This study addresses the issue of overtourism caused by the spread of social media, where a landscape’s "Instagrammability" leads to a concentration of visitors in unexpected locations, as exemplified by the "Mount Fuji Lawson" incident. The research aims to develop a method for predicting potential tourist hotspots by analyzing the landscape potential of an area based on a landmark’s location and urban structure. Using the Tokyo Skytree as a case study, we created a simplified Digital Surface Model (DSM) for Tokyo’s 23 wards by combining a Digital Elevation Model (DEM) with building height data. We analyzed the visibility of the Skytree from various points, focusing on its distinct structural heights (e.g., 50m, 350m, 450m). By linking this potential visibility with the actual locations of photos shared on Flickr, we found a high degree of accuracy in our model. The analysis revealed that Skytree images most often capture the section above 150m, and common compositions include combinations with urban elements, water features, and vegetation. This research demonstrates the effectiveness of using a custom-built DSM for wide-area visibility analysis in urban settings and highlights the importance of combining landscape potential with real-world human behavior data from social media.
Shimazu et al. (Thu,) studied this question.