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Learning in neural spatial interaction models: A statistical perspective

Author

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  • Manfred M. Fischer

    (Department of Economic Geography and Geoinformatics, Vienna University of Economics and Business Administration, Rossauer Laende 23/1, A-1090 Vienna, Austria (e-mail: Manfred.Fischer@wu-wien.ac.at))

Abstract

. In this paper we view learning as an unconstrained non-linear minimization problem in which the objective function is defined by the negative log-likelihood function and the search space by the parameter space of an origin constrained product unit neural spatial interaction model. We consider Alopex based global search, as opposed to local search based upon backpropagation of gradient descents, each in combination with the bootstrapping pairs approach to solve the maximum likelihood learning problem. Interregional telecommunication traffic flow data from Austria are used as test bed for comparing the performance of the two learning procedures. The study illustrates the superiority of Alopex based global search, measured in terms of Kullback and Leibler's information criterion.

Suggested Citation

  • 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.
    • Handle: RePEc:kap:jgeosy:v:4:y:2002:i:3:d:10.1007_s101090200090
    DOI: 10.1007/s101090200090
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    References listed on IDEAS

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    1. Fischer, Manfred M. & Reismann, Martin, 2002. "A Methodology for Neural Spatial Interaction Modeling," MPRA Paper 77794, University Library of Munich, Germany.
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    Cited by:

    1. Fischer, Manfred M., 2006. "Neural Networks. A General Framework for Non-Linear Function Approximation," MPRA Paper 77776, University Library of Munich, Germany.
    2. Manfred M. Fischer, 2009. "Principles of Neural Spatial Interaction Modeling," Advances in Spatial Science, in: Michael Sonis & Geoffrey J. D. Hewings (ed.), Tool Kits in Regional Science, chapter 8, pages 199-214, Springer.
    3. Rafael Lata & Sidonia Proff & Thomas Brenner, 2018. "The influence of distance types on co-patenting and co-publishing in the USA and Europe over time," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 61(1), pages 49-71, July.
    4. Javier Rubio-Herrero & Jesús Muñuzuri, 2023. "Sparse regression for data-driven deterrence functions in gravity models," Annals of Operations Research, Springer, vol. 323(1), pages 153-174, April.

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    1. Rafael Lata & Sidonia Proff & Thomas Brenner, 2018. "The influence of distance types on co-patenting and co-publishing in the USA and Europe over time," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 61(1), pages 49-71, July.
    2. Manfred M. Fischer & Thomas Scherngell & Eva Jansenberger, 2005. "The Geography of Knowledge Spillovers between High-Technology Firms in Europe - Evidence from a Spatial Interaction Modelling Perspective," ERSA conference papers ersa05p5, European Regional Science Association.
    3. Fischer, Manfred M., 2006. "Neural Networks. A General Framework for Non-Linear Function Approximation," MPRA Paper 77776, University Library of Munich, Germany.
    4. Manfred M. Fischer, 2009. "Principles of Neural Spatial Interaction Modeling," Advances in Spatial Science, in: Michael Sonis & Geoffrey J. D. Hewings (ed.), Tool Kits in Regional Science, chapter 8, pages 199-214, Springer.

    More about this item

    Keywords

    Key words: Maximum likelihood learning; local search; global search; backpropagation of gradient descents; Alopex procedure; origin constrained neural spatial interaction model;
    All these keywords.

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics

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