Spatial patterns and their influencing factors for China's catering industry

The catering industry is a vital component of urban service sectors, reflecting a city's cultural vibrancy and economic competitiveness. Its spatial distribution reveals insights into urban structure, economy, and culture, informing urban planning and business location decisions. China's rapid urbanization and economic growth have significantly boosted the catering industry. Previous research has examined restaurant location patterns using GIS techniques, highlighting clustering around CBDs and population density effects. Studies have also investigated the diversity of restaurant types, the impact of income levels on dining choices, and the influence of food culture and tourism on restaurant distribution. However, a comprehensive national-level analysis integrating various socioeconomic factors remains limited. This study aims to investigate the spatial distribution characteristics of Chinese urban catering culture, including the patterns and interrelationships between local and international restaurants, and their correlation with regional economic development, consumption power, and urbanization.

Existing literature demonstrates the significance of the catering industry as a reflection of urban culture and economic vitality. Studies using GIS techniques have shown clustered restaurant distributions, particularly near CBDs, with spillover effects in other urban areas. Research has explored the relationship between population density and restaurant diversity, as well as the impact of income levels on restaurant choice. The influence of tourism and regional food culture on restaurant location patterns has also been addressed. However, a comprehensive analysis of the spatial distribution of various restaurant types across China, considering diverse socioeconomic indicators, is lacking.

This study employed a comprehensive spatial analysis using data from the AMAP database, containing over 6.09 million restaurant POIs. After data cleaning, 4.49 million data points across 336 cities were analyzed. Restaurants were classified into four categories: CRs, WRs, FFRs, and DDRs. Ten socioeconomic indicators, including urban population, population density, urbanization rate, GDP per capita, GDP per unit area, proportion of tertiary industry in GDP, total retail sales, per capita disposable income, and total tourism revenue, were collected from the China Statistical Yearbook 2021. Spatial analysis methods included kernel density estimation (using a Gaussian kernel function and reference method for bandwidth selection) to illustrate the spatial clustering of different restaurant types. Exploratory spatial data analysis (ESDA), including global and local Moran's I index analysis, were used to assess spatial autocorrelation. Finally, geographically weighted regression (GWR) was employed (using the AIC method for bandwidth selection) to identify the spatial heterogeneity of the influencing factors on restaurant distribution.

The study revealed a total of 4.49 million restaurants, with CRs, FFRs, DDRs, and WRs accounting for 67%, 21%, 10%, and 2%, respectively. Spatial distribution showed a gradual decrease in restaurant density from southeast to northwest China. High-density areas were concentrated in the Pearl River Economic Delta, Yangtze River Economic Delta, Chongqing, and Chengdu regions. Kernel density estimation further highlighted these clusters for each restaurant type. Global spatial autocorrelation analysis using Moran's I indicated significant positive spatial autocorrelation for all restaurant types and the total number of restaurants. Local spatial autocorrelation analysis revealed High-High clusters (high restaurant density surrounded by high density) primarily in eastern coastal cities and Low-Low clusters (low density surrounded by low density) in western regions. GWR analysis showed that influencing factors varied spatially. In western and southwestern regions, GDP per unit area, total tourism revenue, per capita disposable income, and total retail sales of social consumption were significant predictors. In northeastern regions, total retail sales, GDP per unit area, urban population, and the proportion of tertiary industry in GDP were more influential. CRs showed a positive correlation with urban population, GDP per unit area, tourism revenue, and retail sales. WRs were positively correlated with urban population, GDP per capita, GDP per unit area, tertiary industry proportion, retail sales, and per capita disposable income. FFRs showed positive correlations with urban population, GDP per unit area, urbanization rate, retail sales, and per capita disposable income. DDRs showed strong positive correlations with urban population, GDP per unit area, and total tourism revenue. The spatial heterogeneity of these relationships was also evident.

The findings address the research question by identifying the spatial patterns and influencing factors of China's diverse restaurant industry. The significant spatial heterogeneity highlights the importance of considering regional variations when planning and managing the catering sector. The strong correlation between restaurant density and socioeconomic factors underscores the industry's sensitivity to economic development and population dynamics. The GWR model effectively captured this spatial non-stationarity, providing more precise insights than traditional OLS regression. The variations in influential factors across different regions reflect unique regional characteristics and development trajectories.

This study provides a comprehensive analysis of the spatial distribution patterns and influencing factors for China's catering industry. The use of kernel density estimation, spatial autocorrelation analysis, and GWR modeling allowed for a detailed understanding of the spatial heterogeneity of these relationships. Findings offer valuable insights for urban planning and strategic decision-making related to the catering sector at different administrative levels. Future research could explore the temporal dynamics of restaurant distribution, investigate the interplay of additional factors (e.g., policy regulations, competition), and delve into the specific characteristics of sub-types within each restaurant category.

The study uses data from a single point in time (2020), limiting its ability to capture temporal changes in restaurant distribution. The reliance on POI data may not fully capture the complexity of the catering industry, as it does not include information about restaurant size, quality, or specific menu offerings. The analysis focuses on city-level data; a finer-grained spatial resolution might reveal more detailed patterns. Finally, the interpretation of the GWR results may be influenced by the selection of the bandwidth.