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

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:

1. Capturing Non-Linear Relationships: Modeling complex interactions between neighborhood characteristics and shooting risk
2. Handling Spatial Autocorrelation: Incorporating spatial structure through lag variables
3. Variable Importance: Identifying which neighborhood features are most predictive
4. 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:

1. Resource Allocation: Targeting violence prevention resources to high-risk areas
2. Prevention Planning: Understanding neighborhood-level drivers of gun violence
3. Policy Evaluation: Assessing how neighborhood changes affect violence risk
4. 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