Visual analysis of air pollution spatio-temporal patterns

Advances in air monitoring methods have made it possible to analyze large-scale air pollution phenomena. Mining potential air pollution information from large-scale air pollution data is an important issue in the current environmental field. Although direct data visualization provides an intuitive presentation, the method is less applicable in long-time domains with high temporal resolution. To better meet the analysis needs of domain experts, we design a visual analysis framework based on friendly multi-view interactions and novel visual view designs. This framework can explore the spatiotemporal dynamics of multiple pollution data. In this paper, a two-stage cluster analysis method is proposed to extract possible transport patterns from large-scale pollutant transport trajectories. This method will be substantially helpful for domain experts to make relevant decisions. At the same time, the index is constructed from long-time series data at the grid point in the specific transport trajectories. This structure can help experts complete the sketch match with custom time resolution. It can assist domain experts in extracting key possible time-varying features. Finally, we verified the validity through spatial and temporal case analysis for pollutant data.