DSTVis: toward better interactive visual analysis of Drones' spatio-temporal data

Maintaining the normal flight of drones is crucial for drone operators. Analyzing the operation status of drones and adjusting flight parameters are essential to achieve this goal. However, as drone technology continues to evolve, the volume and complexity of spatio-temporal data related to drone flight status have grown exponentially. The complexity of this data poses a challenge to effective visualization, which can impact operators' analysis and decision-making. Currently, there is limited research on identifying flight attributes from a large collection of drone time series data. Two challenges were identified: (1) visual clutter from spatio-temporal data; (2) effective integration of time and space properties. By collaborating with domain experts, we addressed two challenges with DSTVis, a novel interactive system for operators to visually analyze spatio-temporal data of drones. For Challenge 1, we designed dynamic interactive views by abstracting and stratifying spatio-temporal data, enabling effective exploration of large amounts of data. For Challenge 2, a two-dimensional map is utilized to integrate time information and assist users in comprehending the spatio-temporal properties. The effectiveness of the system is evaluated with a usage scenario on a real-world historical dataset and received positive feedback from experts.