<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Principal component analysis is usually a linear combination of all variables, which is very detrimental to the interpretation of the results. Therefore, this paper proposes sparse principal component analysis for analyzing the impact of the Xinguang epidemic on tourism, which can find linear combinations containing only a few input variables, effectively filter out sparse principal components, and achieve the purpose of explaining a high percentage of changes with sparse linear combinations. By observing the trend of accommodation, food and beverage, and tourism turnover data in Guizhou Qiannan region before and after the epidemic from 2015 to 2021, it was confirmed that the data of 2020 was mainly used as the dataset. For sparse principal component analysis, four data sets were selected, each with 14 indicators and 32 samples. The findings revealed that the tourism topics of interest during the epidemic were more spread out and had a lower concentration of links among each other. In the accommodation industry, the 1st principal component is turnover, which has a common factor variance of 0.995 and an eigenvalue of 13.408. In the catering industry, the 2 principal components can be interpreted as the major category of operating costs and the major category of turnover, with component matrix values of (0.997, -0.073) and (0.996, 0.064), respectively.</p>
</abstract>

Principal component analysis is usually a linear combination of all variables, which is very detrimental to the interpretation of the results. Therefore, this paper proposes sparse principal component analysis for analyzing the impact of the Xinguang epidemic on tourism, which can find linear combinations containing only a few input variables, effectively filter out sparse principal components, and achieve the purpose of explaining a high percentage of changes with sparse linear combinations. By observing the trend of accommodation, food and beverage, and tourism turnover data in Guizhou Qiannan region before and after the epidemic from 2015 to 2021, it was confirmed that the data of 2020 was mainly used as the dataset. For sparse principal component analysis, four data sets were selected, each with 14 indicators and 32 samples. The findings revealed that the tourism topics of interest during the epidemic were more spread out and had a lower concentration of links among each other. In the accommodation industry, the 1st principal component is turnover, which has a common factor variance of 0.995 and an eigenvalue of 13.408. In the catering industry, the 2 principal components can be interpreted as the major category of operating costs and the major category of turnover, with component matrix values of (0.997, -0.073) and (0.996, 0.064), respectively.

Exploring the Impact of the New Crown Epidemic on Tourism in Qiannan, Guizhou Using Principal Component Analysis Methods

Qiannan Normal University for Nationalities

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

Principal component analysis is usually a linear combination of all variables, which is very detrimental to the interpretation of the results. Therefore, this...

Exploring the Impact of the New Crown Epidemic on Tourism in Qiannan, Guizhou Using Principal Component Analysis Methods

Published Online: Jan 31, 2024

Page range: -

Received: Dec 26, 2023

Accepted: Dec 31, 2023

DOI: https://doi.org/10.2478/amns-2024-0282

Keywords
Sparse principal components, Linear combination, Thematic classification, Tourism industry

© 2024 Yuanyuan Zou, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Exploring the Impact of the New Crown Epidemic on Tourism in Qiannan, Guizhou Using Principal Component Analysis Methods

Published Online: Jan 31, 2024

Page range: -

Received: Dec 26, 2023

Accepted: Dec 31, 2023

DOI: https://doi.org/10.2478/amns-2024-0282

KeywordsSparse principal components, Linear combination, Thematic classification, Tourism industry

© 2024 Yuanyuan Zou, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Keywords
Sparse principal components, Linear combination, Thematic classification, Tourism industry