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Abstract ObjectiveThe 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 MethodsWe 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. ResultsSessions 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. DiscussionWe 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. ConclusionCoronaViz’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. 

Current Computer-Aided Design (CAD) tools lack proper support for guiding novice users towards designs ready for fabrication. We propose Shape Structuralizer (SS), an interactive design support system that repurposes surface models into structural constructions using rods and custom 3Dprinted joints. Shape Structuralizer embeds a recommendation system that computationally supports the user during design ideation by providing design suggestions on local refinements of the design. This strategy enables novice users to choose designs that both satisfy stress constraints as well as their personal design intent. The interactive guidance enables users to repurpose existing surface mesh models, analyze them in-situ for stress and displacement constraints, add movable joints to increase functionality, and attach a customized appearance. This also empowers novices to fabricate even complex constructs while ensuring structural soundness. We validate the Shape Structuralizer tool with a qualitative user study where we observed that even novice users were able to generate a large number of structurally safe designs for fabrication.