Dataset

The dataset used in this study consists of three primary variables extracted from Google Maps Reviews: Username, Review Date, and Place. These variables represent tourists’ digital footprints and serve as the basis for reconstructing mobility patterns across destinations in Central Lombok. The dataset is sourced entirely from publicly available Google Maps pages, ensuring that the data collection process remains non-intrusive and ethically compliant.The Username variable contains the display names of users who posted reviews. This variable functions as an identifier that enables the study to group reviews by individual users. By organizing entries under each username, the research reconstructs sequential visit patterns, making it possible to infer how tourists move from one destination to another. Although usernames do not represent verified identities, they consistently mark the behavior of distinct users within the dataset.The Review Date variable records the date each user submitted a review. This variable is essential for establishing the chronological order of visits. By sorting reviews by date for each user, the analysis identifies directed visit flows in the form of source–target relationships between destinations. This chronological structure is fundamental for building the mobility network using Social Network Analysis.The Place variable represents the destinations visited and reviewed by tourists. Each entry corresponds to a single location and forms the nodes in the mobility network. The frequency of reviews for each place also provides an indication of its popularity and relevance within the tourism system.Together, these three variables provide the minimum yet sufficient structure required to model tourist mobility using SNA. Despite not including textual review content, ratings, or geospatial coordinates within the Excel file itself, the dataset effectively captures the temporal and relational dimensions of tourist behavior. While review dates may not always reflect the exact visit dates and only represent active reviewers, the dataset remains a reliable and scalable foundation for mapping movement patterns in data-limited tourism environments.