Spatial Analysis on Value-Based Quadtrees of Rasterized Vector Data

TL;DR

This paper introduces a value-based quadtree index that enables efficient joint spatial analysis of vector and raster data, significantly reducing query latency while maintaining accuracy.

Contribution

The novel value-based quadtree index bridges vector and raster data analysis, improving performance in spatial data science tasks.

Findings

Achieved 90% reduction in median Point-in-Polygon query latency.

Maintained query accuracy at the same level as existing methods.

Abstract

Mobility data science offers insights into the complex interconnections of spatial data of moving objects and their surroundings, often based on a combination of vector and raster data. For example, mobility traces are usually in vector format, weather data are often in raster format. Yet, available spatial analysis tools for exploratory data science push data scientists towards one or the other, providing only limited support for the respective other. In this paper, we contribute to this problem space with a value-based quadtree index, which serves as a bridge builder to support joint spatial analysis on vector and raster data leveraging their unique autocorrelation property. We achieve a 90% reduction in median Point-in-Polygon query latency, while keeping the accuracy of query responses at equal level.