A Big Data Analytics Method for Tourist Behaviour Analysis

© 2016 Elsevier B.V. Big data generated across social media sites have created numerous opportunities for bringing more insights to decision-makers. Few studies on big data analytics, however, have demonstrated the support for strategic decision-making. Moreover, a formal method for analysing social media-generated big data for decision support is yet to be developed, particularly in the tourism sector. Using a design science research approach, this study aims to design and evaluate a ‘big data analytics’ method to support strategic decision-making in tourism destination management. Using geotagged photos uploaded by tourists to the photo-sharing social media site, Flickr, the applicability of the method in assisting destination management organisations to analyse and predict tourist behavioural patterns at specific destinations is shown, using Melbourne, Australia, as a representative case. Utility was confirmed using both another destination and directly with stakeholder audiences. The developed artefact demonstrates a method for analysing unstructured big data to enhance strategic decision making within a real problem domain. The proposed method is generic, and its applicability to other big data streams is discussed

A Big Data Analytics Method for Tourist Behaviour Analysis

© 2016 Elsevier B.V. Big data generated across social media sites have created numerous opportunities for bringing more insights to decision-makers. Few studies on big data analytics, however, have demonstrated the support for strategic decision-making. Moreover, a formal method for analysing social media-generated big data for decision support is yet to be developed, particularly in the tourism sector. Using a design science research approach, this study aims to design and evaluate a ‘big data analytics’ method to support strategic decision-making in tourism destination management. Using geotagged photos uploaded by tourists to the photo-sharing social media site, Flickr, the applicability of the method in assisting destination management organisations to analyse and predict tourist behavioural patterns at specific destinations is shown, using Melbourne, Australia, as a representative case. Utility was confirmed using both another destination and directly with stakeholder audiences. The developed artefact demonstrates a method for analysing unstructured big data to enhance strategic decision making within a real problem domain. The proposed method is generic, and its applicability to other big data streams is discussed

Towards developing a Healthcare Situation Monitoring Method for Smart City Initiatives: A Citizen Safety Perspective

Research in Smart City development has been proliferated over the past few years, which focused heavily on various supporting service sectors, such as healthcare. However, little effort has been made to design health surveillance support systems, which is also important for the advancement of public healthcare monitoring as an essential smart city initiatives. From an information system (IS) design perspective, this paper introduces a social media-based health surveillance supporting method, which can automatically extricates relevant online posts for health symptom management and prediction. We describe and demonstrate an IS design approach in this paper for hay-fever prediction solution concept based on Twitter posts. This concept can be applicable to fully functional solution design by relevant practitioners in this field

A developed GPS trajectories data management system for predicting tourists' POI

One of the areas that have challenges in the use of internet of things (IoT) is the field of tourism and travel. The issue here is how to employ this technology to serve the tourism and managing the produced data. This work is focus on the use of tourists' trajectories that are collected from global positioning system (GPS) mobile sensors as a source of information. The aim of work is to predict preferred tourism places for tourists by tracking tourists' behavior to extract the tourism places that have been visited by such tourists. Density based clustering algorithm is mainly used to extract stay points and point of interest (POI). By projecting GPS location (for user and places) on the Google map, the type and name of places favored by the tourists are determined. K nearest neighbor (KNN) algorithm with haversine distance has been adopted to find the nearest places for tourists. The evaluation of the obtained results shows superior and satisfactory performance that can reach the objective behind this work

Depression and anxiety detection through the closed-loop method using DASS-21

The change of information and communication technology has brought many changes in daily life. The way humans interacting is changing. It is possible to express each form of communication directly and instantly. Social media has contributed data in size, diversity and capacity and quality. Based on it, the idea was to see and measure the tendency of depression and anxiety through social media using the Closed-Loop method using Facebook text mining posts. Through the stages of pre-processing including text extraction using the Naïve Bayes machine learning model for text classification, the early signs of depression and anxiety are measured using DASS-21 parameter. In total, 22,934 Facebook posts were contributed as training and learning data collected from July 2017 until July 2018. As a results, analysis and mapping of social demographics of users that are usually as a trigger of depression, and anxiety, such as grief, illness, household affairs, children education and others are available

