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Hierarchical Clustering through Spatial Interaction Data. The Case of Commuting Flows in South-Eastern France

  • Giovanni Fusco
  • Matteo Caglioni
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6782)

Abstract

Regional scientists’ methods to partition space in functional areas meet complex system analysts’ methods to detect communities in networks. A common concern is the detection of hierarchical sets of clusters representing underlying structures. In this paper modularity optimization in complex networks is compared to polarized functional area definition through dominant flows. Different approaches to the significance of dominant flows are also tested, namely threshold and Multiple Linkage Analysis approaches.

Both methods are applied recursively in order to obtain a hierarchical clustering of municipalities in the PACA region (France) based on commuting flows in 1999. The comparison focuses on the geographical meaning of the results of the analyses. Modularity optimization and dominant flow results agree in many points and highlight the inadequacy of official methods integrating administrative boundaries in functional area definition. When they differ, they offer complementary views on the urban structure of the PACA region.

Keywords

Functional Areas Complex Networks Modularity Optimization Significant Dominant Flows Multiple Linkage Analysis PACA Region 

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References

  1. Andersen, A.: Commuting Areas in Denmark. AKF, Copenhagen (2000)Google Scholar
  2. Barabási, A.: Linked: How Everything is Connected to Everything Else. Plume, 304 p (2003)Google Scholar
  3. Berroir, S., Mathian, H., Saint-Julien, T., Sanders, L.: Mobilités et polarisations, In: Bonnet, M., Aubertel, P. (eds.) La ville aux limites de la mobilité, pp. 71–82. PUF, Paris (2006)Google Scholar
  4. Berry, B.: Growth Centers in the American Urban System. Ballinper, Cambridge (1973)Google Scholar
  5. Blondel, V., Guillaume, J., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment 10(10008), 12 (2008)Google Scholar
  6. Brown, L., Holmes, J.: The delimitation of functional regions, nodal regions, and hierarchies by functional distance approaches. Journal of Regional Science 1, 57–72 (1971)CrossRefGoogle Scholar
  7. Casado-Diaz, J.: Local labour market areas in Spain. Regional Studies 34, 843–856 (2000)CrossRefGoogle Scholar
  8. Clauset, A., Newman, M., Moore, C.: Finding community structure in very large networks. Phys. Rev. 70(066111), 1–6 (2004)Google Scholar
  9. Dauphiné, A.: Espace, Région et Système. Economica, Paris (1979)Google Scholar
  10. De Montis, A., Caschili, S., C.: Commuter networks and community detection: a method for planning sub regional areas. Physics and Society 19 (2011) arXiv:1103.2467Google Scholar
  11. Fortunato, S.: Community detection in graphs. Physics Reports (2010) arXiv:0906.0612v2Google Scholar
  12. Freeman, L.: A set of measures of centrality based betweenness. Sociometry 40, 35–41 (1977)CrossRefGoogle Scholar
  13. Fujita, M.: Urban Economic Theory. Cambridge University Press, Cambridge (1989)CrossRefGoogle Scholar
  14. Fusco, G.: Modelling Urban Networks from Spatial Interaction Data. In: Scarlatti, F., Rabino, G. (eds.) Advances in Models and Methods for Planning, Pitagora, pp. 63–72 (2009)Google Scholar
  15. Fusco, G., Decoupigny, F.: Logiques réticulaires dans l’organisation métropolitaine en région Provence-Alpes-Côte d’Azur, In: XLVe colloque de l”ASRDLF, Rimouski (2008), http://asrdlf2008.uqar.qc.ca/Papiers%20en%20ligne/FUSCO%20G.%20et%20DECOUPIGNY%20F._texte%20ASRDLF%202008.pdf
