George Kimwomi, Mvurya Mgala

Abstract

Governments employ social protection systems in their efforts to eradicate poverty in line with the Sustainable Development Goals (SDGs) of the United Nations (UN). They however face challenges in reliably identifying vulnerable populations which deserve social protection. This results in wrongful inclusions and exclusions thereby hampering progress towards achievement of the SDGs. This study explores the transformative impact of leveraging data and analytics to map vulnerable populations as a crucial step in developing evidence-based poverty reduction strategies. Leveraging on household data maintained by government agents and advanced analytics of the current the digital age can provide unprecedented insights into poverty dynamics. The study examines methodologies, tools, and successful applications of data-driven approaches used in identifying and mapping vulnerable populations across diverse contexts. The review purposely delves into the integration of machine learning and big data analytics in uncovering intricate poverty patterns for affected populations. The paper underscores the implications of accurate poverty mapping for evidence-based policymaking, enabling tailored interventions and resource allocation. Ethical considerations, privacy safeguards, and community engagement are highlighted. In conclusion, the paper advocates for collaborative efforts to maximize the potential of data and analytics in designing impactful, evidence-based interventions for vulnerable populations worldwide.

Key words: Data analytics, Poverty mapping, Evidence-based strategies, Vulnerable populations