Evaluating Neural Spatial Interaction Modelling by Bootstrapping

Manfred M. Fischer, Martin Reismann

    Publication: Working/Discussion PaperWU Working Paper


    This paper exposes problems of the commonly used technique of splitting the available
    data in neural spatial interaction modelling into training, validation, and test sets that
    are held fixed and warns about drawing too strong conclusions from such static splits.
    Using a bootstrapping procedure, we compare the uncertainty in the solution stemming
    from the data splitting with model specific uncertainties such as parameter
    initialization. Utilizing the Austrian interregional telecommunication traffic data and
    the differential evolution method for solving the parameter estimation task for a fixed
    topology of the network model [ i.e. J = 9] this paper illustrates that the variation due to
    different resamplings is significantly larger than the variation due to different parameter
    initializations. This result implies that it is important to not over-interpret a model,
    estimated on one specific static split of the data. (authors' abstract)
    Original languageEnglish
    Place of PublicationVienna
    PublisherWU Vienna University of Economics and Business
    Publication statusPublished - 2000

    Publication series

    NameDiscussion Papers of the Institute for Economic Geography and GIScience

    WU Working Paper Series

    • Discussion Papers of the Institute for Economic Geography and GIScience

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