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.
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Reismann, M., Hlavácková-Schindler, K. (2006). Neural Network Modelling of Constrained Spatial Interaction Flows. In: Spatial Analysis and GeoComputation. Springer, Berlin, Heidelberg . https://doi.org/10.1007/3-540-35730-0_12
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DOI: https://doi.org/10.1007/3-540-35730-0_12
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