Social network analysis (criminology)

From Wikipedia, the free encyclopedia
Jump to navigation Jump to search

Social network analysis in criminology views social relationships in terms of network theory, consisting of nodes (representing individual actors within the network) and ties (which represent relationships between the individuals, such as offender movement, co-offenders, crime groups, etc.) These networks are often depicted in a social network diagram, where nodes are represented as points and ties are represented as lines.

Key terms[edit]

Offender Movement
is the movement of deviants from one location to another (i.e. from home to the location of criminal acts). Notable scholars: Gisela Bichler, Lucia Summers
Co-Offenders
refers to the relationship between two deviant individuals. Notable scholars: Carlo Morselli, Aili Malm, Gisela Bichler, Jean McGloin, Jerzy Sarnecki, Diane Haynie, Andrew Papachristos
Crime Groups
consists of the social group that participates in the different aspect of a deviant action. Notable scholars: Mangai Natarajan, Aili Malm, Francesco Calderoni, David Bright

Key concepts[edit]

Crime Pattern Theory (CPT)
Crime pattern theory consists of four key points: the complexity of the criminal event, that crime is not random, criminal opportunities are not random, and that offenders and victims are not pathological in their use of time and space.[1]

Graph theory[edit]

Centrality measures are used to determine the relative importance of a vertex or node within the overall network (i.e. how influential a person is within a criminal network, or, for locations, how important an area is to criminal behaviors). There are four main centrality measures used in criminology network analysis:

Degree
Historically first and conceptually simplest is degree centrality, which is defined as the number of links incident upon a node (i.e., the number of ties that a node has). The degree can be interpreted in terms of the immediate risk of a node for catching whatever is flowing through the network. In the case of a directed network (where ties have direction), we usually define two separate measures of degree centrality, namely indegree and outdegree.
Betweenness
Betweenness centrality quantifies the number of times a node acts as a bridge along the shortest path between two other nodes. It was introduced as a measure for quantifying the control of a human on the communication between other humans in a social network by Linton Freeman. In his conception, vertices that have a high probability to occur on a randomly chosen shortest path between two randomly chosen vertices have a high betweenness.
Eigenvector
Eigenvector centrality is a measure of the influence of a node in a network. It assigns relative scores to all nodes in the network based on the concept that connections to high-scoring nodes contribute more to the score of the node in question than equal connections to low-scoring nodes.
Closeness
The farness of a node s is defined as the sum of its distances to all other nodes, and its closeness is defined as the inverse of the farness. Thus, the more central a node is the lower its total distance to all other nodes. Closeness can be regarded as a measure of how fast it will take to spread information from one node to all other nodes sequentially. In the classic definition of the closeness centrality, the spread of information is modeled by the use of shortest paths. This model might not be the most realistic for all types of communication scenarios.

Co-offenders[edit]

"Legitimate strengths in criminal networks"

This article is a study of "...how legitimate world actors contribute to structuring a criminal network... [and] also underscores the facilitating role that some participants have in criminal settings".[2] The basis of Morselli and Giguere's article is from a case study of an illegal drug importation network, monitored by law-enforcement over a period of two years. Their findings were that "...a minority of these actors were critical to the network in two ways: (1) they were active in bringing other participants (including traffickers) into the network; and (2) they were influential directors of relationships with both non-traffickers and traffickers."[2]

"Networks of Collaborating Criminals: Assessing the Structural Vulnerability of Drug Markets"

This article analyzes an illicit drugs commodity chain which requires identifying the collaborating actors who are located within activity niches that link the raw materials to the market absorption, either through retail or consumption. "...uncovering the structure of connections among individuals involved in criminal enterprise will contribute to our understanding of how illicit markets function. In turn, this will lead to policy directives aimed at key pressure points to maximize crime prevention efforts".[3] The created network should capture the roles, functions, and structures of the groups involved in the illicit drug commodity chain and reveal the links in the supply chain (i.e. source, supply, sales, and feeders). Using the created network the resiliency is determined by assessing the clusters in subgroups, identifying pivotal individuals holding central positions, and quantifying the potential to disrupt commodity and information flow by identifying the specific nodes to be removed for maximum effect. This study has three hypothesis: "Hypothesis 1: Individuals involved in production and transporting...will exhibit high small-world properties....Hypothesis 2: those involved in supplying drugs will be characterized by both small-world and scale-free properties as suppliers tend to belong to loosely organized clusters of people...with a few highly connected individuals – hubs. Hypothesis 3: Networked individuals involved in sales will be characterized by high small-world properties."[3]

The application of social network analysis during the collaboration between criminals and terrorists when both use smuggling tunnels was explored by Lichtenwald and Perri.[4] Lichtenwald and Perri referenced many of the notable scholars and key papers in the field.[5][6][7][8][9][10][11][12]

Offender movement[edit]

"Magnetic Facilities: Identifying the convergence Settings of Juvenile Delinquents"

Magnetic facilities refer to the attractiveness of a location for deviant behavior. This study looked at the self-nominated hangouts of 5,082 delinquent youth living in Southern California. The structure of these networks remained relatively constant over the time the study was conducted. The centrality statistics used were in-degree and betweenness to identify facilities operating as stable regional convergence locations. "Explaining the linkage between urban planning and crime patterns, Brantingham[13][14] argues that four factors – accessibility through high-volume transportation conduits, placement, juxtaposition, and the operation of facilities – can account for the criminogenic capacity of specific places".[15] Of the locations included in the network, the top twenty with the highest in-degree centrality scores in both the valued and dichotomous networks were investigated for the following three characteristics: facility type – coded as public space, freestanding building, attached or terrace style structure, or mall facility; place type – coded as movies, video store, fast food restaurant, shopping, outdoor recreation, school, and other; and school accessibility – counting the number of schools within a 5-mile radius of the property.[15]

