Temporal decomposition and semantic \nenrichment of mobility flows
Maynooth University ePrints and eTheses Archive (Maynooth University)Published 1 September 2013Open access
Cathal Coffey
Citations33
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Abstract
Mobility data has increasingly grown in volume over the past decade as loc- \nalisation technologies for capturing mobility \nows have become ubiquitous. \nNovel analytical approaches for understanding and structuring mobility data \nare now required to support the back end of a new generation of space-time GIS \nsystems. This data has become increasingly important as GIS is now an essen- \ntial decision support platform in many domains that use mobility data, such \nas \neet management, accessibility analysis and urban transportation planning. \nThis thesis applies the machine learning method of probabilistic topic mod- \nelling to decompose and semantically enrich mobility \now data. This process \nannotates mobility \nows with semantic meaning by fusing them with geograph- \nically referenced social media data. This thesis also explores the relationship \nbetween causality and correlation, as well as the predictability of semantic \ndecompositions obtained during a case study using a real mobility dataset.
Keywords
Social Sciences
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