Issue No. 05 - September/October (2017 vol. 37)
Zhiguang Zhou , Zhejiang University of Finance and Economics
Zhifei Ye , Zhejiang University of Finance and Economics
Yanan Liu , Zhejiang University of Finance and Economics
Fang Liu , Zhejiang University of Finance and Economics
Yubo Tao , Zhejiang University
Weihua Su , Zhejiang Gongshang University
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.
Air pollution, Visualization, Hierarchical systems, Cluster approximation
Z. Zhou, Z. Ye, Y. Liu, F. Liu, Y. Tao and W. Su, "Visual Analytics for Spatial Clusters of Air-Quality Data," in IEEE Computer Graphics and Applications, vol. 37, no. 5, pp. 98-105, 2017.