Reproducibility package for Fine-Scale Spatial Disaggregation Of Statistical Data Via Graph Neural Networks

Fine-grained spatial data are critical for informed decision-making in domains ranging from economic planning to environmental management. However, many statistics are only available for coarse administrative units, necessitating techniques for fine-scale spatial disaggregation. In this paper, we introduce a graph neural network (GNN) based framework for disaggregating aggregated indicators to a finer spatial resolution. The GNN approach leverages graph representations of spatial units to incorporate both feature information and spatial relationships, addressing challenges of heterogeneity and data sparsity. The approach also adopts the H3 hierarchical hexagonal indexing system to define fine-resolution cells, providing a globally consistent, multi-resolution spatial grid well suited to graph-based modeling. We demonstrate the framework using gross domestic product (GDP) as a representative example, disaggregating national or regional GDP to fine-resolution cells. While illustrated with GDP, the proposed methodology is applicable to a broad class of aggregate indicators, offering a flexible and scalable tool for spatial analysis of economic, social, and environmental statistics. Our results show that the framework produces high-resolution estimates that are consistent with known aggregates and aligned with ancillary covariate patterns. This general-purpose approach to spatial disaggregation enables more detailed mapping of indicators like GDP and beyond, unlocking finer insights from coarse data.