Comparar métodos

	Spatial Conflict Analysis	Regresión espacial (modelos de retardo espacial y de error espacial)
Campo	International Relations	Econometría
Familia	Regression model	Regression model
Año de origen	2002	1988
Autor original	Spatial-analysis-of-conflict literature (e.g., Michael Ward & Kristian Skrede Gleditsch)	Luc Anselin
Tipo	Spatial regression / spatial-statistical modeling of conflict	Spatial regression (cross-sectional)
Fuente seminal	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.	Anselin, L. (1988). Spatial Econometrics: Methods and Models. Kluwer Academic Publishers.
Alias	Spatial Analysis of War and Peace, Geographic Conflict Modeling, Spatial Econometrics of Conflict, Georeferenced Conflict Analysis	spatial econometrics, spatial lag model, spatial error model, SAR / SEM
Relacionados	3	5
Resumen	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.	Spatial regression is a family of regression models that build geographic neighbourhood relationships directly into the model, introduced by Luc Anselin in his 1988 treatment of spatial econometrics. It splits into a spatial lag model, where spatial dependence sits in the dependent variable, and a spatial error model, where the dependence sits in the error term.