Computational Spatial Networks

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.

This study evaluates the exposure of school children to gun violence along their walkable routes to and from school in Englewood, Chicago. Using acoustic sensor detection technology (ShotSpotter) alongside realistic network routing models, the research quantifies both direct and indirect gunfire exposure during daily school commutes. The findings show that children walking to school face frequent and geographically concentrated gunfire exposure, which varies significantly depending on specific walking routes and neighborhood social vulnerability. This work demonstrates the power of integrating spatial routing algorithms with acoustic gunshot data to guide school safety policies, active transit planning, and targeted community policing.

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.

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.

In progress A Novel Integration of Social, Perceptual, and Built Environmental Data Integrating AI, Deep Learning, and Spatial Analytics to Investigate Environmental Correlates of Child Neglect

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.