## 2 Literature Review
Additionally, [PERSON] and his team, using Shanghai as a case study and employing POI data coupled with kernel density analysis, meticulously described the spatial pattern of the catering industry in an urban three-dimensional context. By establishing Ordinary Least Squares (OLS), spatial lag, and spatial error models, they explored the multifaceted factors influencing Shanghai's catering industry's spatial structure, revealing that it is shaped by an amalgamation of demographic, economic, transportation, and spatial elements, distributed in a ring-shaped pattern around the city [39]. [PERSON] and associates assessed the spatial distribution traits of the catering industry in the urban area of Lhasa, a prominent highland tourism city. Utilizing nearest neighbor analysis, standard deviation ellipse, kernel density, and buffer zone methodologies, their findings indicated that the catering industry in Lhasa's urban area generally exhibits significant clustering characteristics, which are highly correlated with the city's transportation and tourism resources [40]. Through rigorous and comprehensive analyses, both domestic and international researchers have increasingly adopted sophisticated spatial econometric methods, seamlessly integrated with Geographic Information Systems (GIS) and other cutting-edge analytical tools [41; 42; 43; 44; 45]. This integrative approach has significantly enhanced the capacity for big data analytics to conduct intricate spatial analyses across various urban components, establishing this method as the foremost and central focus in the realm of contemporary urban studies. Predominantly, the spatial distribution of urban catering industries is characterized by patterns of central aggregation and peripheral dispersion. The key factors influencing these spatial dynamics include proximity to urban centers, the accessibility of transportation networks, the purchasing power of consumers, residential population density, urban land values, and the varied models of commercial operations [46; 47; 48]. In addition, the primary driving mechanisms shaping these distribution patterns cover a broad spectrum of elements [49; 50; 51]. These elements include the efficiency and reach of transportation networks, the availability and supply chain dynamics of goods, the fluidity and competitiveness of local labor markets, the pace and impact of technological innovations, and the level of trust and reliability perceived in products and services by consumers [52; 53; 54]. Together, these factors are instrumental in defining the spatial organization and operational dynamics of the catering industry within diverse urban landscapes, crucially impacting their efficiency and growth in today's rapidly urbanizing world [55; 56; 57]. In summary, a comprehensive array of existing research from international scholars has profoundly enhanced our understanding of the spatial organization and influencing dynamics of the catering industry. These studies have spanned diverse research methodologies, subject areas, and technological innovations, introducing significant theoretical frameworks such as the correlation effects between catering and information technology, spatial diffusion models for catering services, and efficiency hypotheses related to the sector. Despite these considerable advances, there persists a distinct lack of in-depth, detailed explorations specifically targeting the spatio-temporal evolution characteristics and the prospective spatial prediction simulations of distinct segments within the catering industry [58; 59; 60]. As the socio-economic landscape continues to undergo significant transformations, particularly in an era marked by increased uncertainties, the catering industry, recognized for its distinctive service attributes, inherent stability, social relevance, and spatial importance, has emerged as a fundamental pillar for socio-economic stability and a vital agent for spatial identification [61].