From indicators to insight: a model to predict depopulation risk in Colombia

Depopulation poses a territorial challenge in Colombia, exacerbated by the long-term effects of armed conflict, inequality, and rural poverty, which have resulted in uneven demographic and development patterns. This study examines the factors influencing territories at risk of demographic decline in Colombia. This is a territorial phenomenon whose implications vary across regions, and are shaped by Colombia’s social, cultural, and geographic diversity. A model that estimates depopulation risk by incorporating economic variables, conflict, poverty, and rurality was developed using machine learning techniques. The Support Vector Machine approach achieved an f1-score of 78.31%, demonstrating its effectiveness as a predictive tool. The framework emphasizes a regional perspective, enabling the identification and comparison of differences between regions through municipal-level variables that capture territorial characteristics. The model provides a practical tool for predicting and preventing depopulation risks, allowing policymakers to design tailored strategies for regions identified as vulnerable. This perspective is particularly relevant in Colombia, where departments are the key administrative units for regional planning and policy design. However, the proposed approach is not limited to the Colombian context. It can be adapted to other national contexts with varying territorial divisions, offering a flexible framework for analysing territorial heterogeneity and regional disparities. By linking data-driven modeling with regional development analysis, the model contributes to the design of informed strategies for addressing demographic decline and its long-term implications for regional development. Therefore, this study contributes to broader discussions on demographic change, territorial inequality, and the design of informed public policies in diverse socio-spatial contexts.