مقایسهٔ روش‌ها

	Conflict Diffusion Analysis	Spatial Conflict Analysis
حوزه	International Relations	International Relations
خانواده	Regression model	Regression model
سال پیدایش	2008	2002
پدیدآور	Conflict-diffusion literature (e.g., Most & Starr; Halvard Buhaug & Kristian Skrede Gleditsch)	Spatial-analysis-of-conflict literature (e.g., Michael Ward & Kristian Skrede Gleditsch)
نوع	Spatial-temporal analysis of conflict contagion	Spatial regression / spatial-statistical modeling of conflict
منبع بنیادین	Buhaug, H., & Gleditsch, K. S. (2008). Contagion or confusion? Why conflicts cluster in space. International Studies Quarterly, 52(2), 215–233.	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.
نام‌های دیگر	Conflict Contagion Analysis, Conflict Spillover Analysis, Spatial Diffusion of War, Conflict Spread Modeling	Spatial Analysis of War and Peace, Geographic Conflict Modeling, Spatial Econometrics of Conflict, Georeferenced Conflict Analysis
مرتبط	3	3
خلاصه	Conflict diffusion analysis studies how conflict spreads from one place to another — across borders, between neighboring regions, over time. It addresses a sharp inferential challenge posed by Buhaug and Gleditsch (2008): conflicts cluster in space, but clustering can reflect either genuine contagion (a war next door actually raises your risk) or merely the fact that neighbors share war-prone conditions. Using spatial-temporal lags of neighboring conflict alongside covariates, and theorizing concrete transmission mechanisms such as refugee flows and transnational ethnic ties, the method tries to separate true diffusion from spurious co-location.	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.