Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/93531
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Land Surveying and Geo-Informatics | en_US |
dc.contributor | School of Hotel and Tourism Management | en_US |
dc.creator | Xu, Y | en_US |
dc.creator | Li, J | en_US |
dc.creator | Belyi, A | en_US |
dc.creator | Park, S | en_US |
dc.date.accessioned | 2022-07-08T01:02:58Z | - |
dc.date.available | 2022-07-08T01:02:58Z | - |
dc.identifier.issn | 0261-5177 | en_US |
dc.identifier.uri | http://hdl.handle.net/10397/93531 | - |
dc.language.iso | en | en_US |
dc.publisher | Pergamon Press | en_US |
dc.rights | © 2020 Elsevier Ltd. All rights reserved. | en_US |
dc.rights | © 2020. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/. | en_US |
dc.rights | The following publication Xu, Y., Li, J., Belyi, A., & Park, S. (2021). Characterizing destination networks through mobility traces of international tourists — A case study using a nationwide mobile positioning dataset. Tourism Management, 82, 104195 is available at https://dx.doi.org/10.1016/j.tourman.2020.104195. | en_US |
dc.subject | Community detection | en_US |
dc.subject | Mobile positioning | en_US |
dc.subject | Network science | en_US |
dc.subject | Tourism big data | en_US |
dc.subject | Tourist mobility | en_US |
dc.title | Characterizing destination networks through mobility traces of international tourists — A case study using a nationwide mobile positioning dataset | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.volume | 82 | en_US |
dc.identifier.doi | 10.1016/j.tourman.2020.104195 | en_US |
dcterms.abstract | This article demonstrates how large-scale tourist mobility data can be linked with network science approaches to better understand tourism destinations and their interactions. By analyzing a mobile positioning dataset that captures the nationality and movement patterns of foreign tourists to South Korea, we employ a few metrics to quantify the network properties of tourism destinations, aiming to reveal the collective dynamics of tourist movements and key differences across nationalities. According to the results, the number of inbound tourists to destinations follows a log-normal distribution, which indicates a notable heterogeneity of destination attractiveness. Although this finding holds across different nationalities, we find that tourists from different countries tended to visit different places in South Korea. A community detection algorithm partitions South Korea into several tourism regions, each covering a set of destinations that are closely connected by tourist flows. The implications for transportation development and regional tourism planning are discussed. | en_US |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | Tourism management, Feb. 2021, v. 82, 104195 | en_US |
dcterms.isPartOf | Tourism management | en_US |
dcterms.issued | 2021-02 | - |
dc.identifier.scopus | 2-s2.0-85088475033 | - |
dc.identifier.eissn | 1879-3193 | en_US |
dc.identifier.artn | 104195 | en_US |
dc.description.validate | 202207 bcfc | en_US |
dc.description.oa | Accepted Manuscript | en_US |
dc.identifier.FolderNumber | LSGI-0046 | - |
dc.description.fundingSource | Others | en_US |
dc.description.fundingText | Hospitality and Tourism Research Centre (HTRC Grant) of the School of Hotel and Tourism Management The Hong Kong Polytechnic University, the Hong Kong Polytechnic University PTeC Grant; the Hong Kong Polytechnic University Start-Up Grant | en_US |
dc.description.pubStatus | Published | en_US |
dc.identifier.OPUS | 26742613 | - |
dc.description.oaCategory | Green (AAM) | en_US |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
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Xu_Characterizing_Destination_Networks.pdf | Pre-Published version | 17.1 MB | Adobe PDF | View/Open |
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