Data-driven nutritional assessment of urban food landscapes: insights from Boston, London, and Dubai
Abstract Urban food landscapes significantly influence dietary habits and health outcomes, with disparities in food access contributing to obesity, particularly in socioeconomically disadvantaged neighborhoods. This study presents a data-driven approach to assess urban food landscapes using restaura...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-025-08098-9 |
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| author | Michele Tufano Fábio Duarte Martina Mazzarello Javad Eshtiyagh Carlo Ratti Guido Camps |
| author_facet | Michele Tufano Fábio Duarte Martina Mazzarello Javad Eshtiyagh Carlo Ratti Guido Camps |
| author_sort | Michele Tufano |
| collection | DOAJ |
| description | Abstract Urban food landscapes significantly influence dietary habits and health outcomes, with disparities in food access contributing to obesity, particularly in socioeconomically disadvantaged neighborhoods. This study presents a data-driven approach to assess urban food landscapes using restaurant menu data from online delivery platforms in Boston, London, and Dubai. Machine learning matched menu items to the U.S. FoodData Central database, enabling the calculation of nutritional indices and neighborhood-level nutrient averages. The analysis revealed significant patterns between urban food landscapes, socioeconomic features, and obesity rates. In London and Boston, higher socioeconomic neighborhoods had better access to nutrient-rich foods, with dietary fibers showing a strong inverse association with obesity (p = 0.001, p = 0.004, respectively). In Dubai, due to limited health data, the analysis focused on food landscapes and rental prices as a proxy of a neighborhood’s socioeconomic profile. This method offers a scalable alternative to traditional food environment studies and can guide policymakers in identifying neighborhoods at risk for obesity and lack of nutritious foods. Future research should extend this method to diverse regions and advocate for standardized, open-access nutritional data to implement targeted and evidence-based nutritional interventions. |
| format | Article |
| id | doaj-art-d57ed22d1edc4b8ab9a5f7d852c7cd17 |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-d57ed22d1edc4b8ab9a5f7d852c7cd172025-08-20T03:05:17ZengNature PortfolioScientific Reports2045-23222025-07-0115111110.1038/s41598-025-08098-9Data-driven nutritional assessment of urban food landscapes: insights from Boston, London, and DubaiMichele Tufano0Fábio Duarte1Martina Mazzarello2Javad Eshtiyagh3Carlo Ratti4Guido Camps5Division of Human Nutrition and Health, Wageningen University and ResearchSenseable City Lab, Department of Urban Studies and Planning, Massachusetts Institute of TechnologySenseable City Lab, Department of Urban Studies and Planning, Massachusetts Institute of TechnologySenseable City Lab, Department of Urban Studies and Planning, Massachusetts Institute of TechnologySenseable City Lab, Department of Urban Studies and Planning, Massachusetts Institute of TechnologyDivision of Human Nutrition and Health, Wageningen University and ResearchAbstract Urban food landscapes significantly influence dietary habits and health outcomes, with disparities in food access contributing to obesity, particularly in socioeconomically disadvantaged neighborhoods. This study presents a data-driven approach to assess urban food landscapes using restaurant menu data from online delivery platforms in Boston, London, and Dubai. Machine learning matched menu items to the U.S. FoodData Central database, enabling the calculation of nutritional indices and neighborhood-level nutrient averages. The analysis revealed significant patterns between urban food landscapes, socioeconomic features, and obesity rates. In London and Boston, higher socioeconomic neighborhoods had better access to nutrient-rich foods, with dietary fibers showing a strong inverse association with obesity (p = 0.001, p = 0.004, respectively). In Dubai, due to limited health data, the analysis focused on food landscapes and rental prices as a proxy of a neighborhood’s socioeconomic profile. This method offers a scalable alternative to traditional food environment studies and can guide policymakers in identifying neighborhoods at risk for obesity and lack of nutritious foods. Future research should extend this method to diverse regions and advocate for standardized, open-access nutritional data to implement targeted and evidence-based nutritional interventions.https://doi.org/10.1038/s41598-025-08098-9 |
| spellingShingle | Michele Tufano Fábio Duarte Martina Mazzarello Javad Eshtiyagh Carlo Ratti Guido Camps Data-driven nutritional assessment of urban food landscapes: insights from Boston, London, and Dubai Scientific Reports |
| title | Data-driven nutritional assessment of urban food landscapes: insights from Boston, London, and Dubai |
| title_full | Data-driven nutritional assessment of urban food landscapes: insights from Boston, London, and Dubai |
| title_fullStr | Data-driven nutritional assessment of urban food landscapes: insights from Boston, London, and Dubai |
| title_full_unstemmed | Data-driven nutritional assessment of urban food landscapes: insights from Boston, London, and Dubai |
| title_short | Data-driven nutritional assessment of urban food landscapes: insights from Boston, London, and Dubai |
| title_sort | data driven nutritional assessment of urban food landscapes insights from boston london and dubai |
| url | https://doi.org/10.1038/s41598-025-08098-9 |
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