Extracting patterns from large movement datasets

Abstract

Extracting useful information from large spatiotemporal datasets is a challenging task that requires suitable visual data representations. Big movement data are particularly hard to visualize since they are prone to visual clutter caused by overlapping and crisscrossing trajectories. Different data aggregation approaches have been developed to address this challenge and to provide analysts with better visualizations for data exploration and data-driven hypothesis generation. However, most approaches for extracting patterns, such as mobility graphs or generalized flow maps, cannot handle large input datasets. This paper presents a flow extraction algorithm that can be used in distributed computing environments and thus make it possible to explore movement patterns in large datasets. We demonstrate its usefulness in a use case exploring maritime vessel movements.