This the first such project of its kind in the world, pooling multiple data sets from a number of police forces for crime prediction, says Donnelly. In the early phases, the team
gathered more than a terabyte of data from local and national police databases, including records of people being stopped and searched and logs of crimes committed. Around 5 million individuals were identifiable from the data. Looking at this data, the software found nearly 1400 indicators that could
help predict crime, including around 30 that were particularly powerful. These included the number of crimes an individual had committed with the help of others and the number of crimes committed by people in that individual’s social group.
The machine learning component of NDAS will use these indicators to predict which individuals known to the police may be on a trajectory of violence similar to that observed in past cases, but who haven’t yet escalated their activity. Such people will be assigned a risk score indicating the likelihood of future offending.
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