The technology that helped transform a crime capital15/09/2016
In this era of Big Data, where every law enforcement officer and vehicle is a sensor, how do agencies efficiently integrate, analyse and disseminate collected information, and transform it into actionable intelligence that improves decision-making?
To understand the patterns, links and correlations of crimes, criminals and victims, the world’s leading law enforcement agencies are increasingly using advanced location-based analytics – commonly known as GIS technology.
Everything law enforcement manages has a location, so analysing from a geographic starting point makes sense.
GIS takes advantage of the ‘where’ in data, providing a platform for crime analysts, investigators, commanders and patrol officers to visualise information in ways that help them identify, predict and ultimately reduce crime.
Transformation of a crime capital
Once the second deadliest nation in western Europe, Scotland provides a compelling use case into how location-based analytics can help to dramatically cut crime rates.
Not too long ago, Scots were three times more likely to be murdered than their English neighbours. Scotland was once also declared the most violent country in the developed world by the United Nations, as more than 2,000 people were subject to an aggravated attack each week.
In Glasgow, Scotland’s most violent city, the local Strathclyde Police set up a Violence Reduction Unit (VRU) to address violent crime by using location intelligence to prioritise the strategic, focused use of resources.
Part of the solution involved collecting data from external sources, such as hospitals, fire departments, schools and social services organisations. They mapped this with other data related to factors known to impact violent crime – including poverty, housing, unemployment and environment.
Hidden trends and patterns in criminal behaviour in the city were revealed, enabling the unit to understand where crimes were happening and why.
Armed with this powerful insight, law enforcers could make predictions about where crimes were likely to occur, so they could discard their previous ‘needle in a haystack’ approach and better target resources to prevent them.
For example, the team mapped knife-crime alongside ‘pathways’ to crime, using transport and vandalism data from bus companies to visualise previously unidentified links between the two. Consequently, the VRU was able to advise local police forces on where to establish the best locations and times for stop-and-search operations.
VRU’s use of location-based analytics to both understand and predict crime led to a 39 per cent fall in all crime – not merely violent crime – in the Glasgow city centre. Statistics from 2015 also show homicide rates in Scotland are at their lowest levels since records began in the 1970s.
Information, collaboration and integration
Location-based analytics can also dismantle internal information silos. With unprecedented amounts of data being collected internally, the ability to share and analyse this information has never been more crucial. The advent of body and dash cameras, and technologies such as digital number plate recognition, mean every individual police officer is now a sensor, collecting and streaming immense amounts of data.
Being able to efficiently share information was a problem faced by Canada’s Vancouver Police Department (VPD), which struggled with having large amounts of mission-critical data stored in disparate, internal management, analysis and project systems.
Apart from the resultant widespread duplication of efforts and data redundancy, these silos also hindered police investigators. Critical elements of investigations were scattered over multiple jurisdictions and could not be easily shared. Often, by the time data was located and consolidated, it was out of date and no longer useful.
VPD used location-based analytics to bring this data together and, via a user-friendly geo-dashboard, provided accessibility across the department. This provided crime analysts with instant access to offender information and datasets to identify suspects, predatory behaviour, resource inefficiencies and response times.
This meant they were able to focus on conducting analysis at a much deeper level, instead of spending significant amounts of time completing non-analytical tasks. The system was also relied on heavily to plan for the 2010 Vancouver Winter Olympics, where it was used to monitor street closures and deploy police, among other tasks.