Poverty, as a GIS use case, refers to the application of spatial analysis and geospatial data to identify, visualize, and evaluate the geographic distribution of economic deprivation across communities, regions, and nations. By integrating socioeconomic indicators — including income levels, unemployment rates, housing quality, and access to essential services — into GIS platforms, analysts can generate detailed poverty maps that reveal spatial patterns, hotspots, and disparities that traditional tabular analysis often obscures.

GIS technology enables professionals to overlay multiple datasets, such as census data, land use classifications, infrastructure networks, and health metrics, facilitating a multidimensional understanding of poverty's root causes and compounding factors. Spatial clustering techniques, including kernel density estimation and Moran's I statistics, help identify concentrated areas of deprivation requiring targeted interventions.

The practical benefits of applying GIS to poverty analysis include improved resource allocation, evidence-based policy development, and more effective program delivery by government agencies, NGOs, and humanitarian organizations. Interactive dashboards and web mapping applications further democratize access to poverty data, enabling stakeholders at all levels to make spatially informed decisions that drive meaningful socioeconomic change.