1. Introduction
At present, the catering industry has assumed a pivotal role in the urban life service sector, establishing a profound connection with the daily lives of urban inhabitants [
1,
2]. In China, with the rapid development of the national economy and the increasing affluence of people’s lives, the spatial layout of the catering industry has long been one of the basic units of the state’s multi-pronged strategy to promote industry-driven development [
3]. Moreover, an important way to study urban dynamics is to study the changes in urban facilities. Among various urban facilities, restaurant layout is decentralized and has a short life cycle, which can fully reflect various local socio-economic attributes, and is more sensitive to recent short-term changes in urban policies [
4]. Therefore, the study of the spatial distribution and evolution of the catering industry can reveal the development of the city and holds significant research value.
The prerequisite for supporting the establishment and rapid growth of cities is to maintain a high degree of dynamism in the agglomeration economy. The agglomeration economy refers to the economic activities that different industries engage in within a specific area [
5,
6], thus promoting a high degree of dependence of industries on economic activities [
7], and is the main intrinsic motivation for the formation of industrial clusters. Industrial clusters are flexible production combinations consisting of a large number of specialized industries or enterprises and related support institutions located in a specific region, rooted in the local social and cultural environment of continuous innovation. Most analyses of industrial clusters are based on case studies [
8,
9], quantitative studies of industries, and the diversity of cross-industry cluster behaviors [
10,
11,
12], while there is limited understanding of the size, shape, evolutionary patterns, and reasons for the formation of service clusters, such as the catering industry.
Catering clusters represent a unique manifestation of industrial clusters, embodying a form of soft infrastructure or social capital [
13]. Specifically, they are a dense concentration of catering enterprises and their related supporting service businesses located within a specific region [
14,
15,
16]. The factors that influence the formation of catering clusters are multifaceted, including historical and cultural drivers, policy and institutional drivers, social opinion drivers, and planning and strategic drivers, among others [
17,
18,
19,
20]. Furthermore, catering clusters come in different types and forms. The clustering of catering enterprises may seem to contradict classical economic theory, which holds that competing firms in close proximity to one another will create greater market competition and reduce profitability [
21]. However, since the catering industry is not primarily technology-based, such clustering can provide multiple advantages in terms of supply and demand. For example, joint promotion and mutual support among enterprises within catering clusters can enhance the visibility and brand image of the entire cluster [
22]. Moreover, research has shown that the spatial relationships of urban and community catering clusters [
23,
24], the agglomeration process of catering clusters [
25], and the variations in cluster size of different catering clusters [
13] all contribute to regional economic development, including the creation of employment opportunities and tax revenue [
8,
14,
26]. Catering clusters that are not severely affected by competition can elevate the visibility and appeal of the entire region, enticing more diners to patronize restaurants and bolstering the growth of the overall catering industry.
The spatial distribution of the catering industry is influenced by several factors, including population density [
27], regional economic conditions [
28], geographical location, and transportation facilities [
29]. Among them, population density has always been the most significant factor affecting catering distribution, as sufficient foot traffic and high customer satisfaction are essential for a restaurant’s long-term viability. However, the prevalence of third-party catering websites has had an impact on catering distribution, making traditional research methods [
30] less accurate in reflecting clustering conditions. Nowadays, with the changes in dining habits brought about by the Internet and smart delivery services, access to multi-period catering data is made possible through online platforms and POI data based on location-based services.
Currently, scholars have explored various aspects of catering clusters using POI data, specifically the identification of catering clusters [
23,
31], the spatial distribution characteristics of catering clusters [
16,
32], the guidance of catering clusters for urban planning [
33,
34], the economic benefits of catering clusters [
35,
36], and the competition and cooperation of catering clusters [
37,
38] and the network relationships of catering clusters [
39,
40]. The emergence of POI data enables a clearer and more accurate analysis of the distribution of restaurant establishments, the development patterns of catering clusters, and the mechanisms of cluster evolution, whether viewed from a macro or micro perspective.
In conclusion, there have been some studies on the evolutionary characteristics of catering clusters [
16,
41], but they have paid little attention to the underlying mechanisms of their formation and lacked a comprehensive framework for understanding their evolutionary process. Furthermore, the advent of the Internet and smart delivery services have significantly transformed dining habits, leading to a more intricate relationship between the spatial distribution of the catering industry and population density. By integrating online ICT catering website data with GIS, it is possible to conduct more detailed and prospective research and analysis on the formation, influencing factors, and evolutionary patterns of catering clusters.
The main contributions of this study are as follows:
- (1)
A novel approach was adopted to identify urban catering clusters, known as the Natural Nearest Neighbor Single Branch Model (NNSBM), which is a soft clustering model. By considering the inherent characteristics of data objects, the density and cluster density of each data point were dynamically measured, resulting in an adaptive clustering method that leads to improved accuracy in identifying catering points.
- (2)
Our research introduced a novel catering division spatial structure model and measurement method that enabled the identification of four distinct types of urban catering spatial structures based on two fundamental dimensions: primacy and concentration. The proposed model enabled us to explore the evolutionary characteristics of catering spatial structures in 106 Chinese cities and establish a comprehensive and clear framework system for the evolution of urban catering clusters.
- (3)
Statistical and economic indicators were introduced to quantitatively evaluate the correlation between population density and catering distribution. Moreover, the degree of influence of population density on catering distribution was analyzed by a bivariate spatial autocorrelation model, and the significance of the two variables was visualized through LISA clustering plots to further systematically dissect the influence of population density on the evolutionary pattern of catering clusters.
