Visual Analysis of Spatial Time Series
Spatial time series refer to (uni- or multivariate) time series that exist at various points in space. Examples include regional precipitation forecasts, air quality sensors in a city or vehicle counters along a road. Visual analysis of such data is necessary to learn more about the observed phenomenon (e.g., traffic), but the spatiotemporal nature of it makes this difficult.
Collect papers that present visualizations or visual analysis tools for this data type and categorize them systematically. Your STAR should include supported analysis tasks, visualization strategies and data mining algorithms (if applicable).
Some starting pointers:
- Uncertainty-aware Visualization of Regional Time Series Correlation in Spatio-temporal Ensembles. Evers, Huesmann and Linsen, 2021. https://diglib.eg.org/handle/10.1111/cgf14326
- Compass: Towards Better Causal Analysis of Urban Time Series. Deng et al., 2022. https://ieeexplore.ieee.org/document/9557222
- Visualization of Time-Varying Weather Ensembles across Multiple Resolutions. Biswas et al., 2017. https://ieeexplore.ieee.org/document/7539581