Identifying online display of Food Hygiene Rating Scheme ratings: Executive summary

The Food Standards Agency (FSA) wish to make display of Food Hygiene Rating Scheme (FHRS) rating mandatory for businesses online, extending the current physical display requirements in Wales and Northern Ireland and making both compulsory in England. This project was undertaken to support the rollout and enforcement of mandatory online display, by providing insight into current practice. Approach We created an automated solution to finding business websites and establishing whether they are displaying an FHRS rating. Data from Google Places was matched against a sample of businesses from the FHRS open data. This allowed us to collect the businesses' websites where they exist. Only an establishment’s own website was included; if the Google data returned a Facebook or other social media page, a chain website, or a presence on an aggregator or booking site (e.g. OpenTable, Trivago), these were excluded. The websites were passed through a matching pipeline that fetches the website images and matches them against reference images of FHRS ratings. Each image received a score from 0 to 100, representing the confidence of the match. The highest scoring images from each website were compiled, and all images scoring higher than a particular threshold (in this case 30) were examined manually to establish whether they were ratings or not. Findings Just over half of the sample (54%) had a business website. We are confident that, where a business has a website, this is being correctly obtained in around 90% of cases. After passing all websites through the matching pipeline, we estimate that the prevalence of online display is around 3% of websites. Takeaways are more likely than other business types to display a rating, while pubs are less likely. We found twice as many FHRS images in England than in Wales or Northern Ireland, despite the initial sample sizes being very similar. Standardised, high quality images are easy to detect, while variations or low image quality/blurring make it more difficult to establish a match. Our process worked in around 80% of cases; where it did not, this was usually due to unusual features in the source code of the websites that our method had not been able to take into account.