## 5 Results and Discussion
### Model Implementation and Results Analysis
The results suggest that there is a significant improvement when GWR is used. In addition, by comparing the results of the five models, the model for nonrush_hour ridership regression is found to perform the best in terms of the R-squared value. We only need the information about population distribution, degree centrality, betweenness centrality, days since opening, the number of shopping places and distance to the city center to use the GWR model to explain 88% of the response variable of nonrush hour ridership. In addition, the relevant data covering the information on the explanatory variables are easily accessible. Figure 4 shows the standardized residuals of the GWR model for average weekday ridership, and for most stations these are relatively small, demonstrating the high accuracy of the model. Overpredictions (red bubbles) and underpredictions (blue bubbles) are randomly distributed in \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Global (OLS)**} & \multicolumn{5}{c}{**Local (GWR)**} \\ \hline
**Variables** & **Estimate** &
\begin{tabular}{c} **Standard** \\ **Error** \\ \end{tabular} & **t(Est/SE)** & **Min** & **Max** & **Mean** & **STD** \\ \hline
**Intercept** & 459.94 & 32.18 & 14.29 & \(-\)1446.82 & 4350.79 & 519.97 & 792.78 \\ \hline
**Pop** & 118.36 & 39.43 & 3.00 & \(-\)471.76 & 400.50 & 80.31 & 170.68 \\ \hline
**Degree** & 21.29 & 40.77 & 0.52 & \(-\)200.12 & 340.02 & 57.51 & 149.68 \\ \hline
**Betweenness** & 48.65 & 42.70 & 1.14 & \(-\)177.32 & 402.69 & 53.90 & 116.54 \\ \hline
**Days\_open** & 135.09 & 33.90 & 3.98 & \(-\)2116.29 & 2539.11 & 153.31 & 593.88 \\ \hline
**Shopping** & 53.01 & 35.73 & 1.48 & \(-\)479.35 & 196.72 & \(-\)20.25 & 152.92 \\ \hline
**Dis\_to\_center** & \(-\)41.24 & 35.55 & \(-\)1.16 & \(-\)592.59 & 2590.58 & 55.57 & 447.78 \\ \hline \multicolumn{5}{c}{**Diagnostic**} \\ \hline
**R-squared** & & 0.33 & & & 0.88 & \\ \hline
**Adjusted R-squared** & & 0.29 & & & 0.77 & \\ \hline
**Sigma** & 349.49 & & & 199.58 & \\ \hline
**AICc** & 1727.10 & & & 1677.79 & \\ \hline
**Residual sum** & & 13,557,658.86 & & 2,475,111.14 & \\
**of squares** & & & & & \\ \hline
**Number of** & & & & 39.67 & \\ \hline
**parameters** & & & & & \\ \hline \multicolumn{5}{c}{**GWR ANOVA Table**} \\ \hline
**Source** & SS & **DF** & **MS** & **F** & \(p\)**-Value** \\ \hline
**Global Residuals** & 1,355,7658.856 & 7.000 & & & \\ \hline
**GWR Improvement** & 11,082,547.717 & 48.864 & 226,804.412 & & \\ \hline
**GWR Residuals** & 2,475,111.139 & 62.136 & 39,833.705 & 5.693781 & 0.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results of the model for Nonrush_ridership. Figure 5, which indicates that our model is well specified. The spatial autocorrelation (Moran's I) test of the regression residuals helps to ensure that they are spatially random (Table 6). The global Moran's I residuals test of the models for the average ridership over the whole week, shown in Table 6, demonstrates that GWR surpasses OLS, as the Moran I's calculation is closer to the expected value in the GWR model. The residuals of the GWR model have a greater likelihood of random distribution (\(p\)-value) and show less variance (z-score). However, the residuals of OLS demonstrate statistically significant clustering characteristics (reflected by the Z-score and \(p\)-value). The local indicator of spatial association (LISA) was proposed to represent local pockets of nonstationarity, assess the influence of individual locations on the magnitude of the global statistic, and identify \"outliers\" [37]. Figure 5 shows the LISA cluster maps of residuals in the OLS and GWR models for average daily ridership over a whole week. The residuals of the OLS model give significantly positive high-value clustering, while in the GWR model almost all of the clusters of residuals are ruled