Tourism flows in large-scale destination systems

Kádár, Bálint; Gede, Mátyás

doi:10.1016/j.annals.2020.103113

Cited by 54 publications

(21 citation statements)

References 81 publications

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“…In particular, our study contributes to the literature about the consumers' feedback on online picture and the role of the hashtags in enhancing a picture's engagement (Messina, 2007) introducing a new and understudied variable: the territorial dimension (Brouder, 2012;Goffi & Cucculelli, 2019;OECD, 2014;Pierce et al, 2011;Richards, 2019). Our findings: 1) show a high use of territorial hashtags to describe pictures related to small destination, 2) demonstrate the importance of including a small destination in a wider territorial dimension to properly promote it 3) contribute to the literature on destination systems which are less studied because of their complexity, since these areas are composed of different clusters of destinations, in which national boundaries have a strong shielding effect in the interregional movements of tourists (Kádár & Gede, 2021) 4) contribute to the literature on multi-destination trips (Önder, 2017) helping to see how destinations can be combined in order to create a leisure trip. Tourists mainly search for information through social media in the travel-planning phase Leung et al (2013).…”

Section: Discussionmentioning

confidence: 60%

The interplay role of destination area hashtags to enhance small destination pictures’ engagement on Instagram

Bellio

Checchinato

2022

SSRN Journal

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Section: Discussionmentioning

confidence: 60%

The interplay role of destination area hashtags to enhance small destination pictures’ engagement on Instagram

Bellio

Checchinato

2022

SSRN Journal

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“…Based on a sample survey of Sicilian tourists, Asero et al (2016) constructed a spatial network of tourism destinations and found that the mobility of tourists affected its shape, dimension and structure [52]. Kádár and Gede (2021) used network and cluster analysis of tourism flows mapped from user-generated Big Data to show how the Danube region is composed of different clusters of destinations, and how national boundaries have a strong shielding effect on the interregional movements of tourists [53]. Zhou (2008) [54], Hunan [55], Yunnan [56], Heilongjiang [57], Hunan [58], Hainan [59] and Xinjiang [60,61] as examples, respectively, analyzing the spatial structure, evolution process and role of the urban tourism economic network and exploring the spatial development model.…”

Section: Research On Network Structure Among Tourism Destinations Basmentioning

confidence: 99%

Research on the Spatial Structure of the European Union’s Tourism Economy and Its Effects

Xie

Hai-jian

Yin

2021

IJERPH

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With the implementation of European integration policies such as the single market, the Euro and the Schengen Visa, the EU member states are developing closer economic ties through tourism, and their level of tourism integration is constantly improving. Taking the 28 EU member states as research objects, this paper constructs a tourism economic connection network among them, measures the strength of their tourism economic connections from 1995 to 2018 by using the modified gravity model and social network method, and analyzes the spatial structure characteristics and effects of the EU tourism economy. The results are as follows: (1) The tourism economic ties of EU member states are growing increasingly close, enhancing network stability. (2) Germany, France, Italy, Austria and the United Kingdom are the top five countries in the degree centrality and closeness centrality rankings, meaning that they are located in the center of the network and have great influence, and the network is becoming increasingly concentrated. Germany, Italy, Sweden, Austria and France play an important intermediary role in the network, and the centrality of most member states has increased. (3) The core areas are mainly concentrated in Western Europe, Southern Europe, Mediterranean mainland countries and Central Europe, while the marginal areas are mainly concentrated in Eastern Europe, Northern Europe and Mediterranean island countries; the network connection density of the core area, the network connection density of the marginal area, and the network connection density between the core and marginal area overall show an increasing trend. (4) Improvements in the complete network connectedness and a reduction in graph efficiency can significantly reduce differences in EU tourism economic development levels and improve spatial equity.

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“…Researchers have also performed a comparative analysis of several commonly used digital footprints [22]. Most of the existing research on tourism focuses on reading tourists' social preferences for tourism destinations by using social media data with geographic locations [23,24], identifying tourism hotspots [25,26], mining data of tourist attractions [27], and analyzing the spatial distribution pattern of tourism microblogs [28][29][30]. For example, geotagged Flickr photos were used to explore the relations between destinations with the network analysis method [26] and identify tourism intensification in cities with fractal analysis [30].…”

Section: Related Work 21 Tourism Datamentioning

confidence: 99%

Influential Factor Detection for Tourism on the Qinghai-Tibet Plateau Based on Social Media Data

Bao

et al. 2021

IJGI

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Tourism is playing an important role in the economic development of the Qinghai-Tibet Plateau (QTP). To better develop tourism in this region, the spatial heterogeneity of influencing factors on tourism needs to be studied. Using the spatial distribution of tourism potential from social media data, this paper analyzes the influencing factors of tourism on the QTP from the perspective of spatial heterogeneity. We extract microblogs related to travel topics connected to the QTP in 2017 from Sina Weibo to capture tourism potential. Then, factors considered from six aspects (tourism resources, amenities, transportation, geography, population, and the economy) are selected, and a geographic detector (Geodetector) is employed to detect the explanatory power of these factors for tourism potential. The results indicate different influential tourism factors in Qinghai and Tibet. In Qinghai, the main factors are hotels, tourist attractions, and road network density, and the explanatory power of the factors mainly comes from eastern and western Qinghai. In Tibet, the main factors are road network density, regional GDP (Gross Domestic Product), and urban land. It is suggested that tourism in the central region of Qinghai can be improved by enhancing the publicity and utilization of tourism resources, and Tibet should enhance tourism resource utilization and improve tourism amenities and infrastructure.

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Tourism flows in large-scale destination systems

Cited by 54 publications

References 81 publications

The interplay role of destination area hashtags to enhance small destination pictures’ engagement on Instagram

The interplay role of destination area hashtags to enhance small destination pictures’ engagement on Instagram

Research on the Spatial Structure of the European Union’s Tourism Economy and Its Effects

Influential Factor Detection for Tourism on the Qinghai-Tibet Plateau Based on Social Media Data

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