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	Global Terrorism Database Analysis	Spatial Conflict Analysis
分野	International Relations	International Relations
系統	Process / pipeline	Regression model
提唱年	2007	2002
提唱者	Gary LaFree & Laura Dugan (START, University of Maryland)	Spatial-analysis-of-conflict literature (e.g., Michael Ward & Kristian Skrede Gleditsch)
種類	Coding and analysis of terrorist incidents	Spatial regression / spatial-statistical modeling of conflict
原典	LaFree, G., & Dugan, L. (2007). Introducing the Global Terrorism Database. Terrorism and Political Violence, 19(2), 181–204.	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.
別名	GTD Analysis, Terrorism Event Data Analysis, START GTD Analysis, Terrorist Incident Database Analysis	Spatial Analysis of War and Peace, Geographic Conflict Modeling, Spatial Econometrics of Conflict, Georeferenced Conflict Analysis
関連	3	3
概要	Global Terrorism Database (GTD) analysis is the quantitative study of terrorism using the open-source incident database maintained by the START center at the University of Maryland and introduced by LaFree and Dugan (2007). The GTD codes tens of thousands of terrorist attacks since 1970 against explicit inclusion criteria, recording each incident's date, location, perpetrator, target, tactic, and human and material consequences. Analysts use it to map trends, profile groups and tactics, and model the causes and effects of terrorism.	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.