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Classifying pedestrian movement behaviour from GPS trajectories using visualization and clustering
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dc.contributor.author | McArdle, Gavin | |
dc.contributor.author | Demsar, Urska | |
dc.contributor.author | van der Spek, Stefan | |
dc.contributor.author | McLoone, Sean | |
dc.date.accessioned | 2015-10-16T14:10:02Z | |
dc.date.available | 2015-10-16T14:10:02Z | |
dc.date.issued | 2014 | |
dc.identifier | 108604458 | |
dc.identifier | d3b72ae7-7592-4a4a-a67e-692fcb75b55d | |
dc.identifier | 84899033025 | |
dc.identifier.citation | McArdle , G , Demsar , U , van der Spek , S & McLoone , S 2014 , ' Classifying pedestrian movement behaviour from GPS trajectories using visualization and clustering ' , Annals of GIS , vol. 20 , no. 2 , pp. 85-98 . https://doi.org/10.1080/19475683.2014.904560 | en |
dc.identifier.issn | 1947-5683 | |
dc.identifier.other | ORCID: /0000-0001-7791-2807/work/48516851 | |
dc.identifier.uri | https://hdl.handle.net/10023/7664 | |
dc.description | Research presented in this paper was funded by a Strategic Research Cluster grant [07/SRC/I1168] by the Science Foundation Ireland under the National Development Plan. Special Issue: Web and wireless GIS | en |
dc.description.abstract | The quantity and quality of spatial data are increasing rapidly. This is particularly evident in the case of movement data. Devices capable of accurately recording the position of moving entities have become ubiquitous and created an abundance of movement data. Valuable knowledge concerning processes occurring in the physical world can be extracted from these large movement data sets. Geovisual analytics offers powerful techniques to achieve this. This article describes a new geovisual analytics tool specifically designed for movement data. The tool features the classic space-time cube augmented with a novel clustering approach to identify common behaviour. These techniques were used to analyse pedestrian movement in a city environment which revealed the effectiveness of the tool for identifying spatiotemporal patterns. | |
dc.format.extent | 7217067 | |
dc.language.iso | eng | |
dc.relation.ispartof | Annals of GIS | en |
dc.subject | Geovisual Analysis | en |
dc.subject | Clustering | en |
dc.subject | Space-time Cube | en |
dc.subject | Movement Data Analysis | en |
dc.subject | GA Mathematical geography. Cartography | en |
dc.subject | QA75 Electronic computers. Computer science | en |
dc.subject.lcc | GA | en |
dc.subject.lcc | QA75 | en |
dc.title | Classifying pedestrian movement behaviour from GPS trajectories using visualization and clustering | en |
dc.type | Journal article | en |
dc.contributor.institution | University of St Andrews. Geography & Sustainable Development | en |
dc.contributor.institution | University of St Andrews. Centre for Geoinformatics | en |
dc.contributor.institution | University of St Andrews. Bell-Edwards Geographic Data Institute | en |
dc.identifier.doi | 10.1080/19475683.2014.904560 | |
dc.description.status | Peer reviewed | en |
dc.date.embargoedUntil | 2015-04-16 |
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