The spatial autocorrelation problem in spatial interaction modelling: a comparison of two common solutions
Daniel A. Griffith (),
Manfred Fischer and
James LeSage ()
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Daniel A. Griffith: University of Texas at Dallas
Letters in Spatial and Resource Sciences, 2017, vol. 10, issue 1, 75-86
Abstract Spatial interaction models of the gravity type are widely used to describe origin-destination flows. They draw attention to three types of variables to explain variation in spatial interactions across geographic space: variables that characterize the origin region of interaction, variables that characterize the destination region of interaction, and variables that measure the separation between origin and destination regions. A violation of standard minimal assumptions for least squares estimation may be associated with two problems: spatial autocorrelation within the residuals, and spatial autocorrelation within explanatory variables. This paper compares a spatial econometric solution with the spatial statistical Moran eigenvector spatial filtering solution to accounting for spatial autocorrelation within model residuals. An example using patent citation data that capture knowledge flows across 257 European regions serves to illustrate the application of the two approaches.
Keywords: Origin-destination flows; Spatial dependence in origin-destination flows; Spatial econometrics; Spatial filtering; Patent citation flows (search for similar items in EconPapers)
JEL-codes: C31 R15 (search for similar items in EconPapers)
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Working Paper: The spatial autocorrelation problem in spatial interaction modelling: a comparison of two common solutions (2016)
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