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

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Spatial Economic Science

Part of the book series: Advances in Spatial Science ((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.

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Author information

Authors and Affiliations

  1. Department of Geography and National Center for Geographic Information Analysis, State University of New York at Buffalo, Amherst, New York, USA

    Jean-Claude Thill

  2. ESRI, Inc., Redlands, California, USA

    Mikhail Mozolin

Authors
  1. Jean-Claude Thill
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  2. Mikhail Mozolin
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Editor information

Editors and Affiliations

  1. Department of Economics Faculty of Statistics, University of Bologna, Piazza Scaravilli, 2, 40126, Bologna, Italy

    Aura Reggiani

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© 2000 Springer-Verlag Berlin Heidelberg

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Thill, JC., Mozolin, M. (2000). Feedforward Neural Networks for Spatial Interaction: Are They Trustworthy Forecasting Tools?. In: Reggiani, A. (eds) Spatial Economic Science. Advances in Spatial Science. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-59787-9_17

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  • DOI: https://doi.org/10.1007/978-3-642-59787-9_17

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-64125-1

  • Online ISBN: 978-3-642-59787-9

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