Linking physical food outlets to online platforms: A cross-sectional machine learning approach to analysing socioeconomic variations in Great Britain

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Understanding the share of outlets offering delivery is important, as it impacts food availability and thus potentially influences dietary behaviours. This paper demonstrates how a machine learning model can efficiently match physical to online outlets. We also analysed how the proportion of physical outlets listed online and online-only outlets varies by area-level deprivation. Methods The physical locations of outlets selling food in Great Britain was obtained from a centrally held register for food hygiene data, while online outlet data was collected through web scraping an OFDS platform. We calculated string distances based on outlet names and postcodes, which were then used to train a Random Forest model to match outlets from the two lists. Area-level deprivation was assessed using the Index of Multiple Deprivation. Results The Random Forest classifier model achieved an F1 score of 90%, a recall of 98%, and a precision of 83%. Overall, the median percentage of physical outlets also listed online was 14% (IQR 0 - 23), and the median percentage of online-only outlets was also 14% (IQR 0 - 27). The proportion of physical outlets listed online and online-only outlets was highest in more deprived areas. For example, compared to the least deprived areas, the most deprived areas were associated with a 6% greater proportion of physical food outlets listed online (95%CI 5%–6%) and a 3% greater proportion of online-only outlets (95%CI 1%–4%). Conclusion This study demonstrates the potential of machine learning techniques to efficiently match physical and online food outlets. This automated approach can provide insights into the relationship between physical and online food availability. Researchers and policymakers can use this method to better understand inequalities in food outlet availability and monitor the expansion of online delivery services. Introduction The expansion of Online Food Delivery Service (OFDS) platforms such as Just Eat and Uber Eats has increased the accessibility of food prepared out-of-home ( 1 ). These platforms connect individuals with a broad range of food outlets, from local restaurants to major fast-food chains. In addition to increasing accessibility to foods from physical food outlets, OFDS platforms have also enabled the growth of online-only food outlets (i.e. dark kitchens). The rise of these platforms presents new challenges for understanding inequalities in food accessibility across socioeconomic areas, especially when trying to compare traditional physical food environments to the newer online space. Previous research evaluated both the number of physical and online food outlets, as well as the ratio between them ( 2 ). However, the lack of linkage between physical and online outlets prevented the accurate estimation of the proportion of physical outlets that were also registered online or, conversely, online outlets with a physical presence. The scale of such a linkage was likely a barrier given the need to connect 79,000 online outlets from one OFDS platform ( 1 ) to a database containing over 620,000 physical food outlets ( 3 ). Linking physical and online food outlets would facilitate more accurate exposure assessments and allow for ongoing monitoring of the food environment, such as identifying which outlets are more likely to have an online presence and exploring variations by socioeconomic position. This study demonstrates a proof of concept by applying machine learning to link datasets from a central food hygiene register with those on popular OFDS platforms in Great Britain (GB), assessing how physical and online-only outlets varied across area-level deprivation. Methods Physical outlet locations were sourced from the Food Standards Agency (FSA)’s hygiene rating scheme data and matched with online outlet data obtained from the OFDS platform Just Eat Takeaway.com (Just Eat) using machine learning. Figure 1 describes the process by which we sourced data to match physical and online food outlets. Cross-sectional analyses were performed at the Middle Layer Super Output Area (MSOA) level, which are geographic units comprising 2,000–6,000 households. The study adhered to the UK Office for National Statistics’ web scraping policy ( 4 ) and used publicly available data. Thus, ethical approval was not required. Download figure Open in new tab Figure 1. Flow of steps used to prepare data for the machine learning approach matching physical and online food outlets using postcode data and similarity metrics Data sources Food Standards Agency To compile a list of physical food outlets across GB, food hygiene data from FSA as of March 2024 was used ( 3 ). Information on the categories of food outlets, such as restaurants and supermarkets, was extracted directly from this dataset. Previous research has shown that FSA data closely aligns with street audits ( 5 ). Outlets without postcode information, primarily those categorised as mobile caterers, were excluded (16%; Figure 1 ). OFDS platform According to Statista, in 2022, Just Eat was the most popular OFDS platform in the United Kingdom, with twice as many users as the next most commonly used platform, Uber Eats ( 6 ). Similar to our previous study ( 7 ), the names and addresses of food outlets on Just Eat delivering to postcode sectors in GB were scraped using a web crawler developed in Python Scrapy framework. Data were collected over three days in March 2024. Physical and online food outlets We included two outcome measures. First, we measured the proportion of physical outlets also listed online, calculated as the number of physical food outlets matched to an online outlet divided by the total number of physical food outlets