Urban public spaces are a key component to the well-being and prosperity of modern society. It has been increasingly important to improve the qualities and maximize the usages of urban public spaces. There is a lack of studies that investigate how people use and perceive urban parks using quantitative analysis of location-based social media reviews. This study tackles this gap by introducing a case study that uses social media reviews (Tripadivisor.com) to understand the perceived site quality and experiences of Bryant Park in New York City. A large dataset including 11,419 Tripadvisor reviews from 10,615 users was collected. LDA (Latent Dirichlet Allocation), a natural language processing and machine learning technique, was used to perform topic modeling analysis that could reveal hidden themes in large amounts of text. The results include five semantic topics and their associated topic terms. A comprehensive overview of the user experiences in Bryant Park were provided along with their weekly and monthly dynamics. The findings provide insights for future public space designers and managers by revealing how users describe the designs and operations of Bryant Park.
Understanding Perceived Site Qualities and Experiences of Urban Public Spaces: A Case Study of Social Media Reviews in Bryant Park, New York City