VESPa 2.0: Data-driven behavior models for visual analytics of movement sequences

Ubiquitous availability of human mobility data has opened up new possibilities to address a multitude of application domains. However, so far, the visual analysis of this data has been hindered by the limited ability to explore and query complex movement sequences and to create models that allow meaningful aggregation. To address this problem, this paper presents a novel analytical approach that allows to automatically create and semiautomatically advance models for large-scale movement behavior. Using a bottom-up procedure, the analyst can first explore movement sequences with assistance of automated sorting and grouping methods. Secondly, findings can be semi automatically extracted and represented using a data-driven modeling language. In an incremental process, the analyst can then further advance the model, use it to query more results, and find regular as well as outlying patterns. We demonstrate the applicability of our approach based on a real-world case study and a user study.