Multiple Bayesian Models for the Sustainable City: The Case of Urban Sprawl

  • Giovanni FuscoEmail author
  • Andrea Tettamanzi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10407)


Several possible models of urban sprawl are developed as Bayesian networks and evaluated in the light of available evidence, also considering the possibility that further, yet unknown models could offer better explanations. A simple heuristic is proposed in order to attribute a likelihood value for the unknown models. The case study of Grenoble (France) is then used to review beliefs in the different model options. The multiple models framework proves particularly interesting for geographers and planners having little available evidence and heavily relying on prior beliefs. This last condition is very frequent in research on sustainable cities. Further options of multiple models evaluations are finally proposed.


Urban sprawl Uncertainty Model selection Belief revision Bayesian networks Grenoble 


  1. 1.
    Foley, J., et al.: Global consequences of land use. Science 309, 570–573 (2005)CrossRefGoogle Scholar
  2. 2.
    Mcdonald, R., Kareiva, P., Forman, R.: The implications of current and future urbanization for the global protected areas. Biolog. Conserv. 141, 1695–1703 (2008)CrossRefGoogle Scholar
  3. 3.
    Camagni, R., Capello, R., Nijkamp, P.: Towards sustainable city policy: an economy-environment-technology nexus. Ecol. Econ. 24, 103–118 (1998)CrossRefGoogle Scholar
  4. 4.
    European Commission: European Sustainable Cities: Report of the Expert Group on the Urban Environment, Sustainable City Project. European Commission, Brussels (1996)Google Scholar
  5. 5.
    Calthorpe, P., Fulton, W.: The Regional City: Planning for the End of Sprawl. Island Press, New York (2001)Google Scholar
  6. 6.
    Duany, A., Plater-Zyberk, E., Speck, J.: Suburban Nation: The Rise of Sprawl and the Decline of the American Dream. North Point Press, New York (2000)Google Scholar
  7. 7.
    Breheny, M.: Urban compaction: feasible and acceptable? Cities 14(4), 209–217 (1997)CrossRefGoogle Scholar
  8. 8.
    Fouchier, V., Merlin, P. (eds.): High Urban Densities – A Solution for our Cities? Consulate General of France in Hong Kong (1994)Google Scholar
  9. 9.
    Charmes, E. (ed.): La densification en débat. Etudes foncières, special issue, 145 (2010)Google Scholar
  10. 10.
    PUCA: Vers des politiques publiques de densification et d’intensification douces? In: Workshop Proceedings (2014).
  11. 11.
    Laugier, R.: L’étalement urbain en France. Synthèse documentaire. Centre de Ressources Documentaires Aménagement Logement Nature. Ministère de l’Ecologie, du Développement Durable, des Transports et du Logement, Paris (2012)Google Scholar
  12. 12.
    Gordon, P., Richardson, H.: Beyond polycentricity: the dispersed metropolis, Los Angeles 1970–1990. J. Am. Plann. Assoc. 62(3), 289–295 (1996)CrossRefGoogle Scholar
  13. 13.
    Gordon, P., Richardson, H.: Are compact cities a desirable planning goal? J. Am. Plann. Assoc. 63(1), 95–106 (1997)CrossRefGoogle Scholar
  14. 14.
    Jensen, F.: Bayesian Networks and Decision Graphs. Springer, Berlin (2001)CrossRefzbMATHGoogle Scholar
  15. 15.
    Korb, K., Nicholson, A.: Bayesian Artificial Intelligence. Chapman & Hall/CRC, Boca Raton (2004)zbMATHGoogle Scholar
  16. 16.
    Fusco, G.: Démarche géo-prospective et modélisation causale probabiliste. Cybergéo, 613 (2012).
  17. 17.
