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联系，通常基于矢量和栅格数据的组合。例如，移动轨迹通常采用矢量格式，而气象数据则常以栅格格式呈现。然而，现有的探索性数据科学空间分析工具倾向于支持其中一种数据格式，对另一种格式的支持较为有限。本文针对这一问题，提出了一种基于价值的四叉树索引，该索引作为桥梁，利用矢量和栅格数据独特的自相关特性，支持两者的联合空间分析。我们将点面查询的中位延迟降低了90%，同时保持查询响应的准确率在同一水平。