Spatial Analysis on Value-Based Quadtrees of Rasterized Vector Data
This addresses a bottleneck for mobility data scientists who need to analyze mixed vector and raster data, though it appears incremental as it builds on existing quadtree methods.
The paper tackles the problem of limited spatial analysis tools for combined vector and raster data by introducing a value-based quadtree index, achieving a 90% reduction in median Point-in-Polygon query latency while maintaining accuracy.
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.