Visual exploration of multivariate movement events in space-time cube

Analyzing large amounts of complex movement data requires appropriate visual and analytical methods. This paper proposes a 2-D staricon based visualization technique for the visual exploration of multivariate movement events in a space-time cube. To test the proposed method, we derive multivariate events from massive real-world floating car data and visually explore spatio-temporal patterns. The experimental results show that our proposed methods are helpful in identifying interesting locations or functional areas, and assist the understanding of dynamic patterns.