Harnessing User-Generated Big Data and Deep Learning for Sustainable Urban Destination Planning: A Spatial Study in Harbin

Abstract

Integrating big data analytics with sustainable development offers a new avenue for understanding complex human-environment interactions in urban spaces, particularly in urban tourism planning. However, limited research has examined how user-generated content (UGC) can translate tourists’ perceived destination images into actionable spatial strategies for sustainable urban management. Focusing on the built-up area within Harbin’s Third Ring Road, this study used visual UGC posted by tourists on Mafengwo and Ctrip as its primary analytical material, linking 488 tourism attractions, 89,375 reviews, and 23,561 valid images. Places365-CNN scene recognition, aggregation to 500 m × 500 m grids, and ArcGIS-based spatial analysis were applied to identify and map perceived destination images across 1139 spatial units. The results showed that: First, Harbin’s dominant perceived destination images exhibited a multi-type composite structure. History and Architecture accounted for 39.2%, while Infrastructure and Nature accounted for 13.9% and 12.6%, respectively. Winter ice-and-snow scenes made up 35% of the top 20 scenes, indicating that ice and snow were not isolated attraction symbols but a visual medium permeating both the natural and built environments. Second, perceived destination images varied across urban rings, shifting from a historical and cultural core in the First Ring, through a functional transition zone in the Second Ring, to an ice-and-snow periphery in the Third Ring; the five administrative districts also developed complementary image profiles. Third, the dominant images formed a cross-shaped spatial framework along the Songhua River waterfront and the city’s north–south axis, with Sun Island, Central Street, and the Chinese Baroque Historic and Cultural Block serving as the three main perceptual centers. This study demonstrates how integrating big data analytics, computer vision, and spatial analysis can support smart-city solutions, sustainable destination management, and destination marketing. It provides policymakers with a replicable methodological blueprint for evidence-based spatial planning and consumer-centered resource governance, thereby contributing to greener and more resilient urban futures.