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Neural Network Modelling of Constrained Spatial Interaction Flows

In: Spatial Analysis and GeoComputation

Author

Listed:
  • M. Reismann
  • K. Hlavácková-Schindler

Abstract

In this chapter 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 generalisation performance measured by ARV and SRMSE.

Suggested Citation

  • M. Reismann & K. Hlavácková-Schindler, 2006. "Neural Network Modelling of Constrained Spatial Interaction Flows," Springer Books, in: Spatial Analysis and GeoComputation, chapter 12, pages 241-268, Springer.
    • Handle: RePEc:spr:sprchp:978-3-540-35730-8_12
    DOI: 10.1007/3-540-35730-0_12
    as

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