For a population of 5,983 residents, Broseley recorded 14 crimes in February 2025, translating to a crime rate of 2.3 per 1,000 people. This figure is 65.7% below the UK average of 6.7 per 1,000, placing the area among the safest in the country. The most significant category was violence and sexual offences, which accounted for 50% of all reported crimes, followed by anti-social behaviour (14.3%) and criminal damage and arson (7.1%). The low overall crime rate may be partly explained by Broseley's built-up area within Shropshire, a county with historically low crime rates due to its rural character and limited urban density. Seasonal factors, such as the winter lull in outdoor activity, may also contribute to the reduced crime levels, as many crimes—particularly those involving public spaces—are influenced by weather and seasonal patterns. Broseley's crime profile contrasts with national trends, where violent crimes typically make up a smaller proportion of total incidents. However, the area's 50% share of violent crimes suggests that local factors, such as community dynamics or policing strategies, may influence the types of crime that occur. The low rate of property crimes, with only 2 incidents (0.3 per 1,000), further reinforces the area's safety, though property crime rates can fluctuate based on economic conditions and local initiatives. Broseley's position as a built-up area within a largely rural county may provide a unique blend of urban and rural characteristics, reducing opportunities for certain crimes while maintaining a close-knit community that could deter others. The data also shows that Broseley's anti-social behaviour rate is 69% below the UK average, suggesting that local efforts to manage community relations may be effective. However, without detailed information on policing strategies or community programs, these conclusions remain speculative. The overall crime picture for February 2025 indicates a continuation of Broseley's low crime trend, though further analysis of monthly and annual data would be needed to identify long-term patterns.