A data-driven perspective for sensing urban functional images: Place-based evidence in Hong Kong

Urban life involves a large variety of urban functions and human activities in a dense context due to the inherent nature of cities. Although technical frameworks have been previously proposed to understand urban functions and activities, there are limited studies that concern the individual places within a city and their detailed characteristics at a local scale. Using points of interest (POIs), we present a data-driven analytical framework to explore urban space containing urban functions and relevant activities by focusing on particular urban places. Urban functions are first extracted and induced by leveraging a latent Dirichlet allocation (LDA) topic modeling technique. We next evaluate the thematic functional differences among the selected places using the location quotient (LQ). Furthermore, tourist functions are assumed to occur in places within a city and carry broadly identifiable information; thus, tourist places are studied by comparing their perceptual experiences using the high-frequency keywords retrieved from tourist reviews on TripAdvisor. By adopting Hong Kong as our case study, the findings reveal considerable diversity of urban functions across different places, while each place displays the distinctive trait of urban functions. Tourist impressions reflected online are primarily consistent with the corresponding functional identities of these places but exhibit additional details associated with emotional and temporal aspects. This study uses a bottom-up assessment of local functions, and discusses their practical implications as related to city branding strategies.