Assessing urban vitality in high-density cities: a spatial accessibility approach using POI reviews and residential data

Urban vitality is a fundamental driver of sustainable urban development, yet conventional assessments predominantly rely on static urban morphology metrics or traditional survey-based approaches, which inadequately capture the dynamic interplay of human activities within urban environments. This study addresses this critical gap by introducing a computational framework that synergizes social media-derived big data with spatial accessibility modeling to systematically quantify urban vitality in high-density urban landscapes. Leveraging open-access APIs from prominent Chinese platforms – Dianping, Amap, and Baidu Maps – combined with the two-step floating catchment area (2SFCA) method, this study evaluates the intricate spatial interactions between Points of Interest (POIs) and residential zones to delineate urban vibrancy patterns in Shenzhen, China. The findings reveal pronounced spatial heterogeneity in urban vitality, identifying 99 high-vibrancy communities with accessibility indices ranging from 0.95 to 2.43, predominantly concentrated in the core districts of Nanshan, Futian, and Luohu. These results underscore the transformative potential of social media data as a scalable and real-time analytical instrument for urban vitality assessment, offering a transferable methodological paradigm applicable to rapidly urbanizing metropolises worldwide. This research advances the global discourse on urban dynamics by demonstrating how big data analytics can augment evidence-based urban planning strategies, particularly within the framework of compact, high-density cities, fostering more adaptive, resilient, and human-centric urban environments.