Ordinary Kriging is a well-recognized spatial interpolation method. It is adopted extensively because it is the best linear unbiased estimator. However, it is too computationally intensive to be used for large spatial dataset or spatial streams. Peter and Matt developed an approach for incremental Kriging interpolation over spatiotemporal streams. This approach maintains a sketch of historical tuples to accelerate the calculation of current Kriging system. It can be applied over a wide variants of covariance models and sliding temporal windows. In addition, it can be applied over not only the conventional Kriging algorithm, but also the approximated Kriging algorithms already available. A draft paper is almost complete, and will be circulated amongst the wider RISER group for comments shortly.