GP-Tree: An in-memory spatial index combining adaptive grid cells with a prefix tree for efficient spatial querying

TL;DR

GP-Tree is a novel in-memory spatial index that combines adaptive grid cells with a prefix tree to improve filtering accuracy and query performance for complex spatial objects, outperforming traditional indexes significantly.

Contribution

This paper introduces GP-Tree, a new spatial index that uses fine-grained grid cell approximations and a prefix tree structure for more efficient spatial querying of complex objects.

Findings

GP-Tree achieves up to ten times faster query performance.

It significantly improves filtering accuracy over traditional indexes.

The approach effectively handles complex spatial objects like trajectories.

Abstract

Efficient spatial indexing is crucial for processing large-scale spatial data. Traditional spatial indexes, such as STR-Tree and Quad-Tree, organize spatial objects based on coarse approximations, such as their minimum bounding rectangles (MBRs). However, this coarse representation is inadequate for complex spatial objects (e.g., district boundaries and trajectories), limiting filtering accuracy and query performance of spatial indexes. To address these limitations, we propose GP-Tree, a fine-grained spatial index that organizes approximated grid cells of spatial objects into a prefix tree structure. GP-Tree enhances filtering ability by replacing coarse MBRs with fine-grained cell-based approximations of spatial objects. The prefix tree structure optimizes data organization and query efficiency by leveraging the shared prefixes in the hierarchical grid cell encodings between parent and child cells. Additionally, we introduce optimization strategies, including tree pruning and node optimization, to reduce search paths and memory consumption, further enhancing GP-Tree's performance. Finally, we implement a variety of spatial query operations on GP-Tree, including range queries, distance queries, and k-nearest neighbor queries. Extensive experiments on real-world datasets demonstrate that GP-Tree significantly outperforms traditional spatial indexes, achieving up to an order-of-magnitude improvement in query efficiency.