Amatsubu: A Semi-static Representation Technique Exposing Spatial Changes in Spatio-temporal Dependent Data

Abstract

Spatio-temporal dependent data, such as weather observation data, is data in which attribute values depend on both the time and space in which they are recorded. Typical visualization methods of such data that are employed in mass communication involve plotting the attribute values at each point in time on a map, and either displaying a series of such maps in time order using animation or displaying them by juxtaposing horizontally or vertically. Such methods are widely known, even by non-experts in analysis, but they have some problems. These methods force readers who want to grasp spatial changes in the attribute values to memorize the representations on the maps. The longer the time-period of data, the higher the cognitive load. In order to address such problems, we develop a novel visualization technique, named "Amatsubu," which statically represents multiple instantaneous values on a single map by overlaying them. We confirm the usefulness of this method through user studies, and also determine a weak point. The weakness is a lack of readability of information for each point in time, which can induce misreadings of spatial changes. We attempt to overcome this issue by introducing animation to Amatsubu, and transforming it into a semi-static representation technique. We confirm the effect of this improvement through another user study.