Spatiotemporal Difference Characteristics and Influencing Factors of Tourism Urbanization in China’s Major Tourist Cities

Xie, Xiaochun; Zhang, Lei; Sun, Hui; Chen, Feifei; Zhou, Chunshan

doi:10.3390/ijerph181910414

Cited by 26 publications

(17 citation statements)

References 21 publications

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“…First of all, the study shows that in recent years, with the implementation of a series of measures taken by the Chinese government to optimize industrial structure and adhere to green and sustainable development, tourism, urbanization, technology and ecological environment protection in various provinces have all developed to varying degrees [ 37 , 38 , 39 ]; this paper analyzes the comprehensive development level of tourism, urbanization, technology and ecological environment in Yunnan–Guizhou–Sichuan region, and finds that the comprehensive development level of the four are all increasing at a high speed, which verifies the existing research results.…”

Section: Discussionmentioning

confidence: 99%

Spatiotemporal Characteristics and Influencing Factors of Tourism–Urbanization–Technology–Ecological Environment on the Yunnan–Guizhou–Sichuan Region: An Uncoordinated Coupling Perspective

Yang

Gong

Luo

et al. 2022

IJERPH

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The tourism, urbanization, technology, and the ecological environment both promote and restrict each other. Coordinating the relationship between the four is of great significance to the realization of high-quality sustainable regional development. Taking the Yunnan–Guizhou–Sichuan region as an example, this paper constructs an uncoordinated coupling model for the tourism–urbanization–technology–ecological environment system. Using exploratory spatial analysis and geographic information systems, this paper reveals the temporal and spatial evolution law affecting the uncoordinated coupling relationship between tourism, urbanization, technology and the ecological environment in the Yunnan–Guizhou–Sichuan region from 2010 to 2020, before establishing a panel Tobit model that is used to explore the factors affecting the four systems. The research shows the following: (1) The level of comprehensive development for tourism, urbanization, technology, and the ecological environment in Yunnan, Guizhou, and Sichuan has increased rapidly. Of all these, the tourism industry was the most affected by COVID-19 in 2020, while the level of urbanization, technology, and ecological environment developments in the three provinces has become similar over time. (2) Uncoordinated development between cities is a prominent problem; while the uncoordinated coupling spatial agglomeration in various regions is relatively stable, the proportion of cities with no significant agglomeration form amounts to more than 70%, with mostly low–low (L–L) and high–high (H–H) agglomeration types. (3) The degree to which uncoordinated coupling exists among the four systems in the Yunnan–Guizhou–Sichuan region is affected by many factors. Only eco-environmental pressure has a significant positive correlation with the degree of uncoordinated coupling, while the tourism scale, economic urbanization, eco-environmental response, and investment in technology have a significant negative correlation. These results provide a theoretical basis and practical references for strengthening the government’s macro-control and promoting collaborative regional development.

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Section: Discussionmentioning

confidence: 99%

Spatiotemporal Characteristics and Influencing Factors of Tourism–Urbanization–Technology–Ecological Environment on the Yunnan–Guizhou–Sichuan Region: An Uncoordinated Coupling Perspective

Yang

Gong

Luo

et al. 2022

IJERPH

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“…This paper differs from other pieces of literature in that (1) the POI data of Xinjiang tourism resources are used, and their spatial distribution characteristics are analyzed. (2) The future outlook of tourism resources development in the context of the epidemic is discussed, and it is hoped to promote the finding of a new model of tourism.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In 2019, China received a total of about 6 billion domestic tourists and 145 million inbound tourists, with the total tourism spending reaching CNY 5,725,092 billion. In recent years, the tourism index has shown a general upward trend [2]. In this context, Xinjiang has positioned tourism as an important strategic pillar industry, and the tourism industry in Xinjiang has been transformed from a large tourism resource area to a strong tourism economy area.…”

Section: Introduction 1theoretical Backgroundmentioning

confidence: 99%

Spatial Pattern Analysis of Xinjiang Tourism Resources Based on Electronic Map Points of Interest

