Development of an Algorithm to Predict Crimes

Scientists at the University of Chicago have developed a new algorithm that predicts crimes with an accuracy of up to 90% by analyzing data and learning patterns. Data scientists and sociologists from the University of Chicago created an advanced algorithm that works through learning patterns in time and geographic locations from public data on violent and property crimes. According to the University of Chicago, the new model isolates crime by examining the time and spatial coordinates of separate events and discovering patterns to predict future events. It divides the city into spatial parts approximately 1000 feet wide and predicts crimes within these areas instead of relying on traditional neighborhood or political boundaries, which are also prone to bias. Apparently, the accuracy of the new algorithm's predictions reached 90% and it worked not only in Chicago but also with data from seven other American cities: Atlanta, Austin, Detroit, Los Angeles, Philadelphia, Portland, and San Francisco. Ishanu Chattopadhyay, an assistant professor at the University of Chicago, stated, "We have created a digital twin of urban environments. If you provide it with data from what happened in the past, it will tell you what will happen in the future." The model was tested and validated using historical data from Chicago on two broad categories of crimes: violent crimes (homicides and assaults) and property crimes (burglary, theft, and car theft). According to a report published recently, the algorithm can predict future crimes one week in advance with an accuracy of up to 90%, according to Oddity Central.