AlphaEarth用空间上下文帮EMS预测在历史稀疏时性能提升了2-6倍，实实在在的效果。
本文研究时空点过程模型在事件历史稀疏时的泛化能力，使用AlphaEarth嵌入作为线性空间上下文。在固定log-Gaussian Cox过程骨干下，对比仅事件模型与加入AlphaEarth的模型。在8个留出区域的紧急医疗服务（EMS）预测任务中，AlphaEarth在所有历史长度（w=1-104周）上均提升留出区域预测性能。尤其在w=1-2周的稀疏历史下，提升达2到6倍；在w=20-104周时提升约10%到20%。
When Context Compensates for Sparse Event History: AlphaEarth for Spatio-Temporal Point-Process Forecasting
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.