ABSTRACT
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.
ABSTRACT
Machine learning (ML) methods can be leveraged to prevent the spread of deadly infectious disease outbreak (e.g., COVID-19). This can be done by applying machine learning methods in predicting and detecting the deadly infectious disease. Most reviews did not discuss about the machine learning algorithms, datasets and performance measurements used for various applications in predicting and detecting the deadly infectious disease. In contrast, this paper outlines the literature review based on two major ways (e.g., prediction, detection) to limit the spread of deadly disease outbreaks. Hence, this study aims to investigate the state of the art, challenges and future works of leveraging ML methods to detect and predict deadly disease outbreaks according to two categories mentioned earlier. Specifically, this study provides a review on various approaches (e.g., individual and ensemble models), types of datasets, parameters or variables and performance measures used in the previous works. The literature review included all articles from journals and conference proceedings published from 2010 through 2020 in Scopus indexed databases using the search terms Predicting Disease Outbreaks and/or Detecting Disease using Machine Learning. The findings from this review focus on commonly used machine learning approaches, challenges and future works to limit the spread of deadly disease outbreaks through preventions and detections.
