Introduction
The past two decades have seen considerable change occur within law enforcement and crime intelligence agencies. For example, along with the rest of society, law enforcement agencies have entered what is commonly referred to as the ‘information age’ (Arquilla 2014; Castells 2004; Medina 2014). The outcome has been a dramatic increase in the amount of information both produced and recorded by law enforcement agencies, creating numerous challenges as to how these data are collected, collated and analysed (Arquilla 2014; Brodeur and Dupont 2006; Hauck et al. 2002). A further change has been the blurring of the lines between the domains of law enforcement and national security following many high-profile terrorist attacks, including the 11 September 2001 hijackings and the 7 July 2005 London bombings (Brodeur and Dupont 2006; Coyne and Bell 2011b; Stainer 2013). There have also been changes in the criminal environment with the emergence of ‘new’ crime problems, including cybercrime and cyber terrorism (Taylor et al. 2014). These changes have forced law enforcement agencies to find new ways of understanding and responding to crime. Many law enforcement agencies have turned to technology to complement longstanding policing and criminal intelligence practices (Taylor et al. 2007). For example, while law enforcement has a long history of attempting to map criminal networks (Harper and Harris 1975), due to access to powerful computing this can now be undertaken far more easily and on a scale that was largely not possible in the past. At the forefront of this more advanced form of criminal network mapping is social network analysis (SNA), a methodology that has received considerable attention as it has the potential to be of significant value to law enforcement agencies contending with a changing policing landscape.
SNA is an analytical tool that examines the social relationships that exist within social entities (Borgatti et al. 2013) such as a criminal network. It is reportedly capable of identifying the overall structure of a network (such as sub-groups), how information flows between members of a network, important individuals and possible targets for disruption (Bright et al. 2012). Reported capabilities such as these have contributed to a great deal of interest in the application of SNA to criminal networks and what it may offer law enforcement as an investigative tool. For example, Borgatti et al. (2009, p. 892) noted that network-related research is a popular topic, with ‘the number of articles in the Web of Science on the topic of “social networks” nearly tripling in the past decade’. Within the field of criminology specifically, ‘to find a manuscript using SNA methods is no longer rare, as both mainstream and specialty journals are now regularly publishing papers using network methods’ (Bouchard and Amirault 2013, p. 119). There is now a large body of literature that has advanced our understanding of criminal networks by providing insight into the structural properties and modus operandi of such groups (Ball 2016; Bright et al. 2015a; Koschade 2006; Morselli 2014). Despite the intense interest in SNA, the research to date has been largely confined to retrospective analyses of past criminal networks, often years after they were in operation (Bouchard and Nash 2015). This is understandable given the well-documented difficulty in gaining access to law enforcement data due to security concerns (Bright et al. 2012; Klerks 1999; Krebs 2002; Sparrow 1991). However, this means we know almost nothing about the use of SNA within operational law enforcement environments, and this constitutes a critical gap in our understanding of the capabilities and limitations of SNA. As Mullins (2012a, p. 19) noted, if we are ‘to take SNA to the next level as an investigative tool, there is a clear need for improved understanding about how it is already being used’ within operational law enforcement environments.
This book provides an in-depth examination of SNA within operational law enforcement environments. It draws on the existing SNA and intelligence literature, as well as qualitative interviews with intelligence analysts from two Australian state law enforcement agencies, to examine whether and how practitioners can utilise the reported capabilities of SNA. The views of intelligence analysts offer unique insights into the role of this analytical methodology within law enforcement. The book provides an original contribution to both intelligence and SNA literature. It is the first study to explore the use of SNA by law enforcement in Australia and, more broadly, the first study to examine the use of SNA from the perspective of intelligence analysts. The primary objectives of this book are twofold: (1) to identify whether SNA is being used by intelligence analysts in operational law enforcement environments in Australia, and if so how; and (2) to determine what challenges intelligence analysts face when applying network analysis concepts and techniques to criminal networks.
This introductory chapter provides the context for this study and defines several key terms, including ‘intelligence-led policing’, ‘crime intelligence’, ‘network analysis’ and ‘social network analysis’. It is important that terms such as network and network analysis are clearly defined, as such concepts are often used in different ways (Whelan 2012). The inconsistent use of such concepts has led to confusion within the intelligence and security fields (Whelan and Dupont 2017). The first section briefly examines some of the historical conceptual frameworks of law enforcement to better understand the latest iteration, intelligence-led policing (ILP). As ILP has been widely adopted by law enforcement agencies both in Australia and abroad (Carter and Carter 2009; Ratcliffe 2016), it is useful to understand the contextual environment in which SNA might be used. This is because the policing model adopted by law enforcement agencies heavily dictates how intelligence is used, including analytical tools like SNA. To achieve this the chapter examines the ‘3-I model’, a framework of how ILP is intended to work within law enforcement agencies, with ‘crime intelligence’ at its core. Attention then turns to an overview of SNA, including an outline of what differentiates it from other forms of analysis and existing claims about what it can offer law enforcement. This chapter then presents the research design of this study, explaining why semi-structured interviews provided the best opportunity for developing new insight into the use of SNA within operational law enforcement environments. The chapter concludes with a brief outline of the book’s structure.
Conceptual Frameworks of Law Enforcement
A number of policing paradigms developed during the second half of the twentieth century. These include: community-oriented policing (or simply community policing), problem-oriented policing and CompStat (computer statistics or comparative statistics). It is important to examine these frameworks as they have influenced the development of ILP, which is both the latest iteration in policing (Ratcliffe 2016), and a focal topic for this study. While each of these policing frameworks has similarities, there are differences in their philosophy and the tactics they employ (Ratcliffe 2016). Community-oriented policing places a heavy focus on ‘community involvement in crime prevention efforts’ (Gill et al. 2014, p. 402). While there is no agreed-upon definition of community-oriented policing (McGarrell et al. 2007; Ratcliffe 2016), Taylor (2006) observed that among the different definitions there are several common themes, including a focus on providing greater autonomy to front-line officers to foster relationships with the community. Community-oriented policing is a heavily studied topic, with much of the focus on assessing its effectiveness (Fruhling 2007; Gill et al. 2014; Liederbach et al. 2008; Van Brunsuhot 2003). For example, a systematic review of 25 stu...