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Regression modelSpatial methods for conflict

Spatial Conflict Analysis

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.

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  1. 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. DOI: 10.1093/pan/10.3.244

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ScholarGate. (2026, June 22). Spatial Analysis and Modeling of Armed Conflict. ScholarGate. https://scholargate.app/it/international-relations/spatial-conflict-analysis

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ScholarGateSpatial Conflict Analysis (Spatial Analysis and Modeling of Armed Conflict). Consultato il 2026-06-24 da https://scholargate.app/it/international-relations/spatial-conflict-analysis · Insieme di dati: https://doi.org/10.5281/zenodo.20539026