Network Spatial Structure of Tourism Destinations from a Pole-Axis Perspective

Abstract

The spatial structure of tourism forms the foundation for strategic planning and regional coordination. Since the COVID-19 outbreak, Chinese tourists have increasingly adopted personalized and niche travel patterns, challenging traditional resource-oriented spatial models and generating new demands for adaptive destination planning. This study aims to mine tourist movement behaviour to reshape the spatial structure of tourism destinations and to integrate relevant theories, thereby providing theoretical insights and practical implications for spatial optimization and sustainable tourism development. Using travelogues from Ctrip, China’s largest online travel platform, the study applies big data mining and social network analysis within the framework of pole–axis theory to examine Shanxi Province. This study is among the few that integrate social network analysis with pole-axis theory to analyse tourism spatial restructuring, thereby extending the application of pole–axis theory from a resource-oriented to a demand-oriented perspective and enhancing the practical utility of social network analysis findings for tourism planning. The results show that tourist behaviour reshapes spatial structures into a “dual-core, four zones, and eight clusters” pattern, which contrasts sharply with the government’s planned “one pole, three zones, and eleven clusters” framework. The findings demonstrate a significant spatial mismatch, underscoring the need to shift from resource-oriented to demand-responsive strategies, and offering important implications for policymakers, destination managers, and scholars concerned with sustainable tourism and spatial planning.