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

<p>As science and technology advance, the tourism industry is increasingly pivoting towards digital and intelligent transformation. The pivotal role of big data in shaping tourism trends is becoming more apparent. This study leverages big data to explore its impact on tourism development and proposes a big data-based model for this purpose. An influence mechanism model tailored to local tourism’s unique characteristics was developed to guide this analysis. Employing data mining techniques, this research utilizes the association rule model to forecast local tourism trends. Additionally, it applies heat evaluation and hotspot analysis models to examine local tourism activities. Spatial characteristics of local tourism are investigated using the nearest neighbor algorithm. An empirical study conducted in Province H illustrates these concepts in action. Here, the entertainment and leisure segment aligns most closely with the overall clustering characteristics, achieving a Z-score of -200.01. The peak hotness months in Province H are May and October, with hotness indices of 0.0438 and 0.0468, respectively. Entertainment and leisure activities make the most significant contribution to this metric, with an average value of 0.0235. Moreover, the prediction error does not exceed 5%, underscoring the validity and practical utility of this study’s methodological approach in analyzing local tourism development. This provides a robust scientific basis for harnessing data to foster local tourism growth.</p>
</abstract>

As science and technology advance, the tourism industry is increasingly pivoting towards digital and intelligent transformation. The pivotal role of big data in shaping tourism trends is becoming more apparent. This study leverages big data to explore its impact on tourism development and proposes a big data-based model for this purpose. An influence mechanism model tailored to local tourism’s unique characteristics was developed to guide this analysis. Employing data mining techniques, this research utilizes the association rule model to forecast local tourism trends. Additionally, it applies heat evaluation and hotspot analysis models to examine local tourism activities. Spatial characteristics of local tourism are investigated using the nearest neighbor algorithm. An empirical study conducted in Province H illustrates these concepts in action. Here, the entertainment and leisure segment aligns most closely with the overall clustering characteristics, achieving a Z-score of -200.01. The peak hotness months in Province H are May and October, with hotness indices of 0.0438 and 0.0468, respectively. Entertainment and leisure activities make the most significant contribution to this metric, with an average value of 0.0235. Moreover, the prediction error does not exceed 5%, underscoring the validity and practical utility of this study’s methodological approach in analyzing local tourism development. This provides a robust scientific basis for harnessing data to foster local tourism growth.

The Role and Strategy of Big Data in Promoting Local Tourism Development

Applied Mathematics and Nonlinear Sciences

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

{"article-title":"The Role and Strategy of Big Data in Promoting Local Tourism Development"}

As science and technology advance, the tourism industry is increasingly pivoting towards digital and intelligent transformation. The pivotal role of big data...

The Role and Strategy of Big Data in Promoting Local Tourism Development

Publié en ligne: 10 juin 2024

Pages: -

Reçu: 14 févr. 2024

Accepté: 29 avr. 2024

DOI: https://doi.org/10.2478/amns-2024-1457

Mots clés
Data mining, Association rules, Heat evaluation model, Hotspot analysis model, Nearest neighbor algorithm, Local tourism development

© 2024 Yanyan Du, published by Sciendo

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

The Role and Strategy of Big Data in Promoting Local Tourism Development

Publié en ligne: 10 juin 2024

Pages: -

Reçu: 14 févr. 2024

Accepté: 29 avr. 2024

DOI: https://doi.org/10.2478/amns-2024-1457

Mots clésData mining, Association rules, Heat evaluation model, Hotspot analysis model, Nearest neighbor algorithm, Local tourism development

© 2024 Yanyan Du, published by Sciendo

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

Mots clés
Data mining, Association rules, Heat evaluation model, Hotspot analysis model, Nearest neighbor algorithm, Local tourism development