Robust visualization of trajectory data

Abstract The analysis of movement trajectories plays a central role in many application areas, such as traffic management, sports analysis, and collective behavior research, where large complex trajectory data sets are routinely collected these days. While automated methods available to extract characteristics statistics on the geometry, patterns, locations that might be associated with important events, human inspection is still required interpret results, derive parameters for compare further impact factors influence shapes their underlying processes. Every step acquisition pipeline introduce artifacts or alterate features, which bias interpretation confound analysis. Thus, visualization well visualizations themselves need take into account corresponding order allow sound without adding removing features putting strain analyst. In this paper, we provide an overview challenges arising robust tasks. We then discuss several contribute improved visualizations. particular, present practical algorithms simplifying semantic uncertainty information directly account. Furthermore, describe complementary approach allows visualize along trajectories.