Air Quality Temporal Analyser: Interactive Temporal Analyses with Visual Predictive Assessments

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dc.contributor.authorHarbola, Shubhien_US
dc.contributor.authorKoch, Steffenen_US
dc.contributor.authorErtl, Thomasen_US
dc.contributor.authorCoors, Volkeren_US
dc.contributor.editorDutta, Soumya and Feige, Kathrin and Rink, Karsten and Zeckzer, Dirken_US
dc.date.accessioned2021-06-12T11:24:10Z
dc.date.available2021-06-12T11:24:10Z
dc.date.issued2021
dc.description.abstractThis work presents Air Quality Temporal Analyser (AQTA), an interactive system to support visual analyses of air quality data with time. This interactive AQTA allows the seamless integration of predictive models and detailed patterns analyses. While previous approaches lack predictive air quality options, this interface provides back-and-forth dialogue with the designed multiple Machine Learning (ML) models and comparisons for better visual predictive assessments. These models can be dynamically selected in real-time, and the user could visually compare the results in different time conditions for chosen parameters. Moreover, AQTA provides data selection, display, visualisation of past, present, future (prediction) and correlation structure among air parameters, highlighting the predictive models effectiveness. AQTA has been evaluated using Stuttgart (Germany) city air pollutants, i:e:, Particular Matter (PM) PM10, Nitrogen Oxide (NO), Nitrogen Dioxide (NO2), and Ozone (O3) and meteorological parameters like pressure, temperature, wind and humidity. The initial findings are presented that corroborate the city’'s COVID lockdown (year 2020) conditions and sudden changes in patterns, highlighting the improvements in the pollutants concentrations. AQTA, thus, successfully discovers temporal relationships among complex air quality data, interactively in different time frames, by harnessing the user's knowledge of factors influencing the past, present and future behavior, with the aid of ML models. Further, this study also reveals that the decrease in the concentration of one pollutant does not ensure that the surrounding air quality would improve as other factors are interrelated.en_US
dc.description.sectionheadersInteractive Digital and Virtual Visualization Techniques for Environmental Data Visualization
dc.description.seriesinformationWorkshop on Visualisation in Environmental Sciences (EnvirVis)
dc.identifier.doi10.2312/envirvis.20211083
dc.identifier.isbn978-3-03868-148-9
dc.identifier.pages43-50
dc.identifier.urihttps://doi.org/10.2312/envirvis.20211083
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/envirvis20211083
dc.publisherThe Eurographics Associationen_US
dc.subjectTime series
dc.subjectenvironmental visualisation
dc.subjectuser interfaces
dc.subjectvisual prediction
dc.subjectmachine learning
dc.subjectmeteorological data
dc.subjectcity planning
dc.subjectvisual analytics
dc.subjectair pollutants
dc.titleAir Quality Temporal Analyser: Interactive Temporal Analyses with Visual Predictive Assessmentsen_US
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