Technology has advanced to a stage where we can map all individual trees across an entire country, or, at the other end of the scale, all individual cells in a biological tissue sample. Increasingly, such point-location datasets are being collected across different scientific fields to better understand complex natural and biological systems. However, these novel datasets are still often analyzed using traditional methods, leading to sub-optimal results. The DIVSPOT project develops statistically grounded, reliable tools for quantifying and visualizing key structural features in such datasets. The methods are developed and evaluated in close collaboration with domain experts. In forest ecology, they support biodiversity mapping, conservation, and planning. In cancer research, they enable improved analysis of tissue samples, contributing to the discovery of clinically relevant patterns and a deeper understanding of cancer biology.

2026

2030