Multiscale Characteristics and Connection Mechanisms of Attraction Networks: A Trajectory Data Mining Approach Leveraging Geotagged Data.
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| Title: | Multiscale Characteristics and Connection Mechanisms of Attraction Networks: A Trajectory Data Mining Approach Leveraging Geotagged Data. |
|---|---|
| Authors: | Jiang, Hongqiang1 (AUTHOR), Wei, Ye2 (AUTHOR), Mei, Lin2,3 (AUTHOR) meil682@nenu.edu.cn, Wang, Zhaobo4 (AUTHOR) |
| Source: | Chinese Geographical Science. Jun2024, Vol. 34 Issue 3, p533-547. 15p. |
| Subject Terms: | *Scale-free network (Statistical physics), *Geotagging, *Data mining, *Urban tourism, *Tourist attractions, *Matthew effect |
| Geographic Terms: | Beijing (China) |
| Reviews & Products: | Bible. Matthew |
| Abstract: | Urban tourism is considered a complex system, and multiscale exploration of the organizational patterns of attraction networks has become a topical issue in urban tourism, so exploring the multiscale characteristics and connection mechanisms of attraction networks is important for understanding the linkages between attractions and even the future destination planning. This paper uses geotagging data to compare the links between attractions in Beijing, China during four different periods: the pre-Olympic period (2004–2007), the Olympic Games and subsequent 'heat period' (2008–2013), the post-Olympic period (2014–2019), and the COVID-19(Corona Virus Disease 2019) pandemic period (2020–2021). The aim is to better understand the evolution and patterns of attraction networks at different scales in Beijing and to provide insights for tourism planning in the destination. The results show that the macro, meso-, and microscales network characteristics of attraction networks have inherent logical relationships that can explain the commonalities and differences in the development process of tourism networks. The macroscale attraction network degree Matthew effect is significant in the four different periods and exhibits a morphological monocentric structure, suggesting that new entrants are more likely to be associated with attractions that already have high value. The mesoscale links attractions according to the common purpose of tourists, and the results of the community segmentation of the attraction networks in the four different periods suggest that the functional polycentric structure describes their clustering effect, and the weak links between clusters result from attractions bound by incomplete information and distance, and the functional polycentric structure with a generally more efficient network of clusters. The pattern structure at the microscale reveals the topological transformation relationship of the regional collaboration pattern, and the attraction network structure in the four different periods has a very similar importance profile structure suggesting that the attraction network has the same construction rules and evolution mechanism, which aids in understanding the attraction network pattern at both macro and micro scales. Important approaches and practical implications for planners and managers are presented. [ABSTRACT FROM AUTHOR] |
| Database: | Energy & Power Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: enr DbLabel: Energy & Power Source An: 177284659 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Multiscale Characteristics and Connection Mechanisms of Attraction Networks: A Trajectory Data Mining Approach Leveraging Geotagged Data. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Jiang%2C+Hongqiang%22">Jiang, Hongqiang</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wei%2C+Ye%22">Wei, Ye</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Mei%2C+Lin%22">Mei, Lin</searchLink><relatesTo>2,3</relatesTo> (AUTHOR)<i> meil682@nenu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Wang%2C+Zhaobo%22">Wang, Zhaobo</searchLink><relatesTo>4</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Chinese+Geographical+Science%22">Chinese Geographical Science</searchLink>. Jun2024, Vol. 34 Issue 3, p533-547. 15p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Scale-free+network+%28Statistical+physics%29%22">Scale-free network (Statistical physics)</searchLink><br />*<searchLink fieldCode="DE" term="%22Geotagging%22">Geotagging</searchLink><br />*<searchLink fieldCode="DE" term="%22Data+mining%22">Data mining</searchLink><br />*<searchLink fieldCode="DE" term="%22Urban+tourism%22">Urban tourism</searchLink><br />*<searchLink fieldCode="DE" term="%22Tourist+attractions%22">Tourist attractions</searchLink><br />*<searchLink fieldCode="DE" term="%22Matthew+effect%22">Matthew effect</searchLink> – Name: SubjectGeographic Label: Geographic Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Beijing+%28China%29%22">Beijing (China)</searchLink> – Name: SubjectProduct Label: Reviews & Products Group: Su Data: <searchLink fieldCode="PS" term="%22Bible%2E+Matthew%22">Bible. Matthew</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Urban tourism is considered a complex system, and multiscale exploration of the organizational patterns of attraction networks has become a topical issue in urban tourism, so exploring the multiscale characteristics and connection mechanisms of attraction networks is important for understanding the linkages between attractions and even the future destination planning. This paper uses geotagging data to compare the links between attractions in Beijing, China during four different periods: the pre-Olympic period (2004–2007), the Olympic Games and subsequent 'heat period' (2008–2013), the post-Olympic period (2014–2019), and the COVID-19(Corona Virus Disease 2019) pandemic period (2020–2021). The aim is to better understand the evolution and patterns of attraction networks at different scales in Beijing and to provide insights for tourism planning in the destination. The results show that the macro, meso-, and microscales network characteristics of attraction networks have inherent logical relationships that can explain the commonalities and differences in the development process of tourism networks. The macroscale attraction network degree Matthew effect is significant in the four different periods and exhibits a morphological monocentric structure, suggesting that new entrants are more likely to be associated with attractions that already have high value. The mesoscale links attractions according to the common purpose of tourists, and the results of the community segmentation of the attraction networks in the four different periods suggest that the functional polycentric structure describes their clustering effect, and the weak links between clusters result from attractions bound by incomplete information and distance, and the functional polycentric structure with a generally more efficient network of clusters. The pattern structure at the microscale reveals the topological transformation relationship of the regional collaboration pattern, and the attraction network structure in the four different periods has a very similar importance profile structure suggesting that the attraction network has the same construction rules and evolution mechanism, which aids in understanding the attraction network pattern at both macro and micro scales. Important approaches and practical implications for planners and managers are presented. [ABSTRACT FROM AUTHOR] |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s11769-024-1417-x Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 15 StartPage: 533 Subjects: – SubjectFull: Scale-free network (Statistical physics) Type: general – SubjectFull: Geotagging Type: general – SubjectFull: Data mining Type: general – SubjectFull: Urban tourism Type: general – SubjectFull: Tourist attractions Type: general – SubjectFull: Matthew effect Type: general – SubjectFull: Beijing (China) Type: general – SubjectFull: Bible. Matthew Type: general Titles: – TitleFull: Multiscale Characteristics and Connection Mechanisms of Attraction Networks: A Trajectory Data Mining Approach Leveraging Geotagged Data. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Jiang, Hongqiang – PersonEntity: Name: NameFull: Wei, Ye – PersonEntity: Name: NameFull: Mei, Lin – PersonEntity: Name: NameFull: Wang, Zhaobo IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2024 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 10020063 Numbering: – Type: volume Value: 34 – Type: issue Value: 3 Titles: – TitleFull: Chinese Geographical Science Type: main |
| ResultId | 1 |
