国际劳工组织（ILO）：使用遥感和随机森林的加纳网格劳动力市场数据（英文版）

This study presents high-resolution (0.005) gridded labor market data, generated by downscaling district-level census data for Ghana using random forest algorithms and remote sensing. It addresses the lack of spatially disaggregated labor market data by mapping 17 employment categories – including age, gender, skills, status, sectors, unemployment, and NEET. Auxiliary data (64 variables) such as land cover, nighttime lights, infrastructure, and points of interest are integrated to capture dem