AnchorGK: Anchor-based Incremental and Stratified Graph Learning Framework for Inductive Spatio-Temporal Kriging

Spatio-temporal kriging is an essential research problem in sensor networks due to the sparsity of deployed sensors. While recent studies consider spatial and temporal correlations, they often overlook the sparse spatial distribution of locations and the incomplete features across locations. To tackle these problems, we propose an Anchor-based Incremental and Stratified Graph Learning Framework for Inductive Spatio-Temporal Kriging (AnchorGK). AnchorGK introduces anchor locations to enable effective data stratification for accurate kriging. Anchor locations are constructed based on feature availability, and strata are subsequently established based on the an- chor locations. This stratification serves two purposes: 1) it ensures that the spatial correlations between unknown areas (no observations) and surrounding known locations are accurately represented and dynamically updated within the graph learning framework, and 2) it facilitates the use of all available features across different strata through a novel incremental representation method. Building on the data stratification, we propose a dual-view graph learning layer that integrates information from relevant features and locations and learns distinct representations for different strata. Finally, kriging is performed based on the obtained strata representations. Experimental results on multiple benchmark datasets demonstrate that AnchorGK consistently outperforms existing state-of-the-art methods. Our codes, datasets, and related materials are given in: https://github.com/xren451/Spatial-interpolation