Visual Quantification, Spatial Distribution and Combination Association of Tourist Attractions in Qingdao Based on Social Media Images

Focusing on the deficiencies of traditional tourism attraction survey methods in terms of accuracy, efficiency, and large-scale visual representation, this study selects Qingdao as the research case, collects tourism image data from the Weibo platform, applies a deep learning model to identify the visual elements of tourism images, and employs kernel density analysis and Apriori association analysis to clarify further the distribution characteristics and associated features of tourist attractions. Its core objective is to systematically reveal the visual composition, spatial distribution patterns, and related features of tourist attractions in the case study area by identifying and extracting tourist attraction elements from images, thereby providing a decision-making basis for effectively identifying tourism demands and their spatial distribution characteristics, as well as for tourism spatial planning. The findings are as follows: Buildings, sea, and other elements are the main components of tourist attractions in Qingdao. Regarding spatial distribution, tourist attractions in Qingdao exhibit the spatial characteristic of “distributed around the bay and converging towards the sea”, with a certain circular structure and multi-level core distribution pattern. Regarding associated features, tourist attractions in Qingdao form combinations centered on buildings, sea, and signs—such as building-centric, sea-centric, cityscape-centric, and sign-centric combinations—around elements including buildings, sea, and signs. The contribution and significance of this study lie in providing technical support for resolving the contradiction between traditional tourist attraction survey methods and precise demands, offering a scientific basis for decision-making in tourism spatial layout planning, and opening up a new path for the intelligent and refined development of tourism resources using massive visual data.