Analysing Trends in the Spatial-Temporal Visitation Patterns of Mainland Chinese Tourists in Sabah, Malaysia based on Weibo Social Big Data

• Main Authors: Rayner Alfred, Zhu Chen, Oliver Valentine Eboy, Zhang Luxuan, Li Renjie
• Format: Article
• Language: English; English
• Published: ResearchGate 2023
• Subjects: QA1-939 Mathematics; QA75.5-76.95 Electronic computers. Computer science
• Online Access: https://eprints.ums.edu.my/id/eprint/37714/1/ABSTRACT.pdf; https://eprints.ums.edu.my/id/eprint/37714/2/FULL%20TEXT.pdf; https://eprints.ums.edu.my/id/eprint/37714/; http://dx.doi.org/10.1016/j.heliyon.2023.e15526

Conducting on-site surveys to assess tourists’ spatial visitation patterns and preferences is both time and labour intensive. However, an assessment of regional visitation patterns based on social media data can be an important decision-making tool for tourism management. In this study, an assessment of the visitation patterns of Chinese mainland tourists in Sabah is conducted to identify high-visitation hotspots and their changes, as well as large-scale and small-scale temporal characteristics. The data is sourced from the Sina Weibo platform using web crawler technology. In this work, a spatial overlay analysis was used to identify the hotspots of Chinese tourists’ visits and the spatial and temporal variations. The results of the study revealed that the hotspots visited by Chinese tourists prior to 2016 have shifted from the southeast coast of Sabah, to the west coast of Sabah. At a small scale, Chinese tourists’ visitation hotspots were mainly concentrated in the urban area along the southwest coast of Kota Kinabalu, shifting to the southeast of the urban area in 2018.