The emergence of conflict is a complex issue with numerous drivers and interactions playing a role. Exploratory dimension-reduction techniques can reveal patterns of association in such complex data. In this study, an existing dataset was reanalyzed using factor analysis for mixed data to visualize the data in two-dimensional space to explore the conditions associated with high levels of conflict. The first dimension was strongly associated with resilience index, control of corruption, income, income inequality, and regime type, while the second dimension was strongly associated with oil production, regime type, conflict level, political terror level, and water stress. Hierarchical clustering from principal components was used to group the observations into five clusters. Country trajectories through the two-dimensional space provided examples of how movement in the first two dimensions reflected changes in conflict, political terror, regime type, and resilience index. These trajectories correspond to the evolution of themes in research on conflict, particularly in terms of considering the importance of climate or environmental variables in stimulating or sustaining conflict. Understanding conditions associated with high conflict can be helpful in guiding the development of future models for prediction and risk assessment.
Copyright: © 2024 Vazquez et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.