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| Conflict Forecasting× | Spatial Conflict Analysis× | |
|---|---|---|
| Fachgebiet | International Relations | International Relations |
| Familie≠ | Machine learning | Regression model |
| Entstehungsjahr≠ | 2019 | 2002 |
| Urheber≠ | Conflict-forecasting community (e.g., Håvard Hegre and the ViEWS team) | Spatial-analysis-of-conflict literature (e.g., Michael Ward & Kristian Skrede Gleditsch) |
| Typ≠ | Operational predictive system for armed conflict | Spatial regression / spatial-statistical modeling of conflict |
| Wegweisende Quelle≠ | Hegre, H., Allansson, M., Basedau, M., Colaresi, M., Croicu, M., Fjelde, H., et al. (2019). ViEWS: A political violence early-warning system. Journal of Peace Research, 56(2), 155–174. DOI ↗ | 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 ↗ |
| Aliasnamen | Political Violence Early Warning, Armed Conflict Forecasting, Conflict Early-Warning Systems, ViEWS-Style Forecasting | Spatial Analysis of War and Peace, Geographic Conflict Modeling, Spatial Econometrics of Conflict, Georeferenced Conflict Analysis |
| Verwandt | 3 | 3 |
| Zusammenfassung≠ | Conflict forecasting is the enterprise of producing calibrated, regularly updated probabilistic predictions of where and when armed conflict will occur, to support early warning and prevention. Exemplified by operational systems such as ViEWS (Hegre et al. 2019), it combines historical conflict data and predictors at fine spatial and temporal resolution, fits and ensembles multiple models, and forecasts violence months ahead — then rigorously evaluates those forecasts against what actually happens. It differs from explanatory conflict analysis by being transparent, prospective, and judged on out-of-sample accuracy rather than on coefficients. | 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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