The Global Database of Events, Language and Tone (GDELT) provides geolocated event records that can be aggregated into weekly spatiotemporal panels of event counts across regions, actors, and event types. These panels are typically sparse, bursty, and overdispersed, so calibrated probabilistic forecasting is essential for monitoring rare surges. We propose Bayesian count regression pipelines that pair deterministic deep temporal encoders with negative binomial (NB2) and zero-inflated negative binomial (ZINB2) likelihood heads. Posterior predictive simulation yields predictive quantiles and right-tail probabilities that support both forecasting and anomaly scoring. For interpretable spillover attribution, we also fit a Bayesian generalised linear model with high-dimensional lagged cross-series predictors and a two-step screen-and-refit procedure under a three-parameter beta-normal (TPBN) shrinkage prior. To connect spillovers to directional statistics, active cross-region effects are mapped to geodesic bearings on the World Geodetic System 1984 ellipsoid (WGS84) and summarised using weighted circular moments, rose diagrams, and bearing-field maps. Simulations with known spillovers and conflict-panel case studies show accurate right-tail behaviour and a practical workflow for detecting and interpreting geopolitical shocks.

翻译：全球事件、语言与语调数据库（GDELT）提供了可聚合为跨区域、行为体及事件类型的周度时空事件计数面板的地理定位事件记录。这些面板通常呈现稀疏性、突发性与过度离散特征，因此校准的概率预测对于监测罕见激增现象至关重要。我们提出贝叶斯计数回归流程，将确定性深度时序编码器与负二项（NB2）及零膨胀负二项（ZINB2）似然函数头配对。后验预测模拟可生成支持预测与异常评分两者的预测分位数及右尾概率。为实现可解释的溢出归因，我们还拟合了一个贝叶斯广义线性模型，该模型包含高维滞后跨序列预测变量，并在三参数贝塔-正态（TPBN）收缩先验下采用两步筛选-重构流程。为将溢出效应与方向统计相关联，我们将活跃的跨区域效应映射至世界大地测量系统1984椭球体（WGS84）的大地测量方位角，并利用加权圆矩、玫瑰图及方位场图进行汇总。基于已知溢出效应的仿真实验与冲突面板案例研究展示了准确的右尾行为，以及检测与解释地缘政治冲击的实用工作流程。