Spatiotemporal data often relates to different levels of granularity in space, time, and data. Yet, bringing these levels together for an integrated visual exploration across levels poses a challenge up to this day. With this paper, we aim to provide a first solution approach to this challenge, which decomposes the data in its various levels to be able to show them side-by-side. Based on this decomposition, we derive a visual exploration approach that consists of a novel multilevel visualization, adjoined traditional spatial and temporal views, as well as of tailored exploration techniques for their concerted use. We exemplify the utility of this approach by case studies on election and poll data from Germany's various administrative levels and different time spans.
Schulz, H.-J., Hadlak, S., & Schumann, H. (2013). A Visualization Approach for Cross-level Exploration of Spatiotemporal Data. In Proceedings of the 13th International Conference on Knowledge Management and Knowledge Technologies - i-Know ’13 (pp. 1–8). New York, New York, USA: ACM Press. https://doi.org/10.1145/2494188.2494199