    Scarella, F.: La ségrégation résidentielle dans l’espace-temps métropolitain. Ph.D. thesis, University of Nice Sophia Antipolis (2014)Google Scholar
  18. 18.
    Marcot, B., Steventon, J., Sutherland, G., McCann, R.: Guidelines for developing and updating Bayesian belief networks applied to ecological modeling and conservation. Can. J. Forest Res. 36, 3063–3074 (2006)CrossRefGoogle Scholar
  19. 19.
    Henrion, M.: Some practical issues in constructing belief networks. In: Kanal, L., Levitt, T., Lemmer, J. (eds.) Uncertainty in Artificial Intelligence, vol. 3, pp. 161–173 (1989). ElsevierGoogle Scholar
  20. 20.
    Diez, F., Drudzel, M.: Canonical probabilistic models for knowledge engineering. Technical report CISIAD-06-01 (2007)Google Scholar
  21. 21.
    Wiel, M.: La transition urbaine ou le passage de la ville pédestre à la ville motorisée. Mardaga, Liège (1999)Google Scholar
  22. 22.
    Bilmes, J.: On virtual evidence and soft evidence in Bayesian networks. UWEE Technical report 2004-0016. University of Washington (2004)Google Scholar
  23. 23.
    Pan, R., Peng, Y., Ding, Z.: Belief update in Bayesian networks using uncertain evidence. In: Proceedings of the 18th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2006, pp. 441–444. IEEE (2006)Google Scholar
  24. 24.
    Josang, A., Haller, J.: Dirichelet reputation systems. In: Proceedings of the 2nd International Conference on Availability, Reliability and Security, ARES 2007 (2007)Google Scholar
  25. 25.
    Raftery, A.: Bayesian model selection in social research. Sociol. Methodol. 25, 111–163 (1995)CrossRefGoogle Scholar
  26. 26.
    Raftery, A.: Rejoinder: model selection in unavoidable in social research. Sociol. Methodol. 25, 185–195 (1995)CrossRefGoogle Scholar
  27. 27.
    Withers, S.: Quantitative methods: Bayesian inference. Bayesian Thinking Prog. Hum. Geogr. 26(4), 553–566 (2002)CrossRefGoogle Scholar
  28. 28.
    DDT Isère: Comment favoriser la densification? Direction Départementale du Territoire 38, Grenoble (2015)Google Scholar
  29. 29.
    AURG: Schéma Directeur de la Région Grenobloise. Agence d’Urbanisme de la Région Grenobloise, Grenoble (2000)Google Scholar
  30. 30.
    GRA: Programme de Rénovation Urbaine de l’agglomération grenobloise. Grenoble Alpes-Métropole, Grenoble (2005)Google Scholar
  31. 31.
    Fusco, G., et al.: Faire science avec l’incertitude : réflexions sur la production des connaissances en Sciences Humaines et Sociales. In: Proceedings of Incertitude et connaissances en SHS: production, diffusion, transfert, MSHS Sud-Est, Nice, halshs-01166287 (2015)Google Scholar
  32. 32.
    Bolstad, W.: Introduction to Bayesian Statistics, 2nd edn. John Wiley, New York (2007)CrossRefzbMATHGoogle Scholar
  33. 33.
    Shafer, G.: A Mathematical Theory of Evidence. Princeton University Press, Princeton (1976)zbMATHGoogle Scholar
  34. 34.
    Smets, P., Kennes, R.: The transferable belief model. Artif. Intell. 66, 191–234 (1994)MathSciNetCrossRefzbMATHGoogle Scholar
  35. 35.
    Dubois, D., Prade, H.: Possibility Theory. Plenum, New York (1988)CrossRefzbMATHGoogle Scholar
  36. 36.
    Dubois, D., Prade, H., Sandri, S.: On possibility/probability transformations. In: Lowen, R., Roubens, M. (eds.) Fuzzy Logic, pp. 103–112. Kluwer, London (1993)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.CNRS, ESPACEUniversité Côte-AzurNiceFrance
  2. 2.CNRS, Inria, I3SUniversité Côte-AzurSophia AntipolisFrance

Personalised recommendations