当前位置: X-MOL 学术International Interactions › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Employing local peacekeeping data to forecast changes in violence
International Interactions ( IF 1.226 ) Pub Date : 2022-04-07 , DOI: 10.1080/03050629.2022.2055010
Lisa Hultman 1 , Maxine Leis 1 , Desirée Nilsson 1
Affiliation  

Abstract

One way of improving forecasts is through better data. We explore how much we can improve predictions of conflict violence by introducing data reflecting third-party efforts to manage violence. By leveraging new sub-national data on all UN peacekeeping deployments in Africa, 1994–2020, from the Geocoded Peacekeeping (Geo-PKO) dataset, we predict changes in violence at the local level. The advantage of data on peacekeeping deployments is that these vary over time and space, as opposed to many structural variables commonly used. We present two peacekeeping models that contain several local peacekeeping features, each with a separate set of additional variables that form the respective benchmark. The mean errors of our predictions only improve marginally. However, comparing observed and predicted changes in violence, the peacekeeping features improve our ability to identify the correct sign of the change. These results are particularly strong when we limit the sample to countries that have seen peacekeeping deployments. For an ambitious forecasting project, like ViEWS, it may thus be highly relevant to incorporate fine-grained and frequently updated data on peacekeeping troops.



中文翻译:

利用当地维和数据预测暴力变化

摘要

改进预测的一种方法是通过更好的数据。我们通过引入反映第三方管理暴力努力的数据来探索我们可以在多大程度上改善对冲突暴力的预测。通过利用地理编码维和 (Geo-PKO) 数据集中 1994-2020 年联合国在非洲的所有维和部署的新地方数据,我们预测了地方一级的暴力变化。维和部署数据的优势在于这些数据随时间和空间而变化,这与许多常用的结构变量相反。我们提出了两个维和模型,它们包含几个本地维和特征,每个模型都有一组单独的附加变量,形成各自的基准。我们预测的平均误差仅略有改善。然而,比较观察到的和预测的暴力变化,维和功能提高了我们识别变化正确迹象的能力。当我们将样本限制在已经部署维和的国家时,这些结果尤其明显。因此,对于像 ViEWS 这样雄心勃勃的预测项目,结合细粒度且经常更新的维和部队数据可能具有高度相关性。

更新日期:2022-04-07
down
wechat
bug