Integrating Spatial Clustering and Machine Learning for Territorial Segmentation and Short-Term Crime Prediction in Tamaulipas, Mexico

Crime prediction and territorial analysis have become increasingly relevant for public security planning, particularly in regions characterized by heterogeneous spatial and temporal crime dynamics. This study proposes an integrated methodological framework that combines spatial clustering and supervised machine learning to support territorial segmentation and short-term crime occurrence prediction in the state of Tamaulipas, Mexico. The proposed approach follows the Knowledge Discovery in Databases (KDD) process and is based on official crime records analyzed at the neighborhood (colonia) level across eleven municipalities. In the first stage, a K-Means clustering algorithm is applied to identify homogeneous territorial patterns based on crime incidence and sociodemographic characteristics. In the second stage, an AdaBoost classifier is implemented to predict the occurrence of crime events using different temporal windows. Model performance is evaluated using precision, recall, F1-score, and accuracy, with particular emphasis on recall due to the operational relevance of minimizing false negatives in public security contexts. The results indicate that the combined spatial and predictive approach supports the understanding of territorial crime dynamics and provides stable predictive performance across municipalities. This integration offers a practical and replicable framework to support data-driven decision-making in public security and territorial planning, particularly in contexts with limited analytical resources.