Community-based tourism is a sustainable form of tourism development where tourists visit residential communities to interact with local lives and cultures for an enhanced travel experience. Identifying and tracking tourist activities in community-based tourism is particularly challenging, as tourists have shared activity spaces with residents. The paper proposes a new method to study the space–time patterns of the tourist flow using Wi-Fi data. Specifically, we have tracked Wi-Fi probe requests over six months in the Shichahai scenic area, a famous community-based tourist attraction in Beijing, China. After deriving the tourist flow from the Wi-Fi data, we have applied the empirical orthogonal function (EOF) method to the identification of the spatial aggregation pattern and the temporality of the tourist flow. A follow-up explanatory analysis examines the environmental impacts, such as weather conditions, air quality, and travel days, on the space–time patterns. The study is among the first to employ Wi-Fi data to study travel behaviours in community-based tourism. The proposed method can shed insights into a better understanding of tourist behaviours in open-space, tourism-oriented urban communities.