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Feedforward Neural Networks for Spatial Interaction: Are They Trustworthy Forecasting Tools?

  • Jean-Claude Thill
  • Mikhail Mozolin
Part of the Advances in Spatial Science book series (ADVSPATIAL)

Abstract

Though it has often been criticized for providing too crude a rendition of processes underpinning revealed patterns of interaction between geo-referenced entities, spatial interaction modelling has persisted as one of the methodological pillars of several spatial sciences, including regional science, geography and transportation (Fotheringham and O’Kelly 1989; Ortuzar and Willumsen 1994; Sen and Smith 1995; Isard et al. 1998). Traditionally, the spatial interaction model is calibrated by one of several well known fitting and optimization techniques, including leastsquares regression, maximum likelihood, or by numerical heuristics.

Keywords

Neural Network Neural Network Model Hide Node Feedforward Neural Network Spatial Interaction 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Jean-Claude Thill
    • 1
  • Mikhail Mozolin
    • 2
  1. 1.Department of Geography and National Center for Geographic Information AnalysisState University of New York at BuffaloAmherstUSA
  2. 2.ESRI, Inc.RedlandsUSA

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