Presentations | 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 .

No Bright Line: How AI-driven Technology Blurs Legal Boundaries Between Prevention and Punishment in Family Regulation

Wed, 22 Oct 2025 00:00:00 +0000

This new article by Dr. Gia E. Barboza-Salerno argues that Predictive Risk Models (PRMs) can be effective when focused on prevention. The argument is grounded in two core principles. The first is a rights-based balancing approach that extends beyond privacy concerns to encompass due process protections tied to legally recognized status.
The second emphasizes that PRMs, rather than serving solely as detection or risk assessment tools, can help identify structural risk factors, guide equitable public investment, and promote accountability, without expanding surveillance or punitive measures that compound systemic harm.

Together, these principles challenge the false dichotomy between prevention and punishment that underlies much of AI’s deployment in family regulation. By integrating a legal and ethical framework rooted in rights protection and structural accountability, the article redefines what responsible and justice-oriented AI looks like in the context of family welfare systems.

Read the full article here: “Barboza-Salerno, G. E. (2025). No bright line: How AI-driven technology blurs legal boundaries between prevention and punishment in family regulation. Family Advocate, 47(2), 00–00. American Bar Association.”

Beyond Urban–Rural Binaries: Remote Sensing Indicators of Built Environment Risk for Firearm Injury in Cook County, Illinois

Wed, 22 Oct 2025 00:00:00 +0000

Presented at the 2025 National Research Conference for the Prevention of Firearm-Related Harms, this study develops a remote-sensing–based framework to identify built environment risk factors for firearm injury in Cook County, Illinois. Using satellite-derived indicators of nighttime light intensity, impervious surface coverage, vegetation (NDVI), and built-up density merged with firearm injury data from 2020–2024, we fit spatial random forest models and Bayesian spatial Poisson models to quantify environmental predictors of firearm harm.

Findings show that built environment characteristics—rather than traditional urban–rural categories—more accurately distinguish high-risk census tracts. High built-up intensity, fragmented greenness, and asymmetric nighttime activity remain strong predictors even after accounting for area-level deprivation. These results demonstrate the utility of remote sensing for identifying structural vulnerability and guiding place-based firearm injury prevention.

📄 Presentation

Download the presentation here .

GIS Day 2025

Wed, 22 Oct 2025 00:00:00 +0000

Congratulations to our undergraduates Oliva McLucas and Malcolm McCarthy for their abstracts accepted to the 2025 GIS Day at The Ohio State University on November 19, 2025.

Olivia McLucas: Introducing a Spatial Adverse Childhood Environments Index using Geographical Information Systems We extend the cumulative Adverse Childhood Experiences (ACEs) framework to the neighborhood scale with spACEs (Spatial Adverse Childhood Environments), an index of structural disadvantage in childhood. The spACES index is constructed from county-level measures in the 1990 Census, including poverty, educational attainment, employment structure, housing burden, single-parent prevalence, and racial residential segregation, which reflect the environments in which the Opportunity Atlas birth cohorts (children born 1978 to 1985) were raised. The Opportunity Atlas links childhood places of residence to adult outcomes, capturing household income rank, educational attainment, incarceration, and family formation (2010-2015), as well as credit outcomes (2020). Using GIS, we visualize geographic variation in spACEs and assess its association with long-term adult outcomes. We propose spACEs as a revised measure of childhood structural adversity that captures the enduring impact of neighborhood disadvantage across the life course.

McCarthy, M.: Mapping Traumatic Brain Injury using Geographic Information Systems Traumatic brain injury (TBI) is a critical public health problem linked to numerous mechanisms, including unintentional injuries such as falls and motor vehicle crashes and intentional injuries such as assaults. Despite the magnitude of this burden, no research has systematically examined the geographic distribution of TBI hospitalizations and their county-level correlates. We utilize county-level hospitalization data from the Washington State Department of Health, filtered to include all TBI-related admissions by year. These records are linked to the Area Deprivation Index (ADI) for the period 2016–2023, encompassing domains of economic hardship and inequality, educational attainment, and financial strength. The merged dataset enables the analysis of spatial and temporal patterns of TBI hospitalization risk and tests whether county-level social and economic disadvantage is associated with higher rates of TBI hospitalization. Using GIS visualization and regression models, we demonstrate how community structural conditions shape the distribution of TBI and inform strategies for prevention.

The presentations will be made available after the conference.

