Urban heritage regeneration by integrating space syntax and data-driven spatial analysis: insights from Yushan historic district, China

Historic urban areas, shaped by deep historical layers and diverse cultural influences, often exhibit distinctive urban patterns. However, their complex spatial fabric presents challenges to conventional spatial analysis methods, limiting the ability to comprehensively capture their unique morphological and functional characteristics. To address this limitation, this study proposes a comprehensive analytical framework that integrates space syntax models with multisource geospatial data to investigate the spatial patterns of historic urban environments. Using the Yushan Historical and Cultural District in Qingdao, China, as a case study, the study applies visibility graph analysis (VGA) and agent-based simulation (ABS) to examine spatial configurations and pedestrian movement patterns. Additionally, point of interest (POI) data are analysed through kernel density estimation (KDE) to reveal functional distributions. The findings identify zones with strong potential for public gathering and highlight areas requiring improvement, while the proposed strategies provide actionable, context-specific guidelines for urban heritage revitalization. This study demonstrates the value of integrating space syntax with data-driven analytical techniques, offering new insights into quantitative spatial analysis within the framework of urban renewal and heritage conservation.