标题： 埃塞俄比亚冲突发生和死亡人数的时空动力学：基于事件级数据的贝叶斯模型和预测见解（1997--2024） 显示英文标题

标题： Spatiotemporal Dynamics of Conflict Occurrence and Fatalities in Ethiopia: A Bayesian Model and Predictive Insights Using Event-level Data (1997--2024)

摘要： 本研究提出了一种时空双贝叶斯模型，利用埃塞俄比亚（1997-2024年）的事件级数据，来自武装冲突地点和事件数据（ACLED）项目，来研究冲突死亡的发生及其数量。死亡被视作两个相关结果：死亡的发生与否以及发生时的死亡数量。该模型结合了协变量的加法固定效应和捕捉时空影响的随机效应，允许结果特定的影响。协变量包括事件类型和季节作为分类变量，靠近城市和边境作为非线性效应，以及人口作为计数模型中的偏移项。一个潜在的时空过程用于解释共享的空间和时间依赖性，空间结构使用Matérn场先验进行建模，并通过集成嵌套拉普拉斯近似（INLA）进行推断。结果表明，死亡风险存在强烈的空间聚类和时间变化，强调了对两个维度进行建模的重要性，以更好地理解和预测。空袭、炮击和袭击显示出最高的死亡可能性和数量，而社区和叛乱分子导致的死亡最多。多次死亡更可能发生在夏季，靠近边境会引发激烈的暴力，而远离城市中心则与低强度事件有关。这些结果为规划、政策和资源分配提供了见解，以保护易受伤害的社区。 显示英文摘要

摘要： This study presents a spatiotemporal dual Bayesian model that examines both the occurrence and number of conflict fatalities using event-level data from Ethiopia (1997-2024), sourced from the Armed Conflict Location and Event Data (ACLED) project. Fatalities are treated as two linked outcomes: the binary occurrence of deaths and the count of deaths when they occur. The model combines additive fixed effects for covariates with random effects capturing spatiotemporal influences, allowing for outcome-specific effects. Covariates include event type and season as categorical variables, proximity to cities and borders as nonlinear effects, and population as an offset term in the count model. A latent spatiotemporal process accounts for shared spatial and temporal dependence, with the spatial structure modeled using a Mat\'ern field prior and inference via Integrated Nested Laplace Approximation (INLA). Results show strong spatial clustering and temporal variation in fatality risk, emphasizing the importance of modeling both dimensions for better understanding and prediction. Airstrikes, shelling, and attacks show the highest fatality likelihood and counts, while communal and rebel actors cause the most deaths. Multiple fatalities are more likely in summer, and proximity to borders drives intense violence, whereas remoteness from urban centers is linked to lower-intensity events. These results provide insight for planning, policy, and resource allocation to protect vulnerable communities.