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dc.contributor.authorSila-Nowicka, Katarzyna
dc.contributor.authorVandrol, Jan
dc.contributor.authorOshan, Taylor Matthew
dc.contributor.authorLong, Jed
dc.contributor.authorDemsar, Urska
dc.contributor.authorFotheringham, Stewart
dc.identifier.citationSila-Nowicka , K , Vandrol , J , Oshan , T M , Long , J , Demsar , U & Fotheringham , S 2016 , ' Analysis of human mobility patterns from GPS trajectories and contextual information ' , International Journal of Geographical Information Science , vol. 30 , no. 5 , pp. 881-906 .
dc.identifier.otherORCID: /0000-0001-7791-2807/work/48516865
dc.descriptionThis work was supported by the EU FP7 Marie Curie ITN GEOCROWD grant (FP7- PEOPLE-2010-ITN-264994).en
dc.description.abstractHuman mobility is important for understanding the evolution of size and structure of urban areas, the spatial distribution of facilities, and the provision of transportation services. Until recently, exploring human mobility in detail was challenging because data collection methods consisted of cumbersome manual travel surveys, space-time diaries or interviews. The development of location-aware sensors has significantly altered the possibilities for acquiring detailed data on human movements. While this has spurred many methodological developments in identifying human movement patterns, many of these methods operate solely from the analytical perspective and ignore the environmental context within which the movement takes place. In this paper we attempt to widen this view and present an integrated approach to the analysis of human mobility using a combination of volunteered GPS trajectories and contextual spatial information. We propose a new framework for the identification of dynamic (travel modes) and static (significant places) behaviour using trajectory segmentation, data mining and spatio-temporal analysis. We are interested in examining if and how travel modes depend on the residential location, age or gender of the tracked individuals. Further, we explore theorised “third places”, which are spaces beyond main locations (home/work) where individuals spend time to socialise. Can these places be identified from GPS traces? We evaluate our framework using a collection of trajectories from 205 volunteers linked to contextual spatial information on the types of places visited and the transport routes they use. The result of this study is a contextually enriched data set that supports new possibilities for modelling human movement behaviour.
dc.relation.ispartofInternational Journal of Geographical Information Scienceen
dc.subjectMovement analysisen
dc.subjectTrajectory segmentationen
dc.subjectTravel mode classificationen
dc.subjectSignificant placesen
dc.subjectGA Mathematical geography. Cartographyen
dc.subjectSDG 9 - Industry, Innovation, and Infrastructureen
dc.subjectSDG 11 - Sustainable Cities and Communitiesen
dc.titleAnalysis of human mobility patterns from GPS trajectories and contextual informationen
dc.typeJournal articleen
dc.contributor.sponsorEuropean Commissionen
dc.contributor.institutionUniversity of St Andrews. Geography & Sustainable Developmenten
dc.contributor.institutionUniversity of St Andrews. Bell-Edwards Geographic Data Instituteen
dc.description.statusPeer revieweden

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