The development of the catering industry in big cities is of great significance for countries to improve the quality of development and improve people’s living standards. In recent years, the urban catering industry has effectively promoted the process of urbanization, and it is significant to study the development characteristics and spatial distribution of the catering industry for the urban pattern. Taking the four central cities (Shanghai, Hangzhou, Nanjing, and Hefei) of China’s Yangtze River Delta (YRD) urban agglomeration as examples, first, the point-of-interest (POI) data of various catering facilities in the city’s main urban area were crawled from the Amap (AutoNavi map) open platform through Python. Second, for the first time, three quantitative indicators were constructed to characterize the development and urbanization of the urban catering industry, namely cuisine localization index (CLI), cuisine diversity index (CDI), and cuisine geographical preference index (CGPI). Third, the overall spatial characteristics of the catering industry in the four central cities and administrative districts were obtained using the methods of kernel density and spatial autocorrelation analysis. The spatial distribution of the catering industry development in each city is displayed through GIS visualization, and its influencing factors are discussed preliminarily through geographically weighted regression (GWR) model. The research shows that: (1) the four central cities in the YRD have formed core catering areas with different agglomeration levels. Different cuisines in the city have the characteristics of partial spatial overlap. (2) In the four central cities of the YRD, there is a significant positive spatial correlation between the catering industry as a whole and individuals. Hangzhou and Hefei have higher CLI (0.38), but the cuisine structure is relatively simple. The CLI of Shanghai and Nanjing is at a low level, but the overall cuisine structure is relatively balanced. (3) The catering industry in the four central cities has a high degree of agglomeration, a wide range of agglomeration, and many agglomeration points. Only Shanghai cuisine, Jiangsu and Zhejiang cuisine, Anhui cuisine, Fujian cuisine, Shandong cuisine, and Hunan cuisine have significant positive correlations in space, and the correlations weaken in turn. (4) The influence intensity of the influencing factors on CLI and CDI is in the order of degree of openness, regional economic level, market vitality, population concentration level, industrial structure. The spatial pattern of catering in a city is greatly affected by the regional economy and population density. This study can provide a reference for research on the spatial distribution of the catering industry in similar urban agglomerations around the world.

The Chinese government regards the night-time economy as one of the essential means to expand domestic demand and enhance sustainable economic development. Scientifically choosing the night-time economic development path of the suburban counties of the Chinese metropolis (SCCM) and proposing a reasonable spatial matching planning strategy is an urgent problem for Chinese local governments. This study takes Anning county, a suburban Kunming metropolitan area, as the research area. Using Python to capture multi-spatial data, such as POI and Baidu heatmap, we use ArcGIS spatial analysis and statistical tools to show the spatial distribution characteristics of the night-time economic formats in Anning County. At the same time, the spatial coupling coordination model is used to calculate the coupling coordination degree of the night-time economic formats distribution and comprehensive traffic distribution (D1), night-time economic formats distribution and night-crowd vitality (D2), and the spatial coupling coordination of the three (D3). It is divided into five spatial matching levels and analyzes the shortage of night-time economic development in each subdistrict. The research results show that the spatial development of the night-time economy in Anning county is unbalanced at the current stage. The northern part of the county has a good development trend, and the Lianran subdistrict has the highest coupling coordination degree (0.995). In contrast, the southern part of the county has the lowest coupling coordination degree due to a lack of economic formats and traffic restrictions (0.115). According to the subdistricts’ differences, the sustainable development strategy of the county’s night-time economy should be formulated from the perspective of the long-term development of metropolitan areas. We hope that this research can provide valuable inspiration and a development reference for relevant countries and regions to stimulate the sustainable power of the night-time economy.