Algorithm Research & Explore
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1779-1784

Home-work location identification method based on spatiotemporal constrained density clustering

Miao Dengfeng1a
Xiao Yuelei1b,2
1. a. School of Computer, b. School of Modern Posts, Xi'an University of Posts & Telecommunications, Xi'an 710121, China
2. Shaanxi Provincial Information Engineering Research Institute, Xi'an 710075, China

Abstract

To accurately identify residential home-work locations from mobile terminal location data, this paper proposed a home-work location identification method based on spatiotemporal constrained density clustering. Firstly, the method used a K-means based DBSCAN spatiotemporal stationary point clustering process to divide the original trajectory points of many days for each resident into different spatiotemporal stagnation point clusters. Then, it used a recognition process of residence point cluster and moving point cluster based on velocity threshold to recognize every spatiotemporal stationary point cluster of each resident as a stationary point cluster or a moving point cluster. After that, it used a DBSCAN important residence point clustering process based on K-nearest distance to divide the residence points of each resident into different important residence point clusters. Finally, it used a KNN home-work location identification process optimized by KD-tree to identify every important residence point of each resident as a home location, a work location, a home-work location or an interest location. The experimental results show that each process of this method is reasonable and effective, and the final recognition effect of home-work locations is better than the time threshold method, the cumulative time method and the information entropy method.

Foundation Support

国家自然科学基金资助项目(61741216)
国家重点研发计划资助项目(2018YFC08242-04)
陕西省科技统筹创新工程计划资助项目(2016KTTSGY01-03)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2021.11.0628
Publish at: Application Research of Computers Printed Article, Vol. 39, 2022 No. 6
Section: Algorithm Research & Explore
Pages: 1779-1784
Serial Number: 1001-3695(2022)06-030-1779-06

Publish History

[2022-01-25] Accepted Paper
[2022-06-05] Printed Article

Cite This Article

苗登逢, 肖跃雷. 基于时空约束密度聚类的职住地识别方法 [J]. 计算机应用研究, 2022, 39 (6): 1779-1784. (Miao Dengfeng, Xiao Yuelei. Home-work location identification method based on spatiotemporal constrained density clustering [J]. Application Research of Computers, 2022, 39 (6): 1779-1784. )

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.

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