Mobility Networks and Drug Overdose Patterns: An Analysis of Baltimore Street Segments

• In Event: Network Criminology I: Social Networks and Substance Use

Abstract

This study investigates how mobility networks—patterns of human movement between geographic locations—influence the spatial distribution of drug overdoses in Baltimore, Maryland. Drawing insights from social network analysis, environmental criminology, and urban sociology, I utilize human mobility data derived from GPS-enabled mobile device applications to capture Baltimore’s mobility network, which is based on patterns of movement to street segments among residents of census block groups across the city. I then measured key mobility network properties to examine how mobility shapes the spatial distribution of drug overdoses, measured from time-stamped and geocoded locations of 911 calls for overdoses throughout 2024. Longitudinal multilevel models comprising weekly observations nested in street segments and census tracts revealed that the odds of a drug overdose occurring in a street segment increased following elevated overdose incidents in segments with high structural equivalence, or those occupying similar positions within the mobility network. These findings suggest urban mobility patterns serve as important pathways for understanding the spatial patterning of drug overdoses. The study advances the understanding of the opioid epidemic by highlighting how the structural dimensions of human mobility networks, rather than mere geographic proximity, shape overdose risk across urban environments.

Author

• Brian Soller, University of Maryland, Baltimore County