TransportMap: Visual transport analysis for spatiotemporal data without trajectory information

It is essential to understand movements in exploring spatiotemporal data. However, many datasets have no explicit trajectory or origin–destination information, making movement analysis an ill-posed problem. Existing methods struggle to effectively simulate the complete movement process, producing results that are infeasible in real-world scenarios and neglecting potential environmental factors. To address these challenges, we propose TransportMap, a novel approach that extracts movements from spatiotemporal data without trajectory information. TransportMap employs a two-step optimal transport algorithm, which is integrated into a visual analysis system that enables interactive adjustment of environmental factors, improving adaptability to complex settings. The resulting movement interpolations are visualized using density maps and vector fields. Quantitative experiments demonstrate that TransportMap outperforms existing methods. Additionally, three real-world case studies validate the effectiveness of our approach in exploring spatiotemporal data with or without user steering.