Integrated Circuit （IC） card can record a resident’s mobile travel， reflecting the resident’s Origin-Destination （OD） information. However， due to the large scale of OD flow data， it is easy to cause visual clutter when visualizing the spatial distribution of OD flow directly. Moreover， multivariate data is difficult to be combined with flow data because it contains a variety of different types of data. To solve the problem that direct visualizing the spatial distribution of large-scale OD data is easy to cause visual occlusion， a flow clustering method based on Orthogonal Nonnegative Matrix Decomposition （ONMF） was proposed. The OD data was clustered before being visualized， so that unnecessary occlusion was reduced. For that it is difficult to combine and analyze multivariate spatio-temporal data with multiple types， a site multivariate time series data view for bus stop was designed. Bus stop flow and four types of multivariate data — air quality， air temperature， relative humidity， and rainfall were coded on the same time series， to improve the spatial utilization rate of the view， and could be compared and analyzed. To assist users to explore and analyze， an interactive visual analysis system was developed based on origin-destination flow and multivariate data， and a variety of interactive operations were designed to improve the efficiency of user exploration. Finally， based on the Singapore IC card dataset， the proposed clustering method was evaluated from clustering effect and running time. In the comparison experiment results， using silhouette coefficient to evaluate the clustering effect， the clustering effect of the proposed method is improved by 0.028 compared with the original method and 0.253 compared with K-means clustering method. The running time comparison results show that its running time is 254 seconds less than that of ONMFS （Orthogonal NMF through Subspace exploration） method with better clustering effect. The effectiveness of the system was verified by case analysis and system function comparison.