Tourism Flow Between Major Cities During China’s National Day Holiday: A Social Network Analysis Using Weibo Check-in Data

An-jun, LI; Mou, Naixia; Zhang, Lingxian; Yang, Tengfei; Liu, Wenbao; Liu, Feng

doi:10.1109/access.2020.3044613

Cited by 17 publications

(10 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Tourism flow is the link between the origin and destination and is at the core of tourism research. Scholars from different fields, such as tourism, geography, and sociology, have conducted numerous discussions on it, and the relevant studies have mainly focused on the flow prediction of tourism flow [21][22][23], spatial distribution [24,25], influencing factors [26,27], network structure [28,29], tourism destination management [30], and tourist behavioral perceptions [31]. Research scales focus on large spatial scales such as international, regional, provincial, and intra-city levels [32][33][34][35][36][37], and research methods such as GIS analysis and social network analysis are widely used in tourism flow research [38,39].…”

Section: Literature Reviewmentioning

confidence: 99%

Spatio-Temporal Distribution of Tourism Flows and Network Analysis of Traditional Villages in Western Hunan

et al. 2022

View full text Add to dashboard Cite

Traditional village tourism is an effective path to rural revitalization and an excellent way to preserve traditional sceneries and cultural heritage. However, research on the subject matter has been limited. To address some of the current research gaps, this study used 2372 online travelogues from popular travel websites to explore the spatio-temporal distribution and network structure of the tourism flow in traditional villages in western Hunan using ArcGIS and social network analysis. The main findings are as follows: (1) In terms of time distribution, the tourism flow in traditional villages in Western Hunan is dominated by short-term tourism and shows significant seasonal differences. (2) The spatial distribution forms a double-core and multipoint spatial pattern with “Fenghuang Ancient City—Zhangjiajie National Forest Park” as core. (3) The network of traditional villages in Western Hunan exhibits a “core-periphery” hierarchical structure, with a network density of 0.275. Aside from the attraction and appeal of the twin cores, Furong Town and Tianmen Mountain constitute the core points of the network structure, and the radiation drive of the core nodes is not pronounced.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Spatio-Temporal Distribution of Tourism Flows and Network Analysis of Traditional Villages in Western Hunan

et al. 2022

View full text Add to dashboard Cite

show abstract

“…But before the spread of internet technology (e.g., cloud computing [7] ) and social media, this information was mainly obtained through public institutions, travel books or resources [8] . Social media shows good interactivity and timeliness, making users more willing to share their lives on social media platforms [9] , [10] . This also makes social media the main carrier of the digital media era and has been widely recognized and supported by the society [11] , [12] .…”

Section: Introductionmentioning

confidence: 99%

Analyzing trends in the spatial-temporal visitation patterns of mainland Chinese tourists in Sabah, Malaysia based on Weibo social big data

Alfred¹,

Chen²,

Eboy³

et al. 2023

Heliyon

View full text Add to dashboard Cite

“…The second refers to mobile signaling data [12]. The third refers to social network data containing location information based on network data mining, such as Weibo check-in data [13] and Baidu index data [14]. Among many, Baidu has been seen as one of the most popular location-based services (LBS) providers in China.…”

Section: Introductionmentioning

confidence: 99%

Characterizing Intercity Mobility Patterns for the Greater Bay Area in China

Yin

2022

IJGI

View full text Add to dashboard Cite

Understanding intercity mobility patterns is important for future urban planning, in which the intensity of intercity mobility indicates the degree of urban integration development. This study investigates the intercity mobility patterns of the Greater Bay Area (GBA) in China. The proposed workflow starts by analyzing intercity mobility characteristics, proceeds to model the spatial-temporal heterogeneity of intercity mobility structures, and then identifies the intercity mobility patterns. We first conduct a complex network analysis, based on weighted degrees and the PageRank algorithm, to measure intercity mobility characteristics. Next, we calculate the Normalized Levenshtein Distance for Population Mobility Structure (NLPMS) to quantify the differences in intercity mobility structures, and we use the Non-negative Matrix Factorization (NMF) to identify intercity mobility patterns. Our results showed an evident ‘Core-Periphery’ differentiation characterized by intercity mobility, with Guangzhou and Shenzhen as the two core cities. An obvious daily intercity commuting pattern was found between Guangzhou and Foshan, and between Shenzhen and Dongguan cities at working time. This pattern, however, changes during the holidays. This is because people move from the core cities to peripheral cities at the beginning of holidays and return at the end of holidays. This study concludes that Guangzhou and Foshan have formed a relatively stable intercity mobility pattern, and the Shenzhen–Dongguan–Huizhou metropolitan area has been gradually formed.

show abstract

Tourism Flow Between Major Cities During China’s National Day Holiday: A Social Network Analysis Using Weibo Check-in Data

Cited by 17 publications

References 47 publications

Spatio-Temporal Distribution of Tourism Flows and Network Analysis of Traditional Villages in Western Hunan

Spatio-Temporal Distribution of Tourism Flows and Network Analysis of Traditional Villages in Western Hunan

Analyzing trends in the spatial-temporal visitation patterns of mainland Chinese tourists in Sabah, Malaysia based on Weibo social big data

Characterizing Intercity Mobility Patterns for the Greater Bay Area in China

Contact Info

Product

Resources

About