Journal of Geographical Systems

, Volume 10, Issue 4, pp 317–344 | Cite as

Modeling network autocorrelation within migration flows by eigenvector spatial filtering

Original Article


Although the assumption of independence among interaction flows frequently is engaged in spatial interaction modeling, in many circumstances it leads to misspecified models and incorrect inferences. An informed approach is to explicitly incorporate an assumed relationship structure among the interaction flows, and to explicitly model the network autocorrelation. This paper illustrates such an approach in the context of U.S. interstate migration flows. Behavioral assumptions, similar to those of the intervening opportunities or the competing destinations concepts, exemplify how to specify network flows that are related to particular origin–destination combinations. The stepwise incorporation of eigenvectors, which are extracted from a network link matrix, captures the network autocorrelation in a Poisson regression model specification context. Spatial autocorrelation in Poisson regression is measured by the test statistic of Jacqmin-Gadda et al. (Stat Med 16(11):1283–1297, 1997). Results show that estimated regression parameters in the spatial filtering interaction model become more intuitively interpretable.


Network autocorrelation Spatial filtering Spatial interaction Eigenvector Migration 

JEL Classification

C21 R23 



The author thanks Michael Tiefelsdorf for useful comments on this research, and anonymous reviewers for their comments and suggestions.


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Copyright information

© Springer-Verlag 2008

Authors and Affiliations

  1. 1.School of Economic, Political and Policy SciencesThe University of Texas at DallasRichardsonUSA

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