An End-to-End Framework for Dynamic Crime Profiling of Places
Shailendra K. Gupta, Shreyanshu Shekhar, Neeraj Goel, Mukesh Kumar Saini · Smart Cities · 2022
Much effort is being made to ensure the safety of people. One of the main requirements of travelers and city administrators is to have knowledge of places that are more prone to criminal activities. To rate a place as a potential crime location, it needs the past crime history at that location. Such data is not easily available in the public domain, but it floats around on the Internet in the form of newspaper and social media posts, albeit in an unstructured manner. Consequently, many works are reported on extracting crime information from news articles, providing piecemeal solutions to the problem. This chapter complements these works by building an end-to-end framework for crime profiling of any given location/area. It customizes individual components of the framework and provides a spatio-temporal integration of crime information. It develops an automated framework that crawls online news articles, analyzes them, and extracts relevant information to create a crime knowledge base that gets dynamically updated in real-time. The crime density can be easily visualized in the form of a heatmap which is generated by the knowledge base. As a case study, it investigates 345,448 news articles published by six daily English newspapers collected for approximately two years. Experimental results show that the crime profiling matches with the ratings calculated manually by various organizations.