Visualization of large scale time-varying scientific data has been a challenging problem due to their ever-increasing size. Identifying and presenting the most informative (or important) aspects of the data plays an important role in facilitating an efficient visualization. In this paper, an information assisted method is presented to locate temporal and spatial data containing salient physical features and accordingly accelerate the visualization process. To locate temporal data, two information-theoretic measures are utilized, i.e. the KL-distance, which measures information dissimilarity of different time steps, and the off-line marginal utility, which measures surprisingly information provided by each time step. To locate spatial data, a character factor is introduced which measures feature abundance of each sub-region. Based on these information measures, the method adaptively picks up important time steps and sub-regions with the maximum information content so that the time-varying data can be effectively visualized in limited time or using limited resources without loss of potential useful physical features. The experiments on the data of radiation diffusion dynamics and plasma physics simulation demonstrate the effectiveness of the proposed method. The method can remarkably improve the way in which scientists analyze and understand large scale time-varying scientific data.