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PublicationConstructing semantic interpretation of routine and anomalous mobility behaviors from big dataAnnually organized VAST Challenges provide a unique opportunity to analyze complex data with available ground truth. In 2014, one of the tasks was to interpret routine and anomalous patterns of human mobility based on big data: trajectories of cars and credit card transactions. We describe a scalable visual analytics approach to solving this problem. Repeatedly visited personal and public places were extracted from trajectories by finding spatial clusters of stop points. Temporal patterns of peoples presence in the places resulted from spatio-temporal aggregation of the data by the places and hourly intervals within the weekly cycle. Based on these patterns, we identified the meanings or purposes of the places: home, work, breakfast, lunch and dinner, etc. Meanings of some places could be refined using the credit card transaction data. By representing the place meanings as points on a 2D plane, we built an abstract semantic space and transformed the original trajectories to trajectories in the semantic space, i.e., performed semantic abstraction of the data. Spatio-temporal aggregation of the transformed trajectories into flows between the semantic places and subsequent clustering of time intervals by the similarity of the flow situations allowed us to reveal and analyze the routine movement behaviors. To detect anomalies, we (a) investigated the visits to the places with unknown meanings, and (b) looked for unusual presence times or visit durations at different semantic places. The analysis is scalable since all tools and methods can be applied to much larger data. Moreover, the semantic data abstraction can serve as a tool for protecting the personal privacy.
PublicationUsing Bluetooth to track mobility patterns: Depicting its potential based on various case studiesDuring the past years the interest in the exploitation of mobility information has increased significantly. A growing number of companies and research institutions are interested in the analysis of mobility data with demand of a high level of spatial detail. Means of tracking persons in our environment can nowadays be fulfilled by utilizing several technologies, for example the Bluetooth technology, offering means to obtain movement data. This paper gives an overview of four case studies in the field of Bluetooth tracking which were conducted in order to provide helpful insights on movement aspects for decision makers in their specific microcosm. Aim is to analyse spatio-temporal validity of Bluetooth tracking, and in doing so, to describe the potential of Bluetooth in pedestrian mobility mining.