Environmental Justice | ISSUES | The Ohio State University

Modeling Firearm Injury Risk Using Spatial Random Forests

Thu, 23 Oct 2025 00:00:00 +0000

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

This study introduces a spatial machine learning framework for predicting firearm injury risk across Chicago census tracts. Using Spatial Random Forest Modeling (SRFM), we integrate social, built, and natural environment indicators, such as social vulnerability, housing burden, green space, traffic volume, and tree canopy, to identify localized structural determinants of firearm violence.

Results demonstrate that racial composition, economic hardship, and environmental stressors jointly predict spatial clustering of firearm injury, while local variable importance reveals that built environment features exert context-specific effects across neighborhoods. These findings highlight the value of spatially aware, data-driven approaches for informing equitable, place-based violence prevention and policy.

Abstract: Gun violence remains a critical public health crisis, particularly in urban centers like Chicago, where firearm-related injuries are among the highest in the nation. These injuries are not randomly distributed; they are spatially concentrated in neighborhoods shaped by structural inequality, including racial segregation, economic disinvestment, and environmental neglect. While random forest models have shown promise in predicting firearm violence and identifying neighborhood-level risk factors, conventional approaches often ignore spatial dependence, leading to biased estimates, inflated variable importance, and reduced geographic generalizability. This study applies spatially explicit machine learning to model non-fatal shootings across Chicago census tracts from 2020 to 2024. A curated set of predictors was compiled, including measures of social and environmental vulnerability, the built environment, and housing inequality. Three research questions guided the analysis: (1) Does incorporating spatial structure via Moran’s Eigenvector Maps (MEMs) in spatial random forests and spatially structured random effects in a Bayesian spatial model improve predictive accuracy compared to non-spatial models? (2) Which neighborhoods exhibit elevated firearm violence risk due to unmeasured spatial processes, as captured by spatial random effects? (3) Does prediction error vary across geographic space, revealing limits to model transferability? Findings suggest that the spatial random forest model outperformed the non-spatial model (pseudo R² = 0.98 vs. test R² = 0.50). Spatial cross-validation revealed geographic variation in accuracy (median nRMSE = 0.76), while spatial random effects from a Bayesian Poisson model identified unexplained latent risk. The enhanced predictive accuracy reveals hidden geographic risk patterns, suggesting that social, structural, and environmental factors can efficiently inform targeted violence prevention strategies.

Download the presentation here .

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