University of Pennsylvania professor Aaron Chalfin reviews current research on the economic impact of crime, and most of the analyzes account for about 2% of the gross domestic product in the United States.This article examines the applications of AI and machine learning in crime prevention.
Below, we cover a range of current applications: Â Gunfire Identification - ShotSpotterThe ShotSpotter Company uses smart city infrastructure to triangulate the location of a gunshot.According to ShotSpotter, only 20 percent of gunfire incidents involve people calling 911, and even when people report the incident, they often provide only vague or inaccurate information.
They state that when and where new crimes occur, they can analyze existing data on past crimes.
Their system highlights possible hotspots on the map, and police should consider patrolling more.A victory highlighted by Tacoma, a Washington firm, saw a 22 percent drop in residential burglaries as soon as the system was adopted.
Tacoma began using Predpole in 2013, and in 2015 the burglaries fell.Since crime is a complex issue for many reasons, it is very difficult to separate the effectiveness of any one tool.
Hart uses that data to predict whether a person is a low, medium or high risk.The city has been testing the system since 2013 and compared its expectations with real-world results.