The monitoring of conflict risk in the humanitarian sector is largely based on simple historic averages. The overarching goal of this work is to assess the potential for using a more statistically rigorous approach to monitor the risk of political violence and conflict events in practice, and thereby improve our understanding of their temporal and spatial patterns, to inform preventative measures. In particular, a Bayesian, spatiotemporal variant of the Hawkes process is fitted to data gathered by the Armed Conflict Location and Event Data (ACLED) project to obtain sub-national estimates of conflict risk in South Asia over time and space. Our model can effectively estimate the risk level of these events within a statistically sound framework, with a more precise understanding of uncertainty than was previously possible. The model also provides insights into differences in behaviours between countries and conflict types. We also show how our model can be used to monitor short and long term trends, and that it is more stable and robust to outliers compared to current practices that rely on historical averages.

翻译：人道主义领域的冲突风险监测主要基于简单的历史平均值。本研究的核心目标是评估在实践中采用更严谨的统计方法监测政治暴力与冲突事件风险的可行性，从而深化对其时空模式的理解，为预防性措施提供依据。具体而言，本研究将霍克斯过程的贝叶斯时空变体拟合至武装冲突地点与事件数据项目收集的数据，以获得南亚地区冲突风险在次国家尺度上随时间与空间变化的估计。我们的模型能够在统计可靠的框架内有效估计此类事件的风险水平，其不确定性量化精度超越以往方法。该模型还能揭示不同国家与冲突类型间的行为差异。我们进一步展示了该模型如何用于监测短期与长期趋势，并证明相较于当前依赖历史平均值的实践，本模型对异常值具有更强的稳定性与鲁棒性。