Spatio-temporal Matrix Factorization Based Air Quality Inference

With rapid urbanization, air pollution has become increasingly severe, making the provision of a spatio-temporal fine-grained air quality distribution essential to support outdoor planning and promote good health. However, the sparseness of air quality stations, the incompleteness of related feature data, and the nonlinear variation of air quality across locations and times pose substantial challenges for accurately inferring air quality in unobserved areas. This study proposes a matrix factorization-based approach to infer air quality by analyzing a real air quality dataset and discovering the low-rank structure of the air quality matrix. This approach fuses knowledge from the low-rank structure, air quality measurements, and various spatio-temporal features. Unlike existing works that address feature recovery, feature extraction, and air quality inference separately, this study unifies these three tasks into a single model. Such integration allows for improved inference performance through the collaborative training and supervision of different tasks. In this model, spatial and temporal feature matrices and the air quality matrix are constructed and collaboratively factorized into spatial and temporal feature representations. By sharing spatio-temporal matrix factors with the air quality matrix, the similarity knowledge of spatial and temporal features is transferred into air quality inference to enhance its performance. The proposed model is evaluated using real data sources obtained in Beijing city. Comparison results with baseline models demonstrate that the proposed model surpasses these models in various metrics, such as inference error and standard deviation, and achieves a better FAC2 result. Additionally, the model effectively reveals the principal spatial and temporal features to a certain extent.