Predictive Crime Analysis: Statistical Approach to Forecast Crime Hotspots Using Recursive Neural Network in Deep Learning

In the contemporary world, concentrating on predicting the crime analysis is important with increasing criminal & unethical activities all over the world, we are in the need to predict these actions and to formulate countermeasures using statistical/ML techniques. Predictive Policing involves data processing on a large scale to predict and prevent possible future crimes/disturbing activities. This includes utilizing past recorded criminal activities, demographic features, and so on. The method should be able to identify the required patterns accurately and efficiently, and it must use methods that will mesh with law enforcement to make it easier for them to effectively handle these incidents. Using a Recursive Neural Network in Deep Learning (RNNDL) compute confidence rate by giving past predictions less weightage and recent predictions higher weightage, we would be able to figure out crime hotspots with a verified historical dataset of crime records efficiently. ©2024 IEEE.