Drug Law Enforcement in an Agent-Based Model: Simulating the Disruption to Street-Level Drug Markets

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Author(s): Anne Dray (Australian National University, Australia), Lorraine Mazerolle (Griffith University, Australia), Pascal Perez (Australian National University, Australia)and Alison Ritter (University of New South Wales, Australia)
Copyright: 2008
Pages: 20
Source title: Artificial Crime Analysis Systems: Using Computer Simulations and Geographic Information Systems
Source Author(s)/Editor(s): Lin Liu (University of Cincinnati, USA)and John Eck (University of Cincinnati, USA)
DOI: 10.4018/978-1-59904-591-7.ch018

Keywords: Digital Crime & Forensics / Information Science Reference / IT Security and Ethics / Security & Forensics

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Abstract

This chapter describes an agent-based model called SimDrugPolicing that explores the relative impact of three law enforcement strategies—standard patrol, hotspot policing, and problem-oriented policing— on an archetypal street-based illicit drug market. Using data from Melbourne (Australia), we simulate the relative effectiveness of these different drug law enforcement approaches. We examine the complex interactions between users, dealers, wholesalers, outreach workers and police to examine the relative effectiveness of the three drug law enforcement strategies, analyzing several outcome indicators such as the number of committed crimes, dealers’ and users’ cash, overdoses and fatal overdoses. Our results show that problem-oriented policing is the most effective approach to disrupting street level drug markets in a simulated urban environment.

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Drug Law Enforcement in an Agent-Based Model: Simulating the Disruption to Street-Level Drug Markets

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