Tropical Geography ›› 2020, Vol. 40 ›› Issue (6): 1015-1025.doi: 10.13284/j.cnki.rddl.003280

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Spatial Distribution Patterns and Factors Influencing the Shanghai Catering Industry Based on POI Data

Jinyue Tang1(), Yijun He3, Na Ta2,3()   

  1. 1.School of Urban and Regional Science, East China Normal University, Shanghai 200241, China
    2.Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University, Shanghai 200241, China
    3.School of Geographic Sciences, East China Normal University, Shanghai 200241, China
  • Received:2020-02-19 Revised:2020-08-03 Online:2020-11-30 Published:2020-12-10
  • Contact: Na Ta E-mail:tangjinyue@126.com;nta@geo.ecnu.edu.cn

Abstract:

Commercial space structure is an important research focus of Urban Geography. Analyzing the spatial distribution of urban commerce is of great significance to urban planning management, within which spatial distribution patterns of the catering industry have always been a focus of research. Quantitative analysis of the catering industry's spatial pattern and influencing factors using big data is a primary trend in recent research. This paper uses Shanghai as a case-study. Based on POI data and using GIS spatial analysis methods and spatial regression models, the spatial distribution patterns, influencing factors, and internal heterogeneity of different catering industry types are investigated. This paper's conclusions are useful for understanding the influence of urban internal spatial elements on the catering industry's spatial pattern. It also provides a location selection reference for the catering industry and analyzes residents' consumption behavior. We find that the catering industry is clustered and multi-centered, and concentrated in the central urban area. The foreign catering industry is highly concentrated within the inner ring, extending from east to west. The fast-food industry is primarily agglomerated in central areas and rural-urban continua where universities cluster. We use a spatial error model to analyze the influencing factors, finding that the catering industry distribution is influenced by four factors: population, economy, transportation, and space. A larger population provides for more consumers in the catering industry, and the spatial concentration of the population can promote the creation of more catering companies. The catering industry tends to assemble in areas with a higher level of regional economic development. Superior transportation conditions can attract catering companies, but the influences of transport facilities differ. Parking facilities and bus stations are vital to the catering industry. In terms of macroeconomic location, catering industries concentrate around regional centers. Densities of catering companies within the inner ring are significantly higher than those outside. The density of catering companies does not show a significant difference between new towns and the Puxi area. Regarding the micro-built environment, the clustering of public, leisure, entertainment, and cultural facilities positively impacts the distribution of catering companies; however, the degree of diversity of surrounding industry types negatively impacts agglomeration. There are also differences in the factors affecting the catering industry's four distribution types: Chinese food, western food, fast food, and dining establishments. Western food companies tend to be located in areas with higher land prices. Chinese food companies have a greater demand for traffic accessibility. The distribution of dining establishments corresponds significantly to the level of local economic development. The distribution of fast-food companies is closely related to cultural and entertainment public facilities. We extrapolate the relevant theories of urban commercial space structures, providing theoretical support to facilitate the catering industry in choosing company locations and conduct future urban planning.

Key words: catering industry, POI, spatial pattern, Spatial Error Model, Shanghai

CLC Number: 

  • K902