A Study of Tourist Sequential Activity Pattern through Location Based Social Network (LBSN)

Abstract

Sequential Pattern Mining (SPM) is an important component in establishing patterns and mining trends of certain activities. In the past, this technique has been used in various fields such as consumer-watch, making future predictions and analyzing and interpreting large datasets for deeply embedded rules and associations. The qualitative details of Singapore tourists’ foursquare check-ins, represented in a tabular form, is an example of a sequential database. Therefore, the Pattern-Growth method which uses Prefix-Span Algorithm is used in this study to obtain the Tourist Sequential Activity Patterns. Insights into tourist movement and activity patterns is deemed beneficial for the tourism sector in many ways, such as designing better travel packages for tourists, maximizing the tourist activity participation and meeting the tourist demands. This research proposes to adopt mobile social media data for effective capturing of tourist activity information in Singapore and utilizes advanced data mining techniques for extracting valuable insights into tourist behavior. The proposed methods and findings of the study have the potential to support tourism managers and policy makers in making better decisions in tourism destination management.