We support the identification of characteristic spatial states in complex spatial-temporal data. Hierarchical clustering is used to automatically detect spatial patterns in large data sets. Visual exploration methods enables geoscientists to identify prominent spatial patterns and explore their temporal context in detail.

Publication: Köthur, P., Sips, M., Unger, A., Kuhlmann, J., Dransch, D. (2014): Interactive visual summaries for detection and assessment of spatiotemporal patterns in geospatial time series. - Information Visualization, 13, 3, p. 283-298.

Video: Interactive visual Summaries for detection and assessment of spatiotemporal patterns (mp4)

In coorperation with GFZ Section 1.3: Earth System Modeling