IDEAS home Printed from https://ideas.repec.org/a/bla/presci/v95y2016i3p613-631.html
   My bibliography  Save this article

Uncovering regional characteristics from mobile phone data: A network science approach

Author

Listed:
  • Guanghua Chi
  • Jean-Claude Thill
  • Daoqin Tong
  • Li Shi
  • Yu Liu

Abstract

No abstract is available for this item.

Suggested Citation

  • Guanghua Chi & Jean-Claude Thill & Daoqin Tong & Li Shi & Yu Liu, 2016. "Uncovering regional characteristics from mobile phone data: A network science approach," Papers in Regional Science, Wiley Blackwell, vol. 95(3), pages 613-631, August.
    • Handle: RePEc:bla:presci:v:95:y:2016:i:3:p:613-631
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1111/pirs.12149
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Lawrence Hubert & Phipps Arabie, 1985. "Comparing partitions," Journal of Classification, Springer;The Classification Society, vol. 2(1), pages 193-218, December.
    2. Hidalgo, Cesar A. & Rodriguez-Sickert, C., 2008. "The dynamics of a mobile phone network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(12), pages 3017-3024.
    3. Chaogui Kang & Yu Liu & Xiujun Ma & Lun Wu, 2012. "Towards Estimating Urban Population Distributions from Mobile Call Data," Journal of Urban Technology, Taylor & Francis Journals, vol. 19(4), pages 3-21, October.
    4. Guldmann, Jean-Michel, 1998. "Intersectoral point-to-point telecommunication flows: theoretical framework and empirical results," Regional Science and Urban Economics, Elsevier, vol. 28(5), pages 585-609, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Chi, Guanghua & Liu, Yu & Shi, Li & Gao, Yong, 2017. "Understanding the effects of administrative boundary in sampling spatially embedded networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 466(C), pages 616-625.
    2. Arnaud Adam & Jean-Charles Delvenne & Isabelle Thomas, 2018. "Detecting communities with the multi-scale Louvain method: robustness test on the metropolitan area of Brussels," Journal of Geographical Systems, Springer, vol. 20(4), pages 363-386, October.
    3. Andreas Erlström & Markus Grillitsch & Ola Hall, 2022. "The geography of connectivity: a review of mobile positioning data for economic geography," Journal of Geographical Systems, Springer, vol. 24(4), pages 679-707, October.
    4. Yang, Yitao & Jia, Bin & Yan, Xiao-Yong & Zhi, Danyue & Song, Dongdong & Chen, Yan & de Bok, Michiel & Tavasszy, Lóránt A. & Gao, Ziyou, 2023. "Uncovering and modeling the hierarchical organization of urban heavy truck flows," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 179(C).
    5. Erlström, Andreas & Grillitsch, Markus & Hall, Ola, 2020. "The Geography of Connectivity: Trails of Mobile Phone Data," Papers in Innovation Studies 2020/6, Lund University, CIRCLE - Centre for Innovation Research.
    6. Shiwei Lu & Shih-Lung Shaw & Zhixiang Fang & Xirui Zhang & Ling Yin, 2017. "Exploring the Effects of Sampling Locations for Calibrating the Huff Model Using Mobile Phone Location Data," Sustainability, MDPI, vol. 9(1), pages 1-18, January.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Miriam Aparicio, 2021. "Resiliency and Cooperation or Regarding Social and Collective Competencies for University Achievement. An Analysis from a Systemic Perspective," European Journal of Social Sciences Education and Research Articles, Revistia Research and Publishing, vol. 8, ejser_v8_.
    2. Yunpeng Zhao & Qing Pan & Chengan Du, 2019. "Logistic regression augmented community detection for network data with application in identifying autism‐related gene pathways," Biometrics, The International Biometric Society, vol. 75(1), pages 222-234, March.
    3. Michael P. B. Gallaugher & Salvatore D. Tomarchio & Paul D. McNicholas & Antonio Punzo, 2022. "Multivariate cluster weighted models using skewed distributions," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 16(1), pages 93-124, March.
