Could Analytics Have Preempted Colorado Theatre Shootout?

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"Could Predictive Analytics be leveraged to identify troubled individuals with violent proclivity and thus preempt mass killings like recent Colorado theatre shootout?" explores Aryng's President and CEO, Piyanka Jain.

Crime Analytics
Armed with the Predictive Policing tool, SCPD cops are able to arrive at the crime scene even before it takes place, thus staying one step ahead of the bad guys.

Time Magazine named Santa Cruz’s Predictive Policing model as one of 2011’s top inventions. Armed with the Predictive Policing model, Law Enforcement Officers from Santa Cruz Police Department (SCPD) are able to arrive at the crime scene even before it takes place, thus staying one step ahead of the bad guys. Could a similar Predictive model have helped prevent Colorado shootout?

The recent theatre shoot out during the midnight show of “The Dark Knight Rises” on July 19th in Aurora Colorado is a far cry from your everyday crime. In the shootout, a fully armored gunman, opened fire at an unsuspecting audience, a few minutes into the movie, killing 12 and injuring 58 other movie-goers. The 24-year old alleged suspect, James E. Holmes, has apparently no history in having committed any crime before and is known by friends and neighbors as a highly intelligent man with a gift for science. So what happened? Why did this allegedly well-mannered, polite man decided to withdraw from his doctoral program last month, bought four guns all within the last couple of months and go on a rampage?

We may never get that answered as it is probably buried deep inside the folds of this suspects’ mind, but what we could do is look at whether this incident could have been predicted and thereby pre-empted. How? By using Predictive Analytics - statistical technique by which past data patterns are used to predict future occurrences.

Last July, two women were arrested after they were seen peering into cars in a parking garage, in downtown Santa Cruz. Upon arrest, one woman was found to have an outstanding warrant against her, other was carrying illegal drugs. But the presence of cops that day, in that garage was not by chance! It was part of pro-active policing done by SCPD since last year utilizing their predictive analytics system. Thanks to Predictive Policing, law enforcement officers can now tell where and when crimes are most likely to occur on that particular day.
In a recent interview with Aryng (http://www.Aryng.com), SCPD crime analyst, Zach Friend described how SCPD was going through a difficult time last year with 20% reduction in staff and 30% increase in calls for service. With less resources available to deal with more, there were called for efficient allocation of their resources, and that they did. SCPD had a significant amount of high quality crime data; with which they contacted Dr George Mohler, Mathematics Professor at Santa Clara University. Professor Mohler along with a research team comprising of criminologist and anthropologist took all of the complex crime data and converted it into simple points that a line-level police officer could use for checks during shifts, even without understanding of algorithms and inputs.

The results: 19% reduction in burglary in last 16 months and 25+ arrests solely because of the tool. LAPD who has also deployed this same tool, has seen 25% reduction in burglaries, saving over $4 Million in costs to the community.

But burglaries are common crimes with higher incidence vs. mass killings. Can Predictive Analytics be used in these scenarios?
Pauline Arrillaga explores how to prevent mass killings like Aurora, citing the work done by forensic clinical psychologist Dewey Cornell, whose team has developed an assessment guideline to identify threatening individuals. These guidelines are being used by most public schools to pre-empt shootouts like Columbine. Such guidelines along with other purported predictors generated with the help of crime experts (like massive purchase of guns by a person who hasn’t owned a gun before) can form the hypothesis to build a predictive model to identify threatening individuals.

Credit industries have long used an individuals’ demographic data married to purchase behavior to predict their credit worthy-ness. This same technique can be used with additional relevant data like gun purchase, crime data etc. to create a “potential threat” score.

Whether, the Predictive Policing model, as it now stands, can predict a crime like the Colorado shootout or not, it is certain that, in the foreseeable future, major police departments and law enforcement agencies would be employing techniques similar to the Predictive Policing model aimed at pre-empting mass crimes and making the world a safer place to live in!

To learn more about the power of analytics, read Aryng’s blog posts and articles on http://www.Aryng.com/blog.
About the Author: Piyanka Jain, President and CEO of Aryng - a premier analytics training and consulting company, is a well-regarded industry thought leader in analytics, keynoting at business and analytics conferences including Predictive Analytics World, Data Science Summit, TDWI Big Data Conference, and regular contributor to publications like Forbes, SAS Knowledge Exchange, B-eye-network, to name a few.

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