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Socio-demographic and geographic repartition of restaurants available on online food delivery platforms in Flanders and Brussels

Bertrand, Elise
(2024)

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BERTRAND_28051900_2024.pdf
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Details

Supervisors
Legrand, Catherine ; Vandevijvere, Stefanie
Faculty
Faculté des sciences
Degree label
Master [120] en science des données, orientation statistique, à finalité spécialisée
Abstract
Online food delivery (OFD) platforms have become increasingly popular in recent years. They are known to lead to unhealthy diets and this has raised public health concerns. In collaboration with Sciensano, which has been working on the physical food environment, this current work aims to address the online food environment. In this prospect, the study has a three-folded objective : to format, enhance and study a database gathering information on restaurants available on three OFD platforms - Uber Eats, Takeaway and Deliveroo - in Flanders and Brussels. The first two chapters of this thesis focus on the construction and enrichment of the database. This involves identifying duplicate restaurants across platforms, categorizing menu items, and classifying restaurant cuisine types. For the latter, a machine learning model has been developed using a Naïve Bayes classifier. Finally, with such information, we conduct multiple analyses linking the database with socio-demographic information. By examining the online food environment, this study seeks to provide tools and primary analyses on the factors contributing to unhealthy dietary patterns facilitated by OFD platforms.