Developing a Prototype Computational Modelling Platform

Research summary

The 21st century has seen policing agencies become involved in an increasingly diverse range of roles, often while managing relatively restricted resources. Consequently, a key priority for applied policing relates to better understanding and anticipating changes in short-, medium- and long-term demand. Changes to offence rates and types, reduced officer numbers, and structural population changes, are all likely to impact police demand in complex ways.

Illustrating the national importance of these issues, the recent (House of Commons, 2018) Home Affairs Committee report concluded that policing is struggling to cope with rises in crime (old and new) and is not fit for purpose, with resources available not matching demand. The authors of that report strongly recommend that police funding is prioritised in the Autumn Budget and the next Comprehensive Spending Review. However, the challenges to policing are not limited to there simply being too few resources. As noted in the Association of Police and Crime Commissioners and National Police Chiefs Council’s Policing Vision 2025 “Most forces do not have a thorough evidence-based understanding of demand, which makes it difficult for them to transform services intelligently and demonstrate they are achieving value for money”.

Problems the police must address vary in terms of scale, harm and whether responses are dealt with locally, regionally or require national coordination. Some priority areas for which demand needs to be better understood (and met) include online crime, high harm crimes against the most vulnerable, and serious and organised crime. However, demand is poorly understood for volume crimes, which are – after a period of reduction – on the rise again. Furthermore, events such as the 2011 UK riots remind us of the need to plan for major incidents of public disorder that require a national response. Even the latter are poorly understood, and no systematic modelling exists to enable law enforcement to plan for such events, despite their clear national significance (personal communication).

As a result, models of police resourcing are required to allow police agencies to better understand the drivers of demand and best optimise the allocation of existing resources to minimise threat, risk and harm to the community. Yet, understanding demand is a non-trivial task. An array of factors, both internal and external to police organisations, influence demand. Moreover, these factors are often highly interdependent (meaning that all choices have opportunity costs) and difficult to model using traditional analytical techniques. In response to these challenges, the aim of this project is to conduct exploratory research to assess how data-driven agent-based models might be applied to better understand the dynamics of police demand and resourcing. To achieve this, the project will analyse data sources describing the drivers of, and responses to, police demand; and develop exploratory computational models that seek to simulate police demand and resourcing dynamics at both individual and organisational scales.

The ultimate goal of the project is to assess the viability of these techniques for developing decision support tools capable of analysing ongoing, and forecasting future, police demand.