Utilizing Artificial Intelligence and Geospatial Analysis to Examine the Urban Built Environment, Social Vulnerability, and Child Neglect in Los Angeles
Abstract
This study uses artificial intelligence and computer vision to analyze street-level imagery from Google Street View across 141 census block groups in Los Angeles, California. By measuring features of the built and natural environment, the research team constructed neighborhood indices for natural surveillance, lighting, land-use mix, and greenness. The findings show that higher quality built environments, specifically better lighting, visibility (natural surveillance), and green space, are associated with lower rates of neighborhood child neglect, even when controlling for area-level deprivation and poverty. This research demonstrates how AI street-level audits can provide actionable data for city planners, social workers, and policymakers to design community-based prevention strategies that protect vulnerable children.
Plain-Language Summary
This study uses artificial intelligence and computer vision to analyze street-level imagery from Google Street View across 141 census block groups in Los Angeles, California. By measuring features of the built and natural environment, the research team constructed neighborhood indices for natural surveillance, lighting, land-use mix, and greenness.
The findings show that higher quality built environments, specifically better lighting, visibility (natural surveillance), and green space, are associated with lower rates of neighborhood child neglect, even when controlling for area-level deprivation and poverty. This research demonstrates how AI street-level audits can provide actionable data for city planners, social workers, and policymakers to design community-based prevention strategies that protect vulnerable children.
Dr. Gia Elise Barboza-Salerno
Director
I am an interdisciplinary scholar whose research integrates law, public health, social work, and advanced statistics to study childhood adversity, violence-related harm, and neighborhood-level inequities. I lead the ISSUES Lab at Ohio State, translating spatial and legal analysis into policy and practice.
Taylor Harrington
PhD Candidate, Methodologist
Taylor Harrington, MS, is a PhD candidate in Public Health at The Ohio State University. To investigate how multi-level factors shape health behaviors and outcomes, she integrates advanced computational and geospatial methods, including machine learning (e.g., computer vision, random forests, structured topic modeling, text mining) and geospatial modeling (e.g., Bayesian spatial modeling, remote sensing). Through this work, she aims to generate actionable evidence to inform equitable health policy and community-level interventions.
Hexin Yang
PhD Candidate, Methodologist
Hexin is interested in studying child and adolescent development across various contexts, particularly in school settings, using advanced statistical analyses.
Dr. Karla Shockley McCarthy
Co-Director
Dr. Karla Shockley, PhD, LMSW, TRCC, FSW, is a Principal Investigator and Senior Research Consultant at the Ohio Colleges of Medicine Government Resource Center, where she leads applied health services and population health research. She uses a collaborative team approach to design and execute mixed methods studies, develops survey instrumentation, oversees qualitative and quantitative analyses, and employs various dissemination methods. Her work centers on translating complex data into actionable insights that inform public health.