Narratives enable readers to assimilate disparate facts. Accompanying maps can make the narratives even more accessible. As work in computer science has begun to generate stories from low-level event/activity data, there is a need for systems that complement these tools to generate maps illustrating spatial components of these stories. While traditional maps display static spatial relationships, story maps need to not only dynamically display relationships based on the flow of the story but also display character perceptions and intentions. In this work, we study cartographic illustrations of historical battles to design a map generation system for reports produced from a multiplayer battle game log. We then create a story and ask viewers to describe mapped events and rate their own descriptions relative to intended interpretations. Some viewers received training prior to seeing the story, which was shown to be effective, though training may have been unnecessary for certain map types. Self-rating correlated highly with expert ratings, revealing an efficient proxy for expert analysis of map interpretability, a shortcut for determining if training is needed for story-telling maps or other novel visualisation techniques. The study's semantic questions and feedback solicitation demonstrate a process for identifying user-centric improvements to story-telling map design.

We present TanGeoMS, a tangible geospatial modeling visualization system that couples a laser scanner, projector, and a flexible physical three-dimensional model with a standard geospatial information system (GIS) to create a tangible user interface for terrain data. TanGeoMS projects an image of real-world data onto a physical terrain model. Users can alter the topography of the model by modifying the clay surface or placing additional objects on the surface. The modified model is captured by an overhead laser scanner then imported into a GIS for analysis and simulation of real-world processes. The results are projected back onto the surface of the model providing feedback on the impact of the modifications on terrain parameters and simulated processes. Interaction with a physical model is highly intuitive, allowing users to base initial design decisions on geospatial data, test the impact of these decisions in GIS simulations, and use the feedback to improve their design. We demonstrate the system on three applications: investigating runoff management within a watershed, assessing the impact of storm surge on barrier islands, and exploring landscape rehabilitation in military training areas.

An important problem in the area of computer graphics is the visualization of large, complex information spaces. Datasets of this type have grown rapidly in recent years, both in number and in size. Images of the data stored in these collections must support rapid and accurate exploration and analysis. This article presents a method for constructing visualizations that are both effective and aesthetic. Our approach uses techniques from master paintings and human perception to visualize a multidimensional dataset. Individual data elements are drawn with one or more brush strokes that vary their appearance to represent the element's attribute values. The result is a nonphotorealistic visualization of information stored in the dataset. Our research extends existing glyph-based and nonphotorealistic techniques by applying perceptual guidelines to build an effective representation of the underlying data. The nonphotorealistic properties the strokes employ are selected from studies of the history and theory of Impressionist art. We show that these properties are similar to visual features that are detected by the low-level human visual system. This correspondence allows us to manage the strokes to produce perceptually salient visualizations. Psychophysical experiments confirm a strong relationship between the expressive power of our nonphotorealistic properties and previous findings on the use of perceptual color and texture patterns for data display. Results from these studies are used to produce effective nonphotorealistic visualizations. We conclude by applying our techniques to a large, multidimensional weather dataset to demonstrate their viability in a practical, real-world setting.