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

Giovanni Fusco 1 Andrea G. B. Tettamanzi 2
2 WIMMICS - Web-Instrumented Man-Machine Interactions, Communities and Semantics
CRISAM - Inria Sophia Antipolis - Méditerranée , Laboratoire I3S - SPARKS - Scalable and Pervasive softwARe and Knowledge Systems
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.
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Contributor : Andrea G. B. Tettamanzi <>
Submitted on : Monday, July 24, 2017 - 1:19:11 PM
Last modification on : Thursday, October 22, 2020 - 11:24:02 AM



Giovanni Fusco, Andrea G. B. Tettamanzi. Multiple Bayesian Models for the Sustainable City: The Case of Urban Sprawl. Computational Science and Its Applications - ICCSA 2017 - 17th International Conference, Jul 2017, Trieste, Italy. pp.392-407, ⟨10.1007/978-3-319-62401-3_29⟩. ⟨hal-01567726⟩



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