Alfred Rayner, Chen Zhu, Eboy Oliver Valentine, Luxuan Zhang, Renjie Li
Creative Advanced Machine Intelligence Research Centre, Faculty of Computing and Informatics, Universiti Malaysia Sabah, Jalan UMS, 88400 Kota Kinabalu, Sabah, Malaysia.
Faculty of Social Sciences and Humanities, Universiti Malaysia Sabah, Jalan UMS, 88400 Kota Kinabalu, Sabah, Malaysia.
Heliyon. 2023 Apr 19;9(5):e15526. doi: 10.1016/j.heliyon.2023.e15526. eCollection 2023 May.
Conducting on-site surveys to assess tourists' spatial visitation patterns and preferences is both time and labor 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. This study provides insights into the applicability of social media big data in regional tourism management and its potential to enhance fieldwork.
开展实地调查以评估游客的空间访问模式和偏好既耗时又费力。然而，基于社交媒体数据对区域访问模式进行评估可以成为旅游管理的重要决策工具。在本研究中，对中国大陆游客在沙巴的访问模式进行了评估，以确定高访问量热点及其变化，以及大规模和小规模的时间特征。数据使用网络爬虫技术从新浪微博平台获取。在这项工作中，采用空间叠加分析来识别中国游客访问的热点以及时空变化。研究结果显示，2016年之前中国游客访问的热点已从沙巴东南海岸转移到沙巴西海岸。在小尺度上，中国游客的访问热点主要集中在哥打基纳巴卢西南海岸沿线的市区，2018年转移到市区东南部。本研究为社交媒体大数据在区域旅游管理中的适用性及其增强实地调查的潜力提供了见解。
