Decoupling Distance and Networks: Hybrid Graph Attention-Geostatistical Methods for Spatio-temporal Risk Mapping

TL;DR

This paper presents a hybrid graph attention and geostatistical modeling framework that improves spatial prediction and uncertainty quantification by combining relational and physical spatial dependencies.

Contribution

It introduces a novel integrated model that combines Graph Attention Networks with Gaussian spatial processes for better spatio-temporal risk mapping.

Findings

Hybrid model outperforms classical geostatistics and standalone GATv2 in predictive accuracy.

The model provides more realistic uncertainty quantification.

It effectively captures complex nonlinear and relational dependencies in spatial data.

Abstract

Accurate spatial prediction and rigorous uncertainty quantification are central to modern spatial epidemiology and environmental risk analysis. We introduce a statistically principled hybrid modelling framework that integrates the nonlinear, attention-based representation learning capabilities of a dynamic Graph Attention Network (GATv2) with a latent Gaussian spatial process from model-based geostatistics (MBG). This framework jointly captures relational dependence encoded in graph structures and continuous spatial dependence governed by physical proximity. We evaluate the proposed model via a controlled simulation study and an applied analysis of malaria prevalence data, comparing its predictive accuracy, calibration, and uncertainty quantification against classical geostatistical models and standalone GATv2 architectures. Our analyses show that GATv2 captures complex nonlinear interactions but fails to account for residual spatial autocorrelation, resulting in miscalibrated predictive distributions. Conversely, geostatistical models provide coherent uncertainty quantification through structured covariance functions yet are constrained by linear predictor assumptions and by their reliance on Euclidean distance to encode spatial structure. By integrating attention mechanisms and nonlinear features with an explicit probabilistic spatial random field, the hybrid model captured the relational dependence, consistently improved predictive accuracy, and provided more realistic uncertainty quantification in both simulation and applied settings. Overall, the findings demonstrate that the hybrid model constitutes a statistically coherent and empirically robust framework for modelling complex spatial and spatio-temporal processes in settings where both distance-based and structure-based dependencies operate.