AirExplorer: visual exploration of air quality data based on time-series querying

摘要: Air pollution has become an important environmental issue, attracting more and attention from many scholars experts recently. Understanding air quality patterns in urban areas is essential for prevention treatment. However, most existing studies usually cannot effectively capture large-scale data, due to lacking effective interaction approaches intuitive methods that reveal sequential multivariable information. In this paper, we present AirExplorer, a novel visual analysis system providing abundant interactive ways views help users explore the time-varying of data. We design time-embedded RadViz view not only shows relationship between data attributes, but also puts temporal variations among observation stations into perspective. Furthermore, suggest time-series querying algorithm, which combines hierarchical Piecewise Linear Representation Dynamic Time Warping, query interest accurately by sketch-based interaction. The experiment results based on real dataset demonstrate our method can understand spatial-temporal multi-dimensional characteristics discover some potential laws patterns. AirExplorer with easy-to-use interactions improve efficiency analyzing