GeoBlocks: A Query-Cache Accelerated Data Structure for Spatial Aggregation over Polygons

TL;DR

GeoBlocks is a new data structure that enables fast, approximate spatial aggregation over arbitrary polygons with controllable error, significantly improving interactive analysis of urban geospatial data.

Contribution

It introduces GeoBlocks, a pre-aggregation method with error bounds for arbitrary polygons and a cache mechanism for workload adaptation, enhancing spatial query performance.

Findings

GeoBlocks achieves up to 1000x faster queries than on-the-fly methods.

The approximation error can be effectively bounded by adjusting grid cell size.

GeoBlocks supports sub-second query latency for interactive spatial analytics.

Abstract

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 polygonal 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.