Network Structure Features and Influencing Factors of Tourism Flow in Rural Areas: Evidence from China

Abstract

Exploring the spatial network structure of tourism flow and its influencing factors is of great significance to the transmission of characteristic culture and the sustainable development of tourism in tourist destinations, especially in backward rural areas. Taking Qiandongnan Miao and Dong Autonomous Prefecture (hereinafter referred to as Qiandongnan Prefecture) as an example, this paper adopts social network analysis and Quadratic Assignment Procedure regression analysis to study the network structural characteristics and influencing factors of tourism flow using online travel blog data. The results show that: (1) There are seasonal changes in tourism flow, but the attractions that tourists pay attention to do not change with the seasons. (2) The tightness of the tourism flow network structure is poor. The core nodes are unevenly distributed, and there are obvious structural holes. (3) The density of the tourism flow network is low. There is a clear core–periphery structure in the network, and the core area has a weak driving effect on the periphery area. There are more cohesive subgroups in the network, but the degree of connectedness between the subgroups varies greatly. (4) Geographical adjacency, transportation accessibility, and tourism resource endowment influence tourism flow network structure. The study found that the influencing factors of tourism flow in rural areas are different from those in urban areas. These results provide useful information for the marketing and development of tourism management departments in rural areas.

Exploring Spatial Correlations of Tourism Ecological Security in China: A Perspective from Social Network Analysis

“…Most existing studies use gravity model to determine the correlation between nodes, which can comprehensively consider the impact of factors including economy and distance. It is widely used in research on tourism flow [32], the tourism economy [33], and tourism efficiency [34]. By using the gravity model to establish a correlation network, TES can be combined with economic distance and geographical distance to better reveal the spatial correlation characteristics of TES.…”

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“…W orbicie zainteresowań badaczy znalazła się także promocja agroturystyki w sieciach internetowych (Chovanová Supeková, 2021). Problematykę subsieci w strukturze terytorialnej turystyki wiejskiej poruszyli Li i inni (Li et al, 2022), którzy podjęli badania w chińskiej prefekturze Qiandongnan. Autorzy analizowali peryferyjne i centralne obszary sieci, w tym spójne ich podgrupy, i wskazali na luźny stopień powiązań pomiędzy nimi.…”

Operating characteristics of the factor flow networks in rural areas: A case study of a typical industrial town in China

“…Factually, most of the related studies have focused on more mobile, data-rich and accessible urban areas, while few on the rural areas [32][33][34]. At present, the research objects of rural network of factor flows were mainly the network of population flows [35][36][37][38][39], traffic flows [40,41], tourism flows [42,43] and information flows [12,44]; research contents included network deconstruction, dynamic evolution, spatio-temporal pattern, influencing factors and optimization strategies; research methods involved in gravity model, field strength model, potential model, breaking point analysis and qualitative study [45][46][47][48][49]. However, these researches mainly focused on the city scale where statistical data are abundant and easily available [50], or the village scale where information flows are dense, such as Taobao village and tourist village [51,52], whereas lacking in the systematic analysis of the town scale.…”