Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/91481
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dc.contributorSchool of Hotel and Tourism Management-
dc.creatorZuo, Y-
dc.creatorChen, H-
dc.creatorPan, J-
dc.creatorSi, Y-
dc.creatorLaw, R-
dc.creatorZhang, M-
dc.date.accessioned2021-11-03T06:54:02Z-
dc.date.available2021-11-03T06:54:02Z-
dc.identifier.urihttp://hdl.handle.net/10397/91481-
dc.language.isoenen_US
dc.publisherMolecular Diversity Preservation International (MDPI)en_US
dc.rights© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).en_US
dc.rightsThe following publication Zuo, Y.; Chen, H.; Pan, J.; Si, Y.; Law, R.; Zhang, M. Spatial Distribution Pattern and Influencing Factors of Sports Tourism Resources in China. ISPRS Int. J. Geo-Inf. 2021, 10, 428 is available at https://doi.org/10.3390/ijgi10070428en_US
dc.subjectChinaen_US
dc.subjectGeographic detectoren_US
dc.subjectInfluencing factorsen_US
dc.subjectSpatial distributionen_US
dc.subjectSports tourismen_US
dc.titleSpatial distribution pattern and influencing factors of sports tourism resources in Chinaen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.volume10-
dc.identifier.issue7-
dc.identifier.doi10.3390/ijgi10070428-
dcterms.abstractSports tourism is an emerging tourism product. In the sports and tourism industry, resource mining is the foundation that provides positive significance for theoretical support. This study takes China’s sports tourism boutique projects as the study object, exploring its spatial distribution pattern through the average nearest neighbor index, kernel density, and spatial autocorrelation. On the strength of the wuli–shili–renli system approach, the entropy value method and geographic detector probe model are used to identify the driving factors affecting the spatial distribution pattern. Findings reveal the following: (1) From 2013 to 2014, the sports tourism resources in China present a distribution pattern with the Yangtze River Delta urban agglomeration as the high-density core area and the Guizhou–Guangxi border area and the western Hubei ecological circle as the sub-density core areas. (2) From 2014 to 2018, China’s sports tourism boutique projects increased by 381, and the regional differences among various provinces tended to converge. The high-density core area remained unchanged. The sub-density cores are now the Yunqian border area of the Karst Plateau, the Qinglong border area of the Qilian Mountains, and the Jinji border area of the Taihang Mountains, shaping the distribution trends of “depending on the city, near the scenery” and “large concentration, small dispersion”. (3) The proportion of provincial sports tourism development classified as being in the coordinated stage is 61.29%. (4) The explanatory power of the factors affecting the spatial layout in descending order is natural resource endowment, sports resource endowment, transportation capacity, industrial support and guidance, market cultivation and development, people’s living standards, software and hardware services, and economic benefit effects. The explanatory power of the interaction of two different factors is higher than that of the single factor.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationISPRS international journal of geo-information, July 2021, v. 10, no. 7, 428-
dcterms.isPartOfISPRS international journal of geo-information-
dcterms.issued2021-07-
dc.identifier.scopus2-s2.0-85109382200-
dc.identifier.eissn2220-9964-
dc.identifier.artn428-
dc.description.validate202110 bcvc-
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_Scopus/WOSen_US
dc.description.pubStatusPublisheden_US
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