Proceedings of the 2023 4th International Conference on Big Data and Social Sciences (ICBDSS 2023)

<Previous Article In Volume
Next Article In Volume>

Principal component and cluster analysis of Macao tourism destination competitiveness based on Big data

Authors
Fu Luo1, Yingying Zhu2, *, Xinxin Wang1, Xiaojun Luo1, Juncong Chen1, Dongmeng Ye1
1School of Management, Guangdong University of Science and Technology, Dongguan, China
2LanZhou University of Finance and Economics, Lanzhou, China
*Corresponding author. Email: 1789302392@qq.com
Corresponding Author
Yingying Zhu
Available Online 27 October 2023.
DOI
10.2991/978-94-6463-276-7_33How to use a DOI?
Keywords
component; cluster analysis; data analysis
Abstract

This study investigates the principal components of the competitiveness of tourism destinations and their intrinsic classification by applying principal component analysis and cluster analysis with Macau tourism destination as the research object. First, a large amount of data on Macau tourism was collected, and the main factors affecting the competitiveness of tourism destinations were identified as "infrastructure and service quality" and "diversity and innovation of tourism products" through principal component analysis. Cluster analysis was then used to classify Macau's tourism destinations into two main categories, one featuring high-quality infrastructure and services, and the other featuring a rich and diverse tourism product. The results have important theoretical and practical implications for enhancing the competitiveness of Macau and other tourism destinations. Future research could further consider temporal factors as well as try to apply the research framework to other tourism destinations.

Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

<Previous Article In Volume
Next Article In Volume>
Volume Title
Proceedings of the 2023 4th International Conference on Big Data and Social Sciences (ICBDSS 2023)
Series
Atlantis Highlights in Social Sciences, Education and Humanities
Publication Date
27 October 2023
ISBN
978-94-6463-276-7
ISSN
2667-128X
DOI
10.2991/978-94-6463-276-7_33How to use a DOI?
Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Fu Luo
AU  - Yingying Zhu
AU  - Xinxin Wang
AU  - Xiaojun Luo
AU  - Juncong Chen
AU  - Dongmeng Ye
PY  - 2023
DA  - 2023/10/27
TI  - Principal component and cluster analysis of Macao tourism destination competitiveness based on Big data
BT  - Proceedings of the 2023 4th International Conference on Big Data and Social Sciences (ICBDSS 2023)
PB  - Atlantis Press
SP  - 308
EP  - 316
SN  - 2667-128X
UR  - https://doi.org/10.2991/978-94-6463-276-7_33
DO  - 10.2991/978-94-6463-276-7_33
ID  - Luo2023
ER  -