Using haptics to convey cause-and-effect relations in climate visualization

We investigate the potential role of haptics in augmenting the visualization of climate data. In existing approaches to climate visualization, dimensions of climate data such as temperature, humidity, wind, precipitation, and cloud water are typically represented using different visual markers and dimensions such as color, size, intensity, and orientation. Since the numbers of dimensions in climate data are large and climate data need to be represented in connection with the topography, purely visual representations typically overwhelm users. Rather than overloading the visual channel, we investigate an alternative approach in which some of the climate information is displayed through the haptic channel in order to alleviate the perceptual and cognitive load of the user. In this approach, haptic feedback is further used to provide guidance while exploring climate data in order to enable natural and intuitive learning of cause-and-effect relationships between climate variables. As the user explores climate data interactively under the guidance of wind forces displayed by a haptic device, she/he can understand better the occurrence of events such as cloud and rain formation and the effect of climate variables on these events. We designed a set of experiments to demonstrate the effectiveness of this multimodal approach. Our experiments with 33 human subjects show that haptic feedback significantly improves the understanding of climate data and the cause-and-effect relations between climate variables, as well as the interpretation of the variations in climate due to changes in terrain.