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Principles of Neural Spatial Interaction Modeling

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  • Manfred M. Fischer

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Abstract

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.

Suggested Citation

  • Manfred M. Fischer, 2003. "Principles of Neural Spatial Interaction Modeling," ERSA conference papers ersa03p526, European Regional Science Association.
  • Handle: RePEc:wiw:wiwrsa:ersa03p526
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    File URL: http://www-sre.wu.ac.at/ersa/ersaconfs/ersa03/cdrom/papers/526.pdf
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    References listed on IDEAS

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    1. 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.
    2. P.-Y. Henin & Jean-Paul Pollin, 1983. "Introduction," Post-Print halshs-00288183, HAL.
    3. Fischer, Manfred M. & Reismann, Martin, 2002. "A Methodology for Neural Spatial Interaction Modeling," MPRA Paper 77794, University Library of Munich, Germany.
    4. Fischer, Manfred M. & Gopal, Sucharita, 1994. "Artificial Neural Networks. A New Approach to Modelling Interregional Telecommunication Flows," MPRA Paper 77822, University Library of Munich, Germany.
    5. 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.
    6. Manfred M. Fischer, 2002. "Learning in neural spatial interaction models: A statistical perspective," Journal of Geographical Systems, Springer, vol. 4(3), pages 287-299, October.
    7. A. Meltzer & Peter Ordeshook & Thomas Romer, 1983. "Introduction," Public Choice, Springer, vol. 41(1), pages 1-5, January.
    8. 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.
    9. Fischer, Manfred M. & Reismann, Martin & Hlavackova-Schindler, Katerina, 2000. "Evaluating Neural Spatial Interaction. Modelling By Bootstrapping," ERSA conference papers ersa00p370, European Regional Science Association.
    10. 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.
    11. Manfred M. Fischer & Martin Reismann, 2001. "Neural Network Modelling of Constrained Spatial Interaction Flows," ERSA conference papers ersa01p165, European Regional Science Association.
    12. A. P. Thirlwall, 1983. "Introduction," Journal of Post Keynesian Economics, Taylor & Francis Journals, vol. 5(3), pages 341-344, March.
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