A City in Motion: How Everyday Routines Channel and Control Crime in Baltimore

Dr. Brian Soller
Associate Professor of Sociology, Anthropology, & Public Health (SAPH) UMBC

Spatial clustering in crime is often treated as a statistical nuisance—modeled as spatial autocorrelation rather than explained. This talk reframes spatial dependence as a social process largely generated by routine human mobility. Integrating methods and insights from sociology, geography, and social network analysis, I use high-resolution GPS location data from a large panel of Baltimore-area residents to construct street-level mobility networks that capture patterns of street use by locals and non-locals, as well as network ties between street blocks formed through shared movement pathways. I show how these mobility-based connections help explain both crime concentration within blocks and spillover effects across connected blocks. No prior knowledge of R or advanced programming is required; rather than focusing on technical mechanics, the talk emphasizes how integrating theory and methods across traditionally siloed fields allows computational social science to identify the social processes that generate spatial patterns.

Brian Soller is an Associate Professor of Sociology at UMBC. He earned his PhD in Sociology from The Ohio State University in 2013. A specialist in quantitative methodology, Brian teaches courses in social statistics, sociological theory, and criminology.  Brian's research focuses on the intersections between urban sociology and social network analysis, specifically focusing on how to integrate methods and insights from these areas to understand variation in health and crime. His work has been published in leading academic journals, including the American Journal of Sociology (AJS), the American Sociological Review (ASR), Social Science & Medicine (SSM), the Journal of Health and Social Behavior (JHSB), and the American Journal of Community Psychology. His current research centers on the impact of routine mobility on crime, health, and aging and utilizes high-resolution digital mobility data to redefine how we understand spatial processes in the digital era.