Predicting crime using deep learning

Abstract

Criminal activities are available in every region of the world influencing social life and financial improvement. As such, it is a major concern of numerous legislatures who are utilizing distinctive advanced innovation to handle such issues. Crime Analysis, a sub branch of criminology, considers the behavioral example of criminal activities and tries to recognize the pointers of such events. Distinguishing the patterns of criminal activity of a place is vital in order to prevent it. Law enforcement organizations can work effectively and respond more rapidly if they have better knowledge about crime patterns in different geological points of a city. Deep learning agents work with data and utilize distinctive systems to discover patterns in data making it exceptionally helpful for predictive analysis. Law enforcement agencies utilize diverse patrolling techniques in light of the data they get the chance to keep a region secure. The aim of this paper is to use deep learning models to predict and classify a criminal incident by type, depending on its occurrence at a given location. The experimentation is conducted on a dataset containing crime records. For this supervised classification problem, we used a new approach - LSTM (Long Short Term Memory) and was able to classify crimes with 64.2% accuracy. CNN (Convolutional Neural Network) & Shallow dense model were used also. Solving the imbalanced class problem, the deep learning agent was able to classify crimes.