Predicting Crisis Behaviour through Socioeconomic Resilience
Predicting Crisis Behaviour through Socioeconomic Resilience

Summary/Abstract: Socioeconomic resilience is increasingly recognised as a critical component of national security, as it shapes population behaviour during crises. This article analyses the relationship between socioeconomic characteristics and population responses to crisis conditions from the perspectives of crisis management and state security policy. Unlike descriptive vulnerability focused approaches, the study conceptualises socioeconomic resilience as a predictive variable and integrates social vulnerability theory, urban geography, and data driven analytics within a unified framework. A Random Forest machine learning model is applied to structured socioeconomic data to capture nonlinear relationships and identify key predictors of evacuation behaviour, population mobility, and social tension. Results indicate that income stability, educational attainment, housing conditions, and access to social infrastructure significantly influence crisis-related behaviour and enable the identification of high-risk population clusters. The findings demonstrate that socioeconomic resilience is an operational, measurable dimension of national security and provide a data driven decision support tool for crisis management and security planning.

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