Classifying Pedestrian Movement Behaviour from GPS Trajectories using Visualisation and Clustering

McArdle, Gavin and Demsar, Urska and van der Spek, Stefan and McLoone, Sean F. (2014) Classifying Pedestrian Movement Behaviour from GPS Trajectories using Visualisation and Clustering. Annals of GIS, 20 (2). pp. 85-98.

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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. %based on the mathematical description of motion in terms of speed and acceleration. These techniques were used to analyse pedestrian movement in a city environment which revealed the effectiveness of the tool for identifying spatiotemporal patterns.

Item Type: Article
Keywords: Geovisual Analysis; Clustering; Space-time Cube; Movement Data Analysis;
Academic Unit: Faculty of Science and Engineering > Research Institutes > National Centre for Geocomputation, NCG
Item ID: 5514
Depositing User: Dr. Gavin McArdle
Date Deposited: 28 Oct 2014 10:36
Journal or Publication Title: Annals of GIS
Publisher: Taylor & Francis
Refereed: Yes
Funders: Science Foundation Ireland

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