How Data Science Can Give Further Understanding on Urban Poverty

This article explores how data science can provide deeper insights into urban poverty, moving beyond traditional statistical analyses. It discusses the use of big data, machine learning, and spatial analysis to identify and understand the multifaceted dimensions of poverty within urban environments. The piece highlights how data science can reveal hidden patterns, correlations, and causal factors related to income inequality, access to resources, housing, and social services. By integrating diverse data sources, it enables more precise targeting of interventions and the development of evidence-based policies for urban development and poverty alleviation.