Akademik Veri Yönetim Sistemi
40th IEEE International Conference on Data Engineering Workshops, ICDEW 2024, Utrecht, Hollanda, 13 - 16 Mayıs 2024, ss.5-12
This study investigates whether the ingredients listed on restaurant menus can provide insights into a city's socioeconomic status. Using data from an online food delivery system, the study compares menu items with local education rates and rental prices. A machine learning model is developed to predict menu prices based on ingredients and socioeconomic factors. An efficiency metric is proposed to cluster restaurants to address autocorrelation, comparing ingredient averages to socioeconomic indicators. The analysis focuses on hundreds of menus, specifically examining pizza and Turkish pita in Ankara, Türkiye. The results indicate that including nearby rental prices significantly improves the accuracy of predicting menu prices, especially for pizza. The study also notes that wealthier areas tend to feature menus with more unique or expensive ingredients, particularly in the case of pizza, aligning with previous research on eating habits and income levels. Key contributions of this research include a comprehensive examination of restaurant menus, insights into how menus vary based on location and cuisine, and the development of Turkish-English word lists for pita and pizza menu items. Our datasets are also shared. This methodology aids in understanding local taste preferences and provides valuable information for strategic decisions regarding restaurant location and menu planning.