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.

翻译：移动数据科学通过整合矢量与栅格数据，揭示了移动对象及其周围环境空间数据的复杂关联。例如，移动轨迹通常以矢量格式存储，而气象数据往往采用栅格格式。然而，现有探索性数据科学的空间分析工具往往偏向其中一种数据格式，对另一类数据的支持极为有限。本文针对该问题提出基于值的四叉树索引方法，该索引作为桥梁支撑矢量与栅格数据的联合空间分析，充分利用两者独特的自相关特性。实验表明，该方法在保持查询响应精度不变的前提下，将面内点查询的中位延迟降低了90%。