The software collects and analyses around 11,000 bits of information about each robbery (time, date, location, type of business, type of crime), about the criminals involved (perceived age, height, body structure, skin, hair, eye colour, clothing), about the observed weapons (type, make, model, colour) and about the observed vehicle (type, make, model, license plate).
This is then combined with police reports, interviews with the victims and surveillance camera footage before using comparisons to establish links in order to identify and predict criminal strategies.
Professor Mastrobuoni added: “Most re-offending occurs within a few days, which means that at any given point in time there is a limited set of unique groups of robbers whose actions need to be predicted by the software. When all of this information then becomes available to patrols out on the streets, it puts the police in the right place at the right time. There is no doubt that this type of micro predictive policing is a highly effective, efficient IT Investment.”
Professor Mastrobuoni is currently working with Essex Police in developing and evaluating effective predictive policing practices.