Facilitated by advancements in conflict event databases, studies have moved towards predicting armed conflict and understanding its determinants subnationally. However, existing statistical models do not capture the diffusion of armed conflict, and hence do not adequately account for its dependence across both time and space, thus inducing potential biases in the estimates of covariates of interest. To address this, we introduce a regression approach that simultaneously captures both spatial and temporal dimension of the diffusion of armed conflict through non-parametric smoothing. Using fine-grained conflict data on Africa, we demonstrate the importance of accounting for this dependence, capturing conflict diffusion up to 550km in distance and 24 months in the past, and observe that diffusion decays exponentially in both space and time. We illustrate the flexibility of our fully interpretable method by studying the role of population in the transmission of conflict. We find that conflict typically breaks out in densely populated areas, and from there diffuses, specifically to lower population areas.
Racek et al. (Mon,) studied this question.