Gang Rivalries Figured Out by UCLA Computer Algorithm That's Almost as Good as Street Knowledge
Researchers at UCLA say they might have found a magic bullet when it comes to solving gang crime: A computer algorhithm.
This 18th Streeter has a high probability of not liking M.S.
A study destined for a future edition of the mathematical journal Inverse Problems analyzed 1,000 gang crimes that happened in the LAPD's Eastside Hollenbeck Division area during a 10-year-period.
The geeks found that ...
... elements of the crimes could be pulled out of the data and then predicted fairly accurately. UCLA:
About 80 percent of the time, the mathematicians could narrow it down to three gang rivalries that were most likely involved in a crime.
Eh. You know what other program can do that? The human mind, specifically that of anyone with knowledge of a neighborhood, including oh, say, gang cops.
(A shooting last summer in Venice had witnesses immediately buzzing that the perpetrators were from one of two rival gangs, and a suspect ended up being from a rival area).
Still, maybe this could be useful in a place like Hollenbeck, where 30 gangs are said to roam. Andrea Bertozzi, UCLA math whiz:
If police believe a crime might have been committed by one of seven or eight rival gangs, our method would look at recent historical events in the area and compute probabilities as to which of these gangs are most likely to have committed crime.
Breaking news, Andrea: Police usually narrow it down a lot further than that. They usually have an idea right off the bat. If a Playboy Gangster Crip is shot, they're going to immediately go looking in rival Mansfield Crip territory, right?
UCLA says the algorithm could be used to solve other kinds of crimes, though. Good thing.
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