Neural Network Modelling of Constrained Spatial Interaction Flows

M. Reismann and K. Hlavácková-Schindler

Chapter 12 in Spatial Analysis and GeoComputation, 2006, pp 241-268 from Springer

Abstract: 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.

Keywords: Neural Network; Neural Network Modelling; Product Unit; Spatial Interaction; Hide Unit (search for similar items in EconPapers)
Date: 2006
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-540-35730-8_12

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DOI: 10.1007/3-540-35730-0_12

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