Perception of urban population characteristics through dietary taste patterns based on takeout data. (December 2022)

• Record Type: Journal Article
• Authors: Xu, Yichen; Hu, Linshu; Gao, Song; Wang, Mengxiao; Ding, Jiale; Qiu, Yining; Zhang, Feng; Du, Zhenhong; Liu, Renyi
• Is Part Of: Cities. Volume 131(2022)
• Publication Date: 2022-12
• Subjects: Takeout -- Dietary taste pattern -- Hot spot analysis -- Socio-demographic characteristics; City planning -- Periodicals; Urban policy -- Periodicals; 711.4
• DOI: 10.1016/j.cities.2022.103910
• Languages: English
• ISSNs: 0264-2751

Abstract

Abstract: Research on commercial activities and their spatial structure is an important topic in urban geography and economic geography. As an online-to-offline business activity, takeout ordering is an important approach for urban residents to solve their catering needs. It can well reflect the working and living conditions of the population and is a source for studying urban life. Based on the takeout data in the city of Hangzhou, China, this study uses the customers' detailed order information to extract three dietary taste patterns: spicy, sweet, and light. We then combine the dietary taste patterns with points of interest and land use data to identify the location attributes of order destination and explore the temporal and spatial distribution characteristics of dietary taste patterns. The results show differences in dietary behavior of people at the community scale, and help us understand the local culture and socio-demographic characteristics from the perspective of diet. Dietary taste pattern is closely related to timing and location, which reflects differences in consumption tendencies, and socio-demographic heterogeneity. This research helps to present the dietary characteristics of different population groups and their spatial distribution, and supplements the content of urban research on foodscape. Highlights: Extracts three dietary patterns of Hangzhou people based on take-out order data Links dietary habits to socio-demographic heterogeneity and sense of place Utilizes a new type of big data from the food delivery industry into urban analytics on foodscape research and planning