Co-working spaces (CWSs) have gradually become a new form of spatial economic activity in large cities in China. This not only innovates the traditional office model, but also helps to realize the efficient utilization of office buildings and the sustainable development of office spaces. Taking Hangzhou as a case study, this paper uses big data analysis technologies including Python and ArcGIS to reveal the distribution characteristics of CWSs. From the perspectives of traffic accessibility, business atmosphere, innovation environment, living convenience, and rental cost, we innovatively constructed an indicator system of factors affecting site selection of CWSs. We then conducted an empirical study to reveal the influence mechanism behind different factors. Our conclusions are as follows: (1) CWSs in Hangzhou generally present a multi-center distribution pattern; (2) based on the different degrees of dependence of the target customer groups on resources such as commerce, capital, and information, the factor that has the most significant impact on the site of CWSs is the regional innovation environment, and its weight is 0.3941. The order of importance of other influencing factors is the convenience of life (0.3147), business atmosphere (0.1352), and traffic conditions (0.1171). The cost of rent has the most negligible impact on site selection, and its weight is only 0.0195. We hope that the research can be used to provide a scientific basis for the rational planning and development guidance of CWSs.

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.