Visual Spatiotemporal Pattern Analysis of Movement and Event Data


Professor Dr. Daniel A. Keim, Konstanz
Universität Konstanz
Fachbereich Informatik und Informationswissenschaft
Arbeitsgruppe Databases, Data Mining und Visualisierung

Professor Dr. Stefan Wrobel, Bonn
Rheinische Friedrich-Wilhelms-Universität Bonn
Institut für Informatik
Informatik III


Visual analytics methods to support the spatiotemportal analysis of movements in a physical space, in particular in a geographical space (Publications)


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.