
ically referenced social media data. This thesis also explores the relationship
between causality and correlation, as well as the predictability of semantic
decompositions obtained during a case study using a real mobility dataset
