Data-Driven Assessment and Renewal Strategies for Public Space Vitality in Aged Residential Areas

Under the background of urban stock renewal, the quality improvement of public spaces in aged residential areas is confronted with challenges such as inefficient data collection, disconnection between subjective and objective evaluations, and insufficient dynamic adaptability. To address these issues, this study develops and applies a data-driven framework to achieve an objective, precise, and diagnostically powerful evaluation. Focusing on the Zhongshan Road Sub-district of Qingdao and based on 46 typical public space samples, we utilize a Fully Convolutional Network (FCN) for the semantic segmentation of panoramic images, deriving nine objective spatial indicators. We then combine morphological classification and Principal Component Analysis (PCA) to uncover latent correlations and use K-means clustering to pinpoint framework blind spots and heterogeneous spatial types. The results not only diagnose the specific vitality deficits of different spatial types but also demonstrate the method’s capacity to support targeted renewal strategies. This data-driven approach provides a reference framework for achieving precise and objective assessment in urban renewal, moving beyond subjective assumptions to offer a robust theoretical and empirical foundation for governance.