IDEAS home Printed from https://ideas.repec.org/a/bit/bsrysr/v9y2018i2p45-54n5.html
   My bibliography  Save this article

The Influence of the Zonation Effect on a System of Hierarchical Functional Regions

Author

Listed:
  • Drobne Samo
  • Lakner Mitja

    (Faculty of Civil and Geodetic Engineering, University of Ljubljana,Ljubljana, Slovenia)

Abstract

Background: Hierarchical functional regions (FRs) can be calculated using data on interactions between basic spatial units (BSUs) and a hierarchical aggregation procedure. However, the results depend on the selected system of initial BSUs. In spatial sciences, this is known as the zonation effect, which is one of the effects of the Modifiable Areal Unit Problem (MAUP). Objectives: In this paper, we analyse the influence of the zonation effect on a system of hierarchical functional regions. Methods/Approach: We compared two systems of hierarchical functional regions of Slovenia modelled by the Intramax aggregation procedure using the inter-municipal labour commuting flows for the same year, but for two different initial sets of municipalities. Besides, we have introduced a new measure to compare systems of hierarchical FRs. Results: The results show that the zonation effect has an influence on hierarchical functional regions. The clustering comparison measure suggested here is a metric measure, which is appropriate for comparing hierarchical FRs. Conclusions: The zonation effect has influence on hierarchical FRs. The clustering comparison measure suggested in this paper is easy to interpret, but it should be adjusted for the number of clusterings

Suggested Citation

  • Drobne Samo & Lakner Mitja, 2018. "The Influence of the Zonation Effect on a System of Hierarchical Functional Regions," Business Systems Research, Sciendo, vol. 9(2), pages 45-54, July.
    • Handle: RePEc:bit:bsrysr:v:9:y:2018:i:2:p:45-54:n:5
    DOI: 10.2478/bsrj-2018-0018
    as

    Download full text from publisher

    File URL: https://doi.org/10.2478/bsrj-2018-0018
    Download Restriction: no
    File URL: https://libkey.io/10.2478/bsrj-2018-0018?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Tristan Kohl & Aleid E Brouwer, 2014. "The Development of Trade Blocs in an Era of Globalisation," Environment and Planning A, , vol. 46(7), pages 1535-1553, July.
    2. Charlie Karlsson & Michael Olsson, 2006. "The identification of functional regions: theory, methods, and applications," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 40(1), pages 1-18, March.
    3. Lawrence Hubert & Phipps Arabie, 1985. "Comparing partitions," Journal of Classification, Springer;The Classification Society, vol. 2(1), pages 193-218, December.
    4. Drobne Samo & Bogataj Marija, 2014. "Regions for Servicing Old People: Case study of Slovenia," Business Systems Research, Sciendo, vol. 5(3), pages 19-36, September.
    Full references (including those not matched with items on IDEAS)

    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. Drobne Samo & Lakner Mitja, 2016. "Use of Constraints in the Hierarchical Aggregation Procedure Intramax," Business Systems Research, Sciendo, vol. 7(2), pages 5-22, September.
    2. Drobne Samo & Garre Alberto & Hontoria Eloy & Konjar Miha, 2020. "Comparison of Two Network-Theory-Based Methods for detecting Functional Regions," Business Systems Research, Sciendo, vol. 11(2), pages 21-35, October.
    3. Alicja Grześkowiak, 2016. "Assessment of Participation in Cultural Activities in Poland by Selected Multivariate Methods," European Journal of Social Sciences Education and Research Articles, Revistia Research and Publishing, vol. 3, January -.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. Irene Vrbik & Paul McNicholas, 2015. "Fractionally-Supervised Classification," Journal of Classification, Springer;The Classification Society, vol. 32(3), pages 359-381, October.
    10. 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.
    11. V. I. Blanutsa, 2022. "Geographic Research of the Platform Economy: Existing and Potential Approaches," Regional Research of Russia, Springer, vol. 12(2), pages 133-142, June.
    12. Batool, Fatima & Hennig, Christian, 2021. "Clustering with the Average Silhouette Width," Computational Statistics & Data Analysis, Elsevier, vol. 158(C).
    13. 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.
    14. Roberto Rocci & Stefano Antonio Gattone & Roberto Di Mari, 2018. "A data driven equivariant approach to constrained Gaussian mixture modeling," 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. 12(2), pages 235-260, June.
    15. Wan-Lun Wang, 2019. "Mixture of multivariate t nonlinear mixed models for multiple longitudinal data with heterogeneity and missing values," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(1), pages 196-222, March.
    16. Matthijs Warrens, 2010. "Inequalities Between Kappa and Kappa-Like Statistics for k×k Tables," Psychometrika, Springer;The Psychometric Society, vol. 75(1), pages 176-185, March.
    17. Redivo, Edoardo & Nguyen, Hien D. & Gupta, Mayetri, 2020. "Bayesian clustering of skewed and multimodal data using geometric skewed normal distributions," Computational Statistics & Data Analysis, Elsevier, vol. 152(C).
    18. Jerzy Korzeniewski, 2016. "New Method Of Variable Selection For Binary Data Cluster Analysis," Statistics in Transition New Series, Polish Statistical Association, vol. 17(2), pages 295-304, June.
    19. Zhu, Xuwen & Melnykov, Volodymyr, 2018. "Manly transformation in finite mixture modeling," Computational Statistics & Data Analysis, Elsevier, vol. 121(C), pages 190-208.
    20. Li, Pai-Ling & Chiou, Jeng-Min, 2011. "Identifying cluster number for subspace projected functional data clustering," Computational Statistics & Data Analysis, Elsevier, vol. 55(6), pages 2090-2103, June.

    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:bit:bsrysr:v:9:y:2018:i:2:p:45-54:n:5. 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: Peter Golla (email available below). General contact details of provider: https://www.sciendo.com .

    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.