Enhancing Brain Injury Surveillance Using Large Language Models and Topic Modeling

Sun, 19 Oct 2025 00:00:00 +0000

Our presentation, Enhancing Brain Injury Surveillance Using Large Language Models and Topic Modeling, was delivered by Dr. Shockley McCarthy at the AI for BI Conference at The Ohio State University on October 17, 2025.

This work introduces an AI-driven framework for improving the detection and contextual understanding of brain injury cases using unstructured medical and administrative text. By applying large language models (LLMs) and structured topic modeling, we extract interpretable patterns from injury narratives and ICD-coded data to identify mechanisms of injury, intent, and underreported cases of intimate partner violence (IPV)–related brain injury.

Integrating methods from public health, law, and computational linguistics, our approach aims to enhance the precision of injury surveillance systems and inform equitable prevention and policy strategies addressing violence-related traumatic brain injury (TBI).

Download the presentation here .

Dr. Barboza-Salerno Addresses Grand Challenge to Prevent Gun Violence

Tue, 06 Jan 2026 00:00:00 +0000

Dr. Gia Barboza-Salerno addresses the Grand Challenge to Prevent Gun Violence.

Detecting Hidden Mechanisms of Abuse-Related Injury in Children: Structured Topic Modeling of Clinical Death Reviews and Legal Opinions

Mon, 24 Aug 2026 00:00:00 +0000

Presented at the ISPCAN World Congress on Child Abuse and Neglect, Melbourne, Australia, August 2426, 2026.

Full title: Detecting Hidden Mechanisms of Abuse-Related Injury in Children: Structured Topic Modeling of Clinical Death Reviews and Legal Opinions Triangulating Multidisciplinary Child Fatality Reviews with Factual and Procedural Narratives from Case Law

Abstract

Existing child maltreatment surveillance systems miss or misclassify between 8090% of maltreatment deaths, and CPS records undercount fatalities by 5576%. Unstructured narrative text clinical death reviews, court opinions, and social work records accounts for 8090% of all organizational data produced, yet it is rarely leveraged for injury surveillance.

This presentation introduces a two-study AI + Structured Topic Modeling (STM) pipeline that integrates two distinct narrative corpora to recover latent, abuse-related injury mechanisms that structured administrative codes routinely obscure.

Study Design

	Study 1: CFRT Death Reviews	Study 2: CourtListener Case Law
Source	Colorado Child Fatality Review Team reports	Appellate opinions retrieved from CourtListener
Documents	223 child-level narrative records	751 opinions → 335 confirmed cases from Ohio
Text	Two free-text fields per report (clinical + administrative)	Facts + procedural history
LLM Extraction	GPT-4o-mini: caregiver behavior, injury mechanism, setting, diagnosis	GPT-4o-mini: 9 structured fields including perpetrator, victim type, legal outcome
STM Models	Risk & Contributing Factors; Circumstances	Injury Mechanism; Long-Term Consequences
Covariate	Incident year (temporal prevalence)	Judicial ruling (affirmed / reversed)

Research Questions

Can large language models transform unstructured child fatality narratives into standardized semantic features caregiver behavior, injury mechanism, setting, diagnosis?
What latent themes emerge, and what typology of risk and incident circumstances do they suggest?
Do the recovered themes reveal recurring patterns of head and neck trauma consistent with abuse-related injury?
How does the prevalence of these themes shift across incident years (20202025)?

Key Findings

Both corpora converge on craniofacial injury as the dominant mechanism routinely undercounted by ICD codes.
Strangulation emerged as a critical lethality predictor; soft-tissue damage can surface 23 days post-injury, requiring clinician and court training on delayed sequelae.
Records document long-term neurological harm (cerebral palsy, pituitary damage, blindness) in children often years before clinical follow-up.
Children represent a structurally distinct injury profile in both datasets, warranting specialized clinical and legal protocols.
“Accidental” injuries (falls, environmental contact) are frequently embedded in an abuse context screening for mechanism, not just diagnosis, is essential.
Appellate opinions and death reviews function as injury-surveillance sources beyond structured administrative fields.

