Big data is helping as a crime forecaster in UK these days and is proving as a helpful tool to reduce burglaries. Since, 2011 using computer algorithms and data have been yielding some great results for the police of Los Angeles and Manchester who ran radical trials.
But from mid 2015, the cops from said regions claim to have valid proof that big data is helping them spot patterns in the way criminals behaved and is thus helping in bringing down crime rate.
For instance, LAPD has valid proof that big data is helping them identify burglary activities in advance. They say that the available crime data with them has proved a fact that chances of burglaries are high within 200m radius of the first burglary. They added that the risk is often greater on the same side of the street that the 1st burglary occurred.
Similar trials are also taking place across UK, from Kent to Yorkshire. The results suggest that predictive policing models can help cut crimes where perpetrators exhibit predictable patterns of behavior.
For instance, in 2011, in Trafford, Manchester, police noted a 26.6% fall in burglaries, compared to a 9.8% fall across Greater Manchester in the same period.
However, the Police officials at Kent divulge a slightly less straightforward experience. They say that it ran a successful four-month trial starting in December 2012, but after rolling out predictive policing across the county in April 2013, recorded an increase in crime for the following year. The rise of crime rate blame was due to failure in deploying resources effectively and inaccurate crime data.
Research shows predictive analysis can identify hotspots more accurately, and separate studies show targeting police patrol and problem-solving in hotspots can reduce crime.
Therefore, forces in the UK and US are testing the effect of combining prediction with action to remove the causes of crime. The Metropolitan Police is currently undertaking the UK’s biggest pilot, assessing three types of predictive software, and is expected to publish findings later in early 2016.