Teaching Geoscience with Visualizations: Using Images, Animations, and Models Effectively

I am a geographer and psychologist, with research interests in many aspects of spatial and geographic perception, cognition, and behavior. My particular interest is in space and place at scales larger than the human body, including both built and natural while my major interest is in the psychology of (near) earth surface space, I am interested in the space of pictures and objects because they are a common means by which people interact with information about larger spaces—sometimes the only way. I believe that many of my research activities over the past two decades have at least an indirect relevance to the study, creation, and use of scientific visualizations. In particular, I have collaborated on various research projects, and written several reviews of research, that have examined perceptual and cognitive aspects of cartographic and virtual information displays, including their role in information acquisition and knowledge development, and their similarities and differences from other modes of knowledge acquisition such as direct sensorimotor experience in Two ongoing lines of research are directly relevant to scientific visualization. First is research being conducted with primary collaborators Drs. Sara Fabrikant and David Mark. This project investigates how users of information spatializations interpret spatial and nonspatial graphical variables as representing similarity relationships among documents represented in the display. " Information spatializations " are computer visualizations in which nonspatial information is depicted spatially. For example, a " point-display spatialization " depicts documents (or other information-bearing entities) as collections (clouds) of points in 2-or 3-dimensional space. Common examples include point displays, network displays, region displays, natural land surfaces, urban land surfaces, and more of large databases commonly use distance as a metaphor to depict semantic (nonspatial) similarities among data items. We call this the Distance-Similarity Metaphor. Our results so far show that this metaphor operates differently depending on the type of graphical metaphor (e.g., points vs. regions) and whether nonspatial visual variables (such as color hue or value) are manipulated. For example, straight-line