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

Jean-Claude Thill and Mikhail Mozolin
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Jean-Claude Thill: State University of New York at Buffalo
Mikhail Mozolin: ESRI, Inc.

Chapter 17 in Spatial Economic Science, 2000, pp 355-381 from Springer

Abstract: 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 (search for similar items in EconPapers)
Date: 2000
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DOI: 10.1007/978-3-642-59787-9_17

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