A Data Analytics Study for Adverse Reactions of Blood Donors by Age, Gender, and Donation Type

The blood donation process is usually very safe, and blood donors are comfortable during the blood donation procedure; however, blood donors occasionally experience various types of adverse reactions during or at the end of blood donation. Some of these reactions are very minor while blood donors sometimes experience serious reactions as well. This study aims to analyze the various types of adverse reactions experienced by the blood donors. The study conducts detailed analysis on a significant amount of real data collected through a blood organization in the southern part of the United States and provides the results regarding the frequency and types of adverse reactions based on multiple attributes such as age, gender, and donation type

A Business Intelligence Solution For Ticket Sales Management In Sintra’s UNESCO Cultural Heritage

Project Work presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Business AnalyticsThe tourism industry has experienced huge growth of business volume in recent years leading to a rapid increase in the amount of data being generated. To leverage these data, tourism companies can greatly benefit from incorporating business intelligence tools in their daily operations, which can contribute to the creation of new value and to sustainable growth. In this work, we develop a business intelligence solution for Parques de Sintra - Monte da Lua (PSML), a public company that manages ticket sales for some of the most visited cultural attractions in Portugal, which are part of the UNESCO Cultural Heritage. We optimize their current transactional database structure for data analysis by following a dimensional modelling methodology. Then, we develop three dashboards on top of the resultant model. Each dashboard aggregates different visuals and provides information from different perspectives of the business, namely sales, attractions, and customers. We analyse the layout and visualization capabilities of the dashboards and provide insights regarding data interpretation. With this work, we provide the PSML team with a tool that can aid in the quick monitoring of their business at different levels and has the potential to inform decisions and strategies in the areas of sales, logistics, advertising, and customer satisfaction

Understanding Demographics and Experience of Tourists in Yellowstone National Park through Social Media

This study compared tourists’ demographic variables between survey data and Twitter data in Yellowstone National Park and explored tourists’ experience through Twitter data. First, there were significant differences in age groups of tourists between social media data and survey data. Compared to survey data, tourists who identified by Twitter data concentrated on middle age groups. Secondly, the spatial distribution of geotagged tweets reflected the road network and main attractions in Yellowstone National Park. The peak visitation season is from June to September in survey data, while, in social media data, the peak visitation season is slightly shorter. Finally, the sentiment analysis was conducted and only 6.7% of tweets were negative, indicating that most tourists in Yellowstone National Park had good experience. Therefore, analyzing Twitter data will be helpful for understanding tourists’ demographics, attitudes and experience in the national parks and improving customer service in the further

Artificial Intelligence in the Tourism Industry: A privacy impasse

Artificial Intelligence (AI) adoption in the tourism industry has resulted with privacy concerns as companies feed a vast amount of consumer data into AI, creating sensitive customer information. Therefore, this research aims at investigating the adequacy of the Personal Data Protection Act 2010 in addressing the privacy challenges raised by AI. Combining the doctrinal methodology and a case study, this research produced systematic means of legal reasoning pertinent to AI applications in the tourism industry. Ensuring privacy and security through every phase of the data lifecycle is pivotal to avoid legal liability for the tourism players while preserving customer confidence. Keywords: Artificial Intelligence and Law, Privacy and Artificial Intelligence, Privacy Engineering Model, Data Protection and Artificial Intelligence eISSN: 2398-4287 © 2022. The Authors. Published for AMER ABRA cE-Bs by e-International Publishing House, Ltd., UK. This is an open-access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer–review under the responsibility of AMER (Association of Malaysian Environment-Behaviour Researchers), ABRA (Association of Behavioural Researchers on Asians), and cE-Bs (Centre for Environment-Behaviour Studies), Faculty of Architecture, Planning & Surveying, Universiti Teknologi MARA, Malaysia. DOI: https://doi.org/10.21834/ebpj.v7iSI7.381