## 3 Data and Methodology
### MGWR Model
By demonstrating the applicability of the MGWR model to high-resolution multi-source data, this study contributes both methodological insight and practical guidance

Figure 7: Coefficient values of ride-hailing usage determinants during holiday evening peak. for improving demand forecasting, optimizing dispatch operations, and promoting the sustainable development of ride-hailing systems. While this study provides valuable insights into the spatial determinants of ride-hailing demand, it has several limitations that warrant consideration: (1) The analysis focuses on built-environment factors at the community level, which may overlook individual or group-level behavioral heterogeneity and personal preferences. (2) The street-view features were extracted using the DeepLabV3+ model, which was pre-trained on public datasets. This limits the types of recognizable environmental elements and may not fully align with subjective human perceptions of streetscapes. (3) Due to data availability constraints, the ride-hailing data used in this study were collected in 2017. While the analysis aims to uncover structural spatial relationships that are relatively stable over time, we acknowledge that changes in mobility patterns or platform operations since then may affect the generalizability of some findings. Further validation using updated or longitudinal data is encouraged. These limitations also suggest several avenues for future research. First, future research could incorporate emotional responses to different street views to bridge the gap between objective visual features and subjective perception. For example, combining panoramic imagery with human sentiment ratings or emotion-aware visual models may yield a deeper understanding of how environmental aesthetics influence ride-hailing behavior. Moreover, with increasing access to fine-grained and multimodal mobility data, it would be valuable to examine the interactions between ride-hailing and other transportation services. For instance, bike-sharing systems, commonly used for short non-motorized trips in dense urban cores, are often spatially and temporally intertwined with ride-hailing. Finally, future studies should investigate the complementary or competitive dynamics between these two services. Understanding their spatial and temporal interplay is essential for optimizing multi-modal transport systems and informing integrated urban mobility strategies. Conceptualization, J.B. and Y.L. ; methodology, J.B. and Y.L. ; software, J.B.; validation, J.B. and Y.L. ; data curation, J.B.; writing--original draft preparation, J.B. and Y.L. ; writing--review and editing, J.B. and Y.L. ; visualization, J.B.; supervision, J.B. and Y.L. ; funding acquisition, Y.L. All authors have read and agreed to the published version of the manuscript. This research was funded by the project of the National Key R&D Program of China (No. 2018 YFB1601301) and the National Natural Science Foundation of China (No. 71961137006). Not applicable. Data Availability Statement: Data will be made available on request. We would like to acknowledge the research support provided by Shanghai Key Laboratory of Urban Renewal and Spatial Optimization Technology. The authors declare no conflicts of interest. ## References

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