## 3 Materials and Methods
### Modeling Spatial Dependence Effect  
The spatial correlation can generally be reflected in the following three aspects. First, when there is a spatial correlation between the dependent variables, the spatial model is expressed by Equation (1), which is called spatial autoregression. \[\mathbf{y}=\lambda\mathbf{W}\mathbf{y}+\mathbf{\varepsilon} \tag{1}\]  
Here, \(\mathbf{W}\) represents the spatial weight matrix, and the commonly used spatial weight matrix is a binary adjacency matrix, which represents the spatial adjacency relationship between TAZs. The built environment variables of each TAZ are used as independent variables. After the independent variables have been introduced, the SAR model equation is shown by Equation (2). That is, the spatial autoregressive model not only explains the  
Figure 5: Distribution of DiDi trips during the ED. \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Category** & **Name** & **Unit** & **Max.** & **Min.** & **Mean** & **S.D.** \\ \hline \multirow{2}{*}{Density} & Population density & 10,000/km\({}^{2}\) & 8.004 & 0.003 & 2.150 & 1.781 \\  & Building density & m\({}^{2}\)/km\({}^{2}\) & 0.378 & 0.001 & 0.201 & 0.086 \\ \hline \multirow{2}{*}{Diversity} & Mixed land use & Nil & 1.891 & 0.486 & 1.357 & 0.227 \\ \hline \multirow{4}{*}{Design} & Road network density & km/km\({}^{2}\) & 17.282 & 0.408 & 7.986 & 3.173 \\  & \% of non-motorized lane & Nil & 1.000 & 0.287 & 0.799 & 0.143 \\  & Road width & m & 19.871 & 6.232 & 11.773 & 2.222 \\  & Road length & km/km\({}^{2}\) & 4.011 & 0.024 & 0.992 & 0.693 \\ \hline \multirow{2}{*}{Public transit} & Bus line density & km/km\({}^{2}\) & 35.069 & 0 & 3.599 & 4.550 \\  & Bus stops density & No./km\({}^{2}\) & 52.283 & 0.100 & 12.936 & 7.777 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Description of built environmental variables. demand for DiDi trips within the built environment of a certain TAZ but also reflects the spatial dependence of the DiDi travel demand among TAZs. \[\mathbf{y}=\lambda\mathbf{W}\mathbf{y}+\mathbf{X}\mathbf{\beta}+\mathbf{\varepsilon} \tag{2}\]  
Second, on the basis of the SAR model, travel demand is not only affected by the built environment variable in the self-TAZ but may also depend on adjacent TAZs. For the built environment variables of the residential area, the spatial Durbin model (SDM) can be constructed, as shown in Equation (3). \[\mathbf{y}=\mathbf{X}\mathbf{\beta}+\mathbf{W}\mathbf{X}\mathbf{\delta}+\mathbf{\varepsilon} \tag{3}\]  
Third, the spatial dependence can also be embodied by the error term when the missing variables are not included in the independent variable but have an impact on the dependent variable, which has a spatial correlation, or the unobservable random variable has a spatial correlation. That is, the impact is the unobserved DiDi travel demand within TAZs. A spatial error model (SEM') can be constructed according to Equation (4) as follows:  
\[\mathbf{y}=\mathbf{X}\mathbf{\beta}+\mathbf{\mu} \tag{4}\]  
Among them, the generation process of the disturbance term is shown in Equation (5) as follows:  
\[\mathbf{\mu}=\rho\mathbf{W}\mathbf{\mu}+\mathbf{\varepsilon},\ \mathbf{\varepsilon}\ \sim\mathbf{N}(\mathbf{0},\sigma^{2}\mathbf{I}_{\mathbf{n}}) \tag{5}\]  
Further, it can be assumed that there is a spatial correlation between the observed and unobserved independent variables. On the basis of SAR, combined with the characteristics of SDM and SEM, the spatial lag and spatial error characteristics were combined to construct multiple spatial correlations to be developed into a new model called the Spatial Durbin Errors Model (SDEM). Previous research has only used a single SDM or SEM' for spatial correlation analysis and did not consider and verify a combination of the two models. The SDEM constructed in this section is shown in Equation (6).