Tourism has become a critical pathway for town construction, everyday-life improvement, and cultural revitalization. Yet, the mechanisms through which destination image is associated with town attraction remain insufficiently understood, particularly at the fine-grained town scale. Drawing on social media photographs and check-in records from 26 characteristic towns in Tianjin, China, this study deconstructs tourist destination image into image genes and examines their associations with town gravity. A VGG19-based image-recognition model was used to identify and aggregate 68 scene types into nine image-gene categories, while check-in data from Weibo and Little Red Book were used to measure destination gravity. The results show that uniqueness image, cultural custom genes, public space genes, sidewalk density, and POI mix are significantly and positively associated with town gravity, whereas animal genes exhibit a significant negative association. These findings provide an empirical basis for policymakers and planners to strengthen distinctive cultural representation, optimize public-space systems and service diversity, and promote the sustainable attractiveness of town destinations.

Urban vitality is often measured within predefined administrative units, blocks, or grids, limiting its translation into planning-oriented spatial diagnosis. This study examines 34 vitality clusters in the Nanjing Metropolitan Area and develops a multi-scale framework linking cluster identification, spatial interpretability, type stratification, morphological differentiation, micro-scale case examination, and planning translation. The identified clusters show substantial correspondence with built-up territories while extending across administrative boundaries in several cases. Five operational area–density groups are distinguished using study-specific thresholds. Their distribution reveals a gradient from high-density cores to medium-density and peripheral low-density clusters, together with an internal pattern of high-vitality cores and lower-vitality edges. After Benjamini–Hochberg correction, 30 of 60 non-redundant variables remained significant, mainly in functional, transport, and morphological dimensions. Micro-scale cases further show the co-occurrence of smaller blocks, continuous frontages, mixed functions, and favorable walking conditions. These findings suggest that vitality-derived clusters can serve as morphologically interpretable, activity-based diagnostic units that complement conventional planning units in stock-based urban regeneration.