Efficient classification of billions of points into complex geographic regions using hierarchical triangular mesh

TL;DR

This paper demonstrates an efficient method for classifying billions of geographic points into complex regions using Hierarchical Triangular Mesh (HTM) combined with SQL Server GIS functions, significantly improving query performance.

Contribution

The authors develop a novel algorithm for HTM tessellation of complex regions and precompute intersections, enhancing spatial query efficiency in large-scale geographic data.

Findings

HTM-based pre-filtering is ten times faster than SQL Server spatial indices.

HTM-based spatial joins are approximately one hundred times faster.

The approach enables scalable classification of billions of geographic points.

Abstract

We present a case study about the spatial indexing and regional classification of billions of geographic coordinates from geo-tagged social network data using Hierarchical Triangular Mesh (HTM) implemented for Microsoft SQL Server. Due to the lack of certain features of the HTM library, we use it in conjunction with the GIS functions of SQL Server to significantly increase the efficiency of pre-filtering of spatial filter and join queries. For example, we implemented a new algorithm to compute the HTM tessellation of complex geographic regions and precomputed the intersections of HTM triangles and geographic regions for faster false-positive filtering. With full control over the index structure, HTM-based pre-filtering of simple containment searches outperforms SQL Server spatial indices by a factor of ten and HTM-based spatial joins run about a hundred times faster.