Principles of Neural Spatial Interaction Modeling
Neural spatial interaction models are receiving much attention in recent years because of their powerful universal approximation properties. They are essentially devices for non-parametric statistical inference, providing an elegant formalism. Neural spatial interaction models have shown considerable successes in a variety of application contexts. The paper discusses a novel modular methodology for neural spatial interaction model identification. We briefly introduce the motivation for the two main constituent components of the methodology: model selection and model adequacy testing. Then we discuss the issues involved in model selection and make a clear distinction between the problems of estimation and model specification. Though major emphasis will be laid on the case of unconstraint spatial interaction, some attention will be paid also to the singly constrained case. The methodology will be illustrated in a real world context. KEYWORDS: Neural Spatial Interaction Models; Model Selection and Model Adequacy Testing; Unconstraint Spatial Interaction.
|Date of creation:||Aug 2003|
|Date of revision:|
|Contact details of provider:|| Postal: Welthandelsplatz 1, 1020 Vienna, Austria|
Web page: http://www.ersa.org
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Manfred M. Fischer & Yee Leung, 1998. "A genetic-algorithms based evolutionary computational neural network for modelling spatial interaction data," ERSA conference papers ersa98p478, European Regional Science Association.
- Fischer, Manfred M. & Reismann, Martin & Hlavackova-Schindler, Katerina, 2000. "Evaluating Neural Spatial Interaction. Modelling By Bootstrapping," ERSA conference papers ersa00p370, European Regional Science Association.
- Manfred M. Fischer & Katerina Hlavácková-Schindler & Martin Reismann, 1999. "articles: A global search procedure for parameter estimation in neural spatial interaction modelling," Papers in Regional Science, Springer;Regional Science Association International, vol. 78(2), pages 119-134.
- Mozolin, M. & Thill, J. -C. & Lynn Usery, E., 2000. "Trip distribution forecasting with multilayer perceptron neural networks: A critical evaluation," Transportation Research Part B: Methodological, Elsevier, vol. 34(1), pages 53-73, January.
- Fischer, Manfred M. & Reismann, Martin, 2002. "A methodology for neural spatial interaction modelling," ERSA conference papers ersa02p034, European Regional Science Association.
- A. P. Thirlwall, 1983. "Introduction," Journal of Post Keynesian Economics, M.E. Sharpe, Inc., vol. 5(3), pages 341-344, April.
- Manfred M. Fischer & Martin Reismann, 2001. "Neural Network Modelling of Constrained Spatial Interaction Flows," ERSA conference papers ersa01p165, European Regional Science Association.
- P.-Y. Henin & Jean-Paul Pollin, 1983. "Introduction," Post-Print halshs-00288183, HAL.
- Fischer, Manfred M & Nijkamp, Peter, 1992.
"Geographic Information Systems and Spatial Analysis,"
The Annals of Regional Science,
Springer;Western Regional Science Association, vol. 26(1), pages 3-17, April.
- Fischer, M.M. & Nijkamp, P., 1992. "Geographic information systems and spatial analysis," Serie Research Memoranda 0054, VU University Amsterdam, Faculty of Economics, Business Administration and Econometrics.
- A. Meltzer & Peter Ordeshook & Thomas Romer, 1983. "Introduction," Public Choice, Springer, vol. 41(1), pages 1-5, January.
When requesting a correction, please mention this item's handle: RePEc:wiw:wiwrsa:ersa03p526. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Gunther Maier)
If references are entirely missing, you can add them using this form.