From Short-Term Deterrence to Long-Term Intervention: Promises and Challenges of Algorithmic Crime Prevention in Urban Spaces

• In Event: The digitalisation of police work and its consequences for citizens

Abstract

This paper discussed advantages, limitations and opportunities of the use of predictive policing tools for crime prevention in urban settings. Our empirically based comparison with the problematic attempts to implement algorithmic crime prevention in rural areas shows that cities provide an ideal setting for the current approaches, due to crime patterns, high incident rates and police organization. However, even in urban settings practical enforcement faces significant hurdles. Police often struggle to allocate sufficient time for preventive patrols, and predictive policing primarily focuses on short-term deterrence rather than addressing root causes. On the other hand, our study identifies and discusses alternative emerging prevention strategies that emphasize community engagement and problem-oriented approaches. These approaches use algorithmic tools within a more context-aware model, shifting the focus from predicting future crimes to addressing present criminogenic conditions. Additionally, they distribute crime prevention responsibilities beyond the police, involving local communities. This shift might envision a more sustainable and effective use of digital tools in urban crime prevention, also integrating broader social engagement.

Authors

• Elena Esposito, Bielefeld University/University of Bologna

• Simon Egbert, Bielefeld University