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Pyneapple-G: Scalable Spatial Grouping Queries

  • University of California at Riverside

Research output: Contribution to journalConference articlepeer-review

2 Scopus citations

Abstract

This paper demonstrates Pynapple-G, an open-source library for scalable spatial grouping queries based on Apache Sedona (formerly known as GeoSpark). We demonstrate two modules, namely, SGPAC and DDCEL, that support grouping points, grouping lines, and polygon overlays. The SGPAC module provides a large-scale grouping of spatial points by highly complex polygon boundaries. The grouping results aggregate the number of spatial points within the boundaries of each polygon. The DDCEL module provides the first parallelized algorithm to group spatial lines into a DCEL data structure and discovers planar polygons from scattered line segments. Exploiting the scalable DCEL, we support scalable overlay operations over multiple polygon layers to compute the layers’ intersection, union, or difference. To showcase Pyneapple-G, we have developed a frontend web application that enables users to interact with these modules, select their data layers or data points, and view results on an interactive map. We also provide interactive notebooks demonstrating the superiority and simplicity of Pyneapple-G to help social scientists and developers explore its full potential.

Original languageEnglish
Pages (from-to)4469-4472
Number of pages4
JournalProceedings of the VLDB Endowment
Volume17
Issue number12
DOIs
StatePublished - 2024
Externally publishedYes
Event50th International Conference on Very Large Data Bases, VLDB 2024 - Guangzhou, China
Duration: 24 Aug 202429 Aug 2024

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