Journal Paper

@article{ondov2024VisualizingMultilayer, title={Visualizing Multilayer Spatiotemporal Epidemiological Data with Animated Geocircles}, author={Brian Ondov and Harsh B. Patel and Ai Te Kuo and John Kastner and Yunheng Han and Hong Wei and Niklas Elmqvist and Hanan Samet}, url={https://doi.org/10.1093/jamia/ocae234}, year={2024}, date={2024-01-01}, journal={Journal of the American Medical Informatics Association}, publisher={Oxford University Press}, address={Oxford, UK}, volume={31}, number={11}, pages={2507--2518}, doi={10.1093/jamia/ocae234}, issn={1067-5027}, abstract={Objective: The COVID-19 pandemic emphasized the value of geospatial visual analytics for both epidemiologists and the general public. However, systems struggled to encode temporal and geospatial trends of multiple, potentially interacting variables, such as active cases, deaths, and vaccinations. We sought to ask (1) how epidemiologists interact with visual analytics tools, (2) how multiple, time-varying, geospatial variables can be conveyed in a unified view, and (3) how complex spatiotemporal encodings affect utility for both experts and non-experts. Materials and Methods: We propose encoding variables with animated, concentric, hollow circles, allowing multiple variables via color encoding and avoiding occlusion problems, and we implement this method in a browser-based tool called CoronaViz. We conduct task-based evaluations with non-experts, as well as in-depth interviews and observational sessions with epidemiologists, covering a range of tools and encodings. Results: Sessions with epidemiologists confirmed the importance of multivariate, spatiotemporal queries and the utility of CoronaViz for answering them, while providing direction for future development. Non-experts tasked with performing spatiotemporal queries unanimously preferred animation to multi-view dashboards. Discussion: We find that conveying complex, multivariate data necessarily involves trade-offs. Yet, our studies suggest the importance of complementary visualization strategies, with our animated multivariate spatiotemporal encoding filling important needs for exploration and presentation. Conclusion: CoronaViz's unique ability to convey multiple, time-varying, geospatial variables makes it both a valuable addition to interactive COVID-19 dashboards and a platform for empowering experts and the public during future disease outbreaks. CoronaViz is open-source and a live instance is freely hosted at http://coronaviz.umiacs.io.}, language={English}, keywords={COVID-19,data visualization,epidemiology,geographic information systems}, }