Linking the Recreation Opportunity Spectrum with Travel Spending: A Spatial Analysis in West Virginia

Dhami, Ishwar; Deng, Jinyang

doi:10.1080/01490400.2016.1252705

Cited by 10 publications

(6 citation statements)

References 47 publications

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“…The result was shown as a local regional clustering pattern in there and a fuzzy edge extension along the Mayang River (Tao et al, 2017). Ishwar Dhami et al used GIS for image processing and mapping the spectrum of recreation opportunities (ros) in west Virginia and found that tourist spending behavior of visitors was significantly correlated with the class of urban residents (Dhami & Deng, 2018). As a result, there are numerous achievements in the field of tourism industry spatial and temporal distribution research, and GIS analysis tools such as kernel density analysis, spatial auto-correlation analysis and geographically weighted regression analysis have become powerful analytical tools for tourism industry spatial and temporal distribution research.…”

Section: Research On Tourism Spatial Scale Changementioning

confidence: 99%

“…Tourism industry studies relating to spatial and temporal distribution of tourists and resources have become a significant point for tourism research, with topics covering various aspects such as the study of spatial distribution patterns of cities in tourism destinations (Tao et al, 2017), analysis of spatial and temporal behavior of tourists and influencing factors (Dhami & Deng, 2018), establishment and application of tourism demand forecasting models (Li et al, 2022), analysis of spatio-temporal evolution of regional tourism (Ming-chuan, 2015) and ecological spatial measurement and analysis of tourism destinations (Pan et al, 2019). Gao et al used big data based on social media comments to explore the impact of COVID-19 on the spatial behavior of urban tourists, taking Nanjing as an example.…”

Section: Literature Reviewmentioning

confidence: 99%

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Changes in the size of domestic tourists in mainland China under the impact of COVID-19

Zhong

Dong

2023

IJTC

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Purpose The purpose of this paper is to explore the changes of the scale of urban tourists in mainland China under the impact of COVID-19 and, specifically, the following questions: how did the scale of domestic tourists change nationwide and in the seven geographic regions? What are the differences in the changes among the seven geographic regions? What are the changes in the hot spot areas and spatial clustering of domestic tourists across the country? Design/methodology/approach Using the data of domestic tourist arrivals in 337 cities in mainland China from 2018 to 2021, this research analyzes the absolute differences and relative differences in the scale of domestic tourists nationwide and in seven geographic divisions with the help of indicators such as range analysis, standard deviation, coefficient of variation and Herfindahl–Hirschman Index and explores the changes in the hot spot areas and spatial concentration degree of the spatial scale of domestic tourists nationwide under the influence of the epidemic using kernel density analysis and spatial auto-correlation analysis. Findings The absolute differences in all seven geographical divisions continue to increase during 2018–2021. The domestic tourism in southwest China is extremely uneven. Absolute differences in the northwest and northeast regions are relatively small, and the development in attracting domestic tourists is more balanced. Relative differences in southwest China are comparatively large, with the trend of uneven development being obvious. The northeast, northwest and eastern regions of China are small, and the development is more balanced. The popularity of domestic tourism in the Beijing–Tianjin–Hebei region, as well as the Yangtze River Delta region, continues to decline and then pick up in 2021. The inland southwest region became a new domestic tourism hot spot in 2021. The size of domestic tourists from 2018 to 2021 in mainland China cities shows a significant positive spatial correlation, and there is a spatial agglomeration phenomenon, but some regional agglomeration types change from 2018 to 2021. Research limitations/implications The impact of the epidemic on the number and spatial scale of domestic tourism in China has been clarified, which makes up for the comparison of domestic tourism changes before and after the epidemic. A clear understanding of the changes in the number and spatial scale of domestic tourists in different regions after the epidemic is conducive to the development of domestic tourism revitalization strategies in accordance with the actual situation of each province and promotes the internal circulation of Chinese tourism. Practical implications This paper tries to clarify the quantitative scale of domestic tourism in different regions after the epidemic, which is conducive to the development of domestic tourism revitalization strategies in cities in different regions according to regional characteristics and the actual situation of each province and to promote the healthy operation of the internal circulation of tourism in China. This paper also tries to show the changes of domestic tourism market hot spots, agglomeration conditions changes before and after the outbreak and the clarity of tourists’ preference space changes. Originality/value Scale of domestic tourists; Absolute difference; Relative difference; Spatial hot spot distribution; Spatial agglomeration change

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Section: Research On Tourism Spatial Scale Changementioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Changes in the size of domestic tourists in mainland China under the impact of COVID-19

