The Communities Detection of the Tourist Flow Network using Mobile Signaling Data in Nanjing, China

The spatio-temporal characteristics of tourist flow is one of the core topics in tourism geography. However, few studies have paid attention to community analysis in tourist flow network. This paper uses mobile signaling data based on random walk approach to identify the communities of tourist flow. A node influence evaluation is established to identify the core nodes of the community. Popularity response concept is introduced and factors influencing the popularity response of the community is analysed. The results indicate: 1) Spatially, it identifies 4 communities, extracting the differential rules of each community, and suggests the location proximality in formation of communities; 2) By comparing different staying period, it reveals the role of core node such as transport hubs, key attractions and emerging attractions in different staying period. This study also reveals that accommodation density, the significance of core node, key tourist attractions and accessibility have significant influence on the formation of communities. By adding a detailed time dimension, the paper identifies tourism communities and community core nodes and analyses the important factors influencing their popularity response, which contributes to the formation of potential tourism community, the marketing of tourism community, and tourism activities prediction.