EnvirVis2022

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https://diglib.eg.org/handle/10.2312/2633196

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Browsing EnvirVis2022 by Subject "H.5.2 [Information Interfaces and Presentation]"

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    AtmoVis: Web Based Visualization of Air Quality Data with Interconnected Windows
    (The Eurographics Association, 2022) Powley, Benjamin T.; Anslow, Craig; Pearce, David James; Dutta, Soumya; Feige, Kathrin; Rink, Karsten; Zeckzer, Dirk
    Air quality has an adverse impact on the health of people living in areas with poor quality air. Hence monitoring is needed to understand the extent of poor air quality. Little work has been done on the effectiveness of visualization techniques for air quality data analysis. Few tools are developed specifically for air quality analysis and many practitioners use general purpose tools, such as spreadsheets or programming. This paper investigates which visualization techniques are most effective in analysing air pollution data. A user study was performed with 20 experienced or expert participants. The participants used a domain specific prototype visualization tool we developed, AtmoVis, to compare spatio-temporal trends among air quality variables using preexisting visualization techniques. AtmoVis allows experts to explore data without the difficulties of programming, or working with spreadsheets. AtmoVis has a windowed layout that connects 6 different visualizations: heat calendar, line plot, monthly rose, site view, monthly averages, and data comparison. The results of the study demonstrated that the heat calendar, line plot, site view, monthly averages, and monthly rose visualizations were effective for analyzing the air quality through AtmoVis. The line plot and the heat calendar were particularly effective for temporal data analysis. AtmoVis was also effective for accessing air quality visualizations and inferring relationships among air quality variables at different monitoring sites. This research can help inform the design of future domain specific interactive tools for air quality analysis. AtmoVis could be extended to include other datasets in the future.