In many applications, spatiotemporal data is generated in&rapidly growing&amounts, and therefore there is a high demand for scalable&analysis&methods, which allow a systematic analysis and have a sound theoretical&basis. Spatiotemporal data, most importantly movement data, involve&geographical space, time, and multidimensional attributes and thereby&pose&significant challenges for the analysis. We plan to develop&theoretical&foundations for the analysis of spatiotemporal data, which&account for possible&variations of the essential properties of the data. We&will thereby identify&the generic analysis tasks for different types of&movement data&(quasi-continuous and event-based) and different views&of movement (trajectory-oriented&and traffic-oriented). The goal of the&project is to develop the appropriate&analysis methods, which combine&visual, interactive, and algorithmic techniques&for a scalable analysis. The&algorithmic techniques will cluster, aggregate,&and summarize&trajectories, traffic situations, events, and corresponding&multidimensional&attributes for providing an overview of the data and extract&significant&patterns of potential interest. Visual and interactive techniques allow&the&user to steer the automated algorithms and input his knowledge into the&analysis process. Visualizing the transformed data also provides&important&feedback to the user and allows him to better understand the&data. A tight&integration of visual and automatic techniques is crucial to&the success of&spatiotemporal analysis.
