Abstract

The technological progress in recent years made it possible, especially in domains with geographical context, to gather high-resolution spatio-temporal data on a massive scale using satellites, trackers, cameras, and sensors. Thus, creating immense challenges in the processing and handling of the data. To lift the analysis to a higher level of abstraction and consequently easing the data complexity, we focus on events, which are special situations of interest in time and space. However, the variability and complexity of spatio-temporal data complicate communication and the necessary exchange of knowledge between domain experts and data analysts. In this talk, we explore how this gap can be bridged with the help of Visual Analytics. We present and discuss recent approaches that are used in various geographic domains, such as collective behavior, meteorology, ecology, and sports. Subsequently, we discuss open challenges and current research.

Bio

Daniel Seebacher is a research associate and Ph.D. student at the Data Analysis and Visualization group of Daniel Keim at the University of Konstanz since 2016. He received his M. Sc. in Computer Science at the University of Konstanz in 2015 for his thesis on multi-modal patent retrieval. His current research interests include the visual analysis of spatio-temporal event data and their context. Concrete research examples include, the analysis of the spread of invasive species, the study of intra-city meteorological phenomena, and the study of actions and interactions in sports such as soccer or tennis.