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

arXiv: https://arxiv.org/abs/2607.01082 · HTML: https://mlsanity.com/paper/2607.01082/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 25 references that are in the map.
- First posted on arXiv 1 Jul 2026.

## Builds on (most cited first, 10 of 10)

- [AlphaEarth Foundations: An embedding field model for accurate and efficient global mapping from sparse label data](https://mlsanity.com/paper/2507.22291/) (2025)
- [Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC](https://mlsanity.com/paper/1507.04544/) (2015)
- [Neural Spatio-Temporal Point Processes](https://mlsanity.com/paper/2011.04583/) (2020)
- [Spatio-temporal Diffusion Point Processes](https://mlsanity.com/paper/2305.12403/) (2023)
- [A Review of Self-Exciting Spatio-Temporal Point Processes and Their Applications](https://mlsanity.com/paper/1708.02647/) (2017)
- [Spatial and Spatio-Temporal Log-Gaussian Cox Processes: Extending the Geostatistical Paradigm](https://mlsanity.com/paper/1312.6536/) (2013)
- [Unlocking Point Processes through Point Set Diffusion](https://mlsanity.com/paper/2410.22493/) (2024)
- [TransFeat-TPP: An Interpretable Deep Covariate Temporal Point Processes](https://mlsanity.com/paper/2407.16161/) (2024)
- [Spatio-Temporal Analysis of Epidemic Phenomena Using the R Package surveillance](https://mlsanity.com/paper/1411.0416/) (2014)
- [A non-separable first-order spatio-temporal intensity for events on linear networks: an application to ambulance interventions](https://mlsanity.com/paper/2106.00457/) (2021)

## Abstract (arXiv)

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× 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.