Spatial Analysis on Value-Based Quadtrees of Rasterized Vector Data
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.
Cite
@article{arxiv.2603.23105,
title = {Spatial Analysis on Value-Based Quadtrees of Rasterized Vector Data},
author = {Diana Baumann and Nils Japke and Tim C. Rese and David Bermbach},
journal= {arXiv preprint arXiv:2603.23105},
year = {2026}
}
Comments
Accepted for publication at the 1st Workshop on Secure and Intelligent Data Spaces (SIDS 2026) in the proceedings of the 27th IEEE International Conference on Mobile Data Management (MDM 2026)