  Learned indexes fit machine learning (ML) models to the data and use them to
make query operations more time and space-efficient. Recent works propose using
learned spatial indexes to improve spatial query performance by optimizing the
storage layout or internal search structures according to the data
distribution. However, only a few learned indexes exploit the query workload
distribution to enhance their performance. In addition, building and updating
learned spatial indexes are often costly on large datasets due to the
inefficiency of (re)training ML models. In this paper, we present WaZI, a
learned and workload-aware variant of the Z-index, which jointly optimizes the
storage layout and search structures, as a viable solution for the above
challenges of spatial indexing. Specifically, we first formulate a cost
function to measure the performance of a Z-index on a dataset for a range-query
workload. Then, we optimize the Z-index structure by minimizing the cost
function through adaptive partitioning and ordering for index construction.
Moreover, we design a novel page-skipping mechanism to improve the query
performance of WaZI by reducing access to irrelevant data pages. Our extensive
experiments show that the WaZI index improves range query time by 40% on
average over the baselines while always performing better or comparably to
state-of-the-art spatial indexes. Additionally, it also maintains good point
query performance. Generally, WaZI provides favorable tradeoffs among query
latency, construction time, and index size.
