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：移动数据科学通过研究移动对象与周边环境的空间数据复杂关联（通常结合矢量与栅格数据）提供洞见。例如，移动轨迹通常采用矢量格式，而气象数据多采用栅格格式。然而，现有的探索性数据科学空间分析工具往往迫使数据科学家二选其一，对另一种数据格式的支持十分有限。本文提出一种价值型四叉树索引作为桥梁构建方案，利用矢量与栅格数据独特的自相关特性，支持两者的协同空间分析。在保持查询响应精度不变的前提下，我们将点-多边形查询的中位数延迟降低了90%。