Spatial Analysis on Value-Based Quadtrees of Rasterized Vector Data

2026-03-24

AI Summary

Existing spatial analysis tools struggle to efficiently support joint analysis of vector and raster data, hindering in-depth exploration of the complex relationships between moving objects and their environments. This work proposes a value-driven quadtree index structure that, for the first time, integrates vector and raster data through spatial autocorrelation characteristics. By combining vector rasterization with optimized Point-in-Polygon query strategies, the approach significantly enhances joint analysis efficiency while preserving result accuracy. Experimental results demonstrate up to a 90% reduction in median query latency for Point-in-Polygon operations, establishing a novel, efficient, and precise paradigm for collaborative analysis of multi-source spatial data.

Technology Category

• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding
• Web Mining and Content Analysis: Bridging structured and unstructured 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.

Problem

• spatial analysis
• vector data
• raster data
• mobility data science
• data integration

Innovation

• value-based quadtree
• vector-raster integration
• spatial indexing
• Point-in-Polygon query
• mobility data science