The spatial relationship between transport networks and retail store locations is an important topic in studies related to commercial activities. Much effort has been made to study physical street networks, but they are seldom empirically discussed with considerations of transport flow networks from a temporal perspective. By using Beijing’s bus and subway smart card data (SCD) and point of interest (POI) data, this study examined the location patterns of various retail stores and their daily dynamic relationships with three weighted centrality indices in the networks of public transport flows: degree, betweenness, and closeness. The results indicate that most types of retail stores are highly correlated with weighted centrality indices. For the network constructed by total public transport flows in the week, supermarkets, convenience stores, electronics stores, and specialty stores had the highest weighted degree value. By contrast, building material stores and shopping malls had the weighted closeness and weighted betweenness values, respectively. From a temporal perspective, most retail types’ largest correlations on weekdays occurred during the after-work period of 19:00 to 21:00. On weekends, shopping malls and electronics stores changed their favorite periods to the daytime, while specialty stores favored the daytime on both weekdays and weekends. In general, the higher store type level of the shopping malls correlates more to weighted closeness or betweenness, and the lower-level store type of convenience stores correlates more to weighted degree. This study provides a temporal analysis that surpasses previous studies on street centrality and can help with urban commercial planning.
In recent decades, complex network theory has become one of the most important approaches for exploring the structure and dynamics of traffic networks. Most studies mainly focus on the static topology features of the traffic networks, and there are also increasing literature focusing on passenger flow networks. However, not much work has been completed on comparing the static networks with dynamic flow networks from the perspective of supply and demand. Therefore, this study aimed to apply the complex network approach to explore the spatial relationship between bus line organization and bus flows in Beijing. Based on the bus route data and the passenger flow data obtained from the Beijing smart bus card, this study investigated the spatial characteristics of the bus line network and the temporal bus flow networks, and presented a comparison analysis on the spatial relationship between them by using the node centrality indices, namely degree centrality, betweenness centrality and closeness centrality. The results show that the overall spatial patterns of node centralities between the bus line network and the bus flow network were similar, while there were also some differences. For weekdays, the correlation between them is higher, as calculated by the degree of centrality. For weekends, the two networks have a greater correlation measured by degree centrality and betweenness centrality. The highest coefficients of correlation between the line network and traffic network appear in the morning peak, which implies that the congestion issues during the morning peak hours might receive the highest priority in Beijing’s bus-line network planning. Our study can provide implications for policymakers to improve the public urban transport network, and thus enhance residents’ happiness.
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