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.