Spatial, Temporal, and Semantic Crime Analysis Using Information Extraction From Online News

Abstract

Crime is a behavioral disorder with various scales that are intimately linked to a variety of circumstances such as spatial, temporal, sociological, and ecological aspects. The massive amounts of crime-related data, which is being published and grows with each passing day, in the form of online news reports have prompted researchers to pursue studies in the field of violence and criminal investigations. In this work, we developed a semantic approach to extract spatiotemporal and crime-related information from news reports to detect crime spatial distribution. The proposed method, in particular, aims to extract geographical and temporal information to detect regions with a high number of criminal cases, as well as to represent semantic knowledge of criminal incidents by annotating spatiotemporal information from their web domains. This approach incorporates the use of Natural Language Processing (NLP) techniques and a crime domain ontology into the information extraction process to automatically retrieve spatial, temporal, and other relevant information about criminal behavior from news reports. Our proposal consists of a comprehensive solution built on a fully functional architecture that has been tested in a use case scenario for the crime news reported in London, United Kingdom.