Visual variable perception in time series dynamic visualization

Abstract

Visualization facilitates understanding by encoding large amounts of complex information into intuitive formats such as graphics and images. In the design of dynamic visualization of time series, visual coding is more important, because a large number of complex and high-dimensional data will follow over time, but users may not be able to process and filter them in time and effectively. The paper evaluates the cognitive load and performance of users under two time pressures of eight different dynamic visual variables through experiments, so as to allocate more significant coding methods to higher priority information and improve the efficiency of information acquisition. The results show that there are significant differences in cognitive load when using different visual variables, and the cognitive load is lower when using angle, symbol number and area coding. However, there is no significant difference in user performance. There was no significant difference in cognitive load between the two time pressure states, but there was significant difference in accuracy. The accuracy under low time pressure is significantly higher than that under high time pressure.