Unveiling the meaning in the image of the city: A novel approach using place reviews and large language models

Yihan Zhu, Markus Brenken, Filip Biljecki and Ye Zhang

Environment and Planning B, 2026, vol. 53, issue 1, 49-67

Abstract: Kevin Lynch’s concept of imageability describes how effectively an environment evokes a mental image in an observer’s mind, which consists of three components—“identity, structure, and meaning†—with the first two being the main components to build Lynch’s cognitive map. Although imageability has significantly influenced urban design and planning, and inspired numerous subsequent research, the “meaning†component has not been clearly studied. The rise of new urban data, particularly the booming availability of reviews of urban spaces on platforms such as TripAdvisor and Google, offers a valuable opportunity to incorporate the meaning into the imageability study. By adapting several open-source algorithms, this research efficiently extracts both objective (e.g. location, number of reviews) and subjective (e.g. ratings, review text) information from the online platform, proposing a novel approach to studying the meaning component through a fine-tuned BERT model. These data and methods enable this research to capture and categorize the meaning component for describing the image of the city, using Singapore as a case study. The results show that: (1) Lynch’s cognitive mapping approach could potentially be enhanced by incorporating the meaning into the study of imageability, it could amplify the existing nodes or landmarks, and create new “nodes†. (2) The proposed “meaning patch†could add new layers to structure of the city image by representing the shared meanings of multiple places, suggesting the potential to be studied as the sixth element to extend the existing imageability framework, and open new agenda for the future studies.

Keywords: imageability; cognitive map; large language models; natural language processing; online review (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:sae:envirb:v:53:y:2026:i:1:p:49-67

DOI: 10.1177/23998083251369143

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