## 4 Results
### Conclusion Summary  
This study investigates the alterations in the quantity of commercial establishments within 5 to 15-min walking radii of subway stations, utilizing point of interest (POI) data from the years 2016, 2018, and 2020. It further explores the relationship between these changes and various spatial measurement parameters, including road network distribution, road network density, and building density. Following this analysis, a gravity model was applied to the 2020 data, post-subway construction, to assess the impact of subway stations on the number and traffic volume of adjacent POIs. The findings revealed a relatively stable linear regression equation, with a \(p\)-value consistently below 0.05, thereby elucidating the correlation between station traffic volume and surrounding commercial POIs. The principal conclusions drawn from this research are as follows:  
1. The establishment and operation of the subway have led to a discernible trend in the distribution and diffusion of urban planning density along rail transit corridors. This development has enhanced the previous pattern of point-like aggregation and circular expansion within the urban landscape, resulting in a configuration characterized by a \"point-circle-layer, overall line shape\". This outcome suggests that the influence of subway construction on the planning and development of strip-shaped urban environments extends beyond the mere establishment and impact of surface stations;the subterranean subway lines also play a constructive role in guiding the planning of surface roadways and building developments. 2. Further validation of the gravitational model concerning the station's influence on surrounding commercial POIs revealed that a higher density of high-traffic commercial POIs does not necessarily correlate with a stronger interaction force with the station. Certain densely concentrated commercial nodes exhibited a weaker gravitational attraction to stations compared to less dense nodes, a phenomenon that is likely attributable to the characteristics of the built environment, including road and building density. Moreover, areas with elevated gravitational values for commercial nodes frequently occur between stations with high visitation rates, indicating that the construction of stations indeed exerts an attractive influence on commercial establishments. 3. Through regression analysis, the interplay of factors affecting station visitation rates can be further examined. In the multiple regression analysis of station popularity against the number of high-frequency POIs, heat values, and the quantity of catering POIs, a predominantly linear trend is observed, with exceptions noted for certain stations. In separate single-variable regression analyses, variations in R\({}^{2}\) and F values, as well as differing Beta values compared to the multiple regression, were identified. Consequently, it can be inferred that passenger flow at stations is influenced by multiple interrelated factors, with the strongest correlation observed with the number of catering POI points. The construction of subway stations significantly contributes to the proliferation of surrounding POIs.