JOURNAL ARTICLE

Exploring the Convergence of Cyber–Physical Space: Multidimensional Modeling of Overtourism Interactions.

  • Published In: Transactions in GIS, 2024, v. 28, n. 7. P. 2425 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Tai, Kaiwing; Liao, Minglei; Liu, Xintao 3 of 3

Abstract

As technological advancements continue to blur the boundaries between cyber and physical spaces, individuals' activities are not limited to physical space and increasingly transcend singular space. Prior research on interactions between cyber and physical spaces oversimplified or even overlooked the interactions between the two spaces due to limited access to human activity big data, for instance, the implications of cyber–physical interactions for tourism. Consequently, this study proposes an overtourism index framework intended to capture travel behaviors in both cyber and physical spaces to address the research gap. Based on extensive social media data from Hong Kong, the proposed framework is tested and validated efficiently to emulate tourism interactions between cyber and physical spaces at fine spatiotemporal resolutions. The results indicate that there is strong convergent interaction among the public in both cyber and physical spaces. Moreover, an online WebGIS interactive platform (https://arcg.is/0mzHyH) has been developed for visualizing these interactions and provides better decision‐making regarding tourism policy in Hong Kong. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Transactions in GIS. 2024/11, Vol. 28, Issue 7, p2425
  • Document Type:Article
  • Subject Area:Sports and Leisure
  • Publication Date:2024
  • ISSN:1361-1682
  • DOI:10.1111/tgis.13245
  • Accession Number:180851258
  • Copyright Statement:Copyright of Transactions in GIS is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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