Visual Exploration of Space-Time Pattern in Multi-Dimensional and Heterogeneous Data Spaces
In many application scenarios data is collected and referenced by its geo-spatial location at a certain point in time (time-stamp). The analysis of such data sets is an important task, since for decision makers, analysts or emergency response teams it is often essential to rapidly extract relevant information from the flood of data.
In emergency management, for example, GPS-navigation plays an important role to provide effective coordination for various organizations to enable more efficient emergency assistant. Therefore, the GPS information from selected vehicles is collected which results in large and complex databases. A research challenge on the one hand is to find efficient methods to handle such massive information flows and on the other hand to provide visualizations that fuse this information together. The aim is to allow analysts to identify space and time patterns and to provide efficient visual awareness and coordinated help in emergency cases.
In the described examples the collected data typically results in large and heterogeneous data sets with geo-spatial information, time stamps, pictures, text and other information. The visual analysis of these massive volumes of data is a challenge for existing visualization techniques. Space-time-patterns can be seen as a series of multivariate profiles. The difficulty is to provide effective visual awareness in multi-dimensional heterogeneous data spaces that represents the available recourses or products as well as different kinds of events and alerts. Effective visual awareness is based on the perception of objects in an environment with a volume of space and time, the comprehension of their meaning, and the projection of their status in the near future.
In our research we study the following questions about space-time pattern:
- How to adequately analyze changing events in time over a geo-spatial context?
- How to enable intelligent situational awareness?
- How to automatically include heterogeneous data from multiple sources (numerical data, text, images,
gps-data, network logs) into the visual data exploration process?