Social Sensing and Geospatial Visual Analytics of Tourist Destination Image and Town-Scale Gravity

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.