## 1 Introduction  
The rapid progression of urbanization has led to a systematic enhancement of transportation systems within urban environments [1]. The urban transportation network is a fundamental element of the urban framework [2], with public transportation constituting a significant segment of this system. To improve the efficiency and speed of urban transit while mitigating issues related to congestion, urban rail transit has increasingly become a favored option in contemporary cities [3]. Lanzhou, characterized as a typical strip-shaped city, exhibits a transportation network that demonstrates greater accessibility in its eastern region compared to the western region, resulting in a symmetrical decline in accessibility from the geometric center of the city [4]. This distinctive feature of the transportation network significantly influences the urban spatial structure and the arrangement of commercial activities [5]. The metro lines in Lanzhou commenced operations in 2019 [6]. Given the unique linear configuration of the strip-shaped city, rail transit not only enhances the urban framework but also affects the vitality and commercial distribution within the city [7]. The commercial activities surrounding urban rail transit stations serve as indicators of the city's economic dynamism and are integral to urban culture and social interactions. Consequently, examining the commercial distribution and evolution within the urban center's rail transit area in Lanzhou is crucial for understanding the transformations in the spatial structure of strip-shaped cities. This investigation holds substantial practical implications for overcoming the limitations imposed by urban geography on urban vitality [8]. It provides valuable insights for informing the development strategies of urban commercial layouts. Additionally, it offers guidance for optimizing rail transit systems in such cities. In recent years, point of interest (POI) data have been extensively utilized in urban spatial research due to their ability to accurately reflect functional distributions and human activity patterns within urban spaces [9]. POI data not only quantify the extent of functional diversity within urban areas but also elucidate the spatial distribution characteristics of urban vitality [10]. In contemporary studies, the analysis of POI data has gained prominence, particularly concerning temporal variations within specific areas, where changes in POI distribution can be effectively visualized [11]. These distributional changes serve as a reliable indicator of shifts in urban layout. Such analysis should prioritize leveraging the spatiotemporal distribution and evolutionary characteristics of POI data. Such an approach is critical for investigating how urban rail transit stations influence urban vitality distribution patterns. Key dimensions requiring systematic examination include: (1) the functional diversity of rail transit stations, (2) spatial accessibility metrics, and (3) pedestrian flow dynamics. A comprehensive understanding of these interconnected factors will elucidate the underlying mechanisms driving vitality distribution in urban systems. This study commenced with a circular layer analysis of the environment surrounding urban rail transit stations, utilizing a temporal framework of six years. The analysis focused on identifying elements within the built environment that exhibited significant changes. To enhance the interpretability of our analytical data, we employed kernel density analysis in ArcGIS. This approach provided more concrete representations of the modified point and line features. Subsequently, we leveraged real-time data extracted from the Baidu Heatmap to pinpoint urban vitality hotspots. After eliminating potential confounding factors, we constructed a city gravity model that integrates metro stations and high-visitation points [12]. This gravity model provided preliminary evidence of the relationship between metro stations and urban vitality.