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Visual Analytics for Spatial Clusters of Air-Quality Data

Published: 01 January 2017 Publication History
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  • Abstract

    With the rapid development of industrial society, air pollution has become a major issue in the modern world. The development and widespread deployment of sensors has enabled the collection of air-quality datasets with detailed spatial and temporal scales. Analyses of these spatiotemporal air-quality datasets can help decision makers explore the major causes of air pollution and find efficient solutions. The authors designed a visual analytics system that uses multidimensional scaling (MDS) to transform the air-quality data from monitor stations into 2D plots and uses hierarchical clustering, Voronoi diagrams, and storyline visualizations to help experts explore various attributes and time scales in the data.

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    Cited By

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    • (2023)Visual Analytics of Air Pollution Transmission Among Urban AgglomerationsAdvances in Computer Graphics10.1007/978-3-031-50075-6_18(225-237)Online publication date: 28-Aug-2023
    • (2022)AQX: Explaining Air Quality Forecast for Verifying Domain Knowledge using Feature Importance VisualizationProceedings of the 27th International Conference on Intelligent User Interfaces10.1145/3490099.3511150(720-733)Online publication date: 22-Mar-2022
    • (2022)Visual analytics of genealogy with attribute-enhanced topological clusteringJournal of Visualization10.1007/s12650-021-00802-x25:2(361-377)Online publication date: 1-Apr-2022
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            Published In

            cover image IEEE Computer Graphics and Applications
            IEEE Computer Graphics and Applications  Volume 37, Issue 5
            2017
            110 pages

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            IEEE Computer Society Press

            Washington, DC, United States

            Publication History

            Published: 01 January 2017

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            View all
            • (2023)Visual Analytics of Air Pollution Transmission Among Urban AgglomerationsAdvances in Computer Graphics10.1007/978-3-031-50075-6_18(225-237)Online publication date: 28-Aug-2023
            • (2022)AQX: Explaining Air Quality Forecast for Verifying Domain Knowledge using Feature Importance VisualizationProceedings of the 27th International Conference on Intelligent User Interfaces10.1145/3490099.3511150(720-733)Online publication date: 22-Mar-2022
            • (2022)Visual analytics of genealogy with attribute-enhanced topological clusteringJournal of Visualization10.1007/s12650-021-00802-x25:2(361-377)Online publication date: 1-Apr-2022
            • (2021)Visual Analysis of Heterogenous Air Pollution DataProceedings of the 4th International Conference on Computer Science and Software Engineering10.1145/3494885.3494940(300-306)Online publication date: 22-Oct-2021
            • (2021)SensorAware: visual analysis of both static and mobile sensor informationJournal of Visualization10.1007/s12650-020-00717-z24:3(597-613)Online publication date: 1-Jun-2021
            • (2020)Visual abstraction and exploration of large-scale geographical social media dataNeurocomputing10.1016/j.neucom.2019.10.072376:C(244-255)Online publication date: 1-Feb-2020
            • (2019)A Visual Analysis Approach for Understanding Durability Test Data of Automotive ProductsACM Transactions on Intelligent Systems and Technology10.1145/334564010:6(1-23)Online publication date: 12-Dec-2019
            • (2019)(ChinaVis 2019) uncertainty visualization in stratigraphic correlation based on multi-source data fusionJournal of Visualization10.1007/s12650-019-00579-022:5(1021-1038)Online publication date: 1-Oct-2019
            • (2019)FuzzyRadar: visualization for understanding fuzzy clustersJournal of Visualization10.1007/s12650-019-00577-222:5(913-926)Online publication date: 1-Oct-2019

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