OPTICS: ordering points to identify the clustering structure
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
A data model and data structures for moving objects databases
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
A foundation for representing and querying moving objects
ACM Transactions on Database Systems (TODS)
Computational data modeling for network-constrained moving objects
GIS '03 Proceedings of the 11th ACM international symposium on Advances in geographic information systems
IEEE Computer Graphics and Applications
Modeling and querying moving objects in networks
The VLDB Journal — The International Journal on Very Large Data Bases
Human-Computer Interaction (3rd Edition)
Human-Computer Interaction (3rd Edition)
Visual analytics tools for analysis of movement data
ACM SIGKDD Explorations Newsletter - Special issue on visual analytics
Indexing the Trajectories of Moving Objects in Symbolic Indoor Space
SSTD '09 Proceedings of the 11th International Symposium on Advances in Spatial and Temporal Databases
Computing with Spatial Trajectories
Computing with Spatial Trajectories
A hybrid approach for spatial web personalization
W2GIS'05 Proceedings of the 5th international conference on Web and Wireless Geographical Information Systems
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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.