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

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

Abstract

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.

Keywords

Urban sprawl Uncertainty Model selection Belief revision Bayesian networks Grenoble 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

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

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