Development and Spatial Validation of a Random Forest Prediction Model for Firearm-Related Injury Risk in Chicago Census Tracts
Abstract
This study describes the development and validation of a machine learning model (random forest) to predict the risk of firearm-related injuries across Chicago neighborhoods. By combining multi-source data, including local crime reports, socioeconomic vulnerabilities, and environmental exposures, the model maps out areas with the highest predicted risk. The model’s predictions were validated against actual administrative records to verify its spatial accuracy. This predictive modeling framework shows how machine learning can help public health officials and community organizations anticipate high-risk spatial hot spots and deploy community-based interventions more effectively.
Plain-Language Summary
This study describes the development and validation of a machine learning model (random forest) to predict the risk of firearm-related injuries across Chicago neighborhoods. By combining multi-source data, including local crime reports, socioeconomic vulnerabilities, and environmental exposures, the model maps out areas with the highest predicted risk.
The model’s predictions were validated against actual administrative records to verify its spatial accuracy. This predictive modeling framework shows how machine learning can help public health officials and community organizations anticipate high-risk spatial hot spots and deploy community-based interventions more effectively.
Dr. Gia Elise Barboza-Salerno
Director
I am an interdisciplinary scholar whose research integrates law, public health, social work, and advanced statistics to study childhood adversity, violence-related harm, and neighborhood-level inequities. I lead the ISSUES Lab at Ohio State, translating spatial and legal analysis into policy and practice.
Hexin Yang
PhD Candidate, Methodologist
Hexin is interested in studying child and adolescent development across various contexts, particularly in school settings, using advanced statistical analyses.
Taylor Harrington
PhD Candidate, Methodologist
Taylor Harrington, MS, is a PhD candidate in Public Health at The Ohio State University. To investigate how multi-level factors shape health behaviors and outcomes, she integrates advanced computational and geospatial methods, including machine learning (e.g., computer vision, random forests, structured topic modeling, text mining) and geospatial modeling (e.g., Bayesian spatial modeling, remote sensing). Through this work, she aims to generate actionable evidence to inform equitable health policy and community-level interventions.