Assessing social determinants of health using geospatial analytics to reduce disparities and inform targeted interventions

Health inequities persist across populations due to uneven distribution of social determinants of health [SDOH], including income, education, housing quality, environmental exposure, and access to healthcare services. Traditional aggregate-level analyses often mask localized vulnerabilities, limiting the effectiveness of public health interventions. This study examines the application of geospatial analytics to assess SDOH at fine spatial resolutions in order to identify disparity hotspots and inform targeted, equity-oriented interventions. By integrating geospatial data with demographic, socioeconomic, environmental, and health outcome datasets, the approach enables spatial pattern detection, risk stratification, and visualization of complex interactions shaping health outcomes. Geographic information systems [GIS], spatial clustering, and predictive spatial modeling are highlighted as critical tools for uncovering place-based inequities and guiding resource allocation. The paper demonstrates how geospatial analytics can support evidence-based policymaking, improve cross-sector planning, and enhance accountability in public health systems. Furthermore, it discusses ethical considerations, data governance challenges, and the importance of community engagement in geospatial health analyses. The findings underscore the value of spatially informed decision-making in reducing health disparities, optimizing intervention targeting, and advancing health equity, particularly in underserved and marginalized communities. Geospatial assessment of SDOH is positioned as a vital component of modern population health strategies and precision public health frameworks.