Visual Analysis of Multivariate Movement Data using Interactive Difference Views

Movement data consisting of a large number of spatio-temporal agent tr ajectories is challenging to visualize, especially when all trajectories are attributed with multiple variates. In this pape r, we demonstrate the visual exploration of such movement data through the concept of interactive dif ference views. By reconfiguring the difference views in a fast and flexible way, we enable temporal trend discovery . We are able to analyze large amounts of such movement data through the use of a frequency-based visualization based on kernel density estimates (KDE), where it is also possible to quantify differences in terms of the units of the visua lized data. Using the proposed techniques, we show how the user can produce quantifiable movement differ ences and compare different categorical attributes (such as weekdays, ship-type, or the general wind direction) , or a range of a quantitative attribute (such as how two hours’ traffic compares to the average). We present resu lts from the exploration of vessel movement data from the Norwegian Coastal Administration, collected by the Automatic Ide ntification System (AIS) coastal tracking. There are many interacting patterns in such movement data, bo th temporal and other more intricate, such as weather conditions, wave heights, or sunlight. In this work we stud y these movement patterns, answering specific questions posed by Norwegian Coastal Administration on potential shipping lane optimizations.