Big data policing is the use of ‘big data’ to anticipate emerging crime trends and patterns, thereby informing both strategic and tactical aspects of policing management. The overarching principle of big data policing is, therefore, that it has the potential to provide untapped insights that will enable the police to disrupt, prevent and/or reduce crime and harm. Big data policing is a very recent field of study in criminology. It can be placed within the scope of intelligence-led policing (ILP), a framework that mainly emphasizes the importance of data-based decision-making processes in the deployment of scarce police resources (Ratcliffe, 2016). Despite the increasing use and commercialization of big data policing, the scientific evidence base and best practices for its use are currently lacking (Hardyns & Rummens, 2018).

In the academic world, evaluation studies on big data policing are on the rise (e.g. Gerstner, 2018). Randomized controlled trials (RCTs) has been conducted in the US, evaluating the effectiveness of big data policing (Hunt et al., 2014; Mohler et al., 2015; Ratcliffe et al., 2021). They show mixed results in terms of reduced crime rates, but are too few in number to draw any strong conclusions about the effectiveness of big data policing (Meijer & Wessels, 2019). However, there is no transparency on the construction of the predictive algorithms or the effectiveness of big data policing software. In addition, US research results and conclusions cannot be applied directly to European settings, as they differ in terms of how law enforcement operates, governmental policies and many important structural features (e.g. urban planning, income inequality) that have a demonstrated and fundamental impact on spatiotemporal concentrations of crime (Sampson, 2012; Pauwels et al., 2018).

The general aim of this PhD research is to evaluate big data policing from a criminological and economic perspective, in order to evaluate the implementation, use and social relevance of big data policing. Besides the evaluation on how effective big data policing is in reducing crime rates, secondary effects should also be taken into account. These secondary effects that are not crime-related are underreported in evaluating studies of intelligence led policing programs (Khalfa & Hardyns, 2023). Focusing on secondary effects, such as a cost-benefit analysis and the evaluation of user-experience of big data policing will allow to provide a more complex and balanced overview of the impact of big data policing. This way, an informed decision can be made to further develop, adjust or abolish certain applications or implementations. This PhD will provide an impetus to strengthen the relationship between research and policy, in conducting the research in collaboration with police officers and experts working on the field.