Machine Learning Approach with Multiple Open-source Data for Mapping and Prediction of Poverty in Myanmar

Poverty is rampant and very crucial issue in developing countries. Therefore, in this paper, we explore the implementation of machine learning on the estimation of poverty by training input data from widely available and accessible open-source, including nighttime lights (NTL) and OpenStreetMap (OSM) data. We propose this approach as a straightforward, cost-effective and alternative option for previous studies which have been done by deep learning. We applied the linear regression and ridge regression algorithm as our baseline models while using random forest regression, gradient boosting regression and xgboost regression to achieve the better performance. We found that our best model can explain approximately 74% of the variation in wealth index from input features of Myanmar. We then created the poverty map in province administrative level for Myanmar, which indicates that conventional machine learning models with open-source data can still be as efficient as deep learning on poverty estimation.