Jiang, S.; Zhang, H.; Wang, H.; Zhou, L.; Tang, G. Using Restaurant POI Data to Explore Regional Structure of Food Culture Based on Cuisine Preference. ISPRS Int. J. Geo-Inf.2021, 10, 38.
Jiang, S.; Zhang, H.; Wang, H.; Zhou, L.; Tang, G. Using Restaurant POI Data to Explore Regional Structure of Food Culture Based on Cuisine Preference. ISPRS Int. J. Geo-Inf. 2021, 10, 38.
Jiang, S.; Zhang, H.; Wang, H.; Zhou, L.; Tang, G. Using Restaurant POI Data to Explore Regional Structure of Food Culture Based on Cuisine Preference. ISPRS Int. J. Geo-Inf.2021, 10, 38.
Jiang, S.; Zhang, H.; Wang, H.; Zhou, L.; Tang, G. Using Restaurant POI Data to Explore Regional Structure of Food Culture Based on Cuisine Preference. ISPRS Int. J. Geo-Inf. 2021, 10, 38.
Abstract
As a result of the influence of geographical environment and historical heritage, food preference has significant regional differentiation characteristics. However, the spatial structure of food culture represented by the cuisine culture at the regional level has not yet been explored from the perspective of geography. This study aims to explore such patterns by focusing on the restaurants of the eight most famous cuisines in Mainland China. Initially, the density based geospatial hotspot detector method is proposed to analyze and mapping the spatial quantitative characteristics of the eight major cuisines. A heuristic method for geographical regionalization based on machine learning was used to analyze spatial distribution patterns in accordance with the proportion of these cuisines in each prefecture-level city. Results show that some types of single-category cuisines have a stronger spatial concentration effect in the present, whereas others have a strong diffusion trend. In the comprehensive analysis of multicategory cuisines, the eight major cuisines formed a new structure of geographical regionalization of Chinese cuisine culture. This study is helpful to understand regional structure characteristics of food preference, and the density based hotspot detector proposed in this paper can also be used in the analysis of other type of POI data.
Keywords
food culture; cultural regionalization; Chinese cuisines; machine learning; spatial struture
Subject
Environmental and Earth Sciences, Atmospheric Science and Meteorology
Copyright:
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.
