Predictive Analytics for Human Trafficking Detection: A Machine Learning Framework Grounded in Spatial Criminology

Objectives: Human trafficking remains a critical global crisis, with millions of individuals exploited annually in forced labor and sexual exploitation (UNODC, 2023). This study aims to address the limitations of traditional detection methods, which often fail to uncover the covert nature of trafficking networks due to fragmented data, reactive frameworks, and jurisdictional inconsistencies. The research introduces a pioneering machine learning-based predictive framework to enhance trafficking detection and prevention. Methods: The framework integrates diverse datasets, including hotline reports (2015–2020), socio-economic indicators across all 50 U.S. states, and satellite imagery of urban and rural regions. Machine learning algorithms analyze socio-economic disparities, transportation vulnerabilities, and infrastructure anomalies. The model employs geospatial analytics and fairness audits to ensure equitable predictions and compliance with ethical standards, such as data anonymization and adherence to the Palermo Protocol. Results: The model demonstrated robust performance metrics, achieving an F1-score of 0.88 and an AUC of 0.92. It successfully identified high-risk areas, including urban transit hubs, rural regions with economic disparities, and border areas with migration activity. Demographic prediction disparities were minimized to below 2%, ensuring fairness across diverse populations. Geospatial heatmaps and feature importance analyses provided actionable insights for targeted interventions. Conclusions: This study advances global anti-trafficking efforts by offering a scalable and ethically robust predictive framework. It provides actionable insights for optimizing law enforcement interventions, improving victim outreach, and informing policy development. Future directions include integrating natural language processing for real-time monitoring, blockchain for secure cross-border data sharing, and NGO collaborations to expand the framework in underserved regions. These advancements represent a significant step toward addressing a pressing humanitarian crisis.