Spatial network structure analysis of urban tourism flows in Nanchang based on crowdsourced data

Abstract

Urban tourism is a vital driver of economic and cultural development in modern cities. This study investigates the spatial network structure and dynamics of urban tourism flows, using Nanchang City as a case study. By integrating crowdsourced data, the DBSCAN clustering algorithm, and social network analysis, we identify tourist Areas of Interest (AOIs) and evaluate their aggregation across five key dimensions. Subsequently, centrality analysis and structural hole measurement are applied to decipher the spatial pattern of tourism flows. The key findings are: (1) The spatial distribution of scenic spots in Nanchang exhibits a distinct cluster centered on the August 1st Memorial Hall area, highlighting the city’s strong potential to cultivate a unique red tourism brand. (2) The tourism flows network reveals a significant structural imbalance, characterized by an over-concentration of visitors at a few core nodes (e.g., Tengwang Pavilion and Bayi Square) and the presence of pronounced structural holes. This pattern indicates intense inter-destination competition and an uneven distribution of tourism benefits. These insights provide an evidence-based foundation for optimizing tourism route planning, mitigating congestion at core scenic spots, and promoting balanced regional development through targeted marketing and product differentiation.