Interpreting Pedestrian Behaviour by Visualising and Clustering Movement Data

  • Gavin McArdle
  • Urška Demšar
  • Stefan van der Spek
  • Seán McLoone
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7820)


Recent technological advances have increased the quantity of movement data being recorded. While valuable knowledge can be gained by analysing such data, its sheer volume creates challenges. Geovisual analytics, which helps the human cognition process by using tools to reason about data, offers powerful techniques to resolve these challenges. This paper introduces such a geovisual analytics environment for exploring movement trajectories, which provides visualisation interfaces, based on the classic space-time cube. Additionally, a new approach, using the mathematical description of motion within a space-time cube, is used to determine the similarity of trajectories and forms the basis for clustering them. These techniques were used to analyse pedestrian movement. The results reveal interesting and useful spatiotemporal patterns and clusters of pedestrians exhibiting similar behaviour.


Geovisual Analysis Clustering Space-time Cube Movement Data Analysis 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Gavin McArdle
    • 1
  • Urška Demšar
    • 2
  • Stefan van der Spek
    • 3
  • Seán McLoone
    • 4
  1. 1.National Centre for GeocomputationNational University of Ireland MaynoothMaynoothIreland
  2. 2.Centre for GeoInformatics, School for Geography and GeosciencesUniversity of St AndrewsFifeScotland, UK
  3. 3.Department of Urbanism, Faculty of ArchitectureDelft University of TechnologyDelftthe Netherlands
  4. 4.Department of Electronic EngineeringNational University of Ireland MaynoothMaynoothIreland

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