Exploring movement, as an important aspect of spatiotemporal processes, has gained new momentum from the availability of large spatiotemporal datasets. This has given rise to the development of new exploratory and analytical techniques to generate new insight into dynamic processes and the spatiotemporal context in which they operate. This study develops a new dynamic visualization tool, called ``DYNAMOVis: Dynamic Visualization of Movement'', developed for the exploratory analysis of movement in relation to the environment and geographic context. DYNAMOVis applies visual variables such as point and line width, color, and directional vector to visualize movement tracks in their attribute space (e.g. movement parameters and context attributes).
Using real case studies from Movement Ecology, we show how hybrid and dynamic visualizations can strengthen spatiotemporal research by facilitating data exploration, generating new hypotheses, discovery of patterns and dependencies, as well as promoting interdisciplinary research collaborations.