# Urban Greenway Planning and Designing Based on MGWR and the Entropy Weight Method
## 4 Discussion  
The weights for calculating the suitability in greenway planning were obtained by the average values of two methods. The MGWR model shows the following importance rankings of the influence of variables on shared bicycle travel: accessibility > intersection density > density of tourist attractions > land use mix > POI entropy index > road density > intermediate > distance to metro station > distance to bus stop. In the previous studies, the weights of variable indices obtained using AHP analysis show that the density has the highest weight (26.53%), followed by accessibility (14.96%), distance to transit (13.32%), and diversity (11.27%) [10]. These results are similar to our results. The entropy weight method is based on the information entropy theory, which is objective and suitable for data-driven scenarios. The MGWR method considers the local characteristics of spatial data and is suitable for spatial analysis. The analytic hierarchy process relies on expert judgment and is suitable for complex decision-making problems. In terms of the suitability of greenway planning, the entropy weight method can objectively reflect the importance of each evaluation index and avoid subjective bias. The MGWR method considered spatial heterogeneity, makes the planning more in line with the actual situation, and improves the scientific accuracy of the planning [39]. The influencing variables have a negative or positive influence on the shared bicycle traveling. The intersection density, density of tourist attractions, accessibility, distance to bus stop, and intermediate variables have a negative influence on shared bicycle travel, while other variables have a positive influence on shared bicycle travel. In particular, the negative effects of road network accessibility, intersection density, and tourist attraction density suggest that bicycle share use may be inhibited in areas of traffic congestion, road complexity, or specific restrictions (e.g., tourist attractions). This may be due to the inconvenience of riding, safety hazards, or parking restrictions in these areas, which reduce users' willingness to ride. On the contrary, the positive effects of mixed land use proportions, the POI entropy index, and road density emphasize the role of urban planning, facility distribution and road conditions in promoting shared bicycle use. A high proportion of mixed land use implies a variety of land uses in the region that can attract different types of users. A high POI entropy index indicates a balanced distribution of facilities and easy access to different resources while cycling. A high road density provides a better cycling environment and more cycling options. In addition, the negative effects of the intermediate and distance to metro station variables further indicate that being in the center of the network or close to a metro station is not always conducive to bicycle sharing. This may be due to the complex traffic conditions and dense pedestrian traffic in these areas or the fact that users prefer other modes of transport. In contrast, the positive effect of the distance to bus stops suggests that people may be more inclined to choose shared bicycles as a way of travel when they are farther away from the bus station. Shared bicycles can provide a more flexible and direct path to the destination, reducing the distance and time to walk to the bus station. Ecm proposed that the entry of the shared bicycle platform has reduced the city's demand for urban buses by an average of 2.7%, indicating that the shared bicycle platform has replaced the passenger volume of urban buses [40]. If shared bikes replace the number of passengers on some city buses, then this portion of the replaced passengers is likely to include groups who choose other modes of travel (such as shared bikes) because they are far away from the bus station, thus indirectly verifying the influence of the distance from the bus station on the choice of shared bike travel.