Built Environment | ISSUES | The Ohio State University

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 .

Disability-related depression inequities are driven by accessibility deficits: Evidence from crowdsourced sidewalk data and machine learning

Tue, 03 Nov 2026 14:45:00 +0000

Session: Mobility, Transit, and Built Environments for Disabled People
Program: Disability
Date: Monday, November 2, 2026 | 2:45 PM – 3:00 PM

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

Thu, 01 Aug 2024 00:00:00 +0000

Plain-Language Summary

This research presentation examines the interplay between the built, natural, and social environments in predicting the risk of child abuse and neglect (CAN). By utilizing machine learning algorithms, the study categorizes environmental determinants of child well-being into key dimensions including urban design, environmental contaminants, and neighborhood socioeconomic conditions.

The study demonstrates that characteristics of the physical and natural environment, such as neighborhood walkability, lighting, and greenness, interact with social vulnerabilities to shape family stress and safety. This work highlights the value of using computational models to translate physical streetscape characteristics into policy-relevant indicators for child maltreatment prevention.

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.

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.

Built Environment | ISSUES | The Ohio State University

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

📄 Presentation

Disability-related depression inequities are driven by accessibility deficits: Evidence from crowdsourced sidewalk data and machine learning

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

Plain-Language Summary

A Community-Based Approach to the Opioid Crisis

In progress

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

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