## 1 Introduction
### Walkability Score and Quantitative Analysis of Street-Level Environments Factory  
Under the 15-min city conceptual framework, research on pedestrian suitability is undergoing a paradigm shift from \"destination accessibility\" to \"process experientiality\". Existing studies predominantly employ Walking Score algorithms, which evaluate neighborhood walkability potential through a linear combination of facility density (D_f) and distance decay functions (f (d) = e\({}^{[-\lambda\Delta\mathrm{d}]}\)). While some researchers have developed amenity weighted systems to calculate composite 15-min city indices or accessibility scores [44], computational complexity constraints have limited their focus to urban core areas. Consequently, these analyses fail to holistically represent citywide accessibility patterns or reveal gradient differences in accessibility between central and suburban zones. Furthermore, such macro-level accessibility approaches exhibit limitations in capturing micro-scale street-built environment characteristics, with scant exploration of spatial interdependencies between walkability metrics and street-built environment features within the 15-min city context. Notably, systematic investigations into mutual influences between these dimensions remain underdeveloped.  
Emerging street view imagery (SVI) technologies provide transformative solutions to these methodological constraints. Deep learning-based geo-parsing enables automated extraction of granular street-built environment elements from street-level visual data. Numerous scholars have leveraged SVI-derived features to assess walking environments or investigate their impact on pedestrian behavior [45; 46]. Integrating such street-built environment quantifications with walkability scoring allows nuanced analysis of intrinsic facility accessibility-walkability relationships. [PERSON] et al. [47] developed an automated quantification of SVI functionality using Google Street View to evaluate population-specific walkability, while [PERSON] et al. [48] demonstrated SVI factors' significant contributions to walking mode choice through Atlanta-based analyses. [PERSON] et al. [49] established empirical links between perceived walkability, urban landscape features, and public health outcomes through comprehensive SVI-based modeling. Recent extensions include gerontologically oriented street-built environment assessments: [PERSON] et al. [50] employed TrueSkill algorithms to characterize elderly-friendly street configurations, and [PERSON] et al. [51] identified pedestrian-oriented SVI elements that disproportionately enhance senior mobility in suburban contexts.  
Therefore, the analysis of walkability scores and street-built environments could be incorporated into urban planning to enhance pedestrian infrastructure, thereby promoting health and well-being among older adults [52; 53]. In this study, we employed the ordinary least squares method to identify significant correlational factors between walkability scores and street environmental characteristics. Subsequently, a geographically weighted regression model was applied to spatially visualize the geographically varying relationships between street environmental factors across different urban locations.