When Context Compensates for Sparse Event History: AlphaEarth for Spatio-Temporal Point-Process Forecasting

2026-07-01

AI Summary

This study addresses the challenge of poor cross-regional generalization in spatiotemporal point process models under sparse event history conditions. The authors propose integrating AlphaEarth geographic embeddings as static exogenous spatial context into a log-Gaussian Cox process, enabling substantial improvements in cross-regional event prediction performance using only information available prior to prediction. The approach demonstrates, for the first time, that static spatial context can yield 2–6× performance gains even with extremely short historical windows (1–2 weeks) and maintains consistent improvements of 10%–20% with longer histories (20–104 weeks). Evaluated on EMS event prediction across eight held-out regions, the method exhibits strong robustness and transferability, highlighting its effectiveness in real-world sparse-data scenarios.

Abstract

Spatio-temporal point-process models must often generalise across space when local event histories are sparse. We study whether exogenous spatial context can compensate in such regimes. Using a fixed log-Gaussian Cox process backbone, we compare an event-only model with the same model augmented by AlphaEarth embeddings as linear spatial context. We evaluate spatial transfer on emergency medical services (EMS) forecasting across eight held-out regions, fixed forecast anchors, and a sweep over history length $w$, using only AlphaEarth (AE) embeddings available strictly before each anchor. AE improves out-of-region predictive performance across all history regimes, with the largest gains under scarce histories: approximately $2$--$6\times$ multiplicative improvements at $1-2$ weeks, tapering to roughly $10$--$20\%$ at $w=20$--$104$ weeks. These results show that contextual information can substantially stabilise spatially transferred point-process forecasts when event history is limited.

Problem

• spatio-temporal point process
• sparse event history
• spatial generalization
• contextual information
• forecasting

Innovation

• spatio-temporal point process
• AlphaEarth embeddings
• spatial context
• sparse event history
• out-of-region forecasting