Urban attractiveness according to ChatGPT: Contrasting AI and human insights

Abstract

The attractiveness of urban environments significantly impacts residents' satisfaction with their living spaces and their overall mood, which in turn, affects their health and well-being. Given the resource-intensive nature of gathering evaluations on urban attractiveness through surveys or inquiries from residents, there is a constant quest for automated solutions to streamline this process and support spatial planning. In this study, we applied an off-the-shelf AI model to automate the analysis of urban attractiveness, using over 1800 Google Street View images of Helsinki, Finland. By incorporating the GPT-4 model, we assessed these images through three criteria-based prompts. Simultaneously, 24 participants, categorised into residents and non-residents, were asked to rate the images. To gain insights into the non-transparent decision-making processes of GPT-4, we employed semantic segmentation to explore how the model uses different image features. Our results demonstrated a strong alignment between GPT-4 and participant ratings, although geographic disparities were noted. Specifically, GPT-4 showed a preference for suburban areas with significant greenery, contrasting with participants who found these areas less attractive. Conversely, in the city centre and densely populated urban regions of Helsinki, GPT-4 assigned lower attractiveness scores than participant ratings. The semantic segmentation analysis revealed that GPT-4's ratings were primarily influenced by physical features like vegetation, buildings, and sidewalk. While there was general agreement between AI and human assessments across various locations, GPT-4 struggled to incorporate contextual nuances into its ratings, unlike participants, who considered both context and features of the urban environment. The study suggests that leveraging AI models like GPT-4 allows spatial planners to gather insights into the attractiveness of different areas efficiently. However, caution is necessary, while we used an off-the-shelf model, it is crucial to develop models specifically trained to understand the local context. Although AI models provide valuable insights, human perspectives are essential for a comprehensive understanding of urban attractiveness.