The big picture of cities: Analysing Flickr photos of 222 cities worldwide. (July 2020) - Taecharungroj, Viriya; Mathayomchan, Boonyanit
Abstract: The purpose of the current research is to introduce a new method involving machine learning that can identify and analyse the city image dimensions (CIDs) of cities worldwide. Unlike traditional methods, this new method can rapidly identify city image dimensions from large sets of user-generated photos in an efficient and scalable manner, which could help city managers more effectively plan city branding strategies and city development policies. Label detection with Google Cloud Vision and dimension identification (or topic extraction) with latent Dirichlet allocation (LDA) modelling were used to analyse 222, 000 photos of 222 cities worldwide from Flickr.com . Theoretically, this study reinforces the existing literature using Big Data, presents alternative ways to identify CIDs, and illustrates diversity within the image dimensions. Highlights: City image dimensions (CIDs) were identified using 222, 000 photos of 222 cities. Google Cloud Vision AI and latent Dirichlet allocation (LDA) were used. Universal CIDs are cityscape, landscape, transport, architecture, recreation. Individual CIDs of Hong Kong, Rome, Montreal, Baghdad were identified. Exploration of regional CIDs and CIDs based on quality of life.