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Neural Network Modeling of Constrained Spatial Interaction Flows: Design, Estimation, and Performance Issues

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  • Manfred M Fischer
  • Martin Reismann
  • Katerina Hlavackova–Schindler

Abstract

In this paper a novel modular product unit neural network architecture is presented to model singly constrained spatial interaction flows. The efficacy of the model approach is demonstrated for the origin constrained case of spatial interaction using Austrian interregional telecommunication traffic data. The model requires a global search procedure for parameter estimation, such as the Alopex procedure. A benchmark comparison against the standard origin constrained gravity model and the two–stage neural network approach, suggested by Openshaw (1998), illustrates the superiority of the proposed model in terms of the generalization performance measured by ARV and SRMSE.

Suggested Citation

  • Manfred M Fischer & Martin Reismann & Katerina Hlavackova–Schindler, 2003. "Neural Network Modeling of Constrained Spatial Interaction Flows: Design, Estimation, and Performance Issues," Journal of Regional Science, Wiley Blackwell, vol. 43(1), pages 35-61, February.
    • Handle: RePEc:bla:jregsc:v:43:y:2003:i:1:p:35-61
    DOI: 10.1111/1467-9787.00288
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    Cited by:

    1. Brian Cushing & Jacques Poot, 2004. "Crossing boundaries and borders: Regional science advances in migration modelling," Advances in Spatial Science, in: Raymond J. G. M. Florax & David A. Plane (ed.), Fifty Years of Regional Science, pages 317-338, Springer.
    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. K. Newbold, 2012. "Migration and regional science: opportunities and challenges in a changing environment," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 48(2), pages 451-468, April.

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