Illustrative Visualization Of Spatio-temporal Data

Identifying and analyzing the time-varying features is important for understanding the spatiotemporal datasets,and illustrative visualization is a powerful tool to solve such problems.While there are numerous studies on illustrative visualization,existing solutions can only efficiently express the large-scale characteristics but hardly show subtle features in a temporal dataset.However,these tiny features often contain important information,so it is important to visualize them.This paper introduces a novel illustrative visualization scheme that employs temporal filtering techniques to disclose desired tiny features,which are further enhanced by an adaptive temporal illustration technique.At the same time the unconcerned context can be suppressed in a similar manner,so that the subtle features are highlighted to facilitate user's observation and exploration.Based on the above techniques,two visual analytics systems are developed for exploring subtle features in spatiotemporal data and dynamic graph datasets.The systems use filter controls to explore the area and frequency of tiny signals,and visualize data using animation and time series diagram to help users understand and compare the data.With the help of visualization tools embedded in the systems,users can interactively manipulate and analyze temporal features.Experiments on several types of real data show that the system can effectively assist users in exploring the subtle features of spatiotemporal data and dynamic graph datasets.