Comparing Close Destination and Route-Based Similarity Metrics for the Analysis of Map User Trajectories
Movement is a ubiquitous phenomenon in the physical and virtual world. Analysing movement can reveal interesting trends and patterns. In the Human-Computer Interaction (HCI) domain, eye and mouse movements reveal the interests and intentions of users. By identifying common HCI patterns in the spatial domain, profiles containing the spatial interests of users can be generated. These profiles can be used to address the spatial information overload problem through map personalisation. This paper presents the analysis and findings of a case study of users performing spatial tasks on a campus map. Mouse movement was recorded and analysed as users performed specific spatial tasks. The tasks correspond to the mouse trajectories produced while interacting with the Web map. When multiple users conduct similar and dissimilar spatial tasks, it becomes interesting to observe the behaviour patterns of these users. Clustering and geovisual analysis help to understand large movement datasets such as mouse movements. The knowledge gained through this analysis can be used to strengthen map personalisation techniques. In this work, we apply OPTICS clustering algorithm to a set of map user trajectories. We focus on two similarity measures and compare the results obtained with both when applied to particular saptial tasks carried out by multiple users. In particular, we show how route-based similarity, an advanced distance measure, performs better for spatial tasks involving scanning of the map area.
KeywordsActivity Recognition Spatial Task Trajectory Data Mouse Movement Route Similarity
Unable to display preview. Download preview PDF.
- 5.Tahir, A., McArdle, G., Bertolotto, M.: Identifying specific spatial tasks through clustering and geovisual analysis. In: 2012 20th International Conference on Geoinformatics (GEOINFORMATICS), pp. 1–6. IEEE (2012)Google Scholar
- 6.Zhu, Y., Zheng, V.W., Yang, Q.: Activity recognition from trajectory data. In: Zheng, Y., Zhou, X. (eds.) Computing with Spatial Trajectories, p. 180. Springer, New York (2011)Google Scholar
- 9.Forlizzi, L., Güting, R., Nardelli, E., Schneider, M.: A data model and data structures for moving objects databases, vol. 29. ACM (2000)Google Scholar
- 11.Speičvcys, L., Jensen, C., Kligys, A.: Computational data modeling for network-constrained moving objects. In: Proceedings of the 11th ACM International Symposium on Advances in Geographic Information Systems, pp. 118–125. ACM (2003)Google Scholar
- 12.Zheng, Y., Zhou, X.: Computing with Spatial Trajectories. Springer-Verlag New York Inc. (2011)Google Scholar
- 13.Dix, A., Finlay, J., Abowd, G.: Human-Computer Interaction. Prentice Hall (2004)Google Scholar
- 15.Tahir, A., McArdle, G., Bertolotto, M.: A geovisual analytics approach for mouse movement analysis. International Journal of Data Mining, Modeling and Management. Special Issue on Spatial Information Mining, Modeling and Management (2012) (in press)Google Scholar