https://doi.org/10.1140/epjds/s13688-025-00605-2
Research
Mining spatial co-location patterns via γ-quasi-clique detection
1
School of Information Science and Technology, Yunnan Normal University, 650500, Kunming, People’s Republic of China
2
Engineering Research Center of Computer Vision and Intelligent Control Technology, Yunnan Normal University, 650500, Kunming, People’s Republic of China
a
This email address is being protected from spambots. You need JavaScript enabled to view it.
Received:
4
June
2025
Accepted:
27
November
2025
Published online:
5
December
2025
Spatial co-location patterns (SCPs) are sets of spatial features with instances frequently co-occurring proximally in geographical space. Extracting SCPs is crucial for understanding urban space structure and function. Given that spatial datasets frequently exhibit non-uniform density distributions, such characteristics impose significant limitations and constraints on classical spatial co-location pattern (SCP) mining algorithms in accurately capturing the nuanced proximity relationships among spatial instances. This paper introduces a new materialized model with γ-quasi-clique to comprehensively and flexibly capture complex instance spatial relations. However, the γ-quasi-clique’s structural relaxation can violate downward closure in certain cases, and exhaustive clique enumeration for co-location identification is computationally challenging. To address these problems, we develop a novel neighbor relationship function and a shared scoring mechanism to avoid instance over-counting in this paper. We also implement candidate feature and pattern filtering strategies to reduce computational complexity, along with a filtering-verification framework for participation instance determination. Compared to Fraction-Score, γ-quasi-clique can capture more complex spatial relationships while reducing time complexity by 30% through shared score computation. Empirical evaluations on three real-world datasets show the γ-quasi-clique framework’s theoretical advantage and the proposed algorithms’ computational efficiency.
Key words: Spatial data mining / Spatial co-location patterns / γ-quasi-clique / Filtering-and-verification framework
© The Author(s) 2025
Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
