Researching Tourism Space in China’s Great Bay Area: Spatial Pattern, Driving Forces and Its Coupling with Economy and Population

Li, Lingfeng; Gao, Quan

doi:10.3390/land12101878

Cited by 4 publications

(3 citation statements)

References 26 publications

(25 reference statements)

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“…Most scholars believe that the economy [41], resources [42], population [43], and service capacity [44] will affect the spatial distribution of tourism areas. As a new form of tourism occurring in rural areas, rural tourism has the typical characteristics of traditional tourism projects, and its evolution in tourism's spatial pattern is influenced by the spatial layout of traditional tourist attractions.…”

Section: Selection and Calculation Of Influencing Factorsmentioning

confidence: 99%

Hotspot Identification and Causal Analysis of Chinese Rural Tourism at Different Spatial and Temporal Scales Based on Tourism Big Data

Fu,

Cai,

Fang

2024

Sustainability

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Rural tourism serves as a crucial means for fostering rural economic prosperity and inheriting rural culture. The assessment of the quality of rural tourism development and the identification of disparities in rural tourism development among regions have become focal points in current research. This paper utilizes tourism big data to establish a system for evaluating rural tourism popularity and proposes a method for identifying rural tourism hotspots. The study explores the spatiotemporal evolution characteristics and formation mechanisms of the cold and hot patterns of rural tourism in China during two periods (pre-pandemic and post-pandemic) and on two spatial scales (provincial and municipal levels). The research findings indicate that (1) the annual variation in rural tourism popularity exhibits a fluctuating upward trend, with significant seasonal variations on a monthly basis. (2) The spatial pattern of rural tourism popularity changes with the scale effect. At the provincial level, hotspot areas form an east–west dual-core pattern, while at the municipal level, hotspot areas demonstrate an evolution from a three-core to a four-core pattern. In the post-pandemic era, rural tourism popularity in the northwest and southwest regions is experiencing a counter-trend growth. (3) At different spatiotemporal scales, influencing factors and their impact intensities vary. At the provincial level, road density and reception capacity consistently play dominant roles, and per capita disposable income significantly influences early-stage popularity enhancement. At the municipal level, resident population and tourism resources influence are the dominant factors, and the influence of air quality and regional media attention gradually strengthens. This article provides a new perspective on quantitative research in rural tourism, offering significant guidance for the rational allocation of resources in rural tourism, regional tourism collaboration, and the sustainable development of rural tourism in the post-pandemic era.

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Section: Selection and Calculation Of Influencing Factorsmentioning

confidence: 99%

Hotspot Identification and Causal Analysis of Chinese Rural Tourism at Different Spatial and Temporal Scales Based on Tourism Big Data

Fu,

Cai,

Fang

2024

Sustainability

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“…To optimize the accuracy of the Naive Bayes classification algorithm in recommending POIs, the tourist interest matching recommendation degree δ MA is introduced into Formula ( 14) recommendation degree δ NB = max P(P a(i) |C (i) )P(C (i) ), and the destination POI P a(i) recommendation model δ (i) is constructed as shown in Formula (18).…”

Section: Improved Poi Recommendation Degree Model Based On Tourism At...mentioning

confidence: 99%

“…Their spatial accessibility is constrained by various factors, such as geographical coordinates, road distances, accessibility time, and transportation tools. Therefore, constructing a tour route model based on the recommended POIs is an effective way to obtain the optimal travel route [18][19][20]. We integrate the urban geospatial constraints and construct a POI tour route recommendation model based on the spatial decision tree algorithm.…”

Section: Poi Tour Route Recommendation Model Based On the Spatial Dec...mentioning

confidence: 99%

Tour Route Recommendation Model by the Improved Symmetry-Based Naive Bayes Mining and Spatial Decision Forest Search

Zhou,

Peng,

Wen

et al. 2023

Symmetry

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In machine learning, classifiers have the feature of constant symmetry when performing the attribute transformation. In the research field of tourism recommendation, tourists’ interests should be mined and extracted by the symmetrical transformation in founding the training dataset and creating the classifier, so as to ensure that the recommendation results meet the individualized interests and needs. In this paper, by applying the feature of constant symmetry in the classifier and analyzing the research background and existing problems of POI tour routes, we propose and construct a tour route recommendation model using improved symmetry-based Naive Bayes mining and spatial decision forest search. First, the POI natural attribute classification model is constructed based on text mining to classify the natural attributes of the destination POIs. Second, the destination POI recommendation model based on the improved symmetry-based Naive Bayes mining and decision forest algorithm is constructed, outputting POIs that match tourists’ interests. On this basis, the POI tour route recommendation model based on a spatial decision tree algorithm is established, which outputs the optimal tour route with the lowest sub-interval cost and route interval cost. Finally, the validation and comparative experiments are designed to output the optimal POIs and tour routes by using the proposed algorithms, and then the proposed algorithm is compared with the commonly used route planning methods, GDM and 360M. Experimental results show that the proposed algorithm can reduce travel costs by 4.56% and 10.36%, respectively, on the optimal tour route compared to the GDM and 360M and by 2.94% and 8.01%, respectively, on the suboptimal tour route compared to the GDM and 360M, which verifies the advantages of the proposed algorithm over the traditional route planning methods.

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Spatial Distribution, Accessibility, and Influencing Factors of the Tourism and Leisure Industry in Qingdao, China

Sun,

Xu,

et al. 2024

Sustainability

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Analysing the spatial distribution patterns of the urban tourism and leisure industry is crucial for meeting the dual demands of residents and visitors for a better quality of life. Most existing studies on spatial distribution characteristics and the accessibility of leisure tourism have been carried out individually and lack the relationship between the tourism and leisure industry and accessibility. Based on Qingdao’s POI data, spatial visualisation methods and Geodetector are used to explore spatial distribution, accessibility, and influencing factors. (1) It is found that the overall and various types of the tourism and leisure industry in Qingdao exhibit clustered distribution, with an imbalance between coastal and inland areas. (2) There are notable spatial disparities in overall accessibility within Qingdao, with most regions falling within the 70-min isochrone. The spatial distribution of accessibility at the county level aligns with the overall accessibility patterns. (3) Various factors influence the spatial distribution of the tourism and leisure industry. Per capita disposable income is the primary factor affecting the spatial layout of Qingdao’s tourism and leisure industry. After interacting with other factors, GDP per capita and tourist trips become key influencing factors. These findings can promote the sustainable development of urban tourism in Qingdao and provide a valuable reference for other cities.

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Researching Tourism Space in China’s Great Bay Area: Spatial Pattern, Driving Forces and Its Coupling with Economy and Population

Cited by 4 publications

References 26 publications

Hotspot Identification and Causal Analysis of Chinese Rural Tourism at Different Spatial and Temporal Scales Based on Tourism Big Data

Hotspot Identification and Causal Analysis of Chinese Rural Tourism at Different Spatial and Temporal Scales Based on Tourism Big Data

Tour Route Recommendation Model by the Improved Symmetry-Based Naive Bayes Mining and Spatial Decision Forest Search

Spatial Distribution, Accessibility, and Influencing Factors of the Tourism and Leisure Industry in Qingdao, China

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