When veteran crime forecaster Richard Berk was contacted by the National Research Council (NRC) (the research arm of the National Academy of Sciences) for a project funded by the National Science Foundation (NSF), he was asked, “What can be done about mass violence?” His first thought: How do you predict a crime that is statistically rare, but increasingly dire? If you can’t predict it, how would you intervene to prevent it?
“If I were to say that for all the schools in Philadelphia there won’t be a mass shooting in the next 12 months, I would be correct 99.99 percent of the time,” says Berk, professor of criminology and statistics and chair of the Department of Criminology. “My forecasts would be very accurate, but virtually useless. Still, the more I thought about the problem, it became an interesting and worthwhile challenge—finding a needle in the haystack.”
NRC’s pitch was rooted in a request for Berk to present any forthcoming research at a conference which would be attended by academics and various federal agency representatives charged with ensuring public safety. As an international leader in the use of machine learning, Berk uses very large datasets with hundreds of thousands of observations and many hundreds of variables to forecast crime.
The first step in this new research undertaking was to acquire the necessary data to formulate a test bed. Berk notes: “You can shoot 20 people, but if nobody dies, it’s not officially mass violence and data on mass violence is very spotty in any case.” Another approach was required.
This story is written by Blake Cole. Read more at Omnia.
