Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor | Department of Land Surveying and Geo-Informatics | en_US |
| dc.contributor.advisor | Shi, Wenzhong (LSGI) | en_US |
| dc.creator | Zhou, Xiaolin | - |
| dc.identifier.uri | https://theses.lib.polyu.edu.hk/handle/200/12470 | - |
| dc.language | English | en_US |
| dc.publisher | Hong Kong Polytechnic University | en_US |
| dc.rights | All rights reserved | en_US |
| dc.title | Network-based tourism attraction analysis using multi-source geotagged data | en_US |
| dcterms.abstract | Tourism is a networked industry. Resources allocated to destination marketing and management should go beyond single and primary attractions. Tourism resources are significantly connected to each other based on tourist interest. Age of Big Data provides new opportunities for investigate tourist behavior and experience, especially with geotagged data tracking tourists’ visitation pattern. Using geotagged information as an indication of tourist movement patterns between attractions, this study connects attractions based on tourists’ common motivations. Meanwhile, tourist movements can report complex routes that reveal an attraction network of the attractiveness propagation. Information network among tourism attractions can then be abstracted and the following analysis is conducted to facilitate destination planning. | en_US |
| dcterms.abstract | In this study, ranking analysis on attraction network is firstly conducted. Targeted at the feature of attraction network (i.e., distance is accounted as cost for tourists), we propose AttractionRank as a reasonable and fairer way to assess tourism attraction’s attractiveness. AttractionRank is an extension of the PageRank algorithm by eliminating the distance-decay effect scrupulously in the attractiveness propagation process. The main rationale is to calibrate the distance-decay effect function and its effect scale via trip distribution estimation and then exclude the effect in the network linkage weight when applying weighted PageRank. Experimental results shows that AttractionRank outperforms other two schemes and the entire ranking results are also statistically significant in data interpretation in practical sense. The results also demonstrate the potential value of AttractionRank for tourism planning in Hong Kong and other regions. | en_US |
| dcterms.abstract | Clustering analysis on that attraction information network is then explored. The study describes the clustering effects by sorting the attractions into four clusters in Hong Kong case. A new framework was used to reveal the characteristics of these intra-cluster attractions from three dimensions: theme, visit volume, and level of importance by attractiveness propagation rank. The framework offers a theoretical contribution to the literature on clustering dimensions by providing a feasible experimentation method, and, from a practical perspective, it can guide destination marketing strategies for attraction networking. This destination-wide clustering effect analysis facilitate destination planning from a broader perspective than analysis that narrowly focus on certain attractions. | en_US |
| dcterms.abstract | As a complex networked industry, attraction nodes in tourism system should not be concentrated on only. Tourist concerns and needs are also interconnected in VacationScape. This study finally draws upon Gunn’s formative concept of organic destination image and applies it in a contemporary regional context so that a comprehensive apprehension about the attitudes of entire tourists can be revealed. This study uses a prominent Chinese social media platform - the Red – to evaluate tourist/user generated content and explores the destination image of the “9 + 2” Greater Bay Area cities in southern China (9 mainland cites plus Macau and Hong Kong SARs). Four clusters of designative and prescriptive image are proposed. The proposed insights can benefit destination planning at local and regional levels by showing the merits of mobilizing tourism resources across cities. | en_US |
| dcterms.extent | ix, 114 pages : color illustrations, maps | en_US |
| dcterms.isPartOf | PolyU Electronic Theses | en_US |
| dcterms.issued | 2023 | en_US |
| dcterms.educationalLevel | Ph.D. | en_US |
| dcterms.educationalLevel | All Doctorate | en_US |
| dcterms.LCSH | Tourism -- Data processing | en_US |
| dcterms.LCSH | Hong Kong Polytechnic University -- Dissertations | en_US |
| dcterms.accessRights | open access | en_US |
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