Zhong

Dong

2023

IJTC

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“…They found that these counties are concentrated in provincial capital cities. Dhami and Deng [43], in their study on the linkage between rural tourism clusters and travel/tourism-generated revenues in West Virginia, USA, classified rural tourism clusters at the county level and spatially analyzed the relationship between the recreation opportunity spectrum classes and tourism spending. In Huang et al's [44] study on the spatial structure of rural tourism in Hubei Province, the urbanization level was identified as the most significant contributing factor.…”

Section: Spatial Analysis In Tourismmentioning

confidence: 99%

A Longitudinal Spatial-Temporal Analysis of Ancient Village Tourism Development in Zhejiang, China

Bao

Jiang

et al. 2022

Sustainability

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The sustainable development of tourism is essential for revitalizing historically and culturally significant ancient villages in China. Despite the longstanding recognition of the relationship between the spatial distribution of village destinations and their sustainable development, there is a dearth of longitudinal studies in village tourism. Using the geographic information system (GIS) spatial analysis method and the exploratory spatial data analysis model, this study explored the spatial-temporal features of ancient village tourism over three important time nodes of rural tourism development (in Zhejiang, China), as well as the contributing factors at both the provincial and prefectural city levels. The findings of this study suggested a spatial inequality in the distribution of ancient villages, in terms of tourism development over time. In particular, tourism development was clustered in the southern region, with a noticeable spillover effect. Meanwhile, transportation, source markets, and physical topography are essential factors contributing to this spatial distribution. The study contributes to ancient village tourism development literature and offers meaningful, practical implications for regional governments and business investors.

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“…Of the three settings (physical, social, and managerial), information on physical setting (particularly remoteness and size) is readily available, while information on social setting and managerial setting is difficult to obtain and usually incomplete for an area at the regional scale. This may explain why some studies only used the physical setting to create a ROS without considering the other two settings [14]. In terms of evidence of humans in the physical setting, land ownership (public vs. private) was used in some studies [14,15], which may not be applicable in the context of China where almost all land is publicly owned.…”

Section: Introductionmentioning

confidence: 99%

“…This may explain why some studies only used the physical setting to create a ROS without considering the other two settings [14]. In terms of evidence of humans in the physical setting, land ownership (public vs. private) was used in some studies [14,15], which may not be applicable in the context of China where almost all land is publicly owned. Some scholars have criticized the use of population and housing construction to represent evidence of humans.…”

Section: Introductionmentioning

confidence: 99%

Classification of Recreation Opportunity Spectrum Using Night Lights for Evidence of Humans and POI Data for Social Setting

Zeng

Zhong

et al. 2021

Sustainability

Self Cite

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The recreation opportunity spectrum (ROS) has been widely recognized as an effective tool for the inventory and planning of outdoor recreational resources. However, its applications have been primarily focused on forest-dominated settings with few studies being conducted on all land types at a regional scale. The creation of a ROS is based on physical, social, and managerial settings, with the physical setting being measured by three criteria: remoteness, size, and evidence of humans. One challenge to extending the ROS to all land types on a large scale is the difficulty of quantifying the evidence of humans and social settings. Thus, this study, for the first time, developed an innovative approach that used night lights as a proxy for evidence of humans and points of interest (POI) for social settings to generate an automatic ROS for Hunan Province using Geographic Information System (GIS) spatial analysis. The whole province was classified as primitive (2.51%), semi-primitive non-motorized (21.33%), semi-primitive motorized (38.60%), semi-developed natural (30.99%), developed natural (5.61%), and highly developed (0.96%), which was further divided into three subclasses: large-natural (0.63%), small natural (0.27%), and facilities (0.06%). In order to implement the management and utilization of natural recreational resources in Hunan Province at the county (city, district) level, the province’s 122 counties (cities, districts) were categorized into five levels based on the ROS factor dominance calculated at the county and provincial levels. These five levels include key natural recreational counties (cities, districts), general natural recreational counties (cities, districts), rural counties (cities, districts), general metropolitan counties (cities, districts), and key metropolitan counties (cities, districts), with the corresponding numbers being 8, 21, 50, 24, and 19, respectively.

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Linking the Recreation Opportunity Spectrum with Travel Spending: A Spatial Analysis in West Virginia

Cited by 10 publications

References 47 publications

Changes in the size of domestic tourists in mainland China under the impact of COVID-19

Changes in the size of domestic tourists in mainland China under the impact of COVID-19

A Longitudinal Spatial-Temporal Analysis of Ancient Village Tourism Development in Zhejiang, China

Classification of Recreation Opportunity Spectrum Using Night Lights for Evidence of Humans and POI Data for Social Setting

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