IDEAS home Printed from https://ideas.repec.org/a/spr/soinre/v146y2019i3d10.1007_s11205-019-02134-8.html
   My bibliography  Save this article

Cross Sectional and Longitudinal Fuzzy Clustering of the NUTS and Positioning of the Italian Regions with Respect to the Regional Competitiveness Index (RCI) Indicators with Contiguity Constraints

Author

Listed:
  • Pierpaolo D’Urso

    (Sapienza University of Rome)

  • Livia De Giovanni

    (LUISS Guido Carli and CEFOP-LUISS)

  • Riccardo Massari

    (Sapienza University of Rome)

  • Francesca G. M. Sica

    (Confindustria and CEFOP-LUISS)

Abstract

In socio-economical clustering often the empirical information is represented by time-varying data generated by indicators observed over time on a set of subnational (regional) units. Usually among these units may exist contiguity relations, spatial but not only. In this paper we propose a fuzzy clustering model of multivariate time-varying data, the longitudinal fuzzy C-Medoids clustering with contiguity constraints. The temporal aspect is dealt with by using appropriate measures of dissimilarity between time trajectories. The contiguity among units is dealt with adding a contiguity matrix as a penalization term in the clustering model. The cross sectional fuzzy C-Medoids clustering with contiguity constraints is obtained considering one instant of time. The model is applied to the classification of the European NUTS on the basis of the observed dynamics of the Basic, Efficiency and Innovation subindexes of the Regional Competitiveness Index (RCI) 2013 and 2016. The positioning of the Italian regions is analyzed through the values of the medoids of the clusters and shows the peculiarities of the regions with respect to the subindexes either in single times or in the dynamic. Two contiguity constraints, one based on the European Western, Southern, Central and Northern geographic areas and one on the level of GDP—taken into account in the computation of the RCI—are also introduced in the models.

Suggested Citation

  • Pierpaolo D’Urso & Livia De Giovanni & Riccardo Massari & Francesca G. M. Sica, 2019. "Cross Sectional and Longitudinal Fuzzy Clustering of the NUTS and Positioning of the Italian Regions with Respect to the Regional Competitiveness Index (RCI) Indicators with Contiguity Constraints," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 146(3), pages 609-650, December.
    • Handle: RePEc:spr:soinre:v:146:y:2019:i:3:d:10.1007_s11205-019-02134-8
    DOI: 10.1007/s11205-019-02134-8
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11205-019-02134-8
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11205-019-02134-8?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
    ---><---

    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. Coppi, Renato & D'Urso, Pierpaolo, 2006. "Fuzzy unsupervised classification of multivariate time trajectories with the Shannon entropy regularization," Computational Statistics & Data Analysis, Elsevier, vol. 50(6), pages 1452-1477, March.
    2. Renato Coppi & Pierpaolo D'Urso, 2002. "Fuzzy K-means clustering models for triangular fuzzy time trajectories," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 11(1), pages 21-40, February.
    3. Dino Pinelli & Roberta Torre & Lucianajulia Pace & Laura Cassio & Alfonso Arpaia, 2017. "The Recent Reform of the Labour Market in Italy: A Review," European Economy - Discussion Papers 072, Directorate General Economic and Financial Affairs (DG ECFIN), European Commission.
    4. Luis García-Escudero & Alfonso Gordaliza & Carlos Matrán & Agustín Mayo-Iscar, 2010. "A review of robust clustering methods," 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. 4(2), pages 89-109, September.
    5. Nicholas Charron & Lewis Dijkstra & Victor Lapuente, 2015. "Erratum to: Mapping the Regional Divide in Europe: A Measure for Assessing Quality of Government in 206 European Regions," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 124(3), pages 1059-1059, December.
    6. Coppi, Renato & D'Urso, Pierpaolo, 2003. "Three-way fuzzy clustering models for LR fuzzy time trajectories," Computational Statistics & Data Analysis, Elsevier, vol. 43(2), pages 149-177, June.
    7. Heungsun Hwang & Wayne Desarbo & Yoshio Takane, 2007. "Fuzzy Clusterwise Generalized Structured Component Analysis," Psychometrika, Springer;The Psychometric Society, vol. 72(2), pages 181-198, June.
    8. Willem Heiser & Patrick Groenen, 1997. "Cluster differences scaling with a within-clusters loss component and a fuzzy successive approximation strategy to avoid local minima," Psychometrika, Springer;The Psychometric Society, vol. 62(1), pages 63-83, March.
    9. Nicholas Charron & Lewis Dijkstra & Victor Lapuente, 2015. "Mapping the Regional Divide in Europe: A Measure for Assessing Quality of Government in 206 European Regions," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 122(2), pages 315-346, June.
    10. Blanca L. Delgado-Márquez & Marcos García-Velasco, 2018. "Geographical Distribution of the European Knowledge Base Through the Lens of a Synthetic Index," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 136(2), pages 477-496, April.
    11. Livia De Giovanni & Francesca G.M.Sica, 2014. "Attrattività e competitività dei territori italiani - I risultati: le dimensioni dell’attrattività territoriale," Rivista di Politica Economica, SIPI Spa, issue 4, pages 115-234, Oct.-Dec..
    12. Paola Annoni & Lewis Dijkstra, 2017. "Measuring and monitoring regional competitiveness in the European Union," Chapters, in: Robert Huggins & Piers Thompson (ed.), Handbook of Regions and Competitiveness, chapter 3, pages 49-79, Edward Elgar Publishing.
    13. Renato Coppi & Pierpaolo D’Urso & Paolo Giordani, 2010. "A Fuzzy Clustering Model for Multivariate Spatial Time Series," Journal of Classification, Springer;The Classification Society, vol. 27(1), pages 54-88, March.
    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. Ondrej Svoboda & Lukas Melecky & Michaela Stanickova, 2024. "The nexus of a regional competitiveness and economic resilience: The evidence-based on V4+4 NUTS 2 regions," E&M Economics and Management, Technical University of Liberec, Faculty of Economics, vol. 27(1), pages 06-23, March.
    2. Alaimo, Leonardo Salvatore & Nigri, Andrea, 2024. "The gender gap in life expectancy and lifespan disparity as social risk indicators for international countries: A fuzzy clustering approach," Socio-Economic Planning Sciences, Elsevier, vol. 91(C).
    3. Andrea Nigri & Susanna Levantesi & Gabriella Piscopo, 2022. "Causes-of-Death Specific Estimates from Synthetic Health Measure: A Methodological Framework," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 162(2), pages 887-908, July.

