Big data and data mining

Researchers at GIFT have access to a range of national and international databases that enable them to integrate large data and extract tourism-specific intelligence. Researchers draw additional expertise from the Big Data laboratory at Griffith University. GIFT staff have advanced skills in various types of statistical analysis and modelling, always pushing the boundaries to bring latest scientific understanding into tailored analytics.

Big Data Analytics

Big Data has challenged the way researchers approach analytical problems, and provided new insights into different domains of research. By incorporating Big Data analytics into tourism, researchers can understand tourist behaviour and destination changes in more detail, and in real time. Improved decision making is the desired outcome.

At GIFT, we are currently working on a number of projects using Big Data, including a Visitor Information Portal (VIP) for tourism sentiment monitoring at the Great Barrier Reef Marine Park and an automated image analysis, using machine learning.

Global Sustainable Tourism Dashboard

Researchers from GIFT are collaborating with the University of Surrey to develop a global dashboard to track the performance of tourism across all dimensions of sustainability: economic, social and environmental. The initiative is supported by the World Travel and Tourism Council (WTTC), World Economic Forum, International Tourism Partnership, and EarthCheck, amongst others. Economic indicators of the number of arrivals, receipts, jobs and investment are collected at a global scale by WTTC and UNWTO amongst others.


2016 Technical Report

Using Twitter data to measure the Commonwealth Games

A mega event attracts visitors to a host destination for the event experience, bringing in economic and non-economic gains to the destination. The user-generated discussions of the mega event on social media sites have important implications in event sales, place making and destination branding. This nexus between events and destinations in marketing and branding is a focal point of this research, which uses text-mining and sequential rule mining methods to examine twitter data related to the 2018 Commonwealth Games (GC2018). The research analyses both tweets discussing GC2018 and tweets posted within the host location area to differentiate visitors from non-visitors to the event and to understand visitors' activities and travel patterns. This unconventional approach will complement conventional visitor survey approach in providing insights on how to capitalise on events for destination marketing and understanding visitor behaviour.

Big Data in Tourism

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