A methodology for neural spatial interaction modeling

Fischer, Manfred M. ORCID: https://orcid.org/0000-0002-0033-2510 and Reismann, Martin (2002) A methodology for neural spatial interaction modeling. Geographical Analysis, 34 (3). pp. 207-228. ISSN 1538-4632


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This paper attempts to develop a mathematically rigid and unified framework for neural spatial interaction modeling. Families of classical neural network models, but also less classical ones such as product unit neural network ones are considered for the cases of unconstrained and singly constrained spatial interaction flows. Current practice appears to suffer from least squares and normality assumptions that ignore the true integer nature of the flows and approximate a discrete-valued process by an almost certainly misrepresentative continuous distribution. To overcome this deficiency we suggest a more suitable estimation approach, maximum likelihood estimation under more realistic distributional assumptions of Poisson processes, and utilize a global search procedure, called Alopex, to solve the maximum likelihood estimation problem. To identify the transition from underfitting to overfitting we split the data into training, internal validation and test sets. The bootstrapping pairs approach with replacement is adopted to combine the purity of data splitting with the power of a resampling procedure to overcome the generally neglected issue of fixed data splitting and the problem of scarce data. In addition, the approach has power to provide a better statistical picture of the prediction variability, Finally, a benchmark comparison against the classical gravity models illustrates the superiority of both, the unconstrained and the origin constrained neural network model versions in terms of generalization performance measured by Kullback and Leibler's information criterion.

Item Type: Article
Keywords: This is the peer reviewed version of the following article, which has been published in final form at http://dx.doi.org/10.1111/j.1538-4632.2002.tb01085.x. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving. To see the final version of this paper please visit the publisher's website. Access to the published version requires a subscription.
Version of the Document: Accepted for Publication
Depositing User: Gertraud Novotny
Date Deposited: 24 Mar 2017 11:07
Last Modified: 04 Nov 2019 14:16
Related URLs:
FIDES Link: https://bach.wu.ac.at/d/research/results/20203/
URI: https://epub.wu.ac.at/id/eprint/5491


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