## 2 Materials and Methods  
### Study Area and Data Preparation  
Beijing is the capital of China and includes both urban and rural areas. As this study addresses public transport flows and retail activity, the analysis is conducted in the urban area of Beijing. Here, an area of approximately 38.64 km\({}^{2}\) within the sixth ring road of Beijing is selected as the case study area. The area covers most urbanized areas of Beijing. The study area is divided into grid cells to conduct further analysis. The appropriate cell size of the study units may affect the results and computational complexity. In previous studies of retail stores and network centralities, a cell size of 1 km \(\times\) 1 km has most commonly been used despite some variances [21]. Considering the road network density, this study selects a cell size of 1 km \(\times\) 1 km (see Figure 1). Point of interest (POI) data are used to construct a dataset of retail stores. The POI data for 2018 are sourced from Autonavi (Gaode), which is a popular electronic navigation map in China that provides information on the names, location, and types of various retail stores. Based on previous studies and the classification of POI data [21; 23; 37], 72 subtypes of retail stores (as illustrated in Table 1) were extracted from the POI dataset. According to the Retail Type Categorization of China (RTCC), they were categorized within six major categories, including shopping malls, supermarkets, convenience stores, specialty stores, electronics stores, and building material stores. A total of 91,243 POI retail stores in Beijing were extracted. The distributions of the six types of POI are shown in Figure 2. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & **Sub-Category** & **Total Counts** \\ \hline Shopping malls & Shopping Plaza, Shopping & 768 \\  & Center, etc. & \\ Supermarkets & Carrefour, Wal-Mart, Hualian, & \\  & Watsons, etc. & 12,756 \\ Convenience stores & 7-ELEVEN, Circle K, etc. & 15,027 \\  & Sports Store, Clothing Store, & \\ Specialty stores & Franchise Store, Personal Care & 27,222 \\  & Items Shop, etc. & \\  & Home Electronics Hypermarket, & \\ Electronics stores & Digital Electronics, Mobile & 7894 \\  & Handsets Sales, etc. & \\  & Furniture Store, Kitchen Supply, & \\ Building material stores & Hardware Store, Lighting, Porcelain & 27,576 \\  & Market, etc. & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Categories and total counts of POI. Figure 1: Case study area: (**a**) Beijing; (**b**) sixth ring road of Beijing. According to the Beijing Statistical Yearbook in 2018, the public transport lines of Beijing sum to a total length of 19,881 km, including 637 km of metro lines. The annual passenger volume of public transport is 7038.18 million, which includes 3848.43 million metro passengers. Approximately 565 bus lines and 22 subway lines pass through the case study area, and there are more than 3000 bus stations and 259 subway stations within the sixth ring road of Beijing. Approximately 7.5 million bus and 2.5 million subway cards swipes are recorded each day. The modal shares of public transport in the urbanized areas of Beijing are more than 30%. The public transit flow data used in this study were obtained from one week of bus and subway smart card data (SCD) from 19 April to 25 April 2015, which were obtained from the Beijing Public Transport Group. In recent years, two big events have serious impacts on public transit in Beijing. In 2014, Beijing started a price reform on its public transport system and adjusted public transportation fares to a higher level since then. Another event happened in 2019: the transport flows were much impacted by COVID-19. Therefore, the year 2015 may well reflect the stage of post-era of price reform and pre-era of COVID-19. We processed the data in two steps. First, the total flow for one week was accumulated by time periods of one day to capture temporal changes in transit flow. Various divisions of time periods have been used to aggregate the datasets in previous studies [38; 39; 40].