Discovering Localized Drivers of Unrest Events using Clustering and XGBoost

Social unrest, a multifaceted phenomenon that is influenced by a variety of interconnected factors, presents substantial obstacles to societal stability and governance. The comprehension of local nuances is frequently restricted by the analysis of drivers of unrest at broad geographic scales or the isolation of specific causes in traditional studies. This paper introduces the SCEIGE framework, which classifies unrest drivers into six essential categories: Socio-demographic, Cultural, Environmental, Infrastructural, Geographic, and Economic. This framework is designed to address these challenges. SCEIGE offers a comprehensive perspective on the fundamental causes of social unrest by modeling geographic spaces at fine resolutions and incorporating a wide range of variables. We further enhance this framework by introducing a novel clustering and machine learning methodology, SC-XG (SCEIGE Clustering with XGBoost), which organizes geographic regions according to SCEIGE patterns. SC-XG not only reveals the local drivers of unrest but also facilitates the predictive analysis of social unrest events. This paper also illustrates the effectiveness of high-resolution SCEIGE geo-rasters in analyzing social unrest and confirms that the drivers of unrest differ across regions, underscoring the necessity of a local-level understanding. We identify critical, region-specific unrest drivers and address the broader implications for predicting and mitigating social unrest globally by applying SC-XG to unrest patterns in India.