Interactive visual query of density maps on latent space via flow-based models

Visual querying of spatiotemporal data has become a dominant mode in the field of visual analytics. Previous studies have utilized well-designed data structures to speed up the querying of spatiotemporal data. However, reducing storage overhead while improving the querying efficiency of data distribution remains a significant challenge. We propose a flow-based neural representation method for efficient visual querying. First, we transform spatiotemporal data into density maps through kernel density estimation. Then, we leverage the data-driven modeling capabilities of a flow-based neural network to achieve a highly latent representation of the data. Various computations and queries can be performed on the latent representation to improve querying efficiency. Our experiments demonstrate that our approach achieves competitive results in visually querying spatiotemporal data in terms of storage overhead and real-time interaction efficiency.