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Modeling network autocorrelation within migration flows by eigenvector spatial filtering

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Abstract

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.

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Notes

  1. See Guldmann (1999) for a more detailed discussion in the definition of geographic space over which intervening opportunity factors may be computed.

  2. Note that eigenvector spatial filtering treats the eigenvectors of the transformed spatial link matrix V as proxies for genuine unobserved variables.

  3. Under “queen” type contiguity, spatial contiguity is defined as sharing a common boundary or vertex.

  4. The 95% confidence intervals for the distance parameters from the unfiltered and spatially filtered model specifications are (−0.4680, −0.3668) and (−0.4395, −0.3729), respectively.

  5. Note that one confounding factor that should be mentioned is the national economic growth that is occurring. Its geographic distribution focuses on warmer climates because of such regional attributes as non-union labor, relatively inexpensive and abundant available land, and lower taxes (Hanson 1998).

  6. The 95% confidence intervals for the distance parameters from the unfiltered and spatially filtered model specifications are (−0.5491, −0.4459) and (−0.4660, −0.4002), respectively.

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Acknowledgments

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

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  1. School of Economic, Political and Policy Sciences, The University of Texas at Dallas, Richardson, TX, 75080, USA

    Yongwan Chun

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Chun, Y. Modeling network autocorrelation within migration flows by eigenvector spatial filtering. J Geogr Syst 10, 317–344 (2008). https://doi.org/10.1007/s10109-008-0068-2

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