By Elizabeth Flock, courtesy of the University of Chicago Magazine
“ I realized I wanted to be doing some kind of service that went beyond just volunteering.”
Brett Goldstein was always a step ahead.
When he was six years old, he started writing programs on his parents’ single-circuit-board computer. By fifth grade he taught himself programming languages, and while at Connecticut College, a professor told him he’d “maxed out” the curriculum.
By his early 30s, Goldstein, SM’05, was making six figures as IT director at OpenTable, an online restaurant reservation company. But inspired by the attacks of Sept. 11, Goldstein took the Chicago Police entry exam on a whim in 2004.
“I realized I wanted to be doing some kind of service that went beyond just volunteering,” Goldstein says.
Two years later, after completing his computer science master’s at UChicago, Goldstein surprised colleagues and friends by joining the Chicago Police Academy, where he “had high hopes” that he’d eventually be able to utilize his data-analysis and computer-science training.
In early 2007 Goldstein was assigned to the Harrison District, one of the toughest neighborhoods on Chicago’s West Side, where according to the Chicago Sun-Times, he “started thinking about how he could design a computer model that could replicate” an officer’s intuition.
After he transferred off the street, and with the help of a $200,000 National Institute of Justice grant, Goldstein launched a predictive-analytics project in 2009. The group would analyze crime data to focus manpower where trouble was most likely to occur. He had begun forming the idea in an intensive UChicago computer science course in which students learned to extract patterns from data.
About nine months after he became director of the department's new Predictive Analytics Group, Goldstein in April 2011 was named Chief Data Officer as part of Chicago mayor-elect Rahm Emanuel’s technology leadership team.
Identifying patterns to predict crime
The CPD wasn’t Goldstein’s first foray into criminal justice. In 1999 he received his master’s degree in the field from Suffolk University, with the goal of working in IT security; part of his role at OpenTable involved network security.
Goldstein admits his choice to actually enter the academy to become a beat cop wasn’t a quick or an easy one: “It was a big decision that required a lot of thought,” he says. “I became more committed with each step of the police screening process.”
While at UChicago, Goldstein reviewed 911 call records from the Oak Park police as part of computer science professor Leo Irakliotis’ intensive data-mining course. The goal was to identify the street corners where people frequently called 911 and hung up. They then predicted the corners most likely to have hang-up offenders.
It’s finding these patterns in seemingly random events that drives Goldstein’s work at the CPD, where he makes forecasts using the entire data system. Data analytics uses time and location patterns to calculate where crime might happen. Goldstein’s group, for example, might show “a wave of burglaries in one neighborhood,” says Irakliotis. “They would use the data to see how it might expand to another neighborhood.”
Taking data to the next phase
Although a number of U.S. police departments now use such figures to draw conclusions about crime, Chicago has one of the largest data sets in the country. The city began collecting electronic data in the late 1990s, earlier than most departments according to CPD spokesman Michael Fitzpatrick, partly thanks to former assistant deputy superintendent Ron Huberman, AM’00, MBA’00. While working in information services, Huberman helped create a central database to house all police intelligence. In 2001 the department partnered with the Oracle Corporation to create CLEAR (Citizen Law Enforcement Analysis and Reporting). The system, now recognized by the U.S. Justice Department as a law enforcement best practice, is used to streamline processes such as filing arrest reports and provides officers with easy-to-access information.
Goldstein teamed with the Illinois Institute of Technology and the Rand Corporation to take this data to the next phase. The Predictive Analytics Group officially launched in August 2010, and although not all Chicago cops were enthusiastic about the program, Goldstein stresses it is a tool for the officers, not a replacement. “You can’t underestimate the guys’ relationship with the community,” he says. He has invited skeptical beat cops to visit his office to discuss how it works.
Part of the officers’ concern comes from the difficulty of evaluating the program’s accomplishments. “It’s hard,” says Fitzpatrick, because “if there’s a likelihood for something to happen and it doesn’t, that’s a success. So how do you gauge that?”
Goldstein doesn’t claim his analytics would be the only reason for department successes or lower crime. Every day his group sends out intelligence showing the areas with the biggest potential for violence. Then the department that deploys officers produces its own information, based on gang conflicts and human intelligence. The two elements are combined with feedback from the patrol division to come up with that day’s patrol plan.
The “key idea,” says Goldstein, “is to give the field commanders the latest and best information to be able to deploy their resources intelligently.”