As individual traffic and public transport in cities are changing, city authorities need to analyze urban geospatial data to improve transportation and infrastructure. To that end, they highly rely on spatial aggregation queries that extract summarized information from point data (e.g., Uber rides) contained in a given region (e.g., a city neighborhood). To support such queries, current analysis tools either allow only predefined aggregates on predefined regions and are thus unsuitable for exploratory analyses, or access the raw data to compute aggregate results on-the-fly, which severely limits the interactivity. At the same time, existing pre-aggregation techniques are inadequate since they maintain aggregates over rectangular regions. As a result, when applied over arbitrary polygonal regions, they induce an approximation error that cannot be bounded. In this paper, we introduce GeoBlocks, a novel pre-aggregating data structure that supports spatial aggregation over arbitrary polygons. GeoBlocks closely approximate polygons using a set of fine-grained grid cells and, in contrast to prior work, allow to bound the approximation error by adjusting the cell size. Furthermore, GeoBlocks employ a trie-like cache that caches aggregate results of frequently queried regions, thereby dynamically adapting to the skew inherently present in query workloads and improving performance over time. In summary, GeoBlocks outperform on-the-fly aggregation by up to three orders of magnitude, achieving the sub-second query latencies required for interactive exploratory analytics.

翻译：随着城市个人交通和公共交通的变化,城市当局需要分析城市地理空间数据以改善交通和基础设施。为此,他们高度依赖空间汇总查询,从特定区域(如城市街区)所含的点数据(如Uber骑车)中提取摘要信息。为了支持这些查询,目前的分析工具要么只允许预先界定的预定义区域总量,因此不适于进行探索分析,要么获取原始数据,以计算飞行总结果,严重限制互动。与此同时,现有的预汇总技术不够充分,因为它们在矩形区域上维持总合。结果,当对任意多角区域(如城市街区)应用时,它们引发了无法约束的近似错误。在本文中,我们引入了GeoBlock, 一个新的预汇总数据结构,支持在任意多边形上的空间整合。 Geblock使用一套精细的网格单元格,与先前的工作不同,通过调整当前地理序列的地理序列大小来限制近似误差。此外,Geoblock系统经常调整当前三次的底径,从而将总体运行结果调整为动态直径。