Peter have been investigating the potential of conducting various spatial analyses using the IBM InfoSphere Streams architecture. Although the current spatial extent of RISERnet is small, the project also consider the issues that arise when scaling to networks of thousands or more nodes. The current investigation considers using spatial stream processing to achieve efficient and accurate spatial interpolation. For example, Kriging is one method used extensively to interpolate spatial data. But the Kriging matrix computations required for massive, scattered sensor readings are computationally expensive, and not well-suited to rapid, online stream processing. We are testing the computational efficiency and accuracy of interpolation stream operators by decomposing the sampled area using different schemes. We will also investigate the impacts of different sampling schemes on the performance of the spatial interpolation in streams. For example, the figure (right) shows a Delaunay and Voronoi decomposition of a simulated wireless sensor networks. Efficient stream processing requires relies on such spatial “windows” which affect both the efficient and accuracy of the resulting interpolation.