## 3 Data and Methodology
### MGWR Model
For instance, the proportion of greenery may promote travel demand in suburban areas, while it may suppress demand in central urban areas. Given these limitations, it is necessary to further analyze the residuals of the model to assess its ability to capture spatial features. Before examining the spatial autocorrelation of the residuals, this study also measured the global Moran's \(I\) values of the main independent variables to assess their spatial distribution patterns. The results in Table 3 indicate that, at the 0.1% significance level (\(p<0.001\)), all the regional characteristic variables exhibit significant positive spatial autocorrelations, as measured by [PERSON]'s \(I\). This suggests that their spatial distributions deviate markedly from randomness and display clear patterns of positive spatial clustering. When the independent variables themselves have strong spatial dependence, a simple OLS regression may fail to fully capture potential neighborhood effects and spatial spillover processes [64], thereby possibly exacerbating the spatial autocorrelation of the residuals. To test the spatial autocorrelation of the residuals in the linear regression model, this study employed the global [PERSON]'s \(I\) statistic. The results in Table 4 show that the [PERSON]'s \(I\) values for the OLS residuals are generally high across all ten time periods, and the Z-values are highly significant. This indicates strong spatial autocorrelation in the residuals, meaning that the OLS model fails to fully account for the spatial characteristics of the dependent variable. Particularly during the morning and evening peak hours on workdays, the residual Moran's \(I\) values display a distinct \"bimodal\" pattern, reflecting a stronger spatial clustering effect. During the overnight periods, whether on workdays or holidays, the Moran's \(I\) values reach their highest point, suggesting that ride-hailing demand in these time periods is highly concentrated in specific areas. \begin{table}
\begin{tabular}{l c c} \hline
**Variable** & **Moran’s Index** & **Z-Score** \\ \hline Population & 0.665 & 31.559 *** \\ Housing Price & 0.878 & 41.208 *** \\ City Center & 0.983 & 46.047 *** \\ Road Mileage & 0.351 & 16.533 *** \\ Bus Stops & 0.333 & 15.723 *** \\ Finance & 0.286 & 13.593 *** \\ Auto Repair & 0.203 & 9.679 *** \\ Buildings & 0.573 & 26.911 *** \\ Greenery & 0.481 & 22.571 *** \\ Sky & 0.303 & 14.263 *** \\ Roads & 0.614 & 28.783 *** \\ Traffic Control & 0.246 & 11.697 *** \\ Sidewalks & 0.433 & 20.339 *** \\ \hline \end{tabular}
\end{table} 
Table 3: Results of Moran’s I test for selected variables. Spatial regression models are applied to provide a more comprehensive explanation of the spatial characteristics of ride-hailing demand and to address the spatial autocorrelation present in the residuals of the OLS model [28]. Specifically, SEM and SLM are employed to analyze spatial diffusion effects and spatial error dependencies in demand. The results reveal that both SEM and SLM significantly reduce the Moran's \(I\) values to near zero in most time periods, with the Z-values becoming non-significant, indicating effective control of spatial autocorrelation. However, these models still do not address the second limitation of the OLS model--the assumption of globally consistent regression coefficients. This assumption fails to reflect the significant spatial heterogeneity that may exist in the factors affecting ride-hailing demand. Therefore, we employ local regression models that allow parameters to vary spatially, thereby identifying spatial differences. ### Model Performance Comparison

To thoroughly evaluate the explanatory power of different models and the added value of incorporating street-view features, this study compares five models: three global models (OLS, SEM, and SLM) and two local models (GWR and MGWR). Model performance is assessed using three metrics: goodness of fit (R\({}^{2}\)), the Akaike Information Criterion (AIC), and log-likelihood.