DescriptionIn Newark, NJ, drug dealing is common, but it is not evenly distributed in every part of the city. Between 2007 and 2009, most drug arrests were made on less than 20% of the streets. The dissertation seeks to explain how the locations for drug dealing are related to their surrounding situational features. It is hypothesized that these features produce criminal opportunities for drug dealing activities: lack of guardianship, accessibility, and crime generators. This dissertation focuses on drug arrests at the micro-level – street segments and intersections. Police arrest records from 2007 to 2009 provided by the Newark City Police Department are analyzed. A matched case-control design is used. Applying a threshold criterion of 5 or more arrests, 104 street segments and 31 intersections having frequent dealing activity per year in 2007 to 2009 are sampled to be the cases. Controls are individually matched with the cases, taking account of their distance from the cases, street length, and the intersecting thoroughfares. The sample size is 135 pairs. Situational data of local drug dealing settings are observed using Google Street View. Inter-rater reliability is assessed to affirm the quality of the data. McNemar’s test is employed to examine the correlations between variables and the drug market. The dissertation also sets forth a conditional logistic regression model to analyze the causal relationships between variables and the drug market. Results show that drug dealing activity tends to occur on specific street segments characterized by abandoned buildings, bus stops, parking lots, vacant land, mailboxes, retail stores (near vacant lands), or the absence of a church. Drug dealing activity tends to occur on specific intersections characterized by parking lots, retail stores, and churches. The presence of a church as a crime generator to the occurrence of drug markets on intersections is one notable finding of this dissertation. The results signal that there are distinguishable situational factors affecting drug activity on streets and intersections, respectively. This dissertation demonstrates the feasibility of using Google Street View for future crime research. Policy implications are provided for making local drug markets more predictable and controllable to the police.