Interpreting pedestrian behaviour by visualising and clustering movement data

Authors:
Gavin McArdle;Urška Demšar;Stefan van der Spek;Seán McLoone
Affiliations:
National Centre for Geocomputation, National University of Ireland Maynooth, Maynooth, Co. Kildare, Ireland;Centre for GeoInformatics, School for Geography and Geosciences, University of St Andrews, Fife, Scotland, UK;Department of Urbanism, Faculty of Architecture, Delft University of Technology, Delft, the Netherlands;Department of Electronic Engineering, National University of Ireland Maynooth, Maynooth, Co. Kildare, Ireland
Venue:
W2GIS'13 Proceedings of the 12th international conference on Web and Wireless Geographical Information Systems
Year:
2013

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Abstract

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.