Dynamic multiscale visualization of flight data

We present a novel set of techniques for visualization of very large data sets encoding flight information obtained from Air Traffic Control. The aims of our visualization are to provide a smooth way to explore the available information and find outlier spatio-temporal patterns by navigating between fine-scale, detail, views on the data and coarse overviews of large areas and long time periods. To achieve this, we extend and adapt several image-based visualization techniques, including animation, density maps, and bundled graphs. In contrast to previous methods, we are able to visualize significantly more information on a single screen, with limited clutter, and also create real-time animations of the data. For computational scalability, we implement our method using GPU-accelerated techniques. We demonstrate our results on several real-world data sets ranging from hours over a country to one month over the entire world.