at MSOA level. Second, we measured the proportion of online-only outlets, calculated as 1 minus the number of online food outlets matched to a physical outlet divided by the total number of online food outlets at MSOA level. To facilitate the matching of physical and online food outlets, outlet names in both datasets were standardised and common terms like ‘takeaway’ were removed ( Figure 1 ). We developed a third variable indicating whether an outlet was part of a popular chain or independent, using a YouGov poll of the most popular UK dining brands, an approach used by Kalbus et al. ( 8 ). After data cleaning, physical outlets were linked to online outlets at the postcode sector (e.g. CB1 3) level instead of the full postcode (e.g. CB1 3PF) to account for potential errors. Jaccard similarity and Levenshtein Distance were applied to food outlet names and postcode units to calculate a score that reflects the similarity between them. For example, after name standardisation ‘Zizzi – Cambridge’ and ‘Zizzi’ had a Jaccard score of 100 (indication of a perfect score). We manually reviewed and annotated links in 40 randomly selected postcode sectors to refine matching criteria ( Figure 1 ; n=146,210 rows of potential matches). This annotated data was used to train a Random Forest classifier model ( Figure 2 ), which then matched the remaining unannotated data. Each physical food outlet could only be linked to one online food outlet, and vice versa, ensuring that no duplicate links were made between physical and online outlets. If multiple online food outlets could be linked to the same physical outlet, only one link was included, and the other online food outlets were assumed to be virtual brands. Download figure Open in new tab Figure 2. Analytical workflow for matching physical and online food outlets and assessing potential socioeconomic variations Socio-demographic information Covariates that were hypothesized to influence the association between area-level deprivation and outcome measures were included: population size, proportion of females, proportion of working-age individuals ( 15 -65), and proportion of households with dependents at the MSOA level (for England and Wales) and the intermediate zone level (for Scotland, hereafter also referred to as MSOA) were extracted from 2021 Census data ( 9 , 10 ). Indices of Multiple Deprivation (IMD) were obtained from governmental websites at the Lower Super Output Area (LSOA) level for England and Wales (2019), and at the data zone level for Scotland (2020) ( 11 , 12 ). The IMD ranks of LSOAs and data zones were averaged to determine the IMD rank at the MSOA level, after which country-specific quintiles were developed based on these ranks. Statistical analyses Model training and evaluation The annotated data was split into training (90%) and test (10%) sets ( Figure 2 ). To address the imbalance in the training data the Synthetic Minority Over-sampling Technique (SMOTE) was applied. SMOTE generates synthetic samples of the underrepresented matched outlets, balancing the dataset and preventing the model from being biased toward the more frequent non-matches. This resulted in an equal distribution of matches and non-matches in the training data. A Random Forest classifier was trained on the training data using 10-fold cross-validation with stratified sampling to maintain class distribution consistency across folds. Cross-validation was employed to generate out-of-sample predictions for the training data, which allowed us to evaluate model performance before retraining it on the full training set. The retrained model was then evaluated on the test set using precision, recall, and F1-score metrics. Finally, the model was applied to the unannotated dataset, following identical data preparation steps, to generate match predictions. Manual inspections of the unannotated dataset were conducted to assess the quality of the matching process. All data preparation and model training were performed using Python. Assessing socioeconomic differences A lookup table of postcode units was used to map both online and physical food outlets to their corresponding MSOA areas, enabling the calculation of the number of outlets in each MSOA area and the subsequent proportion measures ( 13 ). We used beta regression models to examine the association between quintiles of area-level deprivation and both outcome measures. Marginal effects (at mean) were calculated to interpret the results. All models were adjusted for the covariates listed above and analyses were performed in R version 4.2.3. Results Matching physical and online food outlets At the MSOA level, the median number of physical outlets was 32 (IQR 20–53) and the median number of online outlets was 5 (IQR 1–11; Table 1 ). The cross-validation results on the training set indicated a mean F1 score of 99%, along with a mean recall and precision of 99%. When applied to the test data, the Random Forest model demonstrated class-specific performance metrics with a score of 100% across all three metrics for non-matches. For matches, it yielded an F1 score of 90%, a recall of 98%, and a precision of 83%. The confusion matrix can be found in Supplementary Table 1. View this table: View inline View popup Download powerpoint Table 1. Food outlet availability characteristics by area-level deprivation at MSOA level in Great Britain (n=8525) The median percentage of physical outlets listed online was 14% (IQR 0-23), while the median for online-only outlets was 14% (IQR 0-27; Table 1 ). Also, a higher percentage of physical restaurants (median 11%) and takeaways (median 40%) were listed online compared to other categories, such as pubs and supermarkets, which had a median of 0% ( Table 2 ). View this table: View inline View popup Download powerpoint Table 2. Median percentage of physical outlets also listed online at MSOA level by physical food outlet