This study employs a computational and theoretical framework to investigate digitally mediated urban experiences through a large-scale spatiotemporal analysis of Airbnb user-generated content. Revisiting Kevin Lynch’s concept of “imageability,” the research reconceptualizes urban legibility as a fluid, hybrid outcome produced by the interaction of material form, platform infrastructures, and aggregated user narratives, rather than as a static property of the built environment. The analysis contends that algorithmic mediation constitutes an interdependent layer of urban legibility, reshaping how paths, nodes, districts, and landmarks are perceived and circulated. Empirically, the study analyzes 365,365 Airbnb reviews (2010–2024) across two contrasting contexts: Broward County, Florida, and Cape Town, South Africa. Utilizing OpenAI GPT-4o for multi-language translation, reviews in over 15 languages were translated into American English. The analysis combines spatial clustering, temporal trend analysis, and sentiment mapping to identify evolving “hotspots” and to examine how digitally amplified reputations attach affective meanings to specific neighborhoods. The findings demonstrate that platform placemaking reorders urban salience rather than displacing the physical city. While material features such as streetscapes and physiography remain foundational, their prominence is increasingly filtered through recommendation systems and feedback loops. In this hybrid environment, imageability is distributed and dynamic: urban elements such as trendy commercial spaces gain significance through the “social layers” of repetition, affective intensity, and algorithmic ranking. These results highlight how platform placemaking reshapes urban spatial imaginaries and underscore the adaptive resilience of digital tourism markets in coproducing the contemporary city.
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Funding
University of Miami Laboratory for Integrative Knowledge (U-LINK), University of Miami,
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