PDF(Pubmed)
Abstract：
Sub-Saharan Africa has suffered frequent outbreaks of armed conflict since the end of the Cold War. Although several efforts have been made to understand the underlying causes of armed conflict and establish an early warning mechanism, there is still a lack of a comprehensive assessment approach to model the incidence risk of armed conflict well. Based on a large database of armed conflict events and related spatial datasets covering the period 2000-2019, this study uses a boosted regression tree (BRT) approach to model the spatiotemporal distribution of armed conflict risk in sub-Saharan Africa. Evaluation of accuracy indicates that the simulated models obtain high performance with an area under the receiver operator characteristic curve (ROC-AUC) mean value of 0.937 and an area under the precision recall curves (PR-AUC) mean value of 0.891. The result of the relative contribution indicates that the background context factors (i.e., social welfare and the political system) are the main driving factors of armed conflict risk, with a mean relative contribution of 92.599%. By comparison, the climate change-related variables have relatively little effect on armed conflict risk, accounting for only 7.401% of the total. These results provide novel insight into modelling the incidence risk of armed conflict, which may help implement interventions to prevent and minimize the harm of armed conflict.
摘要：
自冷战结束以来，撒哈拉以南非洲地区经常爆发武装冲突。尽管已经做出了一些努力来了解武装冲突的根本原因并建立预警机制，仍然缺乏全面的评估方法来很好地模拟武装冲突的发生风险。本研究基于2000-2019年期间的大型武装冲突事件数据库和相关空间数据集，使用增强回归树（BRT）方法对撒哈拉以南非洲地区武装冲突风险的时空分布进行建模。准确性评估表明，模拟模型获得了高性能，接受者操作特征曲线下面积（ROC-AUC）平均值为0.937，精确召回曲线下面积（PR-AUC）平均值为0.891。相对贡献的结果表明背景背景因素(即，社会福利和政治制度)是武装冲突风险的主要驱动因素，平均相对贡献率为92.599%。相比之下,与气候变化相关的变量对武装冲突风险的影响相对较小，仅占总数的7.401%。这些结果为武装冲突的发生风险建模提供了新的见解，这可能有助于实施干预措施，以防止和尽量减少武装冲突的伤害。
