  Due to the significant increase in the size of spatial data, it is essential
to use distributed parallel processing systems to efficiently analyze spatial
data. In this paper, we first study learned spatial data partitioning, which
effectively assigns groups of big spatial data to computers based on locations
of data by using machine learning techniques. We formalize spatial data
partitioning in the context of reinforcement learning and develop a novel deep
reinforcement learning algorithm. Our learning algorithm leverages features of
spatial data partitioning and prunes ineffective learning processes to find
optimal partitions efficiently. Our experimental study, which uses Apache
Sedona and real-world spatial data, demonstrates that our method efficiently
finds partitions for accelerating distance join queries and reduces the
workload run time by up to 59.4%.
