Drawing Patterns in Human Trafficking Data Through Covariance Analysis

Human trafficking remains one of the most prevalent humanitarian issues of our time, with millions of individuals trafficked across state, country, and continental lines each year. One of the main issues with tackling trafficking is the lack of data available to us that can help shed light into where and when trafficking occurs.

Our project builds upon the foundation provided by the largest anonymized dataset of human trafficking data available–the global synthetic dataset from the Counter Trafficking Data Collaborative. We apply statistical methods to impute missing values in this data, and then conduct statistical analysis to find correlations between different data trends over time. Finally, we utilize a large language model to provide these trends with the appropriate historical / geopolitical context. Through this effort, we hope to identify and enhance our understanding of the factors that drive trafficking in order to help combat it more effective.