Harnessing Data Science to Understand and Address Poverty: A Process-Based Perspective

Abstract

Poverty remains a critical global issue, intricately connected to healthcare, education, environmental sustainability, and economic disparity. This study investigates the potential of data science as a transformative framework for understanding and addressing poverty. By leveraging big data analytics, machine learning, and real-time data processing, the research uncovers actionable insights into the structural and dynamic factors contributing to poverty. The study emphasizes predictive modeling to identify at-risk populations, optimize social program delivery, and harness localized data for context-sensitive policy development. It also addresses the challenges of digital exclusion and the ethical use of data in poverty-related interventions, aiming to support sustainable and equitable development.