Broughton (North Northamptonshire) Crime Rate

Broughton (North Northamptonshire) has seen a 9.8% year-on-year increase in crime, with drug-related crimes surging by 100%. Despite this, the area remains 24.7% below the UK average, highlighting its overall safety. The sharp rise in drug crimes contrasts with declines in violence and sexual offences, suggesting evolving local dynamics.

Broughton (North Northamptonshire) is a small built-up area with a mix of residential and retail spaces. Its proximity to major transport routes and a moderate population density likely influence its crime profile. The area's retail presence may contribute to shoplifting rates, while its commuter demographics could explain patterns in vehicle crime. The overall lower crime rate compared to the UK average suggests a relatively stable community environment.

The most surprising finding is that shoplifting in Broughton is 96% above the UK average (14.7 vs 7.5 per 1,000). This likely reflects the area's retail density and potential vulnerabilities in store security. Anti-social behaviour is 2% above the UK average, which may stem from social dynamics in local public spaces. Despite this, violence and sexual offences are 50% below the UK average, possibly due to effective community policing or lower population density. The sharp 100% YoY increase in drugs is concerning but remains below the UK average. This could indicate evolving drug distribution networks or increased reporting. Property crime rates are 5.2% below the UK average, suggesting that residential security measures may be effective in deterring burglary and vehicle crime.

Crime in Broughton (North Northamptonshire) has increased by 9.8% year-on-year, with drugs rising sharply by 100% YoY. This surge in drug-related crime contrasts with declines in violence and sexual offences (down 7.1% YoY) and burglary (down 20% YoY). The monthly trend shows a peak in May 2025 (8.6 per 1,000/month) followed by a steady decline, likely due to seasonal factors such as reduced outdoor activity in July. The drug crime spike may be influenced by increased availability or changes in enforcement patterns, though it remains below the UK average.

How To Keep Safe in Broughton (North Northamptonshire)

Residents should focus on securing high-value items, particularly during peak theft hours in the afternoon. For shoplifting prevention, businesses can install perimeter detection systems and ensure staff are trained to identify suspicious behaviour. Anti-social behaviour can be mitigated by reporting incidents promptly through local community channels. Given the sharp rise in drug-related crimes, staying informed about local initiatives and supporting community policing efforts may help address this trend.

Crime in Broughton shows distinct patterns by time of day. Theft peaks in the afternoon (64% of afternoon crimes), likely linked to retail activity and foot traffic. Vehicle crime rises sharply at night (52% of night-time crimes), possibly due to unattended vehicles in residential areas. Burglary is most common in the evening (18%), aligning with typical home entry times. The evening also sees a spike in 'other' crimes (30%), which may include public order issues. These patterns suggest that targeted security measures during peak hours could reduce crime opportunities.

Broughton (North Northamptonshire) has a crime rate of 68.5 per 1,000, 110% higher than Mawsley Village (32.6 per 1,000) but 71% lower than Kettering (117.4 per 1,000). This suggests that while Broughton is safer than Kettering, its rate is still higher than the rural Mawsley Village, likely due to differences in population density and economic activity.

For Broughton (North Northamptonshire) residents, where shoplifting is 96% above the UK average, proactive perimeter security is essential. AI-powered systems like scOS can detect suspicious activity at property boundaries, triggering lighting and audio deterrents to disrupt opportunistic offenders. Multi-camera monitoring ensures full coverage of vulnerable areas, while behavioural pattern learning reduces false alarms by distinguishing regular visitors from potential threats. These features align with the area's need to prevent retail theft and other opportunistic crimes.