Exploring machine learning trends in poverty mapping: A review and meta-analysis

Abstract

Machine Learning (ML) has rapidly advanced as a transformative tool across numerous fields, offering new avenues for addressing poverty-related challenges. This study provides a comprehensive review and meta-analysis of 215 peer-reviewed articles published on Scopus from 2014 to 2023, underscoring the capacity of ML methods to enhance poverty mapping through satellite data analysis. Our findings highlight the significant role of ML in revealing micro-geographical poverty patterns, enabling more granular and accurate poverty assessments. By aggregating and systematically evaluating findings from the past decade, this meta-analysis uniquely identifies overarching trends and methodological insights in ML-driven poverty mapping, distinguishing itself from previous reviews that primarily synthesize existing literature. The nighttime light index emerged as a robust indicator for poverty estimation, though its predictive power improves significantly when combined with daytime features like land cover and building data. Random Forest consistently demonstrated high interpretability and predictive accuracy as the most widely adopted ML model. Key contributions from regions such as the United States, China, and India illustrate the substantial progress and applicability of ML techniques in poverty mapping. This research seeks to provide policymakers with enhanced analytical tools for nuanced poverty assessment, guiding more effective policy decisions aimed at fostering equitable development on a global scale.