category as determined by the FSA Area-level deprivation differences The most deprived areas had the greatest median number of physical and online food outlets ( Table 1 ). In the most deprived areas, 20% (IQR 13 - 27) of physical outlets were also listed online, compared to 9% (IQR 2-19) in the least deprived areas. The percentage of online-only outlets was 17% (IQR 0 - 27) in the most deprived areas and 7% (IQR 0 - 27) in the least deprived areas. We found that the proportion of physical outlets also listed online was lower in less deprived areas (Q3-5 versus Q1; Table 3 ). For example, lower deprived areas (Q4 and Q5) was associated with a 6% (95%CI 5% - 6%) lower proportion of physical outlets listed online compared to the most deprived area (Q1). Also, the proportion of online-only outlets was lower in less deprived areas (Q3, Q4 and Q5) compared to the most deprived area. View this table: View inline View popup Download powerpoint Table 3. Marginal effects of the association between quartiles of area-level deprivation, and the proportion of physical outlets also listed online (n=8503) and online-only outlets (n=7162) Discussion We matched physical and online food outlets in GB using a Random Forest model, which achieved high cross-validation scores. Analyses revealed that the proportion of physical outlets listed online and online-only outlets was greatest in more deprived areas. This study demonstrates the efficiency of machine learning for tasks that would otherwise be labour-intensive to perform manually. Our Random Forest model likely reduced human error and made linkages a less tedious task, achieving high F1, precision, and recall scores (83% to 100%). Despite initial concerns about overfitting, the consistent performance across training and test sets—supported by previous research ( 14 )—confirms the model’s reliability. Around 82% of online outlets were successfully matched to physical ones, aligning with our annotated dataset. Additional data cleaning could further improve model performance, as discrepancies were primarily due to name variations (e.g. ‘AFC’ vs. ‘AFC Fried Chicken’) and chain outlets with multiple purposes (e.g. supermarkets with instore food concessions). We found that the proportion of physical outlets listed online and online-only outlets declined with decreasing deprivation. Previous GB research similarly showed greater availability of online and physical food outlets in more deprived areas ( 2 , 15 ), which may contribute to unhealthy dietary behaviours, obesity, and socioeconomic differences in health outcomes ( 16 , 17 ). A strength of this study was using highly current data with minimal time difference between datasets. The inclusion of a broad range of outlets in the FSA dataset may have affected the results, as focusing on outlets more likely to go online (i.e. takeaways and restaurants) could reduce the number of non-matches and enhance model precision. Additionally, while Just Eat is more widely available and popular, relying on a single OFDS platform may limit generalizability ( 18 ). Future research should incorporate additional datasets, such as those from other OFDS platforms. Monitoring the proportion of physical outlets listed online and online-only outlets is important for understanding how the food environment is evolving and for identifying potential impacts on dietary behaviours, health outcomes, and socioeconomic inequalities. Conclusion This study demonstrated the effectiveness of Random Forest models in matching physical food outlets from a centrally held register for food hygiene data to their online counterparts on the most popular UK OFDS platform. The results show that the median percentage of physical outlets with an online presence is 14%, and the median percentage of online-only outlets is also 14%. The current method shows potential for ongoing monitoring of both physical and digital food environments. Data Availability Food Standards Agency data is publicly available. Code to webscrape data from Online Food Delivery Service platforms can be found online. https://ratings.food.gov.uk/ https://github.com/YuruHuang/Online-food-delivery Declarations Consent for publication Not applicable. Availability of data and materials All data used was publicly available. The code used to process the data and run the Random Forest classifier can be found on Github ( https://github.com/Jodyyy16/Linking_physical_to_online_FO ). Competing interests The authors declare no competing interest. Funding JCH and JA were supported by the Medical Research Council [Unit Programme number MC_UU_00006/7]. The funders played no role in the design of the study, the collection, analysis, and interpretation of data, or the writing of the manuscript. For the purpose of Open Access, the authors have applied a Creative Commons Attribution (CC BY) licence to any Author Accepted Manuscript version arising. Authors’ contributions The authors’ responsibilities were as follows – JCH: study design, conducted analyses and prepared the initial draft of the manuscript; YH: data collection, reviewed and edited the manuscript; JA: study design, supervision, and reviewed and edited the manuscript. All authors read and approved the final manuscript. Declaration of generative AI and AI-assisted technologies in the writing process During the preparation of this work the author(s) used ChatGPT4o in order to improve readability and language of the manuscript. After using this tool, the authors reviewed and edited the content as needed and take full responsibility for the content of the published article. Acknowledgements We would like to thank Dr Mark Kattenberg at the Netherlands Bureau for Economic Policy Analysis for reviewing the manuscript. References 1. ↵ Hoenink JC , Huang Y , Keeble M , Mackenbach JD , Pinho MG , Burgoine T , et al. 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