Researching a Local Heroin Market as a Complex Adaptive System

Abstract

This project applies agent-based modeling (ABM) techniques to better understand the operation, organization, and structure of a local heroin market. The simulation detailed was developed using data from an 18-month ethnographic case study. The original research, collected in Denver, CO during the 1990s, represents the historic account of users and dealers who operated in the Larimer area heroin market. Working together, the authors studied the behaviors of customers, private dealers, street-sellers, brokers, and the police, reflecting the core elements pertaining to how the market operated. After evaluating the logical consistency between the data and agent behaviors, simulations scaled-up interactions to observe their aggregated outcomes. While the concept and findings from this study remain experimental, these methods represent a novel way in which to understand illicit drug markets and the dynamic adaptations and outcomes they generate. Extensions of this research perspective, as well as its strengths and limitations, are discussed.

The social impact in a high-risk community: A cellular automata model

“…As well, individuals using these types of drugs will find other ways to support their habit including participating in the drug market. For instance, some people involved in the market acted a brokers between the user and the dealer and that brokers engaged in this activity to consistently access drugs for personal consumption [13].…”

Application of Space-Time Scan Statistics to Describe Geographic and Temporal Clustering of Visible Drug Activity

“…The plausibility of this is supported by prior studies, including our prior qualitative research conducted in Baltimore, which have linked drug activity displacement to urban redevelopment, 6,35,36 local media reports, 32 and the geography of residential relocation due to EBDI, which resembles the clustering of drug activity in North and Northeast Baltimore. 31 The potential for drug activity to be displaced as a result of urban redevelopment suggests that spatial inequities in drug activity may not be resolved entirely by such efforts.…”

“…For example, an evaluation of public housing demolitions in Atlanta, Georgia, as part of the Housing Opportunities for People Everywhere initiative, demonstrated that while public housing demolition was associated with overall crime reductions in the city, the decline was greater in areas where public housing was demolished and lower in areas where public housing residents relocated. 34 Additionally, our prior qualitative research, 35 and ethnographic studies conducted in Brooklyn, New York City, 36 and Denver, Colorado, 6 have reported an association between urban redevelopment and the displacement of drug activity.…”

“…1 In contrast to person-specific drug markets that are largely sustained through social networks, hidden, and may not be geographically bounded, 5 visible drug markets are characterized by place, facilitating accessibility, drug tourism (e.g., traffic of non-residents to a given neighborhood to purchase drugs), and profit. [5][6][7] Visible drug market activity bears negative implications upon residents living in proximity by glorifying risk behaviors to youth, stigmatizing and marginalizing communities, 5 and serving as a potential determinant of violence, 8 sexually transmitted diseases, 9 and drug abuse. 10,11 Baltimore City is among several US cities where visible drug market activity occurs in some communities and has been associated with economic deprivation, poor housing stock, and social disorder.…”

An analysis of crime hot spots using GPS tracking data of children and agent-based simulation modeling

“…Third, simulation modeling is flexible enough to accommodate a wide range of criminological topics and research questions, including the criminal justice system (Alimadad et al 2008, Auerhahn 2008, repeat victimization (Johnson 2008), offender mobility (Brantingham andTita 2008, Hayslett-McCall et al 2008), the diffusion of crime over space and time (Kim 2008), forecasting (Malleson and Brantingham 2009), law enforcement patrol (Szakas et al 2008), and the evaluation of intervention strategies (Dray et al 2008a(Dray et al , 2008b. Furthermore, simulation modeling has been used to study various types of crimes, including burglary (Birks et al 2008, Johnson 2008, Malleson and Brantingham 2009, Birks et al 2012, street robbery (Liu et al 2005, Groff 2007a, 2007b, drug offense (Dray et al 2008a, 2008b, Hoffer et al 2009), vehiclerelated crimes , Wei 2008, and white-collar crime (Kim and Xiao 2008). Fourth, minimal cost is involved in simulation modeling.…”