Classifying pedestrian movement behaviour from GPS trajectories using visualization and clustering

Gavin McArdle, Urska Demsar, Stefan van der Spek, Sean McLoone

Research output: Contribution to journalArticlepeer-review

24 Citations (Scopus)
2 Downloads (Pure)


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.
Original languageEnglish
Pages (from-to)85-98
JournalAnnals of GIS
Issue number2
Early online date16 Apr 2014
Publication statusPublished - 2014


  • Geovisual Analysis
  • Clustering
  • Space-time Cube
  • Movement Data Analysis


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