Revealing the User Behavior Pattern Using HNCORS RTK Location Big Data

被引:5
|
作者
Ao, Minsi [1 ,2 ]
Dong, Mingxu [1 ]
Chu, Bin [1 ,3 ]
Zeng, Xiangqiang [1 ]
Li, Chenxi [1 ]
机构
[1] Hunan Inst Geomat Sci & Technol, HNCORS Data Ctr, Changsha 410007, Hunan, Peoples R China
[2] Cent S Univ, Sch Geosci & Infophys, Changsha 410007, Hunan, Peoples R China
[3] Wuhan Univ, GNSS Res Ctr, Wuhan 430079, Hubei, Peoples R China
来源
IEEE ACCESS | 2019年 / 7卷
关键词
Location based big data; global positioning system; spatial-temporal analysis; user behavior; kernel density analysis; HNCORS;
D O I
10.1109/ACCESS.2019.2902577
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Hunan continuously operating reference station network is one of the most important infrastructures of the regional geospatial datum in Hunan province, China. It provides the official 24-h RTK service to the public. How to reveal the user behavior pattern by spatio-temporal analysis on location-based big data is significant for the service promotion. With procedures, such as cleaning, sampling, and so on, the usage count, fixing rate, and network delay data from August 2017 to July 2018 are first analyzed on multiple spatial and temporal scales. The results show that user behavior is strongly correlated to the surveying field work habits. Overall, the usage count is much more in the central and eastern, developed, and plain or hill area, while it is less in the western, underdeveloped, mountain and lake area. The suburbs are the most popular area. The usage count is also correlated to the local economic profile. Meanwhile, the Huaihua and Shaoyang cities need to be paid more attention to promotions. The hot spots revolution in 24 h can be divided into six stages as sleeping, recovery, first and second busy stages, adjustment, and dormancy when the hot spot successively increased and decreased around the Changsha-Zhuzhou-Xiangtan urban agglomeration and other 11 urban centers in the Hunan province.
引用
收藏
页码:30302 / 30312
页数:11
相关论文
共 50 条
  • [1] Framework for Analyzing User Behavior using Big Data Technology
    Jatwani, Poonam
    Tomar, Pradeep
    Dhingra, Vandana
    [J]. PROCEEDINGS OF THE 8TH INTERNATIONAL CONFERENCE CONFLUENCE 2018 ON CLOUD COMPUTING, DATA SCIENCE AND ENGINEERING, 2018, : 598 - 603
  • [2] Using NTFS Cluster Allocation Behavior to Find the Location of User Data
    Karresand, Martin
    Axelsson, Stefan
    Dyrkolbotn, Geir Olav
    [J]. DIGITAL INVESTIGATION, 2019, 29 : S51 - S60
  • [3] Internet User Behavior Analysis Based on Big Data
    He, Jiangnan
    Yin, Xiaoyin
    [J]. IWCMC 2021: 2021 17TH INTERNATIONAL WIRELESS COMMUNICATIONS & MOBILE COMPUTING CONFERENCE (IWCMC), 2021, : 432 - 435
  • [4] Location recommendation system using big data
    Lee, Ki-Young
    Kang, Jeong-Jin
    Ahn, Hye-Kyoung
    Kim, Kyu-Ho
    Choi, Gyoo-Seok
    Choi, Sung-Jai
    Oh, Sun-Jin
    [J]. International Journal of Multimedia and Ubiquitous Engineering, 2014, 9 (05): : 317 - 325
  • [5] Location Based Service User Experience based on User Behavior Data by Using Theory of Inventive Problem Solving
    Kim, Song-Kyoo
    [J]. MANAGEMENT AND SERVICE SCIENCE, 2011, 8 : 23 - 27
  • [6] Big Data Enabled User Behavior Characteristics in Mobile Internet
    Jiang, Shuai
    Wei, Baoshan
    Wang, Tong
    Zhao, Zhenbang
    Zhang, Xing
    [J]. 2017 9TH INTERNATIONAL CONFERENCE ON WIRELESS COMMUNICATIONS AND SIGNAL PROCESSING (WCSP), 2017,
  • [7] User and Entity Behavior Analysis under Urban Big Data
    Tian, Zhihong
    Luo, Chaochao
    Lu, Hui
    Su, Shen
    Sun, Yanbin
    Zhang, Man
    [J]. ACM/IMS Transactions on Data Science, 2020, 1 (03):
  • [8] Construction of Network User Behavior Spectrum in Big Data Environment
    Xu, Mengyao
    Yan, Fangfei
    Wang, Biao
    Yi, Shuping
    Yi, Qian
    Xiong, Shiquan
    [J]. INTELLIGENT COMPUTING AND INTERNET OF THINGS, PT II, 2018, 924 : 133 - 143
  • [9] Language independent Big-Data system for the prediction of user location on Twitter
    Alonso-Lorenzo, Jaime
    Costa-Montenegro, Enrique
    Fernandez-Gavilanes, Milagros
    [J]. 2016 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA), 2016, : 2437 - 2446
  • [10] Big Data Analysis to Observe Check-in Behavior Using Location-Based Social Media Data
    Rizwan, Muhammad
    Wan, Wanggen
    [J]. INFORMATION, 2018, 9 (10)