Xizhi Zhao|Bailang Yu|Yan Liu|Zuoqi Chen|Qiaoxuan Li|Congxiao Wang|Jianping Wu
Estimation of Poverty Using Random Forest Regression with Multi-Source Data: A Case Study in Bangladesh
East China Normal University|University of Queensland|East China Normal University|University of Queensland|East China Normal University|East China Normal University|East China Normal University|East China Normal University
spatially explicit reliable data poverty critical policy makers researchers however data remain scarce particularly developing countries current research limited using environmental data different sources isolation estimate poverty despite fact poverty complex phenomenon cannot quantified either theoretically practically one single data type study proposes random forest regression rfr model estimate poverty 10 km 10 km spatial resolution combining features extracted multiple data sources including national polarorbiting partnership visible infrared imaging radiometer suite nppviirs daynight band dnb nighttime light ntl data google satellite imagery land cover map road map division headquarter location data household wealth index wi drawn demographic health surveys dhs program used reflect poverty level trained rfr model using data bangladesh applied model bangladesh nepal evaluate models accuracy results show r2 actual estimated wi bangladesh 070 indicating good predictive power model wi estimation r2 actual estimated wi 061 nepal also indicates good generalization ability model furthermore negative correlation observed district average wi poverty head count ratio hcr bangladesh pearson correlation coefficient 06 using gini importance identify proximity urban areas important variable explain poverty contribute 379 explanatory power compared study used ntl google satellite imagery isolation estimate poverty method increases accuracy estimation given data use globally publicly available methodology reported study would also applicable countries regions estimate extent poverty