In accordance with regulations and requirements, the editorial department's website domain has been changed to arocmag.cn. The original domain (arocmag.com) has been redirecting to new domain since Jan. 1st, 2025.
See More
App. Res. of Comp. Accepted Paper
Vol. 43, 2026 No. 3

Spatial co-location core pattern mining based on adaptive scale neighborhood and K-shell decomposition

Chen Xueyao
Lu Junli
Duan Peng
Tang Mingxiang
Dept. of Mathematics&Computer Science, Yunnan Minzu University, Kunming 650500, China
DOI: 10.19734/j.issn.1001-3695.2025.07.0251

Abstract

With the development of spatial information technology and the rapid growth of urban spatial data, spatial co-location pattern mining plays an important role in revealing potential associations among spatial objects. To address the limitations of traditional methods in expressing spatial object dominance and neglecting instance-scale differences, this study proposed a spatial co-location kernel pattern mining method for urban functional analysis. The method constructed an adaptive scale neighborhood relation based on instance area and applied a spherical neighborhood search strategy. It introduced an area-aware mechanism and a dynamic threshold radius to enhance the accuracy and efficiency of neighborhood determination. The approach applied the K-shell decomposition technique to build a graph structure and automatically identified core features with strong centrality and structural stability. It further implemented a feature-pair-based parallel mining strategy that accelerated neighborhood computation and frequent pattern generation. Experiments on real urban POI datasets showed that the method increased the overall participation rate of mined patterns by about 10% and improved computational efficiency by an average of 33.8% compared with other methods. The results confirm that the proposed method offers significant advantages in pattern quality and computational efficiency and presents strong potential for practical applications.

Foundation Support

国家自然科学基金资助项目(12361104)
兴滇英才青年拔尖人才资助项目(XDYC-QNRC-2022-0518)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2025.07.0251
Publish at: Application Research of Computers Accepted Paper, Vol. 43, 2026 No. 3

Publish History

[2025-11-17] Accepted Paper

Cite This Article

陈雪瑶, 芦俊丽, 段鹏, 等. 自适应规模邻近与K壳分解的空间并置核模式挖掘 [J]. 计算机应用研究, 2026, 43 (3). (2025-11-18). https://doi.org/10.19734/j.issn.1001-3695.2025.07.0251. (Chen Xueyao, Lu Junli, Duan Peng, et al. Spatial co-location core pattern mining based on adaptive scale neighborhood and K-shell decomposition [J]. Application Research of Computers, 2026, 43 (3). (2025-11-18). https://doi.org/10.19734/j.issn.1001-3695.2025.07.0251. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

Indexed & Evaluation

  • The Second National Periodical Award 100 Key Journals
  • Double Effect Journal of China Journal Formation
  • the Core Journal of China (Peking University 2023 Edition)
  • the Core Journal for Science
  • Chinese Science Citation Database (CSCD) Source Journals
  • RCCSE Chinese Core Academic Journals
  • Journal of China Computer Federation
  • 2020-2022 The World Journal Clout Index (WJCI) Report of Scientific and Technological Periodicals
  • Full-text Source Journal of China Science and Technology Periodicals Database
  • Source Journal of China Academic Journals Comprehensive Evaluation Database
  • Source Journals of China Academic Journals (CD-ROM Version), China Journal Network
  • 2017-2019 China Outstanding Academic Journals with International Influence (Natural Science and Engineering Technology)
  • Source Journal of Top Academic Papers (F5000) Program of China's Excellent Science and Technology Journals
  • Source Journal of China Engineering Technology Electronic Information Network and Electronic Technology Literature Database
  • Source Journal of British Science Digest (INSPEC)
  • Japan Science and Technology Agency (JST) Source Journal
  • Russian Journal of Abstracts (AJ, VINITI) Source Journals
  • Full-text Journal of EBSCO, USA
  • Cambridge Scientific Abstracts (Natural Sciences) (CSA(NS)) core journals
  • Poland Copernicus Index (IC)
  • Ulrichsweb (USA)