Abstract: Currently,environmental data in cities are still sampled by fixed stations as the mainstream sampling method,but the high cost of full sampling makes it difficult to be scaled up on a large scale.In this context,the method of extrapolating the remaining unsampled data through local sampling and inference algorithm has become a hot topic in current research.Existing studies usually use two different models for active sampling and missing inference,respectively,which suffer from the shortcomings of high computational cost and easy accumulation of errors.Based on this,this paper proposes a spatiotemporal active-sampling and joint inference(SAJI) integration model.The model can not only select the sampling sites with high prediction accuracy,but also determine their own active sampling time.Finally,the missing values of all sites can be inferred jointly by using Multiple Mea-surement Vector(MMV) recovery algorithm.The experimental results show that compared with the baseline algorithms,SAJI can make full use of spatiotemporal correlation to obtain valuable prefilled values for the unsampled sites and achieve the highest inference accuracy using the subsequent joint inference algorithm at low sampling rates.

Key words: Spatiotemporal active sampling, Spatiotemporal correlation, Genetic algorithm, Compressed sensing, Joint inference