Temporal decomposition and semantic \nenrichment of mobility flows

Cathal Coffey

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Human Mobility and Location-Based Analysis Geographic Information Systems Studies Data Management and Algorithms

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.

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References

1969Investigating Causal Relations by Econometric Models and Cross-spectral Methods 2010Investigating Causal Relations by Econometric Models and Cross-Spectral Methods 2012Discovering regions of different functions in a city using human mobility and POIs 2021Exploring Millions of Footprints in Location Sharing Services 2012Inferring land use from mobile phone activity 2009Eigenplaces: Analysing Cities Using the Space–Time Structure of the Mobile Phone Network 2011SHARED BICYCLES IN A CITY: A SIGNAL PROCESSING AND DATA ANALYSIS PERSPECTIVE 2012Inferring land use from mobile phone activity

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2014Inferring Travel Purpose from Crowd-Augmented Human Mobility Data 2014Explorative public transport flow analysis from uncertain social media data 2020Recent Progress in Activity-Based Travel Demand Modeling: Rising Data and Applicability 2016Research on social media feeds – A GIScience perspective 2018e-mission: An Open-Source, Smartphone Platform for Collecting Human Travel Data

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