方法对比

	Conflict Forecasting	Spatial Conflict Analysis
领域	International Relations	International Relations
方法族	Machine learning	Regression model
起源年份	2019	2002
提出者	Conflict-forecasting community (e.g., Håvard Hegre and the ViEWS team)	Spatial-analysis-of-conflict literature (e.g., Michael Ward & Kristian Skrede Gleditsch)
类型	Operational predictive system for armed conflict	Spatial regression / spatial-statistical modeling of conflict
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.	Ward, M. D., & Gleditsch, K. S. (2002). Location, location, location: An MCMC approach to modeling the spatial context of war and peace. Political Analysis, 10(3), 244–260.
别名	Political Violence Early Warning, Armed Conflict Forecasting, Conflict Early-Warning Systems, ViEWS-Style Forecasting	Spatial Analysis of War and Peace, Geographic Conflict Modeling, Spatial Econometrics of Conflict, Georeferenced Conflict 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.	Spatial conflict analysis models armed conflict while taking geography seriously: conflict is not randomly scattered but clusters in space, and a place's risk depends on its neighbors. Building on georeferenced data and spatial-statistical methods — as in Ward and Gleditsch's (2002) MCMC approach to the spatial context of war and peace — it uses spatial weights, tests for spatial autocorrelation, and fits spatial regression models so that conflict, peace, and their predictors are analyzed as interdependent across locations rather than as isolated observations.