LatentMap: Effective auto-encoding of density maps for spatiotemporal data visualizations

Abstract

In the study of spatiotemporal data visualization, compression and morphing of density maps are challenging tasks. Many existing methods require adjustment of multiple parameters and rich experience, but still cannot get accuracy or smooth results. In this paper, we propose a GAN-based method (LatentMap) to explore the latent space of density maps, which is an end-to-end method. First, we find that small latent codes can be used as the results of compression, which can greatly save transmission time in a front-end system. We collect density maps to make a dataset. Our model learns from the dataset and samples from the Gaussian distribution to encode and decode density maps. Second, based on the latent codes, we explore the smooth dynamic visualization of density maps, and our method can generate dynamic and smooth results. We show the results of our method in a variety of situations and evaluations from multiple aspects. The results demonstrate the effectiveness and practicality of our approach. Our method has practical applications, such as speed up front-end loading, completing or predicting stream data information and visual query.