<b>Global Mapping of Child Labor</b>

Using child labor data from the Multiple Indicator Cluster Surveys (MICS) database and Demographic and Health Surveys (DHS) database from 2001 to 2020, combined with environmental, socio-economic, and demographic data in the study area, and used statistical methods to model. Introducing machine learning to interpret nonlinear effects significantly improves the predictive accuracy of the model, resulting in the creation of a grid map of child labor distribution with a resolution of 5 × 5 kilometers.