    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. B. Lafuente-Rego & P. D’Urso & J. A. Vilar, 2020. "Robust fuzzy clustering based on quantile autocovariances," Statistical Papers, Springer, vol. 61(6), pages 2393-2448, December.
    2. Vaishali Mirge & Kesari Verma & Shubhrata Gupta, 2017. "Dense traffic flow patterns mining in bi-directional road networks using density based trajectory clustering," 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. 11(3), pages 547-561, September.
    3. Renato Coppi & Pierpaolo D’Urso & Paolo Giordani, 2010. "A Fuzzy Clustering Model for Multivariate Spatial Time Series," Journal of Classification, Springer;The Classification Society, vol. 27(1), pages 54-88, March.
    4. Pierpaolo D’Urso & María Ángeles Gil, 2017. "Fuzzy data analysis and classification," 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. 11(4), pages 645-657, December.
    5. Jan Fagerberg & Martin Srholec, 2023. "Capabilities, diversification & economic dynamics in European Regions," The Journal of Technology Transfer, Springer, vol. 48(2), pages 623-644, April.
    6. Pierpaolo D’Urso & Livia Giovanni & Riccardo Massari & Dario Lallo, 2013. "Noise fuzzy clustering of time series by autoregressive metric," METRON, Springer;Sapienza Università di Roma, vol. 71(3), pages 217-243, November.
    7. Alfano, Vincenzo, 2024. "Unlocking the importance of perceived governance: The impact on COVID-19 in NUTS-2 European regions," Social Science & Medicine, Elsevier, vol. 343(C).
    8. Alice Medioli & Pier Luigi Marchini & Tatiana Mazza, 2024. "The impact of corruption and public governance quality on family firm business strategy," Business Strategy and the Environment, Wiley Blackwell, vol. 33(1), pages 55-69, January.
    9. Andrés Rodríguez-Pose & Roberto Ganau, 2022. "Institutions and the productivity challenge for European regions," Journal of Economic Geography, Oxford University Press, vol. 22(1), pages 1-25.
    10. Andrés Rodríguez‐Pose & Roberto Ganau & Kristina Maslauskaite & Monica Brezzi, 2021. "Credit constraints, labor productivity, and the role of regional institutions: Evidence from manufacturing firms in Europe," Journal of Regional Science, Wiley Blackwell, vol. 61(2), pages 299-328, March.
    11. Jesús Peiró-Palomino & Andrés J. Picazo-Tadeo, 2018. "Assessing well-being in European regions. Does government quality matter?," Working Papers 2018/06, Economics Department, Universitat Jaume I, Castellón (Spain).
    12. Fabio La Rosa & Sergio Paternostro & Francesca Bernini, 2023. "Corporate and regional governance antecedents of the Legality Rating of private Italian companies," Journal of Management & Governance, Springer;Accademia Italiana di Economia Aziendale (AIDEA), vol. 27(1), pages 297-329, March.
    13. Williams, Colin C. & Horodnic, Adrian V., 2017. "Rethinking informal payments by patients in Europe: An institutional approach," Health Policy, Elsevier, vol. 121(10), pages 1053-1062.
    14. Andrés Rodríguez-Pose & Vinko Muštra, 2022. "The economic returns of decentralisation: Government quality and the role of space," Environment and Planning A, , vol. 54(8), pages 1604-1622, November.
    15. Emanuela Marrocu & Raffaele Paci & Stefano Usai, 2022. "Direct and indirect effects of universities on European regional productivity," Papers in Regional Science, Wiley Blackwell, vol. 101(5), pages 1105-1133, October.
    16. Nan Zhang, 2015. "Changing a ‘culture’ of corruption: Evidence from an economic experiment in Italy," Rationality and Society, , vol. 27(4), pages 387-413, November.
    17. Elizabeth Ann Maharaj & Pierpaolo D’Urso & Don Galagedera, 2010. "Wavelet-based Fuzzy Clustering of Time Series," Journal of Classification, Springer;The Classification Society, vol. 27(2), pages 231-275, September.
    18. Ardielli Eva, 2019. "Use of TOPSIS Method for Assessing of Good Governance in European Union Countries," Review of Economic Perspectives, Sciendo, vol. 19(3), pages 211-231, September.
    19. Zhang, Min & Rodríguez-Pose, Andrés, 2024. "Government reform and innovation performance in China," LSE Research Online Documents on Economics 122728, London School of Economics and Political Science, LSE Library.
    20. De Luca, Giacomo & Lisi, Domenico & Martorana, Marco & Siciliani, Luigi, 2021. "Does higher Institutional Quality improve the Appropriateness of Healthcare Provision?," Journal of Public Economics, Elsevier, vol. 194(C).

    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:spr:soinre:v:146:y:2019:i:3:d:10.1007_s11205-019-02134-8. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.