用高斯过程分析冲突时空趋势，实现精准预测与机制洞察。
The Currents of Conflict: Decomposing Conflict Trends with Gaussian Processes
- 基于高斯过程建模冲突事件的时空演化模式。
- 可捕捉冲突陷阱、扩散及暴露风险等关键现象。
- 仅需历史冲突数据，适合政策预测与风险评估者。
本文提出一种新方法，利用高度时序和空间粒度的冲突事件数据，结合高斯过程估计暴力冲突的时空趋势。该方法可揭示冲突陷阱、扩散效应及总体时空暴露特征；在其他估计任务中可用于控制这些现象；还可将已估测的时空模式外推至未来时间点，实现先进的冲突预测。重要的是，该框架仅依赖单一数据源——过去冲突模式，即可达成上述成果。
原文摘要 · Abstract (English)
I present a novel approach to estimating the temporal and spatial patterns of violent conflict. I show how we can use highly temporally and spatially disaggregated data on conflict events in tandem with Gaussian processes to estimate temporospatial conflict trends. These trends can be studied to gain insight into conflict traps, diffusion and tempo-spatial conflict exposure in general; they can also be used to control for such phenomenons given other estimation tasks; lastly, the approach allow us to extrapolate the estimated tempo-spatial conflict patterns into future temporal units, thus facilitating powerful, stat-of-the-art, conflict forecasts. Importantly, these results are achieved via a relatively parsimonious framework using only one data source: past conflict patterns.
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