## 3 Data and Methodology
### MGWR Model
Following [80; 87], lower AIC and higher log-likelihood values indicate a better model fit, while higher R\({}^{2}\) values reflect stronger explanatory power. To assess the contribution of street-view imagery, we further re-estimated each model excluding all the street-level features and report the corresponding results in parentheses in Table 5. This side-by-side comparison demonstrates the incremental improvement brought by the inclusion of street-level visual attributes in modeling spatial variations in ride-hailing demand. \begin{table}
\begin{tabular}{l c c c c c c} \hline \multirow{2}{*}{**Time Period**} & \multicolumn{3}{c}{**Moran’s Index**} & \multicolumn{3}{c}{**Z-Score**} \\ \cline{2-7}  & **OLS** & **SEM** & **SLM** & **OLS** & **SEM** & **SLM** \\ \hline Workday & & & & & & \\ Morning Peak & 0.179 & 0.007 & 0.018 & 8.437 *** & 0.425 & 0.899 \\ Morning Day & 0.160 & \(-\)0.008 & \(-\)0.020 & 7.569 *** & \(-\)0.318 & \(-\)0.865 \\ Evening Peak & 0.183 & \(-\)0.005 & 0.001 & 8.635 *** & \(-\)0.159 & 0.111 \\ Evening & 0.142 & \(-\)0.001 & \(-\)0.006 & 6.737 *** & 0.040 & \(-\)0.183 \\ Overnight & 0.254 & 0.006 & 0.016 & 11.972 *** & 0.343 & 0.843 \\ \hline Holiday & & & & & & \\ Morning Peak & 0.144 & 0.003 & 0.004 & 6.821 *** & 0.221 & 0.261 \\ Morning Day & 0.118 & \(-\)0.002 & \(-\)0.014 & 5.618 *** & \(-\)0.014 & \(-\)0.600 \\ Evening Peak & 0.109 & 0.000 & \(-\)0.009 & 5.158 *** & 0.057 & \(-\)0.338 \\ Evening & 0.105 & 0.001 & \(-\)0.008 & 5.033 *** & 0.108 & \(-\)0.286 \\ Overnight & 0.284 & 0.006 & 0.015 & 13.388 *** & 0.342 & 0.792 \\ \hline \end{tabular}

* Significant at 0.001 level.

\end{table} 
Table 4: Results of [PERSON]’s I test for models’ residuals. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multirow{2}{*}{**Models**} & \multicolumn{1}{c}{**[PERSON]**} & \multicolumn{1}{c}{**[PERSON]**} & \multicolumn{1}{c}{**[PERSON]**} & \multicolumn{1}{c}{**[PERSON]**} & \multicolumn{1}{c}{**[PERSON]**} & \multicolumn{1}{c}{**[PERSON]**} \\ \cline{2-13}  & & **Missing Peak** & **Missing Day** & **Evening Peak** & **Evening Peak** & **Even



#### 4.4.1 Global Model Results

The results in Table 5 show that the OLS model has the highest AIC and log-likelihood values in all the time periods, indicating that it is the poorest-performing model. Additionally, its R\({}^{2}\) value is significantly lower than that of all the other models except SEM, suggesting that OLS fails to effectively capture the spatial characteristics of ride-hailing demand. Among the global spatial models, SLM outperforms SEM by significantly improving the R\({}^{2}\) value, with an increase of up to 39% during the overnight period on holidays, indicating that the lag model is more appropriate for addressing spatial autocorrelation issues in this case. This conclusion is also supported by the results of the Lagrange multiplier (LM) test in Table 6, which includes LM-Error (spatial error test), LM-Lag (spatial lag test), and the corresponding Robust LM values, along with their significance (\(p\)-values). The LM-Error test is used to detect spatial correlation in the error terms [90]. If significant, it suggests that unobserved factors in the model cause neighboring regions' error terms to influence each other, indicating that SEM should be used for adjustment. On the other hand, the LM-Lag test is used to determine whether the dependent variable exhibits spatial lag effects. If significant, it indicates that ride-hailing demand in one area is influenced not only by its own factors but also by the demand in neighboring areas, making SLM the more appropriate model. In Table 6, the LM-Error and LM-Lag tests are significant (\(p<0.001\)) across all the time periods, suggesting that ride-hailing demand exhibits spatial dependence and requires spatial regression models. However, the Robust LM-Error test is not significant (\(p>0.05\)) in any of the time periods, while the Robust LM-Lag test is significant (\(p<0.001\)) across all the periods.