Brushing techniques have a long history with the first interactive selection tools appearing in the 1990's. Since then, many additional techniques have been developed to address selection accuracy, scalability and flexibility issues. Selection is especially difficult in large datasets where many visual items tangle and create overlapping. This paper investigates a novel brushing technique which not only relies on the actual brushing location but also on the shape of the brushed area. Firstly, the user brushes the region where trajectories of interest are visible. Secondly, the shape of the brushed area is used to select similar items. Thirdly, the user can adjust the degree of similarity to filter out the requested trajectories. This technique encompasses two types of comparison metrics, the piece-wise Pearson correlation and the similarity measurement based on information geometry. We apply it to concrete scenarios with datasets from air traffic control, eye-tracking data and GPS trajectories.

Interactive technique to improve brushing in dense trajectory datasets by taking into account the shape of the brush.