Chang

Simayi

et al. 2022

IJERPH

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This study considers the Point of Interest data of tourism resources in Xinjiang and studies their spatial distribution by combining geospatial analysis methods, such as the average nearest neighbor index, standard deviation ellipse, kernel density analysis, and hotspot analysis, to explore their spatial distribution characteristics. Based on the analysis results, the following conclusions are made. Different categories of tourism resource sites have different spatial distributions, and all categories of tourism resources in Xinjiang are clustered in Urumqi city. The geological landscape resource sites are widely distributed and have a ring-shaped distribution in the desert area of southern Xinjiang. The biological landscape resources are distributed in a strip along the Tianshan Mountains. The water landscape resources are concentrated in the northern Xinjiang area. The site ruins are mostly distributed in the western region of Xinjiang. The distributions of the architectural landscape and entertainment and shopping resources are highly coupled with the distribution of cities. The distributions of the six categories of tourism resource points are in the northeast-southwest direction. The centripetal force and directional nature of the resource points of the water landscape are not obvious. The remaining five categories of resource points have their own characteristics. The distribution of resources in the site ruins is relatively even, and there are many hotspot areas in the geomantic and architectural landscapes, which are mainly concentrated in Bazhou and other places. The biological landscape has many cold-spot areas, distributed in areas such as Altai in northern Xinjiang and Hotan in southern Xinjiang. The remaining four categories have cold-spot and hotspot areas with different distributions. Tourism is an important thrust for economic development. The study of the distribution of tourism resources on the spatial distribution of tourism resources has clear guidance for later tourism development, can help the tourism industry optimize the layout of resources, and can promote tourism resources to achieve maximum benefits. The government can implement effective control and governance.

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“…Previous studies of the spatial distribution of tourism attractions mostly applied mathematical statistics, GIS, and the analysis of influencing factors through geographic detectors. These studies suffer from a certain level of uncertainty due to the application of key technologies (Zhu and Chen, 2008;Wu et al, 2013;Wu et al, 2017;Li et al, 2018;Zhu et al, 2019;Xie et al, 2021). For example, the use of a geographic detector model to analyze the factors influencing the spatial distribution of scenic spots will provide different results under different spatial scales.…”

Section: Overviewmentioning

confidence: 99%

“…There have been fruitful research results in the research on tourist attractions at home and abroad. They mainly include the price system of scenic spots (Zhu et al, 2020;Wang et al, 2021), the energy consumption of different types of scenic spots (Hu et al, 2016), the satisfaction of tourists (Ge, 2018;Bai et al, 2019;Liu and Hao, 2020), the spatial behavior of tourists in scenic spots (Wu et al, 2017;Wang et al, 2018), the smart tourism scenic spot system (Zhu and Chen, 2008;Xie et al, 2021), and the spatial structure of scenic spots based on fractal theory (Wu et al, 2013;Li et al, 2018). Domestic scholars' research mainly focuses on the spatial distribution pattern, spatial structure characteristics, and the influencing factors of scenic spots.…”

Section: Introductionmentioning

confidence: 99%

Simulation of the spatial pattern of scenic spots combining optimal scale and deep learning

et al. 2022

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With the development of deep learning and big data technology, artificial neural network methods are used to simulate new areas with high potential to develop tourist attractions. They break through the limitation of the lifespan development of domestic tourist attractions and improve the credibility of results caused by the sample size and scale effect. This study applied the data for 906 scenic spots in Northwest China by a geographic detector model and deep learning technology to explore the dominant factors explaining their spatial distribution under the optimal research spatial scale and to simulate new areas with a high potential for development as tourist attractions. The main conclusions of this study were as follows. 1) The results were more reliable for Northwest China under a research spatial scale of 150 km × 150 km. 2) The leading factors affecting the development of tourist attractions in Northwest China were normalized difference vegetation index (NDVI), distance from the city, population, and transportation accessibility. 3) The results identified areas that are highly suitable for tourist attractions, showed regional maturity in either the natural environment or social development. This study can act as a reference for further exploration and the application of artificial intelligence technology in scenic spots.

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Spatiotemporal Difference Characteristics and Influencing Factors of Tourism Urbanization in China’s Major Tourist Cities

Cited by 26 publications

References 21 publications

Spatiotemporal Characteristics and Influencing Factors of Tourism–Urbanization–Technology–Ecological Environment on the Yunnan–Guizhou–Sichuan Region: An Uncoordinated Coupling Perspective

Spatiotemporal Characteristics and Influencing Factors of Tourism–Urbanization–Technology–Ecological Environment on the Yunnan–Guizhou–Sichuan Region: An Uncoordinated Coupling Perspective

Spatial Pattern Analysis of Xinjiang Tourism Resources Based on Electronic Map Points of Interest

Simulation of the spatial pattern of scenic spots combining optimal scale and deep learning

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