Classifying pedestrian movement behaviour from GPS trajectories using visualization and clustering
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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.
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 . DOI: 10.1080/19475683.2014.904560
Annals of GIS
© 2014 Taylor & Francis. This work is made available online in accordance with the publisher’s policies. This is the author created, accepted version manuscript following peer review and may differ slightly from the final published version. The final published version of this work is available at https://dx.doi.org/10.1080/19475683.2014.904560
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
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