Visualizing movement trajectories in R using moveVis: Article published in the latest issue of Methods in Ecology and Evolution

Visualizing movement trajectories in R using moveVis: Article published in the latest issue of Methods in Ecology and Evolution

May 12, 2020

Figure 1: Migratory movements of white storks Ciconia ciconia on a Mapbox satellite base map
Figure 2: Migratory movements of white storks Ciconia ciconia on a temporally interpolated MODIS MOD13Q1 NDVI time series

This month, our open-access paper on visualizing movement trajectories in R using moveVis has been published in the latest issue of Methods in Ecology and Evolution. The article describes the moveVis user functions, explains their technical implementation, provides use cases and discusses its strengths and limitations.

The visualization of movement trajectories sometimes is not easy. Spatial data without a temporal component can often be sufficiently visualized using a plot or map of two dimensions, x and y. Movement trajectories, however, are spatio‐temporal data that represent the change in the spatial location of tracked objects or individuals over time. To account for their temporal component, the representation of time in a third dimension is required. While, in certain cases, it can be sufficient to use a static spatial plot to indicate time, e.g. by using a colour palette or by adding a z axis (space-time cubes), a spatio-temporal animation directly relates the temporal dimension of the data to actual time.

To ease the creation of such animations, the R package moveVis has been developed. moveVis automates the processing of movement and environmental data to turn them into an animation. We deem moveVis to be a useful tool for visually exploring and interpreting movement patterns, including potential interactions of individuals with each other and their environment, and communicate such patterns appropriately to different kinds of audiences.

The online version of our open-access paper includes a detailed worked example using migratory movement trajectories of white storks, the resulting video animations (video 1, video 2 & video 3) and an overview of all moveVis functions and their purposes.

To get started using moveVis, we recommend to have a look at our examples and documentation on movevis.org. The source code of moveVis is openly available on GitHub and has been published under GPL-3. If you have ideas on how to improve moveVis (e.g. missing features that could be useful) or if you encounter bugs or have other problems, feel free to open an issue on GitHub for discussion.

This blog post has also been published at AniMove.org.

Reference:
Schwalb-Willmann, J.; Remelgado, R.; Safi, K.; Wegmann, M. (2020). moveVis: Animating movement trajectories in synchronicity with static or temporally dynamic environmental data in R. Methods Ecol Evol. 2020; 11: 664–669. https://doi.org/10.1111/2041-210X.13374

you may also like:

Deep learning course by Thorsten Hoeser

Deep learning course by Thorsten Hoeser

This week Thorsten Hoeser, an expert in deep learning and data science, taught AI methods in remote sensing at our International EAGLE Earth Observation MSc Program. In this special module, Thorsten covered essential topics on the cutting-edge techniques for...

New Team Member: Sofia Haag

New Team Member: Sofia Haag

Sofia Haag joined the EORC in February 2025 as a research assistant for the EO4CAM project. After completing her Bachelor's degree in Geography at the University of Heidelberg, she pursued her Master's in Applied Physical Geography at the University of Würzburg. Sofia...

“Super-Test-Site Würzburg” consortium meeting

“Super-Test-Site Würzburg” consortium meeting

The core team of our “Super-Test-Site Würzburg” consortium (University of Würzburg, the Karlsruhe Institute of Technology, the Friedrich-Alexander-University Erlangen-Nürnberg and the German Aerospace Center) met again in Würzburg on the 18th of February 2025....