Towards an evidence-based model for big data policing: Evaluating the statistical-methodological, criminological and legal and ethical conditions

Introduction

Big data policing is an innovative strategy that uses historical data to forecast when and where there is a high risk of new crime events in order to use police resources more efficiently and proactively, and ultimately reduce crime rates.

Components of Big Data Policing

Big data policing models can consist of variables based on:

• Crime data available in police databases (e.g. previous crime events)
• Socio-economic data (e.g. poverty index, residential mobility)
• Opportunity characteristics (e.g. the presence of shops, distance to nearest highway)
• Data from new technologies (e.g. intelligent cameras)
• Other known predictors of crime (e.g. police patrol intensity)

Research Gaps

However, there are several research gaps that need to be addressed:

1. Knowledge about and expertise in big data policing in Europe is currently fragmented.
2. There is a lack of interdisciplinarity with regard to big data policing studies, and yet the involvement of several disciplines is required when studying the issue.
3. There is a lack of scientific evaluations of big data policing models.

Project Objectives

The overarching objective of this ERC project is to unite and integrate the statistical-methodological, criminological, legal, and ethical dimensions of big data policing in an evidence-based model that will be tested by different randomized controlled trials.

Approach

This model will be built on the principles of an international (i.e. European) and interdisciplinary approach. The latter aim should be enabled by incorporating and conducting different PhD tracks focusing on these specific dimensions, which should allow better knowledge, insights, and understanding of big data policing to be developed.

Innovation and Transparency

This approach is innovative and radically different from the existing commercial and economic initiatives, which lack transparency on their predictive reliability and validity, effectiveness, and legal and ethical safeguards.