A Prevention-Focused Geospatial Epidemiology Framework for Identifying Multilevel Vulnerability Across Diverse Settings

TL;DR

This paper introduces a geospatial framework to identify and address public health vulnerabilities by combining data on environment, structure, and behavior for targeted prevention.

Contribution

A novel geospatial architecture that translates multilevel data into localized risk signatures for precision prevention strategies.

Findings

Geospatial epidemiology reveals spatially patterned vulnerabilities influenced by ecological, structural, and built-environment factors.

Integrating spatial analytics improves early risk identification and enables equitable, context-aware public health interventions.

Abstract

What are the main findings?
Geospatial epidemiology uncovers spatially patterned vulnerabilities driven by ecological, structural, and built-environment determinants.The proposed architecture translates multilevel data into localized risk signatures for precision prevention strategies.

Geospatial epidemiology uncovers spatially patterned vulnerabilities driven by ecological, structural, and built-environment determinants.

The proposed architecture translates multilevel data into localized risk signatures for precision prevention strategies.

What are the implications of the main findings?
Spatial intelligence enables earlier identification of risk, context-aware clinical screening, and targeted community-level interventions.Integrating geospatial analytics across systems helps overcome systemic data fragmentation, advancing equitable, context-responsive public health action.

Spatial intelligence enables earlier identification of risk, context-aware clinical screening, and targeted community-level interventions.

Integrating geospatial analytics across systems helps overcome systemic data fragmentation, advancing equitable, context-responsive public health action.

Background/Objectives: Geographic Information Systems (GIS) offer essential capabilities for identifying spatial concentrations of vulnerability and strengthening context-aware prevention strategies. This manuscript describes a geospatial architecture designed to generate anticipatory, place-based risk identification applicable across diverse community and institutional environments. Interpersonal Violence (IPV), one of several preventable harms that benefit from this spatially informed analysis, remains a critical public health challenge shaped by structural, ecological, and situational factors. Methods: The conceptual framework presented integrates de-identified surveillance data, ecological indicators, environmental and temporal dynamics into a unified spatial epidemiological model. Multilevel data layers are geocoded, spatially matched, and analyzed using clustering (e.g., Getis-Ord Gi*), spatial dependence metrics (e.g., Moran’s I), and contextual modeling to support anticipatory identification of elevated vulnerability. Framework Outputs: The model is designed to identify spatial clustering, mobility-linked risk patterns, and emerging escalation zones using neighborhood disadvantage, built-environment factors, and situational markers. Outputs are intended to support both clinical decision-making (e.g., geocoded trauma screening, and context-aware discharge planning), and community-level prevention (e.g., targeted environmental interventions and cross-sector resource coordination). Conclusions: This framework synthesizes behavioral theory, spatial epidemiology, and prevention science into an integrative architecture for coordinated public health response. As a conceptual foundation for future empirical research, it advances the development of more dynamic, spatially informed, and equity-focused prevention systems.

Figures