A method to visualize the evolution of multiple interacting spatial systems

Integrated modeling approaches are being increasingly used to simulate the behavior of, and the interaction between, several interdependent systems. They are becoming more and more important in many fields, including, but not being limited to, civil engineering, hydrology and climate impact research. It is beneficial when using these approaches to be able to visualize both, the intermediary and final results of scenario-based analyses that are conducted in both, space and time. This requires appropriate visualization techniques that enable to efficiently navigate between multiple such scenarios. In recent years, several innovative visualization techniques have been developed that allow for such navigation purposes. These techniques, however, are limited to the representation of one system at a time. Improvements are possible with respect to the ability to visualize the results related to multiple scenarios for multiple interdependent spatio-temporal systems. To address this issue, existing multi-scenario navigation techniques based on small multiples and line graphs are extended by multiple system representations and inter-system impact representations. This not only allows to understand the evolution of the systems under consideration but also eases identifying events where one system influences another system significantly. In addition, the concept of selective branching is described that allows to remove otherwise redundant information from the visualization by considering the logical and temporal dependencies between these systems. This visualization technique is applied to a risk assessment methodology that allows to determine how different environmental systems (i.e. precipitation, flooding, and landslides) influence each other as well as how their impact on civil infrastructure affects society. The results of this work are concepts for improved visualization techniques for multiple interacting spatial systems. The successful validation with domain experts of the enhanced small multiples technique proved its usefulness in a use case scenario based on a risk assessment methodology.