The characteristic analysis and pattern classification of Beijing’s commercial districts based on multi-source geographical big data

Commercial districts, as key hubs for diverse commercial activities, play a crucial role in urban life. Yet in recent years, the vitality of commercial districts is declining due to impacts including COVID-19 pandemic. Although various strategies for revitalizing commercial districts have been proposed, it’s essential to first understand their conditions before any interventions being made. Traditional studies primarily focus on location, operational, and service characteristics of commercial districts, yet they suffer from limitations such as poor data usability, a lack of a comprehensive analysis and an oversimplified classification scheme. Geographical big data has provided unprecedented perspectives for commercial district research, thereby presenting new opportunities for overcoming the limitations of traditional studies. To this end, using multisource geographical big data, we delineated commercial districts within Beijing’s 5th Ring Road through clustering algorithms, developed a comprehensive characteristic system from four dimensions of commercial scale and composition, vitality and temporal heterogeneity, radiation and location and environment, and established a set of criteria to classify them into different patterns. The result shows that there are 67 commercial districts within Beijing’ 5th Ring Road, and they differ across different dimensions, according to which we can classify them into seven patterns, including City-level Core, Regional Core, Specialized Comparison, Suburban Hub, Regional Hub, Weekday-oriented Local and Weekend-oriented Local. Our study serves as an example of commercial districts research in the geographical big data era. The result provides practical guidance for the precise planning and refined management of commercial districts, supporting the revitalization of commercial district.