## 4 Discussion
### Application of Research Models and Results  
Owing to technological constraints and limited data availability, streetscape measurement has traditionally relied heavily on expert evaluations and empirical analysis. Nevertheless, this traditional approach is inherently subjective and constrained in its applicability. This study overcomes these challenges by introducing an innovative image-based methodology for streetscape metric assessment. The analysis reveals that this image-based approach not only facilitates rapid and accurate evaluation of street spatial quality but also enables straightforward comparisons of street images across road segments and temporal periods. This study develops a comprehensive methodology for urban street spatial quality planning, centered around a \"quality-accessibility\" assessment matrix that facilitates the entire process of data-driven enhancement in large-scale urban street renewal projects. First, the \"quality-accessibility\" assessment matrix enables the quantification of quality and accessibility across various street design alternatives. This empowers planners to identify street designs that align with the city's unique characteristics and residents' needs, such as incorporating sidewalks, bike lanes, and green belts, thereby enhancing street comfort and safety. Second, it enables an evaluation of the service levels and coverage of the public transportation network, facilitating the identification of public transit de  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Categories/Percentage** & **Spatial Distribution** \\ \hline LL = 16.3\% & \\ \hline \hline \end{tabular}
\end{table}
Table 5: _Cont._mand across various regions, quantifying the accessibility levels of various road network alternatives, enabling the selection of optimal road layouts. This ensures that the road network capacity accommodates increasing traffic demands while simultaneously enhancing traffic efficiency.  
Data-driven urban street research enhances the integration of precision and scale in urban street space analysis, supporting the evaluation of current conditions, renewal planning, and management implementation for large-scale urban street space renewal efforts while fostering significant improvements in street space quality. Conceptually, using data-driven methods to uncover urban issues--rather than relying on subjective impressions--enables efficient, rapid, and scientific problem-oriented urban analysis, advancing the scientific rigor of urban planning and design practices. This approach offers a city-wide evaluation framework that assigns comprehensive scores to street blocks while also tracking their spatial quality over time to identify specific issues and inform site selection for targeted street renewal initiatives.  
The approach to future urban street renewal can be guided by principles of problem orientation, categorization, and integration, with corresponding planning guidelines and recommended renewal sequences proposed accordingly. Specifically, the renewal planning guidelines outline tailored strategies for planning and design at two distinct levels: the section space and the street unit. At the road space level, it leverages the spatial characteristics of urban street elements to analyze their influence on spatial quality, proposing improvement directions for each element type based on their functional and aesthetic value. At the street unit level, the analysis refines road section spaces by integrating features of high-quality types and design best practices, primarily offering renewal planning guidelines to enhance the spatial structure of lower-quality street types. The proposed renewal timeline integrates the practical implementation characteristics of renewal efforts, with phased arrangements--short-term, medium-term, and long-term--based on a prioritization framework, thereby reinforcing implementation management. This research represents a pioneering effort to establish a holistic framework for evaluating street quality.