Gangs of New York

Here’s my latest piece for BBC Future, pre-editing.

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One of the big challenges in fighting organized crime is precisely that it is organized. It is run like a business, sometimes literally, with chains of command and responsibility, different specialized ‘departments’, recruitment initiatives and opportunities for collaboration and trade. This structure can make crime syndicates and gangs highly responsive and adaptable to attempts at disruption by law-enforcement services.

That’s why police forces are keen to discover how these organizations are arranged: to map the networks that link individual members. This structure is quite fluid and informal compared to most legitimate businesses, but it’s not random. In fact, violent street gangs seem to be organized along rather similar lines to insurgent groups that stage armed resistance to political authority, such as guerrilla forces in areas of civil war, for instance in being affiliations of cells each with their own leaders. It’s for this reason that some law-enforcement agencies are hoping to learn from military research. A team at the West Point US Military Academy in New York has just released details of a software package it has developed to aid intelligence-gathering by police dealing with street gangs. The program, called ORCA (Organization, Relationship, and Contact Analyzer), can use real-world data acquired from arrests and questioning of suspects to deduce the network structure of the gangs.

ORCA can figure out the likely affiliations of individuals who will not admit to being members of any specific gang, as well as the sub-structure of gangs (the ‘gang ecosystem’) and the identity of particularly influential members, who tend to dictate the behaviour of others.

There are many reasons why this sort of information would be important to the police. The ecosystem structure of a gang can reveal how it operates. For example, many gangs fund themselves through drug dealing, which tends to happen by the formation of “corner crews”: small groups that congregate on a particular street corner to sell drugs. And having some knowledge of the links and affiliations between different gangs can highlight dangers that call for more focused policing. If a gang perpetrates some violent action on a rival gang, police will often monitor the rival gang more closely because of the likelihood of retaliation. But gangs know this, and so the rivals might instead ask an allied gang to carry out a reprisal instead. So police need to be aware of such alliances.

The roles of highly influential members of a social network are familiar from other studies of such networks – for example, in viral marketing and the epidemiology of infectious diseases. These individuals typically have a larger than average number of links to others, and their choices and actions are quickly adopted by others. An influential gang member who is prone to risky, radicalizing or especially violent behaviour can induce others to follow suit – so it can be important to identify these individuals and perhaps to monitor them more closely.

In developing ORCA, West Point graduate Paulo Shakarian, who has a doctorate in computer sciences and has worked in the past as an adviser to the Iraqi National Police, and his coworkers have drawn on the large literature that has grown over the past decade on the mapping of social networks. These studies have shown that the way a network operates – how information and influence spread through it, for example – depends crucially on what mathematicians call its topology: the shape of the links between people. For example, spreading happens quite differently on a grid (like the street network of Manhattan, where there are many alternative routes between two points), or a tree (where points are connected by the repeated splitting of branches), or a ‘small world’ network (where there are generally many shortcuts so that any point can be reached from any other in relatively few jumps). Many studies in this new mathematical science of networks have been concerned to deduce the community structure of the network: how it can be decomposed into smaller clusters that are highly connected internally but more sparsely linked to other modules. It’s this kind of analysis that enables ORCA to figure out the ecosystems of gangs.

One of the features of ORCA is an algorithm – a set of rules – that assigns each member of the network a probability of belonging to a particular gang. If an individual admits to this, the assignment can be awarded 100% probability. But if he will not, then any known associations he has with other individuals can be used to calculate a probable ‘degree of membership’. The program can also identify ‘connectors’ who are trusted by different gangs to mediate liaisons between them, for example to broker deals that allow one gang to conduct drug sales on the territory of another.

Shakarian and colleagues tested ORCA using police data on almost 1500 individuals belonging to 18 gangs, collected from 5418 arrests in that district over three years. These gangs were known to be racially segregated, and the police told the West Point team that one racial group was know to form more centrally organized gang structures than the other. ORCA confirmed that the latter, more decentralized group tended to be composed of more small modules, rather than larger, branched networks.

Although the West Point team can’t disclose details, they say that they are working with a “major metropolitan police department” to test their program and to integrate it with information on the geographical distributions of gangs and how they change over time. One can’t help suspecting that the developers of games such as Grand Theft Auto, which unfolds in a complex netherworld of organized crime gangs, will also be taking an interest to improve the realism of its fictional scenarios.

Reference: D. Paulo et al., preprint http://www.arxiv.org/abs/1306.6834 (2013).