## 4 Data and Research Method
### Identify the Pattern of Intra-Urban Population Movement Using Taxis' Origin and Destination Data  
The first step toward identifying population movement patterns was to preprocess the data and extract passenger pick-up and drop-off locations. The pick-up and drop-off locations were obtainable when the passenger's status changes. When the passenger's previous status was set to 0 and the current passenger status was set to 1, the sample point represented a pick-up location. If the passenger's previous status was 1 and the passenger's current status was 0, it was assumed that the current sample point is a drop-off location [36; 37]. A total of 112,050 pick-up locations and 105,256 drop-off locations were extracted. Points that originated outside the study area or were terminated outside the study area were identified and then removed. A total of 12,204 pick-up locations and 18,258 drop-off locations were excluded, and finally, 99,846 pick-up locations and 86,998 drop-off locations were retained. Excluding pick-up and drop-off locations comprised only 10.9% and 17.3% of the total pick-up and drop-off locations. Finally, the regional number of pick-up and drop-off locations was calculated. Figure 3 depicts the spatial distribution of pick-up and drop-off locations in the research area. First, before the data analysis step, it was necessary to assign pick-up and drop-off locations to the study area. After data preparation, Moran' I statistic was used for analyzing the spatiotemporal pattern of the urban population movement. In spatial statistics, recognizing patterns and discovering the trend in spatial data have particular importance. [PERSON]'s I spatial autocorrelation statistic makes it possible to measure spatial differences among all samples. To calculate [PERSON]'s I index, first, a score of Z and a \(p\)-value must be obtained, and then the evaluation and significance of Moran statistic should be considered. The value of [PERSON]'s I varies between \(-1\) and \(1\). Negative Moran values indicate a negative trend in spatial autocorrelation and positive values indicate a positive trend in the autocorrelation of space, and the zero value represents a random spatial pattern [38] The value of the Global Moran's I is calculated by Equation (1) [39]. \[I=\frac{n}{s_{0}}\times\frac{\sum_{i=1}^{n}\sum_{j=1}^{n}w_{ij}(x_{i}-\overline {x})\big{(}x_{j}-\overline{x}\big{)}}{\sum_{i=1}^{n}(x_{i}-\overline{x})^{2}} \tag{1}\]  
Figure 3: Spatial distribution of pick-up and drop-off zones. If \(n\) represents spatial units number specified with \(i\) and \(j\); then \(X\) is the desired variable; \(\overline{X}\) is the mean of \(X\); \(W_{ij}\) is a part of the spatial weights matrix and the number of points in a study unit. \(S_{0}\) also indicates the sum of all the elements. In general, spatial autocorrelations depend on Z values. Spatial clustering is a high value if the Z score is both positive and high. However, if the Z score is negative and low, spatial clustering is of low value. Additionally, when Z score is statistically significant, its values are larger than the expected values of 1.96 or more than 2.58 [40]. The z-score can be shown mathematically as Equation (2) [41; 42]. \[E(I)=\frac{-1}{(n-1)}\ and\ V(I)=E\Big{[}I^{2}\Big{]}-E[I]^{2}\ \Bigg{\{}z=\frac{1-E(I)}{ \sqrt{V(I)}}\Bigg{\}} \tag{2}\]  
The value of \(p\) represents a probability. If this value is very small, there is a low probability of the spatial pattern observed being the outcome of accidental processes; then, the null hypothesis can be rejected. If the yielded value of \(p\) is less than 0.01, it indicates a probability of 1% of this clustered pattern being the product of a random process [40]. It is also necessary to use local spatial autocorrelation to determine the location of hot spots and cold spots. The statistic of Getis-Ord-Gi was used to explore more precisely the spatial distribution of hot spots and cold spots. When this statistic is obtained, the confidence level is considered to be 90%, 95%, and 99%, respectively.