Belief Rule Based Expert System to Identify the Crime Zones
This work addresses crime prediction for public safety agencies, but it appears incremental as it applies existing methods to a specific dataset.
The paper tackles crime zone identification by applying a Belief Rule Base algorithm to detect frequent patterns in crime hotspots and using an expert system to forecast crime types, with results aimed at raising awareness and aiding crime prediction.
This paper focuses on Crime zone Identification. Then, it clarifies how we conducted the Belief Rule Base algorithm to produce interesting frequent patterns for crime hotspots. The paper also shows how we used an expert system to forecast potential types of crime. In order to further analyze the crime datasets, the paper introduces an analysis study by combining our findings of the Chittagong crime dataset with demographic information to capture factors that could affect neighborhood safety. The results of this solution could be used to raise awareness of the dangerous locations and to help agencies predict future crimes at a specific location in a given time.