Analysis and prediction of crime patterns using big data

Abstract

Nowadays crimes are increasing at a high rate which is a great challenge for the police department of a city. A huge amount of data on different types of crimes taking place in different geographic locations is collected and stored annually. It is highly essential to analyze data so that potential solutions for solving and mitigating the crime incidents and predicting similar incident patterns for future becomes possible. Then it can be carried out using big data and various machine learning techniques in conjunction. The paper introduced a solution to the crime prediction problem using Naive Bayes classifier, which includes finding the most likely criminal of a particular crime incident when the history of similar crime incidents has been provided with the incident-level crime data. The incident-level crime data is provided as a crime dataset which includes incident date and location, crime type, criminal ID and the acquaintances are the attributes or crime parameters. The acquaintances are the suspects whose names are either directly involved in the incident or indirectly the acquaintances of the criminal. Acquiring a real-time crime dataset is a difficult process in practice due to confidentiality principle. So, crime dataset are used for the inputs using the state of the art methods. The proposed system is tested for the crime prediction problem using the data learning, and the experimental results show that the proposed system provides better results and finding of the potential solutions and crime patterns.