Discovering spatial patterns of tourist flow with multi-layer transport networks

Abstract

Transport is an integral part of the tourism industry. The tourist traveling patterns are highly dependent on the transport infrastructure in a tourism destinations. Based on the Social Network Theory, four centrality indicators and the transition possibility are adopted to explore the tourism flow network related to different modes of transportation and tourist transition capabilities in tourism destinations. A complex tourist itinerary visualization model uncovers the spatial structure of multi-layer tourist flows. Dunhuang, a rapidly growing tourist destination in Northwest China, reveals this patterning. Results show that: (1) The spatial structure and flow rate of the tourism flow network is significantly different when different transport modes are observed. (2) The tourists transition capability for different transport modes in the same region varies according to the geographic location and infrastructure level. (3) Tourists manifest different movement patterns among the available transport networks within a destination area.