Development and Spatial Validation of a Random Forest Prediction Model for Firearm-Related Injury Risk in Chicago Census Tracts

Wed, 01 Oct 2025 00:00:00 +0000

Presentation at Women in Statistics and Data Science Conference

Our presentation, Modeling Firearm Injury Risk Using Spatial Random Forests, was delivered by Dr. Gia Barboza-Salerno at the Women in Statistics and Data Science on November 12, 2025 in Cincinnati, OH.

Abstract

This research develops and validates a spatial random forest prediction model for firearm-related injury risk at the census tract level in Chicago. By incorporating spatial autocorrelation and neighborhood-level predictors, the model achieves high predictive accuracy in identifying areas at elevated risk for gun violence.

Methodology

Spatial Random Forest Approach

The spatial random forest model extends traditional random forest methods by:

Capturing Non-Linear Relationships: Modeling complex interactions between neighborhood characteristics and shooting risk
Handling Spatial Autocorrelation: Incorporating spatial structure through lag variables
Variable Importance: Identifying which neighborhood features are most predictive
Robust Predictions: Providing out-of-sample risk estimates

Data Sources

Shooting incident data from Chicago
Census tract-level socioeconomic indicators
Built environment features
Spatial lag variables to capture spillover effects

Model Validation

Cross-validation for predictive performance assessment
Spatial autocorrelation diagnostics
Comparison with traditional regression approaches

Key Findings

The predicted shooting risk map (shown above) demonstrates:

High Spatial Variation: Risk scores range from 0 to over 80 predicted incidents
Concentrated Risk: Certain neighborhoods show elevated firearm violence risk
Model Accuracy: Spatial random forest successfully captures fine-grained risk patterns
Spatial Clustering: Violence risk exhibits significant spatial autocorrelation

Applications

This spatial prediction model has important applications for:

Resource Allocation: Targeting violence prevention resources to high-risk areas
Prevention Planning: Understanding neighborhood-level drivers of gun violence
Policy Evaluation: Assessing how neighborhood changes affect violence risk
Early Warning: Detecting emerging hotspots before violence escalates

Implications

The research demonstrates that spatial random forest models can:

Improve prediction of firearm violence risk beyond traditional methods
Identify actionable neighborhood-level intervention targets
Account for complex spatial dependencies in violence patterns
Support evidence-based violence prevention strategies

Conference: Women in Statistics and Data Science Conference
Location: Cincinnati, Ohio
Date: October 1-3, 2025
Authors: Gia Barboza-Salerno, Hexin Yang, Taylor Harrington

Development and Spatial Validation of a Random Forest Prediction Model for Firearm-Related Injury Risk in Chicago Census Tracts

Wed, 01 Jan 2025 00:00:00 +0000

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.

Random Forest | ISSUES | The Ohio State University

Development and Spatial Validation of a Random Forest Prediction Model for Firearm-Related Injury Risk in Chicago Census Tracts

Presentation at Women in Statistics and Data Science Conference

Abstract

Methodology

Spatial Random Forest Approach

Data Sources

Model Validation

Key Findings

Applications

Implications

Development and Spatial Validation of a Random Forest Prediction Model for Firearm-Related Injury Risk in Chicago Census Tracts

Plain-Language Summary