Many studies in this new mathematical science of networks have been concerned with deducing the community structure of the network: how it can be decomposed into smaller clusters that are highly connected internally, but 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 his colleagues tested Orca using police data on almost 1,500 individuals belonging to 18 gangs, collected from 5,418 arrests in an undisclosed 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 known to form more centrally organised gang structures than the other. Analysis using Orca confirmed that the gang structures of this second, more decentralised racial group were mostly composed of 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 organised crime gangs, will also take an interest.