方法对比

	Conflict Forecasting	UCDP Conflict Data Analysis
领域	International Relations	International Relations
方法族	Machine learning	Process / pipeline
起源年份	2019	2013
提出者	Conflict-forecasting community (e.g., Håvard Hegre and the ViEWS team)	Uppsala Conflict Data Program (Ralph Sundberg & Erik Melander for UCDP-GED)
类型	Operational predictive system for armed conflict	Coding and analysis of organized-violence events and conflicts
开创性文献	Hegre, H., Allansson, M., Basedau, M., Colaresi, M., Croicu, M., Fjelde, H., et al. (2019). ViEWS: A political violence early-warning system. Journal of Peace Research, 56(2), 155–174.	Sundberg, R., & Melander, E. (2013). Introducing the UCDP Georeferenced Event Dataset. Journal of Peace Research, 50(4), 523–532.
别名	Political Violence Early Warning, Armed Conflict Forecasting, Conflict Early-Warning Systems, ViEWS-Style Forecasting	UCDP Analysis, UCDP Georeferenced Event Dataset Analysis, Uppsala Conflict Data Analysis, Organized Violence Event Analysis
相关	3	3
摘要	Conflict forecasting is the enterprise of producing calibrated, regularly updated probabilistic predictions of where and when armed conflict will occur, to support early warning and prevention. Exemplified by operational systems such as ViEWS (Hegre et al. 2019), it combines historical conflict data and predictors at fine spatial and temporal resolution, fits and ensembles multiple models, and forecasts violence months ahead — then rigorously evaluates those forecasts against what actually happens. It differs from explanatory conflict analysis by being transparent, prospective, and judged on out-of-sample accuracy rather than on coefficients.	UCDP conflict data analysis is the coding and quantitative study of organized violence using the datasets of the Uppsala Conflict Data Program. UCDP distinguishes three categories of organized violence — state-based armed conflict, non-state conflict, and one-sided violence against civilians — and codes them from the level of individual fatal events up to annual conflict dyads. The Georeferenced Event Dataset (UCDP-GED), introduced by Sundberg and Melander (2013), pins each event to a place and date, enabling fine-grained spatial and temporal analysis of where and when violence occurs.