  Spatial indexes are crucial for the analysis of the increasing amounts of
spatial data, for example generated through IoT applications. The plethora of
indexes that has been developed in recent decades has primarily been optimised
for disk. With increasing amounts of memory even on commodity machines,
however, moving them to main memory is an option. Doing so opens up the
opportunity to use additional optimizations that are only amenable to main
memory. In this paper we thus explore the opportunity to use light-weight
machine learning models to accelerate queries on spatial indexes. We do so by
exploring the potential of using interpolation and similar techniques on the
R-tree, arguably the most broadly used spatial index. As we show in our
experimental analysis, the query execution time can be reduced by up to 60%
while simultaneously shrinking the index's memory footprint by over 90%