    4. Riccardo Rastelli & Michael Fop, 2020. "A stochastic block model for interaction lengths," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 14(2), pages 485-512, June.
    5. Scrucca, Luca, 2016. "Identifying connected components in Gaussian finite mixture models for clustering," Computational Statistics & Data Analysis, Elsevier, vol. 93(C), pages 5-17.
    6. Laura Bocci & Donatella Vicari, 2019. "ROOTCLUS: Searching for “ROOT CLUSters” in Three-Way Proximity Data," Psychometrika, Springer;The Psychometric Society, vol. 84(4), pages 941-985, December.
    7. Wu, Han-Ming & Tien, Yin-Jing & Chen, Chun-houh, 2010. "GAP: A graphical environment for matrix visualization and cluster analysis," Computational Statistics & Data Analysis, Elsevier, vol. 54(3), pages 767-778, March.
    8. Zhongnan Jin & Jie Min & Yili Hong & Pang Du & Qingyu Yang, 2024. "Multivariate Functional Clustering with Variable Selection and Application to Sensor Data from Engineering Systems," INFORMS Joural on Data Science, INFORMS, vol. 3(2), pages 203-218, October.
    9. Pietro Coretto & Christian Hennig, 2016. "Robust Improper Maximum Likelihood: Tuning, Computation, and a Comparison With Other Methods for Robust Gaussian Clustering," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(516), pages 1648-1659, October.
    10. José E. Chacón, 2021. "Explicit Agreement Extremes for a 2 × 2 Table with Given Marginals," Journal of Classification, Springer;The Classification Society, vol. 38(2), pages 257-263, July.
    11. F. Marta L. Di Lascio & Andrea Menapace & Roberta Pappadà, 2024. "A spatially‐weighted AMH copula‐based dissimilarity measure for clustering variables: An application to urban thermal efficiency," Environmetrics, John Wiley & Sons, Ltd., vol. 35(1), February.
    12. Browne, Ryan P., 2022. "Revitalizing the multivariate elliptical leptokurtic-normal distribution and its application in model-based clustering," Statistics & Probability Letters, Elsevier, vol. 190(C).
    13. Tang, Jinjun & Liu, Fang & Wang, Yinhai & Wang, Hua, 2015. "Uncovering urban human mobility from large scale taxi GPS data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 438(C), pages 140-153.
    14. Jerzy Korzeniewski, 2016. "New Method Of Variable Selection For Binary Data Cluster Analysis," Statistics in Transition new series, Główny Urząd Statystyczny (Polska), vol. 17(2), pages 295-304, June.
    15. Yifan Zhu & Chongzhi Di & Ying Qing Chen, 2019. "Clustering Functional Data with Application to Electronic Medication Adherence Monitoring in HIV Prevention Trials," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 11(2), pages 238-261, July.
    16. Irene Vrbik & Paul McNicholas, 2015. "Fractionally-Supervised Classification," Journal of Classification, Springer;The Classification Society, vol. 32(3), pages 359-381, October.
    17. Maurizio Vichi & Carlo Cavicchia & Patrick J. F. Groenen, 2022. "Hierarchical Means Clustering," Journal of Classification, Springer;The Classification Society, vol. 39(3), pages 553-577, November.
    18. Batool, Fatima & Hennig, Christian, 2021. "Clustering with the Average Silhouette Width," Computational Statistics & Data Analysis, Elsevier, vol. 158(C).
    19. Jajcay, Nikola, 2018. "Spatial and temporal scales of atmospheric dynamics," Thesis Commons ar8ks, Center for Open Science.
    20. Patrick D. Shay & Stephen S. Farnsworth Mick, 2017. "Clustered and distinct: a taxonomy of local multihospital systems," Health Care Management Science, Springer, vol. 20(3), pages 303-315, September.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bla:presci:v:95:y:2016:i:3:p:613-631. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: http://www.blackwellpublishing.com/journal.asp?ref=1056-8190 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.