Leveraging tourist trajectory data for effective destination planning and management: A new heuristic approach
Understanding tourist movements provides insights for destination planning, service design and marketing. The key challenge is to develop a tool that can capture the value in the tourist mobility data. This study presents a new heuristic approach that combines adaptive spatial clustering with freque...
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| Published in | Tourism management (1982) Vol. 89; p. 104437 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier Ltd
01.04.2022
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| Subjects | |
| Online Access | Get full text |
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| Abstract | Understanding tourist movements provides insights for destination planning, service design and marketing. The key challenge is to develop a tool that can capture the value in the tourist mobility data. This study presents a new heuristic approach that combines adaptive spatial clustering with frequent pattern mining to improve the performance and efficiency of trajectory data analytics. The aim is to fully leverage the semantic information in the tourist data and the duration that tourists stay at an attraction. Anonymous mobile positioning data from 741 tourists to one of China's leading destinations are used to illustrate the application of the new analytical approach. The results reveal a four-level destination spatial structure ranging from core to peripheral areas. The findings provide practical implications for facilitating intra-destination cooperation and optimizing destination resource allocation and service design. |
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| AbstractList | Understanding tourist movements provides insights for destination planning, service design and marketing. The key challenge is to develop a tool that can capture the value in the tourist mobility data. This study presents a new heuristic approach that combines adaptive spatial clustering with frequent pattern mining to improve the performance and efficiency of trajectory data analytics. The aim is to fully leverage the semantic information in the tourist data and the duration that tourists stay at an attraction. Anonymous mobile positioning data from 741 tourists to one of China's leading destinations are used to illustrate the application of the new analytical approach. The results reveal a four-level destination spatial structure ranging from core to peripheral areas. The findings provide practical implications for facilitating intra-destination cooperation and optimizing destination resource allocation and service design. |
| ArticleNumber | 104437 |
| Author | Li, Mengling Zhang, Yangyu Zheng, Weimin Lin, Zhibin |
| Author_xml | – sequence: 1 givenname: Weimin orcidid: 0000-0002-4450-5428 surname: Zheng fullname: Zheng, Weimin organization: School of Management, Xiamen University, 422 South Siming Road, Xiamen, 361005, China – sequence: 2 givenname: Mengling surname: Li fullname: Li, Mengling organization: School of Management, Xiamen University, 422 South Siming Road, Xiamen, 361005, China – sequence: 3 givenname: Zhibin orcidid: 0000-0001-5575-2216 surname: Lin fullname: Lin, Zhibin email: zhibin.lin@durham.ac.uk organization: Durham University Business School, Mill Hill Lane, Durham, DH1 3LB, United Kingdom – sequence: 4 givenname: Yangyu surname: Zhang fullname: Zhang, Yangyu organization: School of Management, Xiamen University, 422 South Siming Road, Xiamen, 361005, China |
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| Keywords | Frequent pattern mining Intra-destination cooperation Tourist mobility Data mining Time-space Core-peripheral |
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