So sánh phương pháp

	UCDP Conflict Data Analysis	Spatial Conflict Analysis
Lĩnh vực	International Relations	International Relations
Họ	Process / pipeline	Regression model
Năm ra đời	2013	2002
Người khởi xướng	Uppsala Conflict Data Program (Ralph Sundberg & Erik Melander for UCDP-GED)	Spatial-analysis-of-conflict literature (e.g., Michael Ward & Kristian Skrede Gleditsch)
Loại	Coding and analysis of organized-violence events and conflicts	Spatial regression / spatial-statistical modeling of conflict
Công trình gốc	Sundberg, R., & Melander, E. (2013). Introducing the UCDP Georeferenced Event Dataset. Journal of Peace Research, 50(4), 523–532.	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.
Tên gọi khác	UCDP Analysis, UCDP Georeferenced Event Dataset Analysis, Uppsala Conflict Data Analysis, Organized Violence Event Analysis	Spatial Analysis of War and Peace, Geographic Conflict Modeling, Spatial Econometrics of Conflict, Georeferenced Conflict Analysis
Liên quan	3	3
Tóm tắt	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.	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.