Where is my child? Spatiotemporal distribution of child trafficking in China and predicting the locations of missing children

In China, the illegal adoption of missing persons and especially of missing children is a major public safety issue that affects social and family stability. Recent work has established a trafficking network developed from a volunteer-managed database of missing people that identifies and locates node cities and critical paths of illegal adoption. In order to evaluate locations where trafficking can be identified and provide direct advice for affected families, this study analyses the temporal and spatial distribution of the missing population and explores factors that affect their transfer. We use spatiotemporal information to construct multiple random forest models to predict the locations of missing person transfer on a larger spatial scale. The proposed independent models achieve very high levels of accuracy: provinces potentially entered, destination regions, relative distances and relative directions. Moreover, an integrated city-level forecasting algorithm can effectively locate the city a missing person was trafficked to. From our driving factor analysis, the transfer paths are strongly correlated with source provinces and regions. The study also shows that the transfer of missing persons is driven by multiple factors rather than by a single element.