5. Discussion
The general approach for catering cluster identification is to use density-based clustering algorithms; for example, Fan and Guo [
31] used DBSCAN for the consumer clustering examination of geo-tagged social network data. Unsupervised clustering algorithms have also been used for clustering catering clusters; for example, Tian and Luan [
14] used GMM to realize spatial clustering, evaluated the spatio-temporal clustering of Shanghai catering clusters, and explored the practical application of catering clusters such as the positional pattern, shape of the clusters, and spatial clustering. However, density-based clustering or unsupervised clustering, appropriate parameters, and sub-models must be manually selected in advance. In contrast, our proposed soft clustering algorithm, Natural Nearest Neighbor Single Branch Model (NNSBM), is adaptive to recognize the target clusters, which greatly reduces the time of manual intervention and improves the recognition accuracy of the catering clusters. In addition, the spatial distribution and evolution of catering activities can reveal urban development at a finer-grained spatio-temporal scale. For example, Zhang and Min [
55] used digital field hierarchical structure mapping and generalized symmetric structure mapping to identify the spatial morphology and evolutionary features of dining in mountainous cities. Wu and Pei [
56] used count regression models to regress restaurant distribution, births, and deaths on different location factors. Different from the above, we proposed a measurement method of a catering spatial structure, based on which a clear evolutionary system was constructed, so that the evolutionary characteristics of catering clusters can be seen at a glance.
As Chinese cities continue to develop, the catering industry has undergone significant advancements, resulting in the expansion of the catering clusters’ spatial structure (as illustrated in
Figure 10a,b). Prior to the emergence of such clusters, catering activities were typically concentrated in certain areas of the city, leading to the main catering cluster. However, as the size of the main catering cluster fails to meet the catering demands of the administrative district, catering activities may relocate from the central area toward the periphery, causing the “central fading” phenomenon. Despite this, the “central fading” of catering activities does not necessarily guarantee the emergence of new catering clusters in the periphery, resulting in the intricate evolution of urban catering spatial structures. For example, if the catering distribution is relatively scarce and dispersed, the spatial structure may shift from Lp-Lc to Lp-Hc or Hp-Hc, as observed in Handan, Luoyang, Xuzhou, and Zhuhai. Conversely, if the distribution is more concentrated and the main catering cluster particularly prominent, the spatial structure may shift from Hp-Lc to Lp-Hc or Hp-Hc, as observed in Nanning, Fuzhou, and Liuzhou. Regardless of the transformation stage, the majority of cities maintain or evolve into the Lp-Hc spatial structure, as depicted in
Figure 10c,d. As depicted in
Figure 8, regardless of the type of evolution, the growth rate of the secondary catering clusters surpasses that of the main catering cluster. Lp-Hc is the ultimate spatial structure for the majority of cities during the evolution of catering clusters.
The model for the division of the catering spatial structure proposed in this manuscript is definitely a priori in nature. To capture the complex and varied changes in the evolution of catering spatial structures, this study classifies cities where such changes occur in nine representative categories, aiming to provide descriptive representations of each type of change. However, it should be noted that this classification does not imply an exact description of the intricate diversity in the evolution process. This is because the “center fading” process observed in catering activities contributes to the complexity and diversity of urban catering spatial structure evolution. The “center fading” process does not necessarily result in the formation of new catering activity clusters in peripheral areas, nor does it inevitably lead to the decline of established catering activity clusters that were formed in the central regions.
Concerning the impact of population density on catering distribution, firstly, it is essential to note that population density exerts a positive influence on catering distribution (
Figure 10e,f). Secondly, the extent to which population density affects different catering spatial structures varies considerably. In the early stages of catering cluster formation, the impact of population density on catering clusters is not especially significant, and the correlation between the two is low (
Figure 9d). Upon examining cities that have undergone spatial structure evolution (
Table 5), as catering clusters enlarge, the impact of population density on catering distribution becomes considerably greater. Once catering clusters reach a specific size (
Table 4), the relationship between population density and catering distribution begins to decline, and the influence weakens, as observed in Jinan and Qingdao. Nonetheless, as catering clusters continue to expand, the correlation between population density and catering distribution begins to rise again, and the impact increases, as evidenced in Beijing and Shanghai. In conclusion, the influence of population density on catering clusters is cyclical, and the correlation between the size of catering clusters and population density is closely interrelated.
Our research has certain limitations that should be acknowledged. Firstly, the NNSBM computational requirements are substantial, resulting in slow convergence speed. Despite pre-dividing the dataset, it remains challenging to determine the optimal number of sub-models in advance, necessitating manual selection. Secondly, the utilization of raster data for population density and the association of POI data with food and beverage points in grid format introduce a significant dependency on the grid size, potentially influencing the correlation results. Therefore, further refinement of the grid size is imperative to achieve a more precise representation.
Undoubtedly, the spatio-temporal evolution of the catering industry is impacted by various factors, including government policies and urban development. Therefore, our study only presents an initial analysis of the spatial structure and spatio-temporal evolution of the catering industry. Our proposed model for an urban catering spatial structure division and the spatio-temporal evolution measurement methods necessitate continual improvement with the expansion of data sources. Furthermore, comprehensive fundamental theories are required to further refine the determinants that influence the distribution of the catering industry.