Predictive policing has come to Canada, specifically, Vancouver. A July 22, 2017 article by Matt Meuse for the Canadian Broadcasting Corporation (CBC) news online describes the new policing tool,
The Vancouver Police Department is implementing a city-wide “predictive policing” system that uses machine learning to prevent break-ins by predicting where they will occur before they happen — the first of its kind in Canada.
Police chief Adam Palmer said that, after a six-month pilot project in 2016, the system is now accessible to all officers via their cruisers’ onboard computers, covering the entire city.
“Instead of officers just patrolling randomly throughout the neighbourhood, this will give them targeted areas it makes more sense to patrol in because there’s a higher likelihood of crime to occur,” Palmer said.
Things got off to a slow start as the system familiarized itself [during a 2016 pilot project] with the data, and floundered in the fall due to unexpected data corruption.
But Special Const. Ryan Prox said the system reduced property crime by as much as 27 per cent in areas where it was tested, compared to the previous four years.
The accuracy of the system was also tested by having it generate predictions for a given day, and then watching to see what happened that day without acting on the predictions.
Palmer said the system was getting accuracy rates between 70 and 80 per cent.
When a location is identified by the system, Palmer said officers can be deployed to patrol that location. …
“Quite often … that visible presence will deter people from committing crimes [altogether],” Palmer said.
Though similar systems are used in the United States, Palmer said the system is the first of its kind in Canada, and was developed specifically for the VPD.
While the current focus is on residential break-ins, Palmer said the system could also be tweaked for use with car theft — though likely not with violent crime, which is far less predictable.
Palmer dismissed the inevitable comparison to the 2002 Tom Cruise film Minority Report, in which people are arrested to prevent them from committing crimes in the future.
“We’re not targeting people, we’re targeting locations,” Palmer said. “There’s nothing dark here.”
If you want to get a sense of just how dismissive Chief Palmer was, there’s a July 21, 2017 press conference (run time: approx. 21 mins.) embedded with a media release of the same date. The media release offered these details,
The new model is being implemented after the VPD ran a six-month pilot study in 2016 that contributed to a substantial decrease in residential break-and-enters.
The pilot ran from April 1 to September 30, 2016. The number of residential break-and enters during the test period was compared to the monthly average over the same period for the previous four years (2012 to 2015). The highest drop in property crime – 27 per cent – was measured in June.
The new model provides data in two-hour intervals for locations where residential and commercial break-and-enters are anticipated. The information is for 100-metre and 500-metre zones. Police resources can be dispatched to that area on foot or in patrol cars, to provide a visible presence to deter thieves.
The VPD’s new predictive policing model is built on GEODASH – an advanced machine-learning technology that was implemented by the VPD in 2015. A public version of GEODASH was introduced in December 2015 and is publicly available on vpd.ca. It retroactively plots the location of crimes on a map to provide a general idea of crime trends to the public.
I wish Chief Palmer had been a bit more open to discussion about the implications of ‘predictive policing’. In the US where these systems have been employed in various jurisdictions, there’s some concern arising after an almost euphoric initial response as a Nov. 21, 2016 article by Logan Koepke for the slate.com notes (Note: Links have been removed),
When predictive policing systems began rolling out nationwide about five years ago, coverage was often uncritical and overly reliant on references to Minority Report’s precog system. The coverage made predictive policing—the computer systems that attempt to use data to forecast where crime will happen or who will be involved—seem almost magical.
Typically, though, articles glossed over Minority Report’s moral about how such systems can go awry. Even Slate wasn’t immune, running a piece in 2011 called “Time Cops” that said, when it came to these systems, “Civil libertarians can rest easy.”
This soothsaying language extended beyond just media outlets. According to former New York City Police Commissioner William Bratton, predictive policing is the “wave of the future.” Microsoft agrees. One vendor even markets its system as “better than a crystal ball.” More recent coverage has rightfully been more balanced, skeptical, and critical. But many still seem to miss an important point: When it comes to predictive policing, what matters most isn’t the future—it’s the past.
Some predictive policing systems incorporate information like the weather, a location’s proximity to a liquor store, or even commercial data brokerage information. But at their core, they rely either mostly or entirely on historical crime data held by the police. Typically, these are records of reported crimes—911 calls or “calls for service”—and other crimes the police detect. Software automatically looks for historical patterns in the data, and uses those patterns to make its forecasts—a process known as machine learning.
Intuitively, it makes sense that predictive policing systems would base their forecasts on historical crime data. But historical crime data has limits. Criminologists have long emphasized that crime reports—and other statistics gathered by the police—do not necessarily offer an accurate picture of crime in a community. The Department of Justice’s National Crime Victimization Survey estimates that from 2006 to 2010, 52 percent of violent crime went unreported to police, as did 60 percent of household property crime. Essentially: Historical crime data is a direct record of how law enforcement responds to particular crimes, rather than the true rate of crime. Rather than predicting actual criminal activity, then, the current systems are probably better at predicting future police enforcement.
Koepke goes on to cover other potential issues with ‘predicitive policing’ in this thoughtful piece. He also co-authored an August 2016 report, Stuck in a Pattern; Early evidence on “predictive” policing and civil rights.
There seems to be increasing attention on machine learning and bias as noted in my May 24, 2017 posting where I provide links to other FrogHeart postings on the topic and there’s this Feb. 28, 2017 posting about a new regional big data sharing project, the Cascadia Urban Analytics Cooperative where I mention Cathy O’Neil (author of the book, Weapons of Math Destruction) and her critique in a subsection titled: Algorithms and big data.
I would like to see some oversight and some discussion in Canada about this brave new world of big data.