Predicting Crime Hotspots: The Role of Security Technologies in Forecasting Residential Burglary in London

• In Event: Predicting crime in space and time: Examining and understanding input, throughput and output

Abstract

Advancements in security technologies have transformed residential crime prevention, yet their role in forecasting crime patterns remains underexplored. While extensive literature exists on situational crime prevention, limited research assesses how security measures—such as smart video doorbells, burglar alarms, motion sensor lighting, and gated communities—contribute to the spatial and temporal prediction of burglary hotspots. This study investigates whether these security features effectively harden targets and how their presence influences crime displacement and forecasting models in London.

As part of a Metropolitan Police-funded study, this research examines the impact of smart video doorbells on residential burglary and other home-related offences. The study addresses three key questions: (1) To what extent do security measures influence the likelihood of burglary incidents? (2) How do variations in security adoption affect crime forecasting models? (3) Can predictive analytics incorporating security measures enhance hotspot identification and resource allocation?

Using Metropolitan Police crime data, combined with spatial security infrastructure mapping, the study employs machine learning techniques and spatial-temporal modelling to assess crime trends and predict future burglary risks. Preliminary findings suggest that while security measures contribute to target hardening, their distribution plays a crucial role in crime hotspot evolution.

By integrating security interventions into crime forecasting models, this study offers valuable insights for law enforcement, policymakers, and urban planners. The findings aim to refine prediction models and improve crime prevention efforts in metropolitan settings.

Authors

• Alina Ristea, Assistant Professor Department of Security and Crime Science, University College London

• Shane Johnson, Dawes Centre for future crime at UCL