AirPollutionViz: visual analytics for understanding the spatio-temporal evolution of air pollution

Spatio-temporal evolution analysis has been a critical topic of air pollution research. However, there are still several difficulties caused by the large scale and dimensionality of the data. Specifically, First, traditional methods deal with such data by simplifying and abstracting, resulting in information loss. Second, most existing visualizations, generally focusing on overall evolution, ignore the exploration of multiple time scales and pattern transitions between subsequences. This paper presents AirPollutionViz, a visual analytics system that enables to analyze the spatio-temporal evolution in two manners: sequence mining and clustering analysis. Concretely, we propose sequence merging to shorten the sequence length and construct a weighted directed graph structure, which promotes efficient querying of sequence patterns by combination with dynamic time warping. We design a novel summary view to display the overview of pollution level changes, together with the improved node-link chart, to support the analysis of air pollution spatio-temporal evolution patterns. We also apply K-means clustering to pollutants, and a scatter plot and map reflect the spatial distribution aggregation. The system supports users’ free exploration across multiple time scales with rich interactions. Case studies with three domain experts and a user study with ten users demonstrate the usefulness and effectiveness of AirPollutionViz.