Modelling Spatial Autocorrelation in Spatial Interaction Data

32 Pages Posted: 6 Mar 2008

See all articles by Manfred M. Fischer

Manfred M. Fischer

Vienna University of Economics and Business - Institute for Economic Geography and GIScience, Department of Socioeconomics

Daniel A. Griffith

University of Texas at Dallas - School of Economic, Political and Policy Sciences

Date Written: December 12, 2007

Abstract

Spatial interaction models of the gravity type are widely used to model origin-destination flows. They draw attention to three types of variables to explain variation in spatial interactions across geographic space: variables that characterise an origin region of a flow, variables that characterise a destination region of a flow, and finally variables that measure the separation between origin and destination regions. This paper outlines and compares two approaches, the spatial econometric and the eigenfunction-based spatial filtering approach, to deal with the issue of spatial autocorrelation among flow residuals. An example using patent citation data that capture knowledge flows across 112 European regions serves to illustrate the application and the comparison of the two approaches.

Keywords: Spatial autocorrelation, spatial interaction models, eigenfunction-based spatial filtering, spatial econometrics

JEL Classification: C13, C31, R15

Suggested Citation

Fischer, Manfred M. and Griffith, Daniel A., Modelling Spatial Autocorrelation in Spatial Interaction Data (December 12, 2007). Available at SSRN: https://ssrn.com/abstract=1102183 or http://dx.doi.org/10.2139/ssrn.1102183

Manfred M. Fischer (Contact Author)

Vienna University of Economics and Business - Institute for Economic Geography and GIScience, Department of Socioeconomics ( email )

Welthandelsplatz 1, D4
Vienna, 1020
Austria

Daniel A. Griffith

University of Texas at Dallas - School of Economic, Political and Policy Sciences ( email )

P.O. Box 830688, GR 31
Richardson, TX 75083
United States

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