AirExplorer: visual exploration of air quality data based on time-series querying
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R E G UL A R P A P E R
Dezhan Qu • Xiaoli Lin • Ke Ren • Quanle Liu • Huijie Zhang
AirExplorer: visual exploration of air quality data based on time-series querying
Received: 7 July 2019 / Revised: 7 July 2019 / Accepted: 18 July 2019 The Visualization Society of Japan 2020
Abstract Air pollution has become an important environmental issue, attracting more and more attention from many scholars and experts recently. Understanding air quality patterns in urban areas is essential for air pollution prevention and treatment. However, most existing studies usually cannot effectively capture air quality patterns from large-scale air quality data, due to lacking effective interaction approaches and intuitive methods that reveal sequential and multivariable information. In this paper, we present AirExplorer, a novel visual analysis system providing abundant interactive ways and intuitive views to help users explore the time-varying and multivariable patterns of air quality data. We design a time-embedded RadViz view that not only shows the relationship between data and multivariable attributes, but also puts the air quality temporal variations among the observation stations into perspective. Furthermore, we suggest a timeseries querying algorithm, which combines hierarchical Piecewise Linear Representation and Dynamic Time Warping, to help users query time-series patterns of interest accurately by a sketch-based interaction. The experiment results based on the real dataset demonstrate that our method can help users understand the spatial-temporal multi-dimensional characteristics effectively and discover some potential laws of air quality patterns. AirExplorer with easy-to-use interactions can improve the efficiency of analyzing air quality data. Keywords Visual analytics Air quality Spatio-temporal Multivariate visualization User-queries Sketching Time series This work was supported by the National Natural Science Foundation of China under Grant 41671379. D. Qu K. Ren H. Zhang School of Information Science and Technique, Northeast Normal University, Changchun, China E-mail: [email protected] K. Ren E-mail: [email protected] D. Qu K. Ren H. Zhang (&) Key Laboratory of Intelligent Information Processing of Jilin Universities, Changchun, China E-mail: [email protected] D. Qu Library, Northeast Normal University, Changchun, China X. Lin College of Computer Science and Engineering, Northeastern University, Shenyang, China E-mail: [email protected] Q. Liu College of Computer Science and Technique, Jilin University, Changchun, China E-mail: [email protected]
D. Qu et al.
1 Introduction In recent years, air pollution has become a serious problem and is threatening human health and the sustainable development of the economy to a great extent. Air quality data collected continuously provide unprecedented opportunities for exploring air quality patterns and pollution sources, which helps environmental departments to prevent and control air pollution. However, due to the heterogeneity
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