Clicca qui per scaricare

Modeling European agri-environmental measure of spatial impact in the region of Sardinia, Italy, through fuzzy clustering means
Titolo Rivista: ECONOMIA AGRO-ALIMENTARE 
Autori/Curatori: Germana Manca 
Anno di pubblicazione:  2015 Fascicolo: 1  Lingua: Inglese 
Numero pagine:  15 P. 13-27 Dimensione file:  352 KB
DOI:  10.3280/ECAG2015-001002
Il DOI è il codice a barre della proprietà intellettuale: per saperne di più:  clicca qui   qui 


The aim of this paper is to demonstrate how a spatial fuzzy clustering mean expands the knowledge of the European agri-environmental initiative impact, named Measure 214, in the Sardinia Region. While sketching out the geographic area covered by the measure for analysis and investigation using fcm is a fruitful approach, their integration with social and economic factors is an essential step in understanding agricultural growth and how it is influenced by environmental policy. This integrated approach shows how agri-environmental measures tend to develop in the region and, geographically, describes the spatial effects. Fuzzy clustering analysis demonstrates how decisions, whether they are related to the pursuit of policies moving towards the agri-environmental initiatives of organic farming and sustainable agriculture, or whether they concern ways of financing the measure’s activities, belong to the sphere of information, able to influence the new phase of agri-environmental financing and to keep it going. The spatial expansion of the measure all over the Region can help identify where the measure has taken root and in which directions it should be steered to achieve sustainable agri-environmental development in the area. Furthermore, the fuzzy cluster analysis highlights the relevance of the results, showing the policy direction that clusters should take in order to improve the measure’s effectiveness.


Keywords: Agri-environmental measure, fuzzy c-means, geographical information system, cap
Jel Code: Q15, Q18

  1. Bezdek, J.C. (1981). Pattern Recognition with Fuzzy Objective Function Algorithms. New York: Plenum Press., DOI: 10.1016/0098-3004(84)90020-7
  2. Cheng, T., Molenaar, M. & Lin, H. (2001). Formalizing fuzzy objects from uncertain classification results. International Journal of Geographical Information Science, 15(1), 27-42., DOI: 10.1080/13658810010004689
  3. Duque, J.C., Ramos, R. & Surinach, J. (2007). Supervised regionalization methods: A survey. International Regional Science Review, 30(3), 195-220., DOI: 10.1177/0160017607301605
  4. Fisher, P. & Wood, J. (1998). What is a mountain? Or the Englishman who went up a Boolean geographical concept but realized it was fuzzy. Geography, 83(3), 247-56.
  5. Fonte, C.C. & Lodwick, W.A. (2004). Areas of fuzzy geographical entities. International Journal of Geographical Information Science, 18(2), 127-50., DOI: 10.1080/13658810310001620933
  6. Franco, S. & Senni, S. (1996). Applicazione della logica fuzzy nella misura dei fenomeni territoriali. Agribusiness Management e Ambiente, 1(4), 85-97.
  7. Franco, S. & Senni, S. (2003). Politiche di Sviluppo Rurale tra Programmazione e Valutazione. Milano: FrancoAngeli.
  8. Kollias, V.J. & Voliotis, A. (1991). Fuzzy-reasoning in the development of geographical information systems: frsis, A prototype soil information system with fuzzy retrieval capabilities. International Journal of Geographical Information Systems, 1(5), 209-23., DOI: 10.1080/02693799108927844
  9. Manca, G. & Curtin K.M. (2012). Fuzzy Analysis for Modeling Regional Delineation and Development: The Case of the Sardinian Mining Geopark. Transactions in gis, 16(1), 55-79., DOI: 10.1111/j.1467-9671.2011.01300.x
  10. Manca, G., Attaway, D. & Waters, N. (2014). Implications of the European agri-environmental program in the region of Sardinia, Italy: Discovering the outcomes using geographical weighted regression. Applied Geography, 50, 24-30., DOI: 10.1016/j.apgeog.2014.01.014
  11. Mason, M. (2005). Ruralita Consumi Alimentari. Una metodologia statistica per l’analisi delle componenti locali. Statistica e Società, 3(2), 12-9.
  12. Montresor, E., Pecci, F. & Pontarollo, N. (2010). Rural development policies at regional level in the enlarged eu. The impact on farm structures. University of Verona Working Paper Series. Department of Economics, University of Verona.
  13. Qiuzhen, C. Sipilainen T. & Sumelius J. (2012). Assessment of agri-environmental public goods proving using fuzzy synthetic evaluation. Discussion Papers, n. 61, Helsinki: Department of Economics and Management.
  14. Robinson, V.B. (1988). Some implications of fuzzy set theory applied to geographic databases. Computers, Environment and Urban Systems, 12(2), 89-97., DOI: 10.1016/0198-9715(88)90012-9
  15. Roubens, M. (1978). Pattern classification problems and fuzzy set. Fuzzy Sets Systems, 1(4), 239-253.
  16. Stefanakis, E., Vazirgiannis M. & Sellis, T. (1999) Incorporating fuzzy set methodologies in a DBMS repository for the application domain of gis. International Journal of Geographical Information Science, 13(7), 657-75., DOI: 10.1080/136588199241058
  17. Tsekouras, E.G. (2007). Implementing hierarchical fuzzy clustering in fuzzy modeling using the weighted fuzzy c-means. In Valente de Oliveira, J. And
  18. Pedrycz, W. (Ed.), Advanced in Fuzzy Clustering and Its Applications. New York: John Wiley and Sons, pp. 246-263.
  19. Tsekouras, E.G. (2005). On the use of the weighted fuzzy c-means in fuzzy modeling. Advances in Engineering Software, 36(5), 287-300., DOI: 10.1016/j.advengsoft.2004.12.001
  20. Wang, F.J. & Hall, G.B. (1996). Fuzzy representation of geographical boundaries in gis. International Journal of Geographical Information Systems, 10(5), 573-590., DOI: 10.1080/02693799608902098
  21. Woodcock, C.E. & Gopal, S. (2000). Fuzzy set theory and thematic maps: accuracy assessment and area estimation. International Journal of Geographical Information Science, 14(2), 153-172., DOI: 10.1080/136588100240895
  22. Xie, X.L. & Beni, G.A. (1991). Validity measure for fuzzy clustering. Ieee Transactions in Pattern Analysis and Machine Intelligence, 13(8), 841-846., DOI: 10.1109/34.85677
  23. Leung, Y. (1983). Fuzzy setap proach to spatial analysis and planning. Anon-technical evaluation. Geografiska Annaler Series B,65 (2), 65-75



  1. Pierpaolo D’Urso, Germana Manca, Nigel Waters, Stefania Girone, Visualizing regional clusters of Sardinia's EU supported agriculture: A Spatial Fuzzy Partitioning Around Medoids in Land Use Policy /2019 pp. 571, DOI: 10.1016/j.landusepol.2019.01.030

Germana Manca, in "ECONOMIA AGRO-ALIMENTARE" 1/2015, pp. 13-27, DOI:10.3280/ECAG2015-001002

   

FrancoAngeli è membro della Publishers International Linking Association associazione indipendente e no profit per facilitare l'accesso degli studiosi ai contenuti digitali nelle pubblicazioni professionali e scientifiche