Assessing the Situational Predictors of Drug Markets across Street Segments and Intersections

Objectives: This study examines the factors that are statistically associated with drug-dealing settings in Newark, NJ, and assesses whether there are systematic differences in these between street segments and on intersections. In doing so, it tests hypotheses consistent with a theory that there are higher and lower levels of social regulation by residents in the two kinds of settings, respectively. Methods: Applying a matched case–control design yields 128 pairs of locations. McNemar’s test and conditional logistic regression are used to uncover statistical associations. Situational data on drug-dealing settings were collected using observations through Google Street View (GSV). Results: A variety of factors are associated with street drug-dealing hot spots, including mailboxes and churches, not previously identified in the literature on street crimes. While findings show differences between drug markets correlates on street segments and intersections, these are only partially consistent with study hypotheses. Conclusions: This study contributes to our understanding of risk factors for drug markets, highlights variations between intersections and street segments in the way drug market risk factors operate, and demonstrates the value of GSV for spatial crime research.