## 3 Data and Variables
### Variables Description
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Variable Name** & **Description** & **Data Sources** & **Mean** & **S.D.** & **VIF** \\ \hline \multicolumn{6}{c}{**Dependent variable**} \\ \hline \multirow{2}{*}{Transfer ridership} & Transfer ridership from bus to & Metro smartcard data of Shanghai on & \multirow{2}{*}{3.82} & \multirow{2}{*}{3.17} \\  & metro, of a metro station (thousand) & 1 September 2016 & & \\ \hline \multicolumn{6}{c}{**Independent variables**} \\ \hline \multicolumn{6}{c}{Bus-metro connection characteristics} \\ \hline \multirow{2}{*}{Bus-network density} & The length of bus-network & \multirow{2}{*}{OpenStreetMap (OSM) data} & \multirow{2}{*}{7.46} & \multirow{2}{*}{3.29} & \multirow{2}{*}{3.46} \\  & centerline per square kilometer & & & \\  & (km/km\({}^{2}\)) & & & \\ \hline \multirow{2}{*}{Network repetition ratio of bus–metro} & The repetition ratio R = L/S, where & \multirow{2}{*}{OpenStreetMap (OSM) data,} & \multirow{2}{*}{0.13} & \multirow{2}{*}{0.05} & \multirow{2}{*}{1.32} \\  & L is the parallel line of bus and & & & & \\ \multicolumn{6}{c}{} \\  & metro, and S is the total bus line per & \multirow{2}{*}{of 2020 Shanghai} & \multirow{2}{*}{0.13} & \multirow{2}{*}{0.05} & \multirow{2}{*}{1.32} \\  & square kilometer (km/km\({}^{2}\)) & & & \\ \hline \multirow{2}{*}{Bus-stop density} & Number of bus stops per square & \multirow{2}{*}{Point-of-interest (POI) data,} & \multirow{2}{*}{6.58} & \multirow{2}{*}{3.13} & \multirow{2}{*}{3.96} \\  & kilometer & & & & \\  & (counts/km\({}^{2}\)) & & & & \\ \hline \multirow{2}{*}{Average transfer distance} & Average transfer distance, from bus & \multirow{2}{*}{Distance crawled from the Baidu Map (map.baidu.com, accessed on 12 April 2022)} & \multirow{2}{*}{0.2} & \multirow{2}{*}{0.05} & \multirow{2}{*}{1.40} \\  & stop to near metro (km) & & & & \\ \hline \multicolumn{6}{c}{**Network attributes of metro stations**} \\ \hline \multirow{2}{*}{Distance to the central station} & Network distance to Jing’an Temple & \multirow{2}{*}{Distance crawled from the Baidu Map (map.baidu.com, accessed on 12 April 2022)} & \multirow{2}{*}{4.81} & \multirow{2}{*}{2.55} & \multirow{2}{*}{6.50} \\  & station (km) & & & & \\ \hline \multicolumn{6}{c}{The closeness contrary,} & \multirow{2}{*}{Distance crawled from the Baidu Map (map.baidu.com, accessed on 12 April 2022)} & \multirow{2}{*}{0.15} & \multirow{2}{*}{0.02} & \multirow{2}{*}{4.40} \\  & \(C=\frac{1}{\sum_{i=1}^{n}d(u,p)}\), where \(n\) is the number of all nodes in network, & & & \\ \multicolumn{6}{c}{} \\ \multirow{2}{*}{Centrality} & \(d(u,v)\) is the shortest distance & 12 April 2022 & & & \\  & between node \(v\) and node \(u\). & & & \\ \hline \multicolumn{6}{c}{**Land-use attributes for trip attraction**} \\ \hline \multirow{2}{*}{Employment density} & Number of jobs per square & Point-of-interest (POI) data, & \multirow{2}{*}{33.34} & \multirow{2}{*}{16.68} & \multirow{2}{*}{2.17} \\  & kilometer (thousand/km\({}^{2}\)) & & & & \\ \hline \multirow{2}{*}{Industrial ratio} & The ratio I = areas, for industrial & \multirow{2}{*}{Land-use data, of 2020 Shanghai} & \multirow{2}{*}{0.05} & \multirow{2}{*}{0.11} & \multirow{2}{*}{2.69} \\  & use/all land areas & & & & \\ \hline \multirow{2}{*}{Commercial ratio} & The ratio C = areas, for commercial & \multirow{2}{*}{Land-use data, of 2020 Shanghai} & \multirow{2}{*}{0.16} & \multirow{2}{*}{0.11} & \multirow{2}{*}{2.01} \\  & use/all land areas & & & & \\ \hline \multicolumn{6}{c}{**Land-use attributes for trip production**} \\ \hline \multirow{2}{*}{Residential ratio} & The ratio R = areas, for residential & \multirow{2}{*}{Land-use data, of 2020 Shanghai} & \multirow{2}{*}{0.38} & \multirow{2}{*}{0.14} & \multirow{2}{*}{2.78} \\  & use/all land areas & & & & \\ \hline \multicolumn{6}{c}{**Other land-use attributes**} \\ \hline \multirow{6}{*}{Land-use diversity} & Entropy index of land use (0–1): & \multirow{2}{*}{\(\frac{-\sum_{i=1}^{n}(s_{i})n(s_{i})}{n(m)}\),} & \multirow{2}{*}{0.61} & \multirow{2}{*}{0.1} & \multirow{2}{*}{2.43} \\  & Where \(m\) is the type of land use, \(s_{i}\) & & & & \\ \multicolumn{6}{c}{} \\ \multirow{2}{*}{Centrality} & is the ratio of type-I land use to the & \multirow{2}{*}{\(\frac{-\sum_{i=1}^{n}(s_{i})n(s_{i})}{n(m)}\),} & \multirow{2}{*}{0.61} & \multirow{2}{*}{0.1} & \multirow{2}{*}{2.43} \\  & Where \(m\) is the type of land use, \(s_{i}\) & & & & \\ \multicolumn{6}{c}{} \\ \multirow{2}{*}{Centrality} & is the ratio of type-I land use to the & \multirow{2}{*}{\(\frac{-\sum_{i=1}^{n}(s_{i})n(s_{i})}{n(m)}\),} & \multirow{2}{*}{0.61} & \multirow{2}{*}{0.1} & \multirow{2}{*}{2.43} \\  & total land area. & & & & \\ \hline \multirow{2}{*}{
\begin{tabular}{c} \multirow{2}{*}{Sweet density} \\ \end{tabular} } & Length of the road/area size & \multirow{2}{*}{OpenStreetMap (OSM) data,} & \multirow{2}{*}{7.67} & \multirow{2}{*}{3.00} & \multirow{2}{*}{2.57} \\  & (km/km\({}^{2}\)) & & & & \\ \hline \hline \end{tabular}
\end{table}