Methodology

The pipeline applies:

PDF parsing (pdftools) and regex isolation of narrative fields
GPT-4o-mini for LLM-based structured field extraction
Structured Topic Modeling (STM) with incident year or judicial ruling as a prevalence covariate
BART zero-shot classification as an independent supervised-style validation check
FREX word scoring (frequency � exclusivity) for topic labeling

Lab Members

This presentation was developed by the ISSUES Lab team at The Ohio State University:

Dr. Gia E. Barboza-Salerno (Founding Director), Dr. Henriikka Weir, Malcolm McCarthy, Dr. Karla Shockley McCarthy, Dr. Amy Watson-Grace, Dr. Keith Warren, Esenia Cassidy, and Olivia McLucas with collaborators Scottye Cash and Suzanne Perkins.

📄 Presentation

Download the presentation slides here .

AERA 2026: Harsher Punishments, Lesser Infractions - Disciplinary Injustice for Native American Students

Sat, 11 Apr 2026 00:00:00 +0000

Presentation at the American Educational Research Association (AERA) Annual Meeting

Date: Saturday, April 11, 2026
Time: 9:45 AM to 11:15 AM PDT (12:45 PM to 2:15 PM EDT)
Location: JW Marriott Los Angeles L.A. LIVE, 4th Floor, Diamond 2

Abstract

Exclusionary discipline disproportionately affects youth of color, but Indigenous student experiences remain underexplored. Using 2019-2020 New Mexico K-12 data, we applied random intercept multilevel modeling to examine the impact of race and infraction severity on odds of receiving harsh discipline (out-of-school suspension, justice system referral) versus in-school suspension.

Key Findings:

White students had 20% lower odds (p = .003) of receiving harsh discipline compared to Native American students
While odds of harsh discipline increased for all students as infraction severity increased, odds of receiving harsh discipline declined more sharply for White, Black, and Latine students compared to Native American students as infraction severity decreased
Native American students faced harsher consequences for less serious infractions, underscoring the need for equity-focused discipline reform

This research highlights critical disparities in school discipline practices and calls for systemic reform to ensure equitable treatment of Indigenous students in New Mexico public schools.

Authors: Allie Watson-Grace, Hannah Yang, and Dr. Gia Barboza-Salerno

This manuscript is currently under review at AERA Open.

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.

A Community-Based Approach to the Opioid Crisis

Mon, 01 Jan 0001 00:00:00 +0000

In progress

Real Help–Real Hope: Where Do We Go From Here?

A Community-Based Approach to the Opioid Crisis

Summary:
This keynote presentation, delivered at the Opioid Treatment Providers of Georgia conference, highlights the need for a comprehensive, public health–centered response to the opioid crisis. It emphasizes how social, environmental, and structural factors—such as poverty, racial inequities, and limited healthcare access—shape opioid-related harms, and critiques punitive policies in favor of community-driven, evidence-based solutions.

Key Details:


Short description:	This presentation critiques the criminalization-focused “War on Drugs” and outlines a public health framework for addressing the opioid crisis, emphasizing prevention, harm reduction, structural analysis, and equity-focused solutions for Georgia and beyond.
Lead developer:	Dr. Gia Elise Barboza-Salerno
Further reading:	Download the presentation slides for full details.
Main data source(s):	Opioid-related mortality, overdose surveillance, Naloxone distribution, treatment access data, regulatory frameworks, and structural vulnerability indicators for Georgia and the United States.
Coverage:	Georgia, with comparisons to national opioid trends.
Citation:	Barboza-Salerno, G.E. (2024). Real Help–Real Hope: Where Do We Go From Here? A Community-Based Approach to the Opioid Crisis. Presented at the Opioid Treatment Providers of Georgia Annual Conference, November 11, 2024.

Check out this accessibility and spatial equity tutorial for more details.

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

Mon, 01 Jan 0001 00:00:00 +0000

The land cover paradox

This paper explores associations between blue- and green- space and suicide mortality in Cook County, Illinois


Short description:	The land cover paradox: Characteristics of blue- and green spaces within and beyond high-risk suicide clusters - ScienceDirect
Lead developer:	Barboza-Salerno
Further reading:	h
Download:	Github repo
Main data source(s):	MLCD land cover, tree- and park- equity
Coverage:	Cook County, IL
Citation:	@article{BARBOZA2025101820, title = {The Land Cover Paradox: {{Characteristics}} of Blue- and Green Spaces within and beyond High-Risk Suicide Clusters}, author = {Barboza, Gia E. and {Watson-Grace}, Amy and {Shockley-McCarthy}, Karla and Harrington, Taylor and Warren, Keith and Steelsmith, Danielle}, year = {2025}, journal = {SSM - Population Health}, pages = {101820}, issn = {2352-8273}, doi = {10.1016/j.ssmph.2025.101820}}

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. Check out this Access our tutorial on na here for more details.

