Commercial space plays a crucial role in urban environments and is among the most dynamic components of cities. Its development often dictates the vitality and economic prosperity of urban areas. However, with rapid urbanization and shifting consumer behaviours, the spatial organization of these areas faces substantial changes, raising critical challenges for effective urban planning. This study aims to identify and analyse the spatial patterns of commercial spaces in Qingdao, China, using advanced data-driven techniques to address these issues. Qingdao was selected as the case study due to its rapid evolution into a major commercial hub in China’s eastern coastal region, with a diverse and competitive commercial landscape shaped by urbanization and economic reforms. This study uses multisource POI data from Qingdao, collected and processed through the Baidu Maps API, with data cleaning and visualization in R Programming. Applying kernel density estimation, spatial autocorrelation, and Moran’s I with local indicators of spatial association (LISA) indices, it examines the distribution and clustering characteristics of urban commercial centres across the city. Results reveal a high concentration of commercial activity in Shibei and Shinan districts, with significantly lower density in peripheral areas such as Pingdu, Laixi, Jiaozhou, and Laoshan. These insights support urban planning efforts to expand commercial development in outer regions, contributing to balanced economic growth and more liveable urban environments. This research enhances understanding of commercial spatial patterns, providing practical guidance for promoting economic vitality and fostering more liveable urban environments through strategic development.
Malek et al. (2025) studied this question.