Surrogate Graph Partitioning for Spatial Prediction
The study aims to create interpretable spatial segments for black-box predictions by solving a graph partitioning problem that minimizes prediction variance within each segment. The exact mathematical formulation is too complex, so an approximation scheme leveraging structural properties of graph partitioning was developed. This scheme efficiently finds spatial segments, proving the approach works in practice.