AirLens: Multi-Level Visual Exploration of Air Quality Evolution in Urban Agglomerations

The precise prevention and control of air pollution is a great challenge faced by environmental experts in recent years. Understanding the air quality evolution in the urban agglomeration is important for coordinated control of air pollution. However, the complex pollutant interactions between different cities lead to the collaborative evolution of air quality. The existing statistical and machine learning methods cannot well support the comprehensive analysis of the dynamic air quality evolution. In this study, we propose AirLens, an interactive visual analytics system that can help domain experts explore and understand the air quality evolution in the urban agglomeration from multiple levels and multiple aspects. To facilitate the cognition of the complex multivariate spatiotemporal data, we first propose a multi-run clustering strategy with a novel glyph design for summarizing and understanding the typical pollutant patterns effectively. On this basis, the system supports the multi-level exploration of air quality evolution, namely, the overall level, stage level and detail level. Frequent pattern mining, city community extraction and useful filters are integrated into the system for discovering significant information comprehensively. The case study and positive feedback from domain experts demonstrate the effectiveness and usability of AirLens.