Abstract
The applications presented in this chapter represent a complete modelling chain, integrating interaction modelling and uncertainty issues. New protocols to extract urban networks from spatial interaction data within a regional space are proposed. Bayesian Networks are later used to produce a model of the evolution of these networks. The results of prospective simulations of the future evolution of regional urban networks are subsequently integrated in a GIS platform to obtain an appropriate cartographic representation. GIS modelling and mapping integrate the probabilistic content of the model results, representing the degree of uncertainty in the knowledge of the future state of every component of the regional system.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Bauer, T., Steinnocher, K.: Per parcel land use classification in urban areas applying a rule-based technique. In: GeoBIT/GIS, vol. 6, pp. 24–27 (2001)
Bayesia, BayesiaLab Documentation, version 4.6, Bayesia, Laval (2009)
Berroir, S., Mathian, H., Saint-Julien, T., Sanders, L.: Mobilités et polarisations: vers des métropoles polycentriques. Les cas des métropoles francilienne et méditerranéenne. In: Bonnet, M., Aubertel, P. (eds.) La ville aux limites de la mobilité, pp. 71–82. PUF, Paris (2006)
Corgne, S.: Modelisation predictive de l’occupation des sols en contexte agricole intensif: Application à la couverture hivernale des sols en Bretagne, PhD dissertation, Université de Rennes 2 – ENST Brest (2004)
Decoupigny, F.: Journey Simulation of a Movement on a Double Scale. In: Mathis, P. (ed.) Graphs and Networks. Multilevel Modelling, pp. 31–46. ISTE, London (2007)
Fusco, G.: Looking for Sustainable Urban Mobility through Bayesian Networks. Scienze Regionali / Italian Journal of Regional Sciences (3), 87–106 (2004) Cybergeo 292, 87–106 (2004), http://www.cybergeo.eu/index2777.html
Fusco, G.: Spatial Dynamics in France. In: Pourret, O., Naïm, P., Marcot, B.G. (eds.) Bayesian Networks: A Practical Guide to Applications, pp. 87–112. John Wiley & Sons, New York (2008)
Fusco, G.: Modelling Urban Networks from Spatial Interaction Data: the ART Platform. In: Rabino, G., Scarlatti, F. (eds.) Advances in models and methods for Planning, pp. 63–72. Bologna, Pitagora (2009)
Fusco, G., Decoupigny, F.: Logiques réticulaires dans l’organisation métropolitaine en région PACA , XLVe colloque de l’ASRDLF, Rimouski 25-27 août, 18 pages (2008), http://asrdlf2008.uqar.qc.ca/Papiers%20en%20ligne/FUSCO%20G.%20et%20DECOUPIGNY%20F._texte%20ASRDLF%202008.pdf
Jansens, D., Wets, G., Brijs, T., Vanhoof, K., Arentze, T., Timmermans, H.: Integrating Bayesian networks and decision trees in a sequential rule-based transportation model. European Journal of Operational Research 175, 16–34 (2006)
Janssens, D., Wets, G., Brijs, T., Vanhoof, K., Timmermans, H.J.P., Arentze, A.: Improving the performance of a multi-agent rule-based model for activity pattern decisions using Bayesian networks. In: Conference Proceedings of the 83rd Annual Meeting of the Transportation Research Board, CD-ROM (2004)
Jensen, F.V.: Bayesian Networks and Decision Graphs. Springer, New York (2001)
Kaufmann, V.: Mobilité Quotidienne et Dynamiques Urbaines - La question du report modal, Presses Polytechniques et Universitaires Romandes, Lausanne (2000)
Kipnis, B.A.: Graph Analysis of Metropolitan Residential Mobility: Methodology and Theoretical Implications. Urban Studies 22, 179–187 (1985)
Korb, K.B., Nicholson, A.E.: Bayesian Artificial Intelligence. Chapman & Hall / CRC, Boca Raton (2004)
Manoj Mumar, P., Sugumaran, R., Zerr, D.: A rule-based classifier using Classification and Regression Tree (CART) approach for urban landscape dynamics. In: IEEE International Geoscience and Remote Sensing Symposium 2002, vol. 2, pp. 1193–1194 (2002)
Nystuen, J.D., Dacey, M.F.: Graph Theory Interpretation of Nodal Regions. In: Papers and Proceedings of the Regional Science Association, vol. 7, pp. 29–42 (1961)
Openshaw, S., Turner, A.: Forecasting global climatic change impacts on Mediterranean agricultural land use in the 21st Century. Cybergeo 120 (2000), http://www.cybergeo.eu/index2255.html
Pearl, J.: Causality – Models, Reasoning and Inference. Cambridge University Press, Cambridge (2000)
Pijanowski, B., Brown, D., Shellito, B., Manik, G.: Using neural networks and GIS to forecast land use changes: a Land Transformation Model. Computers, Environment and Urban Systems 26, 553–575 (2002)
Pijanowski, B., Pithadia, S., Shellito, B., Alexandridis, K.: Calibrating a neural network-based urban change model for two metropolitan areas of the Upper Midwest of the United States. International Journal of Geographical Information Science 19(2), 197–215 (2005)
Rabino, G., Occelli, S.: Understanding spatial structure from network data: theoretical considerations and applications. Cybergeo (29) (1997), http://www.cybergeo.eu/index2199.html
Torres, J.F., Huber, M.: Learning a Causal Model from Household Survey Data Using a Bayesian Belief Network. In: Prooceedings of the 82nd Meeting of the Transportation Research Board, Washington D.C., CD-ROM (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Fusco, G. (2010). Uncertainty in Interaction Modelling: Prospecting the Evolution of Urban Networks in South-Eastern France. In: Jeansoulin, R., Papini, O., Prade, H., Schockaert, S. (eds) Methods for Handling Imperfect Spatial Information. Studies in Fuzziness and Soft Computing, vol 256. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14755-5_14
Download citation
DOI: https://doi.org/10.1007/978-3-642-14755-5_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-14754-8
Online ISBN: 978-3-642-14755-5
eBook Packages: EngineeringEngineering (R0)