Study on Tourism Flow Network Patterns on May Day Holiday

Abstract

The development of tourism is based on tourism flow and studying a tourism flow network can help to elucidate its mechanism of operation. Transportation network is the path to realize the spatial displacement of tourism flow. This study used “Tencent migration” big data to explore the spatial distribution characteristics and rules of tourism flow in China, providing suggestions for the development of tourism. The results demonstrate that the 361 cities studied can be divided into three types: destination-oriented, tourist-origin-oriented, and destination-oriented and tourist-origin-oriented. There are significant differences in the quantity of flow, the area of concentration, and the factors affecting the flow in the three types of cities. The larger the flow of tourism between cities, the higher the network level, and the wider the network range. The high-level nodes are closely related, while the peripheral nodes are more widely distributed, with weak attractiveness and inconvenient traffic, forming a “core-edge” structure. Different network patterns are established for different modes of transportation. The degree of response of different types of transportation to distance is the main factor influencing the network patterns of diverse paths. These findings have practical implications for the choice of appropriate travel destinations and transportation modes for tourists.

Evaluation and Spatial Characteristics of Cooperation among Tourist Attractions Based on a Geographic Information System: A Case Study of The Yangtze River Delta Region, China

“…This spatial pattern is consistent with the spatial structure characteristics of tourism flows, indicating that Shanghai, Nanjing, and Hangzhou are more connected with other cities. This finding supports the view of Wu et al [47] and Zhang et al [48].…”

A study on the evaluation of competitiveness in the aviation logistics industry cluster in Zhengzhou

“…Finally, the values of quantitative indicators were replaced by the corresponding numbers. As some indicators are negatively correlated with the competitiveness of logistics industry clusters, such as the urban registered unemployment rate, industrial wastewater discharge, and industrial dust emissions, they were converted into inverse indicators [29][30][31] .…”

“…Government support for the development of logistics industry clusters is not only reflected in planning and development, financial support, tax incentives, financial credit, and land supply, but also in clean and efficient administration, simplified procedures, and convenient services. To ensure the objectivity of the entropy weight assignment, only quantitative indicators were selected, while qualitative indicators were ignored, which may underestimate the government's support for logistics industry clusters [29][30][31] .…”

Evolution and Driving Mechanism of Tourism Flow Networks in the Yangtze River Delta Urban Agglomeration Based on Social Network Analysis and Geographic Information System: A Double-Network Perspective

“…This study mainly discusses the influence of destination objective factors on the formation of tourism flow networks. Current studies on influencing factors of tourism flow networks mainly focus on the reasons for the flow of tourists from their source place to destination [37][38][39], among which, the "thrust-pull-resistance" driving mechanisms, constructed from the tourism system theory perspective, is widely used [40]. However, previous studies have paid little attention to the factors affecting the flow of tourists in different destinations.…”