Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| UCDP Conflict Data Analysis× | Spatial Conflict Analysis× | |
|---|---|---|
| Nozare | International Relations | International Relations |
| Saime≠ | Process / pipeline | Regression model |
| Izcelsmes gads≠ | 2013 | 2002 |
| Autors≠ | Uppsala Conflict Data Program (Ralph Sundberg & Erik Melander for UCDP-GED) | Spatial-analysis-of-conflict literature (e.g., Michael Ward & Kristian Skrede Gleditsch) |
| Tips≠ | Coding and analysis of organized-violence events and conflicts | Spatial regression / spatial-statistical modeling of conflict |
| Pirmavots≠ | Sundberg, R., & Melander, E. (2013). Introducing the UCDP Georeferenced Event Dataset. Journal of Peace Research, 50(4), 523–532. 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 ↗ |
| Citi nosaukumi | UCDP Analysis, UCDP Georeferenced Event Dataset Analysis, Uppsala Conflict Data Analysis, Organized Violence Event Analysis | Spatial Analysis of War and Peace, Geographic Conflict Modeling, Spatial Econometrics of Conflict, Georeferenced Conflict Analysis |
| Saistītās | 3 | 3 |
| Kopsavilkums≠ | UCDP conflict data analysis is the coding and quantitative study of organized violence using the datasets of the Uppsala Conflict Data Program. UCDP distinguishes three categories of organized violence — state-based armed conflict, non-state conflict, and one-sided violence against civilians — and codes them from the level of individual fatal events up to annual conflict dyads. The Georeferenced Event Dataset (UCDP-GED), introduced by Sundberg and Melander (2013), pins each event to a place and date, enabling fine-grained spatial and temporal analysis of where and when 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. |
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