## 4 Data Analysis
### Spatiotemporal Patterns
#### 4.2.3 Urban Morphology Impact Analysis  
Urban morphology affects the travel behavior of residents. The most relevant factor of the trips by cyclist access to rail transit is the characteristics of urban morphology within the bicycle service area around the station. There are differences between bicycle service areas at different stations, and bicycle service areas of adjacent stations may overlap. Therefore, the collections of traffic zones in which the ends of trips accessing rail transit are located are considered bicycle service areas in this study. Several indicators can describe urban morphology, the most typical of which are the 3D or 5D indicators proposed by [PERSON], [PERSON], and [PERSON] et al. [34; 35]. Three kinds of indicators are employed in this paper: mixed land-use indicators (including four categories of POI points: residence, office and school, business and public service), built environment indicator (number of intersections), and traffic facilities indicator (number of bus stations). Analysis of variance (ANOVA) was adopted to determine whether the six indicators above are significantly different between the four major categories of stations. The results indicate that the \(p\)-values are much lower than 0.001, and the test results of the six indicators are extraordinarily significant, proving significant differences in mixed land-use, built environment, and traffic facilities at various stations. The ANOVA results indicate that the six indicators are significantly different among the four categories of stations, but this does not mean there is a significant difference between each indicator at any two categories of stations. The Tukey test is a post hoc test that compares the differences between means of values. The value of the Tukey test is given by taking the absolute value of the difference between pairs of means and dividing it by the standard error of the mean as determined by the ANOVA test. Thus, a Tukey HSD analysis was used to determine which indicators of which two stations are significantly different, and the results are shown in Figure 5. Every segment of straight lines represents the distribution of confidence interval at 95% of the difference in two corresponding categories of stations. If the straight line intersects with line y = 0, the difference is insignificant; otherwise, the difference is significant. The difference is positive if the straight line is located above line y = 0 and negative if below the line y = 0. Combining the analysis of the spatial-temporal pattern and the results of ANOVA and Tukey HSD, the following conclusions can be drawn:  
1. The POI numbers of residence, office and school have significant effects on Cluster 4 with regard to their bike-sharing operation characteristics. Compared with other categories, the POI numbers for residence are much lower, while the POI numbers for office and school are much larger. 2. The POI numbers of bus stations, intersections and businesses have significant effects on Cluster 3 with regard to their bike-sharing operation characteristics. Compared with other categories, the POI numbers for the three above are much lower. The Cluster 3 stations are mainly located in the periphery of the city where the commercial development, built environment, and traffic facilities are inferior to those at the center of the city. 3. The POI numbers of public service have significant effects on Clusters 3 and 4 with regard to their bike-sharing operation characteristics. Compared with other categories, the POI numbers for the public service of Clusters 4 are much larger. Similarly, as the Cluster 3 stations are mainly located in the suburban areas, its indicator of public service is significantly lower. According to the above analysis, the stations with uniform land use and relatively strong residential functions of the surrounding area have usage patterns similar to the features shown in Figure 4c.