Comparer des méthodes

	ACLED Event Analysis	Spatial Conflict Analysis
Domaine	International Relations	International Relations
Famille	Process / pipeline	Regression model
Année d'origine	2010	2002
Auteur d'origine	Clionadh Raleigh, Andrew Linke, Håvard Hegre & Joakim Karlsen	Spatial-analysis-of-conflict literature (e.g., Michael Ward & Kristian Skrede Gleditsch)
Type	Disaggregated coding and analysis of political-violence events	Spatial regression / spatial-statistical modeling of conflict
Source fondatrice	Raleigh, C., Linke, A., Hegre, H., & Karlsen, J. (2010). Introducing ACLED: An armed conflict location and event dataset. Journal of Peace Research, 47(5), 651–660.	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.
Alias	ACLED Analysis, Armed Conflict Location and Event Data, Political Violence Event Analysis, Disaggregated Conflict Event Analysis	Spatial Analysis of War and Peace, Geographic Conflict Modeling, Spatial Econometrics of Conflict, Georeferenced Conflict Analysis
Apparentées	3	3
Résumé	ACLED event analysis is the disaggregated study of political violence and protest using the Armed Conflict Location and Event Data project, introduced by Raleigh, Linke, Hegre, and Karlsen (2010). ACLED codes individual events — battles, violence against civilians, riots, protests, explosions and remote violence, and strategic developments — with their date, location, actors, and any fatalities, updated on a near-weekly basis. Its fine granularity and timeliness make it a workhorse for mapping, monitoring, and modeling where, when, and by whom political 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.