## 5 Results and Discussion  
### Spatial Autocorrelation Test for Variables  
Establishing whether the candidate variables are spatially autocorrelated is necessary before the GWR model can be implemented. A spatial autocorrelation test can detect the degree of spatial correlation of the variables, which will provide theoretical support for the feasible application of spatial models. Moran's I, proposed by [PERSON] (1950) [35], is a correlation coefficient that measures the spatial autocorrelation. The estimated Moran's I values of the response variables and all of the candidate explanatory variables are higher than the expected I values, indicating that the variables have positive spatial autocorrelations (Table 3). In the tables, we use the concise expressions weekly_ridership, weekday_ridership, weekend_ridership, evenrush_ridership, and nonrush_ridership to denote the average daily ridership in a week, average weekday ridership, average weekend ridership, evening rush hour ridership, and nonrush hour ridership, respectively. The Moran scatter plot can directly reflect the spatial autocorrelation of variables, and the plot has four quadrants. A strong positive spatial correlation is observed when the values are distributed in the first and third quadrants, and a negative spatial correlation will emerge if they fall in the second and fourth quadrants. Figure 3 presents the Moran scatter plots of several variables.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **Variable** & **Moran’s I** & **Expected I** & \(p\)**-Value** \\ \hline \multirow{4}{*}{**Response Variables**} & Weekly\_ridership & 0.241166216 & \(-\)0.008547009 & 1.214e\(-\)06 \\  & Weekday\_ridership & 0.263893648 & \(-\)0.008547009 & 1.441e\(-\)07 \\  & Weekend\_ridership & 0.166122759 & \(-\)0.008547009 & 0.0004006 \\  & Evenrush\_ridership & 0.277295727 & \(-\)0.008547009 & 3.899e\(-\)08 \\  & Nonrush\_ridership & 0.247797251 & \(-\)0.008547009 & 4.637e\(-\)07 \\ \hline \multirow{4}{*}{**Candidate Explanatory Variables**} & Residence & 0.393652311 & \(-\)0.008547009 & 1.349e\(-\)14 \\  & Restaurant & 0.341715207 & \(-\)0.008547009 & 1.692e\(-\)11 \\  & Shopping & 0.231551952 & \(-\)0.008547009 & 3.923e\(-\)06 \\  & School & 0.289176791 & \(-\)0.008547009 & 4.164e\(-\)11 \\  & Offices & 0.174946826 & \(-\)0.008547009 & 1.365e\(-\)05 \\  & Bank & 0.388310167 & \(-\)0.008547009 & 1.978e\(-\)14 \\  & Bus & 0.312294327 & \(-\)0.008547009 & 7.898e\(-\)10 \\  & Hospital & 0.211271593 & \(-\)0.008547009 & 1.263e\(-\)05 \\  & Hotel & 0.466480947 & \(-\)0.008547009 & \(<\)2.2e\(-\)16 \\  & Dis\_to\_center & 0.959152856 & \(-\)0.008547009 & \(<\)2.2e\(-\)16 \\  & Degree & 0.125833878 & \(-\)0.008547009 & 0.005471 \\  & Betweenness & 0.241057563 & \(-\)0.008547009 & 1.674e\(-\)06 \\  & Pop & 0.710332939 & \(-\)0.008547009 & \(<\)2.2e\(-\)16 \\  & Days\_open & 0.383186771 & \(-\)0.008547009 & 2.108e\(-\)13 \\ \hline \end{tabular}
\end{table}
Table 3: Moran’s I test for spatial autocorrelation of all variables.  
Figure 3: Moran scatter plots of explanatory variables.  
None of the Moran's I values presented in Figure 3 are 0, indicating that they are not randomly distributed in space. In addition, most belong to the first and third quadrants, which indicates that the variables show significantly positive spatial autocorrelation. The three explanatory variables with the highest Moran's I values are population, distance to the city center, and days since station opened, as their Moran's I values are greater than 0.3 [36]. The results of the Moran's I test thus provide a theoretical foundation for the rationale of the follow-up study.