Extracting Network Patterns of Tourist Flows in an Urban Agglomeration Through Digital Footprints: The Case of Greater Bay Area

Jiarui Mou

doi:10.1109/access.2022.3149640

Abstract

Network patterns of tourist flows can reveal differences in tourism resources among destinations from the perspective of network science, providing valuable suggestions for tourism managers and policymakers to promote the balanced and sustainable development of tourism. This paper focuses on urban agglomerations, a highly developed spatial form of integrated cities, and proposes a research framework to extract the network patterns of tourist flows through digital footprints. Based on an illustrative case study using geo-located travel blog data from Qunar.com, we built a tourist flow network for the Guangdong-Hong Kong-Macao Greater Bay Area (GBA), China. The analysis shows: (1) GBA’s tourist flow network is obviously heterogeneous, showing a pattern of “four cores and three poles”; (2) the strong “administrative barrier effect,” revealed by community detection within the network, is the main obstacle to integrating regional tourism; (3) strengthening the infrastructure of tourism mediation cities such as Guangzhou, Zhuhai, and Shenzhen, so as to avoid the “structural hole” caused by the tourist flows between cities, is an urgent issue that the GBA government needs to address. To summarize, the research framework can provide a theoretical basis and concrete suggestions for the planning and management of the tourism industry in urban agglomerations.

Highlights

Urban agglomerations are highly developed spatial forms of integrated cities, and are the spatial carrier of regional economic and industrial development [1]
Tourist flow networks with scale-free characteristics are usually heterogeneous [49], that is, there are fewer nodes with larger degree values and more nodes with smaller degree values, indicating that most of the tourist flows in Greater Bay Area (GBA) are mainly concentrated in a few attractions
This paper proposed a novel research framework for extracting the network patterns of tourist flows in urban agglomeration through digital footprints

Summary

Introduction

Urban agglomerations are highly developed spatial forms of integrated cities, and are the spatial carrier of regional economic and industrial development [1]. As an important driving factor for economic growth in cities, is an external manifestation of the integrated development process of urban agglomerations [2, 3]. Tourist flow, generated by the movement of tourists between destinations, is an important metric to evaluate tourism development [7]. Current tourist flow studies mainly focus on theoretical frameworks [8], influencing factors [9], flow patterns [10], and prediction models [11], among which analysis of the spatial characteristics of tourist flow has been a research hotspot in recent years. Scholars have begun to introduce the concept of “digital footprints” [12], which facilitate detailed modeling of tourist movement

Methods

Results

Discussion

Conclusion