"Examining Juvenile Delinquency within Activity Space: Building a Context for Offender Travel Patterns"

This study looked at 2,563 delinquent youths in Southern California and assumed that crime locations were within the offenders activity space. The highlight of the study is the "...need to infuse a place-oriented approach to studying journey-to-crime".[16] An individual’s behavior is influenced by numerous factors, of which spatial awareness emerges from the routine travel to and from activity nodes (i.e. work, school, shopping, and recreation sites). "Recent efforts to enhance journey-to-crime research: examine intraurban criminal migration using travel demand models; explore spatial-temporal constraints posed by routine activities; investigate how co-offending dynamics impact target selection; describe the journey away from crime sites; scrutinize subgroup variation; and assess the utility of distance decay models".[16] Using the crime prevention theory (CPT), it asserts that offenders operate within their familiar settings which are learned as the delinquent travels between activity nodes along constant paths.

See also[edit]

References[edit]

  1. ^ Brantingham, P. L. & Brantigham, P. J. "Crime Pattern Theory" (PDF). Archived from the original (PDF) on 2015-05-18. 
  2. ^ a b Carlo Morselli and Cynthia Giguere (2006). Legitimate strengths in criminal networks. Crime, Law & Social Change, 185-200.
  3. ^ a b Aili Malm & Gisela Bichler (2011). "Networks of Collaborating Criminals: Assessing the Structural Vulnerability of Drug Markets". Journal of Research in Crime and Delinquency. 48: 271–297. doi:10.1177/0022427810391535. 
  4. ^ Lichtenwald, T.G. & Perri, F.S. (2013). "Terrorist use of smuggling tunnels" (PDF). International Journal of Criminology and Sociology. 2: 210–226. 
  5. ^ Papachristos, A. V. (2011). The coming of a networked criminology. In J. MacDonald (ED.), Measuring crime and criminality (pp. 101–140). New Brunswick, N.J.: Transaction Publishers.
  6. ^ Morselli, C. (2009). Inside criminal networks, studies of organized crime. Springer Social Sciences-Criminology and Criminal Justice, 8, ISBN 978-0-387-09526-4.
  7. ^ Malm, A. E., Kinney, J. B.,& Pollard, N.R. (2008). "Social network and Distance Correlates of Criminal Associates Involved in Illicit Drug Production". Security Journal. 21: 77–94. doi:10.1057/palgrave.sj.8350069. 
  8. ^ Qin, J., Xu, J.J., Hu, D., Sageman, M., & Chen, H. (2005). Analyzing terrorist networks: A case study of the Global Salafi Jihad network. IEEE International Conference on Intelligence and Security Informatics, ISI 2005, Atlanta, GA, USA, May 19–20, 2005. Proceedings.
  9. ^ Shelley, J., Picarelli, A.I., Hart, D.M., Craig-Hart, P.A., Williams, P.,Simon, S., & Covill, L. (2005). "Methods and motives: Exploring links between transnational organized crime and international terrorism" (PDF). 
  10. ^ Sageman, M. (2004). Understanding Terror Networks. Philadelphia: University of Pennsylvania Press
  11. ^ Snijders, T. A. B. (2001). "The statistical evaluation of social network dynamics". Sociological Methods. 31: 361–95. doi:10.1111/0081-1750.00099. 
  12. ^ Coles, N. (2001). "It's not what you know-it's who you know that counts: Analyzing serious crime groups as social networks". British Journal of Criminology. 41: 580–594. doi:10.1093/bjc/41.4.580. 
  13. ^ P. Brantingham & P. Brantingham (1994). "The Influence of Street Networks on the Patterning of Property Offenses". In Clarke. Crime Prevention Studies (PDF). 2. Monsey, N.Y.: Criminal Justice Press. 
  14. ^ Brantingham, P. J., & Brantingham, P. L. (1998). "Environmental criminology: From theory to urban planning practice". Studies on Crime and Crime Prevention. 7: 31–60. 
  15. ^ a b Bichler, Gisela M., Aili Malm, and Janet Enriquez (2010). "Magnetic Facilities: Identifying the Convergence Settings of Juvenile Delinquents". Crime & Delinquency. 60: 1–28. doi:10.1177/0011128710382349. 
  16. ^ a b Bichler, Gisela C.-M., Jill Christie-Merral, and Dale Sechrest (2011). "Examining Juvenile Delinquency within Activity Space: Building a Context for Offender Travel Patterns". Journal of Research in Crime and Delinquency. 48: 472–506. doi:10.1177/0022427810393014. 
  • Jean Marie McGloin & David S. Kirk (2011). "An overview of social network analysis". Journal of Criminal Justice Education. 2: 169–181. doi:10.1080/10511251003693694. 
  • Natarajan M (2006). "Understanding the Structure of a Large heroin Distribution Network: A Quantitative Analysis of Qualitative Data". J Quant Criminol. 22: 171–192. doi:10.1007/s10940-006-9007-x. 
  • McGloin J. M. (2005). "Policy and Intervention Consideration of a Network Analysis of Street Gangs". Criminology & Public Policy. 4: 607–636. doi:10.1111/j.1745-9133.2005.00306.x.