Crime reduction through public healthcare: Interpretable machine learning for mental health service impacts in Greater London

The relationship between crime, mental health service access, and socioeconomic deprivation in publicly-funded healthcare systems allowing impactful policy interventions offers an alternative lens to crime prevention that remains underexplored.

We address this critical gap through an analysis of street-level crime data, mental health referral information, and socioeconomic metrics across Greater London, using both traditional statistical methods and machine learning techniques to identify relevant relationships and spatial patterns to reveal a persistent positive association between crime rates and mental health referrals as a proxy for service access.

The prevailing prevention hypothesis is contrasted with a nuanced U-shaped relationship suggesting a contrast between preventive effects at lower service levels and demand-driven responses to crime exposure for higher referral rates.

Subsequent analyses, focussing on explainable artificial intelligence, show distinct crime category patterns, with a cluster analysis identifying four borough typologies with distinct combinations of crime rates, mental health service access, and deprivation levels, requiring multifaceted approaches rather than universal solutions.

This research provides one of the first comprehensive studies on this topic for the UK's publicly-funded healthcare system and introduces interpretation-oriented approaches to uncover the patterns essential to evidence-based policies.