Horizon Scanning Through Automated Information Prioritisation

It is no secret that police forces are seeking to advance their repertoire of analytical and predictive tools to deal with emerging crimes.However, the volume, variety, and velocity of data recorded by police forces and available through open sources means that analysing such data can be challenging. Current efforts typically resort to manual data inspection impeding understanding of problems at scale and over time. Despite these obstacles, horizon scanning (HS) is set to become a prominent aspect of future policing, by enabling police forces to identify emerging threats, anticipate imminent crimes and to ultimately extinguish future methods of perpetration. In essence, HS activities aim to aid the police in staying one step ahead of criminals. The objective of this research is to develop a novel automated technology that can identify early warning signals of future crime problems. The project is split into three main themes: (i) utilising what the police know, (ii) what the community knows, and (iii) what the media knows. Each part will build on techniques from the previous, to create a single, comprehensive, HS system. In order to develop research output that is relevant in the real world and to resolve current blockades in HS, this research will foster close working relationships with law enforcement agencies.To achieve the objectives, the research will begin with a conceptual piece that defines the constitutional motifs of the research such as the characterisation of new crime types and/or methods of perpetration. This will inform subsequent work on automatically detecting and monitoring (novel) crime trends. Data sources will then be considered and will include developing a framework for an open-source intelligence repository (OSINTR) to be created to automatically collect, and store, a large quantity of data from different sources. The repository will include diverse sources such as news reports, social media data and community forum posts. Data sharing arrangements are also being made to gain access to policing data. Overall, the collected data will reflect the three main themes of this project.To develop the method, simple synthetic data will initially be used to evaluate various natural language processing techniques for their usefulness in extracting descriptions of the actions and behaviours exhibited by an offender whilst committing a crime. This will then be replicated on crime reports to establish performance in this context and facilitate recommendations for police data collection practices. Machine learning methods such as anomaly detection (similar to those used by banks to identify fraudulent transactions) will then be implemented to test the extracted information's potential for monitoring changes in crime. The next part of the research will utilise the OSINTR and machine learning (where computers learn about patterns from the data, without being explicitly programmed), and different datatypes to further the methods used earlier in theme extraction and detection. The analysis here will speak to the themes of what the community and the media know about emerging and future crime problems. The research will then conclude with a final study on predictive ability (e.g., anticipating tomorrow's crime headlines today) by utilising the different data sources and employing self-learning algorithms. The purpose of this is to generate short problem snippets that analysts can then investigate further.

The primary objectives of the research are to develop an automated HS system that
Automatically collates information from a variety of open data sources
Can extract crime related information from different data types
Monitors anomalous changes in crime and methods of perpetration
Can potentially make predictions in the form of early warnings concerning future problems

The EPSRC research areas relevant to this research are
AI technologies
Information systems
Natural language processing
Operational Research