Through spatial analytics, police are able to predict where residential break-and-enters will occur and place police patrols accordingly
Police in Vancouver, British Columbia, are deploying a machine learning solution that uses an algorithm to deconstruct crime patterns and help predict future offences.
Through spatial analytics, police are able to predict where residential break-and-enters will occur and place police patrols accordingly.
The department first trialled this technology with a pilot that reduced burglary by more than one fifth month over month. Now they are making the intelligence-led approach common practice.
“Every 28 days, our management reviews crime trends, crime clustering, and crime issues across the city,” said Ryan Prox, special constable in charge of crime analytics advisory and development unit, Vancouver Police, on a blog on the Esri site.
“It has driven us to more of an evidence-based policing approach with accountability on how our officers are being deployed, the resources being allocated, and the net return on our actions.”
The new tool follows the Compstat policing model, a performance management system that combines computer statistics and a geographic information system (GIS) to aid crime reduction strategies.
This practice originated in New York City during the 1990s and has since spread globally. When done correctly, it has proven to be an effective policing tool to help agencies identify problems that reduce crime and improve the quality of life at the neighbourhood level.
“It has driven us to more of an evidence-based policing approach with accountability on how our officers are being deployed, the resources being allocated, and the net return on our actions”
For Vancouver Police, the application of machine learning started with geospatial engineers and statisticians who developed an algorithm to pinpoint property crime patterns.
The crime forecasting tool, developed by Esri partner Latitude Geographics, predicts crime location within a 100-metre circle and a two-hour time window by reviewing what took place in the past. Over time, the tool has proven to have a greater than 80 per cent accuracy, Esri claims.
“We have properly trained people that can use the technology, enterprise solutions and data warehouses to look at the big picture and trends, and a process supported by a progressive management,” added Prox. “It takes all three.”
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