Bath Crime Rate

Bath's crime rate of 111.1 per 1,000 residents is 22% above the UK average, with shoplifting being 95% higher than national levels. This stark contrast highlights the impact of tourism on local crime patterns, as the city's historic shopping districts and university campuses create unique opportunities for opportunistic theft. While overall crime has decreased by 6.5% year-on-year, the persistent elevation of shoplifting rates underscores the need for targeted local solutions.

Bath is a historic built-up area in Somerset, renowned for its Georgian architecture and cultural heritage. As a major tourist destination and home to the University of Bath, the area blends residential, academic, and commercial zones. This mix likely influences crime patterns, with retail theft and anti-social behaviour more prevalent in busy shopping districts, while burglary rates reflect the density of homes in quieter residential areas.

Bath's crime profile reveals a combination of local factors. The most surprising finding is the 95% above-average rate for shoplifting, driven by Bath's status as a tourist hotspot with a high concentration of retail outlets. This aligns with patterns in other tourist towns where opportunistic theft is common. Public order crimes are 70% above the UK average, likely linked to Bath's vibrant nightlife and events in the city centre. Meanwhile, vehicle crime has fallen sharply by 50.5% YoY, possibly due to improved car park security in tourist areas. The area's property crime rate (41.0 per 1,000) is 28.5% above the UK average, with burglary and theft from the person showing mixed trends. These patterns suggest that Bath's unique blend of tourism, academia, and historic infrastructure creates distinct crime dynamics.

Crime in Bath has decreased by 6.5% year-on-year, with vehicle crime falling sharply by 50.5% — the most significant drop among all categories. This decline may be linked to improved parking security in tourist areas and reduced commuter traffic post-pandemic. Shoplifting rates have also fallen by 25%, though they remain 95% above the UK average. The overall decrease contrasts with the 40% rise in burglary, which may reflect changing patterns in residential targeting. The monthly trend shows a peak in October (10.0 per 1,000/month), tied to university term patterns and increased tourism during that period.

How To Keep Safe in Bath

Residents in Bath should focus on securing valuables in high-traffic retail areas, as shoplifting is 95% above the UK average. For property owners, scOS's AI monitoring can detect unauthorised movement around homes, especially during late hours when vehicle crime spikes. Public areas should be approached with awareness, given the 70% above-average rate of public order crimes. Avoid leaving items unattended in gardens or on doorsteps, as this aligns with patterns of opportunistic theft observed in Bath's retail and residential zones.

Crime in Bath exhibits clear temporal patterns. Theft peaks in the afternoon (12pm-6pm) at 52% of all incidents, likely tied to retail activity and commuter movements. Vehicle crime spikes at night (12am-6am) at 28%, reflecting increased opportunities in less-monitored parking areas. Burglary is most common in the evening (6pm-12am) and night, with 45% of incidents occurring during these hours, possibly due to reduced household presence. The area's higher crime rates during evening and night hours contrast with Bathampton's lower rates, where quieter residential areas may see fewer late-night activities.

Compared to nearby areas, Bath's crime rate is significantly higher. Bathampton has a rate of 21.9 per 1,000 — 407% lower than Bath — likely due to its smaller, more rural character. Batheaston, with a rate of 35.5 per 1,000, is 213% lower than Bath but still experiences higher crime than Bath's surrounding villages. These differences reflect Bath's larger population and tourist infrastructure.

For Bath residents, where shoplifting is 95% above the UK average, proactive security measures are essential, scOS offers AI-powered boundary detection that identifies potential threats at property perimeters, distinguishing between legitimate visitors and suspicious activity. This system uses contextual analysis — such as differentiating a delivery driver during the day from an unknown figure at night — to reduce false alarms. Its ethernet-based cameras avoid Wi-Fi vulnerabilities, providing continuous monitoring of high-risk areas like retail zones and residential gardens.