## 1 Introduction  
Location is a key factor for the commercial success of retail stores, as consumers tend to patronize stores that have higher access advantages [1, 2]. The configuration of a city's transport network has been found to have significant impacts on the distribution of retail service activities [3, 4, 5, 6, 7]. Additionally, in urban planning and design, the locations of retail services are important for city growth and vitality [8]. Therefore, location analysis of retail stores is important for retail investment decisions and urban planning. Location analysis has been increasingly applied to the retail sector with the growing computing power and the advent of big data [9]. While many location-allocation models have been developed and used for location decision making of retail stores [10, 11], identifying the spatial pattern of retail locations is still an important and basic research task to date. Generally, many factors may affect the location retail stores, which made it a complex and multi-dimensional problem [12]. Among these factors, transportation is often regarded as a key element for retail locations. There is much empirical literature focusing on exploring the spatial relationships between physical street networks and retail stores. Various accessibility indices are optional to capture the convenience of retail stores in physical street networks [13; 14]. Among them, the centrality features of a store are critical for the commercial competition of market areas according to the central place theory and spatial interaction theory [15]. Based on the approaches of space syntax or complex networks, the centrality features of a transport network can be measured by various centrality indices [16]. The multiple centrality assessment (MCA) model, which groups several indices together, has been applied to examine the relationship between street centrality and the spatial distributions of retail stores [17]. Different cities around the world have been examined, and the findings indicate that the centralities of the physical street network may well explain the retail distributions [18; 19; 20; 21]. The study of [PERSON] and [PERSON] et al. [22] first examined the differences among location preferences for different types of retail stores. Later, new data sources as point of interest (POI) data were introduced [23]. As the relationship between various types of stores and multiple centrality indices of street networks across regions and cities were examined, it was revealed that different store types may correlate to different spatial networks centralities [23; 24], which is helpful for retail location selection and planning. While these previous studies have focused on examining the physical street network, few quantitative empirical studies have examined centrality in networks with transport flows. However, the location advantage of attracting transport flows is one important factor that influences the location selection of commercial services. The transport flows can reflect where people would like to go, and the correlation for retail stores is an important element for commercial development. According to the classic Hotelling model [25], the location strategy serves to obtain maximum flows, which are not necessarily geometric central points in space [26]. In addition, the flow network has a temporal attribute. Exploring temporal dynamics in flow networks may provide some possible insights into retail location patterns [27], as temporal factors such as store opening hours and individuals' travel time cannot be addressed by the static location analyses on the physical street network [28; 29]. For retail location, it was recognized early that accessibility by public transport is a key issue for a store [30]. Several studies have verified that public transport has a substantial impact on retail patterns in city centers when compared to those of out-of-town malls [31; 32; 33; 34].