Examining the Interplay Between the Natural and Socio-physical Environment Using Machine Learning Algorithms

Mon, 01 Jan 0001 00:00:00 +0000

Our work on the socio-physical and natural enviornment was presented at the ISPCAN 2024 international conference.

No previous research has comprehensively examined how different aspects of the sociophysical and built environments increase child abuse and neglect (CAN) risk. Characteristics of the built and natural environment are underexplored in research on CAN. Five categories of environmental determinants of child well-being include: (i) urban design, (ii) contaminants, (iii) the parenting environment, (iv) socio-economic conditions, and (v) climate change1

Download the presentation here .

Integrating AI, Deep Learning, and Spatial Analytics to Investigate Environmental Correlates of Child Neglect

Mon, 01 Jan 0001 00:00:00 +0000

In progress

Integrating AI, Deep Learning, and Spatial Analytics to Investigate Environmental Correlates of Child Neglect

Summary:
The presentation discusses how to use computer vision to create indices of the built and perceptual environment and examine their association with child neglect.

Key Details:


Short description:	The presentation discusses how to use computer vision to create indices of the built and perceptual environment and examine their association with child neglect.
Lead developer:	Dr. Gia Elise Barboza-Salerno
Further reading:	Download the [presentation slides](/Lightning talk- nationwide.pdf) for full details.
Main data source(s):	ZenSVI, Mapillary, Area Deprivation Index, Tree Equity Scores.
Coverage:	Los Angeles, California.
Citation:	Barboza-Salerno, G.E. (2025). Integrating AI, Deep Learning, and Spatial Analytics to Investigate Environmental Correlates of Child Neglect. Presented at the 3rd Annual Family Violence Research-Practice Networking Event, Aug 13, 2025.

Check out the code using ZenSVI on our github page.

Utilizing AI-Driven Technology and Geospatial Analysis to Explore Landscape Morphology, Social Vulnerability, and Child Neglect in Los Angeles

Mon, 01 Jan 0001 00:00:00 +0000

Our work on Utilizing AI-Driven Technology and Geospatial Analysis to Explore Landscape Morphology, Social Vulnerability, and Child Neglect in Los Angeles will be presented at ISUF 2025 in Torino, Italy.

No previous research has comprehensively examined how different aspects of the sociophysical and built environments increase child abuse and neglect (CAN) risk. Characteristics of the built and natural environment are underexplored in research on CAN. Five categories of environmental determinants of child well-being include: (i) urban design, (ii) contaminants, (iii) the parenting environment, (iv) socio-economic conditions, and (v) climate change.

Presentations | ISSUES | The Ohio State University

Modeling Firearm Injury Risk Using Spatial Random Forests

No Bright Line: How AI-driven Technology Blurs Legal Boundaries Between Prevention and Punishment in Family Regulation

Beyond Urban–Rural Binaries: Remote Sensing Indicators of Built Environment Risk for Firearm Injury in Cook County, Illinois

📄 Presentation

GIS Day 2025

Enhancing Brain Injury Surveillance Using Large Language Models and Topic Modeling

Dr. Barboza-Salerno Addresses Grand Challenge to Prevent Gun Violence

Detecting Hidden Mechanisms of Abuse-Related Injury in Children: Structured Topic Modeling of Clinical Death Reviews and Legal Opinions

Abstract

Study Design

Research Questions

Key Findings

Methodology

Lab Members

📄 Presentation

AERA 2026: Harsher Punishments, Lesser Infractions - Disciplinary Injustice for Native American Students

Abstract

Applying Social Geography to Structural Mitigation in Capital Defense

Applying Social Geography to Structural Mitigation in Capital Defense

A Community-Based Approach to the Opioid Crisis

In progress

Real Help–Real Hope: Where Do We Go From Here?

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

The land cover paradox

This paper explores associations between blue- and green- space and suicide mortality in Cook County, Illinois

Examining the Interplay Between the Natural and Socio-physical Environment Using Machine Learning Algorithms

Integrating AI, Deep Learning, and Spatial Analytics to Investigate Environmental Correlates of Child Neglect

In progress

A Novel Integration of Social, Perceptual, and Built Environmental Data

Utilizing AI-Driven Technology and Geospatial Analysis to Explore Landscape Morphology, Social Vulnerability, and Child Neglect in Los Angeles