Visualizing Crime Hotspots by Analysing Online Newspaper Articles

With improvements in technology, India is growing at a fast pace, which has led to a great deal of urbanization. However, instead of reducing, the rate of crime has increased these past couple of years. The general public must be educated on how safe an area is, so that they may take the appropriate actions to protect themselves. Every day, we see many local crimes published in internet news articles, but not everyone has the time to read them all. They contain information that can be used to determine the safety of a location. Thus, in this paper, we propose an end-to-end solution based on Natural Language Processing to inform users of the crime rate in their area. We create a model that analyzes crimes mentioned in local news articles and collects data such as location and incident type. The model uses the concept of Named Entity Recognition to extract the locations and the crime that has occurred. To take advantage of the benefits of transfer learning, we built the model using Google's BERT framework. It was trained on CONELL2003 with custom modifications and was put to the test using real-time data gathered from several online news outlets’ crime pieces. Our model has an F1 score of 83.87% and a validation accuracy of 96%. The information collected via internet was visualized on a heat map using bokeh package. We display metrics such as name of the location, number of crimes occurred in that area and the recent most crime that has occurred which provides a quick overview and benefits our users.