  16. Girvan, M., Newman, M.: Community structure in social and biological networks. In: Proceedings of the National Academy of Science, vol. 99, pp. 7821–7826 (2002)Google Scholar
  17. Goodman, J.: The Definition and Analysis of Local Labour Markets: Some Empirical Problems. British Journal of Industrial Relations 8(2), 179–196 (1970)CrossRefGoogle Scholar
  18. Haggett, P., Cliff, A., Frey, A.: Locational Analysis in Human Geography. Edward Arnold, London (1977)Google Scholar
  19. Johansson, B. (ed.): Infrastructure, Market Potential and Endogenous Economic Growth. Department of Civil Engineering. Kyoto University, Japan (1998)Google Scholar
  20. Karlsson, C.: Clusters, Functional Regions and Cluster Policies. JIBS and CESIS Electronic Working Paper Series (84) (2007)Google Scholar
  21. Kaufman, L., Rousseeuw, P.: Finding Groups in Data: An Introduction to Cluster Analysis. John Wiley & Sons, England (1990)CrossRefzbMATHGoogle Scholar
  22. Kipnis, B.: Graph Analysis of Metropolitan Residential Mobility: Methodology and Theoretical Implications. Urban Studies 22, 179–187 (1985)CrossRefGoogle Scholar
  23. Lagnerö, M.: Local Labour Markets. Working Paper. Statistics, Stockholm (2003)Google Scholar
  24. Lancichinetti, A., Fortunato, S.: Community detection algorithms: a comparative analysis. Physics and Society (2010) arXiv:0908.1062v2Google Scholar
  25. Lombardo, S., Rabino, G.: Urban Structures, Dynamic modelling and Clustering. In: J, Hauer (eds.) Urban Dynamics and Spatial Choice Behaviour, pp. 203–217 (1989)Google Scholar
  26. Newman, M.: Fast algorithm for detecting community structure in networks. Phys. Rev. 69(066133), 1–5 (2004)Google Scholar
  27. Nystuen, J., Dacey, M.: A Graph Theory Interpretation of Nodal Regions. Papers and Proceedings of the Regional Science Association 7, 29–42 (1968)CrossRefGoogle Scholar
  28. Palla, G., Derényi, I., Farkas, I., Vicsek, T.: Uncovering the overlapping community structure of complex networks in nature and society. Nature 435, 814–818 (2005)CrossRefGoogle Scholar
  29. Porter, M., Onnela, J., Mucha, P.: Communities in Networks. Notices of the AMS 56(9), 1082–1097 (2009)MathSciNetzbMATHGoogle Scholar
  30. Rabino, G., Occelli, S.: Understanding spatial structure from network data: theoretical considerations and applications. Cybergeo, n°29 (1997), http://cybergeo.revues.org/2199
  31. Tolbert, C., Sizer, M.: US Commuting Zones and Labour Market Areas. A 1990 Update. US Department of Agriculture, Washington DC (1996)Google Scholar
  32. Van der Haegen, H., Pattyn, M.: An operationalization of the concept of city region in West-European perspective: the Belgian city regions. Tijdschrift voor Economische en Sociale Geografie 61, 70–77 (1980)CrossRefGoogle Scholar
  33. Van der Laan, M., Pollardy, K., Bryanz, J.: A New Partitioning Around Medoids Algorithm. University of California, Berkeley, Working Paper Series, Paper 105, 1–12 (2002)Google Scholar
  34. Van Nuffel, N.: Determination of the Number of Significant Flows in Origin-Destination Specific Analysis: the case of Commuting in Flanders. Regional Studies 41.4, 509–524 (2007)CrossRefGoogle Scholar
  35. Wasserman, S., Faust, K.: Social Network Analysis: Methods and Applications. Cambridge University Press, Cambridge (1994)CrossRefzbMATHGoogle Scholar
  36. Weaver, J.: Crop combination regions in the Midwest, Geog. Review 44, 175–200 (1954)CrossRefGoogle Scholar
  37. Willaert, D., Surkyn, J., Lesthaeghe, R.: Stadsvlucht, verstedelijking en interne migraties in Vlaanderen en Belgie. Vrije Universiteit Brussel, Brussels (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Giovanni Fusco
    • 1
  • Matteo Caglioni
    • 1
  1. 1.UMR ESPACEUniversity of Nice Sophia-AntipolisNiceFrance

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