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Log-linear models with dependent spatial data

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  • B Fingleton

Abstract

Log-linear models are an appropriate means of determining the magnitude and direction of interactions between categorical variables that in common with other statistical models assume independent observations. Spatial data are often dependent rather than independent and thus the analysis of spatial data by log-linear models may erroneously detect interactions between variables that are spurious and are the consequence of pairwise correlations between observations. A procedure is described in this paper to accommodate these effects that requires only very minimal assumptions about the nature of the autocorrelation process given systematic sampling at intersection points on a square lattice.

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  • B Fingleton, 1983. "Log-linear models with dependent spatial data," Environment and Planning A, Pion Ltd, London, vol. 15(6), pages 801-813, June.
  • Handle: RePEc:pio:envira:v:15:y:1983:i:6:p:801-813
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    References listed on IDEAS

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    1. Boheim, Rene & Taylor, Mark P, 2002. "Tied Down or Rome to Move? Investigating the Relationships between Housing Tenure, Employment Status and Residential Mobility in Britain," Scottish Journal of Political Economy, Scottish Economic Society, vol. 49(4), pages 369-392, September.
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    Cited by:

    1. Julie Gallo & Coro Chasco, 2008. "Spatial analysis of urban growth in Spain, 1900–2001," Empirical Economics, Springer, pages 59-80.

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