Toward satisfactory public accessibility: A crowdsourcing approach through online reviews to inclusive urban design

As urban populations grow, the need for accessible urban design has become urgent. Traditional methods for assessing public perceptions of accessibility, such as surveys and interviews, are often resource-intensive and geographically limited in scope. Crowdsourcing via online reviews offers a valuable alternative to understanding public perceptions, and advancements in large language models (LLMs) can facilitate their use. In this study, we examine over one million Google Maps reviews from points of interests (POIs) across the United States and fine-tune the Llama 3 model using the Low-Rank Adaptation (LoRA) technique to identify public sentiment toward accessibility. At the POI level, most categories – restaurants, retail, hotels, and healthcare – show negative sentiments, indicating persistent barriers across key sectors. Socio-spatial regression analysis reveals that more positive sentiment is associated with areas that have higher proportions of white residents and greater socioeconomic advantage. Conversely, more negative sentiment is expressed in areas with higher concentrations of elderly and highly-educated populations. Interestingly, no clear link is found between the presence of disabilities and public sentiments, but a significant positive relationship does exist between disability-friendly scores and public perception. Overall, our findings demonstrate the value of crowdsourcing with LLM-enhanced analysis in identifying accessibility challenges and informing inclusive urban design, offering actionable insights for planners, policymakers, and advocates striving toward more equitable cities. © 2025 Elsevier Ltd.