Graph partitioning improves interpretable spatial prediction
Interpretable Graph Partitioning for Spatial Prediction
Machine Learning
Summary
Predicting values in places where you have little data is tricky, especially when you want the results to be easy to understand. The authors present a method that groups nearby points on a map into connected regions so the predictions can be simplified but still follow complex borders. They add rules to avoid strange connections that can make the groups confusing. Their approach makes it easier to explain predictions while keeping regions meaningful and spatially clear.
What this means in practice
- •For geospatial analysts: Create interpretable regional summaries from complex prediction models applied to spatial data points without predefined units.
- •For environmental modelers: Generate spatially coherent and explainable partitions to better understand environmental variable predictions across irregular terrains.
Authors
Yuta Shikuri
Abstract
Spatial prediction estimates unobserved values from spatially distributed observations. Although flexible predictive models are increasingly available, their practical use remains limited when interpretability is essential. In this study, we formulate a graph partitioning problem to construct a global surrogate model that approximates black-box predictions with simpler representations. Specifically, we select connected subgraphs to minimize within-cluster residuals in vertex-level predictions. Although this formulation can be viewed as a variant of spatial regionalization, it operates on model-based predictions at individual locations rather than predefined areal units. With many locations, this point-level formulation may produce artificial connections between regions, reducing interpretability. We therefore introduce a constraint that excludes such connections while retaining flexible partition boundaries. We theoretically analyze how this constraint affects the resulting partition. We also develop a computational procedure for adjusting partition boundaries. Experimental results show that the proposed method yields spatially coherent partitions with flexible boundaries.