Spatiotemporal clustering is a process of grouping objects based on proximity within the spatial and temporal component.
With this technique, large datasets can be summarized and aggregated to permit a more practical analysis and provide meaningful insights.
Due to the possible volativity in both the spatial and temporal component, clusters may
- split or merge
- change their location
- exist only at specific times
- change in shape or size
and thus alter their intrinsic characteristics.
The effective visualization of clusters in spatiotemporal sequences, especially in large datasets, thus remains a challenging endeavour.
- Evaluate and compare existing approaches for visual cluster detection in spatiotemporal datasets
- Develop a novel interactive, visual spatiotemporal clustering technique
- Compare approach to existing spatiotemporal clustering algorithms
- Interest in spatiotemporal data
- Basic knowledge about information visualization, data mining and geographic information systems
- Scope: Bachelor / Master
- 3 Month Project, 3 / 6 Month Thesis
- Start: immediately
- Bach, B., A Descriptive Framework for Temporal Data Visualizations Based on Generalized Space-Time Cubes. Computer Graphics Forum, 36 (2017). https://doi.org/10.1111/cgf.12804
- Andrienko, G., Andrienko, A., Interactive cluster analysis of diverse types of spatiotemporal data. SIGKDD Explor. Newsl. 11, 2 (2009). https://doi.org/10.1145/1809400.1809405
- Chen, W., Huang, Z., Wu, F., Zhy, M., Guan, H., Maciejewski, R. , A Visual Analysis Approach for Exploring Spatio-Temporal Urban Data. IEEE Trans Vis Comput Graph. (2018). https://doi.org/10.1109/TVCG.2017.2758362