Principles of Neural Spatial Interaction Modeling

Abstract

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may Principles of Neural SpatialInteraction Modelling MANFRED M. FISCHER Department of Economic Geography & Geoinformatics Vienna University of Economics and Business Administration AbstractNeural spatial interaction models represent the most recent innovation in the design of spatial interaction models. They are receiving increasing attention in recent years because of their powerful universal approximation properties. In essence they are devices for non-parametric statistical inferences, providing an elegant formalism for spatial interaction modelling. This contribution meets an urgent demand for methodological guidelines on how to develop robust applications that work from a statistical perspective. It introduces various components of a methodology for neural spatial interaction modelling that consists of a model specification framework to produce consistent estimators, a parameter estimation framework to compute parameter estimates that optimize an explicit fitness criterion and a framework to evaluate the model performance.JEL classification: C31, C45, R19

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“…Recent developments have brought several new impulses to the theory and context of spatial interaction modelling, with inspirations from economic, ecological, mathematical or behavioural concepts (see more in Wilson, 2010). Openshaw (1998) and particularly Fischer (1998Fischer ( , 2009, or Fischer, Reismann (2002), introduce discussion of the neural network modelling of unconstrained and singly constrained spatial interactions, which dwells on the previous bases of the families of spatial interaction models. Chen (2009) uses a new cross-correlation function in the traditional model that incorporates a time dimension into the modelling.…”