Predicting Poverty with Machine Learning and Geospatial Data

Abstract

Abstract This chapter focuses on Sustainable Development Goal (SDG) 1, which aims to end poverty by 2030. Although significant progress has been made in poverty reduction, but the pace has slowed, especially after the COVID-19 pandemic. As of 2024, 8.9% of people global population live in extreme poverty, while 23.6% lives in poverty in low- and middle-income countries. South Asia, including India, continue to faces serious challenges especially in accurately measuring poverty. Traditional household surveys, while useful, are often costly, time-consuming, and outdated. To address this gap, this study explores the use of machine learning (ML) technique the combine geospatial and survey data to improve poverty prediction in India. It incorporates indicators such as nightlight intensity, land temperature, rainfall, vegetation, and points of interest. Among the ML models tested, the Random Forest algorithm produced the most accurate results. Nightlight intensity and point of interest density emerged as the most important predictors. These findings highlights the potential of ML tools to generate faster and more precise poverty estimates at local levels, offering valuable support for targeted policymaking.