Utilizing Artificial Intelligence and Geospatial Analysis to Examine the Urban Built Environment, Social Vulnerability, and Child Neglect in Los Angeles

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

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.

Artificial Intelligence | ISSUES | The Ohio State University

Utilizing Artificial Intelligence and Geospatial Analysis to Examine the Urban Built Environment, Social Vulnerability, and Child Neglect in Los Angeles

Plain-Language Summary