Review Article

Geospatial Social Vulnerability Models for Predicting Firearm Injury Hotspots in Underserved U.S. Communities: A Critical Narrative Review

Abstract

Firearm injury remains a major public health challenge in the United States, disproportionately affecting socially disadvantaged communities and contributing to persistent health inequities. Geographic disparities in firearm violence are strongly influenced by structural factors such as poverty, residential segregation, housing instability, limited healthcare access, and neighborhood disinvestment, highlighting the need for place-based prevention strategies. Recent advances in geographic information systems (GIS), spatial epidemiology, and geospatial analytics have substantially improved the identification of firearm injury hotspots through techniques such as hotspot analysis, spatial autocorrelation, and predictive modeling. However, existing approaches remain fragmented, frequently emphasizing historical firearm incidents or demographic characteristics while inadequately integrating multidimensional measures of social vulnerability into predictive frameworks. This narrative review critically synthesizes current evidence on geospatial approaches for predicting firearm injury hotspots, evaluates commonly used spatial analytical methods and social vulnerability indices, examines the strengths and limitations of existing predictive models, and identifies methodological and translational challenges limiting their public health application. Building upon these findings, the review proposes an Integrated Geospatial Social Vulnerability Framework for Firearm Prediction (IGSVF) that combines social determinants of health, neighborhood vulnerability assessment, geospatial analytics, and predictive risk modeling into a unified conceptual approach for identifying high-risk communities. The proposed framework emphasizes equitable resource allocation, precision public health, and evidence-informed violence prevention by integrating spatial intelligence with multidimensional social vulnerability assessment. Future research should prioritize prospective validation, standardized vulnerability measures, explainable artificial intelligence, and real-time surveillance to improve model transparency, generalizability, and policy relevance. Integrating geospatial intelligence with comprehensive social vulnerability assessment offers a promising pathway toward more equitable and effective firearm injury prevention in underserved U.S. communities. 

Authors affiliation:

Nimotalai Olusola Kassim (ORCID): Department of Public Health, School of Health Sciences, University of New Haven, Connecticut, United States of America.

How to Cite: N.O. Kassim. Geospatial Social Vulnerability Models for Predicting Firearm Injury Hotspots in Underserved U.S. Communities: A Critical Narrative Review. Journal of Public Health and Environmental Research, 2(2):11–25, 2026. https://doi.org/10.58612/jpher222