  A corpus of recent work has revealed that the learned index can improve query
performance while reducing the storage overhead. It potentially offers an
opportunity to address the spatial query processing challenges caused by the
surge in location-based services. Although several learned indexes have been
proposed to process spatial data, the main idea behind these approaches is to
utilize the existing one-dimensional learned models, which requires either
converting the spatial data into one-dimensional data or applying the learned
model on individual dimensions separately. As a result, these approaches cannot
fully utilize or take advantage of the information regarding the spatial
distribution of the original spatial data. To this end, in this paper, we
exploit it by using the spatial (multi-dimensional) interpolation function as
the learned model, which can be directly employed on the spatial data.
Specifically, we design an efficient SPatial inteRpolation functIon based Grid
index (SPRIG) to process the range and kNN queries. Detailed experiments are
conducted on real-world datasets, and the results indicate that our proposed
learned index can significantly improve the performance in comparison with the
traditional spatial indexes and a state-of-the-art multi-dimensional learned
index.
