Spatial-Analysis | ISSUES | The Ohio State University

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

Applying Social Geography to Structural Mitigation in Capital Defense

Wed, 02 Jul 2025 00:00:00 +0000

Summary:
This project applies social geography and spatial data analysis to strengthen structural mitigation arguments in capital defense. By documenting how neighborhood conditions—including poverty, segregation, environmental stressors, and disinvestment—shape an individual’s developmental context and legal outcomes, we provide place-based, legally admissible evidence aligned with Ohio law and ABA mitigation guidelines.

Research Questions:

RQ1: How can spatial analysis of public data reveal community-level risk factors—such as poverty, segregation, and housing instability—that contribute to life trajectories relevant to culpability and sentencing?
RQ2: In what ways can social geography and structural disadvantage be systematically incorporated into mitigation investigations to support fair, contextualized legal outcomes in death penalty cases?


Short description:	This project integrates spatial data and social geography to produce legally admissible, objective evidence of how systemic community-level factors influence life outcomes relevant to culpability and sentencing, advancing structural mitigation in capital defense.
Lead developer:	ProfGEBS
Further reading:	Review the presentation slides from the Ohio Bar Association’s Death Penalty Defense Seminar
Download:	Github repo
Main data source(s):	U.S. Census, Area Deprivation Index, Urban Institute data, Cook County parcel data, historical redlining maps
Coverage:	Ohio and Cook County, Illinois
Citation:	Barboza-Salerno, G. (2025). Applying Social Geography to Structural Mitigation in Capital Defense. Presented at the Ohio Bar Association Death Penalty Defense Seminar, approved by the Supreme Court of Ohio for capital case CLE requirements.

Check out this environmental risk mapping tutorial for a deeper look at spatial methods used in this work.

Evaluating Child Fatality Reports using Structural Topic Models

Wed, 02 Jul 2025 00:00:00 +0000

An examination of the effect of area-level characteristics on juvenile justice and child welfare referrals using multivariate Bayesian spatial modeling

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

Plain-Language Summary

This study examined the spatial overlap of child welfare and juvenile justice involvement using multivariate Bayesian spatial modeling. Results show a significant positive association between juvenile justice and child maltreatment referrals across neighborhoods. After adjusting for residential instability, immigrant concentration, and residential segregation, children in the least deprived 20% of neighborhoods were 95.2% and 55.5% less likely to be referred to child welfare or justice systems, respectively, compared to those in the most deprived 20%.

Street-View Measures of Urban Greenness, Area-Level Deprivation, and the Risk of Firearm-Related Homicide: A Matched Case-Control Study

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

Plain-Language Summary

This study examined how pedestrian-level green space relates to firearm-related and natural deaths in Cook County, Illinois, using a matched case-control design. Various greenness measures, including street-view green imagery, tree canopy coverage, and green space along walkable streets, were inversely associated with firearm homicide risk. Protective effects of tree canopy and localized greenery were stronger in less deprived neighborhoods, while neighborhood racial composition influenced the relationship between park proximity and homicide risk. Overall, street-level green space is linked to lower homicide odds and may offer greater protection in vulnerable communities.

A Multilevel Analysis of Individual and Community Factors Associated With Case Dispositions Following Child Maltreatment Investigations

Tue, 18 Jun 2024 00:00:00 +0000

Plain-Language Summary

This study investigates how individual case characteristics and county-level social vulnerabilities influence the decisions made by child protective services (CPS) investigators when recommending services for families. Analyzing over 12,000 cases across New Mexico, the study looks at factors such as child age, abuse type, prior history, and county-level deprivation (e.g., housing and transportation deficits).

The results show that county-level characteristics significantly influence whether a family receives support services. Even when families present with similar risk levels, those living in counties with higher structural vulnerabilities are subject to different service disposition outcomes. The findings highlight the need for standardized decision-making tools to ensure equitable treatment and service distribution across different geographic regions.