Highlighting space–time patterns: Effective visual encodings for interactive decision‐making
Mike Sips, J. Schneidewind, Daniel A. Keim · International Journal of Geographical Information Systems · 2007
The research reported in this paper focuses on integrating analytical and visual methods in order to explore complex patterns in geo‐related multivariate data sets and to understand the changes in patterns over time. The goal is to provide techniques that are able to analyse real‐world Data Warehouses, a typical architecture to manage such geo‐related multidimensional data sets, in order to support the analyst's decision‐making process. Challenges arise because real‐world applications usually have to deal with millions of records, with dozens of dimensions, and spatio‐temporal context. Therefore, a tight integration of automated analysis and interactive visualizations is needed (as proposed in the context of Visual Analytics). Our approach uses the well‐studied capabilities provided by Data Warehouses supporting knowledge discovery and decision‐making to analyse spatio‐temporal behaviour of pattern in high‐dimensional spaces. The topic of the paper is to show possible interplays between automated analysis and geo‐spatial visualization.