Big Data in Tourism Geographies: Understanding Travel Patterns in Time and Space
Big data analytics have enhanced tourism research and helps the development of new knowledge and support decision-making. Understanding the dispersal of visitors and connections between destinations is vital for destination management. This information informs the creation of linkages between destinations, helps to adjust transport infrastructure, and assist in developing stakeholder cooperation. Traditionally, travel patterns were measured through border crossing statistics, airline data, or surveys. These data are often static and due to time-lags in collection and analysis may not be optimal. Also, data are usually focused on total arrivals or spending and fail to provide nuanced details of traveller behaviour. The availability of big data offers new opportunities in addition to traditional data sources and techniques to analyse travel patterns. Big data is characterised by the 4Vs: volume, variety, velocity, and veracity. Therefore, handling big data requires new technologies to store, process, and analyse. Advances in computer science, technology, and communication, and in equipment and services, have opened new avenues through which information can be collected and generated. For big data analytics in tourism research, there are three main data sources. These are dominated by user-generated data, data generated from devices, and transaction data such as online booking data. The advantages of using big data analytics to understand visitor travel patterns can be seen in the following four aspects: Flexible data collection; High spatial-temporal resolution; Cost-effectiveness; Contents richness. This collection aims to enable understanding how to use big data analytics to analyse visitors' travel patterns, including what specific data can be collected, advantages and disadvantages; how to analyse and visualise the new type of data for modelling travel patterns; and how to develop theoretical contributions using big data analytics for tourism geographers.