The Spatial Distribution, Influencing Factors, and Development Path of Inbound Tourism in China—An Empirical Analysis of Market Segments Based on Travel Motivation

Huang, Heping; Zhong, Wei; Lai, Qingsheng; Qiu, Yishu; Jiang, Hong

doi:10.3390/su12062508

Cited by 11 publications

(7 citation statements)

References 52 publications

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“…This growth will certainly take place in line with the discovery of various new tourist destinations in various regions. In China, the tourism market growth of inbound and outbound tourism changed markedly in the past few years [2]. This potential tourism industry growth was also found in Thailand [3].…”

Section: Introductionmentioning

confidence: 85%

“…All of them can be seen in Table VII. Tourist Experience 4 [12], [13], [19], [20] Tourist Motivation 6 [17], [18], [11], [3], [6], [5] TOTAL 13 [18], [19], [11], [12], [6], [5] Paper/ Report 6 [1], [4], [2], [3], [13], [20] TOTAL 13…”

Section: A Tourismmentioning

confidence: 99%

“…Exploratory Factor Analysis through K-means, ANOVA 1 [5] Statistical Model 6 [4], [19], [11], [2], [12], [6] TOTAL 7…”

Section: ∑ Papers Selected Papersmentioning

confidence: 99%

See 2 more Smart Citations

Smart Tourism Recommendation Model: A Systematic Literature Review

Huda¹,

Ramadhan²,

Trisetyarso³

et al. 2021

IJACSA

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The tourism industry has become a potential sector to leverage economic growth. Many attractions are detected on several platforms. Machine learning and data mining are some potential technologies to improve the service of tourism by providing recommendations for a specific attraction for tourists according to their location and profile. This research applied for a systematic literature review on tourism, digital tourism, smart tourism, and recommender system in tourism. This research aims to evaluate the most relevant and accurate techniques in tourism that focused on recommendations or similar efforts. Several research questions were defined and translated into search strings. The result of this research was promoting 41 research that discussed tourism, digital tourism, smart tourism, and recommender systems. All of the literature was reviewed on some aspects, in example the problem addressed, methodology used, data used, strength, and the limitation that can be an opportunity for improvement in future research. This study proposed some references for further study based on reviewed papers regarding tourism management, tourist experience, tourist motivation, and tourist recommendation system. The opportunities for a further research study can be conducted with more data usage especially for a smart recommender system in tourism through many types of recommendation techniques such as content-based, collaborative filtering, demographic, knowledge-based, community-based, and hybrid recommender systems.

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Section: Introductionmentioning

confidence: 85%

Section: A Tourismmentioning

confidence: 99%

See 1 more Smart Citation

Smart Tourism Recommendation Model: A Systematic Literature Review

Huda¹,

Ramadhan²,

Trisetyarso³

et al. 2021

IJACSA

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show abstract

“…Researchers have adopted various methods to measure the impact of each of these factors on local tourism activities. For traditional tourism statistics, researchers use the ANP model [35], statistical analysis [32], stepwise regression and clustering [45], and other means for analysis. For digital footprint data, researchers use the exponential random graph model [46] and complex network index [20] to measure the importance of factors.…”

Section: Tourism Impact Factor Analysismentioning

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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“…As a core component of tourism, scenic spots and their spatial distribution and accessibility not only determine the spatial behavior of tourists, but also profoundly affect the formulation of regional tourism development strategies [ 2 ]. Therefore, research on scenic spots has been highly valued regionally and globally [ 3 ]. In addition, an expansion in the research of methodologies has occurred, especially related to the interpretation and classification [ 4 , 5 ], competition [ 6 , 7 ], tourists’ perception [ 8 , 9 ], and spatial distribution and evolution [ 10 – 12 ] of scenic spots.…”

Section: Introductionmentioning

confidence: 99%

Spatial distribution evolution and accessibility of A-level scenic spots in Guangdong Province from the perspective of quantitative geography

Liao

Zhang

2021

PLoS ONE

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As a typical representative of tourism resources, the spatial distribution of A-level scenic spots has a profound impact on the layout of tourism industry. Scenic spot accessibility is also important for the development of tourism. However, the relationship of regional accessibility and spatial distribution of A-level scenic spots are understudied. The study used quantitative geography and geographic information system spatial analysis methods and analyzed the evolution of spatial distribution and regional accessibility of A-level scenic spots in Guangdong Province from 2001 to 2020. The results present the following: 1. Agglomeration distribution is the main distribution type of A-level scenic spots in Guangdong Province, and the spatial distribution is unbalanced. 2. From 2001 to 2020, the spatial distribution of A-level scenic spots in 21 prefecture-level cities of Guangdong Province has gradually developed from "wide gap" to "relatively reasonable." 3. Distribution density of A-level scenic spots in Guangdong Province has evolved into the main core area of high density. 4. Center of the gravity of A-level scenic spots in Guangdong Province developed from east to west during 2002–2007 and moved to the east after 2007. 5. Accessibility between A-level scenic spots and tourist source areas in Guangdong Province is good, with an evident aggregation phenomenon. This study reveals the spatial distribution evolution law and regional accessibility of A-level scenic spots, which is conducive to healthy, sustainable, and stable development of tourism in Guangdong Province.

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The Spatial Distribution, Influencing Factors, and Development Path of Inbound Tourism in China—An Empirical Analysis of Market Segments Based on Travel Motivation

Cited by 11 publications

References 52 publications

Smart Tourism Recommendation Model: A Systematic Literature Review

Smart Tourism Recommendation Model: A Systematic Literature Review

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

Spatial distribution evolution and accessibility of A-level scenic spots in Guangdong Province from the perspective of quantitative geography

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