Next Article in Journal
Integrating Risk Assessment into Spatial Planning: RiskOTe Decision Support System
Previous Article in Journal
Semi-Supervised Ground-to-Aerial Adaptation with Heterogeneous Features Learning for Scene Classification
Article Menu
Issue 5 (May) cover image

Export Article

Open AccessArticle
ISPRS Int. J. Geo-Inf. 2018, 7(5), 183; https://doi.org/10.3390/ijgi7050183

Geographic Prevalence and Mix of Regional Cuisines in Chinese Cities

1
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, 129 Luoyu Road, Wuhan 430079, China
2
Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong, China
3
Department of Geography, University of Tennessee, Knoxville, TN 37916, USA
4
Guangzhou Institute of Geography, 100 Xianlie Zhong Road, Guangzhou 510070, China
5
Department of Urban Planning and Design, The University of Hong Kong, Hong Kong, China
*
Author to whom correspondence should be addressed.
Received: 15 March 2018 / Revised: 30 April 2018 / Accepted: 7 May 2018 / Published: 11 May 2018
Full-Text   |   PDF [4815 KB, uploaded 11 May 2018]   |  

Abstract

Previous research on the geographies of food put a considerable focus on analyzing how different types of food or ingredients are consumed across different places. Little is known, however, about how food culture is manifested through various cooking traditions as well as people’s perceptions over different culinary styles. Using a data set captured from one of the largest online review sites in China (www.dianping.com), this study demonstrates how geo-referenced social review data can be leveraged to better understand the geographic prevalence and mix of regional cuisines in Chinese cities. Based on information of millions of restaurants obtained in selected cities (i.e., provincial capitals and municipalities under direct supervision of the Chinese central government), we first measure by each city the diversity of restaurants that serve regional Chinese cuisines using the Shannon entropy, and analyze how cities with different characteristics are geographically distributed. A hierarchical clustering algorithm is then used to further explore the similarities of consumers’ dining options among these cities. By associating each regional Chinese cuisine to its origin, we then develop a weighted distance measure to quantify the geographic prevalence of each cuisine type. Finally, a popularity index (POPU) is introduced to quantify consumers’ preferences for different regional cuisines. We find that: (1) diversity of restaurants among the cities shows an “east–west” contrast that is in general agreement with the socioeconomic divide in China; (2) most of the cities have their own unique characteristics, which are mainly driven by a large market share of the corresponding local cuisine; (3) there exists great heterogeneity of the geographic prevalence of different Chinese cuisines. In particular, Chuan and Xiang, which are famous for their spicy taste, are widely distributed across the mainland China and (4) among the top-tier restaurants ranked by the consumers in a city, the local cuisine is not usually favored, while other cuisines are favored by consumers in many different cities. This study demonstrates the use of social review data as a cost-effective approach of studying urban gastronomy and its relationship with human activities. View Full-Text
Keywords: food geography; regional cuisine; spatial analytics; China food geography; regional cuisine; spatial analytics; China
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Zhu, J.; Xu, Y.; Fang, Z.; Shaw, S.-L.; Liu, X. Geographic Prevalence and Mix of Regional Cuisines in Chinese Cities. ISPRS Int. J. Geo-Inf. 2018, 7, 183.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
ISPRS Int. J. Geo-Inf. EISSN 2220-9964 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top