Progress in positioning technologies has enabled the collection of huge amounts of data about movement of diverse types of objects in various domains, which has raised a demand for scalable methods for analyzing such data. In response, a number of methods and tools have appeared recently in data mining, geographic visualization, information visualization, and visual analytics. However, most of these approaches deal with movement data alone without taking into account the spatiotemporal context of the movement, which includes the properties of different places and different times and various spatial, temporal, and spatiotemporal objects affecting and/or being affected by the movement. This project aims at developing theoretical foundations and novel scalable methods for analyzing movement in context with the use of explicit context information available in the form of datasets as well as implicit context information available in the mind of human analyst. The methods will combine interactive visual interfaces with computational techniques for supporting synergistic collaboration of human and computer. The project will develop approaches enabling the transition from exploratory analysis of movement data to creation of explicit formal models representing the results of the visual analytics processes. In the theoretical part, the project will develop a conceptual model of movement, its spatiotemporal context, and possible relations between movement and context. On this basis, the project will build a taxonomy of analysis tasks and a taxonomy of methods, which will provide guidelines for choosing analytical methods and tools depending on the analysis goals and characteristics of the data to analyze. The developed theory and methodology will be verified through creation of prototype software tools and their evaluation in real application scenarios.