Mapping the popularity of urban restaurants using social media data

Nowadays, geographers show growing interest in providing location-based services for urban residents. It is of great practical significance to screen and recommend the most popular restaurants to consumers, as dining is important to every urban dweller. Consumer review website (CRW) has emerged as an active social media platform in catering industry. This paper demonstrated how to quantify the popularity of urban restaurants (PUR) by using CRW, namely, Dianping.com. An applied popularity index (PI) was developed to quantify PUR, based on the consumer review scores (food, service, and decoration) and physical data (evaluation frequency and restaurant grade) for 8259 restaurants within the Hangzhou city, China. All the information, together with the geographic location data, was harvested from the corresponding Application Programming Interface (API) platform. PUR was then mapped by using a geographic information system (GIS). Results showed that restaurants of high popularity were generally concentrated in old urban districts, whereas those in new urban districts presented low popularity. The kernel density distribution of PI also highlighted the geography that the PI values declined from the central city toward the outskirts. Locational associations between PUR and urban functional units (bank, shopping mall, school, cinema, hotel, bar, and scenic spot) generally presented a similar tendency. Restaurants with high PI values were in high proximity to urban functional units, whereas those with low PI values were located far away from other functional units. It implied that restaurants with high popularity tended to be located in high mixture with urban functional units. Although we used Dianping.com to perform the analysis, the presented methodology can be extended to other types of CRWs. Our study is believed to provide new insights into applied geographic sciences.