High-dimensional data is commonly acquired in various fields. Traditionally such data was often abstract, i.e., in tabular form. In recent years researchers in fields such as life sciences to cultural heritage research have started acquiring spatially resolved high-dimensional data. The exploration and analysis of such data is a challenging task. Visual Analytics and Dimensionality Reduction are commonly used to support exploration and hypothesis generation for abstract high-dimensional data, but adaption to spatially resolved data is necessary. In this talk, Professor Höllt will present the work on dimensionality reduction carried out at the Computer Graphics and Visualization group at TU Delft. He will discuss a number of different methods and how they enable and support applications for example in spatial biology