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：移动数据科学揭示了移动对象及其周围环境空间数据之间的复杂关联，通常基于矢量数据与栅格数据的组合。例如，移动轨迹通常采用矢量格式，而气象数据则多为栅格格式。然而，在探索性数据科学中，现有的空间分析工具往往偏向于其中一种数据类型，对另一种仅提供有限支持。针对这一问题，本文提出了一种基于价值的四叉树索引，作为构建桥梁以支持利用矢量与栅格数据独特自相关特性的联合空间分析。我们在保持查询响应精度同等水平的前提下，实现了中位点-面查询延迟降低90%的效果。