Spatially explicit and reliable data on poverty is critical for both policy makers and researchers. However, such data remain scarce particularly in developing countries. Current research is limited in using environmental data from different sources in isolation to estimate poverty despite the fact that poverty is a complex phenomenon which cannot be quantified either theoretically or practically by one single data type. This study proposes a random forest regression (RFR) model to estimate poverty at 10 km× 10 km spatial resolution by combining features extracted from multiple data sources, including the National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) Day/Night Band (DNB) nighttime light (NTL) data, Google satellite imagery, land cover map, road map and division headquarter location data. The household wealth index (WI) drawn from the Demographic and Health Surveys (DHS) program was used to reflect poverty level. We trained the RFR model using data in Bangladesh and applied the model to both Bangladesh and Nepal to evaluate the model’s accuracy.
This project produced publications as below:
Zhao, X., Yu, B., Liu, Y., Chen, Z., Li, Q., Wang, C., & Wu, J. (2019). Estimation of Poverty Using Random Forest Regression with Multi-Source Data: A Case Study in Bangladesh. Remote Sensing, 11(4), 375.
Zhao, X., Yu, B., Liu, Y., Yao, S., Lian, T., Chen, L., ... & Wu, J. (2018). NPP-VIIRS DNB daily data in natural disaster assessment: evidence from selected case studies. Remote Sensing, 10(10), 1526.