Mining, indexing, and querying historical spatiotemporal data
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining Frequent Spatio-Temporal Sequential Patterns
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Visual analytics tools for analysis of movement data
ACM SIGKDD Explorations Newsletter - Special issue on visual analytics
TrajPattern: mining sequential patterns from imprecise trajectories of mobile objects
EDBT'06 Proceedings of the 10th international conference on Advances in Database Technology
On discovering moving clusters in spatio-temporal data
SSTD'05 Proceedings of the 9th international conference on Advances in Spatial and Temporal Databases
A conceptual framework and taxonomy of techniques for analyzing movement
Journal of Visual Languages and Computing
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The increasing pervasiveness of location-acquisition technologies (GPS, GSM networks, etc.) is leading to the collection of large spatio-temporal datasets and to the opportunity of discovering usable knowledge about movement behaviour, which fosters novel applications and services. In this paper, we apply a trajectory pattern extraction framework, called T-Pattern, to a real-world dataset, describing mobility of citizens within an urban area. The mining tool adopted is able to provide useful insights both in terms of common movements followed in the city, and, as by-product of the mining engine, in terms of spatial distribution and temporal evolution of the traffic density. Both kinds of results are provided in the paper in a visual form, aimed at helping the analyst to better interpret them and link them to his/her existing background knowledge of the domain.