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Towards Scalable and Expressive Spatial Grouping Queries

Published: 22 November 2024 Publication History

Abstract

The rapid expansion of location-based services has generated an unprecedented volume of spatial data. Spatial grouping queries, which categorize sets of spatial objects based on their geographical properties and data characteristics, are fundamental in uncovering significant patterns from large-scale spatial data. This query type is applicable to both spatial points and spatial polygons. In the realm of spatial polygons, regionalization serves as a crucial grouping query by organizing spatial polygons into regions. For spatial points, hotspot detection over spatial networks identifies clusters that demonstrate areas of high concentration. However, existing algorithms often lack scalability, struggle with large datasets, and fail to offer the expressiveness needed to handle flexible, user-defined constraints. To address these limitations, this paper introduces novel algorithms tailored for enhanced spatial grouping queries, including both regionalization and hotspot detection.

References

[1]
H. Alrashid, Y. Liu, and A. Magdy. SMP: Scalable Max-P Regionalization. In ACM SIGSPATIAL, pages 1--4, 2022.
[2]
H. Alrashid, Y. Liu, and A. Magdy. PAGE: Parallel Scalable Regionalization Framework. ACM TSAS, 9(3):1--26, 2023.
[3]
Y. Liu, Y. Kang, A. Mahmood, and A. Magdy. Scalable Evaluation of Local K-Function for Radius-Accurate Hotspot Detection in Spatial Networks. In ACM SIGSPATIAL, pages 1--12, 2023.
[4]
Y. Liu, A. R. Mahmood, A. Magdy, and S. Rey. PRUC: P-regions with User-defined Constraint. In Proceedings of the VLDB Endowment, pages 491--503, 2021.

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Published In

cover image ACM Conferences
SIGSPATIAL '24: Proceedings of the 32nd ACM International Conference on Advances in Geographic Information Systems
October 2024
743 pages
This work is licensed under a Creative Commons Attribution International 4.0 License.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 November 2024

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Author Tags

  1. Hotspot Detection
  2. Spatial Grouping Query
  3. Spatial Regionalization

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  • Short-paper
  • Research
  • Refereed limited

Funding Sources

  • Google-CAHSI research grant
  • Microsoft unrestricted gift
  • National Science Foundation, USA

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SIGSPATIAL '24
Sponsor:

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SIGSPATIAL '24 Paper Acceptance Rate 37 of 122 submissions, 30%;
Overall Acceptance Rate 257 of 1,238 submissions, 21%

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